System

Abstract

An object of a system according to an embodiment is to simplify a procedure of a final tax return and reduce a burden on a user.SOLUTION: A system includes an income inputting part, a generation AI, an expense calculating part, a document generating part, and a question responding part. The income input unit inputs income information of the user. The generating AI parses the revenue information into the appropriate revenue items. An expense calculation part inputs expense information of a user. The generating AI analyzes the expense information and categorizes it into appropriate expense items. The document generation unit automatically generates a necessary document. The production AI formats the paper documents produced by the paper document production component for submission. The question handling unit answers the question from the user in real time. The generation AI parses the question and provides an appropriate answer.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, the tax return procedure was complicated, making it difficult for freelancers and business owners to manage their income and expenses.

[0005] The system according to the embodiment aims to simplify the tax return filing procedure and reduce the burden on users. [Means for solving the problem]

[0006] The system according to the embodiment comprises an income input unit, a generation AI, an expense calculation unit, a document generation unit, and a question response unit. The income input unit inputs the user's income information. The generation AI analyzes the income information and classifies it into appropriate income items. The expense calculation unit inputs the user's expense information. The generation AI analyzes the expense information and classifies it into appropriate expense items. The document generation unit automatically generates the necessary documents. The generation AI formats the documents generated by the document generation unit into a submission format. The question response unit answers questions from the user in real time. The generation AI analyzes the questions and provides appropriate answers. [Effects of the Invention]

[0007] The system according to the embodiment can simplify the tax return filing procedure and reduce the burden on users. [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) The tax return support system according to an embodiment of the present invention is a system in which a user inputs income and expense information, a generation AI analyzes and classifies the information into appropriate categories, automatically generates necessary documents, and answers questions in real time. As a result, the tax return support system enables efficient and simple tax return procedures.

[0029] The tax return support system according to the embodiment includes an income input unit, an expense calculation unit, a document generation unit, and a question response unit. The income input unit inputs a user's income information. For example, if a user inputs, "This year, I earned 5 million yen from freelance work and 1 million yen from part-time work," the generation AI analyzes the information and categorizes it into the appropriate income category. Furthermore, when a user inputs income information, the income input unit helps the generation AI analyze the information and input it in the appropriate format. For example, if a user inputs, "This year, I earned 5 million yen from freelance work and 1 million yen from part-time work," the generation AI analyzes the information and categorizes it into the appropriate income category. The expense calculation unit inputs a user's expense information. For example, if a user inputs, "This year, I earned 100,000 yen from freelance work and 1 million yen from part-time work," the generation AI analyzes the information and categorizes it into the appropriate expense category. Furthermore, when a user inputs expense information, the generation AI helps the generation AI analyze the information and categorize it into the appropriate expense category. For example, if a user inputs "100,000 yen per year for transportation expenses and 50,000 yen for office supplies expenses," the generation AI analyzes this and classifies it into the appropriate expense category. The document generation unit automatically generates the necessary documents. For example, the generation AI automatically generates the necessary documents based on the user's input information and formats them for submission. The document generation unit also helps the generation AI automatically generate the necessary documents based on the user's input information and formats them for submission. For example, the generation AI automatically generates the necessary documents based on the user's input information and formats them for submission. The question response unit answers questions from the user in real time. For example, if a user asks, "In which category should I classify this expense?", the generation AI analyzes the question and suggests the appropriate expense category. The question response unit also helps the generation AI analyze the user's question and provide the appropriate answer. For example, if a user asks, "In which category should I classify this expense?", the generation AI analyzes the question and suggests the appropriate expense category. As a result, the tax return support system according to the embodiment enables tax return procedures to be carried out efficiently and simply.

[0030] The income input unit can analyze a user's past income data, make income predictions, and suggest tax strategies. For example, the generation AI in the income input unit analyzes a user's income data over the past few years to identify patterns of income increase and decrease. Based on this, the unit predicts future income and suggests appropriate tax strategies. For example, if income is on the rise, the unit suggests specific deductions as tax-saving strategies. The income input unit also analyzes a user's past income data to identify seasonal fluctuations in income. For example, if freelance work is divided into busy and slow seasons, the unit suggests appropriate tax strategies. The generation AI in the income input unit also predicts future income based on the user's past income data and suggests appropriate investment and savings plans as tax strategies. For example, if income is stable, the unit recommends long-term investments. This allows the unit to predict future income and suggest appropriate tax strategies.

