System

Abstract

To provide a system for efficiently providing saving advice on the basis of household account book data.SOLUTION: A system includes a household account book input part, a report generation part, and an advice provision part. The household account book input part inputs household account book data of the user. A report generation part generates a monthly end report on the basis of the household account book data inputted by the household account book input part. The advice providing unit provides saving advice based on the month-end report generated by the report generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to efficiently provide money-saving advice based on household account book data.

[0005] The system according to the embodiment aims to efficiently provide saving advice based on household account book data. [Means for solving the problem]

[0006] The system according to the embodiment includes a household account book input unit, a report generation unit, and an advice providing unit. The household account book input unit inputs household account book data of a user. The report generation unit generates a month-end report based on the household account book data input by the household account book input unit. The advice providing unit provides saving advice based on the month-end report generated by the report generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently provide saving advice based on household account book data. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) In the household management system according to an embodiment of the present invention, a user inputs monthly household accounts, and a generation AI creates a report at the end of the month and provides advice on saving money for each household. This allows the household management system to organize the user's household accounts and reduce wasteful spending.

[0029] A household management system according to an embodiment includes a household account book input unit, a report generation unit, and an advice providing unit. The household account book input unit inputs a user's household account book data. For example, the user inputs their monthly income and expenses into the household account book. The report generation unit generates a month-end report based on the household account book data input by the household account book input unit. For example, a generation AI analyzes the input household account book data and creates a month-end report. The advice providing unit provides savings advice based on the month-end report generated by the report generation unit. For example, the generation AI provides savings advice for each household based on the month-end report. As a result, the household management system according to an embodiment generates a month-end report based on the user's household account book data and provides savings advice, enabling the user to organize their household finances and reduce unnecessary spending.

[0030] In the household ledger input unit, the generation AI automatically classifies expenditure items based on data entered by the user and can request confirmation from the user. For example, in the household ledger input unit, the generation AI analyzes expenditure data entered by the user and automatically classifies them into categories such as food, utilities, and entertainment. The classification results are presented to the user and confirmation is requested, thereby maintaining data accuracy. In addition, in the household ledger input unit, the generation AI analyzes expenditure data entered by the user in real time and classifies it into the appropriate category. For example, supermarket purchases are classified as food expenses and movie tickets as entertainment expenses, and confirmation is requested from the user. In addition, in the household ledger input unit, the generation AI automatically analyzes expenditure data entered by the user and classifies it by category. The classification results are notified to the user and corrections are requested as necessary, thereby maintaining data consistency. In this way, the accuracy of the data can be maintained by automatically classifying data entered by the user and requesting confirmation.

[0031] The household account book input unit allows users to simply upload a photo of a receipt, and the generation AI automatically extracts the data and reflects it in the household account book. For example, in the household account book input unit, a user takes a photo of a receipt and uploads it to the app. The generation AI performs image analysis, automatically extracts expenditure items and amounts, and reflects them in the household account book. In addition, when a user uploads a photo of a receipt, the generation AI extracts text information using OCR technology and automatically enters the expenditure items and amounts in the household account book. In addition, when a user uploads a photo of a receipt, the generation AI performs image analysis, automatically extracts expenditure data, and reflects it in the household account book. This allows data to be extracted and reflected in the household account book simply by uploading a photo of the receipt, eliminating the need for manual input.

[0032] The household account book input unit uses voice recognition technology to allow the user to input data simply by speaking. For example, when the user speaks about expenditure details, the voice recognition technology converts the content into text and automatically enters it into the household account book. For example, input is completed by simply speaking, "I spent 5,000 yen at the supermarket." The household account book input unit also uses voice recognition technology to build a system that allows the user to input expenditure data simply by speaking. For example, if the user speaks, "I spent 3,000 yen on gas," it is automatically entered into the household account book as transportation expenses. Furthermore, when the user enters expenditure details by voice, the voice recognition technology analyzes the content, classifies it into the appropriate category, and reflects it in the household account book. This allows the user to input data simply by speaking, eliminating the hassle of inputting data.

[0033] The household account book input unit can link the household account book data with other household management apps to achieve centralized data management. The household account book input unit, for example, links the household account book data with other household management apps to achieve centralized data management. For example, it links with a bank app or a credit card app to automatically import expenditure data. The household account book input unit also links data with other household management apps to ensure centralized data management even when a user uses multiple apps. For example, it links with a household account book app to automatically synchronize expenditure data. The household account book input unit also links the household account book data with other household management apps to enable a user to manage all expenditure data on a single platform. In this way, by linking the household account book data with other household management apps and achieving centralized data management, data is consistently managed even when a user uses multiple apps.

[0034] The report generation unit can predict this month's spending trends based on past data and include the prediction results in the report. In the report generation unit, for example, the generation AI analyzes past household ledger data and predicts this month's spending trends. The prediction results are included in the end-of-month report and presented to the user. In addition, the report generation unit predicts this month's spending trends based on past spending data and reflects the prediction results in the report. For example, the report generation unit predicts this month's food expenses and utility expenses from past data. In addition, the report generation unit predicts this month's spending trends based on past household ledger data and predicts this month's spending trends. By including the prediction results in the end-of-month report, the user can see into future spending. In this way, by predicting this month's spending trends based on past data and including the prediction results in the report, the user can see into future spending.

[0035] The report generation unit can add detailed analysis results for each expense item to the report, allowing the user to understand specific spending patterns. For example, the report generation unit adds detailed analysis results for each expense item to an end-of-month report, allowing the user to understand specific spending patterns. For example, the report generation unit shows a breakdown of expenses for each item, such as food expenses, utility expenses, and entertainment expenses. The report generation unit also has the generation AI perform a detailed analysis for each expense item and reflect the results in the end-of-month report. For example, the breakdown of food expenses shows expenses for eating out and expenses for ingredients. The report generation unit also adds detailed analysis results for each expense item to the end-of-month report, allowing the user to understand specific spending patterns. For example, the breakdown of utility expenses shows electricity, gas, and water. In this way, by adding detailed analysis results for each expense item to the report, the user can understand specific spending patterns.

