System

Abstract

An object of a system according to an embodiment is to simply and smoothly advance a final tax return process.SOLUTION: A system includes an information collection part, an information arrangement part, a declaration preparation part, a troubleshooting part, a progress management part, and a law information provision part. The information collection unit collects input information from a user. The information organization unit organizes the information collected by the information collection unit. The declaration preparation part automatically prepares an Tax Return Form based on the information arranged by the information arrangement part. The troubleshooting unit predicts a trouble that occurs in the course of the final tax return and provides advice. The progress management unit manages a progress status of the final tax return and transmits a reminder. The statute information provision unit provides the latest statutes and rules related to the final tax return.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, the process of filing tax returns was complicated and difficult, especially for self-employed individuals who also have side jobs.

[0005] The system according to the embodiment aims to make the tax return process simple and smooth. [Means for solving the problem]

[0006] The system according to the embodiment includes an information collection unit, an information organization unit, a tax return preparation unit, a troubleshooting unit, a progress management unit, and a legal information provision unit. The information collection unit collects information input by a user. The information organization unit organizes the information collected by the information collection unit. The tax return preparation unit automatically prepares a tax return based on the information organized by the information organization unit. The troubleshooting unit predicts problems that may arise during the tax return filing process and provides advice. The progress management unit manages the progress of tax returns and sends reminders. The legal information provision unit provides the latest laws and regulations related to tax returns. [Effects of the Invention]

[0007] The system according to the embodiment allows the tax return process to proceed easily and smoothly. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The tax return support system according to an embodiment of the present invention is a system that allows people who work part-time or as sole proprietors to file tax returns easily, simply, and smoothly. In this system, a generation AI collects and organizes basic information for tax returns, automatically creates tax returns, and performs troubleshooting. As a result, the tax return support system can streamline the tax return process and perform troubleshooting.

[0029] The tax return support system according to the embodiment includes an information collection unit, an information organization unit, a tax return preparation unit, a troubleshooting unit, a progress management unit, and a legal information provision unit. The information collection unit collects input information from a user. For example, the information can be collected in the form of text input, voice input, or image input. The information collection unit also supports retrieval from a database or input through a user interface. The information organization unit organizes the collected information. For example, the information can be organized using a data classification method or an organization algorithm. The tax return preparation unit automatically prepares a tax return based on the organized information. For example, the information can enter income and expense data into appropriate forms and perform necessary calculations. The troubleshooting unit predicts problems that may arise during the tax return process and provides advice. For example, the troubleshooting unit provides appropriate answers to questions about the eligibility of specific deductions and how to record expenses. The progress management unit manages the progress of tax return preparation and sends reminders. For example, the information collection unit can send reminders when the filing deadline is approaching to prompt the user to take necessary action. The legal information provision unit provides the latest laws and regulations related to tax returns. For example, the system collects and notifies users of the latest information on tax reforms, the addition of new deductions, etc. This allows the tax return support system to streamline the tax return process and troubleshoot problems.

[0030] The information collection unit automatically analyzes the user's past transaction history, learns income and expense patterns, and can predict the next input. For example, the information collection unit uses a generation AI to analyze the user's transaction history over the past year and learn income and expense patterns. For example, it automatically recognizes monthly income and recurring expenses and predicts the next input. The information collection unit also uses a generation AI to automatically classify income and expense categories based on the user's transaction history and provide predicted data for the next tax return. For example, it automatically classifies expenses such as transportation and communication costs. The information collection unit also uses a generation AI to analyze the user's transaction history and learn income and expense patterns based on seasonal fluctuations and specific events. For example, it predicts year-end bonuses or expenses related to specific projects. This allows the next input to be predicted based on the user's past transaction history.

[0031] The information collection unit can automatically import transaction data from the user's bank account and credit card, and automatically classify income and expenses. For example, the generation AI in the information collection unit automatically imports transaction data from the user's bank account and credit card, and automatically classifies income and expenses. For example, it automatically recognizes payroll transfers and credit card expenditures. The information collection unit also analyzes transaction data from the user's bank account and credit card, and the generation AI automatically classifies income and expense categories. For example, it automatically classifies expenses such as food and utility bills. The information collection unit also allows the generation AI to learn income and expense patterns based on the user's transaction data and provide predicted data for the next tax return. For example, it automatically recognizes recurring income and expenses. This allows the generation AI to automatically import transaction data from the user's bank account and credit card, and automatically classify income and expenses.

[0032] The information collection unit supports voice input, allowing the user to collect and organize information simply by speaking. For example, the information collection unit uses a generation AI to support voice input, collecting and organizing income and expense information simply by speaking. For example, if a user says, "My income this month is 500,000 yen," it is automatically registered as income data. The information collection unit also uses voice recognition technology to convert the user's voice input into text data and organize the income and expense information. For example, if a user says, "My transportation expenses are 10,000 yen," it is automatically registered as expense data. The information collection unit also uses a generation AI to analyze voice input and automatically categorize income and expenses. For example, if a user says, "My food expenses are 20,000 yen," it is automatically classified as food expenses. This allows the user to collect and organize the necessary information simply by speaking.

[0033] The information collection unit can work with other accounting software to seamlessly integrate data. For example, the generation AI in the information collection unit works with other accounting software to seamlessly integrate data. For example, it automatically imports data from the accounting software used by the user. The information collection unit also connects with other accounting software via API, allowing the generation AI to automatically obtain and organize income and expense data. For example, it obtains data in real time from cloud accounting software. The information collection unit also synchronizes data with other accounting software to centrally manage income and expense information. For example, it integrates data from multiple accounting software and organizes the information required for tax returns. This allows it to work with other accounting software to seamlessly integrate data.

[0034] The tax return preparation unit can analyze past tax returns and automatically select the optimal tax return format. In the tax return preparation unit, for example, a generation AI analyzes past tax returns and automatically selects the optimal tax return format. For example, an appropriate form is selected based on the content of the past tax return. The tax return preparation unit also analyzes the user's past tax return data, and the generation AI suggests the optimal tax return format. For example, a form is selected based on income and expense patterns. In the tax return preparation unit, a generation AI analyzes past tax returns and automatically selects the optimal tax return format. For example, an appropriate form is selected when there are many specific deduction items. This makes it possible to analyze past tax returns and automatically select the optimal tax return format.

