system

Abstract

The system according to this embodiment aims to simplify the tax return filing process. [Solution] The system according to the embodiment comprises an input unit, a generation unit, a verification unit, and a submission unit. The input unit receives information. The generation unit generates a declaration form based on the information entered by the input unit. The verification unit verifies the declaration form generated by the generation unit. The submission unit submits the declaration form verified by the verification unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the procedure for final tax return is complicated and there is room for reducing man-hours.

[0005] The system according to the embodiment aims to simplify the procedure for final tax return.

Means for Solving the Problems

[0006] The system according to the embodiment includes an input unit, a generation unit, a confirmation unit, and a submission unit. The input unit inputs information. The generation unit generates a tax return form based on the information input by the input unit. The confirmation unit confirms the tax return form generated by the generation unit. The submission unit submits the tax return form confirmed by the confirmation unit.

Effects of the Invention

[0007] The system according to this embodiment can simplify the tax return filing process. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The tax return support system according to an embodiment of the present invention is a system that simplifies tax filing by utilizing generative AI. Tax filing is an annual task, and many people struggle to complete it. In particular, the preparation for e-Tax and paper applications are cumbersome for the elderly and those unfamiliar with personal computers and smartphones. The tax return support system aims to simplify the filing process by assisting in the creation of tax returns using generative AI. First, the user inputs or provides necessary information via voice, and the generative AI analyzes this information to clearly indicate the items required for creating the tax return. For example, when filing a blue return, the user inputs income, necessary expenses, deductions, etc., and the generative AI compiles this information into a tax return form, outputting it as a submission form or image. This allows users to handle tax filing without stress and reduces the workload. The generative AI has the ability to learn input patterns and generate new data. This enables efficient routine tasks and creative suggestions. For example, it can suggest the optimal filing method based on past filing data. The tax return support system targets all people who file tax returns. The target audience includes self-employed individuals, the elderly, sole proprietors, and even salaried employees with capital gains from the sale of stocks or land, among others. During tax filing season, tax offices become congested with long lines, and the filing process is time-consuming and labor-intensive. By using a tax filing support system, the filing process is simplified, reducing the workload for both taxpayers and tax office staff. Furthermore, by utilizing generation AI, not only is the creation of tax returns made easier, but the verification and correction of submitted documents can also be simplified. For example, if there is an error in the declaration, the generation AI automatically suggests corrections and notifies the user. This reduces filing errors and enables accurate filing. The tax filing support system not only simplifies tax filing but also reduces the burden on taxpayers and improves the operational efficiency of tax offices. Taxpayers can easily complete complex filing tasks, and tax offices can reduce the time spent verifying and correcting declarations. This is expected to contribute to overall efficiency and productivity improvement in society. In short, the tax filing support system can reduce the burden on taxpayers and improve the operational efficiency of tax offices.

[0029] The tax return support system according to the embodiment comprises an input unit, a generation unit, a verification unit, and a submission unit. The input unit is used by the user to input information. The input unit can input information using, for example, a keyboard or a touchscreen. The input unit can also input information using voice input. For example, it can convert the user's voice into text using speech recognition technology. Furthermore, the input unit can improve input efficiency by referring to past input history. For example, it has a function to automatically complete previously entered information. The generation unit generates a tax return based on the information entered by the input unit using generation AI. The generation unit can automatically generate the contents of the tax return using, for example, natural language generation AI. The generation unit can also learn from past tax return data and propose the optimal filing method. For example, it can propose the most efficient filing method based on past tax return data. The verification unit verifies the contents of the tax return generated by the generation unit. The verification unit checks, for example, whether there are any errors in the contents of the tax return. The verification unit can also make correction suggestions if there are errors. For example, it can automatically detect data entry errors or calculation errors and make correction suggestions. The submission unit submits the tax return that has been verified by the verification unit. The submission unit can submit the tax return in various ways, such as online or by mail. The submission unit also has a function to monitor the progress of the submission in real time. For example, it can check whether the submission has been completed and notify the user. As a result, the tax return support system according to this embodiment can handle everything from information input to tax return generation, verification, and submission in an integrated manner, simplifying the tax return process.

[0030] The input section provides an interface for users to input information. Specifically, information can be entered using a keyboard or touchscreen. With keyboard input, users can input letters and numbers by pressing physical keys, and with a touchscreen, similar input is possible by tapping a virtual keyboard on the screen. Information can also be entered using voice input. With voice input, speech recognition technology is used to convert the user's voice into text. For example, if a user says, "My income is 5 million yen," that voice is converted into text and automatically reflected in the input field. Furthermore, the input section has a function to improve input efficiency by referring to past input history. For example, there is a function that automatically completes previously entered information, saving the user the trouble of re-entering information they have already entered. This allows users to input information quickly and accurately. The input section also has a backup function that saves the information entered by the user in real time and prevents data loss in the event of accidental loss. This allows users to input information with peace of mind.

[0031] The generation unit uses a generation AI to generate tax returns based on information entered by the input unit. The generation AI utilizes natural language generation technology to automatically create tax returns in the appropriate format based on user input. For example, when a user enters information such as income, expenses, and deductions, the generation AI analyzes this data and generates an accurate tax return based on tax laws. The generation unit can also learn from past tax return data and suggest the optimal filing method. For instance, it can suggest the most efficient filing method based on past data, helping users maximize their deductions. Furthermore, the generation unit automatically calculates each item on the tax return and attaches necessary documents based on user input. This eliminates the need for users to manually perform complex calculations and prepare documents, significantly simplifying the filing process. The generation unit also temporarily saves the generated tax return and provides an interface for users to review and modify it. This allows users to check the generated tax return and make corrections as needed.

[0032] The verification unit is responsible for checking the contents of the tax return generated by the generation unit. The verification unit has an automated verification function to check for errors in the contents of the tax return. For example, it automatically detects data entry errors and calculation errors and suggests corrections to the user. Specifically, if there are inconsistencies in the input of income or expenses, or errors in the calculation of deductions, the verification unit will highlight the relevant parts and suggest appropriate correction methods. The verification unit also uses a checklist based on tax laws and regulations to verify that the tax return meets legal requirements. This allows the user to submit the tax return with confidence. Furthermore, the verification unit also has a function to re-verify the tax return after the user has made corrections, performing a final check. This allows the user to proceed to the next step only after confirming that the contents of the tax return are completely accurate. The verification unit also provides an interface for the user to check the contents of the tax return, allowing the user to review the generated tax return on screen and make corrections as needed.

