system

Abstract

We provide a system aimed at simplifying tax procedures. [Solution] The specific processing unit 290 of the data processing device 12 in the system performs the following: collection processing to automatically import income and expense data; analysis processing to analyze the data collected by the collection processing; generation processing to generate a tax return based on the data analyzed by the analysis processing; provision processing to provide the tax return generated by the generation processing; and notification processing to notify the tax payment deadline based on the tax return provided by the provision processing.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that the process from the import of income and expense data to the generation of a tax return form and the notification of the tax payment deadline is time-consuming and difficult to perform efficiently.

[0005] The system according to the embodiment aims to simplify tax procedures.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, a provision unit, and a notification unit. The collection unit automatically imports income and expense data. The analysis unit analyzes the data collected by the collection unit. The generation unit generates a tax return based on the data analyzed by the analysis unit. The provision unit provides the tax return generated by the generation unit. The notification unit notifies the taxpayer of the tax payment deadline based on the tax return provided by the provision unit. [Effects of the Invention]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The system for simplifying tax procedures according to an embodiment of the present invention is a system that automatically imports and analyzes income and expense data, generates and provides tax returns, and notifies taxpayers of the tax payment deadline. This system works in conjunction with accounting services to provide an automated system that automatically imports income and expense data. Next, by managing tax-related documents and data in the cloud, it becomes accessible from anywhere, reducing the workload of accounting and improving work efficiency. Furthermore, by building a tax return system and connecting it to e-TAX, it reduces manual work and prevents errors. In addition, it introduces a tool that can calculate the tax amount in real time based on data such as income, expenses, and deductions, making it easier to grasp the prospect of the tax amount. A reminder function incorporating a tax payment deadline notification function is created to prevent missing deadlines. An AI chatbot is introduced to provide guides on frequently asked questions and inquiries, reducing the burden on staff by automating question and answer. It analyzes tax payment history and provides feedback to support proactive measures. By linking with financial institutions via API and importing expense data into the accounting system, it automates data entry work. The target audience includes sole proprietors, freelancers, small and medium-sized enterprises, individuals with side businesses, and general individuals unfamiliar with tax matters. To address the challenges these target groups face—understanding the complex types and mechanisms of taxes, adapting to legal changes, and dealing with cumbersome tasks—the system reduces workload and minimizes errors through automation and integration with various systems. It utilizes generation AI to automatically generate tax returns, provide support via chatbots, perform data analysis and predictions, and check for errors. As a result, the system simplifies tax procedures, handling everything from automatic import of income and expense data to the generation, provision, and notification of tax returns.

[0029] The system for simplifying tax procedures according to the embodiment comprises a collection unit, an analysis unit, a generation unit, a provision unit, and a notification unit. The collection unit automatically imports income and expense data. The collection unit automatically acquires data from accounting software, for example, using API integration. The collection unit can also import data uploaded by users using a file import function. Furthermore, the collection unit can instantly import data from financial institutions using a real-time data acquisition function. The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes data trends, for example, using statistical analysis. The analysis unit can also identify data patterns using machine learning algorithms. Furthermore, the analysis unit can extract useful information from the data using data mining techniques. The generation unit generates tax returns based on the data analyzed by the analysis unit. The generation unit creates tax returns in a standard format, for example, using a template-based generation method. The generation unit can also create customized tax returns according to user data using a dynamic generation method. The provision unit provides the tax returns generated by the generation unit. The provisioning unit, for example, sends tax returns to users via email. The provisioning unit can also make tax returns available for download through a web portal. Furthermore, the provisioning unit can integrate tax returns with other systems via APIs. The notification unit notifies taxpayers of the tax deadline based on the tax returns provided by the provisioning unit. The notification unit can remind users, for example, using push notifications. It can also notify taxpayers of the tax deadline via SMS. Furthermore, it can notify taxpayers of the tax deadline via email. This allows the system, which simplifies tax procedures, to handle everything from automatic import of income and expense data to the generation, provision, and notification of tax returns.

[0030] The data collection unit automatically imports income and expense data. For example, it automatically retrieves data from accounting software using API integration. Specifically, the data collection unit periodically retrieves data on the user's income and expenses using the accounting software's API. This eliminates the need for users to manually enter data. The data collection unit can also import data uploaded by users using its file import function. For example, when a user uploads a CSV or Excel file, the data collection unit analyzes these files, extracts the necessary data, and imports it into the system. Furthermore, the data collection unit can instantly import data from financial institutions using its real-time data acquisition function. For example, it can use a bank's API to acquire the user's account transaction information in real time and reflect it in the system as income and expense data. This allows the data collection unit to efficiently collect income and expense data from diverse data sources, significantly reducing the user's workload. In addition, the data collection unit is equipped with functions to detect duplicate data and cleanse data to maintain data integrity. This ensures that the collected data is accurate and consistent, leading to reliable results in subsequent analysis and generation processes.

[0031] The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes data trends using statistical analysis. Specifically, it visualizes monthly and annual income and expenditure trends in graphs and charts based on collected income and expense data. The analysis unit can also identify data patterns using machine learning algorithms. For example, by training on past data, it can predict future income and expenses. Furthermore, the analysis unit can extract useful information from the data using data mining techniques. For example, it can detect months with abnormally high specific expense items and conduct detailed analysis to identify the cause. In this way, the analysis unit can analyze the collected data from multiple perspectives and provide useful insights to users. In addition, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data and issue warnings early. For example, it can detect sudden large expenditures or fluctuations in income and notify users to encourage early action. In this way, the analysis unit can not only grasp the situation in real time but also handle long-term risk management and anomaly detection, improving the reliability and security of the entire system.

[0032] The generation unit generates tax returns based on data analyzed by the analysis unit. For example, the generation unit creates standardized tax returns using a template-based generation method. Specifically, it generates returns quickly and accurately by automatically embedding the analyzed data into pre-prepared templates. The generation unit can also create customized tax returns tailored to the user's data using a dynamic generation method. For example, it automatically adjusts the necessary items and formatting according to the user's industry and income type to generate an optimal tax return. Furthermore, the generation unit has a function to automatically verify the content of the generated tax return, checking for errors and deficiencies. This allows users to submit their tax returns with confidence. The generation unit can also generate various documents and reports required by the user. For example, it automatically creates monthly and annual income and expenditure reports, expense statements, and various documents for submission to the tax office, significantly reducing the user's workload. This allows the generation unit to quickly and accurately generate a variety of documents tailored to the user's needs, streamlining tax procedures.

[0033] The provisioning unit provides tax returns generated by the generation unit. The provisioning unit sends the returns to users, for example, via email. Specifically, it attaches the generated returns in PDF format and sends them to the user's registered email address. The provisioning unit can also make returns available for download via a web portal. Users can log in to a dedicated web portal and download the generated returns at any time. Furthermore, the provisioning unit can integrate returns with other systems via API. For example, it can integrate with a company's internal system or the tax office's online filing system to automatically send returns. This allows the provisioning unit to provide returns to users in a variety of ways, improving convenience. In addition, the provisioning unit has a function to manage the history of provided returns and allow easy reference to past returns. This allows users to check past returns and make corrections or resubmissions as needed. By considering user convenience to the fullest and enhancing the methods of providing and managing returns, the provisioning unit can make tax procedures smoother.

[0034] The notification unit notifies users of the tax payment deadline based on the tax return provided by the service provider. The notification unit reminds users, for example, using push notifications. Specifically, it sends push notifications via a smartphone app when the tax payment deadline is approaching to alert users. The notification unit can also notify users of the tax payment deadline using SMS. It sends an SMS to the user's registered phone number informing them of the tax payment deadline to ensure that the information is reliably transmitted. Furthermore, the notification unit can also notify users of the tax payment deadline using email. It sends a reminder email to the user's registered email address stating the tax payment deadline to prevent delays in tax payment. In this way, the notification unit can notify users of the tax payment deadline in a variety of ways, enabling them to proceed with the tax payment process smoothly. In addition to tax payment deadlines, the notification unit can also provide other important tax-related notifications. For example, it can notify users of information regarding tax law revisions and the introduction of new tax rules, providing them with the latest information. In this way, the notification unit helps users stay informed of the latest tax information and take appropriate action. The notification section is equipped with a variety of notification methods and information provision functions to improve user convenience and streamline tax procedures.

[0035] The data collection unit can automatically import income and expense data in conjunction with accounting software. For example, the data collection unit can automatically retrieve data from accounting software using API integration. The data collection unit can exchange data with accounting software via API, enabling real-time import of income and expense data. Furthermore, the data collection unit can import data files exported from accounting software using a file exchange function. For example, it can import CSV files and save them to the database. Additionally, the data collection unit can directly retrieve data from the accounting software's database using database integration. For example, it can extract necessary data using SQL queries and import it into the system. This enables automatic import of income and expense data in conjunction with accounting software. Some or all of the above-described processes in the data collection unit may be performed using AI, or not. For example, the data collection unit can input data acquired from accounting software into AI, allowing the AI ​​to perform data formatting and filtering.

[0036] The analysis unit can calculate tax amounts in real time based on data such as income, expenses, and deductions. For example, the analysis unit integrates income and expense data and calculates tax amounts considering deductions. The analysis unit updates data in real time and calculates tax amounts based on the latest information. The analysis unit can also use machine learning algorithms to build predictive models for tax amounts from historical data. For example, it can learn from past income and expense data to predict future tax amounts. Furthermore, the analysis unit can use data mining techniques to extract useful patterns from the data and reflect them in the calculation of tax amounts. For example, it can analyze the impact of specific expense categories on tax amounts. This makes it easier to get a clear picture of tax amounts by calculating tax amounts in real time. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input income and expense data into AI and have the AI ​​perform the calculation of tax amounts.