[0035] The tax return preparation unit can provide a tax return template specialized for the user's industry or occupation. For example, the generation AI in the tax return preparation unit provides a tax return template specialized for the user's industry or occupation. For example, a template for sole proprietors in the delivery industry is automatically generated. The tax return preparation unit also provides a tax return template according to the user's industry or occupation. For example, a template for freelance designers is automatically generated. The generation AI in the tax return preparation unit also provides a tax return template specialized for the user's industry or occupation. For example, a template for sole proprietors in the food and beverage industry is automatically generated. This makes it possible to provide a tax return template specialized for the user's industry or occupation.

[0036] The tax return preparation unit generates a draft tax return and can provide an interface that allows the user to make corrections in real time. In the tax return preparation unit, for example, the generation AI generates a draft tax return and provides an interface that allows the user to make corrections in real time. For example, the user makes corrections based on the draft. In addition, the generation AI provides an interface that allows the user to make corrections to the draft tax return in real time. For example, the contents of the draft are edited and immediately reflected. In addition, the tax return preparation unit generates a draft tax return and provides an interface that allows the user to make corrections in real time. For example, the contents of the draft are checked and corrections are entered. In this way, a draft tax return can be generated and an interface that allows the user to make corrections in real time can be provided.

[0037] The tax return preparation unit can automatically create tax-related documents. In the tax return preparation unit, for example, the generation AI automatically creates not only final tax returns but also other tax-related documents (e.g., tax payment certificates). For example, it generates tax payment certificates based on income and expense data. In addition, in the tax return preparation unit, the generation AI automatically creates other tax-related documents based on the user's income and expense data. For example, it generates tax payment certificates and deduction certificates. In addition, in the tax return preparation unit, the generation AI automatically creates other tax-related documents along with final tax returns. For example, it generates tax payment certificates and income certificates. This allows other tax-related documents to be automatically created.

[0038] The troubleshooting unit can learn from past trouble cases, predict similar troubles, and provide advice in advance. For example, the generation AI of the troubleshooting unit learns from past trouble cases, predicts similar troubles, and provides advice in advance. For example, it predicts troubles related to specific deduction items and provides appropriate advice. The troubleshooting unit also analyzes the user's past trouble cases, and the generation AI predicts similar troubles. For example, it predicts troubles related to how expenses are recorded and provides advice. The troubleshooting unit also learns from past trouble cases, predicts similar troubles, and provides advice in advance. For example, it predicts troubles related to errors in filling out tax returns and provides advice. In this way, the system can learn from past trouble cases, predict similar troubles, and provide advice in advance.

[0039] The troubleshooting unit can analyze the user's question history and provide the most appropriate answer. In the troubleshooting unit, for example, the generation AI analyzes the user's past question history and provides the most appropriate answer. For example, if a similar question has been asked in the past, appropriate advice is provided based on the answer. In addition, the troubleshooting unit provides related information based on the user's question history. For example, relevant tax laws and regulations are presented based on the content of past questions. In addition, the troubleshooting unit analyzes the user's question history and provides the most appropriate answer. For example, the generation AI learns past questions and answers and quickly answers similar questions. This makes it possible to analyze the user's question history and provide the most appropriate answer.

[0040] The troubleshooting unit can share trouble cases from other users and provide community-based solutions. For example, the generation AI can share trouble cases from other users and provide community-based solutions. For example, it can present solutions to users who have faced the same problem. The troubleshooting unit can also store trouble cases from other users in a database, and the generation AI can provide advice based on that. For example, it can present solutions that refer to past cases. The troubleshooting unit can also share trouble cases from other users and provide community-based solutions. For example, it can provide a forum where users can exchange opinions with each other. This allows other users to share trouble cases and provide community-based solutions.

[0041] The troubleshooting unit can provide a video tutorial when troubleshooting. For example, the troubleshooting unit provides a video tutorial when the generation AI is troubleshooting. For example, a video explaining how to solve a specific problem. In addition, the troubleshooting unit allows the generation AI to suggest an appropriate video tutorial depending on the problem the user is facing. For example, a video explaining how to fill out a tax return. In addition, the troubleshooting unit provides a video tutorial when the generation AI is troubleshooting. For example, a video explaining how to record expenses. In this way, a video tutorial can be provided when troubleshooting.

[0042] The progress management unit can analyze the user's schedule and suggest the optimal timing for reporting work. In the progress management unit, for example, the generation AI analyzes the user's schedule and suggests the optimal timing for reporting work. For example, it finds the user's free time and suggests reporting work. The progress management unit also analyzes the user's calendar and schedule and the generation AI suggests the optimal timing for reporting work. For example, it suggests reporting work on days when there are no important appointments. The progress management unit also analyzes the user's schedule and suggests the optimal timing for reporting work. For example, it suggests reporting work that avoids the user's peak work hours. In this way, the user's schedule can be analyzed and the optimal timing for reporting work can be suggested.

[0043] The progress management unit visualizes the progress status in real time and can present the user with a specific next step. In the progress management unit, for example, the generation AI visualizes the progress status in real time and presents the user with a specific next step. For example, the current progress status is displayed in a graph and the next work to be done is presented. The progress management unit also analyzes the user's progress in real time and the generation AI presents the specific next step. For example, the incomplete tasks are listed and the next work to be done is presented. In addition, the progress management unit visualizes the progress status in real time and presents the user with a specific next step. For example, the progress status is displayed on a dashboard and the next work to be done is presented. This makes it possible to visualize the progress status in real time and present the user with a specific next step.

[0044] The progress management unit can work in conjunction with other task management tools to centrally manage the progress of tax returns. In the progress management unit, for example, the generation AI works in conjunction with other task management tools to centrally manage the progress of tax returns. For example, it imports data from task management tools and integrates the progress status. The progress management unit also works in conjunction with other task management tools via API, so that the generation AI can centrally manage the progress of tax returns. For example, it synchronizes data from task management tools in real time. The progress management unit also works in conjunction with other task management tools so that the generation AI can centrally manage the progress of tax returns. For example, it automatically updates tasks in the task management tool and visualizes the progress status. This allows the generation AI to work in conjunction with other task management tools to centrally manage the progress of tax returns.

