system

Abstract

The system according to this embodiment aims to perform efficient and accurate tax processing by utilizing everyday life data. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a processing unit, and a declaration unit. The collection unit collects the user's daily life data. The analysis unit analyzes the data collected by the collection unit. The processing unit performs tax processing based on the analysis results obtained by the analysis unit. The declaration unit files a tax return based on the documents created by the processing unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, the method including the 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, efficient and accurate tax processing using daily life data has not been sufficiently carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to perform efficient and accurate tax processing by utilizing daily life data of users.

Means for Solving the Problems

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a processing unit, and a filing unit. The collection unit collects daily life data of users. The analysis unit analyzes the data collected by the collection unit. The processing unit performs tax processing based on the analysis results obtained by the analysis unit. The filing unit makes a final tax return based on the documents created by the processing unit. [Effects of the Invention]

[0007] The system according to this embodiment can perform efficient and accurate tax processing by utilizing everyday life data. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The tax processing system according to an embodiment of the present invention is a system that collects a user's daily life data, automatically manages income and expenses, and automates the process up to filing a tax return. This tax processing system collects the user's daily life data, analyzes it using AI, performs tax processing, and completes the tax return. For example, the tax processing system collects the user's daily life data. This includes methods such as taking pictures of receipts using a smartphone camera or automatically obtaining transaction history from banks and credit cards. Next, the tax processing system uses AI to analyze the collected data. The AI ​​classifies the user's income and expenses and extracts the information necessary for tax processing. Furthermore, the tax processing system uses AI to perform tax processing based on the analysis results. The AI ​​calculates the tax amount based on the user's income and expenses and creates the necessary documents. Finally, the tax processing system files the tax return based on the documents created by the AI. The AI ​​submits the necessary documents to the tax office and completes the user's tax return. As a result, the tax processing system can perform efficient and accurate tax processing by utilizing the user's daily life data.

[0029] The tax processing system according to this embodiment comprises a collection unit, an analysis unit, a processing unit, and a declaration unit. The collection unit collects the user's daily life data. The user's daily life data includes, but is not limited to, purchase history, exercise data, and meal records. The collection unit includes, for example, a method of taking pictures of receipts using a smartphone camera. For example, the collection unit takes pictures of receipts using a smartphone camera and saves them as image data. The collection unit also includes a method of automatically obtaining bank and credit card transaction history. For example, the collection unit automatically obtains bank and credit card transaction history using an API. The analysis unit analyzes the data collected by the collection unit. The analysis unit classifies the user's income and expenses and extracts information necessary for tax processing. For example, the analysis unit analyzes the data using statistical analysis and machine learning algorithms. The processing unit performs tax processing based on the analysis results obtained by the analysis unit. For example, the processing unit calculates the tax amount based on the user's income and expenses and prepares the necessary documents. For example, the processing unit calculates the tax amount based on the application of tax rates and the calculation method of deductions. The filing unit files the tax return based on the documents created by the processing unit. The filing unit completes the user's tax return by, for example, submitting the necessary documents to the tax office. For example, the filing unit submits the documents using electronic filing or paper filing methods. As a result, the tax processing system according to the embodiment can automatically collect and analyze the user's daily life data, perform tax processing, and complete the tax return.

[0030] The data collection unit may include a method of taking a picture of a receipt using a smartphone camera. For example, the data collection unit may take a picture of a receipt using a smartphone camera and save it as image data. For example, the data collection unit may take a picture of a receipt using a smartphone camera and convert the contents of the receipt into text data using image recognition technology. Alternatively, the data collection unit may take a picture of a receipt using a smartphone camera and manage the image data using a dedicated app. For example, the data collection unit may use a dedicated app to save the image data of the receipt to the cloud for later access. This makes data collection easier by taking a picture of a receipt using a smartphone camera. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit may input the image data of the receipt taken with a smartphone camera into a generating AI and have the generating AI perform the conversion from image data to text data.

[0031] The data collection unit may include methods for automatically acquiring bank and credit card transaction history. For example, the data collection unit may use APIs to automatically acquire bank and credit card transaction history. For example, the data collection unit may use a bank's API to acquire transaction history and store it in a database. The data collection unit may also use a credit card company's API to acquire transaction history and store it in a database. For example, the data collection unit may acquire credit card transaction history in real time and reflect it in the database. The data collection unit may also periodically acquire bank and credit card transaction history and update the database. For example, the data collection unit may acquire transaction history on a regular schedule, such as daily or weekly, to keep the database up to date. This makes data collection more efficient by automatically acquiring bank and credit card transaction history. Some or all of the above processes in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit may input the acquired transaction history data into a generating AI and have the generating AI perform data classification and organization.

[0032] The analytics department can classify users' income and expenses and extract information necessary for tax processing. For example, the analytics department can classify users' income and expenses. For example, the analytics department can classify data based on categories such as income, expenses, and deductions. The analytics department can also classify data using machine learning algorithms. For example, the analytics department can use machine learning algorithms to classify users' expenses into categories such as food, transportation, and entertainment. The analytics department can also classify data using statistical analysis. For example, the analytics department can use statistical analysis to classify users' income into categories such as salary, bonuses, and side income. Furthermore, the analytics department extracts information necessary for tax processing from the classified data. For example, the analytics department extracts taxable income from income data and deductible expenses from expense data. The analytics department can also extract applicable deductions from deduction item data. For example, the analytics department extracts applicable deductions such as medical expense deductions and mortgage interest deductions. This improves the accuracy of tax processing by classifying users' income and expenses and extracting information necessary for tax processing. Some or all of the above-described processes in the analysis department may be performed using AI, for example, or without AI. For example, the analysis department can input classified data into a generating AI and have the generating AI extract the information necessary for tax processing.

[0033] The processing unit can calculate the tax amount based on the user's income and expenses and create the necessary documents. For example, the processing unit can calculate the tax amount based on the user's income and expenses. For example, the processing unit can calculate the tax amount based on the application of tax rates and the calculation method of deductions. The processing unit can also create the necessary documents. For example, the processing unit can create necessary documents such as tax returns and supporting documents. For example, the processing unit can automatically create tax returns based on the user's income and expenses. The processing unit can also automatically generate necessary supporting documents. For example, the processing unit can automatically generate supporting documents such as receipts for medical expense deductions and certificates for mortgage interest deductions. This automates tax processing by calculating the tax amount based on the user's income and expenses and creating the necessary documents. Some or all of the above processing in the processing unit may be performed using AI, for example, or not using AI. For example, the processing unit can input the user's income and expense data into a generating AI and have the generating AI perform the tax calculation and document creation.

[0034] The tax filing department can submit the necessary documents to the tax office and complete the user's tax return. For example, the tax filing department can submit the necessary documents to the tax office. For example, the tax filing department can submit the documents using electronic filing methods. Alternatively, the tax filing department can also submit the documents using paper filing methods. For example, the tax filing department can print the created documents and mail them to file a paper return. This automates the tax filing process by submitting the necessary documents to the tax office and completing the user's tax return. Some or all of the above processes in the tax filing department may be performed using AI, for example, or not using AI. For example, the tax filing department can input the created documents into a generating AI and have the generating AI execute the electronic filing procedure.

