System

Abstract

An object of a system according to an embodiment is to support appropriate tax processing according to the situation of each person and each company.SOLUTION: A system includes a collection part, a creation part, a declaration part, and a proposal part. A collection part collects the input of the situations of each person and each company by an account book or hearing. The creation unit analyzes the input collected by the collection unit and creates a ledger document according to the situation. A declaration part supports tax declaration on the basis of the book document prepared by the preparation part. The proposal unit proposes a legal tax saving proposal based on the tax return supported by the return unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has faced the challenge of making it difficult to deal with the complexity and frequent changes in tax laws and to carry out appropriate tax procedures.

[0005] The system according to the embodiment aims to support appropriate tax processing according to the circumstances of each individual and each company. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a creation unit, a declaration unit, and a proposal unit. The collection unit collects inputs from accounts or interviews about the situation of each person and company. The creation unit analyzes the inputs collected by the collection unit and creates accounting documents according to the situation. The declaration unit supports tax returns based on the accounting documents created by the creation unit. The proposal unit proposes legal tax-saving plans based on the tax returns supported by the declaration unit. [Effects of the Invention]

[0007] The system according to the embodiment can support appropriate tax processing according to the circumstances of each individual and each company. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A tax advice system according to an embodiment of the present invention collects input from ledgers and interviews about the situation of each individual or company, analyzes it using a generation AI to create appropriate accounting documents, and supports tax returns. In the tax advice system, the generation AI analyzes the input from ledgers and interviews about the situation of each individual or company, and supports the creation of appropriate accounting documents and tax returns according to the situation. The tax advice system also clearly explains the intent behind tax laws and proposes legal tax-saving measures whenever possible. For example, the tax advice system collects input from ledgers and interviews about the situation of each individual or company. For example, the generation AI collects detailed accounting data and interview content and analyzes it. Next, the tax advice system creates appropriate accounting documents based on the data collected by the generation AI and supports tax return procedures. For example, the generation AI automatically prepares corporate tax returns, income tax returns, and consumption tax returns. Next, the generation AI clearly explains the intent behind tax laws and proposes legal tax-saving measures whenever possible. For example, it suggests ways to reduce tax burdens by applying specific deductions and tax reduction measures. This allows tax professionals to efficiently handle complex tax procedures and prepare satisfactory tax returns. This allows the tax advice system to support the creation of appropriate accounting documents and tax returns tailored to the circumstances of each individual or company, and to suggest legal tax-saving ideas. For example, tax professionals can easily create accurate accounting documents, reducing the effort required for tax returns. It also allows tax professionals to prepare satisfactory tax returns and suggests ways to reduce tax burdens.

[0029] A tax advice system according to an embodiment includes a collection unit, a preparation unit, a filing unit, and a proposal unit. The collection unit collects input about the status of each individual or company through bookkeeping or interviews. The collection unit collects detailed information about bookkeeping data and interview content, for example. The collection unit can collect input in the form of electronic bookkeeping, paper bookkeeping, or face-to-face interviews. The preparation unit analyzes the input collected by the collection unit and prepares bookkeeping documents according to the situation. The preparation unit automatically prepares, for example, corporate tax returns, income tax returns, and consumption tax returns based on the collected data. The preparation unit uses a generation AI to prepare bookkeeping documents based on the collected data. The filing unit supports tax filing based on the bookkeeping documents prepared by the preparation unit. The filing unit supports tax filing procedures based on the created bookkeeping documents, for example. The filing unit can also support tax filing procedures using a generation AI. The proposal unit proposes legal tax-saving ideas based on the tax filing supported by the filing unit. The suggestion unit suggests ways to reduce tax burdens, for example, by applying specific deductions or tax reduction measures. The suggestion unit can also suggest legal tax saving plans using generative AI. As a result, the tax advice system according to the embodiment can support the creation of appropriate accounting documents and tax returns according to the circumstances of each individual or company, and suggest legal tax saving plans.

[0030] The collection unit can collect ledger data or interview content. The collection unit, for example, collects ledger data. The ledger data includes, for example, sales data, expense data, asset data, etc. The collection unit, for example, collects interview content. The interview content includes, for example, interview questions, questionnaire content, etc. By collecting ledger data and interview content in detail, more accurate input can be obtained. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the collection unit can input ledger data into a generation AI, which then analyzes and collects the data.

[0031] The preparation unit can automatically prepare corporate tax returns, income tax returns, and consumption tax returns based on the collected data. The preparation unit, for example, prepares a corporate tax return based on the collected data. The corporate tax return includes, for example, return items and necessary attached documents. The preparation unit, for example, prepares an income tax return based on the collected data. The income tax return includes, for example, return items and necessary attached documents. The preparation unit, for example, prepares a consumption tax return based on the collected data. The consumption tax return includes, for example, return items and necessary attached documents. This reduces the effort required for tax return filing by automatically preparing accounting documents based on the collected data. Some or all of the above-mentioned processing in the preparation unit may be performed using, or without, a generation AI. For example, the preparation unit can input the collected data into a generation AI, which then automatically prepares accounting documents.

[0032] The reporting department can support tax return procedures based on the created bookkeeping documents. The reporting department, for example, supports tax return procedures based on the created bookkeeping documents. Tax return procedures include, for example, support for preparing tax returns, management of filing deadlines, and tax consultations. This enables accurate tax returns by supporting tax return procedures based on the created bookkeeping documents. Some or all of the above-mentioned processing in the reporting department may be performed using, or without, a generation AI, for example. For example, the reporting department can input the created bookkeeping documents into a generation AI, which then supports the tax return procedures.

[0033] The suggestion unit can suggest a method for reducing the tax burden by applying deductions or tax reduction measures. The suggestion unit, for example, suggests a method for reducing the tax burden by applying deductions or tax reduction measures. Deductions or tax reduction measures include, for example, specific deduction items, details of tax incentives, etc. This makes it possible to suggest a method for reducing the tax burden by applying specific deductions or tax reduction measures. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the suggestion unit can input information on deductions or tax reduction measures into the generation AI, which then suggests a method for reducing the tax burden.

[0034] The collection unit can automatically acquire the latest information to respond to annual tax reforms. The collection unit, for example, automatically acquires the latest information to respond to annual tax reforms. Acquisition of the latest information includes, for example, acquiring data from official government websites and using APIs. By automatically acquiring the latest information to respond to annual tax reforms, processing can always be based on the latest tax laws. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the collection unit can input the latest information into the generation AI, which then analyzes and acquires the latest information.

[0035] The proposal unit can propose a tax treatment method for a company of a certain industry or size. The proposal unit, for example, proposes the optimal tax treatment method for a company of a specific industry or size. Industry and size include, for example, manufacturing, retail, number of employees, and sales. By proposing the optimal tax treatment method for a company of a specific industry or size, more effective tax treatment becomes possible. Some or all of the above-mentioned processing in the proposal unit may be performed using, or without, a generation AI, for example. For example, the proposal unit can input information on industry and size into the generation AI, which then proposes the optimal tax treatment method.

[0036] The collection unit can analyze the user's past accounting data and interview content and select the optimal collection method. The collection unit, for example, analyzes the user's past accounting data, and the generation AI proposes the most efficient collection method. The collection unit, for example, analyzes the user's past interview content, and the generation AI selects the optimal question format. The collection unit, for example, has the generation AI propose the optimal collection timing based on the user's past data collection history. In this way, the optimal collection method can be selected by analyzing the user's past data. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the collection unit can input the user's past data into the generation AI, and the generation AI can select the optimal collection method.

