system

Abstract

The system according to the embodiment aims to efficiently manage receipts and invoices and to efficiently process tax returns. [Solution] The system according to the embodiment includes a reception unit, an analysis unit, a declaration unit, and an advice unit. The reception unit receives photos of receipts or invoices. The analysis unit analyzes the photos received by the reception unit and performs accounting. The declaration unit processes information necessary for filing a tax return based on the information analyzed by the analysis unit. The advice unit analyzes the user's income and expenditure data based on the information processed by the declaration unit and provides advice on asset formation.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem that the management of receipts and invoices and the procedures for filing tax returns are complicated and difficult to carry out efficiently.

[0005] The system according to the embodiment aims to efficiently manage receipts and invoices and to efficiently process tax returns. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a declaration unit, and an advice unit. The reception unit receives photos of receipts or invoices. The analysis unit analyzes the photos received by the reception unit and performs accounting. The declaration unit processes information necessary for filing a tax return based on the information analyzed by the analysis unit. The advice unit analyzes the user's income and expenditure data based on the information processed by the declaration unit and provides advice on asset formation. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently manage receipts and invoices and process tax returns. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A personal financial planner system according to an embodiment of the present invention allows users to attach photos of receipts and invoices using a messaging app. AI automatically performs accounting and processes the information required for filing tax returns. In this system, users attach photos of receipts and invoices using a messaging app, and the AI ​​automatically analyzes the photos and performs accounting. Furthermore, users can also submit the necessary information for filing tax returns simply by sending data and photos via the messaging app. Users can perform all operations using natural language. Furthermore, the AI ​​analyzes the user's income and expenditure data and provides optimal asset formation advice. This allows users to efficiently manage their assets. Furthermore, securing customer income and expenditure data can be utilized in various financial services. For example, a user can attach photos of receipts and invoices using a messaging app. For example, a user can take a photo of a meal receipt or utility bill and send it to the messaging app. This information is then input into the AI. The AI ​​then analyzes the input photo and performs accounting. The AI ​​recognizes the text information contained in the photo and classifies it into the appropriate category. For example, meal receipts are accounted for as "food expenses" and utility bills as "utilities expenses." Information required for filing tax returns is also processed in the same way. Users simply send the data and photos required for filing their tax returns via a messaging app, and the AI ​​automatically processes and prepares the tax return. Users can perform all operations using natural language, such as issuing commands like "Start my tax return." Furthermore, the AI ​​analyzes users' income and expenditure data and provides optimal asset formation advice. For example, it analyzes the balance between income and expenditure and offers savings and investment advice. This allows users to efficiently manage their assets. Furthermore, by securing customer income and expenditure data, it can be used in various financial services. For example, it can be used for loan screening and insurance proposals. This allows the personal financial planner system to automatically analyze users' income and expenditure data and provide optimal asset formation advice.

[0029] A personal financial planner system according to an embodiment includes a reception unit, an analysis unit, a filing unit, and an advice unit. The reception unit accepts photos of receipts and invoices using a messaging app. For example, a user can take photos of meal receipts, utility bills, etc. and send them to the messaging app. The reception unit accepts these photos and inputs them into an AI. The analysis unit uses AI to analyze the photos accepted by the reception unit and perform accounting. For example, the AI ​​recognizes text information contained in the photos and classifies them into appropriate categories. For example, meal receipts are classified as "food expenses" and utility bills are classified as "utilities expenses." The analysis unit can also use OCR technology to convert the text information in the photos into text data for accounting purposes. The filing unit automatically processes information necessary for filing tax returns based on the information analyzed by the analysis unit. For example, the filing unit automatically calculates information such as income, expenses, and deductions and prepares a tax return. The filing unit can also automatically process information necessary for filing tax returns based on information stored in a database. The advice unit analyzes the user's income and expenditure data based on the information processed by the reporting unit and provides optimal asset formation advice. For example, the advice unit analyzes the balance between income and expenditure and provides savings and investment advice. The advice unit can also use data mining and statistical analysis to analyze the user's income and expenditure data and provide optimal asset formation advice. As a result, the personal financial planner system according to the embodiment can automatically analyze the user's income and expenditure data and provide optimal asset formation advice. Some or all of the above-described processing in the advice unit may be performed using, for example, AI, or may be performed without AI. For example, the advice unit can provide advice using an AI model that inputs the user's income and expenditure data and outputs optimal asset formation advice. Furthermore, the advice unit can secure the user's income and expenditure data and utilize it in various financial services. For example, the advice unit can perform loan screening and insurance proposals based on the user's income and expenditure data. This allows the user to efficiently manage their assets.

[0030] The reception unit can accept photos of receipts or invoices using a messaging app. The reception unit, for example, accepts a user sending a photo of a receipt or invoice using a messaging app. For example, a user can take a photo of a meal receipt or utility bill and send it to the messaging app. The reception unit accepts these photos and inputs them into an AI. This allows the user to easily send photos of receipts or invoices using a messaging app. Some or all of the above-described processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input photo data accepted via the messaging app into a generation AI and have the generation AI analyze the photo data.

[0031] The analysis unit can use AI to recognize text information contained in photos and classify them into appropriate categories. The analysis unit, for example, uses AI to analyze photos received by the reception unit and perform accounting. For example, AI recognizes text information contained in photos and classifies them into appropriate categories. For example, meal receipts are classified as "food expenses" and utility bills are classified as "utilities expenses." The analysis unit can also use OCR technology to convert text information in photos into text data and perform accounting. This allows AI to automatically analyze text information in photos and classify it into appropriate categories, thereby streamlining accounting work. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input photo data into a generation AI and have the generation AI recognize text information and classify it into categories.

[0032] The declaration unit can automatically process information necessary for filing a tax return using AI. The declaration unit automatically processes information necessary for filing a tax return, for example, based on information analyzed by the analysis unit. For example, the declaration unit automatically calculates information such as income, expenses, and deductions, and prepares a tax return. The declaration unit can also automatically process information necessary for filing a tax return based on information stored in a database. This reduces the burden on users by having AI automatically process the information necessary for filing a tax return. Some or all of the above-mentioned processing in the declaration unit may be performed using AI, for example, or may be performed without using AI. For example, the declaration unit can input information analyzed by the analysis unit into a generation AI and have the generation AI process the information necessary for filing a tax return.

[0033] The advice unit can analyze the user's income and expenditure data using AI and provide savings and investment advice. The advice unit, for example, analyzes the user's income and expenditure data based on information processed by the reporting unit and provides optimal asset formation advice. For example, the advice unit analyzes the balance between income and expenditure and provides savings and investment advice. The advice unit can also analyze the user's income and expenditure data using data mining and statistical analysis and provide optimal asset formation advice. As a result, the AI ​​analyzes the user's income and expenditure data and provides optimal asset formation advice, thereby improving the efficiency of the user's asset management. Some or all of the above-mentioned processing in the advice unit may be performed using AI, for example, or may be performed without using AI. For example, the advice unit can input the user's income and expenditure data into the generation AI and have the generation AI execute savings and investment advice.

