system

Abstract

The system according to this embodiment aims to enable users to understand their spending patterns and effectively manage their household finances. [Solution] The system according to the embodiment comprises a classification unit, an advice unit, a setting unit, and a notification unit. The classification unit classifies expenditures into categories. The advice unit provides advice based on the expenditure data classified by the classification unit. The setting unit sets a budget. The notification unit provides a notification when the budget set by the setting unit is exceeded.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult for a user to grasp their own spending patterns and effectively manage household finances.

[0005] The system according to the embodiment aims to enable a user to grasp their own spending patterns and effectively manage household finances.

Means for Solving the Problems

[0006] The system according to the embodiment includes a classification unit, an advice unit, a setting unit, and a notification unit. The classification unit classifies expenditure categories. The advice unit provides advice based on the expenditure data classified by the classification unit. The setting unit sets a budget. The notification unit gives a notification when the budget set by the setting unit is exceeded. [Effects of the Invention]

[0007] The system according to this embodiment allows users to understand their spending patterns and effectively manage their household finances. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

[0019] The smart device 14 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The household budget management system according to an embodiment of the present invention is a system that proposes new functions to support the user's household budget management by utilizing an electronic payment system. The household budget management system adds a function to categorize expenses, allowing the user to divide expenses into categories such as food, transportation, and leisure. This makes it easier for the user to manage their spending patterns and identify unnecessary expenses. For example, if food expenses are higher than other expenses, the user can receive advice on reducing food expenses. It also suggests advantageous coupon information that can help reduce expenses. Next, budget setting and automatic investment functions are added, allowing the user to set a monthly budget or for the system to automatically set a budget based on past data. If the budget is exceeded, the user can receive a notification, and the "Household Budget Helper" will display advice on how to control household expenses and save money. If the user lives within the set budget, they will be offered more advantageous coupons, etc. If there is a surplus budget, it is possible to automatically transfer the surplus funds to an investment trust through pre-configuration. This enables the user to achieve sound household budget management, improve their awareness of saving, and learn how to use money wisely. For example, with the visualization of expenses and support for budget management, it is possible to reduce unnecessary expenses and increase savings. Furthermore, through saving challenges and advice, users can become more financially conscious, avoid unnecessary spending, and make smart purchase and investment choices, thereby securing funds for their future goals. In this way, the household budget management system can efficiently manage users' spending and support household budget management through budget setting and notifications.

[0029] The household budget management system according to this embodiment comprises a classification unit, an advice unit, a setting unit, and a notification unit. The classification unit classifies expenditures into categories. For example, the classification unit classifies expenditures into categories such as food, transportation, and leisure. The classification unit can perform classifications based on the type, amount, and frequency of expenditures. The advice unit provides advice based on the expenditure data classified by the classification unit. For example, the advice unit provides saving advice for categories where expenditures have increased. The advice unit can also suggest advantageous coupon information that can reduce expenditures. The advice unit analyzes the user's spending patterns and provides optimal advice. The setting unit sets a budget. The setting unit allows the user to set a monthly budget or automatically sets a budget based on past data. The setting unit sets the budget based on specific budget setting methods and criteria. The notification unit provides a notification when the budget set by the setting unit is exceeded. The notification unit notifies the user when the budget is exceeded, allowing them to curb spending. The notification unit can also display advice on how to control household finances and save money when the budget is exceeded. As a result, the household budget management system according to this embodiment can efficiently manage the user's expenses and support household budget management through budget setting and notifications.

[0030] The classification unit categorizes expenditures. For example, it classifies expenditures into categories such as food, transportation, and leisure. Specifically, it analyzes the expenditure data entered by the user and automatically determines which category each expenditure belongs to. The classification unit can classify expenditures based on factors such as type, amount, and frequency. For example, expenditures related to food are automatically classified into the food category based on purchase history at supermarkets and convenience stores. Transportation expenses are classified into the transportation category based on public transport usage history and payments at gas stations. Leisure expenses are classified into the leisure category based on expenditures at movie theaters and restaurants. Furthermore, the classification unit also provides a function for users to manually correct categories, enabling more accurate classification. The classification unit improves classification accuracy by analyzing expenditure data using AI and learning from past data and patterns. For example, the AI ​​learns which category specific stores or services belong to based on the user's past expenditure history, and can quickly and accurately classify new expenditure data when it is entered. This allows the classification unit to efficiently manage the user's expenditure data and support accurate household budget management.

[0031] The Advice Department provides advice based on spending data categorized by the Classification Department. For example, the Advice Department provides saving advice for categories where spending has increased. Specifically, it analyzes the user's spending patterns, identifies the cause of increased spending in a particular category, and proposes specific saving methods. For example, if spending in the food category has increased, the Advice Department provides information on special offers and coupons, offering tips for saving. If transportation costs have increased, it can recommend using public transportation or suggest carpooling. The Advice Department can also suggest advantageous coupon information that can reduce spending. For example, it collects coupon information from partner stores and services and notifies the user at the appropriate time. Furthermore, the Advice Department analyzes the user's spending patterns and provides optimal advice. By using AI to analyze the user's past spending data and predict future spending trends, it can provide more effective advice. For example, if spending related to a particular season or event tends to increase, it can provide saving advice beforehand, encouraging spending within budget. This allows the Advice Department to efficiently manage the user's spending and curb unnecessary spending.

[0032] The settings section allows users to set budgets. Users can either set monthly budgets or the system automatically sets budgets based on past data. Specifically, it provides an interface for users to manually set budgets for each category, enabling flexible budget setting tailored to user needs. It also includes a function where AI automatically suggests budgets based on past spending data. For example, it automatically calculates and suggests appropriate budgets for each category based on average spending over the past three months. The settings section sets budgets based on specific budget setting methods and criteria. For example, it can adjust the budget for each category according to the user's set savings target or spending reduction target. Furthermore, the settings section provides a function to flexibly adjust budgets according to the user's lifestyle and income fluctuations. For example, if income increases, it readjusts the budget to increase savings; conversely, if income decreases, it suggests a budget to reduce spending. In this way, the settings section supports optimal budget setting according to the user's financial situation, enabling efficient household financial management.

[0033] The notification unit sends notifications when the budget set by the settings unit is exceeded. The notification unit can help curb spending by notifying users when the budget is exceeded. Specifically, when the user exceeds their set budget, it sends real-time notifications via smartphone or email. For example, if the budget for the food category is exceeded, it immediately sends a notification to encourage a review of spending. The notification unit can also display advice on how to control household finances and save money when the budget is exceeded. For example, it can suggest specific saving methods and alternatives for the category that has exceeded the budget, helping users reduce unnecessary spending. Furthermore, the notification unit has a function to periodically monitor the user's spending and send reminders to encourage spending within the budget. For example, it can notify users of their spending status mid-month and encourage them to maintain spending within the budget. The notification unit can also collect user feedback and continuously improve the accuracy and effectiveness of its notifications. For example, it can analyze user behavior after receiving notifications to find more effective notification methods and timing. This allows the notification unit to support users in managing their spending quickly and appropriately, improving the efficiency of household finance management.

