system

Abstract

The system according to this embodiment aims to streamline the tax return filing process and provide prompt answers to user questions. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a generation unit, and a conversation unit. The reception unit receives input of declaration details. The analysis unit analyzes the information received by the reception unit. The generation unit generates an appropriate declaration form based on the information analyzed by the analysis unit. The conversation unit answers the user's questions based on the declaration form generated by the generation 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, including 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, although the procedure for final tax return has been simplified, there is a problem that manual work or a visit to the tax office is required, which is not efficient.

[0005] The system according to the embodiment aims to streamline the procedure for final tax return and quickly answer the user's questions.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and a conversation unit. The reception unit receives input of declaration details. The analysis unit analyzes the information received by the reception unit. The generation unit generates an appropriate declaration form based on the information analyzed by the analysis unit. The conversation unit answers the user's questions based on the declaration form generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can streamline the tax return filing process and provide quick answers to user questions. [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 labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The AI ​​system according to an embodiment of the present invention is a system for further simplifying the tax return filing process due to the widespread use of My Number cards. This system begins when a user says or inputs "I want to file a tax return for XX." Next, the AI ​​analyzes this information and automatically generates an appropriate tax return form. This tax return form is created by reading the user's My Number card and automatically inputting the necessary information. Furthermore, the AI ​​answers the user's questions in a conversational format, supporting the filing process. This allows the user to easily complete their tax return without having to go to the tax office. For example, a user says or inputs "I want to file a blue return." This information is input into the AI. Next, the AI ​​analyzes the input information and automatically generates an appropriate tax return form. The AI ​​reads the user's My Number card and automatically inputs the necessary information. For example, it obtains information such as the user's income and deductions from the My Number card and reflects it in the tax return form. Furthermore, the AI ​​answers the user's questions in a conversational format. For example, to a question such as "How do I calculate this deduction?", the AI ​​provides an appropriate answer. This allows the user to easily resolve any parts they don't understand or want to ask about in detail. This system allows users to easily complete their tax returns without having to go to the tax office. For example, by using AI to file their returns at home, users can avoid waiting times and crowds at the tax office. Furthermore, since the AI ​​automatically generates the appropriate tax return forms, manual data entry is also eliminated. In this way, using AI further simplifies the tax return process and improves user convenience. For example, it can handle various tax returns, such as consumption tax, gift tax, financial statements, and income and expenditure statements. This means that even if a tax return user is unsure which form to use, the AI ​​will provide the appropriate form, allowing them to file with confidence. As a result, the AI ​​system simplifies the user's tax return process and generates tax returns quickly and accurately.

[0029] The AI ​​system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and a conversation unit. The reception unit receives input of declaration details. The reception unit can receive declaration details by, for example, the user saying or typing, "I want to file a declaration for XX." The analysis unit analyzes the information received by the reception unit. The analysis unit analyzes the declaration details entered by the user and extracts information necessary to generate an appropriate declaration form. The generation unit generates an appropriate declaration form based on the information analyzed by the analysis unit. The generation unit generates the declaration form by, for example, reading the user's My Number Card and automatically entering the necessary information. The conversation unit answers the user's questions based on the declaration form generated by the generation unit. The conversation unit provides appropriate answers to questions such as, for example, "How do I calculate this deduction?". As a result, the AI ​​system can consistently perform everything from inputting declaration details to analysis, generating the declaration form, and answering questions. Some or all of the above-described processes in the reception unit, analysis unit, generation unit, and conversation unit may be performed using AI, for example, or without using AI. For example, the reception unit inputs the user's declaration details into the AI, the analysis unit uses the AI ​​to analyze the declaration details, the generation unit uses the AI ​​to generate the declaration form, and the conversation unit uses the AI ​​to answer the user's questions.

[0030] The reception desk accepts the input of declaration details. For example, the reception desk can accept declaration details when the user says or types "I want to declare XX." Specifically, the reception desk uses speech recognition technology to convert the user's voice input into text data, or receives text data entered by the user using a keyboard or touchscreen. The speech recognition technology accurately recognizes the user's speech, taking into account noise and accent differences to generate accurate text data. Furthermore, the reception desk has a function to temporarily store the user's input and prompt the user for confirmation as needed. For example, if the user says "I want to check my declaration details," the reception desk displays the entered content, allowing the user to review and correct it. The reception desk also has a function to automatically suggest similar declaration details by referring to the user's past declaration history. This allows users to enter declaration details quickly and accurately. In addition, the reception desk supports multiple languages ​​and can accept declaration details in different languages. This makes it possible to accommodate international users and improves the system's versatility.

[0031] The analysis unit analyzes the information received by the reception unit. For example, the analysis unit analyzes the declaration content entered by the user and extracts information necessary to generate an appropriate declaration form. Specifically, the analysis unit uses natural language processing technology to grammatically and semantically analyze the user's input and extract the necessary information. For example, if a user enters "I want to declare medical expense deductions," the analysis unit extracts the keyword "medical expense deductions" and collects related information. The analysis unit also classifies the user's input and selects an appropriate declaration form template. Furthermore, the analysis unit refers to the user's past declaration history and relevant laws and regulations to verify the accuracy of the declaration content. For example, it checks for inconsistencies with past declarations and compliance with the latest laws and regulations. The analysis unit uses AI to perform these analyses and extract information quickly and accurately. The AI ​​uses machine learning algorithms to learn from past data and improve analysis accuracy. As a result, the analysis unit can efficiently analyze the user's input and provide information necessary to generate an appropriate declaration form.

[0032] The generation unit generates an appropriate tax return based on the information analyzed by the analysis unit. For example, the generation unit generates the tax return by reading the user's My Number Card and automatically entering the necessary information. Specifically, the generation unit enters the appropriate data into each item of the tax return based on the information provided by the analysis unit. For example, it automatically enters the user's name, address, income information, deduction items, etc., to complete the tax return. The generation unit uses AI to perform these processes and also has a function to automatically detect and correct user input errors and deficiencies. For example, if there is a contradiction in the entered data, the generation unit will ask the user for confirmation and urge them to enter the correct information. In addition, the generation unit automatically obtains the necessary information by reading the user's My Number Card and reflects it in the tax return. This allows the user to generate an accurate tax return without any effort. Furthermore, the generation unit also has a function to save the generated tax return in PDF or electronic file format, allowing the user to download or print it. This enables the generation unit to provide users with quick and accurate tax return generation and streamline the tax filing process.

[0033] The conversational unit answers user questions based on the declaration form generated by the generation unit. For example, the conversational unit provides appropriate answers to questions such as, "How do I calculate this deduction?" Specifically, the conversational unit refers to the contents of the generated declaration form and generates answers to the user's questions. The conversational unit uses AI and natural language processing technology to understand the user's questions and provide appropriate answers. For example, if a user asks, "How do I calculate the medical expense deduction?", the conversational unit refers to the laws and regulations and calculation methods related to the medical expense deduction and explains the specific calculation procedure. The conversational unit also provides answers to the user's questions in a conversational format to support user understanding. For example, if a user asks an additional question, the conversational unit will also address that question and provide a detailed explanation. Furthermore, the conversational unit refers to the user's past question history and provides answers to similar questions, enabling quick and accurate responses. This allows the conversational unit to resolve user doubts and anxieties and proceed smoothly with the declaration process. In addition, the conversational unit can collect user feedback and continuously improve the accuracy and quality of its answers. This allows the conversational team to provide users with high-quality support and streamline the reporting process.

[0034] The reading unit can read My Number cards. The reading unit can obtain user information, for example, by reading the IC chip of the My Number card. The reading unit can also obtain user information by scanning a QR code (registered trademark). This allows the reading unit to automatically obtain information from the My Number card. Some or all of the above processing in the reading unit may be performed using AI, for example, or without AI. For example, the reading unit can input the IC chip of the My Number card into AI and obtain information using AI.

[0035] The generation unit can reflect information obtained from the My Number Card into the tax return form. For example, the generation unit can automatically input information such as the user's income and deductions obtained from the My Number Card into the tax return form. The generation unit can also manually verify the information obtained from the My Number Card and reflect it into the tax return form. In this way, the generation unit can automatically reflect information obtained from the My Number Card into the tax return form. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input information obtained from the My Number Card into AI and reflect it into the tax return form using AI.