[0037] The notification unit can provide a tax payment deadline notification function. For example, the notification unit can remind users using push notifications. When the tax payment deadline approaches, the notification unit sends a push notification to the user's smartphone or tablet to inform them of the deadline. The notification unit can also notify users of the tax payment deadline using SMS. For example, it can send an SMS to the user's mobile phone to remind them of the tax payment deadline. Furthermore, the notification unit can also notify users of the tax payment deadline using email. For example, it can send a tax payment deadline notification to the user's email address. This prevents users from missing the deadline through the tax payment deadline notification function. Some or all of the above processes in the notification unit may be performed using AI or not. For example, the notification unit can input tax payment deadline data into AI and have the AI ​​calculate the optimal notification timing.

[0038] The AI ​​chatbot can provide guides for frequently asked questions and inquiries. For example, the AI ​​chatbot can automatically respond to user questions based on an FAQ list. The AI ​​chatbot analyzes the question entered by the user, searches for relevant FAQs, and provides an answer. The AI ​​chatbot can also accept user questions through an inquiry form and provide appropriate guidance. For example, if a user enters a question about a specific tax procedure, the AI ​​chatbot will display a guide regarding that procedure. Furthermore, the AI ​​chatbot can use natural language processing technology to understand user questions and generate appropriate answers. For example, if a user asks, "How do I submit my tax return?", the AI ​​chatbot will explain the procedure for submitting a tax return. By providing guides for frequently asked questions and inquiries, the AI ​​reduces the burden on staff through the automation of question and answer. Some or all of the above processes in the AI ​​chatbot may be performed using AI or not. For example, the AI ​​chatbot can input a user's question into the AI ​​and have the AI ​​generate the answer.

[0039] The analysis unit can analyze tax payment history and provide feedback. For example, it can analyze past tax amounts and payment dates to identify tax payment patterns. Based on the tax payment history, the analysis unit can predict future tax amounts and propose tax payment schedules. The analysis unit can also analyze tax payment methods and propose the most suitable method. For example, it can determine whether electronic tax payment is appropriate based on past tax payment history. Furthermore, the analysis unit can assess tax risks based on tax payment history and propose risk mitigation measures. For example, if a certain deduction has not been applied based on past tax payment history, it can propose the application of that deduction. In this way, the analysis of tax payment history and feedback provide support for proactive measures. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input tax payment history data into AI and have the AI ​​generate feedback.

[0040] The Integration Department can connect with financial institutions via APIs and import expense data into the accounting system. For example, the Integration Department can obtain data from financial institutions using a REST API. The Integration Department obtains transaction data and account information through the financial institution's API and imports it into the accounting system. The Integration Department can also exchange data with financial institutions using a SOAP API. For example, it can obtain expense data from financial institutions using SOAP messages and import it into the system. Furthermore, the Integration Department can directly connect with the financial institution's database and obtain the necessary data. For example, it can extract expense data from the financial institution's database using an SQL query and import it into the system. This enables the automatic import of expense data by connecting with financial institutions via APIs. Some or all of the above processes in the Integration Department may be performed using AI or not. For example, the Integration Department can input data obtained from financial institutions into AI and have the AI ​​perform data formatting and filtering.

[0041] The data collection unit can analyze the user's past income and expense data and select the optimal acquisition method. For example, the data collection unit can analyze the data acquisition methods the user has used in the past (manual, API integration, etc.) and propose the optimal method. Based on the past data acquisition history, the data collection unit selects the most efficient acquisition method. The data collection unit can also analyze patterns in the user's past income and expense data and propose the optimal acquisition timing. For example, it can optimize the timing of income and expense data acquisition based on past data patterns. Furthermore, the data collection unit can select the most efficient acquisition method based on the user's past data acquisition history. For example, it can analyze past data acquisition history and propose the optimal acquisition method. In this way, the optimal data acquisition method can be selected by analyzing past data. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input past income and expense data into AI and have the AI ​​select the optimal acquisition method.

[0042] The data collection unit can filter income and expense data based on the user's current financial situation and areas of interest. For example, the data collection unit can analyze the user's current financial situation and prioritize the acquisition of only important data. The data collection unit prioritizes the acquisition of highly relevant data according to the user's financial situation. The data collection unit can also filter data based on the user's areas of interest (such as specific expense categories). For example, it can prioritize the acquisition of data related to expense categories of interest to the user. Furthermore, the data collection unit can prioritize the acquisition of highly relevant data according to the user's current income situation. For example, it can prioritize the acquisition of important income data based on the user's income situation. This allows for the priority acquisition of highly relevant data by filtering data based on the user's financial situation and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's financial situation data into AI and have the AI ​​perform the data filtering.

[0043] The data collection unit can prioritize the acquisition of highly relevant data by considering the user's geographical location when acquiring income and expense data. For example, if the user is in a specific region, the data collection unit will prioritize the acquisition of expense data related to that region. Based on the geographical location, the data collection unit will acquire the most relevant income data. Furthermore, if the user is on the move, the data collection unit can acquire relevant data based on their current location. For example, while the user is on the move, it will acquire expense data related to their current location and reflect it in the system. In addition, the data collection unit can acquire the most relevant income data based on the user's geographical location. For example, if the user is in a specific region, it will prioritize the acquisition of income data related to that region. This allows for the priority acquisition of highly relevant data by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's geographical location into the AI ​​and have the AI ​​prioritize data acquisition.

[0044] The data collection unit can analyze a user's social media activity and obtain relevant data when acquiring income and expense data. For example, the data collection unit can analyze a user's social media activity and obtain relevant expense data. The data collection unit filters relevant data based on social media posts, likes, follower counts, etc. The data collection unit can also acquire income data based on a user's social media posts about their income. For example, if a user makes a post about their income on social media, the data is acquired and reflected in the system. Furthermore, the data collection unit can filter and acquire relevant data from a user's social media activity. For example, it can analyze a user's social media activity and prioritize the acquisition of highly relevant data. This allows for the efficient acquisition of relevant data by analyzing a user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user social media data into AI and have the AI ​​acquire relevant data.

[0045] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on important data and a simplified analysis on less important data. The analysis unit evaluates the importance of the data and determines the priority of the analysis. The analysis unit can also determine the priority of the analysis according to the importance of the data. For example, it can apply multiple analysis algorithms to important data to provide detailed analysis results. Furthermore, the analysis unit can adjust the analysis schedule based on the importance of the data. For example, it can prioritize the analysis of important data and provide results quickly. This allows for efficient analysis by adjusting the level of detail of the analysis according to the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the importance of the data into the AI ​​and have the AI ​​adjust the level of detail of the analysis.

[0046] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit applies an income-specific analysis algorithm to income data. The analysis unit identifies the data category and selects the optimal analysis algorithm. The analysis unit can also apply an expense-specific analysis algorithm to expense data. For example, it applies an algorithm that identifies expense classifications and patterns to expense data. Furthermore, the analysis unit can apply a deduction-specific analysis algorithm to deduction data. For example, it applies an algorithm that evaluates the conditions for applying deductions to deduction data. By applying the appropriate analysis algorithm according to the data category, the accuracy of the analysis is improved. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the data category into the AI ​​and have the AI ​​select the optimal analysis algorithm.

[0047] The analysis unit can determine the priority of analysis based on the data submission date during the analysis process. For example, the analysis unit may prioritize analyzing data with approaching deadlines. The analysis unit evaluates the data submission date and determines the analysis priority. The analysis unit can also postpone analyzing data with distant submission dates. For example, it may postpone analyzing data with distant deadlines and prioritize analyzing data with approaching deadlines. Furthermore, the analysis unit can adjust the analysis schedule based on the submission date. For example, it may optimize the analysis schedule based on the submission deadline to perform analysis efficiently. This enables efficient analysis by determining the analysis priority based on the data submission date. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the data submission dates into the AI ​​and have the AI ​​determine the analysis priority.

[0048] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis process. For example, the analysis unit may prioritize the analysis of highly relevant data. The analysis unit evaluates the relevance of the data and determines the order of analysis. The analysis unit can also postpone the analysis of less relevant data. For example, it may postpone the analysis of less relevant data and prioritize the analysis of highly relevant data. Furthermore, the analysis unit can adjust the analysis schedule based on the relevance of the data. For example, it may optimize the analysis schedule based on the relevance of the data to perform the analysis efficiently. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the relevance of the data into the AI ​​and have the AI ​​determine the order of analysis.

[0049] The generation unit can adjust the level of detail of the generated data based on its importance. For example, it can generate a detailed tax return for important data. The generation unit evaluates the importance of the data and determines the generation priority. It can also generate a simplified tax return for less important data. For example, it can provide a tax return with concise information for less important data. Furthermore, the generation unit can determine the generation priority based on the importance of the data. For example, it can prioritize the generation of important data and provide results quickly. This allows for efficient generation by adjusting the level of detail of the generated data according to its importance. Some or all of the above processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input the importance of the data into the generation AI and have the generation AI adjust the level of detail of the generated data.

[0050] The generation unit can apply different generation algorithms depending on the data category during generation. For example, the generation unit applies an income-specific generation algorithm to income data. The generation unit identifies the data category and selects the optimal generation algorithm. The generation unit can also apply an expense-specific generation algorithm to expense data. For example, it applies an algorithm that identifies expense classifications and patterns to expense data. Furthermore, the generation unit can apply a deduction-specific generation algorithm to deduction data. For example, it applies an algorithm that evaluates the conditions for applying deductions to deduction data. By applying the appropriate generation algorithm according to the data category, the accuracy of generation is improved. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the data category into the generation AI and have the generation AI select the optimal generation algorithm.

[0051] The generation unit can determine the generation priority based on the data submission timing during generation. For example, the generation unit may prioritize generating data with approaching deadlines. The generation unit evaluates the data submission timing and determines the generation priority. The generation unit can also postpone generating data with distant submission deadlines. For example, it may postpone generating data with distant deadlines and prioritize generating data with approaching deadlines. Furthermore, the generation unit can adjust the generation schedule based on the submission timing. For example, it may optimize the generation schedule based on the submission deadline to generate data efficiently. This enables efficient generation by determining the generation priority based on the data submission timing. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input the data submission timing into the generation AI and have the generation AI determine the generation priority.