[0035] The data collection unit can analyze the user's past data collection history and select the optimal collection method. For example, the data collection unit can prioritize suggesting data collection methods that the user has frequently used in the past. For example, the data collection unit can analyze the user's past data collection history, identify the most frequently used method, and prioritize suggesting that method. The data collection unit can also select the most efficient collection method from the user's past data collection history. For example, the data collection unit can analyze the past data collection history and select a method with high collection efficiency. The data collection unit can also analyze the user's past data collection history and customize the collection method. For example, the data collection unit can suggest a collection method tailored to the user's preferences based on the user's past data collection history. This allows the optimal collection method to be selected by analyzing the user's past data collection history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past data collection history into a generating AI and have the generating AI select the optimal collection method.

[0036] The data collection unit can filter data based on the user's current lifestyle and areas of interest during data collection. For example, if the user is traveling, the data collection unit will prioritize collecting travel-related spending data. For example, the data collection unit will understand the user's current lifestyle and, if it determines that the user is traveling, will prioritize collecting travel-related spending data. The data collection unit can also prioritize collecting health-related spending data if the user is interested in health. For example, the data collection unit will understand the user's areas of interest and, if it determines that the user is interested in health, will prioritize collecting health-related spending data. The data collection unit can also prioritize collecting spending data related to a specific event if the user is participating in that event. For example, the data collection unit will understand the user's current lifestyle and, if it determines that the user is participating in that event, will prioritize collecting spending data related to that event. This allows for the collection of highly relevant data by filtering the data based on the user's current lifestyle and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's lifestyle and areas of interest into a generating AI, and have the generating AI perform filtering.

[0037] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location. For example, if the user is in a specific region, the data collection unit can prioritize the collection of spending data related to that region. For example, if the data collection unit understands the user's geographical location and determines that the user is in a specific region, it will prioritize the collection of spending data related to that region. The data collection unit can also collect data based on the geographical information of the travel destination if the user is traveling. For example, if the data collection unit understands the user's geographical location and determines that the user is traveling, it will prioritize the collection of spending data based on the geographical information of the travel destination. The data collection unit can also prioritize the collection of spending data at a specific store if the user is in that store. For example, if the data collection unit understands the user's geographical location and determines that the user is in a specific store, it will prioritize the collection of spending data at that store. This allows for the priority collection 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, for example, or without AI. For example, the data collection unit can input the user's geographical location information into the generating AI, allowing the AI ​​to collect highly relevant data.

[0038] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, the data collection unit can collect data based on spending information shared by the user on social media. For example, the data collection unit can analyze a user's social media activity and collect data based on shared spending information. The data collection unit can also collect spending data on locations where the user has checked in on social media. For example, the data collection unit can analyze a user's social media activity and collect spending data on locations where the user has checked in. The data collection unit can also collect purchase data for products mentioned by the user on social media. For example, the data collection unit can analyze a user's social media activity and collect purchase data for products mentioned. In this way, relevant data can be collected 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, for example, or without AI. For example, the data collection unit can input user social media activity data into a generating AI and have the generating AI collect relevant data.

[0039] The analysis department can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis department can perform detailed analysis on important data and simplified analysis on less important data. For instance, the analysis department can assess the importance of the data and apply detailed statistical analysis or machine learning algorithms to important data. The analysis department can also perform simplified aggregation or basic statistical analysis on less important data. For example, the analysis department can perform simplified aggregation on daily expenditure data and detailed analysis on important expenditure items. Similarly, the analysis department can perform detailed analysis on important income items and simplified analysis on recurring income items. For example, the analysis department can perform detailed analysis on important income items such as salaries and bonuses and simplified analysis on recurring income items such as side job income. By adjusting the level of detail of the analysis based on the importance of the data, efficient analysis becomes possible. Some or all of the above processes in the analysis department may be performed using AI, for example, or not. For example, the analysis department can input the importance of the data into the generating AI and have the generating AI adjust the level of detail in the analysis.

[0040] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply an income-specific analysis algorithm to income data. For instance, it can input income data into an income-specific machine learning algorithm to analyze income patterns and trends. The analysis unit can also apply an expenditure-specific analysis algorithm to expenditure data. For example, it can input expenditure data into an expenditure-specific statistical analysis algorithm to analyze expenditure categories and trends. The analysis unit can also apply an investment-specific analysis algorithm to investment data. For example, it can input investment data into an investment-specific machine learning algorithm to analyze investment returns and risks. By applying different analysis algorithms depending on the data category, more accurate analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data categories into a generating AI and have the generating AI apply the optimal analysis algorithm.

[0041] The analysis unit can determine the priority of analysis based on the data collection timing during the analysis process. For example, the analysis unit can prioritize the analysis of the most recent data and postpone the analysis of older data. For example, the analysis unit can evaluate the data collection timing and prioritize the analysis of the most recent data. The analysis unit can also prioritize the analysis of data collected during a specific period. For example, the analysis unit can prioritize the analysis of data from a period specified by the user. The analysis unit can also prioritize the analysis of data from a period specified by the user. For example, the analysis unit can prioritize the analysis of data from a period specified by the user. This allows for the prioritization of analysis based on the data collection timing, thereby prioritizing the analysis of the most recent data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection timing into a generating AI and have the generating AI determine the analysis priority.

[0042] 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 can prioritize the analysis of highly relevant data and postpone the analysis of less relevant data. For example, the analysis unit can evaluate the relevance of the data and prioritize the analysis of highly relevant data. The analysis unit can also prioritize the analysis of data related to a specific category. For example, the analysis unit can prioritize the analysis of data in a category specified by the user. This allows for the prioritization of analysis of highly relevant data 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, for example, or without AI. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI adjust the order of analysis.

[0043] The processing unit can analyze the user's past tax history and select the optimal processing method during tax processing. For example, the processing unit can select the optimal tax processing method from the user's past tax history. For example, the processing unit analyzes the user's past tax history and selects the most efficient tax processing method. The processing unit can also analyze the user's past tax history and propose an efficient tax processing method. For example, the processing unit proposes an efficient tax processing method based on the user's past tax history. The processing unit can also provide a customized tax processing method based on the user's past tax history. For example, the processing unit customizes and provides the user with the most suitable tax processing method based on the user's past tax history. This allows the optimal tax processing method to be selected by analyzing the user's past tax history. Some or all of the above processing in the processing unit may be performed using AI, for example, or without AI. For example, the processing unit can input the user's past tax history into a generating AI and have the generating AI select the optimal tax processing method.

[0044] The processing unit can customize the processing methods based on the user's current living situation when processing tax returns. For example, if the user changes jobs, the processing unit can process tax returns based on the new workplace. For example, the processing unit can understand the user's current living situation and, if it determines that the user has changed jobs, process tax returns based on the new workplace. The processing unit can also process tax returns that take into account the spouse's information if the user gets married. For example, the processing unit can understand the user's current living situation and, if it determines that the user has gotten married, process tax returns that take into account the spouse's information. The processing unit can also process tax returns that take into account the user's new address if the user moves. For example, the processing unit can understand the user's current living situation and, if it determines that the user has moved, process tax returns based on the new address. By customizing tax returns based on the user's current living situation, more appropriate tax returns can be processed. Some or all of the above processing in the processing unit may be performed using AI, for example, or without AI. For example, the processing unit can input the user's living situation data into a generating AI and have the generating AI perform the customization of the processing methods.