[0031] The income input unit can analyze income information, identify seasonal fluctuations and trends, and suggest the timing for filing a tax return. For example, the generation AI in the income input unit analyzes the user's income data and identifies seasonal fluctuations in income. For example, if income is concentrated in a specific month, the income input unit suggests the timing for filing a tax return that matches that month. The income input unit also analyzes the user's income data and identifies income trends. For example, if income is increasing year by year, the income input unit suggests the optimal timing for filing a tax return based on that trend. The generation AI in the income input unit also identifies seasonal fluctuations and trends in income based on the user's income data, and suggests the optimal timing for filing a tax return as a tax measure. For example, filing a tax return at a time when income is decreasing can reduce the tax burden. This makes it possible to suggest the optimal timing for filing a tax return based on the seasonal fluctuations and trends of income.

[0032] The expense calculation unit can analyze the user's past expense data, make expense forecasts, and propose expense reduction measures. For example, the generation AI in the expense calculation unit analyzes the user's expense data from the past few years and identifies patterns of expense increases and decreases. Based on this, it predicts future expenses and proposes optimal expense reduction measures. For example, it can propose reductions in specific expense items. The expense calculation unit also analyzes the user's past expense data and identifies seasonal fluctuations in expenses. For example, if expenses are concentrated in a specific month, it can propose expense reduction measures tailored to that month. The expense calculation unit also uses the generation AI to predict future expenses based on the user's past expense data and propose appropriate investments and savings plans as expense reduction measures. For example, it can recommend savings measures tailored to periods when expenses are increasing. This makes it possible to predict future expenses and propose optimal expense reduction measures.

[0033] The expense calculation unit can analyze expense information, identify seasonal fluctuations and trends, and propose expense management methods. For example, the generation AI in the expense calculation unit analyzes the user's expense data and identifies seasonal fluctuations in expenses. For example, if expenses are concentrated in a specific month, the expense calculation unit proposes an expense management method tailored to that month. The expense calculation unit also analyzes the user's expense data and identifies expense trends. For example, if expenses are increasing year by year, the expense calculation unit proposes the optimal expense management method based on that trend. The generation AI in the expense calculation unit also identifies seasonal fluctuations and trends in expenses based on the user's expense data, and proposes appropriate savings measures and investment plans as expense management methods. For example, it recommends a management method tailored to periods when expenses decrease. This makes it possible to propose the optimal expense management method based on seasonal fluctuations and trends in expenses.

[0034] The document generation unit can analyze the user's past tax return data and improve the accuracy of document generation. For example, the document generation unit uses a generation AI to analyze the user's tax return data from the past few years and improve the accuracy of automatically generating required documents. For example, the document generation unit automatically completes required document items based on the content of past tax returns. The document generation unit also analyzes the user's past tax return data and identifies patterns of documents required for tax returns. For example, it automatically generates documents corresponding to specific income sources or expense items. The document generation unit also builds a system in which the generation AI uses the user's past tax return data to improve the accuracy of automatically generating required documents. For example, it automatically adjusts the document format by referring to the content of past tax returns. This allows the document generation unit to analyze past tax return data and improve the accuracy of automatically generating documents.

[0035] The document generation unit can be equipped with a function that analyzes input information and automatically reminds users of deadlines for submission. For example, the document generation unit adds a function in which a generation AI analyzes information input by the user and automatically reminds users of deadlines for document submission. For example, it sends a notification when the deadline approaches. The document generation unit also identifies deadlines for document submission based on information input by the user and provides a reminder function. For example, it automatically adds deadlines to a calendar. The document generation unit also builds a system in which a generation AI analyzes information input by the user and reminds users of deadlines for document submission. For example, it sends reminders by email or app notifications when the deadline approaches. This allows users to automatically be reminded of deadlines for document submission.