[0052] The generation unit can adjust the generation order based on the relevance of the data during generation. For example, the generation unit can prioritize the generation of highly relevant data. The generation unit evaluates the relevance of the data and determines the generation order. The generation unit can also postpone the generation of less relevant data. For example, it can postpone the generation of less relevant data and prioritize the generation of highly relevant data. Furthermore, the generation unit can adjust the generation schedule based on the relevance of the data. For example, it can optimize the generation schedule based on the relevance of the data to perform generation efficiently. This makes efficient generation possible by adjusting the generation order based on the relevance of the data. Some or all of the above processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input the relevance of the data into the generation AI and have the generation AI determine the generation order.

[0053] The service provider can select the optimal service method by referring to the user's past tax payment history at the time of service provision. For example, the service provider can analyze the user's past tax payment history and select the most frequently used service method. Based on the past tax payment history, the service provider can propose the optimal service method. The service provider can also propose a service method suitable for a specific time period based on the user's past tax payment history. For example, it can analyze the past tax payment history and select the most suitable service method for a specific time period. Furthermore, the service provider can select the most efficient service method based on the user's past tax payment history. For example, it can analyze the past tax payment history and propose the most efficient service method. In this way, the optimal service method can be selected by referring to the past tax payment history. Some or all of the above processes in the service provider may be performed using AI or not. For example, the service provider can input the user's past tax payment history into AI and have the AI ​​select the optimal service method.

[0054] The delivery unit can select the optimal delivery method by considering the user's device information at the time of delivery. For example, if the user is using a smartphone, the delivery unit will provide a delivery method that matches the screen size. The delivery unit selects the optimal delivery method based on the device information. Furthermore, if the user is using a tablet, the delivery unit can also provide a delivery method optimized for a larger screen. For example, when the user is using a tablet, it will provide a delivery method optimized for a larger screen. In addition, if the user is using a desktop, the delivery unit can provide a delivery method that includes detailed information. For example, when the user is using a desktop, it will provide a delivery method that includes detailed information. This allows the delivery unit to select the optimal delivery method by considering the user's device information. Some or all of the above processing in the delivery unit may be performed using AI or not. For example, the delivery unit can input the user's device information into AI and have the AI ​​select the optimal delivery method.

[0055] The notification unit can select the optimal notification method by referring to the user's past tax payment history when issuing a notification. For example, the notification unit can analyze the user's past tax payment history and select the most frequently used notification method. The notification unit proposes the optimal notification method based on the past tax payment history. The notification unit can also propose a notification method suitable for a specific time based on the user's past tax payment history. For example, it can analyze the past tax payment history and select the most suitable notification method for a specific time. Furthermore, the notification unit can select the most efficient notification method based on the user's past tax payment history. For example, it can analyze the past tax payment history and propose the most efficient notification method. In this way, the optimal notification method can be selected by referring to the past tax payment history. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input the user's past tax payment history into AI and have the AI ​​select the optimal notification method.

[0056] The notification unit can select the optimal notification method by considering the user's device information when sending a notification. For example, if the user is using a smartphone, the notification unit provides a notification method that is appropriate for the screen size. The notification unit selects the optimal notification method based on the device information. The notification unit can also provide a notification method optimized for a larger screen if the user is using a tablet. For example, it provides a notification method optimized for a larger screen when the user is using a tablet. Furthermore, the notification unit can provide a notification method that includes detailed information if the user is using a desktop. For example, it provides a notification method that includes detailed information when the user is using a desktop. This allows the system to select the optimal notification method by considering the user's device information. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input the user's device information into AI and have AI select the optimal notification method.

[0057] The AI ​​chatbot unit can provide the optimal response by referring to the user's past question history when responding to chatbot inquiries. For example, the AI ​​chatbot unit can analyze the user's past question history and provide the most relevant response. The AI ​​chatbot unit proposes the optimal response based on the past question history. The AI ​​chatbot unit can also propose the optimal response to a specific question based on the user's past question history. For example, it can analyze the past question history and select the optimal response to a specific question. Furthermore, the AI ​​chatbot unit can provide the most efficient response based on the user's past question history. For example, it can analyze the past question history and propose the most efficient response. In this way, the optimal response can be provided by referring to the past question history. Some or all of the above processes in the AI ​​chatbot unit may be performed using AI or not. For example, the AI ​​chatbot unit can input the user's past question history into the AI ​​and have the AI ​​select the optimal response.

[0058] The AI ​​chatbot unit can provide the optimal response by considering the user's device information when responding to a chatbot. For example, if the user is using a smartphone, the AI ​​chatbot unit will provide a response that is appropriate for the screen size. The AI ​​chatbot unit selects the optimal response based on the device information. Furthermore, if the user is using a tablet, the AI ​​chatbot unit can provide a response optimized for a larger screen. For example, when the user is using a tablet, it will provide a response optimized for a larger screen. In addition, if the user is using a desktop, the AI ​​chatbot unit can provide a response that includes detailed information. For example, when the user is using a desktop, it will provide a response that includes detailed information. In this way, the optimal response can be provided by considering the user's device information. Some or all of the above processing in the AI ​​chatbot unit may be performed using AI or not using AI. For example, the AI ​​chatbot unit can input the user's device information into the AI ​​and have the AI ​​select the optimal response.

[0059] The integration unit can select the optimal integration method by referring to the user's past financial transaction history during integration. For example, the integration unit can analyze the user's past financial transaction history and select the most frequently used integration method. Based on the past financial transaction history, the integration unit proposes the optimal integration method. The integration unit can also propose an integration method suitable for a specific period based on the user's past financial transaction history. For example, it can analyze the past financial transaction history and select the optimal integration method for a specific period. Furthermore, the integration unit can select the most efficient integration method based on the user's past financial transaction history. For example, it can analyze the past financial transaction history and propose the most efficient integration method. In this way, the optimal integration method can be selected by referring to the past financial transaction history. Some or all of the above processing in the integration unit may be performed using AI or not. For example, the integration unit can input the user's past financial transaction history into AI and have the AI ​​select the optimal integration method.

[0060] The integration unit can select the optimal integration method by considering the user's geographical location information during integration. For example, if the user is in a specific region, the integration unit will integrate with financial institutions related to that region. The integration unit selects the optimal integration method based on geographical location information. Furthermore, if the user is on the move, the integration unit can also select the optimal integration method based on the user's current location. For example, if the user is on the move, it will integrate with financial institutions related to the user's current location. In addition, the integration unit can select the most relevant integration method based on the user's geographical location information. For example, if the user is in a specific region, it will integrate with financial institutions related to that region. This allows the integration unit to select the optimal integration method by considering the user's geographical location information. Some or all of the above processing in the integration unit may be performed using AI or not. For example, the integration unit can input the user's geographical location information into AI and have the AI ​​select the optimal integration method.

[0061] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0062] The data collection unit can analyze the user's past income and expense data and select the optimal data acquisition method. For example, it can analyze the data acquisition methods the user has used in the past (manual, API integration, etc.) and suggest the most suitable method. It can also select the most efficient acquisition method based on past data acquisition history. Furthermore, it can analyze patterns in the user's past income and expense data and suggest the optimal acquisition timing. In this way, the optimal data acquisition method can be selected by analyzing past data. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input past income and expense data into AI and have the AI ​​select the optimal data acquisition method.

[0063] The analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, it can perform a detailed analysis on important data and a simplified analysis on less important data. It can also evaluate the importance of the data and determine the priority of the analysis. Furthermore, it can adjust the analysis schedule based on the importance of the data. This allows for efficient analysis by adjusting the level of detail according to the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the importance of the data into the AI ​​and have the AI ​​adjust the level of detail of the analysis.

[0064] The generation unit can apply different generation algorithms depending on the data category. For example, a generation algorithm specific to income can be applied to income data. Similarly, a generation algorithm specific to expenses can be applied to expense data. Furthermore, a generation algorithm specific to deductions can be applied to deduction data. By applying the appropriate generation algorithm according to the data category, the accuracy of generation can be improved. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input the data category into the generation AI and have the generation AI select the optimal generation algorithm.

[0065] The service provider can select the optimal service method by referring to the user's past tax payment history. For example, it can analyze the user's past tax payment history and select the most frequently used service method. It can also suggest the optimal service method based on the past tax payment history. Furthermore, it can suggest a service method suitable for a specific period based on the user's past tax payment history. In this way, the optimal service method can be selected by referring to the past tax payment history. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's past tax payment history into AI and have the AI ​​select the optimal service method.

[0066] The notification unit can select the optimal notification method by referring to the user's past tax payment history. For example, it can analyze the user's past tax payment history and select the most frequently used notification method. It can also suggest the optimal notification method based on the past tax payment history. Furthermore, it can suggest a notification method suitable for a specific time based on the user's past tax payment history. In this way, the optimal notification method can be selected by referring to the past tax payment history. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input the user's past tax payment history into AI and have the AI ​​perform the selection of the optimal notification method.

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

[0068] Step 1: The data collection unit automatically imports income and expense data. For example, the data collection unit automatically retrieves data from accounting software using API integration. The data collection unit can also import data uploaded by users using its file import function. Furthermore, the data collection unit can instantly import data from financial institutions using its real-time data acquisition function. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes data trends, for example, using statistical analysis. The analysis unit can also identify data patterns using machine learning algorithms. Furthermore, the analysis unit can extract useful information from the data using data mining techniques. Step 3: The generation unit generates tax returns based on the data analyzed by the analysis unit. The generation unit can, for example, use a template-based generation method to create tax returns in a standard format. Alternatively, the generation unit can use a dynamic generation method to create customized tax returns based on the user's data. Step 4: The provisioning unit provides the tax return generated by the generation unit. The provisioning unit sends the return to the user, for example, via email. The provisioning unit can also make the return available for download through a web portal. Furthermore, the provisioning unit can integrate the return with other systems via an API. Step 5: The notification unit notifies the taxpayer of the tax deadline based on the tax return provided by the service provider. The notification unit may, for example, use push notifications to remind the user. The notification unit may also notify the taxpayer of the tax deadline using SMS. Furthermore, the notification unit may also notify the taxpayer of the tax deadline using email.

