system

Abstract

The system according to this embodiment aims to centrally manage communication charges and points, optimize data usage, integrate with smart home appliances, plan life events, and share information with family members. [Solution] The system according to the embodiment comprises a management unit, a point management unit, an optimization unit, a collaboration unit, a planning unit, and a sharing unit. The management unit manages communication charges. The point management unit manages points managed by the management unit. The optimization unit optimizes data usage managed by the point management unit. The collaboration unit collaborates with smart home appliances optimized by the optimization unit. The planning unit plans life events linked by the collaboration unit. The sharing unit performs family sharing planned by the planning 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, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult to centrally manage communication fees, points, optimize data usage, cooperate with smart home appliances, plan life events, and family sharing.

[0005] The system according to the embodiment aims to centrally manage communication fees, points, optimize data usage, cooperate with smart home appliances, plan life events, and family sharing.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a management unit, a point management unit, an optimization unit, a collaboration unit, a planning unit, and a sharing unit. The management unit manages communication charges. The point management unit manages points managed by the management unit. The optimization unit optimizes data usage managed by the point management unit. The collaboration unit collaborates with smart home appliances optimized by the optimization unit. The planning unit plans life events linked by the collaboration unit. The sharing unit performs family sharing planned by the planning unit. [Effects of the Invention]

[0007] The system according to this embodiment can centrally manage communication charges and points, optimize data usage, integrate with smart home appliances, plan life events, and share information with family members. [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 labeled communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), 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). [[ID=二十]]

[0019] [[ID=二十一]] The smart device 14 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The household finance management and life planning system according to an embodiment of the present invention is a system that supports users' household finance management and life planning by linking with communication services and related services. This system allows for centralized management of daily expenses and income, and enables users to plan for future life events. For example, the household finance management and life planning system includes communication charge management, point linking, data usage optimization, smart home appliance integration, life event planning, and family sharing functions. The communication charge management function centrally manages mobile phone and internet charges for communication services, allowing users to understand their monthly communication expenses in detail. This helps reduce unnecessary spending. The point linking function links with electronic payment services to manage point usage history and balances. Furthermore, it supports effective use of points by suggesting advantageous ways to use them. The data usage optimization function analyzes the user's data usage and suggests the optimal plan. This allows for the provision of data saving methods and advantageous campaign information. The smart home appliance integration function links with smart home appliances (smart speakers, IoT devices, etc.) to provide support for more convenient and efficient household finance management. The life event planning function integrates with life planning services (insurance, mortgages, etc.) to allow users to plan for future life events such as marriage, children's education, home purchase, and retirement finances. The family sharing function allows all family members to share an account and check household finances in real time. This enables budget setting and expenditure management for the entire family. The household finance management and life planning system utilizes generative AI to analyze the user's income and expenditure patterns and provide customized saving advice and budget setting suggestions. It also uses generative AI to send timely notifications based on the user's financial activity. Furthermore, the generative AI can simulate financial plans for the user's future life events and set specific savings goals. A chatbot is provided that instantly answers user questions using natural language processing technology. In this way, the household finance management and life planning system can efficiently support the user's household finance management and life planning.

[0029] The household budget management and life planning system according to this embodiment comprises a management unit, a points management unit, an optimization unit, a linkage unit, a planning unit, and a sharing unit. The management unit manages communication charges. For example, the management unit can centrally manage users' communication charges and understand monthly communication expenses in detail. The management unit can also analyze communication service pricing plans and propose the optimal plan. For example, the management unit can manage the payment history of communication charges and provide advice to reduce unnecessary spending. The points management unit manages points. For example, the points management unit links with electronic payment services and manages point usage history and balances. For example, the points management unit can suggest advantageous ways to use points to support their effective use. For example, the points management unit can manage the expiration date of points and send notifications to prevent them from expiring. The optimization unit optimizes data usage. For example, the optimization unit analyzes users' data usage and proposes the optimal plan. For example, the optimization unit can provide information on how to save on data usage and advantageous campaign information. The optimization unit can, for example, analyze data usage patterns and provide advice to reduce wasteful data usage. The integration unit integrates with smart home appliances. The integration unit integrates with smart home appliances (smart speakers, IoT devices, etc.) to provide support for more convenient and efficient household budget management. The integration unit can also, for example, provide an interface to simplify the operation of smart home appliances. The integration unit can also, for example, analyze the usage of smart home appliances and suggest the optimal way to use them. The planning unit plans life events. The planning unit integrates with life planning services (insurance, mortgages, etc.) to create plans for future life events such as marriage, children's education, home purchase, and retirement financial planning. The planning unit can also, for example, provide tools to support life event planning. The planning unit can also, for example, collect and provide users with the information necessary for life event planning. The sharing unit facilitates family sharing. The sharing unit allows all family members to share an account and check the household budget in real time.The shared section can, for example, provide tools to support budgeting and expense management for the entire family. The shared section can also, for example, analyze family income and expense patterns and provide customized advice. This allows the household financial management and life planning system according to the embodiment to efficiently support the user's household financial management and life planning.

[0030] The management department manages communication charges. For example, the management department can centrally manage users' communication charges and have a detailed understanding of their monthly communication expenses. Specifically, the management department obtains data directly from users' communication service providers and automatically compiles monthly communication expenses. This allows users to see their communication expenses at a glance and easily identify unnecessary spending. The management department can also analyze communication service pricing plans and suggest the most suitable plan. For example, based on the user's past communication usage data, it can evaluate whether the current plan is optimal and suggest a more cost-effective plan. Furthermore, the management department can manage communication payment history and provide advice to reduce unnecessary spending. For example, it can analyze past payment history to identify unnecessary expenses such as late fees and excessive data usage charges and provide specific advice on how to avoid them. This allows users to efficiently manage and save on communication costs.