[0034] The classification unit can categorize expenses into categories such as food, transportation, and leisure. The classification unit can categorize expenses based on the type, amount, and frequency of the expenses. This makes it easier for users to understand their spending patterns by categorizing expenses. Some or all of the above processing in the classification unit may be performed using AI, for example, or without AI. For example, the classification unit can input expense data into a generating AI and have the generating AI perform the categorization of expenses.

[0035] The advice unit can provide saving advice for categories where spending has increased. For example, the advice unit can provide saving advice for categories where spending has increased. The advice unit can provide specific methods and suggestions for reducing spending. By providing saving advice for categories where spending is high, unnecessary spending can be reduced. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input spending data into a generating AI and have the generating AI execute saving advice.

[0036] The advice unit can suggest advantageous coupon information that can reduce spending. For example, the advice unit can suggest advantageous coupon information that can reduce spending. The advice unit can provide optimal coupon information based on the user's spending patterns. In this way, by suggesting advantageous coupon information, the user's spending can be reduced. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input coupon information into a generating AI and have the generating AI suggest optimal coupon information.

[0037] The settings unit allows users to set a monthly budget or automatically set a budget based on past data. For example, the settings unit can set a budget based on specific budget setting methods and criteria. This enables users to manage their spending systematically by setting a budget. Some or all of the above processes in the settings unit may be performed using AI, or not. For example, the settings unit can input past spending data into a generating AI and have the generating AI execute the optimal budget setting.

[0038] The notification unit can issue a notification if the budget is exceeded. For example, the notification unit will issue a notification if the budget is exceeded. The notification unit can notify the user of the budget overrun and curb spending. This allows the user to curb spending by notifying them when the budget is exceeded. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input budget overrun data into a generating AI and have the generating AI generate the notification.

[0039] The notification unit can display advice on how to control household finances and save money when the budget is exceeded. For example, the notification unit can display advice on how to control household finances and save money when the budget is exceeded. The notification unit can provide the user with specific saving methods and advice on household financial management. By providing advice when the budget is exceeded, the user can effectively manage their household finances. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input budget exceedance data into a generating AI and have the generating AI generate advice.

[0040] The configuration unit can automatically transfer surplus funds to an investment trust if there is a budget surplus. For example, the configuration unit can automatically transfer surplus funds to an investment trust if there is a budget surplus. The configuration unit can transfer the user's surplus funds to an investment trust for efficient management. This allows the user to efficiently increase their assets by automatically transferring surplus funds to an investment trust. Some or all of the above processing in the configuration unit may be performed using AI, for example, or without AI. For example, the configuration unit can input surplus fund data into a generating AI and have the generating AI execute the transfer to the investment trust.

[0041] The classification unit can analyze the user's past spending history and select the optimal classification method when classifying expenses. For example, the classification unit can prioritize displaying frequently used categories based on the user's past spending history. The classification unit can analyze the user's past spending patterns and suggest the optimal category classification. The classification unit can automatically classify specific spending items based on the user's past spending history. This enables optimal spending classification by analyzing past spending history. Some or all of the above processes in the classification unit may be performed using AI, for example, or without AI. For example, the classification unit can input past spending data into a generating AI and have the generating AI select the optimal classification method.

[0042] The classification unit can filter expenditures based on the user's current lifestyle and areas of interest. For example, if a user starts a new hobby, the classification unit can add expenditures related to that hobby as a new category. If a user moves, the classification unit can suggest expenditure categories that match the new living environment. If a user participates in a specific event, the classification unit can classify expenditures related to that event as a special category. This allows for more appropriate expenditure management by filtering expenditures based on the user's current lifestyle and areas of interest. Some or all of the above processing in the classification unit may be performed using AI, for example, or not. For example, the classification unit can input the user's lifestyle data into a generating AI and have the generating AI perform the filtering.

[0043] The classification unit can prioritize highly relevant expenditures when classifying spending, taking into account the user's geographical location. For example, if the user is in a specific region, the classification unit can prioritize expenditures related to that region. If the user is traveling, the classification unit can prioritize expenditures related to travel. If the user has made purchases at a specific store, the classification unit can prioritize expenditures related to that store. In this way, highly relevant expenditures can be prioritized by considering geographical location. Some or all of the above processing in the classification unit may be performed using AI, for example, or without AI. For example, the classification unit can input geographical location data into a generating AI and have the generating AI perform the preferential classification of expenditures.

[0044] The classification unit can analyze a user's social media activity and classify relevant expenditures when classifying spending. For example, if a user posts on social media about attending a specific event, the classification unit can classify the expenditure related to that event. If a user posts on social media about purchasing a specific product, the classification unit can classify the expenditure related to that product. If a user posts on social media about visiting a specific place, the classification unit can classify the expenditure related to that place. In this way, relevant expenditures can be appropriately classified by analyzing social media activity. Some or all of the above processing in the classification unit may be performed using AI, for example, or not using AI. For example, the classification unit can input social media data into a generating AI and have the generating AI perform the expenditure classification.

[0045] The advice unit can adjust the level of detail of its advice based on the importance of the expenditure when providing advice. For example, it can provide detailed advice for important expenditure categories, and concise advice for unnecessary expenditure categories. The advice unit can adjust the level of detail of its advice in stages according to the importance of the expenditure. This allows for the provision of more appropriate advice by adjusting the level of detail based on the importance of the expenditure. Some or all of the above processing in the advice unit may be performed using AI, for example, or not using AI. For example, the advice unit can input expenditure data into a generating AI and have the generating AI perform the adjustment of the level of detail of the advice.

[0046] The advice unit can apply different advice algorithms depending on the expenditure category when providing advice. For example, for the food category, the advice unit can provide advice on reducing food expenses. For the transportation category, the advice unit can provide advice on reducing transportation expenses. For the leisure category, the advice unit can provide advice on reducing leisure expenses. By applying different advice algorithms depending on the expenditure category, more effective advice can be provided. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input expenditure data into a generating AI and have the generating AI execute the application of the advice algorithm.

[0047] The advisory unit can prioritize advice based on the timing of expense submissions when providing advice. For example, the advisory unit will prioritize advice when important expenses are submitted. If unnecessary expenses are submitted, the advisory unit may postpone providing advice. The advisory unit can adjust the priority of advice in stages according to the timing of expense submissions. This allows for the provision of more appropriate advice by prioritizing advice based on the timing of expense submissions. Some or all of the above processes in the advisory unit may be performed using AI, for example, or not using AI. For example, the advisory unit can input expense data into a generating AI and have the generating AI determine the priority of advice.