[0036] The report generation unit can display reports in different formats (e.g., graphs and charts) to make it easier for the user to understand visually. For example, the report generation unit displays a month-end report in graph or chart format to make it easier for the user to understand visually. For example, the expenditure percentage is shown in a pie chart. The report generation unit also displays reports in different formats to make it easier for the user to understand visually. For example, the monthly expenditure trend is shown in a line graph. The report generation unit also displays a month-end report in graph or chart format to make it easier for the user to understand visually. For example, a comparison between expenditure items is shown in a bar graph. In this way, by displaying the report in different formats, it is easier for the user to understand visually.

[0037] The report generation unit may add a function that allows reports to be shared with other family members, thereby facilitating spending management across the entire family. The report generation unit may add a function that allows, for example, end-of-month reports to be shared with other family members, thereby facilitating spending management across the entire family. For example, reports may be shared via email or social media. The report generation unit may also add a report sharing function that allows family members to add comments to reports. The report generation unit may also add a function that allows end-of-month reports to be shared with other family members, thereby facilitating spending management across the entire family. For example, family members may provide feedback on shared reports. Thus, by adding a function that allows reports to be shared with other family members, spending management across the entire family may be promoted.

[0038] The advice providing unit allows the generation AI to analyze the effectiveness of past money-saving advice and prioritize providing the most effective advice. For example, the advice providing unit allows the generation AI to analyze the effectiveness of past money-saving advice and prioritize providing the most effective advice. For example, if reviewing mobile phone charges is effective, it will prioritize suggesting that. The advice providing unit also allows the generation AI to analyze the effectiveness of past money-saving advice and prioritize providing advice that was highly effective. For example, if a method for saving on food costs is effective, it will prioritize suggesting that. The advice providing unit also allows the generation AI to analyze the effectiveness of past money-saving advice and prioritize providing the most effective advice. For example, if a method for saving on utility bills is effective, it will prioritize suggesting that. In this way, by analyzing the effectiveness of past money-saving advice and prioritize providing the most effective advice, it is possible to suggest the optimal money-saving method for the user.

[0039] The advice providing unit can include a specific amount of savings and detailed steps on how to save in the content of the advice. For example, the advice providing unit may include a specific amount of savings and detailed steps on how to save in the saving advice provided by the generation AI. For example, the advice providing unit may present a specific amount, such as 2,000 yen saved per month by changing a mobile phone plan. The advice providing unit may also include a specific amount of savings and detailed steps on how to save in the content of the advice. For example, as a way to save on food costs, the advice providing unit may present specific steps, such as saving 5,000 yen per month by buying in bulk once a week. The advice providing unit may also include a specific amount of savings and detailed steps on how to save in the saving advice provided by the generation AI. For example, as a way to save on utility bills, the advice providing unit may present specific steps, such as saving 10,000 yen per year by using eco-friendly home appliances. In this way, by including a specific amount of savings and detailed steps on how to save in the content of the advice, it becomes easier for the user to actually put savings into practice.

[0040] The advice providing unit can provide the advice in video format, making it easier to understand through visual and auditory channels. For example, the advice providing unit provides the saving advice provided by the generation AI in video format, making it easier to understand through visual and auditory channels. For example, a video explaining how to save money is created and provided to the user. The advice providing unit also provides the advice in video format, making it easier to understand through visual and auditory channels. For example, a video explaining how to save money using animation is created and provided to the user. The advice providing unit also provides the saving advice provided by the generation AI in video format, making it easier to understand through visual and auditory channels. For example, a video demonstrating how to save money is created and provided to the user. By providing the advice in video format, it becomes easier to understand through visual and auditory channels.

[0041] The advice providing unit can add a function that allows advice to be shared with other users, thereby promoting information exchange within the community. The advice providing unit can, for example, add a function that allows money-saving advice provided by the generation AI to be shared with other users, thereby promoting information exchange within the community. For example, it can provide a function for sharing advice on social media. The advice providing unit can also add a function that allows advice to be shared with other users, thereby promoting information exchange within the community. For example, it can provide a bulletin board for sharing advice. The advice providing unit can also add a function that allows money-saving advice provided by the generation AI to be shared with other users, thereby promoting information exchange within the community. For example, it can provide a chat function for sharing advice. By adding a function that allows advice to be shared with other users, it can promote information exchange within the community.

[0042] The advice providing unit allows the generation AI to analyze the user's past expenditure data and suggest the best saving method for individual needs. For example, the generation AI analyzes the user's past expenditure data and suggests the best saving method for individual needs. For example, it suggests a specific method for reducing hobby expenses. The advice providing unit also allows the generation AI to analyze the user's past expenditure data and suggest the best saving method for individual needs. For example, it suggests a specific method for reducing food expenses. The advice providing unit also allows the generation AI to analyze the user's past expenditure data and suggest the best saving method for individual needs. For example, it suggests a specific method for reducing utility expenses. In this way, by analyzing the user's past expenditure data and suggesting the best saving method for individual needs, it is possible to provide the best saving method for the user.

[0043] The advice providing unit can collect detailed information about the user's hobbies and non-negotiables and customize advice based on that. For example, the generation AI collects detailed information about the user's hobbies and non-negotiables and customizes advice based on that. For example, it suggests specific ways to reduce hobby expenses. The advice providing unit can also collect information about the user's hobbies and non-negotiables and customize advice based on that. For example, it suggests specific ways to reduce hobby expenses. The advice providing unit can also collect detailed information about the user's hobbies and non-negotiables and customize advice based on that. For example, it suggests specific ways to reduce hobby expenses. In this way, by collecting detailed information about the user's hobbies and non-negotiables and customizing advice based on that, it is possible to provide more appropriate advice to the user.