[0045] The progress management unit can provide incentives to the user according to the progress status. For example, the generation AI provides incentives to the user according to the progress status. For example, a reward is provided when a specific progress is achieved. The progress management unit also analyzes the user's progress status and the generation AI provides incentives. For example, bonus points are provided when progress is going well. The progress management unit also provides incentives to the user according to the generation AI according to the progress status. For example, an incentive is provided along with an encouraging message when progress is lagging. In this way, incentives can be provided to the user according to the progress status.

[0046] The legal information provision unit can analyze the history of changes to laws and regulations and predict the impact on the user. In the legal information provision unit, for example, the generation AI analyzes the history of changes to laws and regulations and predicts the impact on the user. For example, it predicts the impact of tax reforms on the user's tax return. In addition, in the legal information provision unit, the generation AI analyzes the history of changes to laws and regulations based on the user's past tax return data and predicts the impact. For example, it predicts whether new deduction items will be applied to the user. In addition, in the legal information provision unit, the generation AI analyzes the history of changes to laws and regulations and predicts the impact on the user. For example, it predicts the impact that tax reforms for a specific industry will have on the user. In this way, it is possible to analyze the history of changes to laws and regulations and predict the impact on the user.

[0047] The legal information providing unit can provide information on laws and regulations specialized for the user's industry or occupation. For example, the generation AI provides information on laws and regulations specialized for the user's industry or occupation. For example, it provides tax law information for sole proprietors in the delivery industry. The legal information providing unit also provides information on laws and regulations according to the user's industry or occupation. For example, it provides tax law information for freelance designers. The legal information providing unit also provides information on laws and regulations specialized for the user's industry or occupation. For example, it provides tax law information for sole proprietors in the food and beverage industry. This makes it possible to provide information on laws and regulations specialized for the user's industry or occupation.

[0048] The legal information provision unit can work in conjunction with other legal-related tools to centrally manage information on laws and regulations. For example, the generation AI in the legal information provision unit works in conjunction with other legal-related tools to centrally manage information on laws and regulations. For example, it automatically obtains the latest legal information from a legal database. The legal information provision unit also works in conjunction with other legal-related tools via API, allowing the generation AI to centrally manage information on laws and regulations. For example, it synchronizes data from legal software in real time. The legal information provision unit also works in conjunction with other legal-related tools to centrally manage information on laws and regulations. For example, it integrates data from multiple legal tools to provide the latest legal information. This allows it to work in conjunction with other legal-related tools to centrally manage information on laws and regulations.

[0049] The legal information provision unit can provide webinars regarding changes in laws and regulations. For example, the generation AI provides webinars regarding changes in laws and regulations. For example, the latest information on tax reforms is explained in a webinar. The legal information provision unit also provides webinars regarding changes in laws and regulations that users can participate in. For example, a webinar is held that includes explanations by experts. The legal information provision unit also provides webinars regarding changes in laws and regulations. For example, the generation AI provides webinars regarding changes in laws and regulations. For example, the webinar explains details about new deduction items. This makes it possible to provide webinars regarding changes in laws and regulations.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The tax return support system can further include a health management unit that monitors the user's health condition. The health management unit, for example, monitors the user's heart rate and stress level in real time and provides advice based on the user's health condition. For example, it suggests breathing techniques to relax if the heart rate is high. The health management unit also analyzes the user's health data, and the generation AI makes suggestions for improving lifestyle habits based on the user's health condition. For example, it suggests appropriate sleep times if the user is sleep deprived. The health management unit also monitors the user's health condition, and the generation AI provides an exercise program for maintaining health. For example, it suggests stretching exercises for users who do a lot of desk work. This makes it possible to monitor the user's health condition and provide advice for maintaining health.

[0052] The tax return support system can further include a refreshment suggestion unit based on the user's hobbies and interests. The refreshment suggestion unit, for example, analyzes the user's hobbies and interests, and the generation AI suggests activities for refreshing. For example, relaxing music is suggested for a user who likes music. The refreshment suggestion unit also analyzes the user's past activity data, and the generation AI suggests new hobbies and interests. For example, new books are suggested for a user who likes reading. The refreshment suggestion unit also analyzes the user's stress level, and the generation AI suggests specific activities for refreshing. For example, refreshing methods such as walking or yoga are suggested. This makes it possible to make refreshment suggestions based on the user's hobbies and interests.

[0053] The tax return support system can further include a learning support unit that supports the user's learning. The learning support unit, for example, provides learning materials to help the user deepen their knowledge about tax returns. For example, it provides online courses to learn the basics of tax law and accounting. The learning support unit also analyzes the user's learning progress, and the generation AI proposes an appropriate learning plan. For example, it provides a customized learning plan based on the user's level of understanding. The learning support unit also analyzes the user's learning history, and the generation AI suggests points that need to be reviewed. For example, it suggests re-studying questions that were previously answered incorrectly. This allows the user to receive learning support and deepen their knowledge about tax returns.

[0054] The tax return support system can further include a feedback collection unit that collects user feedback. The feedback collection unit, for example, provides an interface for users to provide feedback after using the system. For example, it may conduct a survey on usability and areas for improvement. The feedback collection unit also analyzes the user feedback, and the generation AI identifies areas for improvement in the system. For example, it may add new functions based on user opinions. The feedback collection unit also makes suggestions to the generation AI to improve system performance based on user feedback. For example, it may suggest improvements to operability or bug fixes. In this way, user feedback can be collected and used to improve the system.