[0037] When collecting input, the collection unit can perform filtering based on the user's current work situation and areas of interest. For example, the collection unit analyzes the user's current work situation and allows the generation AI to collect only relevant data. For example, the collection unit prioritizes collecting data required by the generation AI based on the user's areas of interest. For example, the collection unit considers the user's work schedule and allows the generation AI to collect data at the optimal timing. This allows highly relevant data to be collected by filtering data based on the user's current work situation and areas of interest. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, the generation AI. For example, the collection unit can input data on the user's work situation and areas of interest to the generation AI, and the generation AI can perform filtering.

[0038] When collecting input, the collection unit can select the optimal collection means depending on the user's input method. For example, if the user prefers voice input, the collection unit causes the generation AI to preferentially collect voice data. For example, if the user prefers text input, the collection unit causes the generation AI to preferentially collect text data. For example, if the user prefers image input, the collection unit causes the generation AI to preferentially collect image data. This allows data to be collected efficiently by selecting the optimal collection means depending on the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, the generation AI. For example, the collection unit can input the user's input data into the generation AI, which then selects the optimal collection means.

[0039] When collecting input, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, when the user is in a specific area, the collection unit prioritizes collecting data related to that area. For example, when the user is moving, the collection unit collects relevant data based on the user's current location. For example, when the user is in a specific location, the collection unit prioritizes collecting data related to that location. In this way, highly relevant data can be collected preferentially by taking the user's geographical location information into account. Some or all of the above-described processing in the collection unit may be performed using, or without, the generation AI. For example, the collection unit can input the user's geographical location information to the generation AI, which can then prioritize collecting highly relevant data.

[0040] The collection unit can analyze the user's social media activities and collect related data when collecting input. For example, the collection unit analyzes the content of the user's social media posts and collects related data. For example, the collection unit collects related data based on the user's social media check-in information. For example, the collection unit collects related data by referring to the activities of the user's friends on social media. In this way, related data can be collected by analyzing the user's social media activities. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, the generation AI, for example. For example, the collection unit can input the user's social media data into the generation AI, which then collects the related data.

[0041] When collecting input, the collection unit can customize the collection method by reflecting the user's past feedback. In the collection unit, for example, the generation AI proposes an optimal collection method based on the user's past feedback. In the collection unit, for example, the generation AI adjusts the collection means by reflecting the user's past feedback. In the collection unit, for example, the generation AI optimizes the collection timing by referring to the user's past feedback. In this way, the optimal collection method can be customized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using the generation AI, for example, or may be performed without using the generation AI. For example, the collection unit can input the user's past feedback into the generation AI, and the generation AI can customize the collection method.

[0042] The creation unit can adjust the level of detail based on the importance of the data when creating the ledger documents. For example, the creation unit causes the generation AI to create ledger documents including detailed explanations for data with high importance. For example, the creation unit causes the generation AI to create ledger documents including concise explanations for data with low importance. For example, the creation unit causes the generation AI to adjust the level of detail of the ledger documents according to the importance of the data. As a result, important data can be described in detail by adjusting the level of detail of the ledger documents based on the importance of the data. Some or all of the above-mentioned processing in the creation unit may be performed using, or without, the generation AI. For example, the creation unit can input the importance of the data to the generation AI, and the generation AI can adjust the level of detail.

[0043] The creation unit can apply different creation algorithms depending on the tax law category when creating accounting documents. For example, when creating accounting documents for corporate tax, the creation unit causes the generation AI to apply an algorithm dedicated to corporate tax. For example, when creating accounting documents for income tax, the creation unit causes the generation AI to apply an algorithm dedicated to income tax. For example, when creating accounting documents for consumption tax, the creation unit causes the generation AI to apply an algorithm dedicated to consumption tax. In this way, by applying different creation algorithms depending on the tax law category, accounting documents optimal for each tax law can be created. Some or all of the above-mentioned processing in the creation unit may be performed using, or without, the generation AI. For example, the creation unit can input the tax law category into the generation AI, and the generation AI can apply the optimal creation algorithm.

[0044] When creating accounting documents, the creation unit can improve accuracy by referring to the user's past accounting documents. The creation unit, for example, analyzes the user's past accounting documents, and the generation AI creates highly accurate accounting documents. The creation unit, for example, refers to the user's past accounting documents, and the generation AI selects the optimal format. The creation unit, for example, uses the user's past accounting documents as a reference, and the generation AI improves the accuracy of the accounting documents. In this way, the accuracy of the accounting documents can be improved by referring to the user's past accounting documents. Some or all of the above-mentioned processing in the creation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the creation unit inputs the user's past accounting documents into the generation AI, and the generation AI improves the accuracy of the accounting documents.

[0045] The creation unit can determine priorities based on the time of data submission when creating ledger documents. For example, the creation unit causes the generation AI to prioritize creating ledger documents for data with an upcoming submission deadline. For example, the creation unit causes the generation AI to postpone creating ledger documents for data with a distant submission deadline. For example, the creation unit causes the generation AI to adjust the order in which ledger documents are created depending on the time of data submission. In this way, by determining priorities based on the time of data submission, ledger documents can be created in time for the submission deadline. Some or all of the above-mentioned processing in the creation unit may be performed using, or without, the generation AI. For example, the creation unit can input the time of data submission to the generation AI, and the generation AI can determine the priorities.

[0046] The creation unit can adjust the order based on the relevance of the data when creating the ledger documents. For example, the creation unit prioritizes highly relevant data when writing the ledger documents. For example, the creation unit puts less relevant data on hold when writing the ledger documents. For example, the creation unit allows the generation AI to adjust the order of the ledger documents according to the relevance of the data. In this way, by adjusting the order based on the relevance of the data, highly relevant data can be written on priority. Some or all of the above-mentioned processing in the creation unit may be performed using the generation AI, for example, or may be performed without using the generation AI. For example, the creation unit can input the relevance of the data into the generation AI, and the generation AI can adjust the order.

[0047] When creating accounting documents, the creation unit can adjust the use of technical terminology according to the user's level of expertise. For example, if the user has technical expertise, the creation unit causes the generation AI to create accounting documents that use a lot of technical terminology. For example, if the user does not have technical expertise, the creation unit causes the generation AI to create accounting documents that avoid technical terminology. For example, the creation unit causes the generation AI to adjust the use of technical terminology according to the user's level of expertise. This makes it possible to create accounting documents that are easy to understand by adjusting the use of technical terminology according to the user's level of expertise. Some or all of the above-mentioned processing in the creation unit may be performed using the generation AI, for example, or may be performed without using the generation AI. For example, the creation unit can input the user's level of expertise into the generation AI, and the generation AI can adjust the use of technical terminology.

[0048] During the reporting procedure, the reporting unit can select the optimal procedure by referring to past reporting data. For example, the reporting unit analyzes the user's past reporting data, and the generation AI proposes the optimal reporting procedure. For example, the reporting unit causes the generation AI to select the most efficient reporting procedure based on the user's past reporting data. For example, the reporting unit causes the generation AI to optimize the reporting procedure by referring to the user's past reporting data. In this way, the optimal reporting procedure can be selected by referring to the past reporting data. Some or all of the above-mentioned processing in the reporting unit may be performed using, or without, the generation AI. For example, the reporting unit can input past reporting data into the generation AI, and the generation AI can select the optimal procedure.

[0049] The declaration unit can customize the declaration procedure based on the user's current work situation during the declaration procedure. For example, the declaration unit analyzes the user's current work situation, and the generation AI proposes the optimal declaration procedure. For example, the declaration unit considers the user's work schedule, and the generation AI efficiently performs the declaration procedure. For example, the declaration unit customizes the declaration procedure according to the user's work content. In this way, by customizing the procedure based on the user's current work situation, an efficient declaration procedure can be performed. Some or all of the above-mentioned processing in the declaration unit may be performed using, or without, the generation AI. For example, the declaration unit can input the user's work situation into the generation AI, and the generation AI can customize the procedure.