[0048] The advice unit can adjust the order of advice based on the relevance of the expenditures when providing advice. For example, the advice unit can prioritize advice for important expenditure categories. For unnecessary expenditure categories, the advice unit can postpone providing advice. The advice unit can adjust the order of advice in stages according to the relevance of the expenditures. This allows for the provision of more effective advice by adjusting the order of advice based on the relevance of the expenditures. Some or all of the above processing in the advice unit may be performed using AI, for example, or not using AI. For example, the advice unit can input expenditure data into a generating AI and have the generating AI perform the adjustment of the order of advice.

[0049] The setting unit can analyze the user's past spending history to select the optimal budget setting method when setting a budget. For example, the setting unit can suggest the optimal budget setting method based on the user's past spending history. The setting unit can analyze the user's past spending patterns and adjust the criteria for budget setting. The setting unit can set a budget for specific spending items based on the user's past spending history. This makes it possible to set an optimal budget by analyzing past spending history. Some or all of the above processes in the setting unit may be performed using AI, for example, or without AI. For example, the setting unit can input past spending data into a generating AI and have the generating AI select the optimal budget setting method.

[0050] The settings unit can customize the budget setting method based on the user's current living situation when setting a budget. For example, if a user starts a new job, the settings unit can set a budget based on that income. If a user moves, the settings unit can set a budget that suits the new living environment. If a user participates in a specific event, the settings unit can reflect the expenses related to that event in the budget. This allows for more appropriate budget management by customizing the budget setting based on the current living situation. Some or all of the above processes in the settings unit may be performed using AI, for example, or not using AI. For example, the settings unit can input living situation data into a generating AI and have the generating AI perform the customization of the budget setting.

[0051] The settings unit can select the optimal budget setting method when setting a budget, taking into account the user's geographical location information. For example, if the user lives in a specific region, the settings unit can set the budget based on the cost of living in that region. If the user is traveling, the settings unit can reflect travel-related expenses in the budget. If the user is shopping at a specific store, the settings unit can set the budget based on the price range of that store. This makes it possible to set an optimal budget by taking geographical location information into consideration. Some or all of the above processing in the settings unit may be performed using AI, for example, or not using AI. For example, the settings unit can input geographical location data into a generating AI and have the generating AI select a budget setting method.

[0052] The budget setting unit can analyze the user's social media activity and propose budget setting methods when setting a budget. For example, if a user posts on social media that they are attending a specific event, the budget setting unit can reflect the expenditure related to that event in the budget. If a user posts on social media that they have purchased a specific product, the budget setting unit can reflect the expenditure related to that product in the budget. If a user posts on social media that they have visited a specific place, the budget setting unit can reflect the expenditure related to that place in the budget. This allows for more appropriate budget setting by analyzing social media activity. Some or all of the above processing in the budget setting unit may be performed using AI, for example, or not using AI. For example, the budget setting unit can input social media data into a generating AI and have the generating AI execute suggestions for budget setting methods.

[0053] The notification unit can select the optimal notification method by referring to the user's past spending history when sending a notification. For example, the notification unit can prioritize frequently used notification methods based on the user's past spending history. The notification unit can analyze the user's past spending patterns and suggest the optimal notification method. The notification unit can select a notification method for a specific spending item based on the user's past spending history. This allows the optimal notification method to be selected by referring to past spending history. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input past spending data into a generating AI and have the generating AI select the optimal notification method.

[0054] The notification unit can select the optimal notification method when a notification is sent, taking into account the user's device information. For example, if the user is using a smartphone, the notification unit can provide a notification method that matches the screen size. If the user is using a tablet, the notification unit can provide a notification method optimized for a larger screen. If the user is using a smartwatch, the notification unit can provide a concise and highly visible notification method. This allows the optimal notification method to be selected by considering the device information. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input device information into a generating AI and have the generating AI select the optimal notification method.

[0055] The notification unit can provide scheduled notifications by referring to the user's calendar information. For example, the notification unit can refer to the appointments registered in the user's calendar and prioritize providing notifications related to important appointments. The notification unit can provide notifications related to specific events from the user's calendar information. Based on the user's calendar information, the notification unit can suggest the most suitable notification method for each appointment. This makes it possible to provide optimal scheduled notifications by referring to the calendar information. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input calendar information into a generation AI and have the generation AI generate scheduled notifications.

[0056] The notification unit can analyze the user's social media activity and provide relevant notifications at the time of notification. For example, if the user posts on social media that they will be attending a specific event, the notification unit can provide a notification related to that event. If the user posts on social media that they have purchased a specific product, the notification unit can provide a notification related to that product. If the user posts on social media that they have visited a specific place, the notification unit can provide a notification related to that place. This makes it possible to provide relevant notifications by analyzing social media activity. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can input social media data into a generating AI and have the generating AI generate relevant notifications.

[0057] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0058] The household budget management system can also include a health management unit that acquires user health data and analyzes the relationship between spending patterns and health status. For example, if a user's food expenses are high, the health management unit can analyze whether those expenses are being used for healthy foods and provide advice on maintaining a healthy diet. Furthermore, if a user is not getting enough exercise, the health management unit can suggest increasing spending on exercise-related activities. Additionally, if a user has high stress levels, it can recommend spending on relaxation and stress relief. In this way, the household budget management system can support spending management that takes the user's health status into consideration.

[0059] A household budget management system can further include a forecasting unit that analyzes the user's purchase history and predicts future spending based on purchase patterns. For example, if a user spends a fixed amount on a specific category each month, the forecasting unit can analyze that pattern and predict the next month's spending. It can also analyze spending patterns related to specific seasons or events and present expected spending during those periods. Furthermore, the forecasting unit can adjust spending predictions according to changes in the user's lifestyle. This makes it easier for users to plan their future spending.

[0060] The household budget management system can also include a regional analysis unit that acquires the user's geographical location information and analyzes spending patterns by region. For example, if a user lives in a specific region, it can provide advice on optimizing spending based on the cost of living in that region. If a user is traveling, it can prioritize displaying spending related to the travel destination region, supporting spending management during the trip. Furthermore, if a user shops at a specific store, it can provide advice on optimizing spending based on the store's price range. In this way, the household budget management system can support spending management based on the user's geographical location information.

[0061] The household budget management system can also include a social analytics unit that analyzes the user's social media activity and categorizes related expenses. For example, if a user posts on social media about attending a specific event, the system can categorize expenses related to that event. Similarly, if a user posts on social media about purchasing a specific product, the system can categorize expenses related to that product. Furthermore, if a user posts on social media about visiting a specific location, the system can categorize expenses related to that location. This allows the household budget management system to support expense management based on social media activity.

[0062] A household budget management system can also include a calendar management unit that references the user's calendar information and manages expenses based on their schedule. For example, it can refer to the appointments registered in the user's calendar and prioritize displaying expenses related to important appointments. It can also predict expenses related to specific events based on the user's calendar information and reflect them in the budget. Furthermore, it can suggest the most suitable expense management method based on the user's calendar information. In this way, the household budget management system can support effective expense management based on calendar information.

[0063] The following briefly describes the processing flow for example form 1.