[0036] The conversational unit can answer user questions in a conversational format. For example, the conversational unit can provide an appropriate answer to a user's question such as, "How do I calculate this deduction?" It can also provide an appropriate answer to a user's question such as, "How do I submit my tax return?" Thus, the conversational unit can answer user questions in a conversational format. Some or all of the above processing in the conversational unit may be performed using AI, for example, or not. For example, the conversational unit can input the user's question into AI and use AI to provide an appropriate answer.

[0037] The generation unit can generate tax returns such as consumption tax returns, gift tax returns, financial statements, and income and expenditure statements. For example, the generation unit can generate a consumption tax return. It can also generate a gift tax return. Furthermore, it can generate financial statements and income and expenditure statements. This allows the generation unit to generate multiple types of tax returns. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input a consumption tax return into AI and generate it using AI.

[0038] The conversational unit can support users as they complete the filing process at home. For example, the conversational unit can provide online help when users complete the filing process at home. It can also provide telephone support when users complete the filing process at home. This allows the conversational unit to support users as they complete the filing process at home. Some or all of the above processes in the conversational unit may be performed using AI, for example, or not. For example, the conversational unit can input the user's filing process into AI and use AI to provide support.

[0039] The reception desk can analyze the user's past declaration history and suggest the optimal input method. For example, the reception desk can automatically display declaration content that the user has frequently entered in the past as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest declaration content to be used during specific time periods based on the user's past declaration history. This allows the reception desk to suggest the optimal input method based on the user's past declaration history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's past declaration history into AI and use AI to suggest the optimal input method.

[0040] The reception system can filter the input content based on the user's current living situation and areas of interest when they enter their declaration information. For example, if a user has recently moved, the reception system can prioritize displaying declaration information related to their new address. It can also prioritize displaying declaration information related to a specific industry if the user works in that industry. Furthermore, if a user frequently uses a particular deduction, the reception system can prioritize displaying declaration information related to that deduction. This allows the reception system to filter the input content based on the user's current living situation and areas of interest. Some or all of the above processing in the reception system may be performed using AI, for example, or not. For example, the reception system can input the user's living situation and areas of interest into an AI and use the AI ​​to filter the input content.

[0041] The reception desk can prioritize inputting highly relevant information when users enter their declarations, taking into account their geographical location. For example, if a user lives in a specific region, the reception desk can prioritize inputting tax benefits related to that region. Similarly, if a user conducts business in a specific region, the reception desk can prioritize inputting declaration information related to that region. Furthermore, if a user moves to a specific region, the reception desk can prioritize inputting declaration information related to that region. This allows the reception desk to prioritize inputting highly relevant information based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's geographical location information into AI and use AI to prioritize inputting highly relevant information.

[0042] The reception desk can analyze a user's social media activity when they enter their declaration information and input relevant content. For example, if a user mentions a specific deduction on social media, the reception desk can prioritize inputting declaration information related to that deduction. Similarly, if a user mentions a specific industry on social media, the reception desk can prioritize inputting declaration information related to that industry. Furthermore, if a user mentions a specific region on social media, the reception desk can prioritize inputting declaration information related to that region. This allows the reception desk to input relevant content based on the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's social media activity into AI and use AI to input relevant content.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the declared information. For example, the analysis unit can perform a detailed analysis on important declared information. It can also perform a simplified analysis on less important declared information. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the declared information. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the declared information. 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 importance of the declared information into the AI ​​and use the AI ​​to adjust the level of detail of the analysis.

[0044] The analysis unit can apply different analysis algorithms depending on the category of the declared information during analysis. For example, the analysis unit can apply an income analysis algorithm to declared income. It can also apply a deduction analysis algorithm to declared deductions. Furthermore, it can apply an expense analysis algorithm to declared expenses. This allows the analysis unit to apply different analysis algorithms depending on the category of the declared information. 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 the category of the declared information into AI and use AI to apply different analysis algorithms.

[0045] The analysis unit can determine the priority of analysis based on the submission date of the declarations during the analysis process. For example, the analysis unit can prioritize the analysis of declarations with approaching submission deadlines. Conversely, the analysis unit can postpone the analysis of declarations with later submission deadlines. Furthermore, the analysis unit can adjust the analysis schedule according to the submission dates. This allows the analysis unit to determine the priority of analysis based on the submission date of the declarations. 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 submission dates of the declarations into the AI ​​and use the AI ​​to determine the priority of analysis.

[0046] The analysis unit can adjust the order of analysis based on the relevance of the declared information during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant declared information. It can also postpone the analysis of less relevant declared information. Furthermore, the analysis unit can adjust the order of analysis according to the relevance of the declared information. In this way, the analysis unit can adjust the order of analysis based on the relevance of the declared information. 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 the relevance of the declared information into AI and use AI to adjust the order of analysis.

[0047] The generation unit can adjust the level of detail in the tax return based on the importance of the declared information during generation. For example, the generation unit can generate a detailed tax return for important declared information. It can also generate a simplified tax return for less important declared information. Furthermore, the generation unit can adjust the level of detail in the tax return according to the importance of the declared information. In this way, the generation unit can adjust the level of detail in the tax return based on the importance of the declared information. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the importance of the declared information into the AI ​​and use the AI ​​to adjust the level of detail in the tax return.

[0048] The generation unit can apply different generation algorithms depending on the category of the declared information during generation. For example, the generation unit can apply an income generation algorithm to declared income. It can also apply a deduction generation algorithm to declared deductions. Furthermore, it can apply an expense generation algorithm to declared expenses. This allows the generation unit to apply different generation algorithms depending on the category of the declared information. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the categories of declared information into AI and use AI to apply different generation algorithms.

[0049] The generation unit can determine the priority of tax returns based on the filing dates of the declarations during the generation process. For example, the generation unit can prioritize generating tax returns for declarations with approaching deadlines. It can also postpone generating tax returns for declarations with later deadlines. Furthermore, the generation unit can adjust the tax return generation schedule according to the filing dates. This allows the generation unit to determine the priority of tax returns based on the filing dates of the declarations. Some or all of the above processing in the generation unit may be performed using AI, for example, or not. For example, the generation unit can input the filing dates of the declarations into the AI ​​and use the AI ​​to determine the priority of the tax returns.

[0050] The generation unit can adjust the order of declarations based on the relevance of the declared information during generation. For example, the generation unit can prioritize the generation of declarations with high relevance. It can also postpone the generation of declarations with low relevance. Furthermore, the generation unit can adjust the generation order of declarations according to the relevance of the declared information. In this way, the generation unit can adjust the order of declarations based on the relevance of the declared information. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the relevance of the declared information into AI and use AI to adjust the order of declarations.

[0051] The conversational unit can provide the most appropriate answer during a conversation by referring to the user's past question history. For example, the conversational unit can provide relevant answers based on the content of questions the user has asked in the past. It can also prioritize answers to questions the user has asked frequently in the past. Furthermore, the conversational unit can analyze the user's past question history and provide the most appropriate answer. In this way, the conversational unit can provide the most appropriate answer based on the user's past question history. Some or all of the above processing in the conversational unit may be performed using AI, for example, or not using AI. For example, the conversational unit can input the user's past question history into AI and use AI to provide the most appropriate answer.

[0052] The conversational unit can customize its responses based on the user's current living situation during a conversation. For example, if the user has recently moved, the conversational unit can provide appropriate answers to questions related to their new address. It can also provide appropriate answers to questions related to a particular industry if the user works in that industry. Furthermore, if the user frequently uses a particular tax deduction, the conversational unit can provide appropriate answers to questions related to that deduction. This allows the conversational unit to customize its responses based on the user's current living situation. Some or all of the above processing in the conversational unit may be performed using AI, for example, or not. For example, the conversational unit can input the user's living situation into an AI and use the AI ​​to customize its responses.

[0053] The conversational unit can provide optimal answers during a conversation by taking into account the user's geographical location. For example, if the user lives in a specific region, the conversational unit can provide appropriate answers to questions related to that region. Similarly, if the user conducts business in a specific region, the conversational unit can provide appropriate answers to questions related to that region. Furthermore, if the user moves to a specific region, the conversational unit can provide appropriate answers to questions related to that region. This allows the conversational unit to provide optimal answers based on the user's geographical location. Some or all of the above processing in the conversational unit may be performed using AI, for example, or without AI. For example, the conversational unit can input the user's geographical location into AI and use AI to provide optimal answers.