[0036] The question response unit can analyze a user's past question history and learn to provide answers. For example, the question response unit uses a generation AI to analyze a user's past question history and identify question patterns. Based on this, the question response unit learns to provide more appropriate answers. For example, answers to similar questions are automatically generated based on the content of past questions. The question response unit also analyzes a user's past question history and identifies question trends. For example, if there are many questions about a particular topic, the question response unit strengthens answers related to that topic. The question response unit also builds a system in which the generation AI learns to provide more appropriate answers based on the user's past question history. For example, it refers to the content of past questions to improve the accuracy of answers. This allows the generation AI to analyze past question history and learn to provide more appropriate answers.

[0037] The question response unit can analyze the content of the question and automatically refer to laws, regulations, and guidelines to provide an answer. For example, the question response unit uses a generation AI to analyze the content of a user's question and automatically refer to relevant laws, regulations, and guidelines to provide an answer. For example, a question about tax law is answered by citing the relevant laws and regulations. The question response unit also identifies relevant laws and guidelines based on the content of the user's question and reflects them in the answer. For example, a question about a specific expense item is answered by referring to the relevant guidelines. The question response unit also builds a system in which the generation AI analyzes the content of a user's question and automatically refer to relevant laws, regulations, and guidelines to provide an answer. For example, the accuracy of the answer is improved by referring to a database of laws and regulations. This makes it possible to analyze the content of the question and provide an answer by referring to relevant laws, regulations, and guidelines.

[0038] The question response unit can integrate the content of a question with question data of other users and provide the optimal answer to a common question. For example, the question response unit uses a generation AI to integrate the content of a user's question with question data of other users and provide the optimal answer to a common question. For example, if the same question is asked multiple times, the question response unit automatically generates the optimal answer to that question. The question response unit also compares the content of a user's question with question data of other users and identifies answers to common questions. For example, it provides the optimal answer based on past question data. The question response unit also builds a system in which the generation AI integrates the content of a user's question with question data of other users and provides the optimal answer to a common question. For example, it references a question database to improve the accuracy of the answer. This makes it possible to provide the optimal answer to a common question.

[0039] The question response unit can analyze the content of the question, perform a comparative analysis with other users, and provide a benchmark for the question. In the question response unit, for example, the generation AI analyzes the content of the user's question and performs a comparative analysis with other users in the same industry or occupation. For example, a benchmark is provided by comparing it with question data from other companies in the same industry. The question response unit also analyzes the content of the user's question and identifies differences in questions from other users. For example, it analyzes differences in questions from users in the same region or age group and provides a benchmark. The question response unit also builds a system in which the generation AI performs a comparative analysis with other users based on the content of the user's question and provides a benchmark for the question. For example, a benchmark is provided based on the median or average of the questions. This makes it possible to provide a benchmark for the question.

[0040] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0041] The tax return support system can also suggest tax deductions according to the type of income when the user enters their income information. For example, it can suggest specific expense deductions for freelance income and salary income deductions for part-time income. By suggesting tax deductions according to the type of income, it can also help the user take optimal tax measures. This allows the user to receive the optimal tax deductions according to the type of income.

[0042] The tax return support system can also analyze the user's past income data, identify seasonal fluctuations and trends in income, and predict future income. For example, based on past income data, it can identify periods of increase and decrease in income and propose tax strategies accordingly. In addition, by identifying income trends, it can predict future income and suggest appropriate investment and savings plans. This allows the user to take optimal tax strategies based on their future income predictions.

[0043] The tax return support system can further analyze the user's income information, identify seasonal fluctuations and trends in income, and suggest the optimal timing for filing. For example, filing a tax return during a period when income increases can reduce the tax burden. Also, by identifying income trends, it can suggest the optimal timing for filing a tax return based on future income predictions. This allows the user to optimally time their tax return based on seasonal fluctuations and trends in income.

[0044] The tax return support system can also analyze the user's past expense data, identify seasonal fluctuations and trends in expenses, and predict future expenses. For example, based on past expense data, it can identify periods of increase and decrease in expenses and propose corresponding cost-cutting measures. In addition, by identifying expense trends, it can predict future expenses and propose appropriate savings plans. This allows the user to take optimal cost-cutting measures based on future expense predictions.

[0045] The tax return support system can also analyze the user's expense information, identify seasonal fluctuations and trends in expenses, and propose optimal expense management methods. For example, cost reductions can be achieved by managing expenses in accordance with periods when expenses increase. In addition, by identifying expense trends, optimal expense management methods based on future expense forecasts can be proposed. This allows users to optimally manage expenses based on seasonal fluctuations and trends in expenses.

