System

Abstract

An object of a system according to an embodiment is to automatically sort sales and expenses and automatically create an Tax Return Form.SOLUTION: A system includes a sales / expense data acquisition part, a sorting part, and a declaration preparation part. The sales and expenses data acquisition unit acquires data of sales and expenses. The sorting unit analyzes the sales and expense data acquired by the sales and expense data acquiring unit and sorts the data into appropriate categories. The Tax Return Form preparing part automatically prepares a declaration based on the sales and expenses sorted by the sorting part.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, manually sorting sales and expense data and preparing tax returns is time-consuming and prone to errors.

[0005] The system according to the embodiment aims to automatically sort sales and expense data and automatically prepare tax returns. [Means for solving the problem]

[0006] The system according to the embodiment includes a sales and expense data acquisition unit, a classification unit, and a tax return preparation unit. The sales and expense data acquisition unit acquires sales and expense data. The classification unit analyzes the sales and expense data acquired by the sales and expense data acquisition unit and sorts the data into appropriate categories. The tax return preparation unit automatically prepares a tax return based on the sales and expense data sorted by the classification unit. [Effects of the Invention]

[0007] The system according to the embodiment can automatically sort sales and expense data and automatically prepare tax returns. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The blue return system according to the embodiment of the present invention is a system that automatically classifies sales and expenses and automatically prepares a tax return. As a result, the blue return system can automatically classify sales and expense data and automatically prepare a tax return.

[0029] The blue return system according to the embodiment includes a sales and expense data acquisition unit, a classification unit, and a tax return preparation unit. The sales and expense data acquisition unit acquires sales and expense data. For example, when a user takes a photo of a receipt with their smartphone and inputs the image into the AI, the sales and expense data acquisition unit analyzes the content and acquires sales and expense data. The sales and expense data acquisition unit can also directly acquire sales and expense data provided as electronic data. For example, it acquires invoice data sent via email. The classification unit analyzes the sales and expense data acquired by the sales and expense data acquisition unit and sorts it into appropriate categories. For example, the generation AI automatically classifies sales data into sales categories and expense data into expense categories. The classification unit can also learn from the user's past transaction history to perform more accurate classification. For example, if invoices from the same business partner have previously been classified as expenses, similar new invoices will automatically be classified as expenses. The tax return preparation unit automatically prepares a tax return based on the sales and expense data sorted by the classification unit. For example, the generation AI compiles sales and expense data and automatically fills in the necessary fields. The tax return preparation section also automatically reflects the latest tax law information to ensure compliance. For example, it automatically applies the latest tax rates and deduction amounts to prepare tax returns. This allows the blue return system to automatically sort sales and expense data and automatically prepare tax returns. For example, users simply input sales and expense data, and the AI ​​automatically sorts and prepares tax returns, eliminating the need for effort. Furthermore, the system can be easily used with a smartphone, making it easy for even time-poor part-timers to use.

[0030] The sales and expense data acquisition unit can use a smartphone camera to analyze the contents of receipts and invoices in real time and instantly input the data. The sales and expense data acquisition unit can, for example, use a smartphone camera to analyze the contents of receipts and invoices in real time and instantly input the data. For example, when a receipt is photographed, the generation AI automatically analyzes the contents and inputs sales and expense data. The sales and expense data acquisition unit can also use a smartphone camera to photograph an invoice and convert the contents into text data using OCR technology. For example, the contents of the invoice can be automatically read and sales and expense data can be input. This further reduces the user's effort by using a smartphone camera to analyze the contents of receipts and invoices in real time and instantly input the data.

[0031] The sorting unit can learn from a user's past transaction history and perform more accurate sorting. For example, the sorting unit uses a generation AI to learn from a user's past transaction history and automatically identify similar transactions and sort them into the appropriate category. For example, if invoices from the same business partner have been classified as expenses in the past, new similar invoices will also be automatically classified as expenses. The sorting unit can also analyze transaction patterns based on the user's transaction history and perform more accurate sorting. For example, if invoices from a particular business partner tend to be classified into a particular category, it can learn that pattern and apply it to new transactions. This improves the accuracy of sorting by learning from a user's past transaction history and performing more accurate sorting.