[0034] The advice unit secures the user's income and expenditure data and can utilize it in multiple financial services. The advice unit, for example, secures the user's income and expenditure data and utilizes it in various financial services. For example, the advice unit can perform loan screening and insurance proposals based on the user's income and expenditure data. In this way, securing the user's income and expenditure data can be utilized in various financial services, such as loan screening and insurance proposals. Some or all of the above-described processing in the advice unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice unit can input the user's income and expenditure data into a generation AI and have the generation AI execute financial service proposals.

[0035] The reception unit can analyze the user's past submission history and select the optimal reception method. The reception unit, for example, analyzes the user's past submission history and selects the optimal reception method. For example, the reception unit analyzes the time periods during which the user frequently submitted in the past and sends reminders during those time periods. The reception unit can also prioritize and suggest submission methods (e.g., photo, PDF) that the user has used in the past. The reception unit can also predict a tendency for submissions to be made on specific days of the week or during specific time periods based on the user's past submission history and suggest the optimal reception method. This allows the analysis of the user's past submission history to suggest the optimal reception method and enable efficient reception. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's submission history data into a generation AI and have the generation AI select the optimal reception method.

[0036] The reception unit can filter receipts and invoices based on the user's current project and areas of interest when receiving them. For example, the reception unit can prioritize receipts and invoices related to the user's current project and areas of interest when receiving receipts and invoices. For example, the reception unit can prioritize receipts and invoices related to the user's current project and areas of interest. The reception unit can also filter receipts and invoices related to the user's areas of interest and prioritize important receipts. Related receipts and invoices can also be automatically classified based on project tags set by the user. By filtering based on the user's current project and areas of interest, important information can be prioritized. Some or all of the above-described processing in the reception unit can be performed using, or without, AI. For example, the reception unit can input data on the user's project and areas of interest into a generation AI and have the generation AI perform the filtering.

[0037] The reception unit can prioritize receiving highly relevant information by taking into account the user's geographical location information when receiving receipts or invoices. For example, the reception unit prioritizes receiving highly relevant information by taking into account the user's geographical location information when receiving receipts or invoices. For example, if the user is in a specific area, receipts and invoices related to that area can be prioritized. Furthermore, receipts for related stores and services can be prioritized based on the user's current location. Furthermore, if the user is traveling, receipts and invoices related to the travel destination can be prioritized. In this way, highly relevant information can be prioritized by taking the user's geographical location information into account. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's geographical location information to the generation AI and cause the generation AI to prioritize receiving highly relevant information.

[0038] The reception unit can analyze the user's social media activity and accept related information when accepting receipts or invoices. For example, the reception unit can analyze the user's social media activity and accept related information when accepting receipts or invoices. For example, the reception unit can prioritize accepting receipts for stores or services that the user has shared on social media. It can also prioritize accepting receipts or invoices for categories of interest based on the user's social media activity. It can also prioritize accepting receipts or invoices related to events or activities that the user has mentioned on social media. This allows the user's social media activity to be analyzed and related information to be accepted preferentially. Some or all of the above-described processing in the reception unit can be performed using, or without, AI. For example, the reception unit can input the user's social media data into a generation AI and cause the generation AI to accept related information.

[0039] The analysis unit can adjust the level of detail of the analysis based on the importance of the receipt or invoice during analysis. The analysis unit, for example, adjusts the level of detail of the analysis based on the importance of the receipt or invoice during analysis. For example, a detailed analysis is performed for important receipts or invoices. A simplified analysis can also be performed for receipts or invoices with low importance. The level of detail of the analysis can also be adjusted based on the importance specified by the user. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the receipt or invoice. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input importance data of receipts or invoices to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0040] The analysis unit can apply different analysis algorithms depending on the category of the receipt or invoice during analysis. For example, the analysis unit applies different analysis algorithms depending on the category of the receipt or invoice during analysis. For example, an analysis algorithm dedicated to food expenses can be applied to a food receipt. An analysis algorithm dedicated to utility expenses can also be applied to a utility invoice. An analysis algorithm dedicated to transportation expenses can also be applied to a transportation receipt. In this way, by applying different analysis algorithms depending on the category of the receipt or invoice, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input category data of the receipt or invoice into the generation AI and cause the generation AI to apply different analysis algorithms.

[0041] The analysis unit can determine the analysis priority based on the submission date of receipts and invoices during analysis. The analysis unit, for example, determines the analysis priority based on the submission date of receipts and invoices during analysis. For example, the analysis unit can prioritize analysis of recently submitted receipts and invoices. Alternatively, the analysis unit can prioritize analysis of the most recent receipts and invoices, leaving older receipts and invoices for later analysis. The analysis priority can also be adjusted based on the submission date specified by the user. This enables efficient analysis by determining the analysis priority based on the submission date of receipts and invoices. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input receipt and invoice submission date data into the generation AI and have the generation AI determine the analysis priority.

[0042] The analysis unit can adjust the order of analysis based on the relevance of receipts and invoices during analysis. The analysis unit, for example, adjusts the order of analysis based on the relevance of receipts and invoices during analysis. For example, receipts and invoices in the same category are analyzed together. Also, highly related receipts and invoices can be analyzed preferentially. The order of analysis can also be adjusted based on the relevance specified by the user. This enables efficient analysis by adjusting the order of analysis based on the relevance of receipts and invoices. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input relevance data of receipts and invoices to the generation AI and have the generation AI adjust the order of analysis.

[0043] The reporting unit can optimize the current reporting by referring to past reporting data when reporting. For example, the reporting unit optimizes the current reporting by referring to past reporting data when reporting. For example, the reporting unit suggests an optimal reporting method based on the user's past reporting data. It can also automatically select frequently used categories from the past reporting data. It can also analyze the user's past reporting data and suggest the most efficient reporting method. In this way, by referring to the past reporting data, the current reporting can be optimized, enabling efficient reporting. Some or all of the above-mentioned processing in the reporting unit may be performed using, for example, AI, or may be performed without using AI. For example, the reporting unit can input past reporting data into a generation AI and have the generation AI optimize the current reporting.