[0046] The tax return support system can also analyze the user's past tax return data to improve the accuracy of document generation. For example, it can automatically complete required document items based on the content of past tax returns. It can also identify patterns of documents required for tax returns and automatically generate documents corresponding to specific sources of income and expense items. This allows the system to analyze past tax return data and improve the accuracy of automatic document generation.

[0047] The tax return support system can also analyze the information entered by the user and provide a function to automatically remind them of the filing deadline. For example, it can send a notification when the filing deadline approaches. It can also automatically add the filing deadline to a calendar and provide a reminder function. This allows users to file their tax return at the appropriate time without forgetting the filing deadline.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The income input unit inputs the user's income information. For example, if a user inputs "This year's income is 5 million yen from freelance work and 1 million yen from part-time work," the generation AI analyzes this and classifies it into the appropriate income category. In addition, when the user inputs income information, the income input unit helps the generation AI analyze the information and input it in the appropriate format. Step 2: The expense calculation unit inputs the user's expense information. For example, if the user inputs "100,000 yen per year for transportation expenses and 50,000 yen for office supplies," the generation AI analyzes this and classifies it into the appropriate expense items. In addition, when the user inputs expense information, the expense calculation unit helps the generation AI analyze the information and classify it into the appropriate expense items. Step 3: The document generation unit automatically generates the necessary documents. For example, the generation AI automatically generates the necessary documents based on the information entered by the user and formats them for submission. The document generation unit also helps the generation AI automatically generate the necessary documents based on the information entered by the user and formats them for submission. Step 4: The question response unit answers questions from users in real time. For example, if a user asks, "Which category should I classify this expense under?", the generation AI analyzes the question and suggests an appropriate expense category. The question response unit also helps the generation AI analyze the user's question and provide an appropriate answer.

[0050] (Example 2) The tax return support system according to an embodiment of the present invention is a system in which a user inputs income and expense information, a generation AI analyzes and classifies the information into appropriate categories, automatically generates necessary documents, and answers questions in real time. As a result, the tax return support system enables efficient and simple tax return procedures.

[0051] The tax return support system according to the embodiment includes an income input unit, an expense calculation unit, a document generation unit, and a question response unit. The income input unit inputs a user's income information. For example, if a user inputs, "This year, I earned 5 million yen from freelance work and 1 million yen from part-time work," the generation AI analyzes the information and categorizes it into the appropriate income category. Furthermore, when a user inputs income information, the income input unit helps the generation AI analyze the information and input it in the appropriate format. For example, if a user inputs, "This year, I earned 5 million yen from freelance work and 1 million yen from part-time work," the generation AI analyzes the information and categorizes it into the appropriate income category. The expense calculation unit inputs a user's expense information. For example, if a user inputs, "This year, I earned 100,000 yen from freelance work and 1 million yen from part-time work," the generation AI analyzes the information and categorizes it into the appropriate expense category. Furthermore, when a user inputs expense information, the generation AI helps the generation AI analyze the information and categorize it into the appropriate expense category. For example, if a user inputs "100,000 yen per year for transportation expenses and 50,000 yen for office supplies expenses," the generation AI analyzes this and classifies it into the appropriate expense category. The document generation unit automatically generates the necessary documents. For example, the generation AI automatically generates the necessary documents based on the user's input information and formats them for submission. The document generation unit also helps the generation AI automatically generate the necessary documents based on the user's input information and formats them for submission. For example, the generation AI automatically generates the necessary documents based on the user's input information and formats them for submission. The question response unit answers questions from the user in real time. For example, if a user asks, "In which category should I classify this expense?", the generation AI analyzes the question and suggests the appropriate expense category. The question response unit also helps the generation AI analyze the user's question and provide the appropriate answer. For example, if a user asks, "In which category should I classify this expense?", the generation AI analyzes the question and suggests the appropriate expense category. As a result, the tax return support system according to the embodiment enables tax return procedures to be carried out efficiently and simply.