[0032] The tax return preparation section automatically reflects the latest information on tax laws, ensuring compliance with the law. For example, the tax return preparation section uses generation AI to obtain the latest information on tax laws in real time and automatically reflect it in the tax return. For example, the latest tax rates and deduction amounts are automatically applied to prepare the tax return. The tax return preparation section also has an update function to keep up with changes in tax laws, allowing it to always prepare tax returns based on the latest laws. For example, if there are any changes to tax laws, the information is automatically obtained and reflected in the tax return. This automatically reflects the latest information on tax laws, ensuring compliance with the law and preventing filing errors.

[0033] The sales and expense data acquisition unit can use the smartphone's voice recognition function to allow the user to input sales and expense data by voice. The sales and expense data acquisition unit can use the smartphone's voice recognition function to allow the user to input sales and expense data by voice. For example, when the user speaks "Sales 1,000 yen," the generation AI automatically enters the data. The sales and expense data acquisition unit can also use voice recognition technology to analyze the user's voice commands and enter appropriate data. For example, when the user speaks "Expenses 500 yen," the content is analyzed and entered as expense data. This allows the smartphone's voice recognition function to allow the user to enter sales and expense data by voice, further reducing the effort required for input.

[0034] The classification unit can refer to the credit information of business partners and perform risk assessments. For example, the classification unit uses generation AI to obtain the credit information of business partners in real time and perform risk assessments of transactions. For example, it can automatically flag invoices from business partners with low credit scores and alert the user. The classification unit can also automatically identify high-risk transactions based on the business partner's credit information and take appropriate action. For example, it can request additional confirmation when classifying an invoice from a business partner with a low credit score as an expense. In this way, by referring to the business partner's credit information and performing risk assessments, high-risk transactions can be prevented before they occur.

[0035] The tax return preparation unit complies with the tax laws of different countries and can automatically file international returns. For example, the generation AI in the tax return preparation unit automatically recognizes the tax laws of different countries and applies the appropriate tax rates and deduction amounts to create final tax returns. For example, it creates returns based on US tax laws. The tax return preparation unit also has a function for handling international transactions and can create returns based on the tax laws of multiple countries. For example, if a user is conducting transactions in multiple countries, it creates returns based on the tax laws of each country. This allows it to comply with the tax laws of different countries and automatically file international returns, preventing international filing errors.

[0036] The sales and expense data acquisition unit can automatically record transaction locations using the smartphone's GPS function and use them for expense classification. The sales and expense data acquisition unit can automatically record transaction locations using the smartphone's GPS function and use them for expense classification. For example, when a user enters expenses while on a business trip, the transaction location is automatically recorded based on the GPS data. The sales and expense data acquisition unit can also classify expenses based on transaction location data. For example, expenses incurred at a specific location can be automatically classified into a specific category. This makes expense management easier by automatically recording transaction locations using the smartphone's GPS function and using them for expense classification.

[0037] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0038] The blue tax return system can also be equipped with a health management unit that monitors the user's health condition. For example, if a user continues working for a long time, the health management unit can send a notification urging the user to take a break. The health management unit can also monitor the user's heart rate and stress level and provide appropriate advice. For example, if the heart rate is high, the health management unit can suggest breathing techniques to help users relax. In this way, by monitoring the user's health condition and providing appropriate advice, it is possible to improve work efficiency.

[0039] The blue return system can also be equipped with a user schedule management section. For example, the schedule management section can link with the user's calendar and remind the user of important filing deadlines. The schedule management section can also suggest optimal work times based on the user's schedule. For example, it can notify the user to complete filing work during their free time. This allows the user's schedule to be managed efficiently, allowing the filing work to proceed smoothly.