[0033] The Submission Department is responsible for submitting tax returns that have been verified by the Verification Department. The Submission Department can accept tax returns through various methods, including online and postal submission. For online submissions, the Submission Department integrates with the tax authority's online system to electronically transmit the return. This allows users to easily submit their returns from home. For postal submissions, the Submission Department assists with printing the return, attaching necessary documents, and mailing them. For example, the Submission Department provides the mailing address and a list of required documents to help users complete the mailing process smoothly. The Submission Department also has a function to monitor the submission progress in real time. For example, it checks whether the submission is complete and notifies the user. This allows users to always know the submission status and proceed with the filing process with peace of mind. Furthermore, the Submission Department has functions to handle situations where verification or corrections are needed after submission. For example, it provides an interface for receiving feedback from the tax authority and making necessary corrections. This allows users to smoothly follow up after submission.

[0034] The generation unit can generate tax returns using a generation AI. For example, the generation unit can automatically generate the contents of the tax return using a natural language generation AI. For example, the generation unit can automatically fill in each item of the tax return based on information entered by the user. The generation unit can also automatically adjust the format of the tax return using the generation AI. For example, the generation unit can select the optimal tax return format according to the user's occupation and source of income. This makes the generation of tax returns more efficient by using a generation AI. Some or all of the above processes in the generation unit are performed using a generation AI. For example, the generation unit can input information entered by the user into the generation AI and have the generation AI execute the generation of the tax return.

[0035] The generation unit can learn from past declaration data and propose the optimal declaration method. For example, the generation unit proposes the most efficient declaration method based on past declaration data. For example, the generation unit analyzes past declaration data and presents the optimal declaration method to the user. The generation unit can also use generation AI to learn from past declaration data and propose new declaration methods. For example, the generation unit proposes the most efficient declaration method based on past declaration data. This streamlines the declaration process by proposing the optimal declaration method based on past data. Some or all of the above processes in the generation unit are performed using generation AI. For example, the generation unit can input past declaration data into the generation AI and have the generation AI propose the optimal declaration method.

[0036] The verification unit can check the contents of the generated tax return and suggest corrections if errors are found. For example, the verification unit checks whether there are any errors in the contents of the tax return. For example, the verification unit can automatically detect data entry errors or calculation errors and suggest corrections. The verification unit can also use generation AI to check the contents of the tax return and suggest corrections if errors are found. For example, the verification unit can use generation AI to analyze the contents of the tax return and detect errors. This enables accurate tax filing by automatically suggesting corrections for errors in the tax return. Some or all of the above processes in the verification unit are performed using AI. For example, the verification unit can input the generated tax return into the AI ​​and have the AI ​​perform error detection and suggest corrections.

[0037] The submission department can submit verified tax returns. The submission department can submit tax returns in various ways, such as online or by mail. For example, the submission department can submit tax returns electronically using an online submission system. The submission department can also automatically generate labels and envelopes for mail submissions. For example, the submission department can print the tax return and generate mailing labels. This simplifies the filing process by automatically submitting verified tax returns. Some or all of the above processes in the submission department are performed using AI. For example, the submission department can input verified tax returns into the AI ​​and have the AI ​​execute the submission procedure.

[0038] The input unit can analyze the user's past input history and suggest the optimal input method. For example, it can automatically display items that the user has frequently entered in the past as suggestions. It can also prioritize suggesting input methods that the user has used in the past (voice, text, etc.). Furthermore, it can predict and suggest items that the user will use at specific times based on their past input history. This streamlines the input process by suggesting the optimal input method based on past input history. Some or all of the above processes in the input unit are performed using AI. For example, the input unit can input the user's past input history data into the AI ​​and have the AI ​​suggest the optimal input method.

[0039] The input section can customize input fields based on the user's current living situation and areas of interest. For example, if the user is self-employed, the input section will prioritize displaying fields related to income and expenses. Similarly, if the user is elderly, the input section can prioritize displaying fields related to pensions and medical expense deductions. Furthermore, if the user is an investor, the input section can prioritize displaying fields related to stocks and real estate. This streamlines the input process by providing input fields tailored to the user's situation. Some or all of the above processing in the input section is performed using AI. For example, the input section can input data on the user's living situation and areas of interest into the AI, allowing the AI ​​to customize the input fields.

[0040] The input section can prioritize displaying highly relevant input items by considering the user's geographical location. For example, if the user lives in a specific region, the input section can display tax incentives relevant to that region. Furthermore, if the user lives in a specific country, the input section can display input items based on that country's tax laws. Additionally, if the user lives in a specific city, the input section can display the city's specific tax regulations. This streamlines the input process by providing input items based on geographical location information. Some or all of the above processing in the input section is performed using AI. For example, the input section can input the user's geographical location information into the AI ​​and have the AI ​​display highly relevant input items.

[0041] The input unit can analyze the user's social media activity and suggest relevant input items. For example, it can suggest input items based on the user's areas of interest that they frequently mention on social media. It can also suggest relevant input items based on location information shared by the user on social media. Furthermore, it can suggest relevant input items based on the accounts the user follows on social media. This streamlines the input process by providing input items based on social media activity. Some or all of the above processing in the input unit is performed using AI. For example, the input unit can input the user's social media activity data into the AI ​​and have the AI ​​suggest relevant input items.

[0042] The generation unit can select the optimal tax return format by referring to past tax return data. For example, the generation unit can select the optimal format based on the tax return format previously used by the user. The generation unit can also select the most efficient format from the user's past tax return data. Furthermore, the generation unit can analyze the user's past tax return data and suggest the most suitable format. This streamlines the tax return process by selecting the optimal format based on past data. Some or all of the above processes in the generation unit are performed using a generation AI. For example, the generation unit can input past tax return data into the generation AI and have the generation AI select the optimal tax return format.

[0043] The generation unit can apply different generation algorithms depending on the user's occupation and source of income. For example, if the user is self-employed, the generation unit will generate a tax return that emphasizes income and expense items. It can also generate a tax return that emphasizes salary income items if the user is a salaried employee. Furthermore, if the user is an investor, the generation unit can generate a tax return that emphasizes investment returns. This streamlines the tax filing process by generating tax returns tailored to the user's occupation and source of income. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can input data on the user's occupation and source of income into the generation AI and have the generation AI apply the generation algorithm.

[0044] The generation unit can determine the generation priority based on the filing date of the tax return. For example, if the filing deadline is approaching, the generation unit will prioritize generating the most important items. If the filing deadline is far away, the generation unit can also generate detailed items in order. Furthermore, if the filing deadline is imminent, the generation unit can apply an algorithm to generate the return quickly. This streamlines the tax filing process by providing priorities according to the filing date. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can input the filing date data of the tax return into the generation AI and have the generation AI determine the generation priority.

[0045] The generation unit can adjust the generation order based on the relevance of the tax return. For example, it can generate important items first and then detailed items later. It can also prioritize the generation of highly relevant items. Furthermore, it can postpone the generation of less relevant items. This streamlines the tax return process by providing a generation order based on relevance. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can input the relevance data of the tax return into the generation AI and have the generation AI adjust the generation order.