[0064] Step 1: The classification unit categorizes expenses. For example, expenses can be classified into categories such as food, transportation, and leisure. The classification unit can also categorize expenses based on type, amount, frequency, etc. Step 2: The advice section provides advice based on the spending data categorized by the classification section. For example, it provides saving advice for categories where spending has increased. The advice section can also suggest useful coupons that can reduce spending. Furthermore, it analyzes the user's spending patterns and provides optimal advice. Step 3: The settings section sets the budget. Users can set a monthly budget, or the budget can be automatically set based on past data. The settings section sets the budget based on specific budget setting methods and criteria. Step 4: The notification unit will send a notification if the budget set in the settings unit is exceeded. The notification unit will notify the user when the budget is exceeded, which can help curb spending. Furthermore, it can also display advice on how to control household finances and save money when the budget is exceeded.

[0065] (Example of form 2) The household budget management system according to an embodiment of the present invention is a system that proposes new functions to support the user's household budget management by utilizing an electronic payment system. The household budget management system adds a function to categorize expenses, allowing the user to divide expenses into categories such as food, transportation, and leisure. This makes it easier for the user to manage their spending patterns and identify unnecessary expenses. For example, if food expenses are higher than other expenses, the user can receive advice on reducing food expenses. It also suggests advantageous coupon information that can help reduce expenses. Next, budget setting and automatic investment functions are added, allowing the user to set a monthly budget or for the system to automatically set a budget based on past data. If the budget is exceeded, the user can receive a notification, and the "Household Budget Helper" will display advice on how to control household expenses and save money. If the user lives within the set budget, they will be offered more advantageous coupons, etc. If there is a surplus budget, it is possible to automatically transfer the surplus funds to an investment trust through pre-configuration. This enables the user to achieve sound household budget management, improve their awareness of saving, and learn how to use money wisely. For example, with the visualization of expenses and support for budget management, it is possible to reduce unnecessary expenses and increase savings. Furthermore, through saving challenges and advice, users can become more financially conscious, avoid unnecessary spending, and make smart purchase and investment choices, thereby securing funds for their future goals. In this way, the household budget management system can efficiently manage users' spending and support household budget management through budget setting and notifications.

[0066] The household budget management system according to this embodiment comprises a classification unit, an advice unit, a setting unit, and a notification unit. The classification unit classifies expenditures into categories. For example, the classification unit classifies expenditures into categories such as food, transportation, and leisure. The classification unit can perform classifications based on the type, amount, and frequency of expenditures. The advice unit provides advice based on the expenditure data classified by the classification unit. For example, the advice unit provides saving advice for categories where expenditures have increased. The advice unit can also suggest advantageous coupon information that can reduce expenditures. The advice unit analyzes the user's spending patterns and provides optimal advice. The setting unit sets a budget. The setting unit allows the user to set a monthly budget or automatically sets a budget based on past data. The setting unit sets the budget based on specific budget setting methods and criteria. The notification unit provides a notification when the budget set by the setting unit is exceeded. The notification unit notifies the user when the budget is exceeded, allowing them to curb spending. The notification unit can also display advice on how to control household finances and save money when the budget is exceeded. As a result, the household budget management system according to this embodiment can efficiently manage the user's expenses and support household budget management through budget setting and notifications.

[0067] The classification unit categorizes expenditures. For example, it classifies expenditures into categories such as food, transportation, and leisure. Specifically, it analyzes the expenditure data entered by the user and automatically determines which category each expenditure belongs to. The classification unit can classify expenditures based on factors such as type, amount, and frequency. For example, expenditures related to food are automatically classified into the food category based on purchase history at supermarkets and convenience stores. Transportation expenses are classified into the transportation category based on public transport usage history and payments at gas stations. Leisure expenses are classified into the leisure category based on expenditures at movie theaters and restaurants. Furthermore, the classification unit also provides a function for users to manually correct categories, enabling more accurate classification. The classification unit improves classification accuracy by analyzing expenditure data using AI and learning from past data and patterns. For example, the AI ​​learns which category specific stores or services belong to based on the user's past expenditure history, and can quickly and accurately classify new expenditure data when it is entered. This allows the classification unit to efficiently manage the user's expenditure data and support accurate household budget management.

[0068] The Advice Department provides advice based on spending data categorized by the Classification Department. For example, the Advice Department provides saving advice for categories where spending has increased. Specifically, it analyzes the user's spending patterns, identifies the cause of increased spending in a particular category, and proposes specific saving methods. For example, if spending in the food category has increased, the Advice Department provides information on special offers and coupons, offering tips for saving. If transportation costs have increased, it can recommend using public transportation or suggest carpooling. The Advice Department can also suggest advantageous coupon information that can reduce spending. For example, it collects coupon information from partner stores and services and notifies the user at the appropriate time. Furthermore, the Advice Department analyzes the user's spending patterns and provides optimal advice. By using AI to analyze the user's past spending data and predict future spending trends, it can provide more effective advice. For example, if spending related to a particular season or event tends to increase, it can provide saving advice beforehand, encouraging spending within budget. This allows the Advice Department to efficiently manage the user's spending and curb unnecessary spending.

[0069] The settings section allows users to set budgets. Users can either set monthly budgets or the system automatically sets budgets based on past data. Specifically, it provides an interface for users to manually set budgets for each category, enabling flexible budget setting tailored to user needs. It also includes a function where AI automatically suggests budgets based on past spending data. For example, it automatically calculates and suggests appropriate budgets for each category based on average spending over the past three months. The settings section sets budgets based on specific budget setting methods and criteria. For example, it can adjust the budget for each category according to the user's set savings target or spending reduction target. Furthermore, the settings section provides a function to flexibly adjust budgets according to the user's lifestyle and income fluctuations. For example, if income increases, it readjusts the budget to increase savings; conversely, if income decreases, it suggests a budget to reduce spending. In this way, the settings section supports optimal budget setting according to the user's financial situation, enabling efficient household financial management.

[0070] The notification unit sends notifications when the budget set by the settings unit is exceeded. The notification unit can help curb spending by notifying users when the budget is exceeded. Specifically, when the user exceeds their set budget, it sends real-time notifications via smartphone or email. For example, if the budget for the food category is exceeded, it immediately sends a notification to encourage a review of spending. The notification unit can also display advice on how to control household finances and save money when the budget is exceeded. For example, it can suggest specific saving methods and alternatives for the category that has exceeded the budget, helping users reduce unnecessary spending. Furthermore, the notification unit has a function to periodically monitor the user's spending and send reminders to encourage spending within the budget. For example, it can notify users of their spending status mid-month and encourage them to maintain spending within the budget. The notification unit can also collect user feedback and continuously improve the accuracy and effectiveness of its notifications. For example, it can analyze user behavior after receiving notifications to find more effective notification methods and timing. This allows the notification unit to support users in managing their spending quickly and appropriately, improving the efficiency of household finance management.