[0052] The income input unit can analyze a user's past income data, make income predictions, and suggest tax strategies. For example, the generation AI in the income input unit analyzes a user's income data over the past few years to identify patterns of income increase and decrease. Based on this, the unit predicts future income and suggests appropriate tax strategies. For example, if income is on the rise, the unit suggests specific deductions as tax-saving strategies. The income input unit also analyzes a user's past income data to identify seasonal fluctuations in income. For example, if freelance work is divided into busy and slow seasons, the unit suggests appropriate tax strategies. The generation AI in the income input unit also predicts future income based on the user's past income data and suggests appropriate investment and savings plans as tax strategies. For example, if income is stable, the unit recommends long-term investments. This allows the unit to predict future income and suggest appropriate tax strategies.

[0053] The income input unit can analyze income information, identify seasonal fluctuations and trends, and suggest the timing for filing a tax return. For example, the generation AI in the income input unit analyzes the user's income data and identifies seasonal fluctuations in income. For example, if income is concentrated in a specific month, the income input unit suggests the timing for filing a tax return that matches that month. The income input unit also analyzes the user's income data and identifies income trends. For example, if income is increasing year by year, the income input unit suggests the optimal timing for filing a tax return based on that trend. The generation AI in the income input unit also identifies seasonal fluctuations and trends in income based on the user's income data, and suggests the optimal timing for filing a tax return as a tax measure. For example, filing a tax return at a time when income is decreasing can reduce the tax burden. This makes it possible to suggest the optimal timing for filing a tax return based on the seasonal fluctuations and trends of income.

[0054] The income input unit can use the emotion estimation function to measure the stress level of the user when entering income information and propose interface improvements. The income input unit, for example, uses the emotion estimation function to measure the stress level of the user when entering income information in real time. For example, it analyzes facial expressions and voice tone to quantify the stress level. Furthermore, if the user's stress level is high, the income input unit makes suggestions to improve the interface. For example, it changes the design of the input form to provide a more intuitive and easy-to-use interface. Furthermore, the income input unit proposes specific improvement measures to reduce the stress of the user when entering income information based on the emotion estimation data. For example, it enhances input guides and help functions to reduce the burden on the user. In this way, the user's stress level can be measured and proposals for interface improvements can be made.

[0055] The expense calculation unit can analyze the user's past expense data, make expense forecasts, and propose expense reduction measures. For example, the generation AI in the expense calculation unit analyzes the user's expense data from the past few years and identifies patterns of expense increases and decreases. Based on this, it predicts future expenses and proposes optimal expense reduction measures. For example, it can propose reductions in specific expense items. The expense calculation unit also analyzes the user's past expense data and identifies seasonal fluctuations in expenses. For example, if expenses are concentrated in a specific month, it can propose expense reduction measures tailored to that month. The expense calculation unit also uses the generation AI to predict future expenses based on the user's past expense data and propose appropriate investments and savings plans as expense reduction measures. For example, it can recommend savings measures tailored to periods when expenses are increasing. This makes it possible to predict future expenses and propose optimal expense reduction measures.

[0056] The expense calculation unit can analyze expense information, identify seasonal fluctuations and trends, and propose expense management methods. For example, the generation AI in the expense calculation unit analyzes the user's expense data and identifies seasonal fluctuations in expenses. For example, if expenses are concentrated in a specific month, the expense calculation unit proposes an expense management method tailored to that month. The expense calculation unit also analyzes the user's expense data and identifies expense trends. For example, if expenses are increasing year by year, the expense calculation unit proposes the optimal expense management method based on that trend. The generation AI in the expense calculation unit also identifies seasonal fluctuations and trends in expenses based on the user's expense data, and proposes appropriate savings measures and investment plans as expense management methods. For example, it recommends a management method tailored to periods when expenses decrease. This makes it possible to propose the optimal expense management method based on seasonal fluctuations and trends in expenses.

[0057] The expense calculation unit can use the emotion estimation function to measure the stress level of a user when entering expense information and propose interface improvements. The expense calculation unit, for example, uses the emotion estimation function to measure the stress level of a user when entering expense information in real time. For example, it analyzes facial expressions and voice tone to quantify the stress level. Furthermore, if the user's stress level is high, the expense calculation unit proposes interface improvements. For example, it changes the design of the input form to provide a more intuitive and easy-to-use interface. Furthermore, based on the emotion estimation data, the expense calculation unit proposes specific improvement measures to reduce the stress a user experiences when entering expense information. For example, it enhances input guides and help functions to reduce the burden on the user. In this way, the user's stress level can be measured and proposals for interface improvements can be made.