[0046] The verification unit can prioritize displaying items that are likely to be errors by referring to past declaration data. For example, the verification unit can prioritize displaying items in which the user has made errors in the past. The verification unit can also prioritize displaying items that are likely to be errors based on the user's past declaration data. Furthermore, the verification unit can analyze the user's past declaration data and suggest items that are likely to be errors. This streamlines the verification process by prioritizing the display of items that are likely to be errors based on past data. Some or all of the above processes in the verification unit are performed using AI. For example, the verification unit can input past declaration data into the AI ​​and have the AI ​​display items that are likely to be errors.

[0047] The verification unit can apply different verification algorithms depending on the user's occupation and source of income. For example, if the user is self-employed, the verification unit will focus on verifying items related to income and expenses. If the user is a salaried employee, the verification unit can focus on verifying items related to salary income. Furthermore, if the user is an investor, the verification unit can focus on verifying items related to investment returns. This streamlines the verification process by providing verification tailored to the user's occupation and source of income. Some or all of the above processing in the verification unit is performed using AI. For example, the verification unit can input data on the user's occupation and source of income into the AI ​​and have the AI ​​execute the application of the verification algorithm.

[0048] The verification unit can adjust the priority of verification based on when the tax return is filed. For example, if the filing deadline is approaching, the verification unit will prioritize checking the most important items. If the filing deadline is far away, the verification unit can also check detailed items in order. Furthermore, if the filing deadline is imminent, the verification unit can apply an algorithm to expedite the verification process. This streamlines the verification process by providing priorities according to the filing date. Some or all of the above processes in the verification unit are performed using AI. For example, the verification unit can input the filing date data of the tax return into the AI ​​and have the AI ​​adjust the verification priority.

[0049] The verification unit can improve the accuracy of its verification by referring to relevant documents in the tax return. For example, the verification unit can check items that are likely to be incorrect based on the relevant documents in the tax return. The verification unit can also perform accurate verification by referring to relevant documents in the tax return. Furthermore, the verification unit can improve the accuracy of its verification based on the relevant documents in the tax return. This makes accurate verification possible by improving the accuracy of verification based on relevant documents. Some or all of the above processes in the verification unit are performed using AI. For example, the verification unit can input the relevant documents data from the tax return into the AI ​​and have the AI ​​perform the verification accuracy improvement.

[0050] The submission unit can suggest the optimal submission method by referring to past submission history. For example, the submission unit can suggest the optimal submission method based on the submission method the user has used in the past. It can also suggest the most efficient submission method based on the user's past submission history. Furthermore, the submission unit can analyze the user's past submission history and suggest the most suitable submission method. This streamlines the submission process by suggesting the optimal submission method based on past submission history. Some or all of the above processes in the submission unit are performed using AI. For example, the submission unit can input past submission history data into the AI ​​and have the AI ​​suggest the optimal submission method.

[0051] The submission unit can apply different submission algorithms depending on the user's occupation and source of income. For example, if the user is self-employed, the unit will suggest a submission method that emphasizes items related to income and expenses. If the user is a salaried employee, the unit can also suggest a submission method that emphasizes items related to salary income. Furthermore, if the user is an investor, the unit can suggest a submission method that emphasizes items related to investment returns. This streamlines the submission process by providing a submission method tailored to the user's occupation and source of income. Some or all of the above processing in the submission unit is performed using AI. For example, the submission unit can input data on the user's occupation and source of income into the AI ​​and have the AI ​​execute the application of the submission algorithm.

[0052] The filing department can adjust the priority of tax returns based on when they are submitted. For example, if the filing deadline is approaching, the department will prioritize submitting the most important items. If the filing deadline is far away, the department can also submit detailed items in order. Furthermore, if the filing deadline is imminent, the department can apply an algorithm to expedite submission. This streamlines the filing process by providing prioritization based on the submission timing. Some or all of the above processes in the filing department are performed using AI. For example, the filing department can input tax return submission timing data into the AI ​​and have the AI ​​adjust the submission priority.

[0053] The submission department can improve the accuracy of the submission by referring to the relevant documents in the declaration. For example, the submission department can identify items that are likely to be incorrect based on the relevant documents in the declaration. The submission department can also make accurate submissions by referring to the relevant documents in the declaration. Furthermore, the submission department can improve the accuracy of the submission based on the relevant documents. This makes accurate submissions possible by improving the accuracy of the submission based on the relevant documents. Some or all of the above processes in the submission department are performed using AI. For example, the submission department can input the relevant documents data from the declaration into the AI ​​and have the AI ​​perform the task of improving the accuracy of the submission.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The tax return support system can prioritize displaying relevant input fields by considering the user's geographical location. For example, if a user lives in a specific region, it can display tax incentives relevant to that region. Furthermore, if a user lives in a specific country, it can display input fields based on that country's tax laws. Additionally, if a user lives in a specific city, it can display the city's unique tax regulations. This streamlines the input process by providing input fields based on geographical location information. Some or all of the above processing in the input section is performed using AI. For example, the input section can input the user's geographical location information into the AI, allowing the AI ​​to display relevant input fields.

[0056] The tax return support system can analyze a user's past input history and suggest the optimal input method. For example, it can automatically display items that the user has frequently entered in the past as suggestions. It can also prioritize suggesting input methods that the user has used in the past (voice, text, etc.). Furthermore, it can predict and suggest items that the user will use at specific times of the day based on their past input history. This streamlines the input process by suggesting the optimal input method based on past input history. Some or all of the above processing in the input section is performed using AI. For example, the input section can input the user's past input history data into the AI ​​and have the AI ​​suggest the optimal input method.

[0057] The tax return support system can customize input fields based on the user's current living situation and areas of interest. For example, if the user is self-employed, fields related to income and expenses will be displayed preferentially. If the user is elderly, fields related to pensions and medical expense deductions can be displayed preferentially. Furthermore, if the user is an investor, fields related to stocks and real estate can be displayed preferentially. This streamlines the input process by providing input fields tailored to the user's situation. Some or all of the above processing in the input section is performed using AI. For example, the input section can input data on the user's living situation and areas of interest into the AI, and have the AI ​​perform the customization of input fields.

[0058] The tax return support system can determine the priority of data generation based on the filing date of the tax return. For example, if the filing deadline is approaching, it will prioritize generating the most important items. If the filing deadline is far away, it can generate detailed items in order. Furthermore, if the filing deadline is imminent, it can apply an algorithm to generate the data quickly. This streamlines the tax return process by providing priorities according to the filing date. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can input the filing date data of the tax return into the generation AI and have the generation AI determine the generation priority.