[0071] The classification unit can categorize expenses into categories such as food, transportation, and leisure. The classification unit can categorize expenses based on the type, amount, and frequency of the expenses. This makes it easier for users to understand their spending patterns by categorizing expenses. Some or all of the above processing in the classification unit may be performed using AI, for example, or without AI. For example, the classification unit can input expense data into a generating AI and have the generating AI perform the categorization of expenses.

[0072] The advice unit can provide saving advice for categories where spending has increased. For example, the advice unit can provide saving advice for categories where spending has increased. The advice unit can provide specific methods and suggestions for reducing spending. By providing saving advice for categories where spending is high, unnecessary spending can be reduced. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input spending data into a generating AI and have the generating AI execute saving advice.

[0073] The advice unit can suggest advantageous coupon information that can reduce spending. For example, the advice unit can suggest advantageous coupon information that can reduce spending. The advice unit can provide optimal coupon information based on the user's spending patterns. In this way, by suggesting advantageous coupon information, the user's spending can be reduced. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input coupon information into a generating AI and have the generating AI suggest optimal coupon information.

[0074] The settings unit allows users to set a monthly budget or automatically set a budget based on past data. For example, the settings unit can set a budget based on specific budget setting methods and criteria. This enables users to manage their spending systematically by setting a budget. Some or all of the above processes in the settings unit may be performed using AI, or not. For example, the settings unit can input past spending data into a generating AI and have the generating AI execute the optimal budget setting.

[0075] The notification unit can issue a notification if the budget is exceeded. For example, the notification unit will issue a notification if the budget is exceeded. The notification unit can notify the user of the budget overrun and curb spending. This allows the user to curb spending by notifying them when the budget is exceeded. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input budget overrun data into a generating AI and have the generating AI generate the notification.

[0076] The notification unit can display advice on how to control household finances and save money when the budget is exceeded. For example, the notification unit can display advice on how to control household finances and save money when the budget is exceeded. The notification unit can provide the user with specific saving methods and advice on household financial management. By providing advice when the budget is exceeded, the user can effectively manage their household finances. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input budget exceedance data into a generating AI and have the generating AI generate advice.

[0077] The configuration unit can automatically transfer surplus funds to an investment trust if there is a budget surplus. For example, the configuration unit can automatically transfer surplus funds to an investment trust if there is a budget surplus. The configuration unit can transfer the user's surplus funds to an investment trust for efficient management. This allows the user to efficiently increase their assets by automatically transferring surplus funds to an investment trust. Some or all of the above processing in the configuration unit may be performed using AI, for example, or without AI. For example, the configuration unit can input surplus fund data into a generating AI and have the generating AI execute the transfer to the investment trust.

[0078] The classification unit can estimate the user's emotions and adjust the criteria for categorizing expenditures based on the estimated emotions. For example, if the user is stressed, the classification unit can provide simple categorization and avoid complex classification. If the user is relaxed, the classification unit can provide detailed categorization to support granular expenditure management. If the user is in a hurry, the classification unit can display only the main categories to allow for quick classification. This allows for more appropriate expenditure management by adjusting the criteria for categorizing expenditures according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the classification unit may be performed using AI or not. For example, the classification unit can input user emotion data into a generative AI and have the generative AI adjust the criteria for categorizing expenditures.

[0079] The classification unit can analyze the user's past spending history and select the optimal classification method when classifying expenses. For example, the classification unit can prioritize displaying frequently used categories based on the user's past spending history. The classification unit can analyze the user's past spending patterns and suggest the optimal category classification. The classification unit can automatically classify specific spending items based on the user's past spending history. This enables optimal spending classification by analyzing past spending history. Some or all of the above processes in the classification unit may be performed using AI, for example, or without AI. For example, the classification unit can input past spending data into a generating AI and have the generating AI select the optimal classification method.

[0080] The classification unit can filter expenditures based on the user's current lifestyle and areas of interest. For example, if a user starts a new hobby, the classification unit can add expenditures related to that hobby as a new category. If a user moves, the classification unit can suggest expenditure categories that match the new living environment. If a user participates in a specific event, the classification unit can classify expenditures related to that event as a special category. This allows for more appropriate expenditure management by filtering expenditures based on the user's current lifestyle and areas of interest. Some or all of the above processing in the classification unit may be performed using AI, for example, or not. For example, the classification unit can input the user's lifestyle data into a generating AI and have the generating AI perform the filtering.

[0081] The classification unit can estimate the user's emotions and determine the priority of expenditure category classification based on the estimated emotions. For example, if the user is stressed, the classification unit can prioritize displaying important expenditure categories and postpone unnecessary expenditures. If the user is relaxed, the classification unit can display detailed expenditure categories to support finer management. If the user is in a hurry, the classification unit can display only major expenditure categories to allow for quick classification. This enables more appropriate expenditure management by determining the priority of expenditure category classification according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the classification unit may be performed using AI or not. For example, the classification unit can input user emotion data into a generative AI and have the generative AI determine the priority of expenditure category classification.

[0082] The classification unit can prioritize highly relevant expenditures when classifying spending, taking into account the user's geographical location. For example, if the user is in a specific region, the classification unit can prioritize expenditures related to that region. If the user is traveling, the classification unit can prioritize expenditures related to travel. If the user has made purchases at a specific store, the classification unit can prioritize expenditures related to that store. In this way, highly relevant expenditures can be prioritized by considering geographical location. Some or all of the above processing in the classification unit may be performed using AI, for example, or without AI. For example, the classification unit can input geographical location data into a generating AI and have the generating AI perform the preferential classification of expenditures.

[0083] The classification unit can analyze a user's social media activity and classify relevant expenditures when classifying spending. For example, if a user posts on social media about attending a specific event, the classification unit can classify the expenditure related to that event. If a user posts on social media about purchasing a specific product, the classification unit can classify the expenditure related to that product. If a user posts on social media about visiting a specific place, the classification unit can classify the expenditure related to that place. In this way, relevant expenditures can be appropriately classified by analyzing social media activity. Some or all of the above processing in the classification unit may be performed using AI, for example, or not using AI. For example, the classification unit can input social media data into a generating AI and have the generating AI perform the expenditure classification.

[0084] The advice unit can estimate the user's emotions and adjust the way it expresses advice based on those emotions. For example, if the user is stressed, the advice unit can provide simple and intuitive advice. If the user is relaxed, the advice unit can provide detailed advice and suggest specific ways to save money. If the user is in a hurry, the advice unit can provide short and to-the-point advice. By adjusting the way advice is expressed according to the user's emotions, more effective advice can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the advice unit may be performed using AI or not using AI. For example, the advice unit can input user emotion data into the generative AI and have the generative AI adjust the way advice is expressed.

[0085] The advice unit can adjust the level of detail of its advice based on the importance of the expenditure when providing advice. For example, it can provide detailed advice for important expenditure categories, and concise advice for unnecessary expenditure categories. The advice unit can adjust the level of detail of its advice in stages according to the importance of the expenditure. This allows for the provision of more appropriate advice by adjusting the level of detail based on the importance of the expenditure. Some or all of the above processing in the advice unit may be performed using AI, for example, or not using AI. For example, the advice unit can input expenditure data into a generating AI and have the generating AI perform the adjustment of the level of detail of the advice.