[0069] (Example of form 2) The system for simplifying tax procedures according to an embodiment of the present invention is a system that automatically imports and analyzes income and expense data, generates and provides tax returns, and notifies taxpayers of the tax payment deadline. This system works in conjunction with accounting services to provide an automated system that automatically imports income and expense data. Next, by managing tax-related documents and data in the cloud, it becomes accessible from anywhere, reducing the workload of accounting and improving work efficiency. Furthermore, by building a tax return system and connecting it to e-TAX, it reduces manual work and prevents errors. In addition, it introduces a tool that can calculate the tax amount in real time based on data such as income, expenses, and deductions, making it easier to grasp the prospect of the tax amount. A reminder function incorporating a tax payment deadline notification function is created to prevent missing deadlines. An AI chatbot is introduced to provide guides on frequently asked questions and inquiries, reducing the burden on staff by automating question and answer. It analyzes tax payment history and provides feedback to support proactive measures. By linking with financial institutions via API and importing expense data into the accounting system, it automates data entry work. The target audience includes sole proprietors, freelancers, small and medium-sized enterprises, individuals with side businesses, and general individuals unfamiliar with tax matters. To address the challenges these target groups face—understanding the complex types and mechanisms of taxes, adapting to legal changes, and dealing with cumbersome tasks—the system reduces workload and minimizes errors through automation and integration with various systems. It utilizes generation AI to automatically generate tax returns, provide support via chatbots, perform data analysis and predictions, and check for errors. As a result, the system simplifies tax procedures, handling everything from automatic import of income and expense data to the generation, provision, and notification of tax returns.

[0070] The system for simplifying tax procedures according to the embodiment comprises a collection unit, an analysis unit, a generation unit, a provision unit, and a notification unit. The collection unit automatically imports income and expense data. The collection unit automatically acquires data from accounting software, for example, using API integration. The collection unit can also import data uploaded by users using a file import function. Furthermore, the collection unit can instantly import data from financial institutions using a real-time data acquisition function. The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes data trends, for example, using statistical analysis. The analysis unit can also identify data patterns using machine learning algorithms. Furthermore, the analysis unit can extract useful information from the data using data mining techniques. The generation unit generates tax returns based on the data analyzed by the analysis unit. The generation unit creates tax returns in a standard format, for example, using a template-based generation method. The generation unit can also create customized tax returns according to user data using a dynamic generation method. The provision unit provides the tax returns generated by the generation unit. The provisioning unit, for example, sends tax returns to users via email. The provisioning unit can also make tax returns available for download through a web portal. Furthermore, the provisioning unit can integrate tax returns with other systems via APIs. The notification unit notifies taxpayers of the tax deadline based on the tax returns provided by the provisioning unit. The notification unit can remind users, for example, using push notifications. It can also notify taxpayers of the tax deadline via SMS. Furthermore, it can notify taxpayers of the tax deadline via email. This allows the system, which simplifies tax procedures, to handle everything from automatic import of income and expense data to the generation, provision, and notification of tax returns.

[0071] The data collection unit automatically imports income and expense data. For example, it automatically retrieves data from accounting software using API integration. Specifically, the data collection unit periodically retrieves data on the user's income and expenses using the accounting software's API. This eliminates the need for users to manually enter data. The data collection unit can also import data uploaded by users using its file import function. For example, when a user uploads a CSV or Excel file, the data collection unit analyzes these files, extracts the necessary data, and imports it into the system. Furthermore, the data collection unit can instantly import data from financial institutions using its real-time data acquisition function. For example, it can use a bank's API to acquire the user's account transaction information in real time and reflect it in the system as income and expense data. This allows the data collection unit to efficiently collect income and expense data from diverse data sources, significantly reducing the user's workload. In addition, the data collection unit is equipped with functions to detect duplicate data and cleanse data to maintain data integrity. This ensures that the collected data is accurate and consistent, leading to reliable results in subsequent analysis and generation processes.

[0072] The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes data trends using statistical analysis. Specifically, it visualizes monthly and annual income and expenditure trends in graphs and charts based on collected income and expense data. The analysis unit can also identify data patterns using machine learning algorithms. For example, by training on past data, it can predict future income and expenses. Furthermore, the analysis unit can extract useful information from the data using data mining techniques. For example, it can detect months with abnormally high specific expense items and conduct detailed analysis to identify the cause. In this way, the analysis unit can analyze the collected data from multiple perspectives and provide useful insights to users. In addition, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data and issue warnings early. For example, it can detect sudden large expenditures or fluctuations in income and notify users to encourage early action. In this way, the analysis unit can not only grasp the situation in real time but also handle long-term risk management and anomaly detection, improving the reliability and security of the entire system.

[0073] The generation unit generates tax returns based on data analyzed by the analysis unit. For example, the generation unit creates standardized tax returns using a template-based generation method. Specifically, it generates returns quickly and accurately by automatically embedding the analyzed data into pre-prepared templates. The generation unit can also create customized tax returns tailored to the user's data using a dynamic generation method. For example, it automatically adjusts the necessary items and formatting according to the user's industry and income type to generate an optimal tax return. Furthermore, the generation unit has a function to automatically verify the content of the generated tax return, checking for errors and deficiencies. This allows users to submit their tax returns with confidence. The generation unit can also generate various documents and reports required by the user. For example, it automatically creates monthly and annual income and expenditure reports, expense statements, and various documents for submission to the tax office, significantly reducing the user's workload. This allows the generation unit to quickly and accurately generate a variety of documents tailored to the user's needs, streamlining tax procedures.

[0074] The provisioning unit provides tax returns generated by the generation unit. The provisioning unit sends the returns to users, for example, via email. Specifically, it attaches the generated returns in PDF format and sends them to the user's registered email address. The provisioning unit can also make returns available for download via a web portal. Users can log in to a dedicated web portal and download the generated returns at any time. Furthermore, the provisioning unit can integrate returns with other systems via API. For example, it can integrate with a company's internal system or the tax office's online filing system to automatically send returns. This allows the provisioning unit to provide returns to users in a variety of ways, improving convenience. In addition, the provisioning unit has a function to manage the history of provided returns and allow easy reference to past returns. This allows users to check past returns and make corrections or resubmissions as needed. By considering user convenience to the fullest and enhancing the methods of providing and managing returns, the provisioning unit can make tax procedures smoother.

[0075] The notification unit notifies users of the tax payment deadline based on the tax return provided by the service provider. The notification unit reminds users, for example, using push notifications. Specifically, it sends push notifications via a smartphone app when the tax payment deadline is approaching to alert users. The notification unit can also notify users of the tax payment deadline using SMS. It sends an SMS to the user's registered phone number informing them of the tax payment deadline to ensure that the information is reliably transmitted. Furthermore, the notification unit can also notify users of the tax payment deadline using email. It sends a reminder email to the user's registered email address stating the tax payment deadline to prevent delays in tax payment. In this way, the notification unit can notify users of the tax payment deadline in a variety of ways, enabling them to proceed with the tax payment process smoothly. In addition to tax payment deadlines, the notification unit can also provide other important tax-related notifications. For example, it can notify users of information regarding tax law revisions and the introduction of new tax rules, providing them with the latest information. In this way, the notification unit helps users stay informed of the latest tax information and take appropriate action. The notification section is equipped with a variety of notification methods and information provision functions to improve user convenience and streamline tax procedures.

[0076] The data collection unit can automatically import income and expense data in conjunction with accounting software. For example, the data collection unit can automatically retrieve data from accounting software using API integration. The data collection unit can exchange data with accounting software via API, enabling real-time import of income and expense data. Furthermore, the data collection unit can import data files exported from accounting software using a file exchange function. For example, it can import CSV files and save them to the database. Additionally, the data collection unit can directly retrieve data from the accounting software's database using database integration. For example, it can extract necessary data using SQL queries and import it into the system. This enables automatic import of income and expense data in conjunction with accounting software. Some or all of the above-described processes in the data collection unit may be performed using AI, or not. For example, the data collection unit can input data acquired from accounting software into AI, allowing the AI ​​to perform data formatting and filtering.

[0077] The analysis unit can calculate tax amounts in real time based on data such as income, expenses, and deductions. For example, the analysis unit integrates income and expense data and calculates tax amounts considering deductions. The analysis unit updates data in real time and calculates tax amounts based on the latest information. The analysis unit can also use machine learning algorithms to build predictive models for tax amounts from historical data. For example, it can learn from past income and expense data to predict future tax amounts. Furthermore, the analysis unit can use data mining techniques to extract useful patterns from the data and reflect them in the calculation of tax amounts. For example, it can analyze the impact of specific expense categories on tax amounts. This makes it easier to get a clear picture of tax amounts by calculating tax amounts in real time. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input income and expense data into AI and have the AI ​​perform the calculation of tax amounts.

[0078] The notification unit can provide a tax payment deadline notification function. For example, the notification unit can remind users using push notifications. When the tax payment deadline approaches, the notification unit sends a push notification to the user's smartphone or tablet to inform them of the deadline. The notification unit can also notify users of the tax payment deadline using SMS. For example, it can send an SMS to the user's mobile phone to remind them of the tax payment deadline. Furthermore, the notification unit can also notify users of the tax payment deadline using email. For example, it can send a tax payment deadline notification to the user's email address. This prevents users from missing the deadline through the tax payment deadline notification function. Some or all of the above processes in the notification unit may be performed using AI or not. For example, the notification unit can input tax payment deadline data into AI and have the AI ​​calculate the optimal notification timing.