[0059] The tax return support system can analyze a user's social media activity and suggest relevant input fields. For example, it can suggest input fields based on the user's frequently mentioned areas of interest on social media. It can also suggest relevant input fields based on location information shared by the user on social media. Furthermore, it can suggest relevant input fields based on the accounts the user follows on social media. This streamlines the input process by providing input fields based on social media activity. Some or all of the above processing in the input section is performed using AI. For example, the input section can input the user's social media activity data into the AI ​​and have the AI ​​suggest relevant input fields.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The input unit allows the user to input information. This can be done using, for example, a keyboard or touchscreen. The input unit can also accept voice input. For example, speech recognition technology can be used to convert the user's voice into text. Furthermore, the input unit can improve input efficiency by referring to past input history. For example, it may have a function to automatically complete previously entered information. Step 2: The generation unit uses a generation AI to generate a tax return based on the information entered by the input unit. The generation unit can, for example, use a natural language generation AI to automatically generate the contents of the tax return. The generation unit can also learn from past tax return data and suggest the optimal filing method. For example, it can suggest the most efficient filing method based on past tax return data. Step 3: The verification unit checks the contents of the declaration form generated by the generation unit. For example, the verification unit checks whether there are any errors in the contents of the declaration form. The verification unit can also suggest corrections if errors are found. For example, it can automatically detect data entry errors or calculation errors and suggest corrections. Step 4: The submission unit submits the declaration form that has been verified by the verification unit. The submission unit can submit the declaration form in various ways, such as online or by mail. The submission unit also has a function to monitor the progress of the submission in real time. For example, it can check whether the submission has been completed and notify the user.

[0062] (Example of form 2) The tax return support system according to an embodiment of the present invention is a system that simplifies tax filing by utilizing generative AI. Tax filing is an annual task, and many people struggle to complete it. In particular, the preparation for e-Tax and paper applications are cumbersome for the elderly and those unfamiliar with personal computers and smartphones. The tax return support system aims to simplify the filing process by assisting in the creation of tax returns using generative AI. First, the user inputs or provides necessary information via voice, and the generative AI analyzes this information to clearly indicate the items required for creating the tax return. For example, when filing a blue return, the user inputs income, necessary expenses, deductions, etc., and the generative AI compiles this information into a tax return form, outputting it as a submission form or image. This allows users to handle tax filing without stress and reduces the workload. The generative AI has the ability to learn input patterns and generate new data. This enables efficient routine tasks and creative suggestions. For example, it can suggest the optimal filing method based on past filing data. The tax return support system targets all people who file tax returns. The target audience includes self-employed individuals, the elderly, sole proprietors, and even salaried employees with capital gains from the sale of stocks or land, among others. During tax filing season, tax offices become congested with long lines, and the filing process is time-consuming and labor-intensive. By using a tax filing support system, the filing process is simplified, reducing the workload for both taxpayers and tax office staff. Furthermore, by utilizing generation AI, not only is the creation of tax returns made easier, but the verification and correction of submitted documents can also be simplified. For example, if there is an error in the declaration, the generation AI automatically suggests corrections and notifies the user. This reduces filing errors and enables accurate filing. The tax filing support system not only simplifies tax filing but also reduces the burden on taxpayers and improves the operational efficiency of tax offices. Taxpayers can easily complete complex filing tasks, and tax offices can reduce the time spent verifying and correcting declarations. This is expected to contribute to overall efficiency and productivity improvement in society. In short, the tax filing support system can reduce the burden on taxpayers and improve the operational efficiency of tax offices.

[0063] The tax return support system according to the embodiment comprises an input unit, a generation unit, a verification unit, and a submission unit. The input unit is used by the user to input information. The input unit can input information using, for example, a keyboard or a touchscreen. The input unit can also input information using voice input. For example, it can convert the user's voice into text using speech recognition technology. Furthermore, the input unit can improve input efficiency by referring to past input history. For example, it has a function to automatically complete previously entered information. The generation unit generates a tax return based on the information entered by the input unit using generation AI. The generation unit can automatically generate the contents of the tax return using, for example, natural language generation AI. The generation unit can also learn from past tax return data and propose the optimal filing method. For example, it can propose the most efficient filing method based on past tax return data. The verification unit verifies the contents of the tax return generated by the generation unit. The verification unit checks, for example, whether there are any errors in the contents of the tax return. The verification unit can also make correction suggestions if there are errors. For example, it can automatically detect data entry errors or calculation errors and make correction suggestions. The submission unit submits the tax return that has been verified by the verification unit. The submission unit can submit the tax return in various ways, such as online or by mail. The submission unit also has a function to monitor the progress of the submission in real time. For example, it can check whether the submission has been completed and notify the user. As a result, the tax return support system according to this embodiment can handle everything from information input to tax return generation, verification, and submission in an integrated manner, simplifying the tax return process.

[0064] The input section provides an interface for users to input information. Specifically, information can be entered using a keyboard or touchscreen. With keyboard input, users can input letters and numbers by pressing physical keys, and with a touchscreen, similar input is possible by tapping a virtual keyboard on the screen. Information can also be entered using voice input. With voice input, speech recognition technology is used to convert the user's voice into text. For example, if a user says, "My income is 5 million yen," that voice is converted into text and automatically reflected in the input field. Furthermore, the input section has a function to improve input efficiency by referring to past input history. For example, there is a function that automatically completes previously entered information, saving the user the trouble of re-entering information they have already entered. This allows users to input information quickly and accurately. The input section also has a backup function that saves the information entered by the user in real time and prevents data loss in the event of accidental loss. This allows users to input information with peace of mind.

[0065] The generation unit uses a generation AI to generate tax returns based on information entered by the input unit. The generation AI utilizes natural language generation technology to automatically create tax returns in the appropriate format based on user input. For example, when a user enters information such as income, expenses, and deductions, the generation AI analyzes this data and generates an accurate tax return based on tax laws. The generation unit can also learn from past tax return data and suggest the optimal filing method. For instance, it can suggest the most efficient filing method based on past data, helping users maximize their deductions. Furthermore, the generation unit automatically calculates each item on the tax return and attaches necessary documents based on user input. This eliminates the need for users to manually perform complex calculations and prepare documents, significantly simplifying the filing process. The generation unit also temporarily saves the generated tax return and provides an interface for users to review and modify it. This allows users to check the generated tax return and make corrections as needed.

[0066] The verification unit is responsible for checking the contents of the tax return generated by the generation unit. The verification unit has an automated verification function to check for errors in the contents of the tax return. For example, it automatically detects data entry errors and calculation errors and suggests corrections to the user. Specifically, if there are inconsistencies in the input of income or expenses, or errors in the calculation of deductions, the verification unit will highlight the relevant parts and suggest appropriate correction methods. The verification unit also uses a checklist based on tax laws and regulations to verify that the tax return meets legal requirements. This allows the user to submit the tax return with confidence. Furthermore, the verification unit also has a function to re-verify the tax return after the user has made corrections, performing a final check. This allows the user to proceed to the next step only after confirming that the contents of the tax return are completely accurate. The verification unit also provides an interface for the user to check the contents of the tax return, allowing the user to review the generated tax return on screen and make corrections as needed.