[0086] The advice unit can apply different advice algorithms depending on the expenditure category when providing advice. For example, for the food category, the advice unit can provide advice on reducing food expenses. For the transportation category, the advice unit can provide advice on reducing transportation expenses. For the leisure category, the advice unit can provide advice on reducing leisure expenses. By applying different advice algorithms depending on the expenditure category, more effective advice can be provided. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input expenditure data into a generating AI and have the generating AI execute the application of the advice algorithm.

[0087] The advice unit can estimate the user's emotions and adjust the length of the advice based on the estimated emotions. For example, if the user is stressed, the advice unit can provide short, concise advice. If the user is relaxed, the advice unit can provide detailed advice and suggest specific ways to save money. If the user is in a hurry, the advice unit can provide short, concise advice. By adjusting the length of the advice according to the user's emotions, more effective advice can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the advice unit may be performed using AI or not using AI. For example, the advice unit can input user emotion data into the generative AI and have the generative AI adjust the length of the advice.

[0088] The advisory unit can prioritize advice based on the timing of expense submissions when providing advice. For example, the advisory unit will prioritize advice when important expenses are submitted. If unnecessary expenses are submitted, the advisory unit may postpone providing advice. The advisory unit can adjust the priority of advice in stages according to the timing of expense submissions. This allows for the provision of more appropriate advice by prioritizing advice based on the timing of expense submissions. Some or all of the above processes in the advisory unit may be performed using AI, for example, or not using AI. For example, the advisory unit can input expense data into a generating AI and have the generating AI determine the priority of advice.

[0089] The advice unit can adjust the order of advice based on the relevance of the expenditures when providing advice. For example, the advice unit can prioritize advice for important expenditure categories. For unnecessary expenditure categories, the advice unit can postpone providing advice. The advice unit can adjust the order of advice in stages according to the relevance of the expenditures. This allows for the provision of more effective advice by adjusting the order of advice based on the relevance of the expenditures. Some or all of the above processing in the advice unit may be performed using AI, for example, or not using AI. For example, the advice unit can input expenditure data into a generating AI and have the generating AI perform the adjustment of the order of advice.

[0090] The settings unit can estimate the user's emotions and adjust the budget setting method based on the estimated emotions. For example, if the user is stressed, the settings unit can provide a simple budget setting method. If the user is relaxed, the settings unit can provide a detailed budget setting method. If the user is in a hurry, the settings unit can provide a method for quickly setting the budget. This allows for more appropriate budget management by adjusting the budget setting method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the settings unit may be performed using AI or not using AI. For example, the settings unit can input user emotion data into the generative AI and have the generative AI adjust the budget setting method.

[0091] The setting unit can analyze the user's past spending history to select the optimal budget setting method when setting a budget. For example, the setting unit can suggest the optimal budget setting method based on the user's past spending history. The setting unit can analyze the user's past spending patterns and adjust the criteria for budget setting. The setting unit can set a budget for specific spending items based on the user's past spending history. This makes it possible to set an optimal budget by analyzing past spending history. Some or all of the above processes in the setting unit may be performed using AI, for example, or without AI. For example, the setting unit can input past spending data into a generating AI and have the generating AI select the optimal budget setting method.

[0092] The settings unit can customize the budget setting method based on the user's current living situation when setting a budget. For example, if a user starts a new job, the settings unit can set a budget based on that income. If a user moves, the settings unit can set a budget that suits the new living environment. If a user participates in a specific event, the settings unit can reflect the expenses related to that event in the budget. This allows for more appropriate budget management by customizing the budget setting based on the current living situation. Some or all of the above processes in the settings unit may be performed using AI, for example, or not using AI. For example, the settings unit can input living situation data into a generating AI and have the generating AI perform the customization of the budget setting.

[0093] The settings unit can estimate the user's emotions and determine budget priorities based on those emotions. For example, if the user is stressed, the settings unit will prioritize budgeting for important spending items. If the user is relaxed, the settings unit can set a detailed budget. If the user is in a hurry, the settings unit can prioritize budgeting for only major spending items. This allows for more appropriate budget management by prioritizing budget settings according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the settings unit may be performed using AI or not. For example, the settings unit can input user emotion data into a generative AI and have the generative AI determine the budget priorities.

[0094] The settings unit can select the optimal budget setting method when setting a budget, taking into account the user's geographical location information. For example, if the user lives in a specific region, the settings unit can set the budget based on the cost of living in that region. If the user is traveling, the settings unit can reflect travel-related expenses in the budget. If the user is shopping at a specific store, the settings unit can set the budget based on the price range of that store. This makes it possible to set an optimal budget by taking geographical location information into consideration. Some or all of the above processing in the settings unit may be performed using AI, for example, or not using AI. For example, the settings unit can input geographical location data into a generating AI and have the generating AI select a budget setting method.

[0095] The budget setting unit can analyze the user's social media activity and propose budget setting methods when setting a budget. For example, if a user posts on social media that they are attending a specific event, the budget setting unit can reflect the expenditure related to that event in the budget. If a user posts on social media that they have purchased a specific product, the budget setting unit can reflect the expenditure related to that product in the budget. If a user posts on social media that they have visited a specific place, the budget setting unit can reflect the expenditure related to that place in the budget. This allows for more appropriate budget setting by analyzing social media activity. Some or all of the above processing in the budget setting unit may be performed using AI, for example, or not using AI. For example, the budget setting unit can input social media data into a generating AI and have the generating AI execute suggestions for budget setting methods.

[0096] The notification unit can estimate the user's emotions and adjust the notification method based on the estimated emotions. For example, if the user is stressed, the notification unit can provide a simple and intuitive notification. If the user is relaxed, the notification unit can provide a detailed notification and suggest specific ways to save money. If the user is in a hurry, the notification unit can provide a short and to-the-point notification. This allows for more effective notifications by adjusting the notification method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI or not using AI. For example, the notification unit can input user emotion data into a generative AI and have the generative AI adjust the notification method.

[0097] The notification unit can select the optimal notification method by referring to the user's past spending history when sending a notification. For example, the notification unit can prioritize frequently used notification methods based on the user's past spending history. The notification unit can analyze the user's past spending patterns and suggest the optimal notification method. The notification unit can select a notification method for a specific spending item based on the user's past spending history. This allows the optimal notification method to be selected by referring to past spending history. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input past spending data into a generating AI and have the generating AI select the optimal notification method.

[0098] The notification unit can estimate the user's emotions and prioritize notifications based on those emotions. For example, if the user is stressed, the notification unit will prioritize important notifications and postpone unnecessary ones. If the user is relaxed, the notification unit can provide detailed notifications and suggest specific ways to save time. If the user is in a hurry, the notification unit will prioritize only key notifications to allow for quick action. This enables more effective notifications by prioritizing them according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input user emotion data into a generative AI and have the generative AI determine the priority of notifications.