[0079] The AI ​​chatbot can provide guides for frequently asked questions and inquiries. For example, the AI ​​chatbot can automatically respond to user questions based on an FAQ list. The AI ​​chatbot analyzes the question entered by the user, searches for relevant FAQs, and provides an answer. The AI ​​chatbot can also accept user questions through an inquiry form and provide appropriate guidance. For example, if a user enters a question about a specific tax procedure, the AI ​​chatbot will display a guide regarding that procedure. Furthermore, the AI ​​chatbot can use natural language processing technology to understand user questions and generate appropriate answers. For example, if a user asks, "How do I submit my tax return?", the AI ​​chatbot will explain the procedure for submitting a tax return. By providing guides for frequently asked questions and inquiries, the AI ​​reduces the burden on staff through the automation of question and answer. Some or all of the above processes in the AI ​​chatbot may be performed using AI or not. For example, the AI ​​chatbot can input a user's question into the AI ​​and have the AI ​​generate the answer.

[0080] The analysis unit can analyze tax payment history and provide feedback. For example, it can analyze past tax amounts and payment dates to identify tax payment patterns. Based on the tax payment history, the analysis unit can predict future tax amounts and propose tax payment schedules. The analysis unit can also analyze tax payment methods and propose the most suitable method. For example, it can determine whether electronic tax payment is appropriate based on past tax payment history. Furthermore, the analysis unit can assess tax risks based on tax payment history and propose risk mitigation measures. For example, if a certain deduction has not been applied based on past tax payment history, it can propose the application of that deduction. In this way, the analysis of tax payment history and feedback provide support for proactive measures. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input tax payment history data into AI and have the AI ​​generate feedback.

[0081] The Integration Department can connect with financial institutions via APIs and import expense data into the accounting system. For example, the Integration Department can obtain data from financial institutions using a REST API. The Integration Department obtains transaction data and account information through the financial institution's API and imports it into the accounting system. The Integration Department can also exchange data with financial institutions using a SOAP API. For example, it can obtain expense data from financial institutions using SOAP messages and import it into the system. Furthermore, the Integration Department can directly connect with the financial institution's database and obtain the necessary data. For example, it can extract expense data from the financial institution's database using an SQL query and import it into the system. This enables the automatic import of expense data by connecting with financial institutions via APIs. Some or all of the above processes in the Integration Department may be performed using AI or not. For example, the Integration Department can input data obtained from financial institutions into AI and have the AI ​​perform data formatting and filtering.

[0082] The data collection unit can estimate the user's emotions and adjust the timing of income and expense data acquisition based on the estimated emotions. For example, if the user is stressed, the data collection unit can acquire income and expense data at night to reduce the user's burden. The data collection unit uses an emotion estimation algorithm to analyze the user's emotions in real time and determine the optimal data acquisition timing. Furthermore, if the user is relaxed, the data collection unit can acquire income and expense data in real time and reflect it immediately. For example, when the user is relaxed, income and expense data can be acquired immediately and reflected in the system. In addition, if the user is in a hurry, the data collection unit can acquire income and expense data quickly and process it immediately. For example, when the user is in a hurry, important data can be prioritized and processed quickly. In this way, the user's burden is reduced by adjusting the timing of data acquisition according to the user's emotions. Emotion estimation is achieved using an emotion estimation function that utilizes an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into the AI ​​and have the AI ​​adjust the timing of data acquisition.

[0083] The data collection unit can analyze the user's past income and expense data and select the optimal acquisition method. For example, the data collection unit can analyze the data acquisition methods the user has used in the past (manual, API integration, etc.) and propose the optimal method. Based on the past data acquisition history, the data collection unit selects the most efficient acquisition method. The data collection unit can also analyze patterns in the user's past income and expense data and propose the optimal acquisition timing. For example, it can optimize the timing of income and expense data acquisition based on past data patterns. Furthermore, the data collection unit can select the most efficient acquisition method based on the user's past data acquisition history. For example, it can analyze past data acquisition history and propose the optimal acquisition method. In this way, the optimal data acquisition method can be selected by analyzing past data. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input past income and expense data into AI and have the AI ​​select the optimal acquisition method.

[0084] The data collection unit can filter income and expense data based on the user's current financial situation and areas of interest. For example, the data collection unit can analyze the user's current financial situation and prioritize the acquisition of only important data. The data collection unit prioritizes the acquisition of highly relevant data according to the user's financial situation. The data collection unit can also filter data based on the user's areas of interest (such as specific expense categories). For example, it can prioritize the acquisition of data related to expense categories of interest to the user. Furthermore, the data collection unit can prioritize the acquisition of highly relevant data according to the user's current income situation. For example, it can prioritize the acquisition of important income data based on the user's income situation. This allows for the priority acquisition of highly relevant data by filtering data based on the user's financial situation and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's financial situation data into AI and have the AI ​​perform the data filtering.

[0085] The data collection unit can estimate the user's emotions and determine the priority of data to acquire based on the estimated emotions. For example, if the user is stressed, the data collection unit can prioritize acquiring only important data to reduce the burden. The data collection unit uses an emotion estimation algorithm to analyze the user's emotions in real time and determine the optimal data acquisition priority. The data collection unit can also acquire all data equally if the user is relaxed. For example, when the user is relaxed, all data is acquired equally and reflected in the system. Furthermore, if the user is in a hurry, the data collection unit can quickly acquire the most important data. For example, when the user is in a hurry, important data is prioritized and processed quickly. In this way, important data can be prioritized by determining the data priority according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into the AI ​​and have the AI ​​determine the priority of data acquisition.

[0086] The data collection unit can prioritize the acquisition of highly relevant data by considering the user's geographical location when acquiring income and expense data. For example, if the user is in a specific region, the data collection unit will prioritize the acquisition of expense data related to that region. Based on the geographical location, the data collection unit will acquire the most relevant income data. Furthermore, if the user is on the move, the data collection unit can acquire relevant data based on their current location. For example, while the user is on the move, it will acquire expense data related to their current location and reflect it in the system. In addition, the data collection unit can acquire the most relevant income data based on the user's geographical location. For example, if the user is in a specific region, it will prioritize the acquisition of income data related to that region. This allows for the priority acquisition of highly relevant data by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's geographical location into the AI ​​and have the AI ​​prioritize data acquisition.

[0087] The data collection unit can analyze a user's social media activity and obtain relevant data when acquiring income and expense data. For example, the data collection unit can analyze a user's social media activity and obtain relevant expense data. The data collection unit filters relevant data based on social media posts, likes, follower counts, etc. The data collection unit can also acquire income data based on a user's social media posts about their income. For example, if a user makes a post about their income on social media, the data is acquired and reflected in the system. Furthermore, the data collection unit can filter and acquire relevant data from a user's social media activity. For example, it can analyze a user's social media activity and prioritize the acquisition of highly relevant data. This allows for the efficient acquisition of relevant data by analyzing a user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user social media data into AI and have the AI ​​acquire relevant data.

[0088] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is tense, the analysis unit provides a simple and easy-to-understand analysis result. The analysis unit uses an emotion estimation algorithm to analyze the user's emotions in real time and determine the optimal presentation method. The analysis unit can also provide a detailed analysis result if the user is relaxed. For example, it provides a detailed analysis result to deepen understanding when the user is relaxed. Furthermore, if the user is in a hurry, the analysis unit can provide a concise analysis result that highlights the key points. In this way, by adjusting the presentation of the analysis according to the user's emotions, it is possible to provide analysis results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into the AI ​​and have the AI ​​adjust how the analysis results are presented.

[0089] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on important data and a simplified analysis on less important data. The analysis unit evaluates the importance of the data and determines the priority of the analysis. The analysis unit can also determine the priority of the analysis according to the importance of the data. For example, it can apply multiple analysis algorithms to important data to provide detailed analysis results. Furthermore, the analysis unit can adjust the analysis schedule based on the importance of the data. For example, it can prioritize the analysis of important data and provide results quickly. This allows for efficient analysis by adjusting the level of detail of the analysis according to the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the importance of the data into the AI ​​and have the AI ​​adjust the level of detail of the analysis.

[0090] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit applies an income-specific analysis algorithm to income data. The analysis unit identifies the data category and selects the optimal analysis algorithm. The analysis unit can also apply an expense-specific analysis algorithm to expense data. For example, it applies an algorithm that identifies expense classifications and patterns to expense data. Furthermore, the analysis unit can apply a deduction-specific analysis algorithm to deduction data. For example, it applies an algorithm that evaluates the conditions for applying deductions to deduction data. By applying the appropriate analysis algorithm according to the data category, the accuracy of the analysis is improved. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the data category into the AI ​​and have the AI ​​select the optimal analysis algorithm.

[0091] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit will provide a short, concise analysis. The analysis unit uses an emotion estimation algorithm to analyze the user's emotions in real time and determine the optimal analysis length. The analysis unit can also provide a detailed analysis when the user is relaxed. For example, when the user is relaxed, it will provide a longer analysis with detailed explanations. Furthermore, if the user is excited, the analysis unit can provide a visually stimulating analysis. For example, when the user is excited, it will provide an analysis with visually appealing graphs or charts. By adjusting the length of the analysis according to the user's emotions, the analysis unit can provide an analysis of an appropriate length for the user. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into the AI ​​and have the AI ​​adjust the length of the analysis.

[0092] The analysis unit can determine the priority of analysis based on the data submission date during the analysis process. For example, the analysis unit may prioritize analyzing data with approaching deadlines. The analysis unit evaluates the data submission date and determines the analysis priority. The analysis unit can also postpone analyzing data with distant submission dates. For example, it may postpone analyzing data with distant deadlines and prioritize analyzing data with approaching deadlines. Furthermore, the analysis unit can adjust the analysis schedule based on the submission date. For example, it may optimize the analysis schedule based on the submission deadline to perform analysis efficiently. This enables efficient analysis by determining the analysis priority based on the data submission date. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the data submission dates into the AI ​​and have the AI ​​determine the analysis priority.

[0093] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis process. For example, the analysis unit may prioritize the analysis of highly relevant data. The analysis unit evaluates the relevance of the data and determines the order of analysis. The analysis unit can also postpone the analysis of less relevant data. For example, it may postpone the analysis of less relevant data and prioritize the analysis of highly relevant data. Furthermore, the analysis unit can adjust the analysis schedule based on the relevance of the data. For example, it may optimize the analysis schedule based on the relevance of the data to perform the analysis efficiently. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the relevance of the data into the AI ​​and have the AI ​​determine the order of analysis.