[0044] The reporting unit can apply different reporting methods to different categories of receipts and invoices when reporting. For example, the reporting unit applies different reporting methods to different categories of receipts and invoices when reporting. For example, a reporting method dedicated to food expenses can be applied to food receipts. A reporting method dedicated to utility expenses can also be applied to utility invoices. A reporting method dedicated to transportation expenses can also be applied to transportation receipts. In this way, by applying different reporting methods to different categories of receipts and invoices, the accuracy of reporting is improved. Some or all of the above-mentioned processing in the reporting unit may be performed using, or without, AI, for example. For example, the reporting unit can input category data of receipts and invoices into a generation AI and have the generation AI apply different reporting methods.

[0045] The reporting department can analyze changes in the reporting based on the submission dates of receipts and invoices at the time of reporting. For example, the reporting department can analyze changes in the reporting based on the submission dates of receipts and invoices at the time of reporting. For example, the reporting department can analyze changes in the reporting based on recently submitted receipts and invoices. The reporting department can also analyze changes in the reporting based on older submitted receipts and invoices. The reporting department can also analyze changes in the reporting based on the submission date specified by the user. This enables efficient reporting by analyzing changes in the reporting based on the submission dates of receipts and invoices. Some or all of the above-mentioned processing in the reporting department may be performed using, for example, AI, or may be performed without using AI. For example, the reporting department can input data on the submission dates of receipts and invoices into a generation AI and have the generation AI analyze changes in the reporting.

[0046] The reporting department can analyze reporting by referring to market data related to receipts and invoices at the time of reporting. For example, the reporting department analyzes reporting by referring to market data related to receipts and invoices at the time of reporting. For example, the reporting department analyzes changes in reporting based on the relevant market data. The reporting department can also automatically select frequently used categories from the market data. The reporting department can also analyze market data and propose the most efficient reporting method. In this way, by referring to the relevant market data, the accuracy of reporting is improved. Some or all of the above-mentioned processing in the reporting department may be performed using, for example, AI, or may be performed without using AI. For example, the reporting department can input the relevant market data into a generation AI and have the generation AI perform analysis of the reporting.

[0047] The advice unit can improve the accuracy of advice by taking into account the interrelationships of income and expenditure data when providing advice. The advice unit, for example, improves the accuracy of advice by taking into account the interrelationships of income and expenditure data when providing advice. For example, the advice unit analyzes the balance between income and expenditure and suggests the optimal savings method. The advice unit can also suggest the optimal investment method by taking into account the interrelationships of income and expenditure data. The advice unit can also provide advice on efficient asset formation based on income and expenditure data. In this way, the accuracy of advice is improved by taking into account the interrelationships of income and expenditure data. Some or all of the above-mentioned processing in the advice unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice unit can input income and expenditure data into a generation AI and have the generation AI analyze the interrelationships and improve the accuracy of advice.

[0048] The advice unit can provide advice taking into consideration the user's attribute information when providing advice. For example, the advice unit can provide advice taking into consideration the user's attribute information when providing advice. For example, the advice unit can propose an optimal savings method based on the user's age and occupation. The advice unit can also propose an optimal insurance plan based on the user's family composition. The advice unit can also propose an optimal investment method based on the user's lifestyle. This makes it possible to provide more appropriate advice by taking into consideration the user's attribute information. Some or all of the above-mentioned processing in the advice unit may be performed using AI, for example, or may be performed without using AI. For example, the advice unit can input the user's attribute information into the generation AI and cause the generation AI to execute advice based on the attribute information.

[0049] The advice unit can provide advice taking into consideration the geographical distribution of income and expenditure data when providing advice. The advice unit, for example, can provide advice taking into consideration the geographical distribution of income and expenditure data when providing advice. For example, the advice unit can suggest an optimal savings method based on the user's place of residence. The advice unit can also suggest an optimal investment method based on the user's place of work. The advice unit can also suggest an optimal insurance plan based on the user's travel destination. In this way, more appropriate advice can be provided by taking the geographical distribution of income and expenditure data into consideration. Some or all of the above-described processing in the advice unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice unit can input geographical distribution data of income and expenditure data into the generation AI and cause the generation AI to execute advice based on the geographical distribution.

[0050] The advice unit can improve the accuracy of advice by referring to literature related to the income and expenditure data when providing advice. The advice unit, for example, improves the accuracy of advice by referring to literature related to the income and expenditure data when providing advice. For example, the advice unit can suggest an optimal savings method by referring to the latest research papers related to the income and expenditure data. It can also suggest an optimal investment method by referring to market reports related to the income and expenditure data. It can also provide advice on efficient asset formation by referring to specialized books related to the income and expenditure data. In this way, the accuracy of advice is improved by referring to literature related to the income and expenditure data. Some or all of the above-mentioned processing in the advice unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice unit can input literature data related to the income and expenditure data into the generation AI and cause the generation AI to execute advice based on the literature.

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

[0052] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the receipt or invoice. For example, a detailed analysis can be performed on important receipts or invoices. A simplified analysis can also be performed on receipts or invoices with low importance. The level of detail of the analysis can also be adjusted based on the importance specified by the user. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the receipt or invoice. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input importance data of receipts or invoices to the generation AI and have the generation AI adjust the level of detail of the analysis.

[0053] The reception unit can analyze the user's past submission history and select the optimal reception method. For example, it can analyze the time periods during which the user frequently submitted work in the past and send reminders during those time periods. It can also prioritize and suggest submission methods (e.g., photos, PDFs) that the user has used in the past. It can also predict a tendency for submissions to be made on specific days of the week or during specific time periods based on the user's past submission history and suggest the optimal reception method. This allows for efficient reception by analyzing the user's past submission history and suggesting the optimal reception method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's submission history data into a generation AI and have the generation AI select the optimal reception method.

[0054] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the receipt or invoice. For example, an analysis algorithm dedicated to food expenses can be applied to a food receipt. An analysis algorithm dedicated to utility expenses can be applied to a utility invoice. An analysis algorithm dedicated to transportation expenses can be applied to a transportation receipt. By applying different analysis algorithms depending on the category of the receipt or invoice, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input category data of the receipt or invoice into the generation AI and have the generation AI apply different analysis algorithms.

[0055] When filing a declaration, the declaration unit can optimize the current declaration by referring to past declaration data. For example, the declaration unit can suggest the optimal declaration method based on the user's past declaration data. It can also automatically select frequently used categories from the past declaration data. It can also analyze the user's past declaration data and suggest the most efficient declaration method. In this way, by referring to the past declaration data, the current declaration can be optimized and an efficient declaration can be made. Some or all of the above-mentioned processing in the declaration unit may be performed using, for example, AI, or may be performed without using AI. For example, the declaration unit can input past declaration data into a generation AI and have the generation AI optimize the current declaration.