[0099] The notification unit can select the optimal notification method when a notification is sent, taking into account the user's device information. For example, if the user is using a smartphone, the notification unit can provide a notification method that matches the screen size. If the user is using a tablet, the notification unit can provide a notification method optimized for a larger screen. If the user is using a smartwatch, the notification unit can provide a concise and highly visible notification method. This allows the optimal notification method to be selected by considering the device information. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input device information into a generating AI and have the generating AI select the optimal notification method.

[0100] The notification unit can provide scheduled notifications by referring to the user's calendar information. For example, the notification unit can refer to the appointments registered in the user's calendar and prioritize providing notifications related to important appointments. The notification unit can provide notifications related to specific events from the user's calendar information. Based on the user's calendar information, the notification unit can suggest the most suitable notification method for each appointment. This makes it possible to provide optimal scheduled notifications by referring to the calendar information. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input calendar information into a generation AI and have the generation AI generate scheduled notifications.

[0101] The notification unit can analyze the user's social media activity and provide relevant notifications at the time of notification. For example, if the user posts on social media that they will be attending a specific event, the notification unit can provide a notification related to that event. If the user posts on social media that they have purchased a specific product, the notification unit can provide a notification related to that product. If the user posts on social media that they have visited a specific place, the notification unit can provide a notification related to that place. This makes it possible to provide relevant notifications by analyzing social media activity. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can input social media data into a generating AI and have the generating AI generate relevant notifications.

[0102] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0103] The household budget management system can also include a health management unit that acquires user health data and analyzes the relationship between spending patterns and health status. For example, if a user's food expenses are high, the health management unit can analyze whether those expenses are being used for healthy foods and provide advice on maintaining a healthy diet. Furthermore, if a user is not getting enough exercise, the health management unit can suggest increasing spending on exercise-related activities. Additionally, if a user has high stress levels, it can recommend spending on relaxation and stress relief. In this way, the household budget management system can support spending management that takes the user's health status into consideration.

[0104] The household budget management system can also include an emotion-prioritizing section that estimates the user's emotions and adjusts spending priorities based on those emotions. For example, if the user is feeling stressed, the emotion-prioritizing section can prioritize displaying spending related to stress relief, supporting the user in relaxing. If the user is feeling happy, the emotion-prioritizing section can recommend spending to maintain that feeling. Furthermore, if the user is feeling sad, the emotion-prioritizing section can suggest spending to alleviate that feeling. In this way, the household budget management system can support spending management that is tailored to the user's emotions.

[0105] A household budget management system can further include a forecasting unit that analyzes the user's purchase history and predicts future spending based on purchase patterns. For example, if a user spends a fixed amount on a specific category each month, the forecasting unit can analyze that pattern and predict the next month's spending. It can also analyze spending patterns related to specific seasons or events and present expected spending during those periods. Furthermore, the forecasting unit can adjust spending predictions according to changes in the user's lifestyle. This makes it easier for users to plan their future spending.

[0106] A household budget management system can also include an emotional advice unit that estimates the user's emotions and customizes spending advice based on those emotions. For example, if the user is stressed, the emotional advice unit can provide simple and intuitive advice to help the user relax. If the user is relaxed, the emotional advice unit can provide detailed advice and suggest specific ways to save money. Furthermore, if the user is in a hurry, the emotional advice unit can provide short and to-the-point advice. In this way, the household budget management system can provide effective advice tailored to the user's emotions.

[0107] The household budget management system can also include a regional analysis unit that acquires the user's geographical location information and analyzes spending patterns by region. For example, if a user lives in a specific region, it can provide advice on optimizing spending based on the cost of living in that region. If a user is traveling, it can prioritize displaying spending related to the travel destination region, supporting spending management during the trip. Furthermore, if a user shops at a specific store, it can provide advice on optimizing spending based on the store's price range. In this way, the household budget management system can support spending management based on the user's geographical location information.

[0108] The household budget management system can also include an emotion notification unit that estimates the user's emotions and adjusts the notification method based on those emotions. For example, if the user is stressed, the emotion notification unit can provide simple and intuitive notifications to help the user relax. If the user is relaxed, the emotion notification unit can provide detailed notifications and suggest specific ways to save money. Furthermore, if the user is in a hurry, the emotion notification unit can provide short and to-the-point notifications. In this way, the household budget management system can provide effective notifications tailored to the user's emotions.

[0109] The household budget management system can also include a social analytics unit that analyzes the user's social media activity and categorizes related expenses. For example, if a user posts on social media about attending a specific event, the system can categorize expenses related to that event. Similarly, if a user posts on social media about purchasing a specific product, the system can categorize expenses related to that product. Furthermore, if a user posts on social media about visiting a specific location, the system can categorize expenses related to that location. This allows the household budget management system to support expense management based on social media activity.

[0110] A household budgeting system can further incorporate an emotional budgeting section that estimates the user's emotions and adjusts the budgeting method based on those emotions. For example, if the user is stressed, the emotional budgeting section can provide a simple budgeting method to help the user relax. If the user is relaxed, the emotional budgeting section can provide a detailed budgeting method and suggest specific saving strategies. Furthermore, if the user is in a hurry, the emotional budgeting section can provide a way to quickly set a budget. In this way, the household budgeting system can support effective budgeting tailored to the user's emotions.

[0111] A household budget management system can also include a calendar management unit that references the user's calendar information and manages expenses based on their schedule. For example, it can refer to the appointments registered in the user's calendar and prioritize displaying expenses related to important appointments. It can also predict expenses related to specific events based on the user's calendar information and reflect them in the budget. Furthermore, it can suggest the most suitable expense management method based on the user's calendar information. In this way, the household budget management system can support effective expense management based on calendar information.

[0112] The household budget management system can also include an emotion-prioritizing section that estimates the user's emotions and determines spending priorities based on those emotions. For example, if the user is stressed, the emotion-prioritizing section can prioritize important spending categories and postpone unnecessary spending. If the user is relaxed, the emotion-prioritizing section can display detailed spending categories to support more precise management. Furthermore, if the user is in a hurry, the emotion-prioritizing section can display only the main spending categories for quick categorization. In this way, the household budget management system can support effective spending management tailored to the user's emotions.

[0113] The following briefly describes the processing flow for example form 2.

[0114] Step 1: The classification unit categorizes expenses. For example, expenses can be classified into categories such as food, transportation, and leisure. The classification unit can also categorize expenses based on type, amount, frequency, etc. Step 2: The advice section provides advice based on the spending data categorized by the classification section. For example, it provides saving advice for categories where spending has increased. The advice section can also suggest useful coupons that can reduce spending. Furthermore, it analyzes the user's spending patterns and provides optimal advice. Step 3: The settings section sets the budget. Users can set a monthly budget, or the budget can be automatically set based on past data. The settings section sets the budget based on specific budget setting methods and criteria. Step 4: The notification unit will send a notification if the budget set in the settings unit is exceeded. The notification unit will notify the user when the budget is exceeded, which can help curb spending. Furthermore, it can also display advice on how to control household finances and save money when the budget is exceeded.