[0050] The reporting unit can improve the reporting procedure by reflecting user feedback. In the reporting unit, for example, the generation AI improves the reporting procedure based on the user's past feedback. In the reporting unit, for example, the generation AI improves the efficiency of the reporting procedure by reflecting user feedback. In the reporting unit, for example, the generation AI optimizes the reporting procedure by referring to user feedback. In this way, the reporting procedure can be improved by reflecting user feedback. Some or all of the above-mentioned processing in the reporting unit may be performed using the generation AI, for example, or may be performed without using the generation AI. For example, the reporting unit can input user feedback into the generation AI, which can improve the procedure.

[0051] The reporting unit can select the optimal reporting procedure by taking into account the user's geographical location information when performing the reporting procedure. For example, if the user is in a specific area, the reporting unit prioritizes reporting procedures related to that area. For example, if the user is traveling, the reporting unit selects the optimal reporting procedure based on the user's current location. For example, if the user is in a specific location, the reporting unit prioritizes reporting procedures related to that location. This allows the optimal reporting procedure to be selected by taking into account the user's geographical location information. Some or all of the above-mentioned processing in the reporting unit may be performed using, or without, a generation AI. For example, the reporting unit can input the user's geographical location information into the generation AI, which can then select the optimal procedure.

[0052] The reporting unit can analyze the user's social media activity and suggest procedures during the reporting procedure. For example, the reporting unit analyzes the user's social media posts and suggests relevant reporting procedures. For example, the reporting unit suggests relevant reporting procedures based on the user's social media check-in information. For example, the reporting unit suggests relevant reporting procedures based on the activity of the user's friends on social media. In this way, relevant reporting procedures can be suggested by analyzing the user's social media activity. Some or all of the above-mentioned processing in the reporting unit may be performed using, or without, a generation AI. For example, the reporting unit can input the user's social media data into a generation AI, which then suggests relevant reporting procedures.

[0053] The declaration unit can customize the declaration procedure by reflecting the user's past feedback. In the declaration unit, for example, the generation AI proposes the optimal declaration procedure based on the user's past feedback. In the declaration unit, for example, the generation AI customizes the declaration procedure by reflecting the user's past feedback. In the declaration unit, for example, the generation AI optimizes the declaration procedure by referring to the user's past feedback. In this way, the optimal declaration procedure can be customized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the declaration unit may be performed using the generation AI, for example, or may be performed without using the generation AI. For example, the declaration unit can input the user's past feedback into the generation AI, which can customize the procedure.

[0054] When proposing a tax saving plan, the suggestion unit can adjust the level of detail based on the importance of the tax law. For example, in the suggestion unit, the generation AI proposes a tax saving plan including a detailed explanation for a tax law with high importance. For example, in the suggestion unit, the generation AI proposes a tax saving plan including a concise explanation for a tax law with low importance. For example, in the suggestion unit, the generation AI adjusts the level of detail of the tax saving plan according to the importance of the tax law. In this way, by adjusting the level of detail based on the importance of the tax law, detailed tax saving plans for important tax laws can be proposed. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, the generation AI. For example, the suggestion unit can input the importance of the tax law to the generation AI, and the generation AI can adjust the level of detail.

[0055] When proposing a tax saving plan, the proposal unit can apply different proposal algorithms depending on the category of tax law. For example, when proposing a tax saving plan for corporate tax, the proposal unit causes the generation AI to apply an algorithm dedicated to corporate tax. For example, when proposing a tax saving plan for income tax, the proposal unit causes the generation AI to apply an algorithm dedicated to income tax. For example, when proposing a tax saving plan for consumption tax, the proposal unit causes the generation AI to apply an algorithm dedicated to consumption tax. In this way, by applying different proposal algorithms depending on the category of tax law, it is possible to propose tax saving plans that are optimal for each tax law. Some or all of the above-mentioned processing in the proposal unit may be performed using, or without, the generation AI. For example, the proposal unit can input the category of tax law into the generation AI, which then applies the optimal proposal algorithm.

[0056] When proposing tax saving plans, the suggestion unit can improve accuracy by referring to the user's past suggestion results. The suggestion unit, for example, analyzes the user's past suggestion results, and the generation AI proposes highly accurate tax saving plans. The suggestion unit, for example, refers to the user's past suggestion results, and the generation AI selects the optimal tax saving plan. The suggestion unit, for example, causes the generation AI to improve the accuracy of the tax saving plans based on the user's past suggestion results. In this way, the accuracy of the tax saving plans can be improved by referring to the user's past suggestion results. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the suggestion unit can input the user's past suggestion results into the generation AI, and the generation AI can improve the accuracy.

[0057] When proposing tax saving plans, the proposal unit can determine the priority based on the timing of tax law revisions. For example, if the tax law revision is imminent, the proposal unit causes the generation AI to prioritize proposing tax saving plans. For example, if the tax law revision is far away, the proposal unit causes the generation AI to postpone proposing tax saving plans. For example, in the proposal unit, the generation AI adjusts the order in which tax saving plans are proposed depending on the timing of tax law revisions. In this way, by determining the priority based on the timing of tax law revisions, tax saving plans that correspond to important revisions can be preferentially proposed. Some or all of the above-mentioned processing in the proposal unit may be performed using, or without, the generation AI. For example, the proposal unit can input the timing of tax law revisions to the generation AI, and the generation AI can determine the priority.

[0058] When proposing tax saving plans, the proposal unit can adjust the order based on the relevance of tax laws. For example, the proposal unit prioritizes incorporating highly relevant tax laws into the tax saving plans. For example, the proposal unit prioritizes incorporating less relevant tax laws into the tax saving plans. For example, the proposal unit allows the generation AI to adjust the order of the tax saving plans according to the relevance of the tax laws. In this way, by adjusting the order based on the relevance of the tax laws, tax saving plans based on highly relevant tax laws can be proposed preferentially. Some or all of the above-mentioned processing in the proposal unit may be performed using, or without, the generation AI. For example, the proposal unit can input the relevance of tax laws into the generation AI, which can then adjust the order.

[0059] When proposing a tax saving plan, the suggestion unit can adjust the use of technical terminology according to the user's level of expertise. For example, if the user has technical expertise, the suggestion unit causes the generation AI to propose a tax saving plan that uses a lot of technical terminology. For example, if the user does not have technical expertise, the suggestion unit causes the generation AI to propose a tax saving plan that avoids technical terminology. For example, the suggestion unit causes the generation AI to adjust the use of technical terminology according to the user's level of expertise. This makes it possible to propose tax saving plans that are easy to understand by adjusting the use of technical terminology according to the user's level of expertise. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, the generation AI. For example, the suggestion unit can input the user's level of expertise into the generation AI, and the generation AI can adjust the use of technical terminology.

[0060] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0061] The collection unit can analyze the user's past purchase history and automatically extract data related to tax returns. For example, the collection unit analyzes the user's past purchase history and automatically extracts items that can be declared as expenses. For example, the collection unit extracts data for a specific period from the user's purchase history and organizes the information necessary for tax returns. For example, the collection unit automatically classifies expenses into specific categories based on the user's purchase history and provides data useful for tax returns. This makes it possible to efficiently collect data necessary for tax returns by utilizing the user's past purchase history.

[0062] The collection unit can customize the collection method based on the user's past feedback. For example, the collection unit analyzes the user's past feedback, and the generation AI proposes the optimal collection method. For example, the collection unit reflects the user's past feedback, and the generation AI adjusts the collection means. For example, the collection unit refers to the user's past feedback, and the generation AI optimizes the collection timing. In this way, the optimal collection method can be customized by reflecting the user's past feedback.

[0063] When creating ledger documents, the creation unit can adjust the level of detail based on the importance of the data. For example, for data with high importance, the generation AI creates ledger documents including detailed explanations. For example, for data with low importance, the creation unit creates ledger documents including concise explanations. For example, for data with low importance, the creation unit has the generation AI adjust the level of detail of the ledger documents according to the importance of the data. In this way, important data can be described in detail by adjusting the level of detail of the ledger documents based on the importance of the data.