[0056] When providing advice, the advice unit can improve the accuracy of the advice by taking into account the interrelationships between income and expenditure data. For example, the advice unit can analyze the balance between income and expenditure and suggest the optimal savings method. The advice unit can also suggest the optimal investment method by taking into account the interrelationships between income and expenditure data. The advice unit can also provide advice on efficient asset formation based on income and expenditure data. In this way, the accuracy of the advice is improved by taking into account the interrelationships between income and expenditure data. Some or all of the above-mentioned processing in the advice unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice unit can input income and expenditure data into a generation AI and have the generation AI analyze the interrelationships and improve the accuracy of the advice.

[0057] When providing advice, the advice unit can take the user's attribute information into consideration. For example, the advice unit can suggest the optimal savings method based on the user's age and occupation. The advice unit can also suggest the optimal insurance plan based on the user's family structure. The advice unit can also suggest the optimal investment method based on the user's lifestyle. This allows more appropriate advice to be provided by taking the user's attribute information into consideration. Some or all of the above-mentioned processing in the advice unit may be performed using AI, for example, or may be performed without using AI. For example, the advice unit can input the user's attribute information into the generation AI and have the generation AI execute advice based on the attribute information.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The reception unit accepts photos of receipts and bills sent by users via a messaging app. For example, a user can take a photo of a meal receipt or utility bill and send it to a messaging app. The reception unit accepts these photos and inputs them into the AI. Step 2: The analysis unit uses AI to analyze the photos received by the reception unit and perform accounting. For example, AI recognizes the text information contained in the photos and classifies them into the appropriate category. For example, a meal receipt would be classified as "food expenses" and a utility bill as "utility expenses." The analysis unit can also use OCR technology to convert the text information in the photos into text data for accounting purposes. Step 3: The declaration section automatically processes the information necessary for filing a tax return based on the information analyzed by the analysis section. For example, the declaration section automatically calculates information such as income, expenses, and deductions, and prepares a tax return. The declaration section can also automatically process the information necessary for filing a tax return based on the information stored in the database. Step 4: The advice unit analyzes the user's income and expenditure data based on the information processed by the reporting unit and provides optimal asset formation advice. For example, the advice unit analyzes the balance between income and expenditure and provides savings and investment advice. The advice unit can also use data mining and statistical analysis to analyze the user's income and expenditure data and provide optimal asset formation advice. As a result, the personal financial planner system according to the embodiment can automatically analyze the user's income and expenditure data and provide optimal asset formation advice. Some or all of the above-described processing in the advice unit may be performed using, for example, AI, or may be performed without AI. For example, the advice unit can provide advice using an AI model that inputs the user's income and expenditure data and outputs optimal asset formation advice. Furthermore, the advice unit can secure the user's income and expenditure data and utilize it in various financial services. For example, the advice unit can perform loan screening and insurance proposals based on the user's income and expenditure data. This allows the user to efficiently manage their assets.

[0060] (Example 2) A personal financial planner system according to an embodiment of the present invention allows users to attach photos of receipts and invoices using a messaging app. AI automatically performs accounting and processes the information required for filing tax returns. In this system, users attach photos of receipts and invoices using a messaging app, and the AI ​​automatically analyzes the photos and performs accounting. Furthermore, users can also submit the necessary information for filing tax returns simply by sending data and photos via the messaging app. Users can perform all operations using natural language. Furthermore, the AI ​​analyzes the user's income and expenditure data and provides optimal asset formation advice. This allows users to efficiently manage their assets. Furthermore, securing customer income and expenditure data can be utilized in various financial services. For example, a user can attach photos of receipts and invoices using a messaging app. For example, a user can take a photo of a meal receipt or utility bill and send it to the messaging app. This information is then input into the AI. The AI ​​then analyzes the input photo and performs accounting. The AI ​​recognizes the text information contained in the photo and classifies it into the appropriate category. For example, meal receipts are accounted for as "food expenses" and utility bills as "utilities expenses." Information required for filing tax returns is also processed in the same way. Users simply send the data and photos required for filing their tax returns via a messaging app, and the AI ​​automatically processes and prepares the tax return. Users can perform all operations using natural language, such as issuing commands like "Start my tax return." Furthermore, the AI ​​analyzes users' income and expenditure data and provides optimal asset formation advice. For example, it analyzes the balance between income and expenditure and offers savings and investment advice. This allows users to efficiently manage their assets. Furthermore, by securing customer income and expenditure data, it can be used in various financial services. For example, it can be used for loan screening and insurance proposals. This allows the personal financial planner system to automatically analyze users' income and expenditure data and provide optimal asset formation advice.

[0061] A personal financial planner system according to an embodiment includes a reception unit, an analysis unit, a filing unit, and an advice unit. The reception unit accepts photos of receipts and invoices using a messaging app. For example, a user can take photos of meal receipts, utility bills, etc. and send them to the messaging app. The reception unit accepts these photos and inputs them into an AI. The analysis unit uses AI to analyze the photos accepted by the reception unit and perform accounting. For example, the AI ​​recognizes text information contained in the photos and classifies them into appropriate categories. For example, meal receipts are classified as "food expenses" and utility bills are classified as "utilities expenses." The analysis unit can also use OCR technology to convert the text information in the photos into text data for accounting purposes. The filing unit automatically processes information necessary for filing tax returns based on the information analyzed by the analysis unit. For example, the filing unit automatically calculates information such as income, expenses, and deductions and prepares a tax return. The filing unit can also automatically process information necessary for filing tax returns based on information stored in a database. The advice unit analyzes the user's income and expenditure data based on the information processed by the reporting unit and provides optimal asset formation advice. For example, the advice unit analyzes the balance between income and expenditure and provides savings and investment advice. The advice unit can also use data mining and statistical analysis to analyze the user's income and expenditure data and provide optimal asset formation advice. As a result, the personal financial planner system according to the embodiment can automatically analyze the user's income and expenditure data and provide optimal asset formation advice. Some or all of the above-described processing in the advice unit may be performed using, for example, AI, or may be performed without AI. For example, the advice unit can provide advice using an AI model that inputs the user's income and expenditure data and outputs optimal asset formation advice. Furthermore, the advice unit can secure the user's income and expenditure data and utilize it in various financial services. For example, the advice unit can perform loan screening and insurance proposals based on the user's income and expenditure data. This allows the user to efficiently manage their assets.

[0062] The reception unit can accept photos of receipts or invoices using a messaging app. The reception unit, for example, accepts a user sending a photo of a receipt or invoice using a messaging app. For example, a user can take a photo of a meal receipt or utility bill and send it to the messaging app. The reception unit accepts these photos and inputs them into an AI. This allows the user to easily send photos of receipts or invoices using a messaging app. Some or all of the above-described processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input photo data accepted via the messaging app into a generation AI and have the generation AI analyze the photo data.