[0067] The Submission Department is responsible for submitting tax returns that have been verified by the Verification Department. The Submission Department can accept tax returns through various methods, including online and postal submission. For online submissions, the Submission Department integrates with the tax authority's online system to electronically transmit the return. This allows users to easily submit their returns from home. For postal submissions, the Submission Department assists with printing the return, attaching necessary documents, and mailing them. For example, the Submission Department provides the mailing address and a list of required documents to help users complete the mailing process smoothly. The Submission Department also has a function to monitor the submission progress in real time. For example, it checks whether the submission is complete and notifies the user. This allows users to always know the submission status and proceed with the filing process with peace of mind. Furthermore, the Submission Department has functions to handle situations where verification or corrections are needed after submission. For example, it provides an interface for receiving feedback from the tax authority and making necessary corrections. This allows users to smoothly follow up after submission.

[0068] The generation unit can generate tax returns using a generation AI. For example, the generation unit can automatically generate the contents of the tax return using a natural language generation AI. For example, the generation unit can automatically fill in each item of the tax return based on information entered by the user. The generation unit can also automatically adjust the format of the tax return using the generation AI. For example, the generation unit can select the optimal tax return format according to the user's occupation and source of income. This makes the generation of tax returns more efficient by using a generation AI. Some or all of the above processes in the generation unit are performed using a generation AI. For example, the generation unit can input information entered by the user into the generation AI and have the generation AI execute the generation of the tax return.

[0069] The generation unit can learn from past declaration data and propose the optimal declaration method. For example, the generation unit proposes the most efficient declaration method based on past declaration data. For example, the generation unit analyzes past declaration data and presents the optimal declaration method to the user. The generation unit can also use generation AI to learn from past declaration data and propose new declaration methods. For example, the generation unit proposes the most efficient declaration method based on past declaration data. This streamlines the declaration process by proposing the optimal declaration method based on past data. Some or all of the above processes in the generation unit are performed using generation AI. For example, the generation unit can input past declaration data into the generation AI and have the generation AI propose the optimal declaration method.

[0070] The verification unit can check the contents of the generated tax return and suggest corrections if errors are found. For example, the verification unit checks whether there are any errors in the contents of the tax return. For example, the verification unit can automatically detect data entry errors or calculation errors and suggest corrections. The verification unit can also use generation AI to check the contents of the tax return and suggest corrections if errors are found. For example, the verification unit can use generation AI to analyze the contents of the tax return and detect errors. This enables accurate tax filing by automatically suggesting corrections for errors in the tax return. Some or all of the above processes in the verification unit are performed using AI. For example, the verification unit can input the generated tax return into the AI ​​and have the AI ​​perform error detection and suggest corrections.

[0071] The submission department can submit verified tax returns. The submission department can submit tax returns in various ways, such as online or by mail. For example, the submission department can submit tax returns electronically using an online submission system. The submission department can also automatically generate labels and envelopes for mail submissions. For example, the submission department can print the tax return and generate mailing labels. This simplifies the filing process by automatically submitting verified tax returns. Some or all of the above processes in the submission department are performed using AI. For example, the submission department can input verified tax returns into the AI ​​and have the AI ​​execute the submission procedure.

[0072] The input unit can estimate the user's emotions and adjust the design of the input interface based on the estimated emotions. For example, if the user is stressed, the input unit can provide a simple interface and minimize the input steps. If the user is relaxed, the input unit can also provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the input unit can prioritize voice input to allow for quick input of necessary information. This makes the input process more comfortable by providing an interface that responds to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the input unit is performed using AI. For example, the input unit can input user emotion data into the AI ​​and have the AI ​​perform interface design adjustments.

[0073] The input unit can analyze the user's past input history and suggest the optimal input method. For example, it can automatically display items that the user has frequently entered in the past as suggestions. It can also prioritize suggesting input methods that the user has used in the past (voice, text, etc.). Furthermore, it can predict and suggest items that the user will use at specific times based on their past input history. This streamlines the input process by suggesting the optimal input method based on past input history. Some or all of the above processes in the input unit are performed using AI. For example, the input unit can input the user's past input history data into the AI ​​and have the AI ​​suggest the optimal input method.

[0074] The input section can customize input fields based on the user's current living situation and areas of interest. For example, if the user is self-employed, the input section will prioritize displaying fields related to income and expenses. Similarly, if the user is elderly, the input section can prioritize displaying fields related to pensions and medical expense deductions. Furthermore, if the user is an investor, the input section can prioritize displaying fields related to stocks and real estate. This streamlines the input process by providing input fields tailored to the user's situation. Some or all of the above processing in the input section is performed using AI. For example, the input section can input data on the user's living situation and areas of interest into the AI, allowing the AI ​​to customize the input fields.

[0075] The input unit can estimate the user's emotions and determine the priority of inputs based on the estimated emotions. For example, if the user is nervous, the input unit may prompt the user to enter important items first and then details later. If the user is relaxed, the input unit may prompt the user to enter detailed items in order. Furthermore, if the user is in a hurry, the input unit may prioritize the most important items. This streamlines the input process by providing input priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the input unit is performed using AI. For example, the input unit can input user emotion data into the AI ​​and have the AI ​​determine the priority of inputs.

[0076] The input section can prioritize displaying highly relevant input items by considering the user's geographical location. For example, if the user lives in a specific region, the input section can display tax incentives relevant to that region. Furthermore, if the user lives in a specific country, the input section can display input items based on that country's tax laws. Additionally, if the user lives in a specific city, the input section can display the city's specific tax regulations. This streamlines the input process by providing input items based on geographical location information. Some or all of the above processing in the input section is performed using AI. For example, the input section can input the user's geographical location information into the AI ​​and have the AI ​​display highly relevant input items.

[0077] The input unit can analyze the user's social media activity and suggest relevant input items. For example, it can suggest input items based on the user's areas of interest that they frequently mention on social media. It can also suggest relevant input items based on location information shared by the user on social media. Furthermore, it can suggest relevant input items based on the accounts the user follows on social media. This streamlines the input process by providing input items based on social media activity. Some or all of the above processing in the input unit is performed using AI. For example, the input unit can input the user's social media activity data into the AI ​​and have the AI ​​suggest relevant input items.

[0078] The generation unit can estimate the user's emotions and adjust the presentation of the generated declaration based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate a declaration with detailed explanations. If the user is in a hurry, the generation unit can also generate a concise and to-the-point declaration. Furthermore, if the user is stressed, the generation unit can generate a visually easy-to-understand declaration. This makes the declaration process more comfortable by generating a declaration that is tailored 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 is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit is performed using the generation AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI adjust the presentation of the declaration.