[0064] The declaration unit can customize the declaration procedure based on the user's current work situation during the declaration procedure. For example, the declaration unit analyzes the user's current work situation, and the generation AI proposes the optimal declaration procedure. For example, the declaration unit considers the user's work schedule, and the generation AI performs the declaration procedure efficiently. For example, the declaration unit customizes the declaration procedure according to the user's work content. In this way, by customizing the procedure based on the user's current work situation, an efficient declaration procedure can be performed.

[0065] When proposing tax saving plans, the proposal unit can apply different proposal algorithms depending on the tax law category. For example, when the proposal unit proposes tax saving plans for corporate tax, the generation AI applies an algorithm dedicated to corporate tax. For example, when the proposal unit proposes tax saving plans for income tax, the generation AI applies an algorithm dedicated to income tax. For example, when the proposal unit proposes tax saving plans for consumption tax, the generation AI applies an algorithm dedicated to consumption tax. In this way, by applying different proposal algorithms depending on the tax law category, it is possible to propose tax saving plans that are optimal for each tax law.

[0066] When proposing tax saving plans, the proposal unit can improve accuracy by referring to the user's past proposal results. For example, the proposal unit analyzes the user's past proposal results, and the generation AI proposes highly accurate tax saving plans. For example, the proposal unit refers to the user's past proposal results, and the generation AI selects the optimal tax saving plan. For example, the proposal unit refers to the user's past proposal results, and the generation AI improves the accuracy of the tax saving plans based on the user's past proposal results. In this way, the accuracy of the tax saving plans can be improved by referring to the user's past proposal results.

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

[0068] Step 1: The collection department collects inputs about the situation of each person and each company through ledgers or interviews. The collection department collects inputs in the form of electronic ledgers, paper ledgers, interviews, etc. Step 2: The creation unit analyzes the input collected by the collection unit and creates accounting documents according to the situation. For example, the creation unit automatically creates corporate tax returns, income tax returns, and consumption tax returns based on the collected data. Generative AI can also be used to create accounting documents based on the collected data. Step 3: The declaration department supports tax returns based on the accounting documents created by the preparation department. The declaration department, for example, supports tax return procedures based on the created accounting documents. Generative AI can also be used to support tax return procedures. Step 4: The proposal department proposes legal tax saving ideas based on the tax return supported by the declaration department. The proposal department suggests ways to reduce the tax burden, for example, by applying certain deductions or tax reduction measures. Generative AI can also be used to propose legal tax saving ideas.

[0069] (Example 2) A tax advice system according to an embodiment of the present invention collects input from ledgers and interviews about the situation of each individual or company, analyzes it using a generation AI to create appropriate accounting documents, and supports tax returns. In the tax advice system, the generation AI analyzes the input from ledgers and interviews about the situation of each individual or company, and supports the creation of appropriate accounting documents and tax returns according to the situation. The tax advice system also clearly explains the intent behind tax laws and proposes legal tax-saving measures whenever possible. For example, the tax advice system collects input from ledgers and interviews about the situation of each individual or company. For example, the generation AI collects detailed accounting data and interview content and analyzes it. Next, the tax advice system creates appropriate accounting documents based on the data collected by the generation AI and supports tax return procedures. For example, the generation AI automatically prepares corporate tax returns, income tax returns, and consumption tax returns. Next, the generation AI clearly explains the intent behind tax laws and proposes legal tax-saving measures whenever possible. For example, it suggests ways to reduce tax burdens by applying specific deductions and tax reduction measures. This allows tax professionals to efficiently handle complex tax procedures and prepare satisfactory tax returns. This allows the tax advice system to support the creation of appropriate accounting documents and tax returns tailored to the circumstances of each individual or company, and to suggest legal tax-saving ideas. For example, tax professionals can easily create accurate accounting documents, reducing the effort required for tax returns. It also allows tax professionals to prepare satisfactory tax returns and suggests ways to reduce tax burdens.

[0070] A tax advice system according to an embodiment includes a collection unit, a preparation unit, a filing unit, and a proposal unit. The collection unit collects input about the status of each individual or company through bookkeeping or interviews. The collection unit collects detailed information about bookkeeping data and interview content, for example. The collection unit can collect input in the form of electronic bookkeeping, paper bookkeeping, or face-to-face interviews. The preparation unit analyzes the input collected by the collection unit and prepares bookkeeping documents according to the situation. The preparation unit automatically prepares, for example, corporate tax returns, income tax returns, and consumption tax returns based on the collected data. The preparation unit uses a generation AI to prepare bookkeeping documents based on the collected data. The filing unit supports tax filing based on the bookkeeping documents prepared by the preparation unit. The filing unit supports tax filing procedures based on the created bookkeeping documents, for example. The filing unit can also support tax filing procedures using a generation AI. The proposal unit proposes legal tax-saving ideas based on the tax filing supported by the filing unit. The suggestion unit suggests ways to reduce tax burdens, for example, by applying specific deductions or tax reduction measures. The suggestion unit can also suggest legal tax saving plans using generative AI. As a result, the tax advice system according to the embodiment can support the creation of appropriate accounting documents and tax returns according to the circumstances of each individual or company, and suggest legal tax saving plans.

[0071] The collection unit can collect ledger data or interview content. The collection unit, for example, collects ledger data. The ledger data includes, for example, sales data, expense data, asset data, etc. The collection unit, for example, collects interview content. The interview content includes, for example, interview questions, questionnaire content, etc. By collecting ledger data and interview content in detail, more accurate input can be obtained. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the collection unit can input ledger data into a generation AI, which then analyzes and collects the data.

[0072] The preparation unit can automatically prepare corporate tax returns, income tax returns, and consumption tax returns based on the collected data. The preparation unit, for example, prepares a corporate tax return based on the collected data. The corporate tax return includes, for example, return items and necessary attached documents. The preparation unit, for example, prepares an income tax return based on the collected data. The income tax return includes, for example, return items and necessary attached documents. The preparation unit, for example, prepares a consumption tax return based on the collected data. The consumption tax return includes, for example, return items and necessary attached documents. This reduces the effort required for tax return filing by automatically preparing accounting documents based on the collected data. Some or all of the above-mentioned processing in the preparation unit may be performed using, or without, a generation AI. For example, the preparation unit can input the collected data into a generation AI, which then automatically prepares accounting documents.

[0073] The reporting department can support tax return procedures based on the created bookkeeping documents. The reporting department, for example, supports tax return procedures based on the created bookkeeping documents. Tax return procedures include, for example, support for preparing tax returns, management of filing deadlines, and tax consultations. This enables accurate tax returns by supporting tax return procedures based on the created bookkeeping documents. Some or all of the above-mentioned processing in the reporting department may be performed using, or without, a generation AI, for example. For example, the reporting department can input the created bookkeeping documents into a generation AI, which then supports the tax return procedures.

[0074] The suggestion unit can suggest a method for reducing the tax burden by applying deductions or tax reduction measures. The suggestion unit, for example, suggests a method for reducing the tax burden by applying deductions or tax reduction measures. Deductions or tax reduction measures include, for example, specific deduction items, details of tax incentives, etc. This makes it possible to suggest a method for reducing the tax burden by applying specific deductions or tax reduction measures. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the suggestion unit can input information on deductions or tax reduction measures into the generation AI, which then suggests a method for reducing the tax burden.

[0075] The collection unit can automatically acquire the latest information to respond to annual tax reforms. The collection unit, for example, automatically acquires the latest information to respond to annual tax reforms. Acquisition of the latest information includes, for example, acquiring data from official government websites and using APIs. By automatically acquiring the latest information to respond to annual tax reforms, processing can always be based on the latest tax laws. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the collection unit can input the latest information into the generation AI, which then analyzes and acquires the latest information.

