system

Abstract

The system according to this embodiment aims to provide support for automatic tracking and analysis of income and expenses, automatic savings through budget setting, and financial planning. [Solution] The system according to the embodiment comprises an analysis unit, a savings unit, and a planning unit. The analysis unit automatically tracks and analyzes income and expenses. The savings unit automatically saves money based on the income and expense data analyzed by the analysis unit, according to the budget setting. The planning unit supports financial planning based on the budget set by the savings unit.

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 character of the chatbot, 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 the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, automatic tracking and analysis of income and expenditure, automatic savings by budget setting, and support for financial planning are not sufficiently carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to perform automatic tracking and analysis of income and expenditure, automatic savings by budget setting, and support for financial planning.

Means for Solving the Problems

[0006] The system according to the embodiment includes an analysis unit, a savings unit, and a planning unit. The analysis unit automatically tracks and analyzes income and expenditure. The savings unit performs automatic savings by budget setting based on the income and expenditure data analyzed by the analysis unit. The planning unit supports financial planning based on the budget set by the savings unit. [Effects of the Invention]

[0007] The system according to this embodiment can automatically track and analyze income and expenses, automatically save money through budget setting, and support financial planning. [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 fund management system according to an embodiment of the present invention is a new application designed to strengthen general users' involvement in the SoftBank economic sphere. This fund management system has the following functions: "Smart Money Manager": It automatically tracks and analyzes income and expenses using bank data and centrally manages transaction history from e-commerce sites and electronic payment services. Users can check an overview of their income and expenses in the messaging app and set a budget to enable automatic savings. This helps reduce wasteful spending and supports smart fund management. "Life Event Planning": It supports financial planning based on bank data for important events such as marriage and home purchase. Users can check their savings progress in the messaging app, easily plan using the benefits of e-commerce sites, and confidently aim to achieve their goals. First, the "Smart Money Manager" function centrally manages bank account data and expenditure information from e-commerce sites and electronic payment services, and automatically tracks and analyzes income and expenses. This makes it easy for users to manage their daily expenses. In addition, it promotes efficient saving while preventing budget overruns through notification functions in the messaging app. For example, users can set monthly expenses and manage their income and expenses within that range. Next, the "Life Event Planning" feature supports financial planning for life events such as marriage and home purchase. Users can regularly check their savings progress using the reminder function in the messaging app. Furthermore, leveraging rewards on e-commerce sites makes planning easier and helps maintain motivation towards achieving goals. For example, a user can set a savings goal for their wedding and track their progress through the messaging app. This app targets young to middle-aged individuals in their late 20s to 40s, catering to a wide range of people, regardless of gender, from singles to married couples and families with children. It is particularly designed for people in professions familiar with digital tools and those who frequently shop online. Users want to make effective use of their time in their busy daily lives and are seeking efficient methods for household budgeting and saving. They also value planning for the future and have a strong interest in financial planning for life events.This app makes it easier for users to manage their daily expenses and plan for major future expenditures. It also solves the problem of scattered information due to using multiple apps and services, enabling centralized management. This allows users to efficiently manage their finances and smoothly prepare for future life events. The financial management system automatically tracks and analyzes the user's income and expenses, facilitates automatic savings through budgeting, and supports financial planning.

[0029] The fund management system according to this embodiment comprises an analysis unit, a savings unit, and a planning unit. The analysis unit automatically tracks and analyzes income and expenses. The analysis unit centrally manages, for example, bank account data and expenditure information from e-commerce sites and electronic payment services, and automatically tracks and analyzes income and expenses. For example, the analysis unit acquires bank account data, collects expenditure information from e-commerce sites, and integrates the history of electronic payment services. Based on this data, the analysis unit can analyze income and expense trends and identify wasteful spending. The savings unit automatically saves money based on budget settings using the income and expense data analyzed by the analysis unit. For example, the savings unit automatically saves money based on a budget set by the user. For example, the savings unit can automatically save a fixed amount from monthly income. The savings unit can also manage the progress of savings based on a goal set by the user. For example, the savings unit can adjust the monthly savings amount to a savings goal set by the user. The planning unit supports fund planning based on the budget set by the savings unit. The planning unit supports financial planning for life events such as marriage and home purchase. For example, the planning unit can calculate the necessary funds for life events set by the user and create a savings plan. The planning unit can also manage the progress of savings based on goals set by the user. For example, the planning unit can periodically notify the user of the progress of savings for life events set by the user. As a result, the financial management system according to this embodiment can support automatic tracking and analysis of income and expenses, automatic savings based on budget setting, and financial planning.

[0030] The analytics unit automatically tracks and analyzes income and expenses. For example, the analytics unit centrally manages spending information from bank account data, e-commerce sites, and electronic payment services, and automatically tracks and analyzes income and expenses. Specifically, the analytics unit uses bank APIs to obtain account data and collects spending information through e-commerce site and electronic payment service APIs. This allows for centralized management of all of the user's income and expenses. The analytics unit updates this data in real time and analyzes trends in income and expenses. For example, it can graph monthly fluctuations in income and expenses and present them visually to the user. The analytics unit also uses AI to identify wasteful spending. The AI ​​performs pattern recognition based on past data to detect abnormal spending and wasteful spending trends. For example, it can send an alert to the user if spending in a particular category suddenly increases. Furthermore, the analytics unit can learn the user's spending patterns and predict future spending. This allows the user to receive specific advice for reviewing their budget and controlling spending. Through these functions, the analytics unit can streamline the user's income and expense management and reduce wasteful spending.

[0031] The savings unit automatically saves money based on budget settings derived from income and expenditure data analyzed by the analysis unit. For example, the savings unit automatically saves money based on a budget set by the user. Specifically, the savings unit analyzes the user's income and expenses and automatically calculates the monthly savings amount. For example, if a user sets a goal to save 10% of their monthly income, the savings unit automatically calculates that amount and transfers it to the designated savings account. The savings unit can also manage the progress of savings based on goals set by the user. For example, if a user sets a goal such as travel or purchasing a new car, the savings unit calculates the amount and time required to achieve that goal and adjusts the monthly savings amount accordingly. Furthermore, the savings unit can flexibly change the savings plan in response to fluctuations in the user's income and expenses. For example, it can increase savings if income increases and decrease savings if expenses increase. In this way, the savings unit supports users in continuing to save money without difficulty. Through these functions, the savings unit can help users achieve their savings goals and prepare for future life events.

[0032] The Planning Department supports financial planning based on the budget set by the Savings Department. The Planning Department supports financial planning for life events such as marriage and home purchase. Specifically, it can calculate the necessary funds for life events set by the user and create a savings plan. For example, if a user is planning to get married, the Planning Department will estimate the costs of the wedding and honeymoon and propose a savings plan. The Planning Department can also manage the progress of savings based on the user's set goals. For example, if a user aims to buy a home, the Planning Department will calculate the monthly savings amount and the time required to achieve the goal, and provide regular updates on the progress. Furthermore, the Planning Department can flexibly modify the financial plan according to the user's life events. For example, if a user sets a new goal or changes an existing goal, the Planning Department will recalculate the savings plan accordingly and propose the optimal plan. In this way, the Planning Department supports users in efficiently preparing the funds necessary for life events. Through these functions, the Planning Department can comprehensively support users' financial planning and help them achieve their future goals.

[0033] The notification unit can notify users of income and expenditure summaries via a messaging app. For example, by notifying users of income and expenditure summaries, the notification unit can facilitate income and expenditure management. For example, the notification unit can notify users of monthly income and expenditure summaries via a messaging app. The notification unit can also notify users of the progress of income and expenditure against a budget set by the user. For example, the notification unit can notify users if they exceed their budget. This makes income and expenditure management easier by notifying users of income and expenditure summaries. Some or all of the above processes in the notification unit may be performed using AI or not. For example, the notification unit can input income and expenditure data into AI and have the AI ​​generate income and expenditure summaries.

[0034] The reminder unit can notify users of their savings progress via reminders. For example, the reminder unit can maintain users' motivation to save by notifying them of their savings progress. For example, the reminder unit can notify users of their monthly savings progress via reminders. Furthermore, the reminder unit can periodically notify users of their savings progress towards their set savings goals. For example, the reminder unit can notify users of their savings progress towards their set savings goals via reminders. This helps maintain users' motivation to save by notifying them of their savings progress. Some or all of the above-described processes in the reminder unit may be performed using AI or not. For example, the reminder unit can input savings data into an AI and have the AI ​​execute the savings progress notification.