[0044] The advice providing unit can customize advice according to different life stages (for example, while raising children, after retirement). In the advice providing unit, for example, the generation AI provides advice according to the user's life stage. For example, to a user who is raising children, it suggests childcare-related saving methods. In addition, in the advice providing unit, the generation AI customizes the advice according to the user's life stage. For example, to a user who has retired, it suggests saving methods suitable for pension life. In addition, in the advice providing unit, the generation AI provides advice according to the user's life stage. For example, to a newly married user, it suggests saving methods suitable for their new life. In this way, by customizing advice according to different life stages, it is possible to provide more appropriate advice to the user.

[0045] The advice providing unit may add a function that allows advice to be shared with other family members, thereby facilitating spending management across the entire family. The advice providing unit may, for example, add a function that allows advice provided by the generation AI to be shared with other family members, thereby facilitating spending management across the entire family. For example, advice may be shared via email or social media. The advice providing unit may also add an advice sharing function to promote spending management across the entire family. For example, it may provide a function that allows family members to add comments to advice. The advice providing unit may also add a function that allows advice provided by the generation AI to be shared with other family members, thereby facilitating spending management across the entire family. For example, family members may provide feedback on shared advice. In this way, by adding a function that allows advice to be shared with other family members, spending management across the entire family may be promoted.

[0046] The advice providing unit can display the most relevant advertisement based on the user's expenditure data, thereby maximizing the advertising effectiveness. The advice providing unit, for example, displays the most relevant advertisement based on the user's expenditure data, thereby maximizing the advertising effectiveness. For example, a discount advertisement for ingredients is displayed to a user with high food expenses. The advice providing unit also displays the most relevant advertisement based on the expenditure data, thereby maximizing the advertising effectiveness. For example, an advertisement for eco-friendly home appliances is displayed to a user with high utility bills. The advice providing unit also displays the most relevant advertisement based on the user's expenditure data, thereby maximizing the advertising effectiveness. For example, a discount advertisement for movies or events is displayed to a user with high entertainment expenses. In this way, the advertising effectiveness can be maximized by displaying the most relevant advertisement based on the user's expenditure data.

[0047] The advice providing unit can optimize the timing of advertisement display and display advertisements at the timing when the user is most interested. The advice providing unit, for example, optimizes the timing of advertisement display and displays advertisements at the timing when the user is most interested. For example, it displays relevant advertisements when the user is entering household accounts. The advice providing unit also optimizes the timing of advertisement display and displays advertisements at the timing when the user is most interested. For example, it displays relevant advertisements when the user is checking an end-of-month report. The advice providing unit also optimizes the timing of advertisement display and displays advertisements at the timing when the user is most interested. For example, it displays relevant advertisements when the user is receiving money-saving advice. In this way, the effectiveness of the advertisements can be maximized by optimizing the timing of advertisement display and displaying advertisements at the timing when the user is most interested.

[0048] The advice providing unit may provide the advertisement in a video format to make it easier to understand through visual and auditory means. For example, the advice providing unit may provide the advertisement in a video format to make it easier to understand through visual and auditory means. For example, a video advertisement explaining how to use a product may be displayed. The advice providing unit may also provide the advertisement in a video format to make it easier to understand through visual and auditory means. For example, an animated advertisement explaining the benefits of a service may be displayed. The advice providing unit may also provide the advertisement in a video format to make it easier to understand through visual and auditory means. For example, a video advertisement demonstrating the features of a product may be displayed. Thus, providing the advertisement in a video format makes it easier to understand through visual and auditory means.

[0049] The advice providing unit can add a function that allows advertisements to be shared with other users, thereby promoting information exchange within the community. The advice providing unit, for example, adds a function that allows advertisements to be shared with other users, thereby promoting information exchange within the community. For example, it provides a function for sharing advertisements on a social networking site. The advice providing unit can also add a function that allows advertisements to be shared with other users, thereby promoting information exchange within the community. For example, it provides a bulletin board for sharing advertisements. The advice providing unit can also add a function that allows advertisements to be shared with other users, thereby promoting information exchange within the community. For example, it provides a chat function for sharing advertisements. By adding a function that allows advertisements to be shared with other users, it is possible to promote information exchange within the community.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The household management system may further include a health management unit. The health management unit can analyze the relationship between health and expenditures by collecting the user's health data and linking it with household account book data. For example, it can analyze the frequency with which the user goes to the gym and their health condition, and indicate how effective expenditures for maintaining their health are. The health management unit can also collect the user's dietary data and analyze the relationship between food expenses and their health condition. For example, it can suggest healthy meals to a user who eats out frequently. The health management unit can also collect the user's exercise data and analyze the relationship between exercise costs and their health condition. This allows the user to manage their household finances while taking into account the balance between health and expenditures.

[0052] The household management system may further include an education management unit. The education management unit collects the user's education-related expenditure data and can predict future education expenses. For example, it can analyze the cost of children's tuition and extracurricular activities and create a future expenditure plan. The education management unit can also provide the user with information on scholarships and grants available to the user. For example, it can present a list of available scholarships to families that meet certain conditions. The education management unit can also suggest ways to save money based on the user's education-related expenditure data. For example, it can suggest ways to purchase textbooks or use online learning. This allows the user to efficiently manage their education expenses.

[0053] The household management system may further include a travel management unit. The travel management unit can collect travel-related expenditure data from the user and optimize travel plans. For example, it can suggest the best travel time and cost based on past travel data. The travel management unit can also provide information on travel discounts and benefits that the user can use. For example, it can present travel benefits that can be obtained by using a specific credit card. The travel management unit can also suggest ways to save money based on the user's travel-related expenditure data. For example, it can suggest early bookings or off-season travel. This allows the user to efficiently manage travel expenses.

[0054] The household management system may further include an energy management unit. The energy management unit can collect the user's energy consumption data and optimize energy efficiency. For example, it can analyze past electricity and gas bills and show energy consumption trends. The energy management unit can also suggest energy-saving methods that the user can use. For example, it can suggest the use of eco-friendly home appliances or the introduction of insulation materials. The energy management unit can also suggest energy-saving methods based on the user's energy consumption data. For example, it can save money by adjusting the time electricity is used. This allows the user to efficiently manage their energy costs.