[0076] The proposal unit can propose a tax treatment method for a company of a certain industry or size. The proposal unit, for example, proposes the optimal tax treatment method for a company of a specific industry or size. Industry and size include, for example, manufacturing, retail, number of employees, and sales. By proposing the optimal tax treatment method for a company of a specific industry or size, more effective tax treatment becomes possible. Some or all of the above-mentioned processing in the proposal unit may be performed using, or without, a generation AI, for example. For example, the proposal unit can input information on industry and size into the generation AI, which then proposes the optimal tax treatment method.

[0077] The collection unit can estimate the user's emotions and adjust the timing of input collection based on the estimated user emotions. For example, when the user is feeling stressed, the collection unit causes the generation AI to temporarily delay input collection and attempt to collect again when the user is relaxed. For example, when the user is relaxed, the collection unit causes the generation AI to quickly collect input and acquire data efficiently. For example, when the user is in a hurry, the collection unit causes the generation AI to quickly collect input and prioritize collecting the minimum amount of data necessary. This allows data to be collected at a more appropriate time by adjusting the timing of input collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI may be 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-mentioned processing in the collection unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the collection unit can input the user's emotional data into the generation AI, and the generation AI can adjust the collection timing based on the emotion.

[0078] The collection unit can analyze the user's past accounting data and interview content and select the optimal collection method. The collection unit, for example, analyzes the user's past accounting data, and the generation AI proposes the most efficient collection method. The collection unit, for example, analyzes the user's past interview content, and the generation AI selects the optimal question format. The collection unit, for example, has the generation AI propose the optimal collection timing based on the user's past data collection history. In this way, the optimal collection method can be selected by analyzing the user's past data. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the collection unit can input the user's past data into the generation AI, and the generation AI can select the optimal collection method.

[0079] When collecting input, the collection unit can perform filtering based on the user's current work situation and areas of interest. For example, the collection unit analyzes the user's current work situation and allows the generation AI to collect only relevant data. For example, the collection unit prioritizes collecting data required by the generation AI based on the user's areas of interest. For example, the collection unit considers the user's work schedule and allows the generation AI to collect data at the optimal timing. This allows highly relevant data to be collected by filtering data based on the user's current work situation and areas of interest. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, the generation AI. For example, the collection unit can input data on the user's work situation and areas of interest to the generation AI, and the generation AI can perform filtering.

[0080] When collecting input, the collection unit can select the optimal collection means depending on the user's input method. For example, if the user prefers voice input, the collection unit causes the generation AI to preferentially collect voice data. For example, if the user prefers text input, the collection unit causes the generation AI to preferentially collect text data. For example, if the user prefers image input, the collection unit causes the generation AI to preferentially collect image data. This allows data to be collected efficiently by selecting the optimal collection means depending on the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, the generation AI. For example, the collection unit can input the user's input data into the generation AI, which then selects the optimal collection means.

[0081] The collection unit can estimate the user's emotions and determine the priority of inputs to be collected based on the estimated user emotions. For example, when the user is stressed, the collection unit causes the generation AI to postpone less important inputs. For example, when the user is relaxed, the collection unit causes the generation AI to prioritize collecting more important inputs. For example, when the user is in a hurry, the collection unit causes the generation AI to quickly collect the most important inputs. This allows important data to be collected preferentially by determining the priority of inputs according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the collection unit can input the user's emotion data into the generation AI, and the generation AI can determine the priority of inputs based on the emotions.

[0082] When collecting input, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, when the user is in a specific area, the collection unit prioritizes collecting data related to that area. For example, when the user is moving, the collection unit collects relevant data based on the user's current location. For example, when the user is in a specific location, the collection unit prioritizes collecting data related to that location. In this way, highly relevant data can be collected preferentially by taking the user's geographical location information into account. Some or all of the above-described processing in the collection unit may be performed using, or without, the generation AI. For example, the collection unit can input the user's geographical location information to the generation AI, which can then prioritize collecting highly relevant data.

[0083] The collection unit can analyze the user's social media activities and collect related data when collecting input. For example, the collection unit analyzes the content of the user's social media posts and collects related data. For example, the collection unit collects related data based on the user's social media check-in information. For example, the collection unit collects related data by referring to the activities of the user's friends on social media. In this way, related data can be collected by analyzing the user's social media activities. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, the generation AI, for example. For example, the collection unit can input the user's social media data into the generation AI, which then collects the related data.

[0084] When collecting input, the collection unit can customize the collection method by reflecting the user's past feedback. In the collection unit, for example, the generation AI proposes an optimal collection method based on the user's past feedback. In the collection unit, for example, the generation AI adjusts the collection means by reflecting the user's past feedback. In the collection unit, for example, the generation AI optimizes the collection timing by referring to the user's past feedback. In this way, the optimal collection method can be customized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using the generation AI, for example, or may be performed without using the generation AI. For example, the collection unit can input the user's past feedback into the generation AI, and the generation AI can customize the collection method.

[0085] The creation unit can estimate the user's emotions and adjust the presentation of the accounting documents based on the estimated user emotions. For example, if the user is feeling stressed, the creation unit causes the generation AI to create simple, highly visible accounting documents. For example, if the user is relaxed, the creation unit causes the generation AI to create accounting documents containing detailed information. For example, if the user is in a hurry, the creation unit causes the generation AI to create concise accounting documents that focus on the main points. This allows for adjusting the presentation of accounting documents according to the user's emotions, thereby creating more appropriate accounting documents. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be 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-described processing in the creation unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the creation unit can input the user's emotion data into the generation AI, and the generation AI can adjust the presentation of accounting documents based on the emotion.

[0086] The creation unit can adjust the level of detail based on the importance of the data when creating the ledger documents. For example, the creation unit causes the generation AI to create ledger documents including detailed explanations for data with high importance. For example, the creation unit causes the generation AI to create ledger documents including concise explanations for data with low importance. For example, the creation unit causes the generation AI to adjust the level of detail of the ledger documents according to the importance of the data. As a result, important data can be described in detail by adjusting the level of detail of the ledger documents based on the importance of the data. Some or all of the above-mentioned processing in the creation unit may be performed using, or without, the generation AI. For example, the creation unit can input the importance of the data to the generation AI, and the generation AI can adjust the level of detail.

[0087] The creation unit can apply different creation algorithms depending on the tax law category when creating accounting documents. For example, when creating accounting documents for corporate tax, the creation unit causes the generation AI to apply an algorithm dedicated to corporate tax. For example, when creating accounting documents for income tax, the creation unit causes the generation AI to apply an algorithm dedicated to income tax. For example, when creating accounting documents for consumption tax, the creation unit causes the generation AI to apply an algorithm dedicated to consumption tax. In this way, by applying different creation algorithms depending on the tax law category, accounting documents optimal for each tax law can be created. Some or all of the above-mentioned processing in the creation unit may be performed using, or without, the generation AI. For example, the creation unit can input the tax law category into the generation AI, and the generation AI can apply the optimal creation algorithm.

[0088] When creating accounting documents, the creation unit can improve accuracy by referring to the user's past accounting documents. The creation unit, for example, analyzes the user's past accounting documents, and the generation AI creates highly accurate accounting documents. The creation unit, for example, refers to the user's past accounting documents, and the generation AI selects the optimal format. The creation unit, for example, uses the user's past accounting documents as a reference, and the generation AI improves the accuracy of the accounting documents. In this way, the accuracy of the accounting documents can be improved by referring to the user's past accounting documents. Some or all of the above-mentioned processing in the creation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the creation unit inputs the user's past accounting documents into the generation AI, and the generation AI improves the accuracy of the accounting documents.