[0035] The rewards unit can facilitate planning by utilizing rewards. The rewards unit can, for example, make it easier to execute a plan by utilizing rewards. The rewards unit can, for example, facilitate planning by utilizing rewards from e-commerce sites. Furthermore, the rewards unit can support the execution of a plan by utilizing rewards in relation to goals set by the user. The rewards unit can, for example, facilitate planning by utilizing rewards in relation to goals set by the user. This makes it easier to execute a plan by utilizing rewards. Some or all of the above processing in the rewards unit may be performed using AI or not. For example, the rewards unit can input reward data into AI and have AI perform the provision of rewards.

[0036] The analysis unit can centrally manage bank account data, e-commerce site spending information, and electronic payment service spending information, and automatically track and analyze income and expenses. For example, the analysis unit can acquire bank account data, collect spending information from e-commerce sites, and integrate electronic payment service history. Based on this data, the analysis unit can analyze income and expense trends and identify wasteful spending. This enables centralized management and automated analysis of income and expense data. Some or all of the above processing in the analysis unit may be performed using AI, or it may be performed without AI. For example, the analysis unit can input income and expense data into AI and have the AI ​​perform the income and expense analysis.

[0037] The planning unit can support financial planning for life events such as marriage and home purchase. For example, the planning unit can calculate the necessary funds for life events set by the user and create a savings plan. The planning unit can also manage the progress of savings based on goals set by the user. For example, the planning unit can periodically notify the user of the progress of savings for life events set by the user. This enables support for financial planning toward life events. Some or all of the above processes in the planning unit may be performed using AI or not. For example, the planning unit can input life event data into AI and have the AI ​​perform financial planning support.

[0038] The analysis unit can improve the accuracy of its analysis by considering the user's past spending patterns when analyzing income and expenditure data. For example, the analysis unit can predict future spending patterns based on the products and services that the user has frequently purchased in the past. The analysis unit can also analyze the user's past spending history and reflect seasonal spending trends. Furthermore, the analysis unit can predict spending during specific events or sales periods based on the user's past spending patterns. This improves the accuracy of the analysis by considering past spending patterns. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input past spending data into AI and have the AI ​​perform the analysis of spending patterns.

[0039] The analysis unit can apply a customized analysis algorithm based on the user's lifestyle when analyzing income and expenditure data. For example, if the user is health-conscious, the analysis unit will prioritize the analysis of health-related expenses. If the user enjoys traveling, the analysis unit can analyze travel-related expenses in detail. Furthermore, if the user is a family, the analysis unit can consider the expenses of the entire family. This enables customized analysis tailored to the user's lifestyle. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input lifestyle data into AI and have the AI ​​perform the customized analysis.

[0040] The analysis unit can reflect region-specific consumption trends by considering the user's geographical location information when analyzing income and expenditure data. For example, the analysis unit can perform analysis based on the average consumption trends of the area where the user lives. It can also reflect consumption trends in areas that the user frequently visits. Furthermore, the analysis unit can incorporate region-specific sales information into the analysis based on the user's geographical location information. This enables analysis that reflects region-specific consumption trends. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input geographical location data into AI and have the AI ​​perform an analysis of region-specific consumption trends.

[0041] The analysis unit can analyze users' social media activity and incorporate relevant consumption information into the analysis when analyzing income and expenditure data. For example, the analysis unit can perform analysis based on products and services mentioned by users on social media. The analysis unit can also reflect consumption trends in areas of interest from users' social media activity. Furthermore, the analysis unit can perform analysis by referring to the consumption trends of users' followers and friends on social media. This makes it possible to perform analysis that takes social media activity into account. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input social media data into AI and have the AI ​​perform the analysis of consumption information.

[0042] The savings function can analyze the user's past savings history to select the optimal savings method during automatic savings. For example, the savings function can suggest the optimal savings method based on the user's past successful savings methods. Furthermore, the savings function can select methods to increase the success rate of savings based on the user's past savings history. In addition, the savings function can analyze the user's past savings history to optimize the timing and amount of savings. This makes it possible to select the optimal savings method considering past savings history. Some or all of the above processes in the savings function may be performed using AI, or not. For example, the savings function can input past savings data into AI and have the AI ​​select the optimal savings method.

[0043] The savings function can customize the savings amount based on the user's current income situation during automatic savings. For example, the savings function can adjust the savings amount based on the user's current income situation. Furthermore, if the user's income increases, the savings function can increase the savings amount. In addition, if the user's income decreases, the savings function can decrease the savings amount. This makes it possible to customize the savings amount according to the current income situation. Some or all of the above processes in the savings function may be performed using AI or not. For example, the savings function can input income data into AI and have the AI ​​perform the customization of the savings amount.

[0044] The savings function can reflect region-specific saving habits by considering the user's geographical location information during automatic savings. For example, the savings function can suggest savings methods based on the saving habits of the area where the user lives. It can also reflect the saving habits of areas the user frequently visits. Furthermore, the savings function can suggest region-specific saving methods based on the user's geographical location information. This enables automatic savings that reflect region-specific saving habits. Some or all of the above processing in the savings function may be performed using AI or not. For example, the savings function can input geographical location data into AI and have the AI ​​perform the task of reflecting region-specific saving habits.

[0045] The savings function can analyze the user's social media activity and reflect relevant savings information during automatic savings. For example, the savings function can make suggestions based on savings methods mentioned by the user on social media. It can also reflect savings methods the user is interested in based on their social media activity. Furthermore, the savings function can make suggestions based on the savings habits of the user's social media followers and friends. This makes it possible to reflect savings information that takes social media activity into account. Some or all of the above processing in the savings function may be performed using AI or not. For example, the savings function can input social media data into AI and have the AI ​​perform the reflection of savings information.

[0046] The planning department can analyze the user's past planning history to select the optimal planning method when creating a financial plan. For example, the planning department can propose the optimal planning method based on the user's past successful financial plans. Furthermore, the planning department can select methods to increase the success rate of plans based on the user's past planning history. In addition, the planning department can analyze the user's past planning history to optimize the timing and content of plans. This makes it possible to select the optimal financial plan that takes past planning history into account. Some or all of the above processes in the planning department may be performed using AI, or not. For example, the planning department can input past planning data into AI and have the AI ​​select the optimal planning method.

[0047] The planning unit can customize financial plans based on the user's current living situation. For example, the planning unit can adjust financial plans based on the user's current income. It can also propose financial plans tailored to the user's lifestyle. Furthermore, the planning unit can provide financial plans that are appropriate for the user's family structure and life stage. This enables customized financial plans that are tailored to the user's current living situation. Some or all of the above processes in the planning unit may be performed using AI or not. For example, the planning unit can input living situation data into AI and have the AI ​​provide customized financial plans.

[0048] The planning unit can reflect region-specific planning habits by considering the user's geographical location information when creating financial plans. For example, the planning unit can propose a planning method based on the planning habits of the area where the user lives. It can also reflect the planning habits of areas that the user frequently visits. Furthermore, the planning unit can propose region-specific planning methods based on the user's geographical location information. This makes it possible to create financial plans that reflect region-specific planning habits. Some or all of the above processes in the planning unit may be performed using AI or not. For example, the planning unit can input geographical location data into AI and have the AI ​​perform the task of reflecting region-specific planning habits.

[0049] The planning department can analyze users' social media activity and reflect relevant planning information when creating financial plans. For example, the planning department can make suggestions based on planning methods mentioned by users on social media. It can also reflect planning methods that users are interested in based on their social media activity. Furthermore, the planning department can make suggestions based on the planning habits of users' followers and friends on social media. This makes it possible to reflect planning information that takes social media activity into account. Some or all of the above processes in the planning department may be performed using AI or not. For example, the planning department can input social media data into AI and have the AI ​​perform the reflection of planning information.

[0050] The notification unit can analyze the user's past notification history to select the optimal notification method when sending a notification. For example, the notification unit can make suggestions based on the notification methods the user has preferred to receive in the past. The notification unit can also select the most effective notification timing from the user's past notification history. Furthermore, the notification unit can analyze the user's past notification history and optimize the notification content. This makes it possible to select the optimal notification method while considering past notification history. 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 past notification data into AI and have the AI ​​select the optimal notification method.