[0054] The conversational unit can analyze the user's social media activity during a conversation and suggest appropriate responses. For example, if the user mentions a specific deduction on social media, the conversational unit can provide appropriate answers to questions related to that deduction. Similarly, if the user mentions a specific industry on social media, the conversational unit can provide appropriate answers to questions related to that industry. Furthermore, if the user mentions a specific region on social media, the conversational unit can provide appropriate answers to questions related to that region. This allows the conversational unit to suggest responses based on the user's social media activity. Some or all of the above processing in the conversational unit may be performed using AI, for example, or without AI. For example, the conversational unit can input the user's social media activity into AI and use AI to suggest responses.

[0055] The reading unit can select the optimal reading method by referring to the user's past reading history during reading. For example, the reading unit can prioritize reading information that the user has frequently read in the past. The reading unit can also prioritize selecting reading methods (scanning, OCR, etc.) that the user has used in the past. Furthermore, the reading unit can select the optimal reading method from the user's past reading history. In this way, the reading unit can select the optimal reading method based on the user's past reading history. Some or all of the above processing in the reading unit may be performed using AI, for example, or without AI. For example, the reading unit can input the user's past reading history into AI and use AI to select the optimal reading method.

[0056] The reading unit can select the optimal reading method while considering the user's geographical location information. For example, if the user lives in a specific region, the reading unit can prioritize reading information related to that region. Similarly, if the user conducts business in a specific region, the reading unit can prioritize reading information related to that region. Furthermore, if the user moves to a specific region, the reading unit can prioritize reading information related to that region. This allows the reading unit to select the optimal reading method based on the user's geographical location information. Some or all of the above processing in the reading unit may be performed using AI, for example, or without AI. For example, the reading unit can input the user's geographical location information into AI and use AI to select the optimal reading method.

[0057] The generation unit can select the optimal method of reflection when reflecting information obtained from the My Number Card in the declaration form by referring to the user's past declaration history. For example, the generation unit can prioritize reflecting information that the user has frequently declared in the past. The generation unit can also prioritize the reflection method (manual, scanning, etc.) that the user has used in the past. Furthermore, the generation unit can select the optimal reflection method from the user's past declaration history. In this way, the generation unit can select the optimal reflection method based on the user's past declaration history. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's past declaration history into AI and use AI to select the optimal reflection method.

[0058] The generation unit can select the optimal method of reflection when reflecting information obtained from the My Number Card in the declaration form, taking into account the user's geographical location information. For example, if the user lives in a specific region, the generation unit can prioritize reflecting information related to that region. Similarly, if the user conducts business in a specific region, the generation unit can prioritize reflecting information related to that region. Furthermore, if the user moves to a specific region, the generation unit can prioritize reflecting information related to that region. This allows the generation unit to select the optimal reflection method based on the user's geographical location information. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's geographical location information into AI and use AI to select the optimal reflection method.

[0059] The generation unit can adjust the level of detail of tax returns, such as consumption tax, gift tax, financial statements, and income and expenditure statements, based on the importance of the declared information. For example, the generation unit can generate detailed tax returns for important declared information. It can also generate simplified tax returns for less important declared information. Furthermore, the generation unit can adjust the level of detail of the tax returns according to the importance of the declared information. In this way, the generation unit can adjust the level of detail of the tax returns based on the importance of the declared information. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input the importance of the declared information into AI and use AI to adjust the level of detail of the tax returns.

[0060] The generation unit can determine the priority of tax returns, such as consumption tax, gift tax, financial statements, and income and expenditure statements, based on the filing date of the returns. For example, the generation unit can prioritize the generation of returns for returns with an approaching filing deadline. It can also postpone the generation of returns for returns with a later filing deadline. Furthermore, the generation unit can adjust the return generation schedule according to the filing date. This allows the generation unit to determine the priority of tax returns based on the filing date of the returns. Some or all of the above processing in the generation unit may be performed using AI, for example, or not. For example, the generation unit can input the filing dates of the returns into the AI ​​and use the AI ​​to determine the priority of the returns.

[0061] The conversational unit can select the most appropriate support method by referring to the user's past filing history when assisting the user with filing procedures at home. For example, the conversational unit can provide relevant support based on questions the user has frequently asked in the past. The conversational unit can also prioritize support methods (chat, voice, etc.) that the user has used in the past. Furthermore, the conversational unit can select the most appropriate support method from the user's past filing history. This allows the conversational unit to select the most appropriate support method based on the user's past filing history. Some or all of the above processing in the conversational unit may be performed using AI, for example, or not using AI. For example, the conversational unit can input the user's past filing history into AI and use AI to select the most appropriate support method.

[0062] The conversational unit can select the most appropriate support method by considering the user's geographical location when assisting a user with filing procedures at home. For example, if the user lives in a specific region, the conversational unit can provide support relevant to that region. Similarly, if the user operates a business in a specific region, the conversational unit can provide support relevant to that region. Furthermore, if the user moves to a specific region, the conversational unit can provide support relevant to that region. This allows the conversational unit to select the most appropriate support method based on the user's geographical location. Some or all of the above processing in the conversational unit may be performed using AI, for example, or without AI. For example, the conversational unit can input the user's geographical location into AI and use AI to select the most appropriate support method.

[0063] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0064] The reception desk can analyze a user's past declaration history and suggest the most suitable input method. For example, it can automatically display declaration content that the user has frequently entered in the past as suggestions. It can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest declaration content that the user will use at specific times of the day based on their past declaration history. In this way, the reception desk can suggest the most suitable input method based on the user's past declaration history.

[0065] The analysis unit can adjust the level of detail of the analysis based on the importance of the declared information. For example, it can perform a detailed analysis on important declared information, and a simplified analysis on less important declared information. Furthermore, it can determine the priority of the analysis according to the importance of the declared information. In this way, the analysis unit can adjust the level of detail of the analysis based on the importance of the declared information.

[0066] The generation unit can apply different generation algorithms depending on the category of the declared information during generation. For example, for declared information related to income, an income generation algorithm can be applied. Similarly, for declared information related to deductions, a deduction generation algorithm can be applied. Furthermore, for declared information related to expenses, an expense generation algorithm can be applied. In this way, the generation unit can apply different generation algorithms depending on the category of the declared information.

[0067] The conversational unit can provide the most appropriate answer during a conversation by referring to the user's past question history. For example, it can provide relevant answers based on questions the user has asked in the past. It can also prioritize answers to questions the user has asked frequently in the past. Furthermore, it can analyze the user's past question history and provide the most appropriate answer. In this way, the conversational unit can provide the most appropriate answer based on the user's past question history.

[0068] The reading unit can select the optimal reading method while considering the user's geographical location information. For example, if the user lives in a specific region, it can prioritize reading information related to that region. Similarly, if the user conducts business in a specific region, it can prioritize reading information related to that region. Furthermore, if the user moves to a specific region, it can prioritize reading information related to that region. In this way, the reading unit can select the optimal reading method based on the user's geographical location information.

[0069] The following briefly describes the processing flow for example form 1.

[0070] Step 1: The reception desk accepts the input of the declaration details. For example, the declaration details can be accepted when the user says "I want to declare XX" or enters them. Step 2: The analysis unit analyzes the information received by the reception unit. For example, it analyzes the declaration details entered by the user and extracts information necessary to generate an appropriate declaration form. Step 3: The generation unit generates an appropriate declaration form based on the information analyzed by the analysis unit. For example, it generates the declaration form by reading the user's My Number Card and automatically entering the necessary information. Step 4: The conversation unit answers user questions based on the declaration form generated by the generation unit. For example, it provides an appropriate answer to a user's question such as, "How do I calculate this deduction?"