[0079] The generation unit can select the optimal tax return format by referring to past tax return data. For example, the generation unit can select the optimal format based on the tax return format previously used by the user. The generation unit can also select the most efficient format from the user's past tax return data. Furthermore, the generation unit can analyze the user's past tax return data and suggest the most suitable format. This streamlines the tax return process by selecting the optimal format based on past data. Some or all of the above processes in the generation unit are performed using a generation AI. For example, the generation unit can input past tax return data into the generation AI and have the generation AI select the optimal tax return format.

[0080] The generation unit can apply different generation algorithms depending on the user's occupation and source of income. For example, if the user is self-employed, the generation unit will generate a tax return that emphasizes income and expense items. It can also generate a tax return that emphasizes salary income items if the user is a salaried employee. Furthermore, if the user is an investor, the generation unit can generate a tax return that emphasizes investment returns. This streamlines the tax filing process by generating tax returns tailored to the user's occupation and source of income. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can input data on the user's occupation and source of income into the generation AI and have the generation AI apply the generation algorithm.

[0081] The generation unit can estimate the user's emotions and adjust the length of the declaration form it generates based on those emotions. For example, if the user is in a hurry, the generation unit can generate a short, concise declaration form. If the user is relaxed, the generation unit can also generate a longer declaration form with more detailed explanations. Furthermore, if the user is stressed, the generation unit can generate a visually easy-to-understand declaration form. This makes the declaration process more comfortable by providing declaration forms of varying lengths 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 is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the generation unit are performed using the generation AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI adjust the length of the declaration form.

[0082] The generation unit can determine the generation priority based on the filing date of the tax return. For example, if the filing deadline is approaching, the generation unit will prioritize generating the most important items. If the filing deadline is far away, the generation unit can also generate detailed items in order. Furthermore, if the filing deadline is imminent, the generation unit can apply an algorithm to generate the return quickly. This streamlines the tax filing process by providing priorities according to the filing date. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can input the filing date data of the tax return into the generation AI and have the generation AI determine the generation priority.

[0083] The generation unit can adjust the generation order based on the relevance of the tax return. For example, it can generate important items first and then detailed items later. It can also prioritize the generation of highly relevant items. Furthermore, it can postpone the generation of less relevant items. This streamlines the tax return process by providing a generation order based on relevance. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can input the relevance data of the tax return into the generation AI and have the generation AI adjust the generation order.

[0084] The verification unit can estimate the user's emotions and adjust the display method of the verification interface based on the estimated emotions. For example, if the user is nervous, the verification unit can provide a simple and highly visible display method. If the user is relaxed, the verification unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the verification unit can provide a concise display method. This makes the verification process more comfortable by providing an interface that responds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the verification unit is performed using AI. For example, the verification unit can input user emotion data into the AI ​​and have the AI ​​adjust the display method of the verification interface.

[0085] The verification unit can prioritize displaying items that are likely to be errors by referring to past declaration data. For example, the verification unit can prioritize displaying items in which the user has made errors in the past. The verification unit can also prioritize displaying items that are likely to be errors based on the user's past declaration data. Furthermore, the verification unit can analyze the user's past declaration data and suggest items that are likely to be errors. This streamlines the verification process by prioritizing the display of items that are likely to be errors based on past data. Some or all of the above processes in the verification unit are performed using AI. For example, the verification unit can input past declaration data into the AI ​​and have the AI ​​display items that are likely to be errors.

[0086] The verification unit can apply different verification algorithms depending on the user's occupation and source of income. For example, if the user is self-employed, the verification unit will focus on verifying items related to income and expenses. If the user is a salaried employee, the verification unit can focus on verifying items related to salary income. Furthermore, if the user is an investor, the verification unit can focus on verifying items related to investment returns. This streamlines the verification process by providing verification tailored to the user's occupation and source of income. Some or all of the above processing in the verification unit is performed using AI. For example, the verification unit can input data on the user's occupation and source of income into the AI ​​and have the AI ​​execute the application of the verification algorithm.

[0087] The verification unit can estimate the user's emotions and determine the priority of the verification process based on those emotions. For example, if the user is nervous, the verification unit may have them review important items first and then review the details later. If the user is relaxed, the verification unit may have them review the detailed items in order. Furthermore, if the user is in a hurry, the verification unit may prioritize the most important items. This streamlines the verification process by providing a priority system tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the verification unit are performed using AI. For example, the verification unit can input user emotion data into the AI ​​and have the AI ​​determine the priority of the verification process.

[0088] The verification unit can adjust the priority of verification based on when the tax return is filed. For example, if the filing deadline is approaching, the verification unit will prioritize checking the most important items. If the filing deadline is far away, the verification unit can also check detailed items in order. Furthermore, if the filing deadline is imminent, the verification unit can apply an algorithm to expedite the verification process. This streamlines the verification process by providing priorities according to the filing date. Some or all of the above processes in the verification unit are performed using AI. For example, the verification unit can input the filing date data of the tax return into the AI ​​and have the AI ​​adjust the verification priority.

[0089] The verification unit can improve the accuracy of its verification by referring to relevant documents in the tax return. For example, the verification unit can check items that are likely to be incorrect based on the relevant documents in the tax return. The verification unit can also perform accurate verification by referring to relevant documents in the tax return. Furthermore, the verification unit can improve the accuracy of its verification based on the relevant documents in the tax return. This makes accurate verification possible by improving the accuracy of verification based on relevant documents. Some or all of the above processes in the verification unit are performed using AI. For example, the verification unit can input the relevant documents data from the tax return into the AI ​​and have the AI ​​perform the verification accuracy improvement.

[0090] The submission unit can estimate the user's emotions and adjust the display of the submission interface based on the estimated emotions. For example, if the user is nervous, the submission unit can provide a simple and highly visible interface. If the user is relaxed, it can also provide an interface with detailed information. Furthermore, if the user is in a hurry, it can provide a concise interface. This makes the submission process more comfortable by providing an interface that responds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the submission unit is performed using AI. For example, the submission unit can input user emotion data into the AI ​​and have the AI ​​adjust the display of the submission interface.

[0091] The submission unit can suggest the optimal submission method by referring to past submission history. For example, the submission unit can suggest the optimal submission method based on the submission method the user has used in the past. It can also suggest the most efficient submission method based on the user's past submission history. Furthermore, the submission unit can analyze the user's past submission history and suggest the most suitable submission method. This streamlines the submission process by suggesting the optimal submission method based on past submission history. Some or all of the above processes in the submission unit are performed using AI. For example, the submission unit can input past submission history data into the AI ​​and have the AI ​​suggest the optimal submission method.