[0045] The processing unit can select the optimal processing method when processing tax returns, taking into account the user's geographical location. For example, if the user lives in a specific region, the processing unit can process tax returns based on the tax laws of that region. For example, if the processing unit determines that the user lives in a specific region, it can process tax returns based on the tax laws of that region. The processing unit can also process tax returns based on international tax laws if the user lives overseas. For example, if the processing unit determines that the user lives overseas, it can process tax returns based on international tax laws. The processing unit can also process tax returns based on the tax laws of the new region if the user moves. For example, if the processing unit determines that the user has moved, it can process tax returns based on the tax laws of the new region. This allows the processing unit to select the optimal tax processing method by taking into account the user's geographical location. Some or all of the processing described above in the processing unit may be performed using AI, for example, or without AI. For example, the processing unit can input the user's geographical location into a generating AI and have the generating AI select the optimal tax processing method.

[0046] The processing unit can analyze a user's social media activity and propose processing methods during tax processing. For example, the processing unit can perform tax processing based on information shared by the user on social media. For example, the processing unit analyzes the user's social media activity and performs tax processing based on the shared information. The processing unit can also perform tax processing considering income and expenses mentioned by the user on social media. For example, the processing unit analyzes the user's social media activity and performs tax processing considering the mentioned income and expenses. The processing unit can also perform tax processing based on information about locations checked in by the user on social media. For example, the processing unit analyzes the user's social media activity and performs tax processing based on information about locations checked in. In this way, by analyzing the user's social media activity, the processing unit can propose relevant tax processing methods. Some or all of the processing described above in the processing unit may be performed using AI, for example, or without AI. For example, the processing unit can input the user's social media activity data into a generating AI and have the generating AI execute the proposal of processing methods.

[0047] The filing unit can select the optimal filing method by referring to the user's past filing history at the time of filing. For example, the filing unit can select the optimal filing method from the user's past filing history. For example, the filing unit can refer to the user's past filing history and select the most efficient filing method. The filing unit can also analyze the user's past filing history and suggest an efficient filing method. For example, the filing unit can suggest an efficient filing method based on the user's past filing history. The filing unit can also provide a customized filing method based on the user's past filing history. For example, the filing unit can customize and provide the user with the most suitable filing method based on the user's past filing history. This allows the optimal filing method to be selected by referring to the user's past filing history. Some or all of the above processing in the filing unit may be performed using AI, for example, or not using AI. For example, the filing unit can input the user's past filing history into a generating AI and have the generating AI select the optimal filing method.

[0048] The reporting unit can customize reporting methods based on the user's current living situation at the time of reporting. For example, if the user changes jobs, the reporting unit can provide reporting methods based on the new workplace. For example, the reporting unit can grasp the user's current living situation and, if it determines that the user has changed jobs, provide reporting methods based on the new workplace. The reporting unit can also provide reporting methods that take into account the spouse's information if the user gets married. For example, the reporting unit can grasp the user's current living situation and, if it determines that the user has gotten married, provide reporting methods that take into account the spouse's information. The reporting unit can also provide reporting methods based on the new address if the user moves. For example, the reporting unit can grasp the user's current living situation and, if it determines that the user has moved, provide reporting methods that take into account the new address. This allows for more appropriate reporting by customizing reporting methods based on the user's current living situation. Some or all of the above processing in the reporting unit may be performed using AI, for example, or not using AI. For example, the reporting unit can input the user's living situation data into a generating AI and have the generating AI perform the customization of reporting methods.

[0049] The filing unit can select the optimal filing method at the time of filing, taking into account the user's geographical location information. For example, if the user lives in a specific region, the filing unit can provide a filing method based on the tax laws of that region. For example, if the filing unit determines that the user lives in a specific region based on the user's geographical location information, it can provide a filing method based on the tax laws of that region. The filing unit can also provide a filing method based on international tax law if the user lives overseas. For example, if the filing unit determines that the user lives overseas based on the user's geographical location information, it can provide a filing method based on international tax law. The filing unit can also provide a filing method based on the tax laws of the new region if the user has moved. For example, if the filing unit determines that the user has moved based on the user's geographical location information, it can provide a filing method based on the tax laws of the new region. This allows the optimal filing method to be selected by taking into account the user's geographical location information. Some or all of the above processing in the filing unit may be performed using AI, for example, or not using AI. For example, the filing unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal filing method.

[0050] The reporting unit can analyze the user's social media activity and suggest reporting methods at the time of reporting. For example, the reporting unit can suggest reporting methods based on information shared by the user on social media. For example, the reporting unit can analyze the user's social media activity and suggest reporting methods based on the shared information. The reporting unit can also suggest reporting methods considering the income and expenses mentioned by the user on social media. For example, the reporting unit can analyze the user's social media activity and suggest reporting methods considering the mentioned income and expenses. The reporting unit can also suggest reporting methods based on information about locations checked in by the user on social media. For example, the reporting unit can analyze the user's social media activity and suggest reporting methods based on information about locations checked in. In this way, relevant reporting methods can be suggested by analyzing the user's social media activity. Some or all of the above processing in the reporting unit may be performed using AI, for example, or not using AI. For example, the reporting unit can input the user's social media activity data into a generating AI and have the generating AI execute the proposal of reporting methods.

[0051] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0052] The data collection unit can collect user health data and adjust the priority of tax processing based on the user's health status. For example, the data collection unit can obtain health data from the user's fitness tracker or smartwatch and process normal tax processing if the user is in good health. Furthermore, if the user is ill or injured, the data collection unit can change the priority of tax processing, prioritizing certain tax treatments such as medical expense deductions. In addition, the data collection unit can analyze the user's health data and adjust the frequency of tax processing based on their health status. For example, if their health deteriorates, the frequency of tax processing can be reduced to lessen the user's burden. This allows for more appropriate tax processing by adjusting the priority and frequency of tax processing based on the user's health status.

[0053] The analytics department can analyze users' purchase history and provide tax-saving advice. For example, it can analyze a user's purchase history to identify products and services that should be avoided for tax purposes. It can also suggest purchase methods that are highly effective for tax savings based on the user's purchase history. For example, it can suggest products and services that offer higher tax savings when purchased at specific times. Furthermore, the analytics department can analyze a user's purchase history and suggest deductions and credits that can be used for tax savings. For example, it can suggest deductions and credits that the user can utilize, such as medical expense deductions or education expense deductions. In this way, by analyzing a user's purchase history, it can provide specific tax-saving advice.

[0054] The tax filing department can provide region-specific tax advice by taking into account the user's geographical location. For example, if the tax filing department lives in a specific region, it can provide tax-saving advice based on the tax laws of that region. Furthermore, if the tax filing department lives overseas, it can provide tax advice based on international tax laws. For example, if the tax filing department determines that the user lives overseas based on their geographical location, it can provide tax-saving advice based on international tax laws. Additionally, if the tax filing department moves, it can provide tax advice based on the tax laws of the new region. For example, if the tax filing department determines that the user has moved based on their geographical location, it can provide tax-saving advice based on the tax laws of the new region. This allows for the provision of optimal region-specific tax advice by considering the user's geographical location.

[0055] The analytics department can analyze users' spending data and provide advice for saving money. For example, it can analyze users' spending data to identify unnecessary expenses. It can also suggest highly effective ways to reduce spending based on user spending data. For example, it can suggest ways to increase savings by reducing spending in specific categories. Furthermore, the analytics department can analyze user spending data and suggest discounts and coupons that can be used to save money. For example, it can suggest discounts and coupons usable at specific stores or services. In this way, by analyzing user spending data, it can provide concrete advice for saving money.

[0056] The tax filing system can analyze a user's past filing history and suggest points to pay attention to when filing. For example, it can analyze a user's past filing history and identify items where errors were frequently made in the past. It can also suggest points to pay attention to when filing based on the user's past filing history. For example, it can display a message specifically highlighting items where errors were frequently made in the past. Furthermore, the tax filing system can analyze a user's past filing history and suggest available deductions and credits when filing. For example, it can suggest deductions and credits that were not used in the past. This allows the system to specifically suggest points to pay attention to when filing by analyzing a user's past filing history.