[0071] (Example of form 2) The AI ​​system according to an embodiment of the present invention is a system for further simplifying the tax return filing process due to the widespread use of My Number cards. This system begins when a user says or inputs "I want to file a tax return for XX." Next, the AI ​​analyzes this information and automatically generates an appropriate tax return form. This tax return form is created by reading the user's My Number card and automatically inputting the necessary information. Furthermore, the AI ​​answers the user's questions in a conversational format, supporting the filing process. This allows the user to easily complete their tax return without having to go to the tax office. For example, a user says or inputs "I want to file a blue return." This information is input into the AI. Next, the AI ​​analyzes the input information and automatically generates an appropriate tax return form. The AI ​​reads the user's My Number card and automatically inputs the necessary information. For example, it obtains information such as the user's income and deductions from the My Number card and reflects it in the tax return form. Furthermore, the AI ​​answers the user's questions in a conversational format. For example, to a question such as "How do I calculate this deduction?", the AI ​​provides an appropriate answer. This allows the user to easily resolve any parts they don't understand or want to ask about in detail. This system allows users to easily complete their tax returns without having to go to the tax office. For example, by using AI to file their returns at home, users can avoid waiting times and crowds at the tax office. Furthermore, since the AI ​​automatically generates the appropriate tax return forms, manual data entry is also eliminated. In this way, using AI further simplifies the tax return process and improves user convenience. For example, it can handle various tax returns, such as consumption tax, gift tax, financial statements, and income and expenditure statements. This means that even if a tax return user is unsure which form to use, the AI ​​will provide the appropriate form, allowing them to file with confidence. As a result, the AI ​​system simplifies the user's tax return process and generates tax returns quickly and accurately.

[0072] The AI ​​system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and a conversation unit. The reception unit receives input of declaration details. The reception unit can receive declaration details by, for example, the user saying or typing, "I want to file a declaration for XX." The analysis unit analyzes the information received by the reception unit. The analysis unit analyzes the declaration details entered by the user and extracts information necessary to generate an appropriate declaration form. The generation unit generates an appropriate declaration form based on the information analyzed by the analysis unit. The generation unit generates the declaration form by, for example, reading the user's My Number Card and automatically entering the necessary information. The conversation unit answers the user's questions based on the declaration form generated by the generation unit. The conversation unit provides appropriate answers to questions such as, for example, "How do I calculate this deduction?". As a result, the AI ​​system can consistently perform everything from inputting declaration details to analysis, generating the declaration form, and answering questions. Some or all of the above-described processes in the reception unit, analysis unit, generation unit, and conversation unit may be performed using AI, for example, or without using AI. For example, the reception unit inputs the user's declaration details into the AI, the analysis unit uses the AI ​​to analyze the declaration details, the generation unit uses the AI ​​to generate the declaration form, and the conversation unit uses the AI ​​to answer the user's questions.

[0073] The reception desk accepts the input of declaration details. For example, the reception desk can accept declaration details when the user says or types "I want to declare XX." Specifically, the reception desk uses speech recognition technology to convert the user's voice input into text data, or receives text data entered by the user using a keyboard or touchscreen. The speech recognition technology accurately recognizes the user's speech, taking into account noise and accent differences to generate accurate text data. Furthermore, the reception desk has a function to temporarily store the user's input and prompt the user for confirmation as needed. For example, if the user says "I want to check my declaration details," the reception desk displays the entered content, allowing the user to review and correct it. The reception desk also has a function to automatically suggest similar declaration details by referring to the user's past declaration history. This allows users to enter declaration details quickly and accurately. In addition, the reception desk supports multiple languages ​​and can accept declaration details in different languages. This makes it possible to accommodate international users and improves the system's versatility.

[0074] The analysis unit analyzes the information received by the reception unit. For example, the analysis unit analyzes the declaration content entered by the user and extracts information necessary to generate an appropriate declaration form. Specifically, the analysis unit uses natural language processing technology to grammatically and semantically analyze the user's input and extract the necessary information. For example, if a user enters "I want to declare medical expense deductions," the analysis unit extracts the keyword "medical expense deductions" and collects related information. The analysis unit also classifies the user's input and selects an appropriate declaration form template. Furthermore, the analysis unit refers to the user's past declaration history and relevant laws and regulations to verify the accuracy of the declaration content. For example, it checks for inconsistencies with past declarations and compliance with the latest laws and regulations. The analysis unit uses AI to perform these analyses and extract information quickly and accurately. The AI ​​uses machine learning algorithms to learn from past data and improve analysis accuracy. As a result, the analysis unit can efficiently analyze the user's input and provide information necessary to generate an appropriate declaration form.

[0075] The generation unit generates an appropriate tax return based on the information analyzed by the analysis unit. For example, the generation unit generates the tax return by reading the user's My Number Card and automatically entering the necessary information. Specifically, the generation unit enters the appropriate data into each item of the tax return based on the information provided by the analysis unit. For example, it automatically enters the user's name, address, income information, deduction items, etc., to complete the tax return. The generation unit uses AI to perform these processes and also has a function to automatically detect and correct user input errors and deficiencies. For example, if there is a contradiction in the entered data, the generation unit will ask the user for confirmation and urge them to enter the correct information. In addition, the generation unit automatically obtains the necessary information by reading the user's My Number Card and reflects it in the tax return. This allows the user to generate an accurate tax return without any effort. Furthermore, the generation unit also has a function to save the generated tax return in PDF or electronic file format, allowing the user to download or print it. This enables the generation unit to provide users with quick and accurate tax return generation and streamline the tax filing process.

[0076] The conversational unit answers user questions based on the declaration form generated by the generation unit. For example, the conversational unit provides appropriate answers to questions such as, "How do I calculate this deduction?" Specifically, the conversational unit refers to the contents of the generated declaration form and generates answers to the user's questions. The conversational unit uses AI and natural language processing technology to understand the user's questions and provide appropriate answers. For example, if a user asks, "How do I calculate the medical expense deduction?", the conversational unit refers to the laws and regulations and calculation methods related to the medical expense deduction and explains the specific calculation procedure. The conversational unit also provides answers to the user's questions in a conversational format to support user understanding. For example, if a user asks an additional question, the conversational unit will also address that question and provide a detailed explanation. Furthermore, the conversational unit refers to the user's past question history and provides answers to similar questions, enabling quick and accurate responses. This allows the conversational unit to resolve user doubts and anxieties and proceed smoothly with the declaration process. In addition, the conversational unit can collect user feedback and continuously improve the accuracy and quality of its answers. This allows the conversational team to provide users with high-quality support and streamline the reporting process.

[0077] The reader unit can read My Number cards. The reader unit can obtain user information, for example, by reading the IC chip of the My Number card. The reader unit can also obtain user information by scanning a QR code. This allows the reader unit to automatically obtain information from the My Number card. Some or all of the above processing in the reader unit may be performed using AI, for example, or without AI. For example, the reader unit can input the IC chip of the My Number card into AI and obtain information using AI.

[0078] The generation unit can reflect information obtained from the My Number Card into the tax return form. For example, the generation unit can automatically input information such as the user's income and deductions obtained from the My Number Card into the tax return form. The generation unit can also manually verify the information obtained from the My Number Card and reflect it into the tax return form. In this way, the generation unit can automatically reflect information obtained from the My Number Card into the tax return form. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input information obtained from the My Number Card into AI and reflect it into the tax return form using AI.

[0079] The conversational unit can answer user questions in a conversational format. For example, the conversational unit can provide an appropriate answer to a user's question such as, "How do I calculate this deduction?" It can also provide an appropriate answer to a user's question such as, "How do I submit my tax return?" Thus, the conversational unit can answer user questions in a conversational format. Some or all of the above processing in the conversational unit may be performed using AI, for example, or not. For example, the conversational unit can input the user's question into AI and use AI to provide an appropriate answer.

[0080] The generation unit can generate tax returns such as consumption tax returns, gift tax returns, financial statements, and income and expenditure statements. For example, the generation unit can generate a consumption tax return. It can also generate a gift tax return. Furthermore, it can generate financial statements and income and expenditure statements. This allows the generation unit to generate multiple types of tax returns. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input a consumption tax return into AI and generate it using AI.

[0081] The conversational unit can support users as they complete the filing process at home. For example, the conversational unit can provide online help when users complete the filing process at home. It can also provide telephone support when users complete the filing process at home. This allows the conversational unit to support users as they complete the filing process at home. Some or all of the above processes in the conversational unit may be performed using AI, for example, or not. For example, the conversational unit can input the user's filing process into AI and use AI to provide support.