[0055] The tax return support system can further include a network utilization unit that utilizes the user's network. The network utilization unit, for example, provides a platform where users can exchange information with each other. For example, it provides a forum for sharing questions and advice about tax returns. The network utilization unit also analyzes the user's network, and the generation AI introduces appropriate experts. For example, it introduces experts such as tax accountants and accountants. The network utilization unit also utilizes the user's network, and the generation AI provides opportunities for collaboration. For example, it provides a platform where users in the same industry can work together to file tax returns. This makes it possible to utilize the user's network to exchange information and introduce experts.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The information collection unit collects input information from the user. For example, information can be collected in the form of text input, voice input, image input, etc. The information collection unit also supports retrieval from a database or input through a user interface. Step 2: The information organizer organizes the collected information, for example, using a data classification method or an organization algorithm. Step 3: The tax return preparation section automatically prepares the tax return based on the organized information, for example by entering income and expense data into the appropriate forms and performing the necessary calculations. Step 4: The Troubleshooting Department anticipates and advises on problems that may arise during the tax return process, such as providing appropriate answers to questions about the eligibility of certain deductions or how to account for expenses. Step 5: The progress management department manages the progress of tax returns and sends reminders. For example, when the filing deadline approaches, it sends reminders to prompt necessary work. Step 6: The Legal Information Department provides the latest laws and regulations related to tax returns. For example, it collects the latest information on tax reforms and the addition of new deduction items, and notifies the user.

[0058] (Example 2) The tax return support system according to an embodiment of the present invention is a system that allows people who work part-time or as sole proprietors to file tax returns easily, simply, and smoothly. In this system, a generation AI collects and organizes basic information for tax returns, automatically creates tax returns, and performs troubleshooting. As a result, the tax return support system can streamline the tax return process and perform troubleshooting.

[0059] The tax return support system according to the embodiment includes an information collection unit, an information organization unit, a tax return preparation unit, a troubleshooting unit, a progress management unit, and a legal information provision unit. The information collection unit collects input information from a user. For example, the information can be collected in the form of text input, voice input, or image input. The information collection unit also supports retrieval from a database or input through a user interface. The information organization unit organizes the collected information. For example, the information can be organized using a data classification method or an organization algorithm. The tax return preparation unit automatically prepares a tax return based on the organized information. For example, the information can enter income and expense data into appropriate forms and perform necessary calculations. The troubleshooting unit predicts problems that may arise during the tax return process and provides advice. For example, the troubleshooting unit provides appropriate answers to questions about the eligibility of specific deductions and how to record expenses. The progress management unit manages the progress of tax return preparation and sends reminders. For example, the information collection unit can send reminders when the filing deadline is approaching to prompt the user to take necessary action. The legal information provision unit provides the latest laws and regulations related to tax returns. For example, the system collects and notifies users of the latest information on tax reforms, the addition of new deductions, etc. This allows the tax return support system to streamline the tax return process and troubleshoot problems.

[0060] The information collection unit automatically analyzes the user's past transaction history, learns income and expense patterns, and can predict the next input. For example, the information collection unit uses a generation AI to analyze the user's transaction history over the past year and learn income and expense patterns. For example, it automatically recognizes monthly income and recurring expenses and predicts the next input. The information collection unit also uses a generation AI to automatically classify income and expense categories based on the user's transaction history and provide predicted data for the next tax return. For example, it automatically classifies expenses such as transportation and communication costs. The information collection unit also uses a generation AI to analyze the user's transaction history and learn income and expense patterns based on seasonal fluctuations and specific events. For example, it predicts year-end bonuses or expenses related to specific projects. This allows the next input to be predicted based on the user's past transaction history.

[0061] The information collection unit can automatically import transaction data from the user's bank account and credit card, and automatically classify income and expenses. For example, the generation AI in the information collection unit automatically imports transaction data from the user's bank account and credit card, and automatically classifies income and expenses. For example, it automatically recognizes payroll transfers and credit card expenditures. The information collection unit also analyzes transaction data from the user's bank account and credit card, and the generation AI automatically classifies income and expense categories. For example, it automatically classifies expenses such as food and utility bills. The information collection unit also allows the generation AI to learn income and expense patterns based on the user's transaction data and provide predicted data for the next tax return. For example, it automatically recognizes recurring income and expenses. This allows the generation AI to automatically import transaction data from the user's bank account and credit card, and automatically classify income and expenses.

[0062] The information collection unit supports voice input, allowing the user to collect and organize information simply by speaking. For example, the information collection unit uses a generation AI to support voice input, collecting and organizing income and expense information simply by speaking. For example, if a user says, "My income this month is 500,000 yen," it is automatically registered as income data. The information collection unit also uses voice recognition technology to convert the user's voice input into text data and organize the income and expense information. For example, if a user says, "My transportation expenses are 10,000 yen," it is automatically registered as expense data. The information collection unit also uses a generation AI to analyze voice input and automatically categorize income and expenses. For example, if a user says, "My food expenses are 20,000 yen," it is automatically classified as food expenses. This allows the user to collect and organize the necessary information simply by speaking.

[0063] The information collection unit can work with other accounting software to seamlessly integrate data. For example, the generation AI in the information collection unit works with other accounting software to seamlessly integrate data. For example, it automatically imports data from the accounting software used by the user. The information collection unit also connects with other accounting software via API, allowing the generation AI to automatically obtain and organize income and expense data. For example, it obtains data in real time from cloud accounting software. The information collection unit also synchronizes data with other accounting software to centrally manage income and expense information. For example, it integrates data from multiple accounting software and organizes the information required for tax returns. This allows it to work with other accounting software to seamlessly integrate data.

[0064] The information collection unit can use the emotion estimation function to detect the stress level of the user when entering data and provide a relaxing interface. The information collection unit, for example, uses the emotion estimation function to detect the stress level of the user when entering data in real time and provide a relaxing interface. For example, relaxing music is played when stress is high. The information collection unit also analyzes the user's facial expressions and voice, and the emotion estimation function detects the stress level. For example, the color tone of the interface is changed when stress is high. The information collection unit also uses the emotion estimation function to detect the stress level of the user when entering data and provide advice to help the user relax. For example, a message encouraging deep breathing is displayed. In this way, the stress level of the user when entering data can be detected and a relaxing interface can be provided.

[0065] The tax return preparation unit can analyze past tax returns and automatically select the optimal tax return format. In the tax return preparation unit, for example, a generation AI analyzes past tax returns and automatically selects the optimal tax return format. For example, an appropriate form is selected based on the content of the past tax return. The tax return preparation unit also analyzes the user's past tax return data, and the generation AI suggests the optimal tax return format. For example, a form is selected based on income and expense patterns. In the tax return preparation unit, a generation AI analyzes past tax returns and automatically selects the optimal tax return format. For example, an appropriate form is selected when there are many specific deduction items. This makes it possible to analyze past tax returns and automatically select the optimal tax return format.