[0057] The analytics department can analyze users' spending data and provide future spending forecasts. For example, it can predict future spending trends based on users' past spending data. It can also provide spending forecasts for specific periods based on users' spending data, such as for the next month or year. Furthermore, the analytics department can analyze users' spending data and provide saving advice for future spending, such as for specific spending categories. In this way, by analyzing users' spending data, it can provide both future spending forecasts and saving advice.

[0058] The following briefly describes the processing flow for example form 1.

[0059] Step 1: The data collection unit collects the user's daily life data. This data includes purchase history, exercise data, and meal records. The data collection unit includes methods such as taking pictures of receipts using a smartphone camera and automatically obtaining bank and credit card transaction history using APIs. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit uses statistical analysis and machine learning algorithms to classify users' income and expenses and extract information necessary for tax processing. Step 3: The processing unit performs tax processing based on the analysis results obtained by the analysis unit. The processing unit calculates the tax amount based on the user's income and expenses and prepares the necessary documents. The tax amount is calculated based on the application of tax rates and the calculation method for deductions. Step 4: The Filing Department files the tax return based on the documents prepared by the Processing Department. The Filing Department submits the necessary documents to the tax office and completes the user's tax return. Documents are submitted using either electronic or paper filing methods.

[0060] (Example of form 2) The tax processing system according to an embodiment of the present invention is a system that collects a user's daily life data, automatically manages income and expenses, and automates the process up to filing a tax return. This tax processing system collects the user's daily life data, analyzes it using AI, performs tax processing, and completes the tax return. For example, the tax processing system collects the user's daily life data. This includes methods such as taking pictures of receipts using a smartphone camera or automatically obtaining transaction history from banks and credit cards. Next, the tax processing system uses AI to analyze the collected data. The AI ​​classifies the user's income and expenses and extracts the information necessary for tax processing. Furthermore, the tax processing system uses AI to perform tax processing based on the analysis results. The AI ​​calculates the tax amount based on the user's income and expenses and creates the necessary documents. Finally, the tax processing system files the tax return based on the documents created by the AI. The AI ​​submits the necessary documents to the tax office and completes the user's tax return. As a result, the tax processing system can perform efficient and accurate tax processing by utilizing the user's daily life data.

[0061] The tax processing system according to this embodiment comprises a collection unit, an analysis unit, a processing unit, and a declaration unit. The collection unit collects the user's daily life data. The user's daily life data includes, but is not limited to, purchase history, exercise data, and meal records. The collection unit includes, for example, a method of taking pictures of receipts using a smartphone camera. For example, the collection unit takes pictures of receipts using a smartphone camera and saves them as image data. The collection unit also includes a method of automatically obtaining bank and credit card transaction history. For example, the collection unit automatically obtains bank and credit card transaction history using an API. The analysis unit analyzes the data collected by the collection unit. The analysis unit classifies the user's income and expenses and extracts information necessary for tax processing. For example, the analysis unit analyzes the data using statistical analysis and machine learning algorithms. The processing unit performs tax processing based on the analysis results obtained by the analysis unit. For example, the processing unit calculates the tax amount based on the user's income and expenses and prepares the necessary documents. For example, the processing unit calculates the tax amount based on the application of tax rates and the calculation method of deductions. The filing unit files the tax return based on the documents created by the processing unit. The filing unit completes the user's tax return by, for example, submitting the necessary documents to the tax office. For example, the filing unit submits the documents using electronic filing or paper filing methods. As a result, the tax processing system according to the embodiment can automatically collect and analyze the user's daily life data, perform tax processing, and complete the tax return.

[0062] The data collection unit may include a method of taking a picture of a receipt using a smartphone camera. For example, the data collection unit may take a picture of a receipt using a smartphone camera and save it as image data. For example, the data collection unit may take a picture of a receipt using a smartphone camera and convert the contents of the receipt into text data using image recognition technology. Alternatively, the data collection unit may take a picture of a receipt using a smartphone camera and manage the image data using a dedicated app. For example, the data collection unit may use a dedicated app to save the image data of the receipt to the cloud for later access. This makes data collection easier by taking a picture of a receipt using a smartphone camera. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit may input the image data of the receipt taken with a smartphone camera into a generating AI and have the generating AI perform the conversion from image data to text data.

[0063] The data collection unit may include methods for automatically acquiring bank and credit card transaction history. For example, the data collection unit may use APIs to automatically acquire bank and credit card transaction history. For example, the data collection unit may use a bank's API to acquire transaction history and store it in a database. The data collection unit may also use a credit card company's API to acquire transaction history and store it in a database. For example, the data collection unit may acquire credit card transaction history in real time and reflect it in the database. The data collection unit may also periodically acquire bank and credit card transaction history and update the database. For example, the data collection unit may acquire transaction history on a regular schedule, such as daily or weekly, to keep the database up to date. This makes data collection more efficient by automatically acquiring bank and credit card transaction history. Some or all of the above processes in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit may input the acquired transaction history data into a generating AI and have the generating AI perform data classification and organization.

[0064] The analytics department can classify users' income and expenses and extract information necessary for tax processing. For example, the analytics department can classify users' income and expenses. For example, the analytics department can classify data based on categories such as income, expenses, and deductions. The analytics department can also classify data using machine learning algorithms. For example, the analytics department can use machine learning algorithms to classify users' expenses into categories such as food, transportation, and entertainment. The analytics department can also classify data using statistical analysis. For example, the analytics department can use statistical analysis to classify users' income into categories such as salary, bonuses, and side income. Furthermore, the analytics department extracts information necessary for tax processing from the classified data. For example, the analytics department extracts taxable income from income data and deductible expenses from expense data. The analytics department can also extract applicable deductions from deduction item data. For example, the analytics department extracts applicable deductions such as medical expense deductions and mortgage interest deductions. This improves the accuracy of tax processing by classifying users' income and expenses and extracting information necessary for tax processing. Some or all of the above-described processes in the analysis department may be performed using AI, for example, or without AI. For example, the analysis department can input classified data into a generating AI and have the generating AI extract the information necessary for tax processing.

[0065] The processing unit can calculate the tax amount based on the user's income and expenses and create the necessary documents. For example, the processing unit can calculate the tax amount based on the user's income and expenses. For example, the processing unit can calculate the tax amount based on the application of tax rates and the calculation method of deductions. The processing unit can also create the necessary documents. For example, the processing unit can create necessary documents such as tax returns and supporting documents. For example, the processing unit can automatically create tax returns based on the user's income and expenses. The processing unit can also automatically generate necessary supporting documents. For example, the processing unit can automatically generate supporting documents such as receipts for medical expense deductions and certificates for mortgage interest deductions. This automates tax processing by calculating the tax amount based on the user's income and expenses and creating the necessary documents. Some or all of the above processing in the processing unit may be performed using AI, for example, or not using AI. For example, the processing unit can input the user's income and expense data into a generating AI and have the generating AI perform the tax calculation and document creation.

[0066] The tax filing department can submit the necessary documents to the tax office and complete the user's tax return. For example, the tax filing department can submit the necessary documents to the tax office. For example, the tax filing department can submit the documents using electronic filing methods. Alternatively, the tax filing department can also submit the documents using paper filing methods. For example, the tax filing department can print the created documents and mail them to file a paper return. This automates the tax filing process by submitting the necessary documents to the tax office and completing the user's tax return. Some or all of the above processes in the tax filing department may be performed using AI, for example, or not using AI. For example, the tax filing department can input the created documents into a generating AI and have the generating AI execute the electronic filing procedure.