[0082] The reception desk can estimate the user's emotions and adjust the input method of the declaration based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. If the user is relaxed, the reception desk can also provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception desk can prioritize voice input to allow for quick input of the declaration. In this way, the reception desk can adjust the input method of the declaration according to the user's emotions. 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 processing in the reception desk may be performed using AI or not using AI. For example, the reception desk can input the user's emotions into an AI, use the AI ​​to estimate the emotions, and adjust the input method of the declaration.

[0083] The reception desk can analyze the user's past declaration history and suggest the optimal input method. For example, the reception desk can automatically display declaration content that the user has frequently entered in the past as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest declaration content to be used during specific time periods based on the user's past declaration history. This allows the reception desk to suggest the optimal input method based on the user's past declaration history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's past declaration history into AI and use AI to suggest the optimal input method.

[0084] The reception system can filter the input content based on the user's current living situation and areas of interest when they enter their declaration information. For example, if a user has recently moved, the reception system can prioritize displaying declaration information related to their new address. It can also prioritize displaying declaration information related to a specific industry if the user works in that industry. Furthermore, if a user frequently uses a particular deduction, the reception system can prioritize displaying declaration information related to that deduction. This allows the reception system to filter the input content based on the user's current living situation and areas of interest. Some or all of the above processing in the reception system may be performed using AI, for example, or not. For example, the reception system can input the user's living situation and areas of interest into an AI and use the AI ​​to filter the input content.

[0085] The reception desk can estimate the user's emotions and prioritize the input content based on the estimated emotions. For example, if the user is stressed, the reception desk can prioritize inputting important items. If the user is relaxed, the reception desk can also prompt the user to input detailed items sequentially. Furthermore, if the user is in a hurry, the reception desk can prompt the user to input the most important items first. In this way, the reception desk can prioritize the input content according to the user's emotions. 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 processing in the reception desk may be performed using AI or not using AI. For example, the reception desk can input the user's emotions into an AI, use the AI ​​to estimate the emotions, and determine the priority of the input content.

[0086] The reception desk can prioritize inputting highly relevant information when users enter their declarations, taking into account their geographical location. For example, if a user lives in a specific region, the reception desk can prioritize inputting tax benefits related to that region. Similarly, if a user conducts business in a specific region, the reception desk can prioritize inputting declaration information related to that region. Furthermore, if a user moves to a specific region, the reception desk can prioritize inputting declaration information related to that region. This allows the reception desk to prioritize inputting highly relevant information based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's geographical location information into AI and use AI to prioritize inputting highly relevant information.

[0087] The reception desk can analyze a user's social media activity when they enter their declaration information and input relevant content. For example, if a user mentions a specific deduction on social media, the reception desk can prioritize inputting declaration information related to that deduction. Similarly, if a user mentions a specific industry on social media, the reception desk can prioritize inputting declaration information related to that industry. Furthermore, if a user mentions a specific region on social media, the reception desk can prioritize inputting declaration information related to that region. This allows the reception desk to input relevant content based on the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's social media activity into AI and use AI to input relevant content.

[0088] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit can provide a simple and easy-to-understand analysis result. If the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is in a hurry, the analysis unit can provide a concise analysis result. In this way, the analysis unit can adjust the presentation of the analysis according to the user's emotions. 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 processing in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input the user's emotions into the AI, use the AI ​​to estimate the emotions, and adjust the presentation of the analysis.

[0089] The analysis unit can adjust the level of detail of the analysis based on the importance of the declared information. For example, the analysis unit can perform a detailed analysis on important declared information. It can also perform a simplified analysis on less important declared information. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the declared information. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the declared information. 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 importance of the declared information into the AI ​​and use the AI ​​to adjust the level of detail of the analysis.

[0090] The analysis unit can apply different analysis algorithms depending on the category of the declared information during analysis. For example, the analysis unit can apply an income analysis algorithm to declared income. It can also apply a deduction analysis algorithm to declared deductions. Furthermore, it can apply an expense analysis algorithm to declared expenses. This allows the analysis unit to apply different analysis algorithms depending on the category of the declared information. 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 the category of the declared information into AI and use AI to apply different analysis algorithms.

[0091] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit can provide a short, concise analysis. If the user is relaxed, the analysis unit can provide a detailed analysis. Furthermore, if the user is in a hurry, the analysis unit can provide a brief analysis. In this way, the analysis unit can adjust the length of the analysis according to the user's emotions. 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 processing in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input the user's emotions into the AI, use the AI ​​to estimate the emotions, and adjust the length of the analysis.

[0092] The analysis unit can determine the priority of analysis based on the submission date of the declarations during the analysis process. For example, the analysis unit can prioritize the analysis of declarations with approaching submission deadlines. Conversely, the analysis unit can postpone the analysis of declarations with later submission deadlines. Furthermore, the analysis unit can adjust the analysis schedule according to the submission dates. This allows the analysis unit to determine the priority of analysis based on the submission date of the declarations. 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 submission dates of the declarations into the AI ​​and use the AI ​​to determine the priority of analysis.

[0093] The analysis unit can adjust the order of analysis based on the relevance of the declared information during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant declared information. It can also postpone the analysis of less relevant declared information. Furthermore, the analysis unit can adjust the order of analysis according to the relevance of the declared information. In this way, the analysis unit can adjust the order of analysis based on the relevance of the declared information. 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 the relevance of the declared information into AI and use AI to adjust the order of analysis.

[0094] The generation unit can estimate the user's emotions and adjust the presentation of the generated declaration based on the estimated emotions. For example, if the user is stressed, the generation unit can generate a simple and highly visual declaration. If the user is relaxed, the generation unit can generate a declaration that includes detailed information. Furthermore, if the user is in a hurry, the generation unit can generate a concise declaration. In this way, the generation unit can adjust the presentation of the generated declaration according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not using AI. For example, the generation unit can input the user's emotions into an AI, use the AI ​​to estimate the emotions, and adjust the presentation of the declaration.

[0095] The generation unit can adjust the level of detail in the tax return based on the importance of the declared information during generation. For example, the generation unit can generate a detailed tax return for important declared information. It can also generate a simplified tax return for less important declared information. Furthermore, the generation unit can adjust the level of detail in the tax return according to the importance of the declared information. In this way, the generation unit can adjust the level of detail in the tax return based on the importance of the declared information. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the importance of the declared information into the AI ​​and use the AI ​​to adjust the level of detail in the tax return.

[0096] The generation unit can apply different generation algorithms depending on the category of the declared information during generation. For example, the generation unit can apply an income generation algorithm to declared income. It can also apply a deduction generation algorithm to declared deductions. Furthermore, it can apply an expense generation algorithm to declared expenses. This allows the generation unit to apply different generation algorithms depending on the category of the declared information. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the categories of declared information into AI and use AI to apply different generation algorithms.

[0097] The generation unit can estimate the user's emotions and adjust the length of the declaration it generates based on the estimated emotions. For example, if the user is stressed, the generation unit can generate a short, concise declaration. If the user is relaxed, the generation unit can generate a longer declaration containing more detailed information. Furthermore, if the user is in a hurry, the generation unit can generate a brief declaration. In this way, the generation unit can adjust the length of the declaration it generates according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not using AI. For example, the generation unit can input the user's emotions into an AI, use the AI ​​to estimate the emotions, and adjust the length of the declaration.

[0098] The generation unit can determine the priority of tax returns based on the filing dates of the declarations during the generation process. For example, the generation unit can prioritize generating tax returns for declarations with approaching deadlines. It can also postpone generating tax returns for declarations with later deadlines. Furthermore, the generation unit can adjust the tax return generation schedule according to the filing dates. This allows the generation unit to determine the priority of tax returns based on the filing dates of the declarations. Some or all of the above processing in the generation unit may be performed using AI, for example, or not. For example, the generation unit can input the filing dates of the declarations into the AI ​​and use the AI ​​to determine the priority of the tax returns.

[0099] The generation unit can adjust the order of declarations based on the relevance of the declared information during generation. For example, the generation unit can prioritize the generation of declarations with high relevance. It can also postpone the generation of declarations with low relevance. Furthermore, the generation unit can adjust the generation order of declarations according to the relevance of the declared information. In this way, the generation unit can adjust the order of declarations based on the relevance of the declared information. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the relevance of the declared information into AI and use AI to adjust the order of declarations.