[0089] The creation unit can estimate the user's emotions and adjust the length of the ledger document based on the estimated user's emotions. For example, if the user is feeling stressed, the creation unit causes the generation AI to create a short, concise ledger document. For example, if the user is relaxed, the creation unit causes the generation AI to create a longer ledger document with detailed explanations. For example, if the user is in a hurry, the creation unit causes the generation AI to create a concise, short ledger document. This allows the creation of more appropriate ledger documents by adjusting the length of the ledger document according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be 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-described processing in the creation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the creation unit can input the user's emotion data into the generation AI, and the generation AI can adjust the length of the ledger document based on the emotion.

[0090] The creation unit can determine priorities based on the time of data submission when creating ledger documents. For example, the creation unit causes the generation AI to prioritize creating ledger documents for data with an upcoming submission deadline. For example, the creation unit causes the generation AI to postpone creating ledger documents for data with a distant submission deadline. For example, the creation unit causes the generation AI to adjust the order in which ledger documents are created depending on the time of data submission. In this way, by determining priorities based on the time of data submission, ledger documents can be created in time for the submission deadline. Some or all of the above-mentioned processing in the creation unit may be performed using, or without, the generation AI. For example, the creation unit can input the time of data submission to the generation AI, and the generation AI can determine the priorities.

[0091] The creation unit can adjust the order based on the relevance of the data when creating the ledger documents. For example, the creation unit prioritizes highly relevant data when writing the ledger documents. For example, the creation unit puts less relevant data on hold when writing the ledger documents. For example, the creation unit allows the generation AI to adjust the order of the ledger documents according to the relevance of the data. In this way, by adjusting the order based on the relevance of the data, highly relevant data can be written on priority. Some or all of the above-mentioned processing in the creation unit may be performed using the generation AI, for example, or may be performed without using the generation AI. For example, the creation unit can input the relevance of the data into the generation AI, and the generation AI can adjust the order.

[0092] When creating accounting documents, the creation unit can adjust the use of technical terminology according to the user's level of expertise. For example, if the user has technical expertise, the creation unit causes the generation AI to create accounting documents that use a lot of technical terminology. For example, if the user does not have technical expertise, the creation unit causes the generation AI to create accounting documents that avoid technical terminology. For example, the creation unit causes the generation AI to adjust the use of technical terminology according to the user's level of expertise. This makes it possible to create accounting documents that are easy to understand by adjusting the use of technical terminology according to the user's level of expertise. Some or all of the above-mentioned processing in the creation unit may be performed using the generation AI, for example, or may be performed without using the generation AI. For example, the creation unit can input the user's level of expertise into the generation AI, and the generation AI can adjust the use of technical terminology.

[0093] The reporting unit can estimate the user's emotions and adjust the reporting procedure method based on the estimated user emotions. For example, if the user is feeling stressed, the reporting unit causes the generation AI to propose a simple and quick reporting procedure. For example, if the user is relaxed, the reporting unit causes the generation AI to propose a reporting procedure with detailed explanations. For example, if the user is in a hurry, the reporting unit causes the generation AI to propose a reporting procedure that can be completed in the shortest time. This allows the reporting procedure method to be adjusted according to the user's emotions, resulting in a more appropriate reporting procedure. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be 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-mentioned processing in the reporting unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the reporting unit can input the user's emotion data into the generation AI, and the generation AI can adjust the reporting procedure method based on the emotion.

[0094] During the reporting procedure, the reporting unit can select the optimal procedure by referring to past reporting data. For example, the reporting unit analyzes the user's past reporting data, and the generation AI proposes the optimal reporting procedure. For example, the reporting unit causes the generation AI to select the most efficient reporting procedure based on the user's past reporting data. For example, the reporting unit causes the generation AI to optimize the reporting procedure by referring to the user's past reporting data. In this way, the optimal reporting procedure can be selected by referring to the past reporting data. Some or all of the above-mentioned processing in the reporting unit may be performed using, or without, the generation AI. For example, the reporting unit can input past reporting data into the generation AI, and the generation AI can select the optimal procedure.

[0095] The declaration unit can customize the declaration procedure based on the user's current work situation during the declaration procedure. For example, the declaration unit analyzes the user's current work situation, and the generation AI proposes the optimal declaration procedure. For example, the declaration unit considers the user's work schedule, and the generation AI efficiently performs the declaration procedure. For example, the declaration unit customizes the declaration procedure according to the user's work content. In this way, by customizing the procedure based on the user's current work situation, an efficient declaration procedure can be performed. Some or all of the above-mentioned processing in the declaration unit may be performed using, or without, the generation AI. For example, the declaration unit can input the user's work situation into the generation AI, and the generation AI can customize the procedure.

[0096] The reporting unit can improve the reporting procedure by reflecting user feedback. In the reporting unit, for example, the generation AI improves the reporting procedure based on the user's past feedback. In the reporting unit, for example, the generation AI improves the efficiency of the reporting procedure by reflecting user feedback. In the reporting unit, for example, the generation AI optimizes the reporting procedure by referring to user feedback. In this way, the reporting procedure can be improved by reflecting user feedback. Some or all of the above-mentioned processing in the reporting unit may be performed using the generation AI, for example, or may be performed without using the generation AI. For example, the reporting unit can input user feedback into the generation AI, which can improve the procedure.

[0097] The reporting unit can estimate the user's emotions and determine the priority of reporting procedures based on the estimated user emotions. For example, if the user is feeling stressed, the reporting unit causes the generation AI to postpone less important reporting procedures. For example, if the user is relaxed, the reporting unit causes the generation AI to prioritize more important reporting procedures. For example, if the user is in a hurry, the reporting unit causes the generation AI to quickly perform the most important reporting procedures. This allows important procedures to be prioritized by determining the priority of reporting procedures according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be 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-mentioned processing in the reporting unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the reporting unit can input the user's emotion data into the generation AI, and the generation AI can determine the priority of reporting procedures based on the emotion.

[0098] The reporting unit can select the optimal reporting procedure by taking into account the user's geographical location information when performing the reporting procedure. For example, if the user is in a specific area, the reporting unit prioritizes reporting procedures related to that area. For example, if the user is traveling, the reporting unit selects the optimal reporting procedure based on the user's current location. For example, if the user is in a specific location, the reporting unit prioritizes reporting procedures related to that location. This allows the optimal reporting procedure to be selected by taking into account the user's geographical location information. Some or all of the above-mentioned processing in the reporting unit may be performed using, or without, a generation AI. For example, the reporting unit can input the user's geographical location information into the generation AI, which can then select the optimal procedure.

[0099] The reporting unit can analyze the user's social media activity and suggest procedures during the reporting procedure. For example, the reporting unit analyzes the user's social media posts and suggests relevant reporting procedures. For example, the reporting unit suggests relevant reporting procedures based on the user's social media check-in information. For example, the reporting unit suggests relevant reporting procedures based on the activity of the user's friends on social media. In this way, relevant reporting procedures can be suggested by analyzing the user's social media activity. Some or all of the above-mentioned processing in the reporting unit may be performed using, or without, a generation AI. For example, the reporting unit can input the user's social media data into a generation AI, which then suggests relevant reporting procedures.

[0100] The declaration unit can customize the declaration procedure by reflecting the user's past feedback. In the declaration unit, for example, the generation AI proposes the optimal declaration procedure based on the user's past feedback. In the declaration unit, for example, the generation AI customizes the declaration procedure by reflecting the user's past feedback. In the declaration unit, for example, the generation AI optimizes the declaration procedure by referring to the user's past feedback. In this way, the optimal declaration procedure can be customized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the declaration unit may be performed using the generation AI, for example, or may be performed without using the generation AI. For example, the declaration unit can input the user's past feedback into the generation AI, which can customize the procedure.