[0051] 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 will prioritize push notifications. If the user is using a tablet, the notification unit can provide a notification method optimized for a larger screen. Furthermore, if the user is using a smartwatch, the notification unit can provide a concise and highly visible notification method. This makes it possible to select the optimal notification method considering device information. 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 device information into the AI ​​and have the AI ​​select the optimal notification method.

[0052] The notification unit can select the optimal notification timing by referring to the user's calendar information when sending a notification. For example, the notification unit can refer to appointments registered in the user's calendar and adjust the notification timing. The notification unit can also prioritize notifications related to specific events based on the user's calendar information. Furthermore, the notification unit can suggest the optimal notification timing based on the user's calendar information and appointments. This makes it possible to select the optimal notification timing while considering calendar information. 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 calendar information into AI and have the AI ​​select the optimal notification timing.

[0053] The notification unit can analyze the user's social media activity and reflect relevant notification information when sending notifications. For example, the notification unit can prioritize notifications related to events mentioned by the user on social media. It can also reflect notifications related to areas of interest based on the user's social media activity. Furthermore, the notification unit can send notifications based on the activity of the user's social media followers and friends. This makes it possible to reflect notification information that takes social media activity into account. 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 social media data into AI and have the AI ​​perform the reflection of notification information.

[0054] The reminder unit can analyze the user's past reminder history to select the optimal reminder method at the time of a reminder. For example, the reminder unit can make suggestions based on the reminder methods the user has preferred to receive in the past. The reminder unit can also select the most effective reminder timing from the user's past reminder history. Furthermore, the reminder unit can analyze the user's past reminder history and optimize the reminder content. This makes it possible to select the optimal reminder method considering past reminder history. Some or all of the above processes in the reminder unit may be performed using AI or not. For example, the reminder unit can input past reminder data into AI and have the AI ​​select the optimal reminder method.

[0055] The reminder unit can select the optimal reminder method by considering the user's device information when a reminder is issued. For example, if the user is using a smartphone, the reminder unit will prioritize push notifications. If the user is using a tablet, the reminder unit can provide a reminder method optimized for a larger screen. Furthermore, if the user is using a smartwatch, the reminder unit can provide a concise and highly visible reminder method. This makes it possible to select the optimal reminder method considering device information. Some or all of the above processing in the reminder unit may be performed using AI or not. For example, the reminder unit can input device information into AI and have the AI ​​select the optimal reminder method.

[0056] The reminder unit can select the optimal reminder timing by referring to the user's calendar information when a reminder is issued. For example, the reminder unit can refer to appointments registered in the user's calendar and adjust the timing of the reminder. The reminder unit can also prioritize reminders related to specific events based on the user's calendar information. Furthermore, the reminder unit can suggest the optimal reminder timing that matches the appointments based on the user's calendar information. This makes it possible to select the optimal reminder timing that takes calendar information into consideration. Some or all of the above processing in the reminder unit may be performed using AI or not. For example, the reminder unit can input calendar information into AI and have the AI ​​perform the selection of the optimal reminder timing.

[0057] The reminder function can analyze the user's social media activity and reflect relevant reminder information when a reminder is issued. For example, the reminder function can prioritize reminders related to events mentioned by the user on social media. It can also reflect reminders related to areas of interest based on the user's social media activity. Furthermore, the reminder function can issue reminders based on the activity of the user's social media followers and friends. This makes it possible to reflect reminder information that takes social media activity into account. Some or all of the above processing in the reminder function may be performed using AI or not. For example, the reminder function can input social media data into AI and have the AI ​​perform the task of reflecting reminder information.

[0058] The rewards unit can analyze a user's past reward usage history to select the most suitable reward when offering one. For example, the rewards unit can suggest the most suitable reward based on the rewards the user has used in the past. Furthermore, the rewards unit can select the most effective reward from the user's past reward usage history. In addition, the rewards unit can analyze the user's past reward usage history and optimize the content of the rewards. This makes it possible to select the most suitable reward considering past reward usage history. Some or all of the above processing in the rewards unit may be performed using AI, or not. For example, the rewards unit can input past reward data into AI and have the AI ​​select the most suitable reward.

[0059] The rewards department can customize rewards when providing them, taking into account the user's current income situation. For example, the rewards department can adjust the content of the rewards based on the user's current income situation. Furthermore, if the user's income increases, the rewards department can provide more expensive rewards. In addition, if the user's income decreases, the rewards department can provide more affordable rewards. This makes it possible to customize rewards according to the user's current income situation. Some or all of the above processing in the rewards department may be performed using AI or not. For example, the rewards department can input income data into AI and have the AI ​​perform the reward customization.

[0060] The rewards unit can provide region-specific rewards by considering the user's geographical location information when offering rewards. For example, the rewards unit can make suggestions based on rewards in the area where the user lives. It can also reflect rewards in areas that the user frequently visits. Furthermore, the rewards unit can suggest region-specific rewards based on the user's geographical location information. This makes it possible to provide region-specific rewards. Some or all of the above processing in the rewards unit may be performed using AI or not. For example, the rewards unit can input geographical location data into AI and have the AI ​​perform the provision of region-specific rewards.

[0061] The rewards department can analyze the user's social media activity and reflect relevant reward information when providing rewards. For example, the rewards department can make suggestions based on rewards mentioned by the user on social media. It can also reflect rewards that the user is interested in based on their social media activity. Furthermore, the rewards department can make suggestions based on the reward usage habits of the user's social media followers and friends. This makes it possible to reflect reward information that takes social media activity into consideration. Some or all of the above processing in the rewards department may be performed using AI or not. For example, the rewards department can input social media data into AI and have the AI ​​perform the reflection of reward information.

[0062] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0063] The fund management system can also include an "investment advisory department." This department can propose optimal investment plans based on the user's income and expenditure data and market data. For example, it can analyze the user's income and expenditure trends and select investment products according to their risk tolerance. Furthermore, the investment advisory department can develop long-term investment strategies based on the user's goals. In addition, the investment advisory department can regularly monitor market trends and revise investment plans as needed. This allows users to efficiently increase their assets.

[0064] The fund management system can also include a "health management department." This department can collect users' health data and provide financial management advice based on their health status. For example, if a user is in good health, it can suggest reducing health-related expenses. The health management department can also help users plan the necessary expenses for maintaining their health. Furthermore, it can support users in selecting insurance products according to their health status. This allows users to balance health and financial management.

[0065] The fund management system can also include an "Education Planning Department." This department can plan the educational expenses of the user's children. For example, it can calculate the necessary educational expenses based on the child's age and chosen school. Furthermore, the Education Planning Department can provide information on scholarships and educational loans and suggest the most suitable funding methods. In addition, the Education Planning Department can advise the user on how to save for educational expenses efficiently. This allows the user to systematically prepare the funds necessary for their child's education.

[0066] The fund management system can also include a "travel planning section." This section can support the user's travel planning. For example, it can calculate the necessary travel expenses based on the destination and duration. Furthermore, it can propose travel plans tailored to the user's budget. Additionally, it can manage expenses during the trip and provide advice to ensure the user enjoys their trip within their budget. This allows users to enjoy their trip in a well-planned manner.

[0067] The fund management system can also include a "retirement planning section." This section can plan the user's living expenses after retirement. For example, it can predict post-retirement income and expenses and calculate the necessary savings. Furthermore, the retirement planning section can provide information on pensions and retirement benefits and propose optimal investment strategies. In addition, the retirement planning section can offer advice to ensure the user enjoys a stable life after retirement. This allows the user to approach retirement with peace of mind.

[0068] The following briefly describes the processing flow for example form 1.

[0069] Step 1: The analysis unit automatically tracks and analyzes income and expenses. The analysis unit centrally manages, for example, bank account data, e-commerce site spending information, and electronic payment service spending information, and automatically tracks and analyzes income and expenses. The analysis unit retrieves bank account data, collects spending information from e-commerce sites, and integrates electronic payment service history. Based on this data, it can analyze income and expense trends and identify wasteful spending. Step 2: The savings unit automatically saves money based on the income and expenditure data analyzed by the analysis unit. The savings unit automatically saves money based on the budget set by the user. For example, a fixed amount can be automatically saved from monthly income. In addition, the progress of savings can be managed and the monthly savings amount adjusted based on the goals set by the user. Step 3: The Planning Department supports financial planning based on the budget set by the Savings Department. The Planning Department supports financial planning necessary for life events such as marriage and home purchase. For example, it can calculate the necessary funds for life events set by the user and create a savings plan. It can also manage the progress of savings based on the goals set by the user and provide regular notifications.