[0115] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0116] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0117] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0118] Each of the multiple elements described above, including the classification unit, advice unit, setting unit, and notification unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the classification unit is implemented by the control unit 46A of the smart device 14 and classifies expenditures into categories such as food, transportation, and leisure. The advice unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides savings advice and information on discount coupons based on the classified expenditure data. The setting unit is implemented by the control unit 46A of the smart device 14 and allows the user to set a monthly budget or automatically sets a budget based on past data. The notification unit is implemented by the specific processing unit 290 of the data processing unit 12 and notifies the user if the budget is exceeded, and displays advice on how to control household finances and save money. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0119] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0120] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0121] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0122] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0123] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0124] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0125] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0126] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0127] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0128] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0129] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0130] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0131] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0132] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0133] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0134] Each of the multiple elements described above, including the classification unit, advice unit, setting unit, and notification unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the classification unit is implemented by the control unit 46A of the smart glasses 214 and classifies expenditures into categories such as food, transportation, and leisure. The advice unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides savings advice and information on discount coupons based on the classified expenditure data. The setting unit is implemented by the control unit 46A of the smart glasses 214 and allows the user to set a monthly budget or automatically sets a budget based on past data. The notification unit is implemented by the specific processing unit 290 of the data processing unit 12 and notifies the user if the budget is exceeded, displaying advice on how to control household finances and save money. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0135] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0136] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0137] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0138] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0139] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0141] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0142] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0143] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0144] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0145] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0146] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0147] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0148] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0149] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0150] Each of the multiple elements described above, including the classification unit, advice unit, setting unit, and notification unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the classification unit is implemented by the control unit 46A of the headset terminal 314 and classifies expenditures into categories such as food, transportation, and leisure. The advice unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides savings advice and information on discount coupons based on the classified expenditure data. The setting unit is implemented by the control unit 46A of the headset terminal 314 and allows the user to set a monthly budget or automatically sets a budget based on past data. The notification unit is implemented by the specific processing unit 290 of the data processing unit 12 and notifies the user when the budget is exceeded, displaying advice on how to control household finances and save money. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0151] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0152] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0153] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0154] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0155] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0156] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0157] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0158] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0159] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0160] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0161] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0162] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0163] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0164] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0165] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0166] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0167] Each of the multiple elements described above, including the classification unit, advice unit, setting unit, and notification unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the classification unit is implemented by the control unit 46A of the robot 414 and classifies expenditures into categories such as food, transportation, and leisure. The advice unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides savings advice and information on discount coupons based on the classified expenditure data. The setting unit is implemented by the control unit 46A of the robot 414 and either allows the user to set a monthly budget or automatically sets a budget based on past data. The notification unit is implemented by the specific processing unit 290 of the data processing unit 12 and notifies the user if the budget is exceeded, displaying advice on how to control household finances and save money. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0168] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0169] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0170] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0171] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0172] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0173] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0174] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0175] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0176] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0177] 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.

[0178] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0179] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0180] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0181] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0182] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0183] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0184] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0185] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0186] (Note 1) A classification section that categorizes expenditures, An advice unit provides advice based on expenditure data classified by the classification unit, A setting section for setting the budget, The system includes a notification unit that provides notification when the budget set by the setting unit is exceeded. A system characterized by the following features. (Note 2) The aforementioned classification unit is Classify expenses into categories such as food, transportation, and leisure. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned advice section, We provide saving advice for categories where spending has increased. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned advice section, We offer information on great coupons that can help you save money. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned setting unit is, Users can set a monthly budget, or the system can automatically set a budget based on past data. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned notification unit, We will send a notification if the budget is exceeded. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned notification unit, When the budget is exceeded, it displays tips on how to control household finances and advice on saving money. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned setting unit is, If there is a surplus in the budget, the excess funds will be automatically transferred to an investment trust. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned classification unit is We estimate user sentiment and adjust the criteria for categorizing spending based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned classification unit is When classifying expenses, the system analyzes the user's past spending history to select the optimal classification method. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned classification unit is When classifying expenses, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned classification unit is It estimates the user's emotions and determines the priority of spending categories based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned classification unit is When classifying expenses, the system prioritizes highly relevant expenses by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned classification unit is When classifying spending, analyze users' social media activity and categorize related spending. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned advice section, It estimates the user's emotions and adjusts the way advice is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned advice section, When providing advice, adjust the level of detail based on the importance of the expenditure. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned advice section, When providing advice, different advice algorithms are applied depending on the category of expenditure. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned advice section, It estimates the user's emotions and adjusts the length of the advice based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned advice section, When providing advice, we prioritize the advice based on when the expenses were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned advice section, When providing advice, we adjust the order of advice based on the relevance of the expenses. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned setting unit is, It estimates user sentiment and adjusts budget setting methods based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned setting unit is, When setting a budget, the system analyzes the user's past spending history to select the optimal budget setting method. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned setting unit is, When setting a budget, customize the budget setting method based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned setting unit is, It estimates user sentiment and prioritizes budget settings based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned setting unit is, When setting a budget, the optimal budget setting method is selected by considering the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned setting unit is, When setting a budget, we analyze users' social media activity and suggest methods for budgeting. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned notification unit, It estimates the user's emotions and adjusts the notification method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned notification unit, When sending a notification, the system will refer to the user's past spending history to select the most suitable notification method. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned notification unit, It estimates the user's emotions and prioritizes notifications based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned notification unit, When sending notifications, the system selects the most suitable notification method, taking into account the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned notification unit, When sending notifications, the system references the user's calendar information to provide notifications based on their schedule. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned notification unit, When sending notifications, the system analyzes the user's social media activity to provide relevant notifications. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0187] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A classification section that categorizes expenditures, An advice unit provides advice based on expenditure data classified by the classification unit, A setting section for setting the budget, The system includes a notification unit that provides notification when the budget set by the setting unit is exceeded. A system characterized by the following features.

2. The aforementioned classification unit is Classify expenses into categories such as food, transportation, and leisure. The system according to feature 1.

3. The aforementioned advice section, We provide saving advice for categories where spending has increased. The system according to feature 1.

4. The aforementioned advice section, We offer information on great coupons that can help you save money. The system according to feature 1.

5. The aforementioned setting unit is, Users can set a monthly budget, or the system can automatically set a budget based on past data. The system according to feature 1.

6. The aforementioned notification unit, We will send a notification if the budget is exceeded. The system according to feature 1.

7. The aforementioned notification unit, When the budget is exceeded, it displays tips on how to control household finances and advice on saving money. The system according to feature 1.

8. The aforementioned setting unit is, If there is a surplus in the budget, the excess funds will be automatically transferred to an investment trust. The system according to feature 1.

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