[0067] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of data collection to lessen the user's burden. For instance, if the data collection unit estimates the user's emotions and determines that they are stressed, it can reduce the data collection frequency to once a week. Conversely, if the user is relaxed, the data collection unit can collect more detailed data to obtain more information. For example, if the data collection unit estimates the user's emotions and determines that they are relaxed, it can increase the data collection frequency to daily. Furthermore, if the user is busy, the data collection unit can automate data collection to reduce the user's effort. For example, if the data collection unit estimates the user's emotions and determines that they are busy, it can fully automate data collection, eliminating the need for manual input by the user. This reduces the user's burden by adjusting the timing of data collection based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. The generating AI may be a text generating AI (e.g., LLM) or a multimodal generating AI, but is not limited to such examples. Some or all of the processing described above in the collection unit may be performed using AI, or not using AI. For example, the collection unit may input user emotion data into the generating AI and have the generating AI perform emotion estimation.

[0068] The data collection unit can analyze the user's past data collection history and select the optimal collection method. For example, the data collection unit can prioritize suggesting data collection methods that the user has frequently used in the past. For example, the data collection unit can analyze the user's past data collection history, identify the most frequently used method, and prioritize suggesting that method. The data collection unit can also select the most efficient collection method from the user's past data collection history. For example, the data collection unit can analyze the past data collection history and select a method with high collection efficiency. The data collection unit can also analyze the user's past data collection history and customize the collection method. For example, the data collection unit can suggest a collection method tailored to the user's preferences based on the user's past data collection history. This allows the optimal collection method to be selected by analyzing the user's past data collection history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past data collection history into a generating AI and have the generating AI select the optimal collection method.

[0069] The data collection unit can filter data based on the user's current lifestyle and areas of interest during data collection. For example, if the user is traveling, the data collection unit will prioritize collecting travel-related spending data. For example, the data collection unit will understand the user's current lifestyle and, if it determines that the user is traveling, will prioritize collecting travel-related spending data. The data collection unit can also prioritize collecting health-related spending data if the user is interested in health. For example, the data collection unit will understand the user's areas of interest and, if it determines that the user is interested in health, will prioritize collecting health-related spending data. The data collection unit can also prioritize collecting spending data related to a specific event if the user is participating in that event. For example, the data collection unit will understand the user's current lifestyle and, if it determines that the user is participating in that event, will prioritize collecting spending data related to that event. This allows for the collection of highly relevant data by filtering the data based on the user's current lifestyle and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's lifestyle and areas of interest into a generating AI, and have the generating AI perform filtering.

[0070] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting only the most important data. For example, if the data collection unit estimates the user's emotions and determines that the user is stressed, it will prioritize collecting only the most important data. The data collection unit can also prioritize collecting detailed data if the user is relaxed. For example, if the data collection unit estimates the user's emotions and determines that the user is relaxed, it will prioritize collecting detailed data. The data collection unit can also prioritize collecting only the minimum necessary data if the user is busy. For example, if the data collection unit estimates the user's emotions and determines that the user is busy, it will prioritize collecting only the minimum necessary data. In this way, by determining the priority of data to collect based on the user's emotions, important data can be collected preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into a generating AI and have the generating AI determine the priority of the data to be collected.

[0071] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location. For example, if the user is in a specific region, the data collection unit can prioritize the collection of spending data related to that region. For example, if the data collection unit understands the user's geographical location and determines that the user is in a specific region, it will prioritize the collection of spending data related to that region. The data collection unit can also collect data based on the geographical information of the travel destination if the user is traveling. For example, if the data collection unit understands the user's geographical location and determines that the user is traveling, it will prioritize the collection of spending data based on the geographical information of the travel destination. The data collection unit can also prioritize the collection of spending data at a specific store if the user is in that store. For example, if the data collection unit understands the user's geographical location and determines that the user is in a specific store, it will prioritize the collection of spending data at that store. This allows for the priority collection 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, for example, or without AI. For example, the data collection unit can input the user's geographical location information into the generating AI, allowing the AI ​​to collect highly relevant data.

[0072] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, the data collection unit can collect data based on spending information shared by the user on social media. For example, the data collection unit can analyze a user's social media activity and collect data based on shared spending information. The data collection unit can also collect spending data on locations where the user has checked in on social media. For example, the data collection unit can analyze a user's social media activity and collect spending data on locations where the user has checked in. The data collection unit can also collect purchase data for products mentioned by the user on social media. For example, the data collection unit can analyze a user's social media activity and collect purchase data for products mentioned. In this way, relevant data can be collected 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, for example, or without AI. For example, the data collection unit can input user social media activity data into a generating AI and have the generating AI collect relevant data.

[0073] 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, the analysis unit can provide simple and easy-to-understand analysis results. For example, if the analysis unit estimates the user's emotions and determines that they are nervous, it can provide analysis results using simple and easy-to-understand graphs and charts. The analysis unit can also provide detailed analysis results if the user is relaxed. For example, if the analysis unit estimates the user's emotions and determines that they are relaxed, it can provide analysis results using detailed reports and data. The analysis unit can also provide concise analysis results if the user is in a hurry. For example, if the analysis unit estimates the user's emotions and determines that they are in a hurry, it can provide concise analysis results that are easy for the user to understand. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generating AI and have the generating AI adjust the way the analysis is expressed.

[0074] The analysis department can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis department can perform detailed analysis on important data and simplified analysis on less important data. For instance, the analysis department can assess the importance of the data and apply detailed statistical analysis or machine learning algorithms to important data. The analysis department can also perform simplified aggregation or basic statistical analysis on less important data. For example, the analysis department can perform simplified aggregation on daily expenditure data and detailed analysis on important expenditure items. Similarly, the analysis department can perform detailed analysis on important income items and simplified analysis on recurring income items. For example, the analysis department can perform detailed analysis on important income items such as salaries and bonuses and simplified analysis on recurring income items such as side job income. By adjusting the level of detail of the analysis based on the importance of the data, efficient analysis becomes possible. Some or all of the above processes in the analysis department may be performed using AI, for example, or not. For example, the analysis department can input the importance of the data into the generating AI and have the generating AI adjust the level of detail in the analysis.

[0075] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply an income-specific analysis algorithm to income data. For instance, it can input income data into an income-specific machine learning algorithm to analyze income patterns and trends. The analysis unit can also apply an expenditure-specific analysis algorithm to expenditure data. For example, it can input expenditure data into an expenditure-specific statistical analysis algorithm to analyze expenditure categories and trends. The analysis unit can also apply an investment-specific analysis algorithm to investment data. For example, it can input investment data into an investment-specific machine learning algorithm to analyze investment returns and risks. By applying different analysis algorithms depending on the data category, more accurate analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data categories into a generating AI and have the generating AI apply the optimal analysis algorithm.

[0076] 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 can provide a short, concise analysis. For example, if the analysis unit estimates the user's emotions and determines that they are in a hurry, it can provide a short, concise report. The analysis unit can also provide a detailed analysis if the user is relaxed. For example, if the analysis unit estimates the user's emotions and determines that they are relaxed, it can provide a report with detailed data and graphs. The analysis unit can also provide an analysis with visually stimulating effects if the user is excited. For example, if the analysis unit estimates the user's emotions and determines that they are excited, it can provide an interactive report with visually stimulating effects. This allows the analysis unit to provide an analysis of an appropriate length for the user by adjusting the length of the analysis based on their emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generating AI and have the generating AI adjust the length of the analysis.