[0055] The household management system may further include an investment management unit. The investment management unit may collect the user's investment data and optimize the investment portfolio. For example, it may analyze past investment performance and make investment suggestions that take into account the balance between risk and return. The investment management unit may also provide information on investment products and services available to the user. For example, it may present information on specific investment trusts and stocks. The investment management unit may also suggest ways to save money based on the user's investment data. For example, it may suggest investment products with low fees. This allows the user to efficiently manage their investment expenses.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The household account book input unit inputs the user's household account book data. For example, the user inputs monthly income and expenses into the household account book. Step 2: The report generation unit generates a month-end report based on the household accounting data entered by the household accounting input unit. For example, the generation AI analyzes the entered household accounting data and creates a month-end report. Step 3: The advice provider provides savings advice based on the end-of-month report generated by the report generator. For example, the generation AI provides savings advice for each household based on the end-of-month report.

[0058] (Example 2) In the household management system according to an embodiment of the present invention, a user inputs monthly household accounts, and a generation AI creates a report at the end of the month and provides advice on saving money for each household. This allows the household management system to organize the user's household accounts and reduce wasteful spending.

[0059] A household management system according to an embodiment includes a household account book input unit, a report generation unit, and an advice providing unit. The household account book input unit inputs a user's household account book data. For example, the user inputs their monthly income and expenses into the household account book. The report generation unit generates a month-end report based on the household account book data input by the household account book input unit. For example, a generation AI analyzes the input household account book data and creates a month-end report. The advice providing unit provides savings advice based on the month-end report generated by the report generation unit. For example, the generation AI provides savings advice for each household based on the month-end report. As a result, the household management system according to an embodiment generates a month-end report based on the user's household account book data and provides savings advice, enabling the user to organize their household finances and reduce unnecessary spending.

[0060] In the household ledger input unit, the generation AI automatically classifies expenditure items based on data entered by the user and can request confirmation from the user. For example, in the household ledger input unit, the generation AI analyzes expenditure data entered by the user and automatically classifies them into categories such as food, utilities, and entertainment. The classification results are presented to the user and confirmation is requested, thereby maintaining data accuracy. In addition, in the household ledger input unit, the generation AI analyzes expenditure data entered by the user in real time and classifies it into the appropriate category. For example, supermarket purchases are classified as food expenses and movie tickets as entertainment expenses, and confirmation is requested from the user. In addition, in the household ledger input unit, the generation AI automatically analyzes expenditure data entered by the user and classifies it by category. The classification results are notified to the user and corrections are requested as necessary, thereby maintaining data consistency. In this way, the accuracy of the data can be maintained by automatically classifying data entered by the user and requesting confirmation.

[0061] The household account book input unit allows users to simply upload a photo of a receipt, and the generation AI automatically extracts the data and reflects it in the household account book. For example, in the household account book input unit, a user takes a photo of a receipt and uploads it to the app. The generation AI performs image analysis, automatically extracts expenditure items and amounts, and reflects them in the household account book. In addition, when a user uploads a photo of a receipt, the generation AI extracts text information using OCR technology and automatically enters the expenditure items and amounts in the household account book. In addition, when a user uploads a photo of a receipt, the generation AI performs image analysis, automatically extracts expenditure data, and reflects it in the household account book. This allows data to be extracted and reflected in the household account book simply by uploading a photo of the receipt, eliminating the need for manual input.

[0062] The household account book input unit uses the emotion estimation function to analyze the emotion of the user when entering data, and can make suggestions to simplify the input if the user is feeling stressed. For example, when the user enters data into the household account book, the emotion estimation function analyzes the user's facial expression and voice tone, and makes suggestions to reduce the number of input items if the user is feeling stressed. The household account book input unit also uses the emotion estimation function to analyze in real time whether the user is feeling stressed while entering data, and presents a simplified input form. The emotion estimation function also measures the user's stress level when entering data into the household account book, and suggests an automatic input function if the user is feeling high. This makes it possible to reduce the burden on the user by making suggestions to simplify the input if the user is feeling stressed.

[0063] The household account book input unit uses voice recognition technology to allow the user to input data simply by speaking. For example, when the user speaks about expenditure details, the voice recognition technology converts the content into text and automatically enters it into the household account book. For example, input is completed by simply speaking, "I spent 5,000 yen at the supermarket." The household account book input unit also uses voice recognition technology to build a system that allows the user to input expenditure data simply by speaking. For example, if the user speaks, "I spent 3,000 yen on gas," it is automatically entered into the household account book as transportation expenses. Furthermore, when the user enters expenditure details by voice, the voice recognition technology analyzes the content, classifies it into the appropriate category, and reflects it in the household account book. This allows the user to input data simply by speaking, eliminating the hassle of inputting data.

[0064] The household account book input unit can link the household account book data with other household management apps to achieve centralized data management. The household account book input unit, for example, links the household account book data with other household management apps to achieve centralized data management. For example, it links with a bank app or a credit card app to automatically import expenditure data. The household account book input unit also links data with other household management apps to ensure centralized data management even when a user uses multiple apps. For example, it links with a household account book app to automatically synchronize expenditure data. The household account book input unit also links the household account book data with other household management apps to enable a user to manage all expenditure data on a single platform. In this way, by linking the household account book data with other household management apps and achieving centralized data management, data is consistently managed even when a user uses multiple apps.

[0065] The household account book input unit can use the emotion estimation function to provide an interface that provides real-time feedback on the emotions of the user when entering data and elicits positive emotions. The household account book input unit, for example, uses the emotion estimation function to analyze the emotions of the user when entering data into the household account book and provide an interface that elicits positive emotions. For example, an encouraging message is displayed while the data is being entered. The household account book input unit also provides an interface in which the emotion estimation function analyzes the emotions of the user when entering data into the household account book and elicits positive emotions. For example, a compliment is displayed when the data entry is completed. The household account book input unit also uses the emotion estimation function to provide real-time feedback on the emotions of the user when entering data into the household account book and provide an interface that elicits positive emotions. For example, positive music is played while the data is being entered. This provides real-time feedback on the emotions of the user when entering data and provides an interface that elicits positive emotions, thereby improving the user's input experience.