[0040] The blue return system may further include a user learning support unit. For example, the learning support unit may provide learning materials for users to learn basic knowledge of tax law and accounting. The learning support unit may also monitor the user's learning progress and provide appropriate feedback. For example, the learning support unit may suggest additional learning materials depending on the user's level of understanding. This allows users to deepen their knowledge of tax law and accounting, thereby enabling them to complete their tax return work more efficiently.

[0041] The blue return system can also be equipped with a user network management unit. For example, the network management unit provides a platform for users to share information with other users. The network management unit can also promote communication between users and enable them to support each other. For example, it can provide a place for users in the same industry to share their experiences and knowledge. This allows users to build a network and support each other, making the filing process more efficient.

[0042] The blue return system can also be equipped with a user energy management unit. For example, the energy management unit monitors energy consumption in the user's work environment and suggests efficient energy usage. The energy management unit can also optimize energy consumption according to the user's work hours. For example, it can suggest ways to reduce energy consumption during times when work is concentrated. This allows for efficient management of the user's energy consumption and reduces costs.

[0043] The processing flow of the first embodiment will be briefly explained below.

[0044] Step 1: The sales and expense data acquisition unit acquires sales and expense data. For example, if a user takes a photo of a receipt with their smartphone and inputs the image into the AI, the sales and expense data acquisition unit analyzes the content and acquires sales and expense data. It can also directly acquire sales and expense data provided as electronic data. For example, it acquires invoice data sent by email. Step 2: The classification unit analyzes the sales and expense data acquired by the sales and expense data acquisition unit and sorts it into appropriate categories. For example, the generation AI automatically classifies sales data into sales categories and expense data into expense categories. The classification unit can also learn from the user's past transaction history to perform more accurate classification. For example, if invoices from the same business partner have been classified as expenses in the past, similar new invoices will also be automatically classified as expenses. Step 3: The tax return preparation unit automatically prepares tax returns based on the sales and expense data sorted by the classification unit. For example, the generation AI aggregates sales and expense data and automatically fills in the necessary fields. The tax return preparation unit also automatically reflects the latest tax law information to ensure compliance. For example, it automatically applies the latest tax rates and deduction amounts to prepare tax returns.

[0045] (Example 2) The blue return system according to the embodiment of the present invention is a system that automatically classifies sales and expenses and automatically prepares a tax return. As a result, the blue return system can automatically classify sales and expense data and automatically prepare a tax return.

[0046] The blue return system according to the embodiment includes a sales and expense data acquisition unit, a classification unit, and a tax return preparation unit. The sales and expense data acquisition unit acquires sales and expense data. For example, when a user takes a photo of a receipt with their smartphone and inputs the image into the AI, the sales and expense data acquisition unit analyzes the content and acquires sales and expense data. The sales and expense data acquisition unit can also directly acquire sales and expense data provided as electronic data. For example, it acquires invoice data sent via email. The classification unit analyzes the sales and expense data acquired by the sales and expense data acquisition unit and sorts it into appropriate categories. For example, the generation AI automatically classifies sales data into sales categories and expense data into expense categories. The classification unit can also learn from the user's past transaction history to perform more accurate classification. For example, if invoices from the same business partner have previously been classified as expenses, similar new invoices will automatically be classified as expenses. The tax return preparation unit automatically prepares a tax return based on the sales and expense data sorted by the classification unit. For example, the generation AI compiles sales and expense data and automatically fills in the necessary fields. The tax return preparation section also automatically reflects the latest tax law information to ensure compliance. For example, it automatically applies the latest tax rates and deduction amounts to prepare tax returns. This allows the blue return system to automatically sort sales and expense data and automatically prepare tax returns. For example, users simply input sales and expense data, and the AI ​​automatically sorts and prepares tax returns, eliminating the need for effort. Furthermore, the system can be easily used with a smartphone, making it easy for even time-poor part-timers to use.