[0058] The document generation unit can analyze the user's past tax return data and improve the accuracy of document generation. For example, the document generation unit uses a generation AI to analyze the user's tax return data from the past few years and improve the accuracy of automatically generating required documents. For example, the document generation unit automatically completes required document items based on the content of past tax returns. The document generation unit also analyzes the user's past tax return data and identifies patterns of documents required for tax returns. For example, it automatically generates documents corresponding to specific income sources or expense items. The document generation unit also builds a system in which the generation AI uses the user's past tax return data to improve the accuracy of automatically generating required documents. For example, it automatically adjusts the document format by referring to the content of past tax returns. This allows the document generation unit to analyze past tax return data and improve the accuracy of automatically generating documents.

[0059] The document generation unit can be equipped with a function that analyzes input information and automatically reminds users of deadlines for submission. For example, the document generation unit adds a function in which a generation AI analyzes information input by the user and automatically reminds users of deadlines for document submission. For example, it sends a notification when the deadline approaches. The document generation unit also identifies deadlines for document submission based on information input by the user and provides a reminder function. For example, it automatically adds deadlines to a calendar. The document generation unit also builds a system in which a generation AI analyzes information input by the user and reminds users of deadlines for document submission. For example, it sends reminders by email or app notifications when the deadline approaches. This allows users to automatically be reminded of deadlines for document submission.

[0060] The document generation unit can use the emotion estimation function to measure the stress level of the user when preparing documents and propose interface improvements. The document generation unit, for example, uses the emotion estimation function to measure the stress level of the user when preparing documents in real time. For example, the document generation unit analyzes facial expressions and voice tone to quantify the stress level. Furthermore, if the user's stress level is high, the document generation unit proposes interface improvements. For example, the document generation unit changes the design of the input form to provide a more intuitive and easy-to-use interface. Furthermore, the document generation unit proposes specific improvement measures to reduce the stress of the user when preparing documents based on the emotion estimation data. For example, the document generation unit enhances input guides and help functions to reduce the burden on the user. In this way, the document generation unit can measure the user's stress level and propose interface improvements.

[0061] The question response unit can analyze a user's past question history and learn to provide answers. For example, the question response unit uses a generation AI to analyze a user's past question history and identify question patterns. Based on this, the question response unit learns to provide more appropriate answers. For example, answers to similar questions are automatically generated based on the content of past questions. The question response unit also analyzes a user's past question history and identifies question trends. For example, if there are many questions about a particular topic, the question response unit strengthens answers related to that topic. The question response unit also builds a system in which the generation AI learns to provide more appropriate answers based on the user's past question history. For example, it refers to the content of past questions to improve the accuracy of answers. This allows the generation AI to analyze past question history and learn to provide more appropriate answers.

[0062] The question response unit can analyze the content of the question and automatically refer to laws, regulations, and guidelines to provide an answer. For example, the question response unit uses a generation AI to analyze the content of a user's question and automatically refer to relevant laws, regulations, and guidelines to provide an answer. For example, a question about tax law is answered by citing the relevant laws and regulations. The question response unit also identifies relevant laws and guidelines based on the content of the user's question and reflects them in the answer. For example, a question about a specific expense item is answered by referring to the relevant guidelines. The question response unit also builds a system in which the generation AI analyzes the content of a user's question and automatically refer to relevant laws, regulations, and guidelines to provide an answer. For example, the accuracy of the answer is improved by referring to a database of laws and regulations. This makes it possible to analyze the content of the question and provide an answer by referring to relevant laws, regulations, and guidelines.

[0063] The question response unit can use the emotion estimation function to analyze the emotion of a user when asking a question and adjust the tone and content of the answer according to the emotion. The question response unit, for example, uses the emotion estimation function to analyze the emotion of a user when asking a question in real time. For example, it analyzes facial expressions and voice tone and calculates an emotion score. Furthermore, the question response unit adjusts the tone and content of the answer if the user's emotion is negative. For example, it provides an answer in a gentle tone to reduce the user's anxiety. Furthermore, the question response unit builds a system that analyzes the emotion of a user when asking a question based on the emotion estimation data and adjusts the tone and content of the answer according to the emotion. For example, it automatically generates an answer according to the emotion score. This makes it possible to adjust the tone and content of the answer according to the user's emotion.