[0077] The analysis unit can determine the priority of analysis based on the data collection timing during the analysis process. For example, the analysis unit can prioritize the analysis of the most recent data and postpone the analysis of older data. For example, the analysis unit can evaluate the data collection timing and prioritize the analysis of the most recent data. The analysis unit can also prioritize the analysis of data collected during a specific period. For example, the analysis unit can prioritize the analysis of data from a period specified by the user. The analysis unit can also prioritize the analysis of data from a period specified by the user. For example, the analysis unit can prioritize the analysis of data from a period specified by the user. This allows for the prioritization of analysis based on the data collection timing, thereby prioritizing the analysis of the most recent data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection timing into a generating AI and have the generating AI determine the analysis priority.

[0078] 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 can prioritize the analysis of highly relevant data and postpone the analysis of less relevant data. For example, the analysis unit can evaluate the relevance of the data and prioritize the analysis of highly relevant data. The analysis unit can also prioritize the analysis of data related to a specific category. For example, the analysis unit can prioritize the analysis of data in a category specified by the user. This allows for the prioritization of analysis of highly relevant data 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, for example, or without AI. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI adjust the order of analysis.

[0079] The processing unit can estimate the user's emotions and adjust the tax processing method based on the estimated emotions. For example, if the user is nervous, the processing unit can provide a simple and easy-to-understand tax processing method. For example, if the processing unit estimates the user's emotions and determines that they are nervous, it can provide a simple and easy-to-understand tax processing method. The processing unit can also provide a detailed tax processing method if the user is relaxed. For example, if the processing unit estimates the user's emotions and determines that they are relaxed, it can provide a detailed tax processing method. The processing unit can also provide a concise tax processing method if the user is in a hurry. For example, if the processing unit estimates the user's emotions and determines that they are in a hurry, it can provide a concise tax processing method. By adjusting the tax processing method based on the user's emotions, optimal tax processing becomes possible for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the processing unit may be performed using AI, for example, or without AI. For example, the processing unit can input user emotion data into a generating AI and have the generating AI adjust the tax processing method.

[0080] The processing unit can analyze the user's past tax history and select the optimal processing method during tax processing. For example, the processing unit can select the optimal tax processing method from the user's past tax history. For example, the processing unit analyzes the user's past tax history and selects the most efficient tax processing method. The processing unit can also analyze the user's past tax history and propose an efficient tax processing method. For example, the processing unit proposes an efficient tax processing method based on the user's past tax history. The processing unit can also provide a customized tax processing method based on the user's past tax history. For example, the processing unit customizes and provides the user with the most suitable tax processing method based on the user's past tax history. This allows the optimal tax processing method to be selected by analyzing the user's past tax history. Some or all of the above processing in the processing unit may be performed using AI, for example, or without AI. For example, the processing unit can input the user's past tax history into a generating AI and have the generating AI select the optimal tax processing method.

[0081] The processing unit can customize the processing methods based on the user's current living situation when processing tax returns. For example, if the user changes jobs, the processing unit can process tax returns based on the new workplace. For example, the processing unit can understand the user's current living situation and, if it determines that the user has changed jobs, process tax returns based on the new workplace. The processing unit can also process tax returns that take into account the spouse's information if the user gets married. For example, the processing unit can understand the user's current living situation and, if it determines that the user has gotten married, process tax returns that take into account the spouse's information. The processing unit can also process tax returns that take into account the user's new address if the user moves. For example, the processing unit can understand the user's current living situation and, if it determines that the user has moved, process tax returns based on the new address. By customizing tax returns based on the user's current living situation, more appropriate tax returns can be processed. Some or all of the above processing in the processing unit may be performed using AI, for example, or without AI. For example, the processing unit can input the user's living situation data into a generating AI and have the generating AI perform the customization of the processing methods.

[0082] The processing unit can estimate the user's emotions and determine the priority of tax processing based on the estimated emotions. For example, if the user is stressed, the processing unit will prioritize important tax processing. For example, if the processing unit estimates the user's emotions and determines that the user is stressed, it will prioritize important tax processing. The processing unit can also prioritize detailed tax processing if the user is relaxed. For example, if the processing unit estimates the user's emotions and determines that the user is relaxed, it will prioritize detailed tax processing. The processing unit can also prioritize the minimum necessary tax processing if the user is busy. For example, if the processing unit estimates the user's emotions and determines that the user is busy, it will prioritize the minimum necessary tax processing. In this way, by determining the priority of tax processing based on the user's emotions, important tax processing can be prioritized. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. 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-described processes in the processing unit may be performed using AI, for example, or without AI. For example, the processing unit can input user emotion data into a generating AI and have the generating AI determine the priority of tax processing.

[0083] The processing unit can select the optimal processing method when processing tax returns, taking into account the user's geographical location. For example, if the user lives in a specific region, the processing unit can process tax returns based on the tax laws of that region. For example, if the processing unit determines that the user lives in a specific region, it can process tax returns based on the tax laws of that region. The processing unit can also process tax returns based on international tax laws if the user lives overseas. For example, if the processing unit determines that the user lives overseas, it can process tax returns based on international tax laws. The processing unit can also process tax returns based on the tax laws of the new region if the user moves. For example, if the processing unit determines that the user has moved, it can process tax returns based on the tax laws of the new region. This allows the processing unit to select the optimal tax processing method by taking into account the user's geographical location. Some or all of the processing described above in the processing unit may be performed using AI, for example, or without AI. For example, the processing unit can input the user's geographical location into a generating AI and have the generating AI select the optimal tax processing method.

[0084] The processing unit can analyze a user's social media activity and propose processing methods during tax processing. For example, the processing unit can perform tax processing based on information shared by the user on social media. For example, the processing unit analyzes the user's social media activity and performs tax processing based on the shared information. The processing unit can also perform tax processing considering income and expenses mentioned by the user on social media. For example, the processing unit analyzes the user's social media activity and performs tax processing considering the mentioned income and expenses. The processing unit can also perform tax processing based on information about locations checked in by the user on social media. For example, the processing unit analyzes the user's social media activity and performs tax processing based on information about locations checked in. In this way, by analyzing the user's social media activity, the processing unit can propose relevant tax processing methods. Some or all of the processing described above in the processing unit may be performed using AI, for example, or without AI. For example, the processing unit can input the user's social media activity data into a generating AI and have the generating AI execute the proposal of processing methods.

[0085] The reporting unit can estimate the user's emotions and adjust the reporting method based on the estimated emotions. For example, if the user is nervous, the reporting unit can provide a simple and easy-to-understand reporting method. For example, if the reporting unit estimates the user's emotions and determines that they are nervous, it can provide a simple and easy-to-understand reporting method. The reporting unit can also provide a detailed reporting method if the user is relaxed. For example, if the reporting unit estimates the user's emotions and determines that they are relaxed, it can provide a detailed reporting method. The reporting unit can also provide a concise reporting method if the user is in a hurry. For example, if the reporting unit estimates the user's emotions and determines that they are in a hurry, it can provide a concise reporting method. In this way, by adjusting the reporting method based on the user's emotions, the optimal reporting method can be provided for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. 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-described processes in the reporting section may be performed using AI, for example, or without AI. For example, the reporting section may input user sentiment data into a generating AI and have the generating AI adjust the reporting method.