[0070] (Example of form 2) The fund management system according to an embodiment of the present invention is a new application designed to strengthen general users' involvement in the SoftBank economic sphere. This fund management system has the following functions: "Smart Money Manager": It automatically tracks and analyzes income and expenses using bank data and centrally manages transaction history from e-commerce sites and electronic payment services. Users can check an overview of their income and expenses in the messaging app and set a budget to enable automatic savings. This helps reduce wasteful spending and supports smart fund management. "Life Event Planning": It supports financial planning based on bank data for important events such as marriage and home purchase. Users can check their savings progress in the messaging app, easily plan using the benefits of e-commerce sites, and confidently aim to achieve their goals. First, the "Smart Money Manager" function centrally manages bank account data and expenditure information from e-commerce sites and electronic payment services, and automatically tracks and analyzes income and expenses. This makes it easy for users to manage their daily expenses. In addition, it promotes efficient saving while preventing budget overruns through notification functions in the messaging app. For example, users can set monthly expenses and manage their income and expenses within that range. Next, the "Life Event Planning" feature supports financial planning for life events such as marriage and home purchase. Users can regularly check their savings progress using the reminder function in the messaging app. Furthermore, leveraging rewards on e-commerce sites makes planning easier and helps maintain motivation towards achieving goals. For example, a user can set a savings goal for their wedding and track their progress through the messaging app. This app targets young to middle-aged individuals in their late 20s to 40s, catering to a wide range of people, regardless of gender, from singles to married couples and families with children. It is particularly designed for people in professions familiar with digital tools and those who frequently shop online. Users want to make effective use of their time in their busy daily lives and are seeking efficient methods for household budgeting and saving. They also value planning for the future and have a strong interest in financial planning for life events.This app makes it easier for users to manage their daily expenses and plan for major future expenditures. It also solves the problem of scattered information due to using multiple apps and services, enabling centralized management. This allows users to efficiently manage their finances and smoothly prepare for future life events. The financial management system automatically tracks and analyzes the user's income and expenses, facilitates automatic savings through budgeting, and supports financial planning.

[0071] The fund management system according to this embodiment comprises an analysis unit, a savings unit, and a planning unit. The analysis unit automatically tracks and analyzes income and expenses. The analysis unit centrally manages, for example, bank account data and expenditure information from e-commerce sites and electronic payment services, and automatically tracks and analyzes income and expenses. For example, the analysis unit acquires bank account data, collects expenditure information from e-commerce sites, and integrates the history of electronic payment services. Based on this data, the analysis unit can analyze income and expense trends and identify wasteful spending. The savings unit automatically saves money based on budget settings using the income and expense data analyzed by the analysis unit. For example, the savings unit automatically saves money based on a budget set by the user. For example, the savings unit can automatically save a fixed amount from monthly income. The savings unit can also manage the progress of savings based on a goal set by the user. For example, the savings unit can adjust the monthly savings amount to a savings goal set by the user. The planning unit supports fund planning based on the budget set by the savings unit. The planning unit supports financial planning for life events such as marriage and home purchase. For example, the planning unit can calculate the necessary funds for life events set by the user and create a savings plan. The planning unit can also manage the progress of savings based on goals set by the user. For example, the planning unit can periodically notify the user of the progress of savings for life events set by the user. As a result, the financial management system according to this embodiment can support automatic tracking and analysis of income and expenses, automatic savings based on budget setting, and financial planning.

[0072] The analytics unit automatically tracks and analyzes income and expenses. For example, the analytics unit centrally manages spending information from bank account data, e-commerce sites, and electronic payment services, and automatically tracks and analyzes income and expenses. Specifically, the analytics unit uses bank APIs to obtain account data and collects spending information through e-commerce site and electronic payment service APIs. This allows for centralized management of all of the user's income and expenses. The analytics unit updates this data in real time and analyzes trends in income and expenses. For example, it can graph monthly fluctuations in income and expenses and present them visually to the user. The analytics unit also uses AI to identify wasteful spending. The AI ​​performs pattern recognition based on past data to detect abnormal spending and wasteful spending trends. For example, it can send an alert to the user if spending in a particular category suddenly increases. Furthermore, the analytics unit can learn the user's spending patterns and predict future spending. This allows the user to receive specific advice for reviewing their budget and controlling spending. Through these functions, the analytics unit can streamline the user's income and expense management and reduce wasteful spending.

[0073] The savings unit automatically saves money based on budget settings derived from income and expenditure data analyzed by the analysis unit. For example, the savings unit automatically saves money based on a budget set by the user. Specifically, the savings unit analyzes the user's income and expenses and automatically calculates the monthly savings amount. For example, if a user sets a goal to save 10% of their monthly income, the savings unit automatically calculates that amount and transfers it to the designated savings account. The savings unit can also manage the progress of savings based on goals set by the user. For example, if a user sets a goal such as travel or purchasing a new car, the savings unit calculates the amount and time required to achieve that goal and adjusts the monthly savings amount accordingly. Furthermore, the savings unit can flexibly change the savings plan in response to fluctuations in the user's income and expenses. For example, it can increase savings if income increases and decrease savings if expenses increase. In this way, the savings unit supports users in continuing to save money without difficulty. Through these functions, the savings unit can help users achieve their savings goals and prepare for future life events.

[0074] The Planning Department supports financial planning based on the budget set by the Savings Department. The Planning Department supports financial planning for life events such as marriage and home purchase. Specifically, it can calculate the necessary funds for life events set by the user and create a savings plan. For example, if a user is planning to get married, the Planning Department will estimate the costs of the wedding and honeymoon and propose a savings plan. The Planning Department can also manage the progress of savings based on the user's set goals. For example, if a user aims to buy a home, the Planning Department will calculate the monthly savings amount and the time required to achieve the goal, and provide regular updates on the progress. Furthermore, the Planning Department can flexibly modify the financial plan according to the user's life events. For example, if a user sets a new goal or changes an existing goal, the Planning Department will recalculate the savings plan accordingly and propose the optimal plan. In this way, the Planning Department supports users in efficiently preparing the funds necessary for life events. Through these functions, the Planning Department can comprehensively support users' financial planning and help them achieve their future goals.

[0075] The notification unit can notify users of income and expenditure summaries via a messaging app. For example, by notifying users of income and expenditure summaries, the notification unit can facilitate income and expenditure management. For example, the notification unit can notify users of monthly income and expenditure summaries via a messaging app. The notification unit can also notify users of the progress of income and expenditure against a budget set by the user. For example, the notification unit can notify users if they exceed their budget. This makes income and expenditure management easier by notifying users of income and expenditure summaries. Some or all of the above processes in the notification unit may be performed using AI or not. For example, the notification unit can input income and expenditure data into AI and have the AI ​​generate income and expenditure summaries.

[0076] The reminder unit can notify users of their savings progress via reminders. For example, the reminder unit can maintain users' motivation to save by notifying them of their savings progress. For example, the reminder unit can notify users of their monthly savings progress via reminders. Furthermore, the reminder unit can periodically notify users of their savings progress towards their set savings goals. For example, the reminder unit can notify users of their savings progress towards their set savings goals via reminders. This helps maintain users' motivation to save by notifying them of their savings progress. Some or all of the above-described processes in the reminder unit may be performed using AI or not. For example, the reminder unit can input savings data into an AI and have the AI ​​execute the savings progress notification.

[0077] The rewards unit can facilitate planning by utilizing rewards. The rewards unit can, for example, make it easier to execute a plan by utilizing rewards. The rewards unit can, for example, facilitate planning by utilizing rewards from e-commerce sites. Furthermore, the rewards unit can support the execution of a plan by utilizing rewards in relation to goals set by the user. The rewards unit can, for example, facilitate planning by utilizing rewards in relation to goals set by the user. This makes it easier to execute a plan by utilizing rewards. Some or all of the above processing in the rewards unit may be performed using AI or not. For example, the rewards unit can input reward data into AI and have AI perform the provision of rewards.