[0100] The conversational unit can estimate the user's emotions and adjust the way the conversation is expressed based on those estimated emotions. For example, if the user is stressed, the conversational unit can speak in a calm tone. If the user is relaxed, it can speak in a friendly tone. Furthermore, if the user is in a hurry, the conversational unit can speak quickly and concisely. In this way, the conversational unit can adjust the way the conversation is expressed according to the user's emotions. 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 processing in the conversational unit may be performed using AI, or not using AI. For example, the conversational unit can input the user's emotions into an AI, use the AI ​​to estimate the emotions, and adjust the way the conversation is expressed.

[0101] The conversational unit can provide the most appropriate answer during a conversation by referring to the user's past question history. For example, the conversational unit can provide relevant answers based on the content of questions the user has asked in the past. It can also prioritize answers to questions the user has asked frequently in the past. Furthermore, the conversational unit can analyze the user's past question history and provide the most appropriate answer. In this way, the conversational unit can provide the most appropriate answer based on the user's past question history. Some or all of the above processing in the conversational unit may be performed using AI, for example, or not using AI. For example, the conversational unit can input the user's past question history into AI and use AI to provide the most appropriate answer.

[0102] The conversational unit can customize its responses based on the user's current living situation during a conversation. For example, if the user has recently moved, the conversational unit can provide appropriate answers to questions related to their new address. It can also provide appropriate answers to questions related to a particular industry if the user works in that industry. Furthermore, if the user frequently uses a particular tax deduction, the conversational unit can provide appropriate answers to questions related to that deduction. This allows the conversational unit to customize its responses based on the user's current living situation. Some or all of the above processing in the conversational unit may be performed using AI, for example, or not. For example, the conversational unit can input the user's living situation into an AI and use the AI ​​to customize its responses.

[0103] The conversational unit can estimate the user's emotions and determine the priority of the conversation based on the estimated emotions. For example, if the user is stressed, the conversational unit can prioritize answering important questions. If the user is relaxed, the conversational unit can also answer detailed questions sequentially. Furthermore, if the user is in a hurry, the conversational unit can answer the most important questions first. In this way, the conversational unit can determine the priority of the conversation according to the user's emotions. 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 processing in the conversational unit may be performed using AI, or not using AI. For example, the conversational unit can input the user's emotions into an AI, use the AI ​​to estimate the emotions, and determine the priority of the conversation.

[0104] The conversational unit can provide optimal answers during a conversation by taking into account the user's geographical location. For example, if the user lives in a specific region, the conversational unit can provide appropriate answers to questions related to that region. Similarly, if the user conducts business in a specific region, the conversational unit can provide appropriate answers to questions related to that region. Furthermore, if the user moves to a specific region, the conversational unit can provide appropriate answers to questions related to that region. This allows the conversational unit to provide optimal answers based on the user's geographical location. Some or all of the above processing in the conversational unit may be performed using AI, for example, or without AI. For example, the conversational unit can input the user's geographical location into AI and use AI to provide optimal answers.

[0105] The conversational unit can analyze the user's social media activity during a conversation and suggest appropriate responses. For example, if the user mentions a specific deduction on social media, the conversational unit can provide appropriate answers to questions related to that deduction. Similarly, if the user mentions a specific industry on social media, the conversational unit can provide appropriate answers to questions related to that industry. Furthermore, if the user mentions a specific region on social media, the conversational unit can provide appropriate answers to questions related to that region. This allows the conversational unit to suggest responses based on the user's social media activity. Some or all of the above processing in the conversational unit may be performed using AI, for example, or without AI. For example, the conversational unit can input the user's social media activity into AI and use AI to suggest responses.

[0106] The reading unit can estimate the user's emotions and adjust the timing of the reading based on the estimated emotions. For example, if the user is stressed, the reading unit can perform a rapid reading. Conversely, if the user is relaxed, the reading unit can perform a detailed reading. Furthermore, if the user is in a hurry, the reading unit can prioritize reading the most important information. This allows the reading unit to adjust the timing of the reading according to the user's emotions. 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 processing in the reading unit may be performed using AI, or not using AI. For example, the reading unit can input the user's emotions into an AI, use the AI ​​to estimate the emotions, and adjust the timing of the reading.

[0107] The reading unit can select the optimal reading method by referring to the user's past reading history during reading. For example, the reading unit can prioritize reading information that the user has frequently read in the past. The reading unit can also prioritize selecting reading methods (scanning, OCR, etc.) that the user has used in the past. Furthermore, the reading unit can select the optimal reading method from the user's past reading history. In this way, the reading unit can select the optimal reading method based on the user's past reading history. Some or all of the above processing in the reading unit may be performed using AI, for example, or without AI. For example, the reading unit can input the user's past reading history into AI and use AI to select the optimal reading method.

[0108] The reading unit can estimate the user's emotions and determine reading priorities based on the estimated emotions. For example, if the user is stressed, the reading unit can prioritize reading important information. If the user is relaxed, the reading unit can sequentially read detailed information. Furthermore, if the user is in a hurry, the reading unit can read the most important information first. In this way, the reading unit can determine reading priorities according to the user's emotions. 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 processing in the reading unit may be performed using AI or not using AI. For example, the reading unit can input the user's emotions into an AI, use the AI ​​to estimate the emotions, and determine reading priorities.

[0109] The reading unit can select the optimal reading method while considering the user's geographical location information. For example, if the user lives in a specific region, the reading unit can prioritize reading information related to that region. Similarly, if the user conducts business in a specific region, the reading unit can prioritize reading information related to that region. Furthermore, if the user moves to a specific region, the reading unit can prioritize reading information related to that region. This allows the reading unit to select the optimal reading method based on the user's geographical location information. Some or all of the above processing in the reading unit may be performed using AI, for example, or without AI. For example, the reading unit can input the user's geographical location information into AI and use AI to select the optimal reading method.

[0110] The generation unit can estimate the user's emotions when reflecting information obtained from the My Number Card into the declaration form, and can determine the priority of information to reflect based on the estimated user's emotions. For example, if the user is feeling stressed, the generation unit can prioritize reflecting important information. If the user is relaxed, the generation unit can also reflect detailed information sequentially. Furthermore, if the user is in a hurry, the generation unit can reflect the most important information first. In this way, the generation unit can determine the priority of information to reflect according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's emotions into an AI, use the AI ​​to estimate the emotions, and determine the priority of information to reflect.

[0111] The generation unit can select the optimal method of reflection when reflecting information obtained from the My Number Card in the declaration form by referring to the user's past declaration history. For example, the generation unit can prioritize reflecting information that the user has frequently declared in the past. The generation unit can also prioritize the reflection method (manual, scanning, etc.) that the user has used in the past. Furthermore, the generation unit can select the optimal reflection method from the user's past declaration history. In this way, the generation unit can select the optimal reflection method based on the user's past declaration history. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's past declaration history into AI and use AI to select the optimal reflection method.

[0112] The generation unit can estimate the user's emotions when reflecting information obtained from the My Number Card in the declaration form, and adjust the display method of the reflected information based on the estimated user's emotions. For example, if the user is stressed, the generation unit can provide a simple and highly visible display method. If the user is relaxed, the generation unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the generation unit can provide a display method that gets straight to the point. In this way, the generation unit can adjust the display method of the reflected information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's emotions into the AI, use the AI ​​to estimate the emotions, and adjust the display method of the reflected information.

[0113] The generation unit can select the optimal method of reflection when reflecting information obtained from the My Number Card in the declaration form, taking into account the user's geographical location information. For example, if the user lives in a specific region, the generation unit can prioritize reflecting information related to that region. Similarly, if the user conducts business in a specific region, the generation unit can prioritize reflecting information related to that region. Furthermore, if the user moves to a specific region, the generation unit can prioritize reflecting information related to that region. This allows the generation unit to select the optimal reflection method based on the user's geographical location information. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's geographical location information into AI and use AI to select the optimal reflection method.