[0066] The report generation unit can predict this month's spending trends based on past data and include the prediction results in the report. In the report generation unit, for example, the generation AI analyzes past household ledger data and predicts this month's spending trends. The prediction results are included in the end-of-month report and presented to the user. In addition, the report generation unit predicts this month's spending trends based on past spending data and reflects the prediction results in the report. For example, the report generation unit predicts this month's food expenses and utility expenses from past data. In addition, the report generation unit predicts this month's spending trends based on past household ledger data and predicts this month's spending trends. By including the prediction results in the end-of-month report, the user can see into future spending. In this way, by predicting this month's spending trends based on past data and including the prediction results in the report, the user can see into future spending.

[0067] The report generation unit can add detailed analysis results for each expense item to the report, allowing the user to understand specific spending patterns. For example, the report generation unit adds detailed analysis results for each expense item to an end-of-month report, allowing the user to understand specific spending patterns. For example, the report generation unit shows a breakdown of expenses for each item, such as food expenses, utility expenses, and entertainment expenses. The report generation unit also has the generation AI perform a detailed analysis for each expense item and reflect the results in the end-of-month report. For example, the breakdown of food expenses shows expenses for eating out and expenses for ingredients. The report generation unit also adds detailed analysis results for each expense item to the end-of-month report, allowing the user to understand specific spending patterns. For example, the breakdown of utility expenses shows electricity, gas, and water. In this way, by adding detailed analysis results for each expense item to the report, the user can understand specific spending patterns.

[0068] The report generation unit can use the emotion estimation function to analyze the emotional impact that the content of the report has on the user and emphasize positive feedback. For example, the report generation unit uses the emotion estimation function to analyze the emotional impact that the content of the month-end report has on the user and emphasize positive feedback. For example, it highlights items that have resulted in successful savings. The report generation unit also uses the emotion estimation function to analyze the emotional impact that the content of the month-end report has on the user and emphasizes positive feedback. For example, it prominently displays items where expenses have decreased. The report generation unit also uses the emotion estimation function to analyze the emotional impact that the content of the month-end report has on the user and emphasizes positive feedback. For example, it displays a congratulatory message when a savings goal is achieved. In this way, the user's motivation can be improved by analyzing the emotional impact that the content of the report has on the user and emphasizing positive feedback.

[0069] The report generation unit can display reports in different formats (e.g., graphs and charts) to make it easier for the user to understand visually. For example, the report generation unit displays a month-end report in graph or chart format to make it easier for the user to understand visually. For example, the expenditure percentage is shown in a pie chart. The report generation unit also displays reports in different formats to make it easier for the user to understand visually. For example, the monthly expenditure trend is shown in a line graph. The report generation unit also displays a month-end report in graph or chart format to make it easier for the user to understand visually. For example, a comparison between expenditure items is shown in a bar graph. In this way, by displaying the report in different formats, it is easier for the user to understand visually.

[0070] The report generation unit may add a function that allows reports to be shared with other family members, thereby facilitating spending management across the entire family. The report generation unit may add a function that allows, for example, end-of-month reports to be shared with other family members, thereby facilitating spending management across the entire family. For example, reports may be shared via email or social media. The report generation unit may also add a report sharing function that allows family members to add comments to reports. The report generation unit may also add a function that allows end-of-month reports to be shared with other family members, thereby facilitating spending management across the entire family. For example, family members may provide feedback on shared reports. Thus, by adding a function that allows reports to be shared with other family members, spending management across the entire family may be promoted.

[0071] The report generation unit can use the emotion estimation function to collect the user's emotional reactions to the report content and reflect them in the creation of the next report. The report generation unit, for example, uses the emotion estimation function to collect the user's emotional reactions to the content of the end-of-month report and reflect them in the creation of the next report. For example, it highlights items with many positive reactions. The report generation unit also uses the emotion estimation function to collect the user's emotional reactions to the report content and reflect them in the creation of the next report. For example, it improves items with many negative reactions. The report generation unit also uses the emotion estimation function to collect the user's emotional reactions to the report content and reflect them in the creation of the next report. For example, it creates a report in a format preferred by the user. In this way, by collecting the user's emotional reactions to the report content and reflecting them in the creation of the next report, it is possible to provide a more appropriate report for the user.

[0072] The advice providing unit allows the generation AI to analyze the effectiveness of past money-saving advice and prioritize providing the most effective advice. For example, the advice providing unit allows the generation AI to analyze the effectiveness of past money-saving advice and prioritize providing the most effective advice. For example, if reviewing mobile phone charges is effective, it will prioritize suggesting that. The advice providing unit also allows the generation AI to analyze the effectiveness of past money-saving advice and prioritize providing advice that was highly effective. For example, if a method for saving on food costs is effective, it will prioritize suggesting that. The advice providing unit also allows the generation AI to analyze the effectiveness of past money-saving advice and prioritize providing the most effective advice. For example, if a method for saving on utility bills is effective, it will prioritize suggesting that. In this way, by analyzing the effectiveness of past money-saving advice and prioritize providing the most effective advice, it is possible to suggest the optimal money-saving method for the user.

[0073] The advice providing unit can include a specific amount of savings and detailed steps on how to save in the content of the advice. For example, the advice providing unit may include a specific amount of savings and detailed steps on how to save in the saving advice provided by the generation AI. For example, the advice providing unit may present a specific amount, such as 2,000 yen saved per month by changing a mobile phone plan. The advice providing unit may also include a specific amount of savings and detailed steps on how to save in the content of the advice. For example, as a way to save on food costs, the advice providing unit may present specific steps, such as saving 5,000 yen per month by buying in bulk once a week. The advice providing unit may also include a specific amount of savings and detailed steps on how to save in the saving advice provided by the generation AI. For example, as a way to save on utility bills, the advice providing unit may present specific steps, such as saving 10,000 yen per year by using eco-friendly home appliances. In this way, by including a specific amount of savings and detailed steps on how to save in the content of the advice, it becomes easier for the user to actually put savings into practice.