[0078] The analysis unit can centrally manage bank account data, e-commerce site spending information, and electronic payment service spending information, and automatically track and analyze income and expenses. For example, the analysis unit can acquire bank account data, collect spending information from e-commerce sites, and integrate electronic payment service history. Based on this data, the analysis unit can analyze income and expense trends and identify wasteful spending. This enables centralized management and automated analysis of income and expense data. Some or all of the above processing in the analysis unit may be performed using AI, or it may be performed without AI. For example, the analysis unit can input income and expense data into AI and have the AI ​​perform the income and expense analysis.

[0079] The planning unit can support financial planning for life events such as marriage and home purchase. For example, the planning unit can calculate the necessary funds for life events set by the user and create a savings plan. The planning unit can also manage the progress of savings based on goals set by the user. For example, the planning unit can periodically notify the user of the progress of savings for life events set by the user. This enables support for financial planning toward life events. Some or all of the above processes in the planning unit may be performed using AI or not. For example, the planning unit can input life event data into AI and have the AI ​​perform financial planning support.

[0080] The analysis unit can estimate the user's emotions and adjust the analysis method of the financial data based on the estimated user emotions. For example, if the user is stressed, the analysis unit will display the analysis results concisely and omit detailed data. If the user is relaxed, the analysis unit will provide detailed analysis results to allow the user to understand them more deeply. Furthermore, if the user is in a hurry, the analysis unit can highlight and display only the important points. This enables the analysis of financial data in accordance with 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 analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into AI and have the AI ​​perform adjustments to the analysis method based on emotions.

[0081] The analysis unit can improve the accuracy of its analysis by considering the user's past spending patterns when analyzing income and expenditure data. For example, the analysis unit can predict future spending patterns based on the products and services that the user has frequently purchased in the past. The analysis unit can also analyze the user's past spending history and reflect seasonal spending trends. Furthermore, the analysis unit can predict spending during specific events or sales periods based on the user's past spending patterns. This improves the accuracy of the analysis by considering past spending patterns. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input past spending data into AI and have the AI ​​perform the analysis of spending patterns.

[0082] The analysis unit can apply a customized analysis algorithm based on the user's lifestyle when analyzing income and expenditure data. For example, if the user is health-conscious, the analysis unit will prioritize the analysis of health-related expenses. If the user enjoys traveling, the analysis unit can analyze travel-related expenses in detail. Furthermore, if the user is a family, the analysis unit can consider the expenses of the entire family. This enables customized analysis tailored to the user's lifestyle. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input lifestyle data into AI and have the AI ​​perform the customized analysis.

[0083] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that gets straight to the point. This makes it possible to display analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the analysis unit may be performed using AI or not using AI. For example, the analysis unit can input user emotion data into an AI and have the AI ​​perform the adjustment of the display method based on the emotions.

[0084] The analysis unit can reflect region-specific consumption trends by considering the user's geographical location information when analyzing income and expenditure data. For example, the analysis unit can perform analysis based on the average consumption trends of the area where the user lives. It can also reflect consumption trends in areas that the user frequently visits. Furthermore, the analysis unit can incorporate region-specific sales information into the analysis based on the user's geographical location information. This enables analysis that reflects region-specific consumption trends. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input geographical location data into AI and have the AI ​​perform an analysis of region-specific consumption trends.

[0085] The analysis unit can analyze users' social media activity and incorporate relevant consumption information into the analysis when analyzing income and expenditure data. For example, the analysis unit can perform analysis based on products and services mentioned by users on social media. The analysis unit can also reflect consumption trends in areas of interest from users' social media activity. Furthermore, the analysis unit can perform analysis by referring to the consumption trends of users' followers and friends on social media. This makes it possible to perform analysis that takes social media activity into account. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input social media data into AI and have the AI ​​perform the analysis of consumption information.

[0086] The savings unit can estimate the user's emotions and adjust the timing of automatic savings based on those emotions. For example, if the user is stressed, the savings unit can delay the timing of savings. Conversely, if the user is relaxed, the savings unit can speed up the timing of savings. Furthermore, if the user is in a hurry, the savings unit can adjust the timing of savings to reduce stress. This makes it possible to adjust the timing of automatic savings 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 savings unit may be performed using AI or not. For example, the savings unit can input user emotion data into AI and have the AI ​​perform emotion-based adjustments to the saving timing.

[0087] The savings function can analyze the user's past savings history to select the optimal savings method during automatic savings. For example, the savings function can suggest the optimal savings method based on the user's past successful savings methods. Furthermore, the savings function can select methods to increase the success rate of savings based on the user's past savings history. In addition, the savings function can analyze the user's past savings history to optimize the timing and amount of savings. This makes it possible to select the optimal savings method considering past savings history. Some or all of the above processes in the savings function may be performed using AI, or not. For example, the savings function can input past savings data into AI and have the AI ​​select the optimal savings method.

[0088] The savings function can customize the savings amount based on the user's current income situation during automatic savings. For example, the savings function can adjust the savings amount based on the user's current income situation. Furthermore, if the user's income increases, the savings function can increase the savings amount. In addition, if the user's income decreases, the savings function can decrease the savings amount. This makes it possible to customize the savings amount according to the current income situation. Some or all of the above processes in the savings function may be performed using AI or not. For example, the savings function can input income data into AI and have the AI ​​perform the customization of the savings amount.

[0089] The savings unit can estimate the user's emotions and determine savings priorities based on those estimated emotions. For example, if the user is stressed, the savings unit can lower the priority of saving. Conversely, if the user is relaxed, the savings unit can raise the priority of saving. Furthermore, if the user is in a hurry, the savings unit can adjust the savings priority to reduce stress. This makes it possible to determine savings priorities in accordance with 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 savings unit may be performed using AI or not. For example, the savings unit can input user emotion data into an AI and have the AI ​​perform emotion-based savings priority determination.

[0090] The savings function can reflect region-specific saving habits by considering the user's geographical location information during automatic savings. For example, the savings function can suggest savings methods based on the saving habits of the area where the user lives. It can also reflect the saving habits of areas the user frequently visits. Furthermore, the savings function can suggest region-specific saving methods based on the user's geographical location information. This enables automatic savings that reflect region-specific saving habits. Some or all of the above processing in the savings function may be performed using AI or not. For example, the savings function can input geographical location data into AI and have the AI ​​perform the task of reflecting region-specific saving habits.

[0091] The savings function can analyze the user's social media activity and reflect relevant savings information during automatic savings. For example, the savings function can make suggestions based on savings methods mentioned by the user on social media. It can also reflect savings methods the user is interested in based on their social media activity. Furthermore, the savings function can make suggestions based on the savings habits of the user's social media followers and friends. This makes it possible to reflect savings information that takes social media activity into account. Some or all of the above processing in the savings function may be performed using AI or not. For example, the savings function can input social media data into AI and have the AI ​​perform the reflection of savings information.

[0092] The planning unit can estimate the user's emotions and adjust the financial planning method based on the estimated emotions. For example, if the user is stressed, the planning unit can propose a concise financial plan. If the user is relaxed, the planning unit can provide a detailed financial plan. Furthermore, if the user is in a hurry, the planning unit can propose a financial plan that highlights only the important points. This makes it possible to adjust the financial plan 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 planning unit may be performed using AI or not. For example, the planning unit can input user emotion data into AI and have the AI ​​perform emotion-based adjustments to the financial plan.

[0093] The planning department can analyze the user's past planning history to select the optimal planning method when creating a financial plan. For example, the planning department can propose the optimal planning method based on the user's past successful financial plans. Furthermore, the planning department can select methods to increase the success rate of plans based on the user's past planning history. In addition, the planning department can analyze the user's past planning history to optimize the timing and content of plans. This makes it possible to select the optimal financial plan that takes past planning history into account. Some or all of the above processes in the planning department may be performed using AI, or not. For example, the planning department can input past planning data into AI and have the AI ​​select the optimal planning method.

[0094] The planning unit can customize financial plans based on the user's current living situation. For example, the planning unit can adjust financial plans based on the user's current income. It can also propose financial plans tailored to the user's lifestyle. Furthermore, the planning unit can provide financial plans that are appropriate for the user's family structure and life stage. This enables customized financial plans that are tailored to the user's current living situation. Some or all of the above processes in the planning unit may be performed using AI or not. For example, the planning unit can input living situation data into AI and have the AI ​​provide customized financial plans.