[0064] The question response unit can integrate the content of a question with question data of other users and provide the optimal answer to a common question. For example, the question response unit uses a generation AI to integrate the content of a user's question with question data of other users and provide the optimal answer to a common question. For example, if the same question is asked multiple times, the question response unit automatically generates the optimal answer to that question. The question response unit also compares the content of a user's question with question data of other users and identifies answers to common questions. For example, it provides the optimal answer based on past question data. The question response unit also builds a system in which the generation AI integrates the content of a user's question with question data of other users and provides the optimal answer to a common question. For example, it references a question database to improve the accuracy of the answer. This makes it possible to provide the optimal answer to a common question.

[0065] The question response unit can analyze the content of the question, perform a comparative analysis with other users, and provide a benchmark for the question. In the question response unit, for example, the generation AI analyzes the content of the user's question and performs a comparative analysis with other users in the same industry or occupation. For example, a benchmark is provided by comparing it with question data from other companies in the same industry. The question response unit also analyzes the content of the user's question and identifies differences in questions from other users. For example, it analyzes differences in questions from users in the same region or age group and provides a benchmark. The question response unit also builds a system in which the generation AI performs a comparative analysis with other users based on the content of the user's question and provides a benchmark for the question. For example, a benchmark is provided based on the median or average of the questions. This makes it possible to provide a benchmark for the question.

[0066] The question response unit uses the emotion estimation function to analyze the emotion of the user when asking a question in real time and provide positive feedback. The question response unit, for example, uses the emotion estimation function to analyze the emotion of the user when asking a question in real time. For example, it analyzes facial expressions and voice tone and calculates an emotion score. Furthermore, if the user's emotion is negative, the question response unit provides positive feedback. For example, it presents encouraging messages and success stories to increase the user's motivation. Furthermore, the question response unit builds a system that analyzes the emotion of the user when asking a question based on the emotion estimation data and provides positive feedback. For example, it automatically generates feedback according to the emotion score. This makes it possible to analyze the user's emotion in real time and provide positive feedback.

[0067] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0068] The tax return support system can also suggest tax deductions according to the type of income when the user enters their income information. For example, it can suggest specific expense deductions for freelance income and salary income deductions for part-time income. By suggesting tax deductions according to the type of income, it can also help the user take optimal tax measures. This allows the user to receive the optimal tax deductions according to the type of income.

[0069] The tax return support system can also analyze the user's past income data, identify seasonal fluctuations and trends in income, and predict future income. For example, based on past income data, it can identify periods of increase and decrease in income and propose tax strategies accordingly. In addition, by identifying income trends, it can predict future income and suggest appropriate investment and savings plans. This allows the user to take optimal tax strategies based on their future income predictions.

[0070] The tax return support system can further analyze the user's income information, identify seasonal fluctuations and trends in income, and suggest the optimal timing for filing. For example, filing a tax return during a period when income increases can reduce the tax burden. Also, by identifying income trends, it can suggest the optimal timing for filing a tax return based on future income predictions. This allows the user to optimally time their tax return based on seasonal fluctuations and trends in income.

[0071] The tax return support system can also use emotion estimation to measure the stress level of users when they enter their income information and suggest improvements to the interface. For example, it can analyze facial expressions and voice tone to quantify the stress level. If the user's stress level is high, it can change the design of the input form to provide a more intuitive and user-friendly interface. This allows it to measure the user's stress level and suggest improvements to the interface.

[0072] The tax return support system can also analyze the user's past expense data, identify seasonal fluctuations and trends in expenses, and predict future expenses. For example, based on past expense data, it can identify periods of increase and decrease in expenses and propose corresponding cost-cutting measures. In addition, by identifying expense trends, it can predict future expenses and propose appropriate savings plans. This allows the user to take optimal cost-cutting measures based on future expense predictions.

[0073] The tax return support system can also analyze the user's expense information, identify seasonal fluctuations and trends in expenses, and propose optimal expense management methods. For example, cost reductions can be achieved by managing expenses in accordance with periods when expenses increase. In addition, by identifying expense trends, optimal expense management methods based on future expense forecasts can be proposed. This allows users to optimally manage expenses based on seasonal fluctuations and trends in expenses.