[0063] The analysis unit can use AI to recognize text information contained in photos and classify them into appropriate categories. The analysis unit, for example, uses AI to analyze photos received by the reception unit and perform accounting. For example, AI recognizes text information contained in photos and classifies them into appropriate categories. For example, meal receipts are classified as "food expenses" and utility bills are classified as "utilities expenses." The analysis unit can also use OCR technology to convert text information in photos into text data and perform accounting. This allows AI to automatically analyze text information in photos and classify it into appropriate categories, thereby streamlining accounting work. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input photo data into a generation AI and have the generation AI recognize text information and classify it into categories.

[0064] The declaration unit can automatically process information necessary for filing a tax return using AI. The declaration unit automatically processes information necessary for filing a tax return, for example, based on information analyzed by the analysis unit. For example, the declaration unit automatically calculates information such as income, expenses, and deductions, and prepares a tax return. The declaration unit can also automatically process information necessary for filing a tax return based on information stored in a database. This reduces the burden on users by having AI automatically process the information necessary for filing a tax return. Some or all of the above-mentioned processing in the declaration unit may be performed using AI, for example, or may be performed without using AI. For example, the declaration unit can input information analyzed by the analysis unit into a generation AI and have the generation AI process the information necessary for filing a tax return.

[0065] The advice unit can analyze the user's income and expenditure data using AI and provide savings and investment advice. The advice unit, for example, analyzes the user's income and expenditure data based on information processed by the reporting unit and provides optimal asset formation advice. For example, the advice unit analyzes the balance between income and expenditure and provides savings and investment advice. The advice unit can also analyze the user's income and expenditure data using data mining and statistical analysis and provide optimal asset formation advice. As a result, the AI ​​analyzes the user's income and expenditure data and provides optimal asset formation advice, thereby improving the efficiency of the user's asset management. Some or all of the above-mentioned processing in the advice unit may be performed using AI, for example, or may be performed without using AI. For example, the advice unit can input the user's income and expenditure data into the generation AI and have the generation AI execute savings and investment advice.

[0066] The advice unit secures the user's income and expenditure data and can utilize it in multiple financial services. The advice unit, for example, secures the user's income and expenditure data and utilizes it in various financial services. For example, the advice unit can perform loan screening and insurance proposals based on the user's income and expenditure data. In this way, securing the user's income and expenditure data can be utilized in various financial services, such as loan screening and insurance proposals. Some or all of the above-described processing in the advice unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice unit can input the user's income and expenditure data into a generation AI and have the generation AI execute financial service proposals.

[0067] The reception unit can estimate the user's emotions and adjust the timing of receiving photos of receipts and invoices based on the estimated user emotions. For example, the reception unit estimates the user's emotions and adjusts the timing of receiving photos of receipts and invoices based on the estimated user emotions. For example, if the user is feeling stressed, the reception timing can be adjusted by sending a reminder. Furthermore, if the user is relaxed, the reception timing can be flexibly set to match the user's pace. Furthermore, if the user is busy, the reception timing can be automatically delayed and a reminder can be sent again later. This allows the reception timing to be adjusted according to the user's emotions, thereby reducing the user's stress and enabling efficient reception. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or without AI. For example, the reception unit can input the user's emotion data into the generation AI and have the generation AI estimate the emotion and adjust the timing of reception.

[0068] The reception unit can analyze the user's past submission history and select the optimal reception method. The reception unit, for example, analyzes the user's past submission history and selects the optimal reception method. For example, the reception unit analyzes the time periods during which the user frequently submitted in the past and sends reminders during those time periods. The reception unit can also prioritize and suggest submission methods (e.g., photo, PDF) that the user has used in the past. The reception unit can also predict a tendency for submissions to be made on specific days of the week or during specific time periods based on the user's past submission history and suggest the optimal reception method. This allows the analysis of the user's past submission history to suggest the optimal reception method and enable efficient reception. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's submission history data into a generation AI and have the generation AI select the optimal reception method.

[0069] The reception unit can filter receipts and invoices based on the user's current project and areas of interest when receiving them. For example, the reception unit can prioritize receipts and invoices related to the user's current project and areas of interest when receiving receipts and invoices. For example, the reception unit can prioritize receipts and invoices related to the user's current project and areas of interest. The reception unit can also filter receipts and invoices related to the user's areas of interest and prioritize important receipts. Related receipts and invoices can also be automatically classified based on project tags set by the user. By filtering based on the user's current project and areas of interest, important information can be prioritized. Some or all of the above-described processing in the reception unit can be performed using, or without, AI. For example, the reception unit can input data on the user's project and areas of interest into a generation AI and have the generation AI perform the filtering.

[0070] The reception unit can estimate the user's emotions and determine the priority of photos to be accepted based on the estimated user emotions. For example, the reception unit estimates the user's emotions and determines the priority of photos to be accepted based on the estimated user emotions. For example, if the user is stressed, important receipts and invoices can be accepted first. Furthermore, if the user is relaxed, the priority of photos to be accepted can be flexibly set. Furthermore, if the user is busy, important photos can be accepted first, leaving less important photos for later. This allows important information to be processed preferentially by determining the priority of photos to be accepted based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using, for example, an AI. For example, the reception unit can input the user's emotion data into the generation AI and have the generation AI execute emotion estimation and photo priority determination.

[0071] The reception unit can prioritize receiving highly relevant information by taking into account the user's geographical location information when receiving receipts or invoices. For example, the reception unit prioritizes receiving highly relevant information by taking into account the user's geographical location information when receiving receipts or invoices. For example, if the user is in a specific area, receipts and invoices related to that area can be prioritized. Furthermore, receipts for related stores and services can be prioritized based on the user's current location. Furthermore, if the user is traveling, receipts and invoices related to the travel destination can be prioritized. In this way, highly relevant information can be prioritized by taking the user's geographical location information into account. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's geographical location information to the generation AI and cause the generation AI to prioritize receiving highly relevant information.

[0072] The reception unit can analyze the user's social media activity and accept related information when accepting receipts or invoices. For example, the reception unit can analyze the user's social media activity and accept related information when accepting receipts or invoices. For example, the reception unit can prioritize accepting receipts for stores or services that the user has shared on social media. It can also prioritize accepting receipts or invoices for categories of interest based on the user's social media activity. It can also prioritize accepting receipts or invoices related to events or activities that the user has mentioned on social media. This allows the user's social media activity to be analyzed and related information to be accepted preferentially. Some or all of the above-described processing in the reception unit can be performed using, or without, AI. For example, the reception unit can input the user's social media data into a generation AI and cause the generation AI to accept related information.

[0073] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user emotions. For example, the analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide a simple, highly visible analysis result. If the user is relaxed, the analysis unit can provide a detailed analysis result. If the user is in a hurry, the analysis unit can provide a concise analysis result that focuses on the main points. By adjusting the presentation method of the analysis according to the user's emotions, it is possible to provide an analysis result that is easy for the user to understand. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the analysis unit can input the user's emotion data into the generation AI and cause the generation AI to estimate emotions and adjust the presentation method of the analysis.