[0114] The generation unit can estimate the user's emotions when generating tax returns such as consumption tax, gift tax, financial statements, and income and expenditure statements, and determine the type of return to generate based on the estimated emotions. For example, if the user is stressed, the generation unit can generate a simple return. If the user is relaxed, the generation unit can generate a detailed return. Furthermore, if the user is in a hurry, the generation unit can generate a concise return. In this way, the generation unit can determine the type of return to generate according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input the user's emotions into an AI, use the AI ​​to estimate the emotions, and determine the type of return to generate.

[0115] The generation unit can adjust the level of detail of tax returns, such as consumption tax, gift tax, financial statements, and income and expenditure statements, based on the importance of the declared information. For example, the generation unit can generate detailed tax returns for important declared information. It can also generate simplified tax returns for less important declared information. Furthermore, the generation unit can adjust the level of detail of the tax returns according to the importance of the declared information. In this way, the generation unit can adjust the level of detail of the tax returns based on the importance of the declared information. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input the importance of the declared information into AI and use AI to adjust the level of detail of the tax returns.

[0116] The generation unit can estimate the user's emotions when generating tax returns such as consumption tax, gift tax, financial statements, and income and expenditure statements, and adjust the display method of the generated tax returns based on the estimated emotions of the user. For example, if the user is stressed, the generation unit can provide a simple and highly visible display method. If the user is relaxed, the generation unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the generation unit can provide a display method that gets straight to the point. In this way, the generation unit can adjust the display method of the generated tax returns according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input the user's emotions into the AI, use the AI ​​to estimate the emotions, and adjust the display method of the generated tax returns.

[0117] The generation unit can determine the priority of tax returns, such as consumption tax, gift tax, financial statements, and income and expenditure statements, based on the filing date of the returns. For example, the generation unit can prioritize the generation of returns for returns with an approaching filing deadline. It can also postpone the generation of returns for returns with a later filing deadline. Furthermore, the generation unit can adjust the return generation schedule according to the filing date. This allows the generation unit to determine the priority of tax returns based on the filing date of the returns. Some or all of the above processing in the generation unit may be performed using AI, for example, or not. For example, the generation unit can input the filing dates of the returns into the AI ​​and use the AI ​​to determine the priority of the returns.

[0118] The conversational unit can estimate the user's emotions when supporting them in completing the filing process at home, and adjust its support based on the estimated emotions. For example, if the user is stressed, the conversational unit can provide simple and easy-to-understand support. If the user is relaxed, it can provide detailed support. Furthermore, if the user is in a hurry, it can provide concise support. In this way, the conversational unit can adjust its support according to the user's emotions. 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 processing in the conversational unit may be performed using AI, or not using AI. For example, the conversational unit can input the user's emotions into an AI, use the AI ​​to estimate the emotions, and adjust its support accordingly.

[0119] The conversational unit can select the most appropriate support method by referring to the user's past filing history when assisting the user with filing procedures at home. For example, the conversational unit can provide relevant support based on questions the user has frequently asked in the past. The conversational unit can also prioritize support methods (chat, voice, etc.) that the user has used in the past. Furthermore, the conversational unit can select the most appropriate support method from the user's past filing history. This allows the conversational unit to select the most appropriate support method based on the user's past filing history. Some or all of the above processing in the conversational unit may be performed using AI, for example, or not using AI. For example, the conversational unit can input the user's past filing history into AI and use AI to select the most appropriate support method.

[0120] The conversational unit can estimate the user's emotions when assisting them with the filing process at home, and prioritize support based on the estimated emotions. For example, if the user is stressed, the conversational unit can prioritize important support. If the user is relaxed, the conversational unit can provide detailed support sequentially. Furthermore, if the user is in a hurry, the conversational unit can provide the most important support first. In this way, the conversational unit can prioritize support according to the user's emotions. 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 processing in the conversational unit may be performed using AI, or not using AI. For example, the conversational unit can input the user's emotions into an AI, use the AI ​​to estimate the emotions, and determine the priority of support.

[0121] The conversational unit can select the most appropriate support method by considering the user's geographical location when assisting a user with filing procedures at home. For example, if the user lives in a specific region, the conversational unit can provide support relevant to that region. Similarly, if the user operates a business in a specific region, the conversational unit can provide support relevant to that region. Furthermore, if the user moves to a specific region, the conversational unit can provide support relevant to that region. This allows the conversational unit to select the most appropriate support method based on the user's geographical location. Some or all of the above processing in the conversational unit may be performed using AI, for example, or without AI. For example, the conversational unit can input the user's geographical location into AI and use AI to select the most appropriate support method.

[0122] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0123] The reception desk can estimate the user's emotions and adjust the input method based on those estimates. For example, if the user is stressed, it can provide a simple interface and minimize the input steps. If the user is relaxed, it can provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, it can prioritize voice input to allow for quick input of the declaration. In this way, the reception desk can adjust the input method according to the user's emotions.

[0124] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on those emotions. For example, if the user is stressed, it can provide simple and easy-to-understand analysis results. If the user is relaxed, it can provide detailed analysis results. Furthermore, if the user is in a hurry, it can provide concise analysis results. In this way, the analysis unit can adjust the presentation of the analysis according to the user's emotions.

[0125] The generation unit can estimate the user's emotions and adjust the presentation of the generated declaration based on those emotions. For example, if the user is stressed, it can generate a simple and highly visual declaration. If the user is relaxed, it can generate a declaration that includes detailed information. Furthermore, if the user is in a hurry, it can generate a declaration that gets straight to the point. In this way, the generation unit can adjust the presentation of the generated declaration according to the user's emotions.

[0126] The conversational unit can estimate the user's emotions and adjust its conversational style based on those emotions. For example, if the user is stressed, it can converse in a calm tone. If the user is relaxed, it can converse in a friendly tone. Furthermore, if the user is in a hurry, it can converse quickly and concisely. In this way, the conversational unit can adjust its conversational style according to the user's emotions.

[0127] The reading unit can estimate the user's emotions and adjust the timing of the reading based on those emotions. For example, if the user is stressed, the reading can be performed quickly. Conversely, if the user is relaxed, a more detailed reading can be performed. Furthermore, if the user is in a hurry, the most important information can be prioritized. In this way, the reading unit can adjust the timing of the reading according to the user's emotions.

[0128] The reception desk can analyze a user's past declaration history and suggest the most suitable input method. For example, it can automatically display declaration content that the user has frequently entered in the past as suggestions. It can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest declaration content that the user will use at specific times of the day based on their past declaration history. In this way, the reception desk can suggest the most suitable input method based on the user's past declaration history.

[0129] The analysis unit can adjust the level of detail of the analysis based on the importance of the declared information. For example, it can perform a detailed analysis on important declared information, and a simplified analysis on less important declared information. Furthermore, it can determine the priority of the analysis according to the importance of the declared information. In this way, the analysis unit can adjust the level of detail of the analysis based on the importance of the declared information.

[0130] The generation unit can apply different generation algorithms depending on the category of the declared information during generation. For example, for declared information related to income, an income generation algorithm can be applied. Similarly, for declared information related to deductions, a deduction generation algorithm can be applied. Furthermore, for declared information related to expenses, an expense generation algorithm can be applied. In this way, the generation unit can apply different generation algorithms depending on the category of the declared information.

[0131] The conversational unit can provide the most appropriate answer during a conversation by referring to the user's past question history. For example, it can provide relevant answers based on questions the user has asked in the past. It can also prioritize answers to questions the user has asked frequently in the past. Furthermore, it can analyze the user's past question history and provide the most appropriate answer. In this way, the conversational unit can provide the most appropriate answer based on the user's past question history.

[0132] The reading unit can select the optimal reading method while considering the user's geographical location information. For example, if the user lives in a specific region, it can prioritize reading information related to that region. Similarly, if the user conducts business in a specific region, it can prioritize reading information related to that region. Furthermore, if the user moves to a specific region, it can prioritize reading information related to that region. In this way, the reading unit can select the optimal reading method based on the user's geographical location information.

[0133] The following briefly describes the processing flow for example form 2.