[0066] The tax return preparation unit can provide a tax return template specialized for the user's industry or occupation. For example, the generation AI in the tax return preparation unit provides a tax return template specialized for the user's industry or occupation. For example, a template for sole proprietors in the delivery industry is automatically generated. The tax return preparation unit also provides a tax return template according to the user's industry or occupation. For example, a template for freelance designers is automatically generated. The generation AI in the tax return preparation unit also provides a tax return template specialized for the user's industry or occupation. For example, a template for sole proprietors in the food and beverage industry is automatically generated. This makes it possible to provide a tax return template specialized for the user's industry or occupation.

[0067] The tax return preparation unit can use the emotion estimation function to provide guidance to reduce anxiety felt by the user when checking the tax return. The tax return preparation unit, for example, uses the emotion estimation function to detect anxiety felt by the user when checking the tax return in real time and provide guidance. For example, a reassuring message is displayed if the user feels anxious. The tax return preparation unit also analyzes the user's facial expression and voice, and the emotion estimation function detects anxiety. For example, if the user feels anxious, the tax return preparation unit provides detailed instructions on how to check the tax return. The tax return preparation unit also uses the emotion estimation function to provide guidance to reduce anxiety felt by the user when checking the tax return. For example, a support chat is provided if the user feels anxious. This makes it possible to provide guidance to reduce anxiety felt by the user when checking the tax return.

[0068] The tax return preparation unit generates a draft tax return and can provide an interface that allows the user to make corrections in real time. In the tax return preparation unit, for example, the generation AI generates a draft tax return and provides an interface that allows the user to make corrections in real time. For example, the user makes corrections based on the draft. In addition, the generation AI provides an interface that allows the user to make corrections to the draft tax return in real time. For example, the contents of the draft are edited and immediately reflected. In addition, the tax return preparation unit generates a draft tax return and provides an interface that allows the user to make corrections in real time. For example, the contents of the draft are checked and corrections are entered. In this way, a draft tax return can be generated and an interface that allows the user to make corrections in real time can be provided.

[0069] The tax return preparation unit can automatically create tax-related documents. In the tax return preparation unit, for example, the generation AI automatically creates not only final tax returns but also other tax-related documents (e.g., tax payment certificates). For example, it generates tax payment certificates based on income and expense data. In addition, in the tax return preparation unit, the generation AI automatically creates other tax-related documents based on the user's income and expense data. For example, it generates tax payment certificates and deduction certificates. In addition, in the tax return preparation unit, the generation AI automatically creates other tax-related documents along with final tax returns. For example, it generates tax payment certificates and income certificates. This allows other tax-related documents to be automatically created.

[0070] The tax return preparation unit can use the emotion estimation function to analyze the emotion a user feels when submitting a tax return and provide follow-up after submission. The tax return preparation unit, for example, uses the emotion estimation function to analyze the emotion a user feels when submitting a tax return in real time and provide follow-up after submission. For example, a reassuring message is displayed after submission. The tax return preparation unit also analyzes the user's facial expressions and voice, and the emotion estimation function analyzes the emotion. For example, a support chat is provided if the user feels anxious after submission. The tax return preparation unit also uses the emotion estimation function to analyze the emotion a user feels when submitting a tax return and provide follow-up after submission. For example, a message of gratitude is displayed after submission. This makes it possible to analyze the emotion a user feels when submitting a tax return and provide follow-up after submission.

[0071] The troubleshooting unit can learn from past trouble cases, predict similar troubles, and provide advice in advance. For example, the generation AI of the troubleshooting unit learns from past trouble cases, predicts similar troubles, and provides advice in advance. For example, it predicts troubles related to specific deduction items and provides appropriate advice. The troubleshooting unit also analyzes the user's past trouble cases, and the generation AI predicts similar troubles. For example, it predicts troubles related to how expenses are recorded and provides advice. The troubleshooting unit also learns from past trouble cases, predicts similar troubles, and provides advice in advance. For example, it predicts troubles related to errors in filling out tax returns and provides advice. In this way, the system can learn from past trouble cases, predict similar troubles, and provide advice in advance.

[0072] The troubleshooting unit can analyze the user's question history and provide the most appropriate answer. In the troubleshooting unit, for example, the generation AI analyzes the user's past question history and provides the most appropriate answer. For example, if a similar question has been asked in the past, appropriate advice is provided based on the answer. In addition, the troubleshooting unit provides related information based on the user's question history. For example, relevant tax laws and regulations are presented based on the content of past questions. In addition, the troubleshooting unit analyzes the user's question history and provides the most appropriate answer. For example, the generation AI learns past questions and answers and quickly answers similar questions. This makes it possible to analyze the user's question history and provide the most appropriate answer.

[0073] The troubleshooting unit can use the emotion estimation function to analyze the emotions of the user when faced with a problem and provide advice to reduce stress. For example, the troubleshooting unit can use the emotion estimation function to analyze the emotions of the user when faced with a problem in real time and provide advice to reduce stress. For example, if the user is feeling anxious, advice to help the user relax is provided. The troubleshooting unit also analyzes the user's facial expressions and voice, and the emotion estimation function analyzes the emotions. For example, if the user is feeling stressed, specific advice to help reduce stress is provided. The troubleshooting unit also uses the emotion estimation function to analyze the emotions of the user when faced with a problem and provide advice to help reduce stress. For example, if the user is feeling impatient, advice to help the user stay calm is provided. In this way, the emotions of the user when faced with a problem can be analyzed and advice to help reduce stress can be provided.

[0074] The troubleshooting unit can share trouble cases from other users and provide community-based solutions. For example, the generation AI can share trouble cases from other users and provide community-based solutions. For example, it can present solutions to users who have faced the same problem. The troubleshooting unit can also store trouble cases from other users in a database, and the generation AI can provide advice based on that. For example, it can present solutions that refer to past cases. The troubleshooting unit can also share trouble cases from other users and provide community-based solutions. For example, it can provide a forum where users can exchange opinions with each other. This allows other users to share trouble cases and provide community-based solutions.