[0074] The analysis unit can adjust the level of detail of the analysis based on the importance of the receipt or invoice during analysis. The analysis unit, for example, adjusts the level of detail of the analysis based on the importance of the receipt or invoice during analysis. For example, a detailed analysis is performed for important receipts or invoices. A simplified analysis can also be performed for receipts or invoices with low importance. The level of detail of the analysis can also be adjusted based on the importance specified by the user. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the receipt or invoice. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input importance data of receipts or invoices to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0075] The analysis unit can apply different analysis algorithms depending on the category of the receipt or invoice during analysis. For example, the analysis unit applies different analysis algorithms depending on the category of the receipt or invoice during analysis. For example, an analysis algorithm dedicated to food expenses can be applied to a food receipt. An analysis algorithm dedicated to utility expenses can also be applied to a utility invoice. An analysis algorithm dedicated to transportation expenses can also be applied to a transportation receipt. In this way, by applying different analysis algorithms depending on the category of the receipt or invoice, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input category data of the receipt or invoice into the generation AI and cause the generation AI to apply different analysis algorithms.

[0076] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, the analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis result. If the user is relaxed, the analysis unit can provide a detailed analysis result. If the user is excited, the analysis unit can provide an analysis result with visually stimulating effects. By adjusting the length of the analysis based on the user's emotions, the analysis unit can provide an optimal analysis result for the user. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI estimate the emotion and adjust the length of the analysis.

[0077] The analysis unit can determine the analysis priority based on the submission date of receipts and invoices during analysis. The analysis unit, for example, determines the analysis priority based on the submission date of receipts and invoices during analysis. For example, the analysis unit can prioritize analysis of recently submitted receipts and invoices. Alternatively, the analysis unit can prioritize analysis of the most recent receipts and invoices, leaving older receipts and invoices for later analysis. The analysis priority can also be adjusted based on the submission date specified by the user. This enables efficient analysis by determining the analysis priority based on the submission date of receipts and invoices. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input receipt and invoice submission date data into the generation AI and have the generation AI determine the analysis priority.

[0078] The analysis unit can adjust the order of analysis based on the relevance of receipts and invoices during analysis. The analysis unit, for example, adjusts the order of analysis based on the relevance of receipts and invoices during analysis. For example, receipts and invoices in the same category are analyzed together. Also, highly related receipts and invoices can be analyzed preferentially. The order of analysis can also be adjusted based on the relevance specified by the user. This enables efficient analysis by adjusting the order of analysis based on the relevance of receipts and invoices. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input relevance data of receipts and invoices to the generation AI and have the generation AI adjust the order of analysis.

[0079] The reporting unit can estimate the user's emotions and adjust the display method of the report based on the estimated user emotions. For example, the reporting unit can estimate the user's emotions and adjust the display method of the report based on the estimated user emotions. For example, if the user is nervous, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. If the user is in a hurry, a display method that focuses on the main points can be provided. This allows the display method of the report to be adjusted according to the user's emotions, thereby providing a report result that is easy for the user to understand. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the reporting unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the reporting unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion and adjust the display method of the report.

[0080] The reporting unit can optimize the current reporting by referring to past reporting data when reporting. For example, the reporting unit optimizes the current reporting by referring to past reporting data when reporting. For example, the reporting unit suggests an optimal reporting method based on the user's past reporting data. It can also automatically select frequently used categories from the past reporting data. It can also analyze the user's past reporting data and suggest the most efficient reporting method. In this way, by referring to the past reporting data, the current reporting can be optimized, enabling efficient reporting. Some or all of the above-mentioned processing in the reporting unit may be performed using, for example, AI, or may be performed without using AI. For example, the reporting unit can input past reporting data into a generation AI and have the generation AI optimize the current reporting.

[0081] The reporting unit can apply different reporting methods to different categories of receipts and invoices when reporting. For example, the reporting unit applies different reporting methods to different categories of receipts and invoices when reporting. For example, a reporting method dedicated to food expenses can be applied to food receipts. A reporting method dedicated to utility expenses can also be applied to utility invoices. A reporting method dedicated to transportation expenses can also be applied to transportation receipts. In this way, by applying different reporting methods to different categories of receipts and invoices, the accuracy of reporting is improved. Some or all of the above-mentioned processing in the reporting unit may be performed using, or without, AI, for example. For example, the reporting unit can input category data of receipts and invoices into a generation AI and have the generation AI apply different reporting methods.

[0082] The reporting unit can estimate the user's emotions and adjust the importance of the reports based on the estimated user emotions. For example, the reporting unit can estimate the user's emotions and adjust the importance of the reports based on the estimated user emotions. For example, when the user is feeling stressed, important reporting items can be displayed with priority. Furthermore, when the user is relaxed, the importance of the reporting items can be flexibly set. Furthermore, when the user is busy, important reporting items can be displayed with priority, leaving less important reporting items for later display. This allows important reporting items to be displayed with priority by adjusting the importance of the reports according to the user's emotions. Estimation of emotions is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. Examples of the generation AI include, but are not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above-described processing in the reporting unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reporting unit can input the user's emotion data into the generation AI and cause the generation AI to estimate emotions and adjust the importance of the reports.

[0083] The reporting department can analyze changes in the reporting based on the submission dates of receipts and invoices at the time of reporting. For example, the reporting department can analyze changes in the reporting based on the submission dates of receipts and invoices at the time of reporting. For example, the reporting department can analyze changes in the reporting based on recently submitted receipts and invoices. The reporting department can also analyze changes in the reporting based on older submitted receipts and invoices. The reporting department can also analyze changes in the reporting based on the submission date specified by the user. This enables efficient reporting by analyzing changes in the reporting based on the submission dates of receipts and invoices. Some or all of the above-mentioned processing in the reporting department may be performed using, for example, AI, or may be performed without using AI. For example, the reporting department can input data on the submission dates of receipts and invoices into a generation AI and have the generation AI analyze changes in the reporting.

[0084] The reporting department can analyze reporting by referring to market data related to receipts and invoices at the time of reporting. For example, the reporting department analyzes reporting by referring to market data related to receipts and invoices at the time of reporting. For example, the reporting department analyzes changes in reporting based on the relevant market data. The reporting department can also automatically select frequently used categories from the market data. The reporting department can also analyze market data and propose the most efficient reporting method. In this way, by referring to the relevant market data, the accuracy of reporting is improved. Some or all of the above-mentioned processing in the reporting department may be performed using, for example, AI, or may be performed without using AI. For example, the reporting department can input the relevant market data into a generation AI and have the generation AI perform analysis of the reporting.