[0134] Step 1: The reception desk accepts the input of the declaration details. For example, the declaration details can be accepted when the user says "I want to declare XX" or enters them. Step 2: The analysis unit analyzes the information received by the reception unit. For example, it analyzes the declaration details entered by the user and extracts information necessary to generate an appropriate declaration form. Step 3: The generation unit generates an appropriate declaration form based on the information analyzed by the analysis unit. For example, it generates the declaration form by reading the user's My Number Card and automatically entering the necessary information. Step 4: The conversation unit answers user questions based on the declaration form generated by the generation unit. For example, it provides an appropriate answer to a user's question such as, "How do I calculate this deduction?"

[0135] 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.

[0136] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0137] 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.

[0138] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, conversation unit, and reading unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14 and receives the user's declaration. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the information received from the reception unit. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a declaration form based on the analyzed information. The conversation unit is implemented by the control unit 46A of the smart device 14 and answers the user's questions. The reading unit is implemented by the camera 42 of the smart device 14 and reads the My Number Card information. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0139] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.).

[0151] 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.

[0152] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0153] 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.

[0154] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, conversation unit, and reading unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214 and receives the user's declaration. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the information received from the reception unit. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a declaration form based on the analyzed information. The conversation unit is implemented by the control unit 46A of the smart glasses 214 and answers the user's questions. The reading unit is implemented by the camera 42 of the smart glasses 214 and reads the information on the My Number Card. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0155] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.).

[0167] 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.

[0168] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0169] 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.

[0170] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, conversation unit, and reading unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314 and receives the user's declaration. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the information received from the reception unit. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and generates a declaration form based on the analyzed information. The conversation unit is implemented by, for example, the control unit 46A of the headset terminal 314 and answers the user's questions. The reading unit is implemented by, for example, the camera 42 of the headset terminal 314 and reads the My Number Card information. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0171] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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).

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.).

[0184] 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.

[0185] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0186] 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.

[0187] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, conversation unit, and reading unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414 and receives the user's declaration. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the information received from the reception unit. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and generates a declaration form based on the analyzed information. The conversation unit is implemented by, for example, the control unit 46A of the robot 414 and answers the user's questions. The reading unit is implemented by, for example, the camera 42 of the robot 414 and reads the information on the My Number Card. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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."

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] (Note 1) The reception desk accepts the input of declaration details, An analysis unit that analyzes the information received by the reception unit, A generation unit that generates an appropriate declaration form based on the information analyzed by the analysis unit, The system includes a conversation unit that answers user questions based on the declaration form generated by the generation unit. A system characterized by the following features. (Note 2) It is equipped with a reader that reads My Number cards. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is The information obtained from the My Number Card will be reflected in the tax return. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned conversation section is, Answer user questions in a conversational format. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is Generate tax returns such as consumption tax, gift tax, financial statements, and income and expenditure statements. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned conversation section is, Support users when they complete the tax filing process at home. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is The system estimates the user's emotions and adjusts the input method for declarations based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is We analyze the user's past declaration history and suggest the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When users enter their declaration information, the system filters the input based on their current living situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and prioritizes input content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When entering declaration information, the system prioritizes inputting highly relevant information, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When entering declaration information, the system analyzes the user's social media activity and inputs relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, the level of detail of the analysis is adjusted based on the importance of the declared information. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of the declared information. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During the analysis, the priority of the analysis will be determined based on when the declared information was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the order of analysis will be adjusted based on the relevance of the declared information. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is We estimate the user's emotions and adjust the way the declaration is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is During generation, the level of detail in the tax return is adjusted based on the importance of the declared information. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is During generation, different generation algorithms are applied depending on the category of the declared information. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is It estimates the user's emotions and adjusts the length of the declaration generated based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is During generation, the priority of the tax returns is determined based on the timing of their submission. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is During generation, the order of the declarations is adjusted based on the relevance of the declared information. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned conversation section is, It estimates the user's emotions and adjusts the way the conversation is expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned conversation section is, During a conversation, the system provides the most appropriate answer by referring to the user's past question history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned conversation section is, During conversations, the system customizes responses based on the user's current life circumstances. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned conversation section is, It estimates the user's emotions and determines conversation priorities based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned conversation section is, During conversations, the system takes the user's geographical location into consideration to provide the most appropriate response. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned conversation section is, During conversations, the system analyzes the user's social media activity and suggests appropriate responses. The system described in Appendix 1, characterized by the features described herein. (Note 31) The reading unit is It estimates the user's emotions and adjusts the timing of readings based on the estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 32) The reading unit is During scanning, the system selects the optimal scanning method by referring to the user's past scanning history. The system described in Appendix 2, characterized by the features described herein. (Note 33) The reading unit is The system estimates the user's emotions and determines the priority of readings based on the estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 34) The reading unit is During scanning, the system selects the optimal scanning method, taking into account the user's geographical location. The system described in Appendix 2, characterized by the features described herein. (Note 35) The generating unit is When reflecting information obtained from My Number cards in tax returns, the system estimates the user's emotions and determines the priority of the information to be reflected based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 36) The generating unit is When reflecting information obtained from My Number Cards into tax returns, the system will refer to the user's past tax return history to select the most appropriate method of reflection. The system described in Appendix 3, characterized by the features described herein. (Note 37) The generating unit is When reflecting information obtained from My Number cards in tax returns, the system estimates the user's emotions and adjusts how the reflected information is displayed based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 38) The generating unit is When reflecting information obtained from My Number Cards in tax returns, the system will select the most appropriate method of reflection, taking into account the user's geographical location. The system described in Appendix 3, characterized by the features described herein. (Note 39) The generating unit is When generating tax returns such as consumption tax, gift tax, financial statements, and income and expenditure statements, the system estimates the user's emotions and determines the type of tax return to generate based on those estimated emotions. The system described in Appendix 5, characterized by the features described herein. (Note 40) The generating unit is When generating tax returns such as consumption tax, gift tax, financial statements, and income and expenditure statements, the level of detail in the return is adjusted based on the importance of the declared information. The system described in Appendix 5, characterized by the features described herein. (Note 41) The generating unit is When generating tax returns such as consumption tax, gift tax, financial statements, and income and expenditure statements, the system estimates the user's sentiment and adjusts how the generated tax returns are displayed based on that estimated sentiment. The system described in Appendix 5, characterized by the features described herein. (Note 42) The generating unit is When generating tax returns such as consumption tax, gift tax, financial statements, and income and expenditure statements, the priority of the tax returns is determined based on the timing of their submission. The system described in Appendix 5, characterized by the features described herein. (Note 43) The aforementioned conversation section is, When supporting users as they complete the filing process at home, the system estimates the user's emotions and adjusts the support method based on those estimated emotions. The system described in Appendix 6, characterized by the features described herein. (Note 44) The aforementioned conversation section is, When assisting users with filing their tax returns at home, the system will refer to the user's past filing history to select the most appropriate support method. The system described in Appendix 6, characterized by the features described herein. (Note 45) The aforementioned conversation section is, When supporting users as they complete the filing process at home, the system estimates the user's emotions and prioritizes support based on those estimated emotions. The system described in Appendix 6, characterized by the features described herein. (Note 46) The aforementioned conversation section is, When supporting users in filing their tax returns at home, the system selects the most appropriate support method by considering the user's geographical location. The system described in Appendix 6, characterized by the features described herein. [Explanation of Symbols]

[0207] 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. The reception desk accepts the input of declaration details, An analysis unit that analyzes the information received by the reception unit, A generation unit that generates an appropriate declaration form based on the information analyzed by the analysis unit, The system includes a conversation unit that answers user questions based on the declaration form generated by the generation unit. A system characterized by the following features.

2. It is equipped with a reader that reads My Number cards. The system according to feature 1.

3. The generating unit is The information obtained from the My Number Card will be reflected in the tax return. The system according to feature 1.

4. The aforementioned conversation section is, Answer user questions in a conversational format. The system according to feature 1.

5. The generating unit is Generate tax returns such as consumption tax, gift tax, financial statements, and income and expenditure statements. The system according to feature 1.

6. The aforementioned conversation section is, Support users when they complete the tax filing process at home. The system according to feature 1.

7. The aforementioned reception unit is The system estimates the user's emotions and adjusts the input method for declarations based on those estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is We analyze the user's past declaration history and suggest the optimal input method. The system according to feature 1.

9. The aforementioned reception unit is When users enter their declaration information, the system filters the input based on their current living situation and areas of interest. The system according to feature 1.

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