[0095] The planning unit can estimate the user's emotions and determine the priority of plans based on those emotions. For example, if the user is stressed, the planning unit can lower the priority of a plan. Conversely, if the user is relaxed, the planning unit can raise the priority of a plan. Furthermore, if the user is in a hurry, the planning unit can adjust the priority of plans to reduce stress. This makes it possible to determine plan priorities in accordance with 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 planning unit may be performed using AI or not. For example, the planning unit can input user emotion data into an AI and have the AI ​​perform emotion-based plan priority determination.

[0096] The planning unit can reflect region-specific planning habits by considering the user's geographical location information when creating financial plans. For example, the planning unit can propose a planning method based on the planning habits of the area where the user lives. It can also reflect the planning habits of areas that the user frequently visits. Furthermore, the planning unit can propose region-specific planning methods based on the user's geographical location information. This makes it possible to create financial plans that reflect region-specific planning habits. Some or all of the above processes in the planning unit may be performed using AI or not. For example, the planning unit can input geographical location data into AI and have the AI ​​perform the task of reflecting region-specific planning habits.

[0097] The planning department can analyze users' social media activity and reflect relevant planning information when creating financial plans. For example, the planning department can make suggestions based on planning methods mentioned by users on social media. It can also reflect planning methods that users are interested in based on their social media activity. Furthermore, the planning department can make suggestions based on the planning habits of users' followers and friends on social media. This makes it possible to reflect planning information that takes social media activity into account. Some or all of the above processes in the planning department may be performed using AI or not. For example, the planning department can input social media data into AI and have the AI ​​perform the reflection of planning information.

[0098] The notification unit can estimate the user's emotions and adjust the timing of notifications based on those emotions. For example, if the user is stressed, the notification unit can reduce the frequency of notifications. Conversely, if the user is relaxed, the notification unit can increase the frequency of notifications. Furthermore, if the user is in a hurry, the notification unit can prioritize sending only important notifications. This makes it possible to adjust the timing of notifications 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. For example, the notification unit can input user emotion data into an AI and have the AI ​​perform emotion-based notification timing adjustments.

[0099] The notification unit can analyze the user's past notification history to select the optimal notification method when sending a notification. For example, the notification unit can make suggestions based on the notification methods the user has preferred to receive in the past. The notification unit can also select the most effective notification timing from the user's past notification history. Furthermore, the notification unit can analyze the user's past notification history and optimize the notification content. This makes it possible to select the optimal notification method while considering past notification history. 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 past notification data into AI and have the AI ​​select the optimal notification method.

[0100] 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 will prioritize push notifications. If the user is using a tablet, the notification unit can provide a notification method optimized for a larger screen. Furthermore, if the user is using a smartwatch, the notification unit can provide a concise and highly visible notification method. This makes it possible to select the optimal notification method considering device information. 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 device information into the AI ​​and have the AI ​​select the optimal notification method.

[0101] The notification unit can estimate the user's emotions and determine notification priorities based on those emotions. For example, if the user is stressed, the notification unit will prioritize only important notifications. If the user is relaxed, the notification unit can display all notifications. Furthermore, if the user is in a hurry, the notification unit can prioritize sending only important notifications. This makes it possible to determine notification priorities 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. For example, the notification unit can input user emotion data into an AI and have the AI ​​perform emotion-based notification prioritization.

[0102] The notification unit can select the optimal notification timing by referring to the user's calendar information when sending a notification. For example, the notification unit can refer to appointments registered in the user's calendar and adjust the notification timing. The notification unit can also prioritize notifications related to specific events based on the user's calendar information. Furthermore, the notification unit can suggest the optimal notification timing based on the user's calendar information and appointments. This makes it possible to select the optimal notification timing while considering calendar information. 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 calendar information into AI and have the AI ​​select the optimal notification timing.

[0103] The notification unit can analyze the user's social media activity and reflect relevant notification information when sending notifications. For example, the notification unit can prioritize notifications related to events mentioned by the user on social media. It can also reflect notifications related to areas of interest based on the user's social media activity. Furthermore, the notification unit can send notifications based on the activity of the user's social media followers and friends. This makes it possible to reflect notification information that takes social media activity into account. 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 social media data into AI and have the AI ​​perform the reflection of notification information.

[0104] The reminder unit can estimate the user's emotions and adjust the timing of reminders based on those emotions. For example, if the user is stressed, the reminder unit can reduce the frequency of reminders. Conversely, if the user is relaxed, the reminder unit can increase the frequency of reminders. Furthermore, if the user is in a hurry, the reminder unit can prioritize sending only important reminders. This allows for adjustment of reminder timing 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 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 reminder unit may be performed using AI or not. For example, the reminder unit can input user emotion data into an AI and have the AI ​​adjust the reminder timing based on emotions.

[0105] The reminder unit can analyze the user's past reminder history to select the optimal reminder method at the time of a reminder. For example, the reminder unit can make suggestions based on the reminder methods the user has preferred to receive in the past. The reminder unit can also select the most effective reminder timing from the user's past reminder history. Furthermore, the reminder unit can analyze the user's past reminder history and optimize the reminder content. This makes it possible to select the optimal reminder method considering past reminder history. Some or all of the above processes in the reminder unit may be performed using AI or not. For example, the reminder unit can input past reminder data into AI and have the AI ​​select the optimal reminder method.

[0106] The reminder unit can select the optimal reminder method by considering the user's device information when a reminder is issued. For example, if the user is using a smartphone, the reminder unit will prioritize push notifications. If the user is using a tablet, the reminder unit can provide a reminder method optimized for a larger screen. Furthermore, if the user is using a smartwatch, the reminder unit can provide a concise and highly visible reminder method. This makes it possible to select the optimal reminder method considering device information. Some or all of the above processing in the reminder unit may be performed using AI or not. For example, the reminder unit can input device information into AI and have the AI ​​select the optimal reminder method.

[0107] The reminder function can estimate the user's emotions and determine the priority of reminders based on those emotions. For example, if the user is stressed, the reminder function will prioritize only important reminders. If the user is relaxed, the reminder function can display all reminders. Furthermore, if the user is in a hurry, the reminder function can prioritize sending only important reminders. This makes it possible to determine the priority of reminders 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 reminder function may be performed using AI or not. For example, the reminder function can input user emotion data into an AI and have the AI ​​perform the emotion-based reminder priority determination.

[0108] The reminder unit can select the optimal reminder timing by referring to the user's calendar information when a reminder is issued. For example, the reminder unit can refer to appointments registered in the user's calendar and adjust the timing of the reminder. The reminder unit can also prioritize reminders related to specific events based on the user's calendar information. Furthermore, the reminder unit can suggest the optimal reminder timing that matches the appointments based on the user's calendar information. This makes it possible to select the optimal reminder timing that takes calendar information into consideration. Some or all of the above processing in the reminder unit may be performed using AI or not. For example, the reminder unit can input calendar information into AI and have the AI ​​perform the selection of the optimal reminder timing.

[0109] The reminder function can analyze the user's social media activity and reflect relevant reminder information when a reminder is issued. For example, the reminder function can prioritize reminders related to events mentioned by the user on social media. It can also reflect reminders related to areas of interest based on the user's social media activity. Furthermore, the reminder function can issue reminders based on the activity of the user's social media followers and friends. This makes it possible to reflect reminder information that takes social media activity into account. Some or all of the above processing in the reminder function may be performed using AI or not. For example, the reminder function can input social media data into AI and have the AI ​​perform the task of reflecting reminder information.

[0110] The rewards unit can estimate the user's emotions and adjust how rewards are provided based on those emotions. For example, if the user is stressed, the rewards unit can provide relaxing rewards. If the user is relaxed, the rewards unit can provide enjoyable rewards. Furthermore, if the user is in a hurry, the rewards unit can provide rewards that can be used quickly. This makes it possible to adjust how rewards are provided 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 rewards unit may be performed using AI or not. For example, the rewards unit can input user emotion data into an AI and have the AI ​​perform the adjustment of how rewards are provided based on those emotions.

[0111] The rewards unit can analyze a user's past reward usage history to select the most suitable reward when offering one. For example, the rewards unit can suggest the most suitable reward based on the rewards the user has used in the past. Furthermore, the rewards unit can select the most effective reward from the user's past reward usage history. In addition, the rewards unit can analyze the user's past reward usage history and optimize the content of the rewards. This makes it possible to select the most suitable reward considering past reward usage history. Some or all of the above processing in the rewards unit may be performed using AI, or not. For example, the rewards unit can input past reward data into AI and have the AI ​​select the most suitable reward.