[0085] The advice unit can estimate the user's emotions and determine the priority of advice based on the estimated user emotions. The advice unit, for example, estimates the user's emotions and determines the priority of advice based on the estimated user emotions. For example, if the user is feeling stressed, important advice can be provided preferentially. Also, if the user is relaxed, the priority of advice can be flexibly set. Also, if the user is busy, less important advice can be put off and important advice can be provided preferentially. In this way, by determining the priority of advice according to the user's emotions, important advice can be provided preferentially. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the advice unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the advice unit can input the user's emotion data into the generation AI and cause the generation AI to estimate emotions and determine the priority of advice.

[0086] The advice unit can improve the accuracy of advice by taking into account the interrelationships of income and expenditure data when providing advice. The advice unit, for example, improves the accuracy of advice by taking into account the interrelationships of income and expenditure data when providing advice. For example, the advice unit analyzes the balance between income and expenditure and suggests the optimal savings method. The advice unit can also suggest the optimal investment method by taking into account the interrelationships of income and expenditure data. The advice unit can also provide advice on efficient asset formation based on income and expenditure data. In this way, the accuracy of advice is improved by taking into account the interrelationships of income and expenditure data. Some or all of the above-mentioned processing in the advice unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice unit can input income and expenditure data into a generation AI and have the generation AI analyze the interrelationships and improve the accuracy of advice.

[0087] The advice unit can provide advice taking into consideration the user's attribute information when providing advice. For example, the advice unit can provide advice taking into consideration the user's attribute information when providing advice. For example, the advice unit can propose an optimal savings method based on the user's age and occupation. The advice unit can also propose an optimal insurance plan based on the user's family composition. The advice unit can also propose an optimal investment method based on the user's lifestyle. This makes it possible to provide more appropriate advice by taking into consideration the user's attribute information. Some or all of the above-mentioned processing in the advice unit may be performed using AI, for example, or may be performed without using AI. For example, the advice unit can input the user's attribute information into the generation AI and cause the generation AI to execute advice based on the attribute information.

[0088] The advice unit can estimate the user's emotions and adjust the display method of the advice based on the estimated user emotions. For example, the advice unit can estimate the user's emotions and adjust the display method of the advice based on the estimated user emotions. For example, if the user is nervous, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. If the user is in a hurry, a display method that focuses on the main points can be provided. This allows the advice display method to be adjusted according to the user's emotions, making it possible to provide advice that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the advice unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the advice unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion and adjust the display method of the advice.

[0089] The advice unit can provide advice taking into consideration the geographical distribution of income and expenditure data when providing advice. The advice unit, for example, can provide advice taking into consideration the geographical distribution of income and expenditure data when providing advice. For example, the advice unit can suggest an optimal savings method based on the user's place of residence. The advice unit can also suggest an optimal investment method based on the user's place of work. The advice unit can also suggest an optimal insurance plan based on the user's travel destination. In this way, more appropriate advice can be provided by taking the geographical distribution of income and expenditure data into consideration. Some or all of the above-described processing in the advice unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice unit can input geographical distribution data of income and expenditure data into the generation AI and cause the generation AI to execute advice based on the geographical distribution.

[0090] The advice unit can improve the accuracy of advice by referring to literature related to the income and expenditure data when providing advice. The advice unit, for example, improves the accuracy of advice by referring to literature related to the income and expenditure data when providing advice. For example, the advice unit can suggest an optimal savings method by referring to the latest research papers related to the income and expenditure data. It can also suggest an optimal investment method by referring to market reports related to the income and expenditure data. It can also provide advice on efficient asset formation by referring to specialized books related to the income and expenditure data. In this way, the accuracy of advice is improved by referring to literature related to the income and expenditure data. Some or all of the above-mentioned processing in the advice unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice unit can input literature data related to the income and expenditure data into the generation AI and cause the generation AI to execute advice based on the literature. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, declaration unit, and advice unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14, and allows a user to send photos of receipts and invoices using a messaging app. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the photos using AI and performs accounting. The declaration unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and automatically processes information necessary for tax returns. The advice unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the user's income and expenditure data and provides optimal asset formation advice. === Hard Collateral 1-2 === Each of the multiple elements including the above-described reception unit, analysis unit, declaration unit, and advice unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214, and allows a user to send photos of receipts or invoices using a messaging app. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the photos using AI and performs accounting. The declaration unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and automatically processes information necessary for tax returns. The advice unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the user's income and expenditure data and provides optimal asset formation advice. === Hard Collateral 1-3 === Each of the multiple elements including the above-described reception unit, analysis unit, declaration unit, and advice unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset-type terminal 314, and allows a user to send photos of receipts and invoices using a messaging app. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the photos using AI and performs accounting. The declaration unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and automatically processes information necessary for filing a tax return. The advice unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the user's income and expenditure data and provides optimal asset formation advice. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, declaration unit, and advice unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414, and allows a user to send photos of receipts and invoices using a messaging app. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the photos using AI and performs accounting. The declaration unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and automatically processes information necessary for tax returns. The advice unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the user's income and expenditure data and provides optimal asset formation advice.

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

[0092] The reception unit can estimate the user's emotions and adjust the timing of receiving receipts and invoices based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can send a reminder to adjust the reception timing. If the user is relaxed, the reception timing can be flexibly set to match the user's pace. If the user is busy, the reception timing can be automatically delayed and a reminder sent again later. This allows the reception timing to be adjusted according to the user's emotions, reducing the user's stress and enabling efficient reception. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit can input the user's emotion data into the generation AI and have the generation AI estimate the emotion and adjust the reception timing.

[0093] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user emotions. For example, if the user is nervous, it can provide a simple, highly visible analysis result. If the user is relaxed, it can provide a detailed analysis result. If the user is in a hurry, it can provide a concise analysis result that focuses on the main points. By adjusting the presentation method of the analysis according to the user's emotions, it is possible to provide an analysis result that is easy for the user to understand. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI estimate emotions and adjust the presentation method of the analysis.

[0094] The reporting unit can estimate the user's emotions and adjust the display method of the report based on the estimated user emotions. For example, if the user is nervous, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. If the user is in a hurry, a display method that focuses on the main points can be provided. By adjusting the display method of the report according to the user's emotions, it is possible to provide a report result that is easy for the user to understand. The estimation of emotions is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the reporting unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reporting unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion and adjust the display method of the report.