[0047] The sales and expense data acquisition unit can use a smartphone camera to analyze the contents of receipts and invoices in real time and instantly input the data. The sales and expense data acquisition unit can, for example, use a smartphone camera to analyze the contents of receipts and invoices in real time and instantly input the data. For example, when a receipt is photographed, the generation AI automatically analyzes the contents and inputs sales and expense data. The sales and expense data acquisition unit can also use a smartphone camera to photograph an invoice and convert the contents into text data using OCR technology. For example, the contents of the invoice can be automatically read and sales and expense data can be input. This further reduces the user's effort by using a smartphone camera to analyze the contents of receipts and invoices in real time and instantly input the data.

[0048] The sorting unit can learn from a user's past transaction history and perform more accurate sorting. For example, the sorting unit uses a generation AI to learn from a user's past transaction history and automatically identify similar transactions and sort them into the appropriate category. For example, if invoices from the same business partner have been classified as expenses in the past, new similar invoices will also be automatically classified as expenses. The sorting unit can also analyze transaction patterns based on the user's transaction history and perform more accurate sorting. For example, if invoices from a particular business partner tend to be classified into a particular category, it can learn that pattern and apply it to new transactions. This improves the accuracy of sorting by learning from a user's past transaction history and performing more accurate sorting.

[0049] The tax return preparation section automatically reflects the latest information on tax laws, ensuring compliance with the law. For example, the tax return preparation section uses generation AI to obtain the latest information on tax laws in real time and automatically reflect it in the tax return. For example, the latest tax rates and deduction amounts are automatically applied to prepare the tax return. The tax return preparation section also has an update function to keep up with changes in tax laws, allowing it to always prepare tax returns based on the latest laws. For example, if there are any changes to tax laws, the information is automatically obtained and reflected in the tax return. This automatically reflects the latest information on tax laws, ensuring compliance with the law and preventing filing errors.

[0050] The sales and expense data acquisition unit can use the smartphone's voice recognition function to allow the user to input sales and expense data by voice. The sales and expense data acquisition unit can use the smartphone's voice recognition function to allow the user to input sales and expense data by voice. For example, when the user speaks "Sales 1,000 yen," the generation AI automatically enters the data. The sales and expense data acquisition unit can also use voice recognition technology to analyze the user's voice commands and enter appropriate data. For example, when the user speaks "Expenses 500 yen," the content is analyzed and entered as expense data. This allows the smartphone's voice recognition function to allow the user to enter sales and expense data by voice, further reducing the effort required for input.

[0051] The classification unit can refer to the credit information of business partners and perform risk assessments. For example, the classification unit uses generation AI to obtain the credit information of business partners in real time and perform risk assessments of transactions. For example, it can automatically flag invoices from business partners with low credit scores and alert the user. The classification unit can also automatically identify high-risk transactions based on the business partner's credit information and take appropriate action. For example, it can request additional confirmation when classifying an invoice from a business partner with a low credit score as an expense. In this way, by referring to the business partner's credit information and performing risk assessments, high-risk transactions can be prevented before they occur.

[0052] The tax return preparation unit complies with the tax laws of different countries and can automatically file international returns. For example, the generation AI in the tax return preparation unit automatically recognizes the tax laws of different countries and applies the appropriate tax rates and deduction amounts to create final tax returns. For example, it creates returns based on US tax laws. The tax return preparation unit also has a function for handling international transactions and can create returns based on the tax laws of multiple countries. For example, if a user is conducting transactions in multiple countries, it creates returns based on the tax laws of each country. This allows it to comply with the tax laws of different countries and automatically file international returns, preventing international filing errors.

[0053] The sales and expense data acquisition unit can use the emotion estimation function to analyze the user's emotions in real time when entering data and provide an interface for reducing stress. The sales and expense data acquisition unit can, for example, use the emotion estimation function to analyze the user's emotions in real time when entering data and provide an interface for reducing stress. For example, if the user is feeling stressed, it can play relaxing music. The sales and expense data acquisition unit can also analyze the user's emotions and provide appropriate feedback. For example, if the user is feeling anxious, it can display an encouraging message. In this way, the emotion estimation function can be used to analyze the user's emotions in real time when entering data and provide an interface for reducing stress, making user operation more comfortable.