[0094] The generation unit can estimate the user's emotions and adjust the presentation of the generated tax return based on those emotions. For example, if the user is stressed, the generation unit will generate a simple and highly legible tax return. The generation unit uses an emotion estimation algorithm to analyze the user's emotions in real time and determine the optimal presentation. The generation unit can also generate a tax return with detailed information if the user is relaxed. For example, it can provide a tax return with detailed information to enhance understanding when the user is relaxed. Furthermore, the generation unit can generate a concise tax return if the user is in a hurry. For example, it can provide a tax return that concisely presents the important points when the user is in a hurry. By adjusting the presentation of the tax return according to the user's emotions, it is possible to provide a tax return that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI adjust the way the tax return is presented.

[0095] The generation unit can adjust the level of detail of the generated data based on its importance. For example, it can generate a detailed tax return for important data. The generation unit evaluates the importance of the data and determines the generation priority. It can also generate a simplified tax return for less important data. For example, it can provide a tax return with concise information for less important data. Furthermore, the generation unit can determine the generation priority based on the importance of the data. For example, it can prioritize the generation of important data and provide results quickly. This allows for efficient generation by adjusting the level of detail of the generated data according to its importance. Some or all of the above processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input the importance of the data into the generation AI and have the generation AI adjust the level of detail of the generated data.

[0096] The generation unit can apply different generation algorithms depending on the data category during generation. For example, the generation unit applies an income-specific generation algorithm to income data. The generation unit identifies the data category and selects the optimal generation algorithm. The generation unit can also apply an expense-specific generation algorithm to expense data. For example, it applies an algorithm that identifies expense classifications and patterns to expense data. Furthermore, the generation unit can apply a deduction-specific generation algorithm to deduction data. For example, it applies an algorithm that evaluates the conditions for applying deductions to deduction data. By applying the appropriate generation algorithm according to the data category, the accuracy of generation is improved. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the data category into the generation AI and have the generation AI select the optimal generation algorithm.

[0097] The generation unit can estimate the user's emotions and adjust the length of the tax return it generates based on those emotions. For example, if the user is in a hurry, the generation unit will generate a short, concise tax return. The generation unit uses an emotion estimation algorithm to analyze the user's emotions in real time and determine the optimal length of the return. The generation unit can also generate a longer tax return with detailed explanations if the user is relaxed. For example, when the user is relaxed, it can provide a longer return with detailed information to enhance understanding. Furthermore, if the user is excited, the generation unit can generate a tax return with visually stimulating effects. For example, when the user is excited, it can provide a return with visually appealing graphs and charts. By adjusting the length of the tax return according to the user's emotions, the system can provide a tax return of an appropriate length for the user. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generation AI. Generation AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input user emotion data into a generation AI and have the generation AI adjust the length of the tax return.

[0098] The generation unit can determine the generation priority based on the data submission timing during generation. For example, the generation unit may prioritize generating data with approaching deadlines. The generation unit evaluates the data submission timing and determines the generation priority. The generation unit can also postpone generating data with distant submission deadlines. For example, it may postpone generating data with distant deadlines and prioritize generating data with approaching deadlines. Furthermore, the generation unit can adjust the generation schedule based on the submission timing. For example, it may optimize the generation schedule based on the submission deadline to generate data efficiently. This enables efficient generation by determining the generation priority based on the data submission timing. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input the data submission timing into the generation AI and have the generation AI determine the generation priority.

[0099] The generation unit can adjust the generation order based on the relevance of the data during generation. For example, the generation unit can prioritize the generation of highly relevant data. The generation unit evaluates the relevance of the data and determines the generation order. The generation unit can also postpone the generation of less relevant data. For example, it can postpone the generation of less relevant data and prioritize the generation of highly relevant data. Furthermore, the generation unit can adjust the generation schedule based on the relevance of the data. For example, it can optimize the generation schedule based on the relevance of the data to perform generation efficiently. This makes efficient generation possible by adjusting the generation order based on the relevance of the data. Some or all of the above processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input the relevance of the data into the generation AI and have the generation AI determine the generation order.

[0100] The service provider can estimate the user's emotions and adjust the display method of the tax return based on the estimated emotions. For example, if the user is stressed, the service provider can provide a simple and highly visible display method. The service provider uses an emotion estimation algorithm to analyze the user's emotions in real time and determine the optimal display method. The service provider can also provide a display method that includes detailed information if the user is relaxed. For example, when the user is relaxed, a display method that includes detailed information is provided to deepen understanding. Furthermore, if the user is in a hurry, the service provider can provide a display method that gets straight to the point. For example, when the user is in a hurry, a display method that concisely shows the important points is provided. In this way, by adjusting the display method of the tax return according to the user's emotions, a display that is easy for the user to understand can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input user emotion data into the AI ​​and have the AI ​​adjust the display method.

[0101] The service provider can select the optimal service method by referring to the user's past tax payment history at the time of service provision. For example, the service provider can analyze the user's past tax payment history and select the most frequently used service method. Based on the past tax payment history, the service provider can propose the optimal service method. The service provider can also propose a service method suitable for a specific time period based on the user's past tax payment history. For example, it can analyze the past tax payment history and select the most suitable service method for a specific time period. Furthermore, the service provider can select the most efficient service method based on the user's past tax payment history. For example, it can analyze the past tax payment history and propose the most efficient service method. In this way, the optimal service method can be selected by referring to the past tax payment history. Some or all of the above processes in the service provider may be performed using AI or not. For example, the service provider can input the user's past tax payment history into AI and have the AI ​​select the optimal service method.

[0102] The service provider can estimate the user's emotions and determine the priority of the tax returns to be provided based on the estimated emotions. For example, if the user is stressed, the service provider will prioritize providing important tax returns. The service provider uses an emotion estimation algorithm to analyze the user's emotions in real time and determine the optimal priority. The service provider can also provide all tax returns equally if the user is relaxed. For example, when the user is relaxed, all returns can be provided equally to allow for better understanding. Furthermore, if the user is in a hurry, the service provider can quickly provide the most important tax returns. For example, when the user is in a hurry, important returns can be prioritized for quick processing. This allows for the priority of important tax returns by determining the priority of tax returns according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input user emotion data into the AI ​​and have the AI ​​determine priorities.

[0103] The delivery unit can select the optimal delivery method by considering the user's device information at the time of delivery. For example, if the user is using a smartphone, the delivery unit will provide a delivery method that matches the screen size. The delivery unit selects the optimal delivery method based on the device information. Furthermore, if the user is using a tablet, the delivery unit can also provide a delivery method optimized for a larger screen. For example, when the user is using a tablet, it will provide a delivery method optimized for a larger screen. In addition, if the user is using a desktop, the delivery unit can provide a delivery method that includes detailed information. For example, when the user is using a desktop, it will provide a delivery method that includes detailed information. This allows the delivery unit to select the optimal delivery method by considering the user's device information. Some or all of the above processing in the delivery unit may be performed using AI or not. For example, the delivery unit can input the user's device information into AI and have the AI ​​select the optimal delivery method.

[0104] The notification unit can estimate the user's emotions and adjust the timing of notifications based on the estimated emotions. For example, if the user is stressed, the notification unit may send a notification at night to reduce the user's burden. The notification unit uses an emotion estimation algorithm to analyze the user's emotions in real time and determine the optimal notification timing. The notification unit can also send notifications in real time when the user is relaxed and reflect them immediately. For example, when the user is relaxed, it can send a notification immediately and reflect it in the system. Furthermore, if the user is in a hurry, the notification unit can send a notification quickly and process it immediately. For example, when the user is in a hurry, it can prioritize important notifications and process them quickly. In this way, the user's burden is reduced by adjusting the timing of notifications according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input user emotion data into the AI ​​and have the AI ​​adjust the timing of notifications.

[0105] The notification unit can select the optimal notification method by referring to the user's past tax payment history when issuing a notification. For example, the notification unit can analyze the user's past tax payment history and select the most frequently used notification method. The notification unit proposes the optimal notification method based on the past tax payment history. The notification unit can also propose a notification method suitable for a specific time based on the user's past tax payment history. For example, it can analyze the past tax payment history and select the most suitable notification method for a specific time. Furthermore, the notification unit can select the most efficient notification method based on the user's past tax payment history. For example, it can analyze the past tax payment history and propose the most efficient notification method. In this way, the optimal notification method can be selected by referring to the past tax payment history. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input the user's past tax payment history into AI and have the AI ​​select the optimal notification method.

[0106] The notification unit can estimate the user's emotions and determine notification priorities based on those emotions. For example, if the user is stressed, the notification unit will prioritize important notifications. The notification unit uses an emotion estimation algorithm to analyze the user's emotions in real time and determine the optimal priority. The notification unit can also distribute all notifications equally if the user is relaxed. For example, when the user is relaxed, it distributes all notifications equally to deepen understanding. Furthermore, if the user is in a hurry, the notification unit can quickly deliver the most important notifications. For example, when the user is in a hurry, it prioritizes important notifications for quick processing. This allows for prioritizing important notifications by determining notification priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input user emotion data into the AI ​​and have the AI ​​determine the priority of notifications.

[0107] The notification unit can select the optimal notification method by considering the user's device information when sending a notification. For example, if the user is using a smartphone, the notification unit provides a notification method that is appropriate for the screen size. The notification unit selects the optimal notification method based on the device information. The notification unit can also provide a notification method optimized for a larger screen if the user is using a tablet. For example, it provides a notification method optimized for a larger screen when the user is using a tablet. Furthermore, the notification unit can provide a notification method that includes detailed information if the user is using a desktop. For example, it provides a notification method that includes detailed information when the user is using a desktop. This allows the system to select the optimal notification method by considering the user's device information. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input the user's device information into AI and have AI select the optimal notification method.