[0074] The advice providing unit can use the emotion estimation function to analyze the timing when the user is likely to accept advice and provide the advice at the optimal timing. The advice providing unit, for example, uses the emotion estimation function to analyze the timing when the user is likely to accept advice and provides the advice at the optimal timing. For example, the advice providing unit transmits advice during a time period when the user is relaxed. The advice providing unit also uses the emotion estimation function to analyze the timing when the user is likely to accept advice and provides the advice at the optimal timing. For example, the advice providing unit transmits advice when the user is feeling positive. The advice providing unit also uses the emotion estimation function to analyze the timing when the user is likely to accept advice and provides the advice at the optimal timing. For example, the advice providing unit transmits advice when the user is not feeling stressed. In this way, the effectiveness of the advice can be maximized by analyzing the timing when the user is likely to accept advice and providing the advice at the optimal timing.

[0075] The advice providing unit can provide the advice in video format, making it easier to understand through visual and auditory channels. For example, the advice providing unit provides the saving advice provided by the generation AI in video format, making it easier to understand through visual and auditory channels. For example, a video explaining how to save money is created and provided to the user. The advice providing unit also provides the advice in video format, making it easier to understand through visual and auditory channels. For example, a video explaining how to save money using animation is created and provided to the user. The advice providing unit also provides the saving advice provided by the generation AI in video format, making it easier to understand through visual and auditory channels. For example, a video demonstrating how to save money is created and provided to the user. By providing the advice in video format, it becomes easier to understand through visual and auditory channels.

[0076] The advice providing unit can add a function that allows advice to be shared with other users, thereby promoting information exchange within the community. The advice providing unit can, for example, add a function that allows money-saving advice provided by the generation AI to be shared with other users, thereby promoting information exchange within the community. For example, it can provide a function for sharing advice on social media. The advice providing unit can also add a function that allows advice to be shared with other users, thereby promoting information exchange within the community. For example, it can provide a bulletin board for sharing advice. The advice providing unit can also add a function that allows money-saving advice provided by the generation AI to be shared with other users, thereby promoting information exchange within the community. For example, it can provide a chat function for sharing advice. By adding a function that allows advice to be shared with other users, it can promote information exchange within the community.

[0077] The advice providing unit can use the emotion estimation function to collect the user's emotional reactions to the advice and reflect them in the next advice. The advice providing unit, for example, uses the emotion estimation function to collect the user's emotional reactions to the advice and reflect them in the next advice. For example, advice that receives a lot of positive reactions is preferentially provided. The advice providing unit also uses the emotion estimation function to collect the user's emotional reactions to the advice and reflect them in the next advice. For example, advice that receives a lot of negative reactions is improved. The advice providing unit also uses the emotion estimation function to collect the user's emotional reactions to the advice and reflect them in the next advice. For example, an advice format that the user prefers is provided. In this way, by collecting the user's emotional reactions to the advice and reflecting them in the next advice, more appropriate advice can be provided to the user.

[0078] The advice providing unit allows the generation AI to analyze the user's past expenditure data and suggest the best saving method for individual needs. For example, the generation AI analyzes the user's past expenditure data and suggests the best saving method for individual needs. For example, it suggests a specific method for reducing hobby expenses. The advice providing unit also allows the generation AI to analyze the user's past expenditure data and suggest the best saving method for individual needs. For example, it suggests a specific method for reducing food expenses. The advice providing unit also allows the generation AI to analyze the user's past expenditure data and suggest the best saving method for individual needs. For example, it suggests a specific method for reducing utility expenses. In this way, by analyzing the user's past expenditure data and suggesting the best saving method for individual needs, it is possible to provide the best saving method for the user.

[0079] The advice providing unit can collect detailed information about the user's hobbies and non-negotiables and customize advice based on that. For example, the generation AI collects detailed information about the user's hobbies and non-negotiables and customizes advice based on that. For example, it suggests specific ways to reduce hobby expenses. The advice providing unit can also collect information about the user's hobbies and non-negotiables and customize advice based on that. For example, it suggests specific ways to reduce hobby expenses. The advice providing unit can also collect detailed information about the user's hobbies and non-negotiables and customize advice based on that. For example, it suggests specific ways to reduce hobby expenses. In this way, by collecting detailed information about the user's hobbies and non-negotiables and customizing advice based on that, it is possible to provide more appropriate advice to the user.

[0080] The advice providing unit uses the emotion estimation function to provide advice that takes into account the emotional state of the user, thereby reducing stress. The advice providing unit, for example, uses the emotion estimation function to provide advice that takes into account the emotional state of the user, thereby reducing stress. For example, the advice providing unit transmits advice when the user is relaxed. The advice providing unit also uses the emotion estimation function to provide advice that takes into account the emotional state of the user, thereby reducing stress. For example, the advice providing unit transmits advice when the user is feeling positive. The advice providing unit also uses the emotion estimation function to provide advice that takes into account the emotional state of the user, thereby reducing stress. For example, the advice providing unit transmits advice when the user is not feeling stressed. In this way, the advice that takes into account the emotional state of the user is provided, thereby reducing stress, thereby improving user satisfaction.

[0081] The advice providing unit can customize advice according to different life stages (for example, while raising children, after retirement). In the advice providing unit, for example, the generation AI provides advice according to the user's life stage. For example, to a user who is raising children, it suggests childcare-related saving methods. In addition, in the advice providing unit, the generation AI customizes the advice according to the user's life stage. For example, to a user who has retired, it suggests saving methods suitable for pension life. In addition, in the advice providing unit, the generation AI provides advice according to the user's life stage. For example, to a newly married user, it suggests saving methods suitable for their new life. In this way, by customizing advice according to different life stages, it is possible to provide more appropriate advice to the user.