[0101] The suggestion unit can estimate the user's emotions and adjust the way the tax saving plan is presented based on the estimated user's emotions. For example, if the user is feeling stressed, the suggestion unit causes the generation AI to propose simple, highly visible tax saving plans. For example, if the user is relaxed, the suggestion unit causes the generation AI to propose tax saving plans with detailed explanations. For example, if the user is in a hurry, the suggestion unit causes the generation AI to propose concise tax saving plans that focus on the main points. This allows the suggestion unit to propose more appropriate tax saving plans by adjusting the way the tax saving plan is presented based on the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be 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-described processing in the suggestion unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the suggestion unit can input the user's emotion data into the generation AI, which can then adjust the way the tax saving plan is presented based on the emotion.

[0102] When proposing a tax saving plan, the suggestion unit can adjust the level of detail based on the importance of the tax law. For example, in the suggestion unit, the generation AI proposes a tax saving plan including a detailed explanation for a tax law with high importance. For example, in the suggestion unit, the generation AI proposes a tax saving plan including a concise explanation for a tax law with low importance. For example, in the suggestion unit, the generation AI adjusts the level of detail of the tax saving plan according to the importance of the tax law. In this way, by adjusting the level of detail based on the importance of the tax law, detailed tax saving plans for important tax laws can be proposed. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, the generation AI. For example, the suggestion unit can input the importance of the tax law to the generation AI, and the generation AI can adjust the level of detail.

[0103] When proposing a tax saving plan, the proposal unit can apply different proposal algorithms depending on the category of tax law. For example, when proposing a tax saving plan for corporate tax, the proposal unit causes the generation AI to apply an algorithm dedicated to corporate tax. For example, when proposing a tax saving plan for income tax, the proposal unit causes the generation AI to apply an algorithm dedicated to income tax. For example, when proposing a tax saving plan for consumption tax, the proposal unit causes the generation AI to apply an algorithm dedicated to consumption tax. In this way, by applying different proposal algorithms depending on the category of tax law, it is possible to propose tax saving plans that are optimal for each tax law. Some or all of the above-mentioned processing in the proposal unit may be performed using, or without, the generation AI. For example, the proposal unit can input the category of tax law into the generation AI, which then applies the optimal proposal algorithm.

[0104] When proposing tax saving plans, the suggestion unit can improve accuracy by referring to the user's past suggestion results. The suggestion unit, for example, analyzes the user's past suggestion results, and the generation AI proposes highly accurate tax saving plans. The suggestion unit, for example, refers to the user's past suggestion results, and the generation AI selects the optimal tax saving plan. The suggestion unit, for example, causes the generation AI to improve the accuracy of the tax saving plans based on the user's past suggestion results. In this way, the accuracy of the tax saving plans can be improved by referring to the user's past suggestion results. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the suggestion unit can input the user's past suggestion results into the generation AI, and the generation AI can improve the accuracy.

[0105] The suggestion unit can estimate the user's emotions and adjust the length of the tax saving suggestions based on the estimated user emotions. For example, if the user is feeling stressed, the suggestion unit causes the generation AI to suggest short, concise tax saving suggestions. For example, if the user is relaxed, the suggestion unit causes the generation AI to suggest longer tax saving suggestions with detailed explanations. For example, if the user is in a hurry, the suggestion unit causes the generation AI to suggest concise, short tax saving suggestions. This allows the length of the tax saving suggestions to be adjusted according to the user's emotions, thereby proposing more appropriate tax saving suggestions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be 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-mentioned processing in the suggestion unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the suggestion unit can input the user's emotion data into the generation AI, which can then adjust the length of the tax saving suggestions based on the emotion.

[0106] When proposing tax saving plans, the proposal unit can determine the priority based on the timing of tax law revisions. For example, if the tax law revision is imminent, the proposal unit causes the generation AI to prioritize proposing tax saving plans. For example, if the tax law revision is far away, the proposal unit causes the generation AI to postpone proposing tax saving plans. For example, in the proposal unit, the generation AI adjusts the order in which tax saving plans are proposed depending on the timing of tax law revisions. In this way, by determining the priority based on the timing of tax law revisions, tax saving plans that correspond to important revisions can be preferentially proposed. Some or all of the above-mentioned processing in the proposal unit may be performed using, or without, the generation AI. For example, the proposal unit can input the timing of tax law revisions to the generation AI, and the generation AI can determine the priority.

[0107] When proposing tax saving plans, the proposal unit can adjust the order based on the relevance of tax laws. For example, the proposal unit prioritizes incorporating highly relevant tax laws into the tax saving plans. For example, the proposal unit prioritizes incorporating less relevant tax laws into the tax saving plans. For example, the proposal unit allows the generation AI to adjust the order of the tax saving plans according to the relevance of the tax laws. In this way, by adjusting the order based on the relevance of the tax laws, tax saving plans based on highly relevant tax laws can be proposed preferentially. Some or all of the above-mentioned processing in the proposal unit may be performed using, or without, the generation AI. For example, the proposal unit can input the relevance of tax laws into the generation AI, which can then adjust the order.

[0108] When proposing a tax saving plan, the suggestion unit can adjust the use of technical terminology according to the user's level of expertise. For example, if the user has technical expertise, the suggestion unit causes the generation AI to propose a tax saving plan that uses a lot of technical terminology. For example, if the user does not have technical expertise, the suggestion unit causes the generation AI to propose a tax saving plan that avoids technical terminology. For example, the suggestion unit causes the generation AI to adjust the use of technical terminology according to the user's level of expertise. This makes it possible to propose tax saving plans that are easy to understand by adjusting the use of technical terminology according to the user's level of expertise. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, the generation AI. For example, the suggestion unit can input the user's level of expertise into the generation AI, and the generation AI can adjust the use of technical terminology. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, creation unit, declaration unit, and suggestion unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects accounting data and interview contents using the camera 42 and microphone 38B of the smart device 14, and the collected data is analyzed by the specific processing unit 290 of the data processing device 12. The creation unit creates accounting documents based on the data collected by the specific processing unit 290 of the data processing device 12. The declaration unit supports tax reporting based on the accounting documents created by the creation unit, and is realized by the specific processing unit 290 of the data processing device 12. The suggestion unit proposes legal tax-saving ideas by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, creation unit, declaration unit, and suggestion unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects accounting data and interview contents using the camera 42 and microphone 238 of the smart glasses 214, and the collected data is analyzed by the specific processing unit 290 of the data processing device 12. The creation unit creates accounting documents based on the data collected by the specific processing unit 290 of the data processing device 12. The declaration unit supports tax reporting based on the accounting documents created by the creation unit, and is realized by the specific processing unit 290 of the data processing device 12. The suggestion unit proposes legal tax-saving ideas by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, creation unit, declaration unit, and suggestion unit described above is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit collects accounting data and interview contents using the camera 42 and microphone 238 of the headset terminal 314, and the collected data is analyzed by the specific processing unit 290 of the data processing device 12. The creation unit creates accounting documents based on the data collected by the specific processing unit 290 of the data processing device 12. The declaration unit supports tax reporting based on the accounting documents created by the creation unit, and is realized by the specific processing unit 290 of the data processing device 12. The suggestion unit proposes legal tax-saving ideas by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, creation unit, declaration unit, and suggestion unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects accounting data and interview contents using the camera 42 and microphone 238 of the robot 414, and the collected data is analyzed by the specific processing unit 290 of the data processing device 12. The creation unit creates accounting documents based on the data collected by the specific processing unit 290 of the data processing device 12. The declaration unit supports tax reporting based on the accounting documents created by the creation unit, and is realized by the specific processing unit 290 of the data processing device 12. The suggestion unit proposes legal tax-saving ideas by the specific processing unit 290 of the data processing device 12.