[0075] The troubleshooting unit can provide a video tutorial when troubleshooting. For example, the troubleshooting unit provides a video tutorial when the generation AI is troubleshooting. For example, a video explaining how to solve a specific problem. In addition, the troubleshooting unit allows the generation AI to suggest an appropriate video tutorial depending on the problem the user is facing. For example, a video explaining how to fill out a tax return. In addition, the troubleshooting unit provides a video tutorial when the generation AI is troubleshooting. For example, a video explaining how to record expenses. In this way, a video tutorial can be provided when troubleshooting.

[0076] The troubleshooting unit uses the emotion estimation function to analyze the emotions felt by the user when solving a problem, and allows the user to share his or her successful experience. For example, the troubleshooting unit uses the emotion estimation function to analyze the emotions felt by the user when solving a problem in real time, and allows the user to share his or her successful experience. For example, if the user feels joy, the user shares the experience with other users. The troubleshooting unit also analyzes the user's facial expressions and voice, and the emotion estimation function analyzes the emotions. For example, if the user feels a sense of accomplishment, the user shares the experience with other users. The troubleshooting unit also uses the emotion estimation function to analyze the emotions felt by the user when solving a problem, and allows the user to share his or her successful experience. For example, if the user feels satisfaction, the user shares the experience with other users. In this way, the emotions felt by the user when solving a problem can be analyzed, and the user can share his or her successful experience.

[0077] The progress management unit can analyze the user's schedule and suggest the optimal timing for reporting work. In the progress management unit, for example, the generation AI analyzes the user's schedule and suggests the optimal timing for reporting work. For example, it finds the user's free time and suggests reporting work. The progress management unit also analyzes the user's calendar and schedule and the generation AI suggests the optimal timing for reporting work. For example, it suggests reporting work on days when there are no important appointments. The progress management unit also analyzes the user's schedule and suggests the optimal timing for reporting work. For example, it suggests reporting work that avoids the user's peak work hours. In this way, the user's schedule can be analyzed and the optimal timing for reporting work can be suggested.

[0078] The progress management unit visualizes the progress status in real time and can present the user with a specific next step. In the progress management unit, for example, the generation AI visualizes the progress status in real time and presents the user with a specific next step. For example, the current progress status is displayed in a graph and the next work to be done is presented. The progress management unit also analyzes the user's progress in real time and the generation AI presents the specific next step. For example, the incomplete tasks are listed and the next work to be done is presented. In addition, the progress management unit visualizes the progress status in real time and presents the user with a specific next step. For example, the progress status is displayed on a dashboard and the next work to be done is presented. This makes it possible to visualize the progress status in real time and present the user with a specific next step.

[0079] The progress management unit can use the emotion estimation function to provide reminders for the user to maintain motivation for progress management. The progress management unit, for example, uses the emotion estimation function to provide reminders for the user to maintain motivation for progress management. For example, if the user is losing motivation, the progress management unit sends an encouraging message. The progress management unit also analyzes the user's facial expressions and voice, and the emotion estimation function detects motivation. For example, if the user is tired, the progress management unit sends a reminder to take a break. The progress management unit also uses the emotion estimation function to provide reminders for the user to maintain motivation for progress management. For example, if the user is approaching a goal, the progress management unit sends an encouraging message. This makes it possible to provide reminders for the user to maintain motivation for progress management.

[0080] The progress management unit can work in conjunction with other task management tools to centrally manage the progress of tax returns. In the progress management unit, for example, the generation AI works in conjunction with other task management tools to centrally manage the progress of tax returns. For example, it imports data from task management tools and integrates the progress status. The progress management unit also works in conjunction with other task management tools via API, so that the generation AI can centrally manage the progress of tax returns. For example, it synchronizes data from task management tools in real time. The progress management unit also works in conjunction with other task management tools so that the generation AI can centrally manage the progress of tax returns. For example, it automatically updates tasks in the task management tool and visualizes the progress status. This allows the generation AI to work in conjunction with other task management tools to centrally manage the progress of tax returns.

[0081] The progress management unit can provide incentives to the user according to the progress status. For example, the generation AI provides incentives to the user according to the progress status. For example, a reward is provided when a specific progress is achieved. The progress management unit also analyzes the user's progress status and the generation AI provides incentives. For example, bonus points are provided when progress is going well. The progress management unit also provides incentives to the user according to the generation AI according to the progress status. For example, an incentive is provided along with an encouraging message when progress is lagging. In this way, incentives can be provided to the user according to the progress status.

[0082] The progress management unit can use the emotion estimation function to analyze the user's emotions regarding progress management and provide positive feedback. The progress management unit, for example, uses the emotion estimation function to analyze the user's emotions regarding progress management in real time and provide positive feedback. For example, if the user is feeling anxious, it sends an encouraging message. The progress management unit also analyzes the user's facial expressions and voice, and the emotion estimation function analyzes the emotions. For example, if the user has positive emotions regarding progress, it sends a compliment. The progress management unit also uses the emotion estimation function to analyze the user's emotions regarding progress management and provide positive feedback. For example, if the user has negative emotions regarding progress, it sends an encouraging message. This makes it possible to analyze the user's emotions regarding progress management and provide positive feedback.

[0083] The legal information provision unit can analyze the history of changes to laws and regulations and predict the impact on the user. In the legal information provision unit, for example, the generation AI analyzes the history of changes to laws and regulations and predicts the impact on the user. For example, it predicts the impact of tax reforms on the user's tax return. In addition, in the legal information provision unit, the generation AI analyzes the history of changes to laws and regulations based on the user's past tax return data and predicts the impact. For example, it predicts whether new deduction items will be applied to the user. In addition, in the legal information provision unit, the generation AI analyzes the history of changes to laws and regulations and predicts the impact on the user. For example, it predicts the impact that tax reforms for a specific industry will have on the user. In this way, it is possible to analyze the history of changes to laws and regulations and predict the impact on the user.

[0084] The legal information providing unit can provide information on laws and regulations specialized for the user's industry or occupation. For example, the generation AI provides information on laws and regulations specialized for the user's industry or occupation. For example, it provides tax law information for sole proprietors in the delivery industry. The legal information providing unit also provides information on laws and regulations according to the user's industry or occupation. For example, it provides tax law information for freelance designers. The legal information providing unit also provides information on laws and regulations specialized for the user's industry or occupation. For example, it provides tax law information for sole proprietors in the food and beverage industry. This makes it possible to provide information on laws and regulations specialized for the user's industry or occupation.