[0112] The rewards department can customize rewards when providing them, taking into account the user's current income situation. For example, the rewards department can adjust the content of the rewards based on the user's current income situation. Furthermore, if the user's income increases, the rewards department can provide more expensive rewards. In addition, if the user's income decreases, the rewards department can provide more affordable rewards. This makes it possible to customize rewards according to the user's current income situation. Some or all of the above processing in the rewards department may be performed using AI or not. For example, the rewards department can input income data into AI and have the AI ​​perform the reward customization.

[0113] The rewards unit can estimate the user's emotions and determine the priority of rewards based on those emotions. For example, if the user is stressed, the rewards unit can prioritize relaxing rewards. If the user is relaxed, the rewards unit can prioritize enjoyable rewards. Furthermore, if the user is in a hurry, the rewards unit can prioritize rewards that can be used quickly. This makes it possible to determine the priority of rewards 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 rewards unit may be performed using AI or not. For example, the rewards unit can input user emotion data into an AI and have the AI ​​perform the determination of emotion-based reward priorities.

[0114] The rewards unit can provide region-specific rewards by considering the user's geographical location information when offering rewards. For example, the rewards unit can make suggestions based on rewards in the area where the user lives. It can also reflect rewards in areas that the user frequently visits. Furthermore, the rewards unit can suggest region-specific rewards based on the user's geographical location information. This makes it possible to provide region-specific rewards. Some or all of the above processing in the rewards unit may be performed using AI or not. For example, the rewards unit can input geographical location data into AI and have the AI ​​perform the provision of region-specific rewards.

[0115] The rewards department can analyze the user's social media activity and reflect relevant reward information when providing rewards. For example, the rewards department can make suggestions based on rewards mentioned by the user on social media. It can also reflect rewards that the user is interested in based on their social media activity. Furthermore, the rewards department can make suggestions based on the reward usage habits of the user's social media followers and friends. This makes it possible to reflect reward information that takes social media activity into consideration. Some or all of the above processing in the rewards department may be performed using AI or not. For example, the rewards department can input social media data into AI and have the AI ​​perform the reflection of reward information.

[0116] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0117] The fund management system can also include an "investment advisory department." This department can propose optimal investment plans based on the user's income and expenditure data and market data. For example, it can analyze the user's income and expenditure trends and select investment products according to their risk tolerance. Furthermore, the investment advisory department can develop long-term investment strategies based on the user's goals. In addition, the investment advisory department can regularly monitor market trends and revise investment plans as needed. This allows users to efficiently increase their assets.

[0118] The fund management system can also include a "health management department." This department can collect users' health data and provide financial management advice based on their health status. For example, if a user is in good health, it can suggest reducing health-related expenses. The health management department can also help users plan the necessary expenses for maintaining their health. Furthermore, it can support users in selecting insurance products according to their health status. This allows users to balance health and financial management.

[0119] The fund management system can also include an "Education Planning Department." This department can plan the educational expenses of the user's children. For example, it can calculate the necessary educational expenses based on the child's age and chosen school. Furthermore, the Education Planning Department can provide information on scholarships and educational loans and suggest the most suitable funding methods. In addition, the Education Planning Department can advise the user on how to save for educational expenses efficiently. This allows the user to systematically prepare the funds necessary for their child's education.

[0120] The fund management system can also include a "travel planning section." This section can support the user's travel planning. For example, it can calculate the necessary travel expenses based on the destination and duration. Furthermore, it can propose travel plans tailored to the user's budget. Additionally, it can manage expenses during the trip and provide advice to ensure the user enjoys their trip within their budget. This allows users to enjoy their trip in a well-planned manner.

[0121] The fund management system can also include a "retirement planning section." This section can plan the user's living expenses after retirement. For example, it can predict post-retirement income and expenses and calculate the necessary savings. Furthermore, the retirement planning section can provide information on pensions and retirement benefits and propose optimal investment strategies. In addition, the retirement planning section can offer advice to ensure the user enjoys a stable life after retirement. This allows the user to approach retirement with peace of mind.

[0122] The fund management system can also include an "emotional analysis unit." This unit can estimate the user's emotions and provide financial management advice based on those emotions. For example, if the user is feeling stressed, it can suggest relaxing spending. If the user is relaxed, the emotional analysis unit can offer advice on saving or investing. Furthermore, the emotional analysis unit can adjust spending priorities according to the user's emotions. This allows users to manage their finances in a way that aligns with their emotions.

[0123] The fund management system can also include a "stress management unit." This unit can estimate the user's emotions and provide stress management advice based on those estimates. For example, if the user is feeling stressed, it can suggest relaxing activities or spending. If the user is relaxed, the unit can offer advice on stress prevention. Furthermore, the unit can plan stress-reducing spending based on the user's emotions. This allows users to manage their finances while simultaneously managing stress.

[0124] The fund management system can also include a "motivation management unit." This unit can estimate the user's emotions and provide advice to maintain motivation based on those estimates. For example, if a user is losing motivation, it can send encouraging messages to help them achieve their goals. Furthermore, if a user is highly motivated, the unit can advise them on setting further goals. Additionally, the unit can plan spending to maintain motivation based on the user's emotions. This allows users to manage their finances while maintaining their motivation.

[0125] The fund management system can also be equipped with an "emotional feedback unit." This unit can estimate the user's emotions and provide feedback based on those emotions. For example, if the user is feeling stressed, it can provide relaxing feedback. Conversely, if the user is relaxed, it can provide positive feedback. Furthermore, the emotional feedback unit can adjust the content of the feedback according to the user's emotions. This allows the user to receive feedback that is appropriate to their feelings.

[0126] The fund management system can also be equipped with an "emotional monitoring unit." This unit continuously monitors the user's emotions and provides financial management advice in response to changes in those emotions. For example, if a user's emotions change drastically, it analyzes the cause and provides appropriate advice. Furthermore, the emotional monitoring unit can understand the user's emotional trends and develop long-term financial management plans. It can also adjust spending and savings plans according to the user's emotions. This allows users to manage their finances in a way that aligns with their emotions.

[0127] The following briefly describes the processing flow for example form 2.

[0128] Step 1: The analysis unit automatically tracks and analyzes income and expenses. The analysis unit centrally manages, for example, bank account data, e-commerce site spending information, and electronic payment service spending information, and automatically tracks and analyzes income and expenses. The analysis unit retrieves bank account data, collects spending information from e-commerce sites, and integrates electronic payment service history. Based on this data, it can analyze income and expense trends and identify wasteful spending. Step 2: The savings unit automatically saves money based on the income and expenditure data analyzed by the analysis unit. The savings unit automatically saves money based on the budget set by the user. For example, a fixed amount can be automatically saved from monthly income. In addition, the progress of savings can be managed and the monthly savings amount adjusted based on the goals set by the user. Step 3: The Planning Department supports financial planning based on the budget set by the Savings Department. The Planning Department supports financial planning necessary for life events such as marriage and home purchase. For example, it can calculate the necessary funds for life events set by the user and create a savings plan. It can also manage the progress of savings based on the goals set by the user and provide regular notifications.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] Each of the multiple elements described above, including the analysis unit, savings unit, planning unit, notification unit, reminder unit, and rewards unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the smart device 14 and collects bank account data and spending information from e-commerce sites to automatically track and analyze income and expenses. The savings unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and performs automatic savings based on the data from the analysis unit. The planning unit is implemented, for example, by the control unit 46A of the smart device 14 and supports financial planning necessary for life events. The notification unit notifies users of an income and expense summary via a messaging app using the output device 40 of the smart device 14. The reminder unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and notifies users of savings progress via reminders. The rewards unit is implemented, for example, by the control unit 46A of the smart device 14 and facilitates planning by utilizing rewards from e-commerce sites. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0133] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] Each of the multiple elements described above, including the analysis unit, savings unit, planning unit, notification unit, reminder unit, and rewards unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the smart glasses 214 and collects bank account data and spending information from e-commerce sites to automatically track and analyze income and expenses. The savings unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and performs automatic savings based on the data from the analysis unit. The planning unit is implemented, for example, by the control unit 46A of the smart glasses 214 and supports financial planning necessary for life events. The notification unit notifies users of an income and expense summary via a messaging app using the speaker 240 of the smart glasses 214. The reminder unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and notifies users of savings progress via reminders. The rewards unit is implemented, for example, by the control unit 46A of the smart glasses 214 and facilitates planning by utilizing rewards from e-commerce sites. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0149] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] Each of the multiple elements described above, including the analysis unit, savings unit, planning unit, notification unit, reminder unit, and benefits unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the headset terminal 314 and collects bank account data and spending information from e-commerce sites to automatically track and analyze income and expenses. The savings unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and performs automatic savings based on the data from the analysis unit. The planning unit is implemented by, for example, the control unit 46A of the headset terminal 314 and supports financial planning necessary for life events. The notification unit notifies users of an income and expense summary via a messaging app using the display 343 of the headset terminal 314. The reminder unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and notifies users of savings progress via reminders. The benefits section is implemented, for example, by the control unit 46A of the headset-type terminal 314, and facilitates planning by utilizing benefits from e-commerce sites. The correspondence between each part and the device or control unit is not limited to the example described above, and various modifications are possible.