[0092] The submission unit can apply different submission algorithms depending on the user's occupation and source of income. For example, if the user is self-employed, the unit will suggest a submission method that emphasizes items related to income and expenses. If the user is a salaried employee, the unit can also suggest a submission method that emphasizes items related to salary income. Furthermore, if the user is an investor, the unit can suggest a submission method that emphasizes items related to investment returns. This streamlines the submission process by providing a submission method tailored to the user's occupation and source of income. Some or all of the above processing in the submission unit is performed using AI. For example, the submission unit can input data on the user's occupation and source of income into the AI ​​and have the AI ​​execute the application of the submission algorithm.

[0093] The submission unit can estimate the user's emotions and determine the priority of submissions based on those emotions. For example, if the user is nervous, the unit may prompt them to submit important items first and then details later. If the user is relaxed, the unit may prompt them to submit detailed items in order. Furthermore, if the user is in a hurry, the unit may prioritize the submission of the most important items. This streamlines the submission process by providing submission priorities that are tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the submission unit is performed using AI. For example, the submission unit can input user emotion data into the AI ​​and have the AI ​​determine the submission priority.

[0094] The filing department can adjust the priority of tax returns based on when they are submitted. For example, if the filing deadline is approaching, the department will prioritize submitting the most important items. If the filing deadline is far away, the department can also submit detailed items in order. Furthermore, if the filing deadline is imminent, the department can apply an algorithm to expedite submission. This streamlines the filing process by providing prioritization based on the submission timing. Some or all of the above processes in the filing department are performed using AI. For example, the filing department can input tax return submission timing data into the AI ​​and have the AI ​​adjust the submission priority.

[0095] The submission department can improve the accuracy of the submission by referring to the relevant documents in the declaration. For example, the submission department can identify items that are likely to be incorrect based on the relevant documents in the declaration. The submission department can also make accurate submissions by referring to the relevant documents in the declaration. Furthermore, the submission department can improve the accuracy of the submission based on the relevant documents. This makes accurate submissions possible by improving the accuracy of the submission based on the relevant documents. Some or all of the above processes in the submission department are performed using AI. For example, the submission department can input the relevant documents data from the declaration into the AI ​​and have the AI ​​perform the task of improving the accuracy of the submission.

[0096] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0097] The tax return support system can estimate the user's emotions and adjust the tax return generation method based on those emotions. For example, if the user is stressed, the generation unit can generate a concise and to-the-point tax return. If the user is relaxed, it can generate a tax return with detailed explanations. Furthermore, if the user is in a hurry, it can generate a visually easy-to-understand tax return. This makes the tax return process more comfortable by generating a tax return that is tailored to the user's emotions. Emotion estimation is achieved using an emotion engine or a generation AI. Some or all of the above-mentioned processes in the generation unit are performed using the generation AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI adjust the tax return generation method.

[0098] The tax return support system can prioritize displaying relevant input fields by considering the user's geographical location. For example, if a user lives in a specific region, it can display tax incentives relevant to that region. Furthermore, if a user lives in a specific country, it can display input fields based on that country's tax laws. Additionally, if a user lives in a specific city, it can display the city's unique tax regulations. This streamlines the input process by providing input fields based on geographical location information. Some or all of the above processing in the input section is performed using AI. For example, the input section can input the user's geographical location information into the AI, allowing the AI ​​to display relevant input fields.

[0099] The tax return support system can estimate the user's emotions and adjust the input interface design based on those emotions. For example, if the user is stressed, it can provide a simple interface and minimize the input steps. If the user is relaxed, it can provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, it can prioritize voice input to allow for quick input of necessary information. This makes the input process more comfortable by providing an interface that responds to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Some or all of the above processing in the input section is performed using AI. For example, the input section can input user emotion data into the AI ​​and have the AI ​​adjust the interface design.

[0100] The tax return support system can analyze a user's past input history and suggest the optimal input method. For example, it can automatically display items that the user has frequently entered in the past as suggestions. It can also prioritize suggesting input methods that the user has used in the past (voice, text, etc.). Furthermore, it can predict and suggest items that the user will use at specific times of the day based on their past input history. This streamlines the input process by suggesting the optimal input method based on past input history. Some or all of the above processing in the input section is performed using AI. For example, the input section can input the user's past input history data into the AI ​​and have the AI ​​suggest the optimal input method.

[0101] The tax return support system can estimate the user's emotions and determine input priorities based on those emotions. For example, if the user is nervous, it can prompt them to enter important items first and then details later. If the user is relaxed, it can prompt them to enter detailed items in order. Furthermore, if the user is in a hurry, it can prioritize the most important items. This streamlines the input process by providing input priorities that match the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Some or all of the above processing in the input section is performed using AI. For example, the input section can input user emotion data into the AI ​​and have the AI ​​determine the input priorities.

[0102] The tax return support system can customize input fields based on the user's current living situation and areas of interest. For example, if the user is self-employed, fields related to income and expenses will be displayed preferentially. If the user is elderly, fields related to pensions and medical expense deductions can be displayed preferentially. Furthermore, if the user is an investor, fields related to stocks and real estate can be displayed preferentially. This streamlines the input process by providing input fields tailored to the user's situation. Some or all of the above processing in the input section is performed using AI. For example, the input section can input data on the user's living situation and areas of interest into the AI, and have the AI ​​perform the customization of input fields.

[0103] The tax return support system can estimate the user's emotions and adjust the presentation of the generated tax return based on those emotions. For example, if the user is relaxed, it can generate a tax return with detailed explanations. If the user is in a hurry, it can generate a concise and to-the-point tax return. Furthermore, if the user is stressed, it can generate a visually easy-to-understand tax return. This makes the tax return process more comfortable by generating a tax return that is tailored to the user's emotions. Emotion estimation is achieved using an emotion engine or a generative AI. Some or all of the above processing in the generation unit is performed using the generative AI. For example, the generation unit can input user emotion data into the generative AI and have the generative AI adjust the presentation of the tax return.

[0104] The tax return support system can determine the priority of data generation based on the filing date of the tax return. For example, if the filing deadline is approaching, it will prioritize generating the most important items. If the filing deadline is far away, it can generate detailed items in order. Furthermore, if the filing deadline is imminent, it can apply an algorithm to generate the data quickly. This streamlines the tax return process by providing priorities according to the filing date. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can input the filing date data of the tax return into the generation AI and have the generation AI determine the generation priority.

[0105] The tax return support system can analyze a user's social media activity and suggest relevant input fields. For example, it can suggest input fields based on the user's frequently mentioned areas of interest on social media. It can also suggest relevant input fields based on location information shared by the user on social media. Furthermore, it can suggest relevant input fields based on the accounts the user follows on social media. This streamlines the input process by providing input fields based on social media activity. Some or all of the above processing in the input section is performed using AI. For example, the input section can input the user's social media activity data into the AI ​​and have the AI ​​suggest relevant input fields.