[0054] The tax return preparation unit can use the emotion estimation function to analyze the emotion of the user when checking the tax return and provide an explanation that gives a sense of security. The tax return preparation unit, for example, uses the emotion estimation function to analyze the emotion of the user when checking the tax return in real time and provide an explanation that gives a sense of security. For example, if the user is feeling anxious, a detailed explanation or a support message is displayed. The tax return preparation unit can also analyze the user's emotion and provide appropriate feedback. For example, an easy-to-understand explanation is displayed so that the user can feel at ease. In this way, the emotion estimation function is used to analyze the emotion of the user when checking the tax return and provide an explanation that gives a sense of security, thereby reducing the user's anxiety.

[0055] The sales and expense data acquisition unit can automatically record transaction locations using the smartphone's GPS function and use them for expense classification. The sales and expense data acquisition unit can automatically record transaction locations using the smartphone's GPS function and use them for expense classification. For example, when a user enters expenses while on a business trip, the transaction location is automatically recorded based on the GPS data. The sales and expense data acquisition unit can also classify expenses based on transaction location data. For example, expenses incurred at a specific location can be automatically classified into a specific category. This makes expense management easier by automatically recording transaction locations using the smartphone's GPS function and using them for expense classification.

[0056] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0057] The blue tax return system can also be equipped with a health management unit that monitors the user's health condition. For example, if a user continues working for a long time, the health management unit can send a notification urging the user to take a break. The health management unit can also monitor the user's heart rate and stress level and provide appropriate advice. For example, if the heart rate is high, the health management unit can suggest breathing techniques to help users relax. In this way, by monitoring the user's health condition and providing appropriate advice, it is possible to improve work efficiency.

[0058] The blue return system can also be equipped with a user schedule management section. For example, the schedule management section can link with the user's calendar and remind the user of important filing deadlines. The schedule management section can also suggest optimal work times based on the user's schedule. For example, it can notify the user to complete filing work during their free time. This allows the user's schedule to be managed efficiently, allowing the filing work to proceed smoothly.

[0059] The blue return system may further include a user learning support unit. For example, the learning support unit may provide learning materials for users to learn basic knowledge of tax law and accounting. The learning support unit may also monitor the user's learning progress and provide appropriate feedback. For example, the learning support unit may suggest additional learning materials depending on the user's level of understanding. This allows users to deepen their knowledge of tax law and accounting, thereby enabling them to complete their tax return work more efficiently.

[0060] The blue return system can also be equipped with a user network management unit. For example, the network management unit provides a platform for users to share information with other users. The network management unit can also promote communication between users and enable them to support each other. For example, it can provide a place for users in the same industry to share their experiences and knowledge. This allows users to build a network and support each other, making the filing process more efficient.

[0061] The blue return system can also be equipped with a user energy management unit. For example, the energy management unit monitors energy consumption in the user's work environment and suggests efficient energy usage. The energy management unit can also optimize energy consumption according to the user's work hours. For example, it can suggest ways to reduce energy consumption during times when work is concentrated. This allows for efficient management of the user's energy consumption and reduces costs.

[0062] The blue tax return system may further include an emotion feedback unit that estimates the user's emotions and provides appropriate feedback based on the estimated emotions. For example, if the user is feeling stressed, the emotion feedback unit may provide advice to help the user relax. The emotion feedback unit may also adjust the progress of work according to the user's emotions. For example, if the user is tired, the emotion feedback unit may send a notification encouraging the user to take a break. This allows the system to analyze the user's emotions in real time and provide appropriate feedback, thereby improving work efficiency.

[0063] The blue tax return system can also include an emotion support unit that estimates the user's emotions and provides appropriate support based on the estimated emotions. For example, if the user feels anxious, the emotion support unit can provide an explanation to reassure the user. The emotion support unit can also adjust the content of the support depending on the user's emotions. For example, if the user feels confused, the emotion support unit can provide easy-to-understand guidance. In this way, the user's emotions can be analyzed in real time and appropriate support can be provided to reduce the user's anxiety.