[0086] The filing unit can select the optimal filing method by referring to the user's past filing history at the time of filing. For example, the filing unit can select the optimal filing method from the user's past filing history. For example, the filing unit can refer to the user's past filing history and select the most efficient filing method. The filing unit can also analyze the user's past filing history and suggest an efficient filing method. For example, the filing unit can suggest an efficient filing method based on the user's past filing history. The filing unit can also provide a customized filing method based on the user's past filing history. For example, the filing unit can customize and provide the user with the most suitable filing method based on the user's past filing history. This allows the optimal filing method to be selected by referring to the user's past filing history. Some or all of the above processing in the filing unit may be performed using AI, for example, or not using AI. For example, the filing unit can input the user's past filing history into a generating AI and have the generating AI select the optimal filing method.

[0087] The reporting unit can customize reporting methods based on the user's current living situation at the time of reporting. For example, if the user changes jobs, the reporting unit can provide reporting methods based on the new workplace. For example, the reporting unit can grasp the user's current living situation and, if it determines that the user has changed jobs, provide reporting methods based on the new workplace. The reporting unit can also provide reporting methods that take into account the spouse's information if the user gets married. For example, the reporting unit can grasp the user's current living situation and, if it determines that the user has gotten married, provide reporting methods that take into account the spouse's information. The reporting unit can also provide reporting methods based on the new address if the user moves. For example, the reporting unit can grasp the user's current living situation and, if it determines that the user has moved, provide reporting methods that take into account the new address. This allows for more appropriate reporting by customizing reporting methods based on the user's current living situation. Some or all of the above processing in the reporting unit may be performed using AI, for example, or not using AI. For example, the reporting unit can input the user's living situation data into a generating AI and have the generating AI perform the customization of reporting methods.

[0088] The reporting unit can estimate the user's emotions and prioritize reports based on the estimated emotions. For example, if the reporting unit is stressed, it will prioritize important reports. For example, if the reporting unit estimates the user's emotions and determines that the user is stressed, it will prioritize important reports. The reporting unit can also prioritize detailed reports if the user is relaxed. For example, if the reporting unit estimates the user's emotions and determines that the user is relaxed, it will prioritize detailed reports. The reporting unit can also prioritize minimal reports if the user is busy. For example, if the reporting unit estimates the user's emotions and determines that the user is busy, it will prioritize minimal reports. In this way, by prioritizing reports based on the user's emotions, important reports can be prioritized. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the reporting section may be performed using AI, for example, or without AI. For example, the reporting section may input user sentiment data into a generating AI and have the generating AI determine the priority of the reporting.

[0089] The filing unit can select the optimal filing method at the time of filing, taking into account the user's geographical location information. For example, if the user lives in a specific region, the filing unit can provide a filing method based on the tax laws of that region. For example, if the filing unit determines that the user lives in a specific region based on the user's geographical location information, it can provide a filing method based on the tax laws of that region. The filing unit can also provide a filing method based on international tax law if the user lives overseas. For example, if the filing unit determines that the user lives overseas based on the user's geographical location information, it can provide a filing method based on international tax law. The filing unit can also provide a filing method based on the tax laws of the new region if the user has moved. For example, if the filing unit determines that the user has moved based on the user's geographical location information, it can provide a filing method based on the tax laws of the new region. This allows the optimal filing method to be selected by taking into account the user's geographical location information. Some or all of the above processing in the filing unit may be performed using AI, for example, or not using AI. For example, the filing unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal filing method.

[0090] The reporting unit can analyze the user's social media activity and suggest reporting methods at the time of reporting. For example, the reporting unit can suggest reporting methods based on information shared by the user on social media. For example, the reporting unit can analyze the user's social media activity and suggest reporting methods based on the shared information. The reporting unit can also suggest reporting methods considering the income and expenses mentioned by the user on social media. For example, the reporting unit can analyze the user's social media activity and suggest reporting methods considering the mentioned income and expenses. The reporting unit can also suggest reporting methods based on information about locations checked in by the user on social media. For example, the reporting unit can analyze the user's social media activity and suggest reporting methods based on information about locations checked in. In this way, relevant reporting methods can be suggested by analyzing the user's social media activity. Some or all of the above processing in the reporting unit may be performed using AI, for example, or not using AI. For example, the reporting unit can input the user's social media activity data into a generating AI and have the generating AI execute the proposal of reporting methods. === Hard Collateral 1-1 === Each of the multiple elements described above, including the collection unit, analysis unit, processing unit, and filing unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the collection unit takes a picture of a receipt using the camera 42 of the smart device 14 and saves it as image data. The collection unit also automatically acquires bank and credit card transaction history via the communication I / F 26 of the data processing unit 12. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and analyzes the collected data and extracts the information necessary for tax processing. The processing unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and calculates the tax amount and prepares the necessary documents. The filing unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and submits the necessary documents to the tax office and completes the tax return. === Hard Collateral 1-2 === Each of the multiple elements described above, including the collection unit, analysis unit, processing unit, and filing unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit uses the camera 42 of the smart glasses 214 to photograph receipts and saves them as image data. The collection unit also automatically acquires bank and credit card transaction history via the communication I / F 26 of the data processing unit 12. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes the collected data and extracts information necessary for tax processing. The processing unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which calculates the tax amount and prepares the necessary documents. The filing unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which submits the necessary documents to the tax office and completes the tax return. === Hard Collateral 1-3 === Each of the multiple elements described above, including the collection unit, analysis unit, processing unit, and declaration unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit takes pictures of receipts using the camera 42 of the headset terminal 314 and saves them as image data. The collection unit also automatically acquires bank and credit card transaction history via the communication I / F 26 of the data processing unit 12. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which analyzes the collected data and extracts information necessary for tax processing. The processing unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which calculates the tax amount and prepares the necessary documents. The declaration unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which submits the necessary documents to the tax office and completes the tax return. === Hard Collateral 1-4 === Each of the multiple elements described above, including the collection unit, analysis unit, processing unit, and filing unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit uses the camera 42 of the robot 414 to photograph receipts and saves them as image data. The collection unit also automatically acquires bank and credit card transaction history via the communication I / F 26 of the data processing unit 12. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 to analyze the collected data and extract information necessary for tax processing. The processing unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 to calculate the tax amount and prepare the necessary documents. The filing unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 to submit the necessary documents to the tax office and complete the tax return.

[0091] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0092] The data collection unit can collect user health data and adjust the priority of tax processing based on the user's health status. For example, the data collection unit can obtain health data from the user's fitness tracker or smartwatch and process normal tax processing if the user is in good health. Furthermore, if the user is ill or injured, the data collection unit can change the priority of tax processing, prioritizing certain tax treatments such as medical expense deductions. In addition, the data collection unit can analyze the user's health data and adjust the frequency of tax processing based on their health status. For example, if their health deteriorates, the frequency of tax processing can be reduced to lessen the user's burden. This allows for more appropriate tax processing by adjusting the priority and frequency of tax processing based on the user's health status.

[0093] The analytics department can analyze users' purchase history and provide tax-saving advice. For example, it can analyze a user's purchase history to identify products and services that should be avoided for tax purposes. It can also suggest purchase methods that are highly effective for tax savings based on the user's purchase history. For example, it can suggest products and services that offer higher tax savings when purchased at specific times. Furthermore, the analytics department can analyze a user's purchase history and suggest deductions and credits that can be used for tax savings. For example, it can suggest deductions and credits that the user can utilize, such as medical expense deductions or education expense deductions. In this way, by analyzing a user's purchase history, it can provide specific tax-saving advice.