[0106] The tax return support system can estimate the user's emotions and adjust the display method of the confirmation interface based on the estimated emotions. For example, if the user is nervous, it can provide a simple and highly visible display method. If the user is relaxed, it can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, it can provide a display method that gets straight to the point. This makes the confirmation process more comfortable by providing an interface that responds to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Some or all of the above processing in the confirmation unit is performed using AI. For example, the confirmation unit can input user emotion data into the AI ​​and have the AI ​​adjust the display method of the confirmation interface.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The input unit allows the user to input information. This can be done using, for example, a keyboard or touchscreen. The input unit can also accept voice input. For example, speech recognition technology can be used to convert the user's voice into text. Furthermore, the input unit can improve input efficiency by referring to past input history. For example, it may have a function to automatically complete previously entered information. Step 2: The generation unit uses a generation AI to generate a tax return based on the information entered by the input unit. The generation unit can, for example, use a natural language generation AI to automatically generate the contents of the tax return. The generation unit can also learn from past tax return data and suggest the optimal filing method. For example, it can suggest the most efficient filing method based on past tax return data. Step 3: The verification unit checks the contents of the declaration form generated by the generation unit. For example, the verification unit checks whether there are any errors in the contents of the declaration form. The verification unit can also suggest corrections if errors are found. For example, it can automatically detect data entry errors or calculation errors and suggest corrections. Step 4: The submission unit submits the declaration form that has been verified by the verification unit. The submission unit can submit the declaration form in various ways, such as online or by mail. The submission unit also has a function to monitor the progress of the submission in real time. For example, it can check whether the submission has been completed and notify the user.

[0109] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0110] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0111] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0112] Each of the multiple elements described above, including the input unit, generation unit, verification unit, and submission unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the input unit can input information using the receiving device 38 of the smart device 14. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12 and generates a declaration form using a generation AI. The verification unit is implemented in the specific processing unit 290 of the data processing unit 12 and verifies the contents of the generated declaration form. The submission unit can submit the declaration form using the communication I / F 44 of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0113] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0114] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0115] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0116] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0117] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0119] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0120] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0121] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0122] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0123] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0124] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0125] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0126] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0127] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0128] Each of the multiple elements described above, including the input unit, generation unit, verification unit, and submission unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the input unit can perform voice input using the microphone 238 of the smart glasses 214. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and generates a declaration form using generation AI. The verification unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and verifies the contents of the generated declaration form. The submission unit can submit the declaration form, for example, using the communication I / F 44 of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0129] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0130] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0131] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0132] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0133] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0135] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0136] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0137] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0138] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0139] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0140] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0141] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0142] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0143] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0144] Each of the multiple elements described above, including the input unit, generation unit, verification unit, and submission unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the input unit can perform voice input using the microphone 238 of the headset terminal 314. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and generates a declaration form using a generation AI. The verification unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and verifies the contents of the generated declaration form. The submission unit can submit the declaration form using, for example, the communication I / F 44 of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0145] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0146] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0147] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0148] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0149] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0151] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0152] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0153] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0154] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0155] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0156] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0157] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0158] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0159] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0160] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0161] Each of the multiple elements described above, including the input unit, generation unit, verification unit, and submission unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the input unit can perform voice input using the microphone 238 of the robot 414. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and generates a declaration form using a generation AI. The verification unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and verifies the contents of the generated declaration form. The submission unit can submit the declaration form using, for example, the communication I / F 44 of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0162] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0163] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0164] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0165] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0166] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0167] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0168] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0169] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0170] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0171] 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.

[0172] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0173] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0174] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0175] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0176] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0177] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0178] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0179] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0180] (Note 1) An input section for entering information, A generation unit that generates a declaration form based on the information entered by the input unit, A verification unit that verifies the declaration form generated by the generation unit, A submission unit for submitting the declaration form that has been verified by the aforementioned verification unit, Equipped with A system characterized by the following features. (Note 2) The generating unit is The AI ​​generates the tax return. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is It learns from past tax return data and suggests the optimal filing method. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned verification unit is Review the generated tax return and propose corrections if any errors are found. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned submission section, Submit the verified declaration form. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned input unit is It estimates the user's emotions and adjusts the input interface design based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned input unit is It analyzes the user's past input history and suggests the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned input unit is Customize input fields based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned input unit is It estimates the user's emotions and determines the priority of inputs based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned input unit is The system prioritizes displaying input fields that are highly relevant to the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned input unit is Analyzes users' social media activity and suggests relevant input fields. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is We estimate the user's emotions and adjust the way the declaration is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is Refer to past tax return data to select the most suitable tax return format. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is Apply different generation algorithms depending on the user's occupation and income source. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is It estimates the user's emotions and adjusts the length of the declaration generated based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is The priority of generation is determined based on the filing date of the tax return. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is Adjust the generation order based on the relevance of the declarations. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned verification unit is It estimates the user's emotions and adjusts how the confirmation interface is displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned verification unit is By referring to past declaration data, items that are more likely to be errors will be displayed preferentially. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned verification unit is Different verification algorithms are applied depending on the user's occupation and source of income. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned verification unit is The system estimates the user's emotions and determines the priority of confirmations based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned verification unit is Prioritizing verification based on the filing date of the tax return. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned verification unit is Referencing relevant documents in the declaration will improve the accuracy of the verification. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned submission section, It estimates the user's emotions and adjusts how the submission interface is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned submission section, We will suggest the optimal submission method by referring to your past submission history. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned submission section, Apply different submission algorithms depending on the user's occupation and income source. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned submission section, The system estimates the user's emotions and determines the priority of submissions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned submission section, Prioritizing filing based on the filing date. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned submission section, Refer to relevant documents in your tax return to improve the accuracy of your submission. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0181] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. An input section for entering information, A generation unit that generates a declaration form based on the information entered by the input unit, A verification unit that verifies the declaration form generated by the generation unit, A submission unit for submitting the declaration form that has been verified by the aforementioned verification unit, Equipped with A system characterized by the following features.

2. The generating unit is The AI ​​generates the tax return. The system according to feature 1.

3. The generating unit is It learns from past tax return data and suggests the optimal filing method. The system according to feature 1.

4. The aforementioned verification unit is Review the generated tax return and propose corrections if any errors are found. The system according to feature 1.

5. The aforementioned submission section, Submit the verified declaration form. The system according to feature 1.

6. The aforementioned input unit is It estimates the user's emotions and adjusts the input interface design based on those estimated emotions. The system according to feature 1.

7. The aforementioned input unit is It analyzes the user's past input history and suggests the optimal input method. The system according to feature 1.

8. The aforementioned input unit is Customize input fields based on the user's current lifestyle and areas of interest. The system according to feature 1.

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