[0064] The blue tax return system can also be equipped with an emotion interface unit that estimates the user's emotion and provides an appropriate interface based on the estimated emotion. For example, if the user is feeling stressed, it can provide an interface that helps the user relax. The emotion interface unit can also adjust the interface design according to the user's emotion. For example, if the user is tired, it can change the color scheme to be easier on the eyes. In this way, the user's emotion can be analyzed in real time and an appropriate interface provided, making the user's operation more comfortable.

[0065] The blue tax return system may further include an emotion advice unit that estimates the user's emotion and provides appropriate advice based on the estimated emotion. For example, if the user is feeling anxious, the emotion advice unit may provide advice to give the user a sense of security. The emotion advice unit may also adjust the content of the advice according to the user's emotion. For example, if the user is feeling stressed, the emotion advice unit may suggest ways to relax. In this way, the user's emotion can be analyzed in real time and appropriate advice can be provided to reduce the user's anxiety.

[0066] The blue tax return system can also include an emotion notification unit that estimates the user's emotions and provides appropriate notifications based on the estimated emotions. For example, if the user is feeling stressed, the emotion notification unit can send a notification encouraging the user to relax. The emotion notification unit can also adjust the content of the notification according to the user's emotions. For example, if the user is tired, the emotion notification unit can send a notification encouraging the user to take a break. This allows the system to analyze the user's emotions in real time and provide appropriate notifications, making user operation more comfortable.

[0067] The processing flow of the second embodiment will be briefly explained below.

[0068] Step 1: The sales and expense data acquisition unit acquires sales and expense data. For example, if a user takes a photo of a receipt with their smartphone and inputs the image into the AI, the sales and expense data acquisition unit analyzes the content and acquires sales and expense data. It can also directly acquire sales and expense data provided as electronic data. For example, it acquires invoice data sent by email. Step 2: The classification unit analyzes the sales and expense data acquired by the sales and expense data acquisition unit and sorts it into appropriate categories. For example, the generation AI automatically classifies sales data into sales categories and expense data into expense categories. The classification unit can also learn from the user's past transaction history to perform more accurate classification. For example, if invoices from the same business partner have been classified as expenses in the past, similar new invoices will also be automatically classified as expenses. Step 3: The tax return preparation unit automatically prepares tax returns based on the sales and expense data sorted by the classification unit. For example, the generation AI aggregates sales and expense data and automatically fills in the necessary fields. The tax return preparation unit also automatically reflects the latest tax law information to ensure compliance. For example, it automatically applies the latest tax rates and deduction amounts to prepare tax returns.

[0069] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0070] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0071] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0072] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0073] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0074] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0075] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0076] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0077] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0078] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0079] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0080] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0081] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0082] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0083] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0084] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0085] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0086] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0087] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0088] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0089] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0090] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0091] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0092] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0093] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0094] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0095] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0096] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0097] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0098] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0099] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0100] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0101] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0102] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0103] 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.

[0104] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0105] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0106] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0107] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0108] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0109] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0110] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0111] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0112] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0113] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0114] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0115] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0116] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0117] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0118] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0119] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0120] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0121] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0122] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0123] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0124] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0125] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0126] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0127] 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.

[0128] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0129] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0130] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0131] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0132] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0133] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0134] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0135] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0136] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A system comprising: a sales and expense data acquisition unit that acquires sales and expense data; a sorting unit that analyzes the sales and expense data acquired by the sales and expense data acquisition unit and sorts it into appropriate categories; and a tax return preparation unit that automatically prepares a tax return based on the sales and expense data sorted by the sorting unit.

2. The system according to claim 1, wherein the sales and expense data acquisition unit analyzes the contents of receipts and invoices in real time using a smartphone camera and immediately inputs the data.

3. 2. The system according to claim 1, wherein the sorting unit learns the user's past transaction history and performs the sorting with higher accuracy.

4. 10. The system of claim 1, wherein the return preparation component automatically updates tax laws to ensure compliance.

5. 2. The system according to claim 1, wherein the sales and expense data acquisition unit analyzes the user's emotions in real time when entering data and provides an interface for reducing stress.

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