[0082] The advice providing unit may add a function that allows advice to be shared with other family members, thereby facilitating spending management across the entire family. The advice providing unit may, for example, add a function that allows advice provided by the generation AI to be shared with other family members, thereby facilitating spending management across the entire family. For example, advice may be shared via email or social media. The advice providing unit may also add an advice sharing function to promote spending management across the entire family. For example, it may provide a function that allows family members to add comments to advice. The advice providing unit may also add a function that allows advice provided by the generation AI to be shared with other family members, thereby facilitating spending management across the entire family. For example, family members may provide feedback on shared advice. In this way, by adding a function that allows advice to be shared with other family members, spending management across the entire family may be promoted.

[0083] The advice providing unit can use the emotion estimation function to collect the user's emotional reactions to the advice and reflect them in the next advice. The advice providing unit, for example, uses the emotion estimation function to collect the user's emotional reactions to the advice and reflect them in the next advice. For example, advice that receives a lot of positive reactions is preferentially provided. The advice providing unit also uses the emotion estimation function to collect the user's emotional reactions to the advice and reflect them in the next advice. For example, advice that receives a lot of negative reactions is improved. The advice providing unit also uses the emotion estimation function to collect the user's emotional reactions to the advice and reflect them in the next advice. For example, an advice format that the user prefers is provided. In this way, by collecting the user's emotional reactions to the advice and reflecting them in the next advice, more appropriate advice can be provided to the user.

[0084] The advice providing unit can display the most relevant advertisement based on the user's expenditure data, thereby maximizing the advertising effectiveness. The advice providing unit, for example, displays the most relevant advertisement based on the user's expenditure data, thereby maximizing the advertising effectiveness. For example, a discount advertisement for ingredients is displayed to a user with high food expenses. The advice providing unit also displays the most relevant advertisement based on the expenditure data, thereby maximizing the advertising effectiveness. For example, an advertisement for eco-friendly home appliances is displayed to a user with high utility bills. The advice providing unit also displays the most relevant advertisement based on the user's expenditure data, thereby maximizing the advertising effectiveness. For example, a discount advertisement for movies or events is displayed to a user with high entertainment expenses. In this way, the advertising effectiveness can be maximized by displaying the most relevant advertisement based on the user's expenditure data.

[0085] The advice providing unit can optimize the timing of advertisement display and display advertisements at the timing when the user is most interested. The advice providing unit, for example, optimizes the timing of advertisement display and displays advertisements at the timing when the user is most interested. For example, it displays relevant advertisements when the user is entering household accounts. The advice providing unit also optimizes the timing of advertisement display and displays advertisements at the timing when the user is most interested. For example, it displays relevant advertisements when the user is checking an end-of-month report. The advice providing unit also optimizes the timing of advertisement display and displays advertisements at the timing when the user is most interested. For example, it displays relevant advertisements when the user is receiving money-saving advice. In this way, the effectiveness of the advertisements can be maximized by optimizing the timing of advertisement display and displaying advertisements at the timing when the user is most interested.

[0086] The advice providing unit can use the emotion estimation function to analyze the emotional impact of an advertisement on a user and preferentially display advertisements that elicit positive emotions. The advice providing unit, for example, uses the emotion estimation function to analyze the emotional impact of an advertisement on a user and preferentially display advertisements that elicit positive emotions. For example, positive advertisements are displayed when the user is relaxed. The advice providing unit also uses the emotion estimation function to analyze the emotional impact of an advertisement on a user and preferentially display advertisements that elicit positive emotions. For example, advertisements are displayed when the user has positive emotions. The advice providing unit also uses the emotion estimation function to analyze the emotional impact of an advertisement on a user and preferentially display advertisements that elicit positive emotions. For example, advertisements are displayed when the user is not feeling stressed. In this way, the advertising effectiveness can be maximized by analyzing the emotional impact of an advertisement on a user and preferentially displaying advertisements that elicit positive emotions.

[0087] The advice providing unit may provide the advertisement in a video format to make it easier to understand through visual and auditory means. For example, the advice providing unit may provide the advertisement in a video format to make it easier to understand through visual and auditory means. For example, a video advertisement explaining how to use a product may be displayed. The advice providing unit may also provide the advertisement in a video format to make it easier to understand through visual and auditory means. For example, an animated advertisement explaining the benefits of a service may be displayed. The advice providing unit may also provide the advertisement in a video format to make it easier to understand through visual and auditory means. For example, a video advertisement demonstrating the features of a product may be displayed. Thus, providing the advertisement in a video format makes it easier to understand through visual and auditory means.

[0088] The advice providing unit can add a function that allows advertisements to be shared with other users, thereby promoting information exchange within the community. The advice providing unit, for example, adds a function that allows advertisements to be shared with other users, thereby promoting information exchange within the community. For example, it provides a function for sharing advertisements on a social networking site. The advice providing unit can also add a function that allows advertisements to be shared with other users, thereby promoting information exchange within the community. For example, it provides a bulletin board for sharing advertisements. The advice providing unit can also add a function that allows advertisements to be shared with other users, thereby promoting information exchange within the community. For example, it provides a chat function for sharing advertisements. By adding a function that allows advertisements to be shared with other users, it is possible to promote information exchange within the community.

[0089] The advice providing unit can use the emotion estimation function to collect the user's emotional reactions to the advertisement and reflect them in the next advertisement display. The advice providing unit, for example, uses the emotion estimation function to collect the user's emotional reactions to the advertisement and reflect them in the next advertisement display. For example, advertisements with many positive reactions are preferentially displayed. The advice providing unit also uses the emotion estimation function to collect the user's emotional reactions to the advertisement and reflect them in the next advertisement display. For example, advertisements with many negative reactions are improved. The advice providing unit also uses the emotion estimation function to collect the user's emotional reactions to the advertisement and reflect them in the next advertisement display. For example, an advertisement format preferred by the user is provided. In this way, by collecting the user's emotional reactions to the advertisement and reflecting them in the next advertisement display, it is possible to provide a more appropriate advertisement for the user.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The household management system may further include a health management unit. The health management unit can analyze the relationship between health and expenditures by collecting the user's health data and linking it with household account book data. For example, it can analyze the frequency with which the user goes to the gym and their health condition, and indicate how effective expenditures for maintaining their health are. The health management unit can also collect the user's dietary data and analyze the relationship between food expenses and their health condition. For example, it can suggest healthy meals to a user who eats out frequently. The health management unit can also collect the user's exercise data and analyze the relationship between exercise costs and their health condition. This allows the user to manage their household finances while taking into account the balance between health and expenditures.