[0085] The legal information providing unit can use the emotion estimation function to provide guidance to help the user reduce anxiety about changes in laws, regulations, and rules. The legal information providing unit, for example, uses the emotion estimation function to detect a user's anxiety about changes in laws, regulations, and rules in real time and provide guidance. For example, if the user is feeling anxious, a reassuring message is displayed. The legal information providing unit also analyzes the user's facial expressions and voice, and the emotion estimation function detects anxiety. For example, if the user is feeling anxious, the legal information providing unit provides a detailed explanation of changes in laws, regulations, and rules. The legal information providing unit also uses the emotion estimation function to provide guidance to help the user reduce anxiety about changes in laws, regulations, and rules. For example, if the user is feeling anxious, a support chat is provided. In this way, guidance can be provided to help the user reduce anxiety about changes in laws, regulations, and rules.

[0086] The legal information provision unit can work in conjunction with other legal-related tools to centrally manage information on laws and regulations. For example, the generation AI in the legal information provision unit works in conjunction with other legal-related tools to centrally manage information on laws and regulations. For example, it automatically obtains the latest legal information from a legal database. The legal information provision unit also works in conjunction with other legal-related tools via API, allowing the generation AI to centrally manage information on laws and regulations. For example, it synchronizes data from legal software in real time. The legal information provision unit also works in conjunction with other legal-related tools to centrally manage information on laws and regulations. For example, it integrates data from multiple legal tools to provide the latest legal information. This allows it to work in conjunction with other legal-related tools to centrally manage information on laws and regulations.

[0087] The legal information provision unit can provide webinars regarding changes in laws and regulations. For example, the generation AI provides webinars regarding changes in laws and regulations. For example, the latest information on tax reforms is explained in a webinar. The legal information provision unit also provides webinars regarding changes in laws and regulations that users can participate in. For example, a webinar is held that includes explanations by experts. The legal information provision unit also provides webinars regarding changes in laws and regulations. For example, the generation AI provides webinars regarding changes in laws and regulations. For example, the webinar explains details about new deduction items. This makes it possible to provide webinars regarding changes in laws and regulations.

[0088] The legal information providing unit can use the emotion estimation function to analyze the emotions a user feels when receiving information about laws and regulations, and provide support to deepen their understanding. For example, the legal information providing unit can use the emotion estimation function to analyze the emotions a user feels when receiving information about laws and regulations in real time, and provide support to deepen their understanding. For example, if the user feels anxious, the legal information providing unit provides additional explanations. The legal information providing unit also analyzes the user's facial expressions and voice, and the emotion estimation function analyzes the emotions. For example, if the user is confused, the legal information providing unit provides easy-to-understand explanations about the information about laws and regulations. The legal information providing unit also uses the emotion estimation function to analyze the emotions a user feels when receiving information about laws and regulations, and provide support to deepen their understanding. For example, if the user has questions, the legal information providing unit provides a Q&A session. This makes it possible to analyze the emotions a user feels when receiving information about laws and regulations, and provide support to deepen their understanding.

[0089] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0090] The tax return support system can further include a health management unit that monitors the user's health condition. The health management unit, for example, monitors the user's heart rate and stress level in real time and provides advice based on the user's health condition. For example, it suggests breathing techniques to relax if the heart rate is high. The health management unit also analyzes the user's health data, and the generation AI makes suggestions for improving lifestyle habits based on the user's health condition. For example, it suggests appropriate sleep times if the user is sleep deprived. The health management unit also monitors the user's health condition, and the generation AI provides an exercise program for maintaining health. For example, it suggests stretching exercises for users who do a lot of desk work. This makes it possible to monitor the user's health condition and provide advice for maintaining health.

[0091] The tax return support system can further include a refreshment suggestion unit based on the user's hobbies and interests. The refreshment suggestion unit, for example, analyzes the user's hobbies and interests, and the generation AI suggests activities for refreshing. For example, relaxing music is suggested for a user who likes music. The refreshment suggestion unit also analyzes the user's past activity data, and the generation AI suggests new hobbies and interests. For example, new books are suggested for a user who likes reading. The refreshment suggestion unit also analyzes the user's stress level, and the generation AI suggests specific activities for refreshing. For example, refreshing methods such as walking or yoga are suggested. This makes it possible to make refreshment suggestions based on the user's hobbies and interests.

[0092] The tax return support system can further include a learning support unit that supports the user's learning. The learning support unit, for example, provides learning materials to help the user deepen their knowledge about tax returns. For example, it provides online courses to learn the basics of tax law and accounting. The learning support unit also analyzes the user's learning progress, and the generation AI proposes an appropriate learning plan. For example, it provides a customized learning plan based on the user's level of understanding. The learning support unit also analyzes the user's learning history, and the generation AI suggests points that need to be reviewed. For example, it suggests re-studying questions that were previously answered incorrectly. This allows the user to receive learning support and deepen their knowledge about tax returns.

[0093] The tax return support system can further include a feedback collection unit that collects user feedback. The feedback collection unit, for example, provides an interface for users to provide feedback after using the system. For example, it may conduct a survey on usability and areas for improvement. The feedback collection unit also analyzes the user feedback, and the generation AI identifies areas for improvement in the system. For example, it may add new functions based on user opinions. The feedback collection unit also makes suggestions to the generation AI to improve system performance based on user feedback. For example, it may suggest improvements to operability or bug fixes. In this way, user feedback can be collected and used to improve the system.

[0094] The tax return support system can further include a network utilization unit that utilizes the user's network. The network utilization unit, for example, provides a platform where users can exchange information with each other. For example, it provides a forum for sharing questions and advice about tax returns. The network utilization unit also analyzes the user's network, and the generation AI introduces appropriate experts. For example, it introduces experts such as tax accountants and accountants. The network utilization unit also utilizes the user's network, and the generation AI provides opportunities for collaboration. For example, it provides a platform where users in the same industry can work together to file tax returns. This makes it possible to utilize the user's network to exchange information and introduce experts.