[0109] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0110] The collection unit can analyze the user's past purchase history and automatically extract data related to tax returns. For example, the collection unit analyzes the user's past purchase history and automatically extracts items that can be declared as expenses. For example, the collection unit extracts data for a specific period from the user's purchase history and organizes the information necessary for tax returns. For example, the collection unit automatically classifies expenses into specific categories based on the user's purchase history and provides data useful for tax returns. This makes it possible to efficiently collect data necessary for tax returns by utilizing the user's past purchase history.

[0111] The creation unit can estimate the user's emotions and adjust the format of the ledger documents based on the estimated user emotions. For example, if the user is feeling stressed, the generation AI selects a simple, highly visible format. For example, if the user is relaxed, the creation unit selects a format including detailed information. For example, if the user is in a hurry, the creation unit selects a concise format that focuses on the main points. In this way, by adjusting the format of the ledger documents according to the user's emotions, more appropriate ledger documents can be created.

[0112] The reporting unit can estimate the user's emotions and adjust the support method for the reporting procedure based on the estimated user emotions. For example, if the user is feeling stressed, the generation AI provides a simple and quick support method. For example, if the user is feeling relaxed, the reporting unit can provide a support method with detailed explanations. For example, if the user is in a hurry, the reporting unit can provide a support method that can be completed in the shortest time. This allows for more appropriate support to be provided by adjusting the support method for the reporting procedure according to the user's emotions.

[0113] The suggestion unit can estimate the user's emotions and adjust the method of suggesting tax-saving suggestions based on the estimated user emotions. For example, if the user is feeling stressed, the generation AI will suggest simple, highly visible tax-saving suggestions. For example, if the user is relaxed, the suggestion unit will suggest tax-saving suggestions that include detailed explanations. For example, if the user is in a hurry, the suggestion unit will suggest concise tax-saving suggestions that focus on the main points. In this way, by adjusting the method of suggesting tax-saving suggestions according to the user's emotions, more appropriate tax-saving suggestions can be suggested.

[0114] The collection unit can estimate the user's emotions and adjust the input collection method based on the estimated user emotions. For example, if the user is feeling stressed, the generation AI temporarily delays input collection and attempts to collect again when the user is relaxed. For example, if the user is relaxed, the collection unit can quickly collect input and acquire data efficiently. For example, if the user is in a hurry, the collection unit can quickly collect input and prioritize collecting the minimum amount of data necessary. This allows the input collection method to be adjusted according to the user's emotions, making it possible to collect data at more appropriate times.

[0115] The collection unit can customize the collection method based on the user's past feedback. For example, the collection unit analyzes the user's past feedback, and the generation AI proposes the optimal collection method. For example, the collection unit reflects the user's past feedback, and the generation AI adjusts the collection means. For example, the collection unit refers to the user's past feedback, and the generation AI optimizes the collection timing. In this way, the optimal collection method can be customized by reflecting the user's past feedback.

[0116] When creating ledger documents, the creation unit can adjust the level of detail based on the importance of the data. For example, for data with high importance, the generation AI creates ledger documents including detailed explanations. For example, for data with low importance, the creation unit creates ledger documents including concise explanations. For example, for data with low importance, the creation unit has the generation AI adjust the level of detail of the ledger documents according to the importance of the data. In this way, important data can be described in detail by adjusting the level of detail of the ledger documents based on the importance of the data.

[0117] The declaration unit can customize the declaration procedure based on the user's current work situation during the declaration procedure. For example, the declaration unit analyzes the user's current work situation, and the generation AI proposes the optimal declaration procedure. For example, the declaration unit considers the user's work schedule, and the generation AI performs the declaration procedure efficiently. For example, the declaration unit customizes the declaration procedure according to the user's work content. In this way, by customizing the procedure based on the user's current work situation, an efficient declaration procedure can be performed.

[0118] When proposing tax saving plans, the proposal unit can apply different proposal algorithms depending on the tax law category. For example, when the proposal unit proposes tax saving plans for corporate tax, the generation AI applies an algorithm dedicated to corporate tax. For example, when the proposal unit proposes tax saving plans for income tax, the generation AI applies an algorithm dedicated to income tax. For example, when the proposal unit proposes tax saving plans for consumption tax, the generation AI applies an algorithm dedicated to consumption tax. In this way, by applying different proposal algorithms depending on the tax law category, it is possible to propose tax saving plans that are optimal for each tax law.

[0119] When proposing tax saving plans, the proposal unit can improve accuracy by referring to the user's past proposal results. For example, the proposal unit analyzes the user's past proposal results, and the generation AI proposes highly accurate tax saving plans. For example, the proposal unit refers to the user's past proposal results, and the generation AI selects the optimal tax saving plan. For example, the proposal unit refers to the user's past proposal results, and the generation AI improves the accuracy of the tax saving plans based on the user's past proposal results. In this way, the accuracy of the tax saving plans can be improved by referring to the user's past proposal results.

[0120] The processing flow of the second embodiment will be briefly explained below.

[0121] Step 1: The collection department collects inputs about the situation of each person and each company through ledgers or interviews. The collection department collects inputs in the form of electronic ledgers, paper ledgers, interviews, etc. Step 2: The creation unit analyzes the input collected by the collection unit and creates accounting documents according to the situation. For example, the creation unit automatically creates corporate tax returns, income tax returns, and consumption tax returns based on the collected data. Generative AI can also be used to create accounting documents based on the collected data. Step 3: The declaration department supports tax returns based on the accounting documents created by the preparation department. The declaration department, for example, supports tax return procedures based on the created accounting documents. Generative AI can also be used to support tax return procedures. Step 4: The proposal department proposes legal tax saving ideas based on the tax return supported by the declaration department. The proposal department suggests ways to reduce the tax burden, for example, by applying certain deductions or tax reduction measures. Generative AI can also be used to propose legal tax saving ideas.

[0122] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0123] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0124] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0125] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0126] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0127] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0128] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0129] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0130] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0131] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0132] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0133] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0134] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0135] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0136] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0137] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0138] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0139] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0140] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0141] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0142] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0143] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0144] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0145] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0146] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0147] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0148] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0149] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0150] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0151] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0152] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0153] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0154] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0155] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0156] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0157] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0158] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0159] 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.

[0160] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0161] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0162] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0163] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0164] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0165] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0166] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0167] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0168] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0169] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0170] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0171] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0172] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0173] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0174] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0175] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0176] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0177] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0178] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0179] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0180] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0181] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0182] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0183] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0184] 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.

[0185] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0186] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0187] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0188] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0189] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0190] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0191] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0192] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0193] [Explanation of symbols]

[0194] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A collection department collects input on the status of each person and company through accounting books or interviews; a creation unit that analyzes the input collected by the collection unit and creates accounting documents according to the situation; a reporting unit that supports tax returns based on the accounting documents prepared by the preparation unit; a suggestion unit that proposes legal tax-saving measures based on the tax return supported by the tax return filing unit; Equipped with A system characterized by:

2. The collecting unit Collect accounting data or interview details 2. The system of claim 1.

3. The creation unit Based on the collected data, corporate tax returns, income tax returns, and consumption tax returns are automatically prepared.

2. The system of claim 1.

4. The reporting unit Assist with tax return procedures based on the prepared accounting documents 2. The system of claim 1.

5. The proposal unit Suggest ways to reduce your tax burden by applying for deductions or tax credits 2. The system of claim 1.

6. The collecting unit Automatically receive the latest information to keep up with annual tax changes 2. The system of claim 1.

7. The proposal unit Proposing tax treatment methods for companies of any industry or size 2. The system of claim 1.

8. The collecting unit Estimate the user's emotions and adjust the timing of input collection based on the estimated user emotions.

2. The system of claim 1.

9. The collecting unit Analyze the user's past accounting data and interview details to select the optimal collection method 2. The system of claim 1.

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