[0092] The household management system may further include an education management unit. The education management unit collects the user's education-related expenditure data and can predict future education expenses. For example, it can analyze the cost of children's tuition and extracurricular activities and create a future expenditure plan. The education management unit can also provide the user with information on scholarships and grants available to the user. For example, it can present a list of available scholarships to families that meet certain conditions. The education management unit can also suggest ways to save money based on the user's education-related expenditure data. For example, it can suggest ways to purchase textbooks or use online learning. This allows the user to efficiently manage their education expenses.

[0093] The household management system may further include a travel management unit. The travel management unit can collect travel-related expenditure data from the user and optimize travel plans. For example, it can suggest the best travel time and cost based on past travel data. The travel management unit can also provide information on travel discounts and benefits that the user can use. For example, it can present travel benefits that can be obtained by using a specific credit card. The travel management unit can also suggest ways to save money based on the user's travel-related expenditure data. For example, it can suggest early bookings or off-season travel. This allows the user to efficiently manage travel expenses.

[0094] The household management system may further include an energy management unit. The energy management unit can collect the user's energy consumption data and optimize energy efficiency. For example, it can analyze past electricity and gas bills and show energy consumption trends. The energy management unit can also suggest energy-saving methods that the user can use. For example, it can suggest the use of eco-friendly home appliances or the introduction of insulation materials. The energy management unit can also suggest energy-saving methods based on the user's energy consumption data. For example, it can save money by adjusting the time electricity is used. This allows the user to efficiently manage their energy costs.

[0095] The household management system may further include an investment management unit. The investment management unit may collect the user's investment data and optimize the investment portfolio. For example, it may analyze past investment performance and make investment suggestions that take into account the balance between risk and return. The investment management unit may also provide information on investment products and services available to the user. For example, it may present information on specific investment trusts and stocks. The investment management unit may also suggest ways to save money based on the user's investment data. For example, it may suggest investment products with low fees. This allows the user to efficiently manage their investment expenses.

[0096] The household management system can further use an emotion estimation function to analyze spending patterns based on the user's emotions. For example, it can analyze the tendency for impulse buying to increase when the user is feeling stressed and provide advice to reduce stress. It can also use the emotion estimation function to analyze the tendency for users to become more conscious of saving when they are feeling positive, and provide saving advice at that time. It can also use the emotion estimation function to provide real-time feedback on spending patterns based on the user's emotional state and make suggestions to reduce wasteful spending. This allows users to manage their spending based on their emotions.

[0097] The household management system can further use an emotion estimation function to set savings goals based on the user's emotions. For example, when the user is feeling positive, the system suggests setting a higher savings goal. Also, when the user is feeling stressed, the system uses the emotion estimation function to suggest setting a more reasonable savings goal. The system also uses the emotion estimation function to adjust the savings goal in real time according to the user's emotional state and set an achievable goal. This allows the user to set a realistic savings goal based on their emotions.

[0098] The household management system can further use the emotion estimation function to introduce a reward system based on the user's emotions. For example, when a user achieves a savings goal, a reward that elicits positive emotions is provided. Also, using the emotion estimation function, when the user is feeling stressed, a suggestion is made to provide a reward that will help the user relax. Furthermore, using the emotion estimation function, the reward system can be adjusted in real time according to the user's emotional state, providing rewards to maintain motivation. In this way, the user can be made more conscious of saving through the emotion-based reward system.

[0099] The household management system can further use an emotion estimation function to predict spending based on the user's emotions. For example, it can predict a tendency for spending to increase when the user is feeling stressed and issue a warning in advance. It can also use the emotion estimation function to predict a tendency for spending to decrease when the user is feeling positive and provide saving advice at that time. It can also use the emotion estimation function to predict spending in real time based on the user's emotional state and make suggestions to reduce wasteful spending. This allows the user to reduce wasteful spending through emotion-based spending predictions.

[0100] The household management system can further use an emotion estimation function to provide money-saving advice based on the user's emotions. For example, when the user has positive emotions, more proactive money-saving advice is provided. In addition, the emotion estimation function can be used to suggest reasonable money-saving advice when the user is feeling stressed. The emotion estimation function can also be used to adjust money-saving advice in real time according to the user's emotional state and provide achievable advice. This allows the user to receive realistic money-saving advice based on their emotions.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The household account book input unit inputs the user's household account book data. For example, the user inputs monthly income and expenses into the household account book. Step 2: The report generation unit generates a month-end report based on the household accounting data entered by the household accounting input unit. For example, the generation AI analyzes the entered household accounting data and creates a month-end report. Step 3: The advice provider provides savings advice based on the end-of-month report generated by the report generator. For example, the generation AI provides savings advice for each household based on the end-of-month report.

[0103] 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.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0105] 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.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] 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.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0132] 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.

[0133] 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.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] 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.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0148] 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.

[0149] 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.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0170] 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 household account book input unit for inputting user household account book data; a report generation unit that generates a month-end report based on the household account book data input by the household account book input unit; an advice providing unit that provides saving advice based on the end-of-month report generated by the report generating unit; A system characterized by:

2. The household account book input unit Based on the data entered by the user, the generation AI automatically classifies the expenditure items and asks the user for confirmation.

2. The system of claim 1.

3. The household account book input unit The user simply uploads a photo of the receipt, and the AI ​​automatically extracts the data and reflects it in the household ledger.

2. The system of claim 1.

4. The household account book input unit Analyze the emotions of the user when inputting information, and if the user is feeling stressed, make suggestions to simplify the input.

2. The system of claim 1.

5. The household account book input unit Using voice recognition technology, data is entered simply by the user speaking.

2. The system of claim 1.

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