[0095] The tax return support system can further include an emotion advice unit that estimates the user's emotions and provides customized advice based on the emotions. The emotion advice unit, for example, analyzes the user's facial expressions and voice, and the emotion estimation function analyzes the user's emotions in real time. For example, if the user is feeling anxious, the emotion advice unit provides advice on how to relax. The emotion advice unit also provides customized advice based on the user's emotions with a generation AI. For example, if the user is feeling stressed, the emotion advice unit provides specific advice on how to reduce stress. The emotion advice unit also analyzes the user's emotions, and the generation AI suggests ways to refresh based on the emotions. For example, if the user is tired, the generation AI suggests activities to refresh. This makes it possible to provide customized advice based on the user's emotions.

[0096] The tax return support system can further include an emotion feedback unit that estimates the user's emotions and provides emotion-based feedback to improve motivation. The emotion feedback unit, for example, analyzes the user's facial expressions and voice, and the emotion estimation function analyzes the user's emotions in real time. For example, if the user is losing motivation, the emotion feedback unit sends an encouraging message. The emotion feedback unit also provides specific feedback to improve motivation based on the user's emotions, with the generation AI. For example, if the user is tired, the emotion feedback unit sends a message encouraging the user to take a break. The emotion feedback unit also analyzes the user's emotions, with the generation AI providing emotion-based positive feedback. For example, if the user has negative emotions about progress, the emotion feedback unit sends an encouraging message. This makes it possible to provide feedback to improve motivation based on the user's emotions.

[0097] The tax return support system can further include an emotion stress management unit that estimates the user's emotions and provides advice for stress management based on the emotions. The emotion stress management unit, for example, analyzes the user's facial expressions and voice, and the emotion estimation function analyzes the user's emotions in real time. For example, if the user is feeling stressed, the emotion stress management unit provides advice on how to relax. The emotion stress management unit also has a generation AI that provides specific advice for stress management based on the user's emotions. For example, if the user is feeling impatient, the emotion stress management unit provides advice on how to stay calm. The emotion stress management unit also analyzes the user's emotions, and the generation AI suggests a method of refreshing based on the emotions. For example, if the user is tired, the generation AI suggests an activity to refresh them. This makes it possible to provide advice for stress management based on the user's emotions.

[0098] The tax return support system can further include an emotion relaxation unit that estimates the user's emotions and provides relaxation methods based on the emotions. The emotion relaxation unit, for example, analyzes the user's facial expressions and voice, and the emotion estimation function analyzes the user's emotions in real time. For example, if the user is tense, it plays music to help the user relax. The emotion relaxation unit also has a generation AI that suggests relaxation methods based on the user's emotions. For example, if the user is feeling stressed, it suggests specific activities to help the user relax. The emotion relaxation unit also analyzes the user's emotions, and the generation AI suggests refreshment methods based on the emotions. For example, if the user is tired, it suggests activities to refresh the user. This makes it possible to provide relaxation methods based on the user's emotions.

[0099] The tax return support system can further include an emotion support unit that estimates the user's emotions and provides customized support based on the emotions. The emotion support unit, for example, analyzes the user's facial expressions and voice, and the emotion estimation function analyzes the user's emotions in real time. For example, if the user is feeling anxious, the emotion support unit sends a reassuring message. The emotion support unit also provides customized support based on the user's emotions with a generation AI. For example, if the user is feeling stressed, the generation AI provides specific advice for reducing stress. The emotion support unit also analyzes the user's emotions, and the generation AI suggests ways to refresh based on the emotions. For example, if the user is tired, the generation AI suggests activities to refresh them. This makes it possible to provide customized support based on the user's emotions.

[0100] The processing flow of the second embodiment will be briefly explained below.

[0101] Step 1: The information collection unit collects input information from the user. For example, information can be collected in the form of text input, voice input, image input, etc. The information collection unit also supports retrieval from a database or input through a user interface. Step 2: The information organizer organizes the collected information, for example, using a data classification method or an organization algorithm. Step 3: The tax return preparation section automatically prepares the tax return based on the organized information, for example by entering income and expense data into the appropriate forms and performing the necessary calculations. Step 4: The Troubleshooting Department anticipates and advises on problems that may arise during the tax return process, such as providing appropriate answers to questions about the eligibility of certain deductions or how to account for expenses. Step 5: The progress management department manages the progress of tax returns and sends reminders. For example, when the filing deadline approaches, it sends reminders to prompt necessary work. Step 6: The Legal Information Department provides the latest laws and regulations related to tax returns. For example, it collects the latest information on tax reforms and the addition of new deduction items, and notifies the user.

[0102] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0103] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0104] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0105] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0106] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0107] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0108] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0109] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0110] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0111] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0112] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0113] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0114] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0115] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0116] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0117] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0118] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0119] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0120] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0121] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0122] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0123] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0124] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0125] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0126] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0127] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0128] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0129] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0130] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0131] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0132] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0133] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0134] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0135] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0136] 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.

[0137] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0138] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0139] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0141] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0142] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0143] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0144] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0145] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0146] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0147] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0148] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0149] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0150] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0151] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0152] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0153] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0154] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0155] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0156] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0157] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0158] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0159] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0160] 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.

[0161] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0162] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0163] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0164] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0165] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0166] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0167] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0168] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

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

Claims

1. an information collection unit that collects input information from a user; an information organizing unit that organizes the information collected by the information collecting unit; a tax return preparation unit that automatically prepares a tax return based on the information organized by the information organization unit; A troubleshooting department that predicts problems that may arise during the tax return process and provides advice. A progress management department that manages the progress of tax returns and sends reminders; A legal information providing department that provides the latest laws and regulations related to tax returns. A system characterized by:

2. The information collecting unit Automatically analyzes the user's past transaction history, learns income and expense patterns, and predicts next inputs 2. The system of claim 1.

3. The information collecting unit Automatically imports user's bank account and credit card transaction data and automatically categorizes income and expenses 2. The system of claim 1.

4. The information collecting unit Supports voice input, allowing users to collect and organize the information simply by speaking.

2. The system of claim 1.

5. The information collecting unit Integrate with other accounting software for seamless data integration 2. The system of claim 1.

6. The information collecting unit Detects the user's stress level when typing and provides a relaxing interface 2. The system of claim 1.

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