[0108] The AI ​​chatbot can estimate the user's emotions and adjust its response method based on the estimated emotions. For example, if the user is tense, the AI ​​chatbot will respond in a calm tone. The AI ​​chatbot uses an emotion estimation algorithm to analyze the user's emotions in real time and determine the optimal response method. The AI ​​chatbot can also respond in a friendly tone if the user is relaxed. For example, when the user is relaxed, it will respond in a friendly tone to provide a sense of familiarity. Furthermore, if the user is in a hurry, the AI ​​chatbot can provide a quick and concise response. For example, when the user is in a hurry, it will provide a response that concisely delivers important information. In this way, by adjusting the chatbot's response method according to the user's emotions, it can provide an appropriate response for the user. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the AI ​​chatbot may be performed using AI or not. For example, the AI ​​chatbot unit can input user emotion data into the AI ​​and have the AI ​​adjust its response methods.

[0109] The AI ​​chatbot unit can provide the optimal response by referring to the user's past question history when responding to chatbot inquiries. For example, the AI ​​chatbot unit can analyze the user's past question history and provide the most relevant response. The AI ​​chatbot unit proposes the optimal response based on the past question history. The AI ​​chatbot unit can also propose the optimal response to a specific question based on the user's past question history. For example, it can analyze the past question history and select the optimal response to a specific question. Furthermore, the AI ​​chatbot unit can provide the most efficient response based on the user's past question history. For example, it can analyze the past question history and propose the most efficient response. In this way, the optimal response can be provided by referring to the past question history. Some or all of the above processes in the AI ​​chatbot unit may be performed using AI or not. For example, the AI ​​chatbot unit can input the user's past question history into the AI ​​and have the AI ​​select the optimal response.

[0110] The AI ​​chatbot can estimate the user's emotions and prioritize its responses based on those emotions. For example, if the user is stressed, the AI ​​chatbot will prioritize responding to important questions. The AI ​​chatbot uses an emotion estimation algorithm to analyze the user's emotions in real time and determine the optimal priority. The AI ​​chatbot can also respond to all questions equally if the user is relaxed. For example, when the user is relaxed, it will respond to all questions equally to deepen understanding. Furthermore, if the user is in a hurry, the AI ​​chatbot can quickly respond to the most important questions. For example, when the user is in a hurry, it will prioritize responding to important questions and process them quickly. In this way, by prioritizing responses according to the user's emotions, it is possible to prioritize responses to important questions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the AI ​​chatbot may be performed using AI or not. For example, the AI ​​chatbot can input user emotion data into the AI ​​and have the AI ​​determine the priority of responses.

[0111] The AI ​​chatbot unit can provide the optimal response by considering the user's device information when responding to a chatbot. For example, if the user is using a smartphone, the AI ​​chatbot unit will provide a response that is appropriate for the screen size. The AI ​​chatbot unit selects the optimal response based on the device information. Furthermore, if the user is using a tablet, the AI ​​chatbot unit can provide a response optimized for a larger screen. For example, when the user is using a tablet, it will provide a response optimized for a larger screen. In addition, if the user is using a desktop, the AI ​​chatbot unit can provide a response that includes detailed information. For example, when the user is using a desktop, it will provide a response that includes detailed information. In this way, the optimal response can be provided by considering the user's device information. Some or all of the above processing in the AI ​​chatbot unit may be performed using AI or not using AI. For example, the AI ​​chatbot unit can input the user's device information into the AI ​​and have the AI ​​select the optimal response.

[0112] The integration unit can estimate the user's emotions and select a financial institution to integrate with based on the estimated emotions. For example, if the user is stressed, the integration unit will prioritize selecting a highly reliable financial institution. The integration unit uses an emotion estimation algorithm to analyze the user's emotions in real time and select the most suitable financial institution. The integration unit can also select a financial institution that the user frequently uses when the user is relaxed. For example, when the user is relaxed, it will select a financial institution that the user frequently uses and integrate with. Furthermore, if the user is in a hurry, the integration unit can select a financial institution that can integrate quickly. For example, when the user is in a hurry, it will select a financial institution that can integrate quickly and integrate with. In this way, by selecting a financial institution according to the user's emotions, the user can integrate with the most suitable financial institution. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the integration unit may be performed using AI or not. For example, the collaboration department can input user emotional data into the AI ​​and have the AI ​​select financial institutions.

[0113] The integration unit can select the optimal integration method by referring to the user's past financial transaction history during integration. For example, the integration unit can analyze the user's past financial transaction history and select the most frequently used integration method. Based on the past financial transaction history, the integration unit proposes the optimal integration method. The integration unit can also propose an integration method suitable for a specific period based on the user's past financial transaction history. For example, it can analyze the past financial transaction history and select the optimal integration method for a specific period. Furthermore, the integration unit can select the most efficient integration method based on the user's past financial transaction history. For example, it can analyze the past financial transaction history and propose the most efficient integration method. In this way, the optimal integration method can be selected by referring to the past financial transaction history. Some or all of the above processing in the integration unit may be performed using AI or not. For example, the integration unit can input the user's past financial transaction history into AI and have the AI ​​select the optimal integration method.

[0114] The collaboration unit can estimate the user's emotions and determine the priority of collaborations based on the estimated emotions. For example, if the user is stressed, the collaboration unit will prioritize important collaborations. The collaboration unit uses an emotion estimation algorithm to analyze the user's emotions in real time and determine the optimal priority. The collaboration unit can also perform all collaborations equally if the user is relaxed. For example, when the user is relaxed, it will perform all collaborations equally to deepen understanding. Furthermore, if the user is in a hurry, the collaboration unit can quickly perform the most important collaborations. For example, when the user is in a hurry, it will prioritize important collaborations and process them quickly. In this way, by determining the priority of collaborations according to the user's emotions, important collaborations can be prioritized. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the collaboration unit may be performed using AI or not. For example, the collaboration unit can input user emotion data into the AI ​​and have the AI ​​determine the priority of collaborations.

[0115] The integration unit can select the optimal integration method by considering the user's geographical location information during integration. For example, if the user is in a specific region, the integration unit will integrate with financial institutions related to that region. The integration unit selects the optimal integration method based on geographical location information. Furthermore, if the user is on the move, the integration unit can also select the optimal integration method based on the user's current location. For example, if the user is on the move, it will integrate with financial institutions related to the user's current location. In addition, the integration unit can select the most relevant integration method based on the user's geographical location information. For example, if the user is in a specific region, it will integrate with financial institutions related to that region. This allows the integration unit to select the optimal integration method by considering the user's geographical location information. Some or all of the above processing in the integration unit may be performed using AI or not. For example, the integration unit can input the user's geographical location information into AI and have the AI ​​select the optimal integration method.

[0116] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0117] The data collection unit can estimate the user's emotions and adjust the timing of income and expense data acquisition based on the estimated emotions. For example, if the user is stressed, income and expense data can be acquired at night to reduce the user's burden. If the user is relaxed, income and expense data can be acquired in real time and reflected immediately. Furthermore, if the user is in a hurry, income and expense data can be acquired quickly and processed immediately. In this way, the user's burden can be reduced by adjusting the timing of data acquisition according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into AI and have the AI ​​adjust the timing of data acquisition.

[0118] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is nervous, it can provide a simple and easy-to-understand analysis result. If the user is relaxed, it can provide a detailed analysis result. Furthermore, if the user is in a hurry, it can provide a concise analysis result. In this way, by adjusting the presentation of the analysis according to the user's emotions, it is possible to provide analysis results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the analysis unit may be performed using AI or not using AI. For example, the analysis unit can input user emotion data into the AI ​​and have the AI ​​adjust the presentation of the analysis result.

[0119] The generation unit can estimate the user's emotions and adjust the presentation of the generated tax return based on the estimated emotions. For example, if the user is nervous, it can generate a simple and highly legible tax return. If the user is relaxed, it can generate a tax return that includes detailed information. Furthermore, if the user is in a hurry, it can generate a concise tax return. By adjusting the presentation of the tax return according to the user's emotions, it is possible to provide a tax return that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using the generation AI or not. For example, the generation unit can input user emotion data into the generation AI and have the generation AI adjust the presentation of the tax return.

[0120] The service provider can estimate the user's emotions and adjust the display method of the tax return based on the estimated emotions. For example, if the user is stressed, a simple and highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that gets straight to the point can be provided. In this way, by adjusting the display method of the tax return according to the user's emotions, a display that is easy for the user to understand can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input user emotion data into AI and have the AI ​​perform the adjustment of the display method.

[0121] The notification unit can estimate the user's emotions and adjust the timing of notifications based on the estimated emotions. For example, if the user is stressed, notifications can be sent at night to reduce the user's burden. If the user is relaxed, notifications can be sent in real time for immediate reflection. Furthermore, if the user is in a hurry, notifications can be sent quickly for immediate processing. In this way, the user's burden can be reduced by adjusting the timing of notifications according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input user emotion data into AI and have the AI ​​adjust the notification timing.

[0122] The data collection unit can analyze the user's past income and expense data and select the optimal data acquisition method. For example, it can analyze the data acquisition methods the user has used in the past (manual, API integration, etc.) and suggest the most suitable method. It can also select the most efficient acquisition method based on past data acquisition history. Furthermore, it can analyze patterns in the user's past income and expense data and suggest the optimal acquisition timing. In this way, the optimal data acquisition method can be selected by analyzing past data. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input past income and expense data into AI and have the AI ​​select the optimal data acquisition method.

[0123] The analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, it can perform a detailed analysis on important data and a simplified analysis on less important data. It can also evaluate the importance of the data and determine the priority of the analysis. Furthermore, it can adjust the analysis schedule based on the importance of the data. This allows for efficient analysis by adjusting the level of detail according to the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the importance of the data into the AI ​​and have the AI ​​adjust the level of detail of the analysis.

[0124] The generation unit can apply different generation algorithms depending on the data category. For example, a generation algorithm specific to income can be applied to income data. Similarly, a generation algorithm specific to expenses can be applied to expense data. Furthermore, a generation algorithm specific to deductions can be applied to deduction data. By applying the appropriate generation algorithm according to the data category, the accuracy of generation can be improved. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input the data category into the generation AI and have the generation AI select the optimal generation algorithm.