[0165] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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).

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.).

[0178] 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.

[0179] 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.

[0180] 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.

[0181] Each of the multiple elements described above, including the analysis unit, savings unit, planning unit, notification unit, reminder unit, and rewards unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the robot 414 and collects bank account data and spending information from e-commerce sites to automatically track and analyze income and expenses. The savings unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and performs automatic savings based on the data from the analysis unit. The planning unit is implemented by, for example, the control unit 46A of the robot 414 and supports financial planning necessary for life events. The notification unit notifies users of an income and expense summary via a messaging app using the speaker 240 of the robot 414. The reminder unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and notifies users of savings progress via reminders. The rewards unit is implemented by, for example, the control unit 46A of the robot 414 and facilitates planning by utilizing rewards from e-commerce sites. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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."

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] (Note 1) An analysis unit that automatically tracks and analyzes income and expenses, A savings unit that automatically saves money based on the income and expenditure data analyzed by the aforementioned analysis unit, The system includes a planning unit that supports financial planning based on the budget set by the savings unit. A system characterized by the following features. (Note 2) It includes a notification unit that notifies users of the income and expenditure summary via a messaging app. The system described in Appendix 1, characterized by the features described herein. (Note 3) It includes a reminder function that notifies users of their savings progress via reminders. The system described in Appendix 1, characterized by the features described herein. (Note 4) It includes a benefits section to simplify planning by utilizing the benefits offered. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit, It centrally manages bank account data, e-commerce site spending information, and electronic payment service spending information, and automatically tracks and analyzes income and expenses. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned planning department, Supporting financial planning necessary for life events such as marriage and home purchase. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit, We estimate user sentiment and adjust the analysis method of revenue and expenditure data based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, When analyzing revenue and expenditure data, we improve the accuracy of the analysis by considering the user's past consumption patterns. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, When analyzing income and expenditure data, a customized analysis algorithm is applied based on the user's lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, When analyzing revenue and expenditure data, the system takes into account the user's geographical location to reflect region-specific consumption trends. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, When analyzing revenue and expenditure data, we analyze users' social media activity and incorporate relevant consumption information into the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned savings section is, It estimates the user's emotions and adjusts the timing of automatic savings based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned savings section is, When automatic savings are initiated, the system analyzes the user's past savings history to select the optimal savings method. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned savings section is, When automatic savings are made, the amount saved will be customized based on the user's current income situation. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned savings section is, It estimates the user's emotions and determines savings priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned savings section is, When automatic savings are made, the system takes into account the user's geographical location to reflect region-specific savings habits. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned savings section is, When automatic savings are made, the system analyzes the user's social media activity and reflects relevant savings information. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned planning department, It estimates user sentiment and adjusts the financial planning method based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned planning department, When creating a financial plan, the system analyzes the user's past planning history to select the optimal planning method. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned planning department, When creating a financial plan, customize the plan based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned planning department, It estimates user sentiment and prioritizes plans based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned planning department, When creating financial plans, consider the user's geographical location to reflect region-specific planning habits. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned planning department, When creating a financial plan, analyze users' social media activity and reflect relevant planning information. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned notification unit, It estimates the user's emotions and adjusts the timing of notifications based on those emotions. The system described in Appendix 2, characterized by the features described herein. (Note 26) The aforementioned notification unit, When sending a notification, the system analyzes the user's past notification history to select the most suitable notification method. The system described in Appendix 2, characterized by the features described herein. (Note 27) 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 2, characterized by the features described herein. (Note 28) The aforementioned notification unit, It estimates the user's emotions and prioritizes notifications based on those emotions. The system described in Appendix 2, characterized by the features described herein. (Note 29) The aforementioned notification unit, When sending a notification, the system will refer to the user's calendar information to select the optimal notification timing. The system described in Appendix 2, characterized by the features described herein. (Note 30) The aforementioned notification unit, When sending notifications, the system analyzes the user's social media activity and reflects relevant notification information. The system described in Appendix 2, characterized by the features described herein. (Note 31) The reminder unit is, It estimates the user's emotions and adjusts the timing of reminders based on those emotions. The system described in Appendix 3, characterized by the features described herein. (Note 32) The reminder unit is, When a reminder is sent, the system analyzes the user's past reminder history to select the most suitable reminder method. The system described in Appendix 3, characterized by the features described herein. (Note 33) The reminder unit is, When a reminder is sent, the system selects the most appropriate reminder method, taking into account the user's device information. The system described in Appendix 3, characterized by the features described herein. (Note 34) The reminder unit is, It estimates the user's emotions and prioritizes reminders based on those emotions. The system described in Appendix 3, characterized by the features described herein. (Note 35) The reminder unit is, When a reminder is sent, the system references the user's calendar information to select the optimal reminder timing. The system described in Appendix 3, characterized by the features described herein. (Note 36) The reminder unit is, When a reminder is sent, the system analyzes the user's social media activity and reflects relevant reminder information. The system described in Appendix 3, characterized by the features described herein. (Note 37) The aforementioned special features section is, We estimate the user's emotions and adjust how we provide rewards based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 38) The aforementioned special features section is, When offering a reward, the system analyzes the user's past reward usage history to select the most suitable reward. The system described in Appendix 4, characterized by the features described herein. (Note 39) The aforementioned special features section is, When offering rewards, customize them to take into account the user's current income situation. The system described in Appendix 4, characterized by the features described herein. (Note 40) The aforementioned special features section is, It estimates the user's emotions and determines the priority of rewards based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 41) The aforementioned special features section is, When offering benefits, we will provide region-specific benefits that take into account the user's geographical location. The system described in Appendix 4, characterized by the features described herein. (Note 42) The aforementioned special features section is, When offering rewards, analyze users' social media activity and reflect relevant reward information. The system described in Appendix 4, characterized by the features described herein. [Explanation of Symbols]

[0201] 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. An analysis unit that automatically tracks and analyzes income and expenses, A savings unit that automatically saves money based on the income and expenditure data analyzed by the aforementioned analysis unit, The system includes a planning unit that supports financial planning based on the budget set by the savings unit. A system characterized by the following features.

2. It includes a notification unit that notifies users of the income and expenditure summary via a messaging app. The system according to feature 1.

3. It includes a reminder function that notifies users of their savings progress via reminders. The system according to feature 1.

4. It includes a benefits section to simplify planning by utilizing the benefits offered. The system according to feature 1.

5. The aforementioned analysis unit, It centrally manages spending information from bank account data, e-commerce sites, and electronic payment services, and automatically tracks and analyzes income and expenses. The system according to feature 1.

6. The aforementioned planning department, Supporting financial planning necessary for life events such as marriage and home purchase. The system according to feature 1.

7. The aforementioned analysis unit, We estimate user sentiment and adjust the analysis method of revenue and expenditure data based on the estimated user sentiment. The system according to feature 1.

8. The aforementioned analysis unit, When analyzing revenue and expenditure data, we improve the accuracy of the analysis by considering the user's past consumption patterns. The system according to feature 1.

9. The aforementioned analysis unit, When analyzing income and expenditure data, a customized analysis algorithm is applied based on the user's lifestyle. The system according to feature 1.

10. The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system according to feature 1.

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