[0074] The tax return support system can also use emotion estimation to measure the stress level of users when they enter expense information and suggest improvements to the interface. For example, it can analyze facial expressions and voice tone to quantify the stress level. If the user's stress level is high, it can change the design of the input form to provide a more intuitive and user-friendly interface. This allows it to measure the user's stress level and suggest improvements to the interface.

[0075] The tax return support system can also analyze the user's past tax return data to improve the accuracy of document generation. For example, it can automatically complete required document items based on the content of past tax returns. It can also identify patterns of documents required for tax returns and automatically generate documents corresponding to specific sources of income and expense items. This allows the system to analyze past tax return data and improve the accuracy of automatic document generation.

[0076] The tax return support system can also analyze the information entered by the user and provide a function to automatically remind them of the filing deadline. For example, it can send a notification when the filing deadline approaches. It can also automatically add the filing deadline to a calendar and provide a reminder function. This allows users to file their tax return at the appropriate time without forgetting the filing deadline.

[0077] The tax return support system can also use emotion estimation to measure the user's stress level when preparing documents and suggest interface improvements. For example, it can analyze facial expressions and voice tone to quantify the stress level. If the user's stress level is high, it can change the design of the input form to provide a more intuitive and user-friendly interface. This allows it to measure the user's stress level and suggest interface improvements.

[0078] The processing flow of the second embodiment will be briefly explained below.

[0079] Step 1: The income input unit inputs the user's income information. For example, if a user inputs "This year's income is 5 million yen from freelance work and 1 million yen from part-time work," the generation AI analyzes this and classifies it into the appropriate income category. In addition, when the user inputs income information, the income input unit helps the generation AI analyze the information and input it in the appropriate format. Step 2: The expense calculation unit inputs the user's expense information. For example, if the user inputs "100,000 yen per year for transportation expenses and 50,000 yen for office supplies," the generation AI analyzes this and classifies it into the appropriate expense items. In addition, when the user inputs expense information, the expense calculation unit helps the generation AI analyze the information and classify it into the appropriate expense items. Step 3: The document generation unit automatically generates the necessary documents. For example, the generation AI automatically generates the necessary documents based on the information entered by the user and formats them for submission. The document generation unit also helps the generation AI automatically generate the necessary documents based on the information entered by the user and formats them for submission. Step 4: The question response unit answers questions from users in real time. For example, if a user asks, "Which category should I classify this expense under?", the generation AI analyzes the question and suggests an appropriate expense category. The question response unit also helps the generation AI analyze the user's question and provide an appropriate answer.

[0080] 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.

[0081] 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.

[0082] 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.

[0083] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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).

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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).

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the 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 specific processing unit 290 using these models.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0114] 7, a 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] In the robot 414, 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 robot 414 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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."

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0147] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A system equipped with a generative AI, an income input unit for inputting income information of a user; A generation AI that analyzes the income information and classifies it into appropriate income items; an expense calculation unit for inputting user expense information; A generation AI that analyzes the expense information and classifies it into appropriate expense items; a document generation unit that automatically generates necessary documents; A generation AI that formats the document generated by the document generation unit into a submission format; a question response unit that answers questions from users in real time; A generation AI that analyzes the question and provides an appropriate answer. A system characterized by:

2. The income input unit Analyze the user's past income data, make income forecasts, and propose tax strategies 2. The system of claim 1.

3. The income input unit Analyze the income information, identify seasonal fluctuations and trends, and suggest filing timing 2. The system of claim 1.

4. The income input unit Measure the stress level when the user inputs the income information, and suggest interface improvements to reduce stress.

2. The system of claim 1.

5. The expense calculation unit Analyze the user's past expense data, make expense forecasts, and propose cost reduction measures 2. The system of claim 1.

6. The expense calculation unit Analyze the expense information, identify seasonal fluctuations and trends, and propose expense management methods 2. The system of claim 1.

7. The expense calculation unit Measure the stress level of the user when entering the expense information, and propose interface improvements to reduce stress.

2. The system of claim 1.

8. The document generation unit Analyze the user's past reporting data to improve document generation accuracy 2. The system of claim 1.

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