[0125] The service provider can select the optimal service method by referring to the user's past tax payment history. For example, it can analyze the user's past tax payment history and select the most frequently used service method. It can also suggest the optimal service method based on the past tax payment history. Furthermore, it can suggest a service method suitable for a specific period based on the user's past tax payment history. In this way, the optimal service method can be selected by referring to the past tax payment history. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's past tax payment history into AI and have the AI ​​select the optimal service method.

[0126] The notification unit can select the optimal notification method by referring to the user's past tax payment history. For example, it can analyze the user's past tax payment history and select the most frequently used notification method. It can also suggest the optimal notification method based on the past tax payment history. Furthermore, it can suggest a notification method suitable for a specific time based on the user's past tax payment history. In this way, the optimal notification method can be selected by referring to the past tax payment history. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input the user's past tax payment history into AI and have the AI ​​perform the selection of the optimal notification method.

[0127] The following briefly describes the processing flow for example form 2.

[0128] Step 1: The data collection unit automatically imports income and expense data. For example, the data collection unit automatically retrieves data from accounting software using API integration. The data collection unit can also import data uploaded by users using its file import function. Furthermore, the data collection unit can instantly import data from financial institutions using its real-time data acquisition function. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes data trends, for example, using statistical analysis. The analysis unit can also identify data patterns using machine learning algorithms. Furthermore, the analysis unit can extract useful information from the data using data mining techniques. Step 3: The generation unit generates tax returns based on the data analyzed by the analysis unit. The generation unit can, for example, use a template-based generation method to create tax returns in a standard format. Alternatively, the generation unit can use a dynamic generation method to create customized tax returns based on the user's data. Step 4: The provisioning unit provides the tax return generated by the generation unit. The provisioning unit sends the return to the user, for example, via email. The provisioning unit can also make the return available for download through a web portal. Furthermore, the provisioning unit can integrate the return with other systems via an API. Step 5: The notification unit notifies the taxpayer of the tax deadline based on the tax return provided by the service provider. The notification unit may, for example, use push notifications to remind the user. The notification unit may also notify the taxpayer of the tax deadline using SMS. Furthermore, the notification unit may also notify the taxpayer of the tax deadline using email.

[0129] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0130] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0131] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0132] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, provision unit, notification unit, AI chatbot unit, and linkage unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the collection unit is implemented by the control unit 46A of the smart device 14 and automatically acquires data from accounting software using API linkage. The analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The generation unit is implemented by the specific processing unit 290 of the data processing device 12 and generates a tax return based on the analyzed data. The provision unit is implemented by the control unit 46A of the smart device 14 and provides the generated tax return to the user. The notification unit is implemented by the control unit 46A of the smart device 14 and notifies the tax payment deadline. The AI ​​chatbot unit is implemented by the specific processing unit 290 of the data processing device 12 and provides a guide to frequently asked questions and inquiries. The collaboration unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, which collaborates with financial institutions via API and imports expense data into the accounting system. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0133] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0134] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0135] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0136] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0137] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0139] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0140] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0141] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0142] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0143] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0144] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0145] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0146] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0147] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0148] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, provision unit, notification unit, AI chatbot unit, and linkage unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit is implemented by the control unit 46A of the smart glasses 214 and automatically acquires data from accounting software using API linkage. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and generates a tax return based on the analyzed data. The provision unit is implemented, for example, by the control unit 46A of the smart glasses 214 and provides the generated tax return to the user. The notification unit is implemented, for example, by the control unit 46A of the smart glasses 214 and notifies the tax payment deadline. The AI ​​chatbot unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and provides a guide to frequently asked questions and inquiries. The collaboration unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, which collaborates with financial institutions via API and imports expense data into the accounting system. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0149] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0150] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0151] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0152] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0153] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0154] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0155] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0156] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0157] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0158] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0159] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0160] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0161] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0162] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0163] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0164] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, provision unit, notification unit, AI chatbot unit, and collaboration unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the headset terminal 314 and automatically acquires data from accounting software using API integration. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a tax return based on the analyzed data. The provision unit is implemented by the control unit 46A of the headset terminal 314 and provides the generated tax return to the user. The notification unit is implemented by the control unit 46A of the headset terminal 314 and notifies the tax payment deadline. The AI ​​chatbot unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides a guide to frequently asked questions and inquiries. The collaboration unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, which collaborates with financial institutions via API and imports expense data into the accounting system. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0165] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0166] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0167] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0168] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0169] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0170] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0171] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0172] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0173] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0174] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0175] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0176] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0177] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0178] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0179] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0180] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0181] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, provision unit, notification unit, AI chatbot unit, and linkage unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the robot 414 and automatically acquires data from accounting software using API linkage. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and generates a tax return based on the analyzed data. The provision unit is implemented, for example, by the control unit 46A of the robot 414 and provides the generated tax return to the user. The notification unit is implemented, for example, by the control unit 46A of the robot 414 and notifies the tax payment deadline. The AI ​​chatbot unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and provides a guide to frequently asked questions and inquiries. The collaboration unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, which collaborates with financial institutions via API and imports expense data into the accounting system. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0182] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0183] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0184] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0185] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0186] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0187] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0188] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0189] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0190] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0191] 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.

[0192] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0193] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0194] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0195] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0196] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0197] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0198] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0199] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0200] (Note 1) A collection unit that automatically imports income and expense data, An analysis unit analyzes the data collected by the aforementioned collection unit, A generation unit that generates a tax return based on the data analyzed by the analysis unit, A providing unit that provides the tax return generated by the generation unit, A notification unit that notifies the tax payment deadline based on the tax return provided by the aforementioned provision unit, Equipped with A system characterized by the following features. (Note 2) The aforementioned collection unit is It automatically imports income and expense data in conjunction with accounting submissions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The system calculates your tax liability in real time based on data such as income, expenses, and deductions. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned notification unit, Provides a tax payment deadline notification function. The system described in Appendix 1, characterized by the features described herein. (Note 5) Equipped with an AI chatbot section, The aforementioned AI chatbot unit is We provide a guide for frequently asked questions and inquiries. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit, Analyze tax payment history and provide feedback. The system described in Appendix 1, characterized by the features described herein. (Note 7) Equipped with a collaboration department, The aforementioned linkage unit is, We connect with financial institutions via API and import expense data into our accounting system. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is The system estimates user sentiment and adjusts the timing of revenue and expense data acquisition based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is Analyze the user's past income and expense data to select the optimal acquisition method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When acquiring income and expense data, filtering is performed based on the user's current financial situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is It estimates the user's emotions and determines the priority of data to acquire based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When acquiring income and expense data, the system prioritizes the acquisition of highly relevant data by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is When acquiring income and expense data, we analyze users' social media activity and obtain relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the priority of analyses is determined based on the timing of data submission. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is We estimate the user's emotions and adjust the way tax returns are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is During generation, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is During generation, different generation algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is It estimates the user's emotions and adjusts the length of the tax return generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is During generation, the generation priority is determined based on when the data was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 25) The generating unit is During generation, the generation order is adjusted based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, We estimate the user's emotions and adjust how tax returns are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing the service, the system will refer to the user's past tax payment history to select the most suitable method of provision. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, It estimates the user's emotions and determines the priority of the tax return forms to be provided based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing the service, the optimal delivery method will be selected, taking into account the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned notification unit, It estimates the user's emotions and adjusts the timing of notifications based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned notification unit, When sending a notification, the system will refer to the user's past tax payment history to select the most appropriate notification method. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned notification unit, It estimates the user's emotions and prioritizes notifications based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned notification unit, When sending notifications, the system selects the most suitable notification method, taking into account the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned AI chatbot unit is It estimates the user's emotions and adjusts the chatbot's response based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned AI chatbot unit is When the chatbot responds, it refers to the user's past question history to provide the most appropriate response. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned AI chatbot unit is It estimates the user's emotions and prioritizes the chatbot's responses based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned AI chatbot unit is When the chatbot responds, it takes the user's device information into consideration to provide the most appropriate response. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned linkage unit is, The system estimates user sentiment and selects partner financial institutions based on the estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned linkage unit is, During integration, the system selects the optimal integration method by referring to the user's past financial transaction history. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned linkage unit is, It estimates the user's emotions and determines the priority of collaborations based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 41) The aforementioned linkage unit is, When integrating, the system selects the optimal integration method by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0201] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that automatically imports income and expense data, An analysis unit analyzes the data collected by the aforementioned collection unit, A generation unit that generates a tax return based on the data analyzed by the analysis unit, A providing unit that provides the tax return generated by the generation unit, A notification unit that notifies the tax payment deadline based on the tax return provided by the aforementioned provision unit, Equipped with A system characterized by the following features.

2. The aforementioned collection unit is It automatically imports income and expense data in conjunction with accounting submissions. The system according to feature 1.

3. The aforementioned analysis unit, The system calculates your tax liability in real time based on data such as income, expenses, and deductions. The system according to feature 1.

4. The aforementioned notification unit, Provides a tax payment deadline notification function. The system according to feature 1.

5. Equipped with an AI chatbot section, The aforementioned AI chatbot unit is We provide a guide for frequently asked questions and inquiries. The system according to feature 1.

6. The aforementioned analysis unit, Analyze tax payment history and provide feedback. The system according to feature 1.

7. Equipped with a collaboration department, The aforementioned linkage unit is, We connect with financial institutions via API and import expense data into our accounting system. The system according to feature 1.

8. The aforementioned collection unit is The system estimates user sentiment and adjusts the timing of revenue and expense data acquisition based on the estimated user sentiment. The system according to feature 1.

9. The aforementioned collection unit is Analyze the user's past income and expense data to select the optimal acquisition method. The system according to feature 1.

10. The aforementioned collection unit is When acquiring income and expense data, filtering is performed based on the user's current financial situation and areas of interest. The system according to feature 1.

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