[0094] The processing unit can estimate the user's emotions and adjust the way tax processing notifications are sent based on those emotions. For example, if the processing unit is stressed, it can provide simple and highly visible notifications. For instance, if it estimates the user's emotions and determines that they are stressed, it sends a concise message or notification. The processing unit can also provide detailed notifications if the user is relaxed. For example, if it estimates the user's emotions and determines that they are relaxed, it sends a notification that includes detailed explanations and guides. Furthermore, if the user is busy, the processing unit can reduce the frequency of notifications and send only important ones. For example, if it estimates the user's emotions and determines that they are busy, it sends only important notifications. This allows the system to provide the most suitable notification method for the user by adjusting the notification method based on their emotions.

[0095] The tax filing department can provide region-specific tax advice by taking into account the user's geographical location. For example, if the tax filing department lives in a specific region, it can provide tax-saving advice based on the tax laws of that region. Furthermore, if the tax filing department lives overseas, it can provide tax advice based on international tax laws. For example, if the tax filing department determines that the user lives overseas based on their geographical location, it can provide tax-saving advice based on international tax laws. Additionally, if the tax filing department moves, it can provide tax advice based on the tax laws of the new region. For example, if the tax filing department determines that the user has moved based on their geographical location, it can provide tax-saving advice based on the tax laws of the new region. This allows for the provision of optimal region-specific tax advice by considering the user's geographical location.

[0096] The data collection unit can estimate the user's emotions and customize the data collection method based on the estimated emotions. For example, if the data collection unit is stressed, it can provide a simple and hassle-free data collection method. For instance, if the data collection unit estimates the user's emotions and determines that they are stressed, it can provide a method for collecting data with a single click. Alternatively, if the data collection unit is relaxed, it can provide a more detailed data collection method. For example, if the data collection unit estimates the user's emotions and determines that they are relaxed, it can provide a detailed input form. Furthermore, if the data collection unit is busy, it can automate data collection, saving the user time and effort. For example, if the data collection unit estimates the user's emotions and determines they are busy, it can fully automate data collection, eliminating the need for manual input. This reduces the user's burden by customizing the data collection method based on their emotions.

[0097] The analytics department can analyze users' spending data and provide advice for saving money. For example, it can analyze users' spending data to identify unnecessary expenses. It can also suggest highly effective ways to reduce spending based on user spending data. For example, it can suggest ways to increase savings by reducing spending in specific categories. Furthermore, the analytics department can analyze user spending data and suggest discounts and coupons that can be used to save money. For example, it can suggest discounts and coupons usable at specific stores or services. In this way, by analyzing user spending data, it can provide concrete advice for saving money.

[0098] The processing unit can estimate the user's emotions and adjust how it reports on the progress of tax processing based on those emotions. For example, if the processing unit is stressed, it can provide a simple and easy-to-understand progress report. For example, if the processing unit estimates the user's emotions and determines that they are stressed, it can provide a concise progress report. Alternatively, if the processing unit is relaxed, it can provide a detailed progress report. For example, if the processing unit estimates the user's emotions and determines that they are relaxed, it can provide a detailed progress report. Furthermore, if the processing unit is busy, it can reduce the frequency of progress reports and provide only important reports. For example, if the processing unit estimates the user's emotions and determines that they are busy, it can provide only important progress reports. This allows the processing unit to provide the most suitable reporting method for the user by adjusting the progress reporting method based on the user's emotions.

[0099] The tax filing system can analyze a user's past filing history and suggest points to pay attention to when filing. For example, it can analyze a user's past filing history and identify items where errors were frequently made in the past. It can also suggest points to pay attention to when filing based on the user's past filing history. For example, it can display a message specifically highlighting items where errors were frequently made in the past. Furthermore, the tax filing system can analyze a user's past filing history and suggest available deductions and credits when filing. For example, it can suggest deductions and credits that were not used in the past. This allows the system to specifically suggest points to pay attention to when filing by analyzing a user's past filing history.

[0100] The data collection unit can estimate the user's emotions and adjust the data collection interface based on those emotions. For example, if the user is stressed, the unit can provide a simple and intuitive interface. If it estimates the user's emotions and determines they are stressed, it can provide an interface that allows data collection with simple operations. The unit can also provide a detailed interface if the user is relaxed. If it estimates the user's emotions and determines they are relaxed, it can provide an interface that allows for detailed settings and customization. Furthermore, if the user is busy, the unit can minimize the operation of the interface. If it estimates the user's emotions and determines they are busy, it can provide an interface that allows data collection with a single click. In this way, the burden on the user is reduced by adjusting the data collection interface based on the user's emotions.

[0101] The analytics department can analyze users' spending data and provide future spending forecasts. For example, it can predict future spending trends based on users' past spending data. It can also provide spending forecasts for specific periods based on users' spending data, such as for the next month or year. Furthermore, the analytics department can analyze users' spending data and provide saving advice for future spending, such as for specific spending categories. In this way, by analyzing users' spending data, it can provide both future spending forecasts and saving advice.

[0102] The following briefly describes the processing flow for example form 2.

[0103] Step 1: The data collection unit collects the user's daily life data. This data includes purchase history, exercise data, and meal records. The data collection unit includes methods such as taking pictures of receipts using a smartphone camera and automatically obtaining bank and credit card transaction history using APIs. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit uses statistical analysis and machine learning algorithms to classify users' income and expenses and extract information necessary for tax processing. Step 3: The processing unit performs tax processing based on the analysis results obtained by the analysis unit. The processing unit calculates the tax amount based on the user's income and expenses and prepares the necessary documents. The tax amount is calculated based on the application of tax rates and the calculation method for deductions. Step 4: The Filing Department files the tax return based on the documents prepared by the Processing Department. The Filing Department submits the necessary documents to the tax office and completes the user's tax return. Documents are submitted using either electronic or paper filing methods.

[0104] 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.

[0105] 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 the following. 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 (for example, 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. 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 a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

[0106] 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.

[0107] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0108] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.).

[0120] 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.

[0121] 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. 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.

[0122] 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.

[0123] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0124] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.).

[0136] 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.

[0137] 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. 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.

[0138] 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.

[0139] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0140] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.).

[0153] 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.

[0154] 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. 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.

[0155] 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.

[0156] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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."

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] [Explanation of Symbols]

[0176] 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 data collection unit that collects data on the user's daily life, An analysis unit analyzes the data collected by the aforementioned collection unit, A processing unit that performs tax processing based on the analysis results obtained by the aforementioned analysis unit, The system includes a tax return unit that files a tax return based on the documents created by the processing unit. A system characterized by the following features.

2. The aforementioned collection unit is This includes how to take a picture of a receipt using your smartphone camera. The system according to feature 1.

3. The aforementioned collection unit is This includes methods for automatically retrieving bank and credit card transaction history. The system according to feature 1.

4. The aforementioned analysis unit is Classify the user's income and expenses and extract the information necessary for tax processing. The system according to feature 1.

5. The aforementioned processing unit, We calculate the tax amount based on the user's income and expenses and prepare the necessary documents. The system according to feature 1.

6. The aforementioned reporting section, Submit the necessary documents to the tax office and complete the user's tax return. The system according to feature 1.

7. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze the user's past data collection history and select the optimal collection method. The system according to feature 1.

9. The aforementioned collection unit is During data collection, filtering is performed based on the user's current lifestyle and areas of interest. The system according to feature 1.

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