[0095] The advice unit can estimate the user's emotions and determine the priority of advice based on the estimated user emotions. For example, if the user is feeling stressed, important advice can be provided preferentially. Furthermore, if the user is relaxed, the priority of advice can be flexibly set. Furthermore, if the user is busy, less important advice can be postponed and important advice can be provided preferentially. Thus, by determining the priority of advice according to the user's emotions, important advice can be provided preferentially. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the advice unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the advice unit can input the user's emotion data into the generation AI and cause the generation AI to estimate emotions and determine the priority of advice.

[0096] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the receipt or invoice. For example, a detailed analysis can be performed on important receipts or invoices. A simplified analysis can also be performed on receipts or invoices with low importance. The level of detail of the analysis can also be adjusted based on the importance specified by the user. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the receipt or invoice. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input importance data of receipts or invoices to the generation AI and have the generation AI adjust the level of detail of the analysis.

[0097] The reception unit can analyze the user's past submission history and select the optimal reception method. For example, it can analyze the time periods during which the user frequently submitted work in the past and send reminders during those time periods. It can also prioritize and suggest submission methods (e.g., photos, PDFs) that the user has used in the past. It can also predict a tendency for submissions to be made on specific days of the week or during specific time periods based on the user's past submission history and suggest the optimal reception method. This allows for efficient reception by analyzing the user's past submission history and suggesting the optimal reception method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's submission history data into a generation AI and have the generation AI select the optimal reception method.

[0098] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the receipt or invoice. For example, an analysis algorithm dedicated to food expenses can be applied to a food receipt. An analysis algorithm dedicated to utility expenses can be applied to a utility invoice. An analysis algorithm dedicated to transportation expenses can be applied to a transportation receipt. By applying different analysis algorithms depending on the category of the receipt or invoice, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input category data of the receipt or invoice into the generation AI and have the generation AI apply different analysis algorithms.

[0099] When filing a declaration, the declaration unit can optimize the current declaration by referring to past declaration data. For example, the declaration unit can suggest the optimal declaration method based on the user's past declaration data. It can also automatically select frequently used categories from the past declaration data. It can also analyze the user's past declaration data and suggest the most efficient declaration method. In this way, by referring to the past declaration data, the current declaration can be optimized and an efficient declaration can be made. Some or all of the above-mentioned processing in the declaration unit may be performed using, for example, AI, or may be performed without using AI. For example, the declaration unit can input past declaration data into a generation AI and have the generation AI optimize the current declaration.

[0100] When providing advice, the advice unit can improve the accuracy of the advice by taking into account the interrelationships between income and expenditure data. For example, the advice unit can analyze the balance between income and expenditure and suggest the optimal savings method. The advice unit can also suggest the optimal investment method by taking into account the interrelationships between income and expenditure data. The advice unit can also provide advice on efficient asset formation based on income and expenditure data. In this way, the accuracy of the advice is improved by taking into account the interrelationships between income and expenditure data. Some or all of the above-mentioned processing in the advice unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice unit can input income and expenditure data into a generation AI and have the generation AI analyze the interrelationships and improve the accuracy of the advice.

[0101] When providing advice, the advice unit can take the user's attribute information into consideration. For example, the advice unit can suggest the optimal savings method based on the user's age and occupation. The advice unit can also suggest the optimal insurance plan based on the user's family structure. The advice unit can also suggest the optimal investment method based on the user's lifestyle. This allows more appropriate advice to be provided by taking the user's attribute information into consideration. Some or all of the above-mentioned processing in the advice unit may be performed using AI, for example, or may be performed without using AI. For example, the advice unit can input the user's attribute information into the generation AI and have the generation AI execute advice based on the attribute information.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The reception unit accepts photos of receipts and bills sent by users via a messaging app. For example, a user can take a photo of a meal receipt or utility bill and send it to a messaging app. The reception unit accepts these photos and inputs them into the AI. Step 2: The analysis unit uses AI to analyze the photos received by the reception unit and perform accounting. For example, AI recognizes the text information contained in the photos and classifies them into the appropriate category. For example, a meal receipt would be classified as "food expenses" and a utility bill as "utility expenses." The analysis unit can also use OCR technology to convert the text information in the photos into text data for accounting purposes. Step 3: The declaration section automatically processes the information necessary for filing a tax return based on the information analyzed by the analysis section. For example, the declaration section automatically calculates information such as income, expenses, and deductions, and prepares a tax return. The declaration section can also automatically process the information necessary for filing a tax return based on the information stored in the database. Step 4: The advice unit analyzes the user's income and expenditure data based on the information processed by the reporting unit and provides optimal asset formation advice. For example, the advice unit analyzes the balance between income and expenditure and provides savings and investment advice. The advice unit can also use data mining and statistical analysis to analyze the user's income and expenditure data and provide optimal asset formation advice. As a result, the personal financial planner system according to the embodiment can automatically analyze the user's income and expenditure data and provide optimal asset formation advice. Some or all of the above-described processing in the advice unit may be performed using, for example, AI, or may be performed without AI. For example, the advice unit can provide advice using an AI model that inputs the user's income and expenditure data and outputs optimal asset formation advice. Furthermore, the advice unit can secure the user's income and expenditure data and utilize it in various financial services. For example, the advice unit can perform loan screening and insurance proposals based on the user's income and expenditure data. This allows the user to efficiently manage their assets.

[0104] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating 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.

[0105] 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 the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0106] 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.

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

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

[0109] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0110] The data processing device 12 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.

[0111] 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.

[0112] 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.

[0113] 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).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating 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.

[0121] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0122] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is 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.

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

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

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0135] 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.

[0136] 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.

[0137] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0138] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is 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.

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

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

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0152] 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.

[0153] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the 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.

[0154] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0155] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is 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.

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

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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."

[0163] 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.

[0164] 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.

[0165] 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.

[0166] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] [Explanation of symbols]

[0176] 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 reception section for receiving a photo of a receipt or invoice; an analysis unit that analyzes and categorizes the photographs received by the reception unit; a declaration unit that processes information necessary for filing a final tax return based on the information analyzed by the analysis unit; An advice unit analyzes the income and expenditure data of the user based on the information processed by the reporting unit and provides advice on asset formation. A system characterized by:

2. The reception unit Accept photos of receipts or invoices using messaging apps 2. The system of claim 1.

3. The analysis unit AI recognizes text information contained in photos and classifies them into appropriate categories 2. The system of claim 1.

4. The reporting unit Automatically process information required for tax returns using AI 2. The system of claim 1.

5. The advice unit AI analyzes users' income and expenditure data and provides advice on savings and investments 2. The system of claim 1.

6. The advice unit Secure user income and expenditure data and utilize it across multiple financial services 2. The system of claim 1.

7. The reception unit Estimate user emotions and adjust the timing of accepting photos of receipts and invoices based on the estimated user emotions 2. The system of claim 1.

8. The reception unit Analyze the user's past submission history and select the optimal acceptance method 2. The system of claim 1.

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