[0031] The Points Management Department manages points. For example, it integrates with electronic payment services to manage point usage history and balances. Specifically, the Points Management Department collects point data from multiple electronic payment services and manages it centrally. This allows users to view multiple point programs through a single interface, making it easier to effectively utilize points. The Points Management Department can also suggest advantageous ways to use points to support their effective use. For example, it can provide information on periods and campaigns with high point redemption rates at specific stores or services, enabling users to maximize their point utilization. Furthermore, the Points Management Department can manage point expiration dates and send notifications to prevent points from expiring. For example, it can send notifications to users when points are nearing expiration, urging them to use their points before they expire. This ensures that users can utilize their points to the fullest without wasting them.

[0032] The optimization unit optimizes data usage. For example, it analyzes a user's data usage and proposes the optimal plan. Specifically, the optimization unit analyzes a user's past data usage patterns and evaluates whether the current plan is optimal. For example, if a user exceeds their monthly data usage limit, it will propose a larger data plan. It can also provide information on how to save data usage and advantageous campaigns. For example, it will suggest ways to save data usage by restricting data usage during specific times or for specific applications. Furthermore, the optimization unit can analyze data usage patterns and provide advice to reduce unnecessary data usage. For example, if an application running in the background is consuming a large amount of data, it will advise changing the settings of that application. This allows users to efficiently manage their data usage and save on communication costs.

[0033] The integration unit connects with smart home appliances. For example, it connects with smart home appliances (smart speakers, IoT devices, etc.) to provide support for more convenient and efficient household budget management. Specifically, the integration unit collects data from smart home appliances and integrates it into the household budget management system. This allows users to centrally manage the usage of smart home appliances and understand energy consumption and appliance usage patterns. The integration unit can also provide an interface to simplify the operation of smart home appliances. For example, users can access the household budget management system via voice commands through a smart speaker to check their household budget in real time and record expenses. Furthermore, the integration unit can analyze the usage of smart home appliances and suggest optimal usage. For example, it can suggest ways to save on electricity bills by adjusting the usage time of energy-intensive appliances. This allows users to use smart home appliances efficiently and manage their household budget more effectively.

[0034] The planning department plans life events. For example, it can collaborate with life planning services (insurance, mortgages, etc.) to create plans for future life events such as marriage, children's education, home purchase, and retirement finances. Specifically, the planning department analyzes the user's current income, expenses, and savings to calculate the funds needed for future life events. For example, it considers expenses such as children's education, home purchase costs, and retirement living expenses to set necessary savings targets. The planning department can also provide tools to support life event planning. For example, it can use simulation tools to create financial plans based on different scenarios. Furthermore, the planning department can collect and provide users with the information necessary for life event planning. For example, it can provide comparative information on insurance products and mortgages to support users in making the best choices. This makes it easier for users to plan for future life events and live with peace of mind.

[0035] The shared section facilitates family sharing. For example, the shared section allows all family members to share an account and monitor household finances in real time. Specifically, the shared section centrally manages the income and expenses of all family members and provides a dashboard for understanding the overall household finances. This allows all family members to check the current financial situation in real time and prevent unnecessary spending. The shared section can also provide tools to support family-wide budgeting and spending management. For example, it can provide a shared budget sheet and spending log tool, making it easier to manage overall spending by allowing each member to record their own expenses. Furthermore, the shared section can analyze family income and spending patterns and provide customized advice. For example, if there is a tendency for spending to increase in a particular month, it can identify the cause and provide specific advice on how to reduce unnecessary spending. This allows all family members to cooperate in managing household finances and using funds efficiently.

[0036] The analysis unit can perform data analysis and prediction. For example, the analysis unit can analyze a user's income and expenditure data and predict future income and expenditure. For example, the analysis unit can use generative AI to analyze a user's income and expenditure patterns and provide customized saving advice and budget setting suggestions. For example, the analysis unit can also make predictions so that the generative AI can send notifications at the appropriate time based on the user's financial activities. This improves the accuracy of the user's household financial management and life planning through data analysis and prediction. Some or all of the above processes in the analysis unit may be performed using generative AI or not. For example, the analysis unit can input user's income and expenditure data into the generative AI, which can analyze the data and output prediction results.

[0037] The notification unit can provide personalized notifications. For example, it can send notifications at the appropriate time based on the user's financial activity. For example, it can use generative AI to analyze the user's income and expenditure patterns and provide customized notifications. For example, the notification unit can use generative AI to simulate a financial plan for the user's future life events and provide notifications to set specific savings goals. This improves the efficiency of household management and life planning by providing users with timely notifications. Some or all of the above processes in the notification unit may be performed using generative AI or not. For example, the notification unit can input the user's financial activity data into generative AI, which can then analyze the data and generate notification content.

[0038] The Planning Department can perform life event planning. For example, the Planning Department can collaborate with life planning services (insurance, mortgages, etc.) to create plans for future life events such as marriage, children's education, home purchase, and retirement financial planning. For example, the Planning Department can use generative AI to simulate financial plans for the user's future life events and set specific savings goals. For example, the Planning Department can use generative AI to analyze the user's income and expenditure patterns and propose a customized life event plan. This can support the user's planning for life events. Some or all of the above processes in the Planning Department may be performed using generative AI or not. For example, the Planning Department can input the user's income and expenditure data into the generative AI, which can then analyze the data and generate a life event plan.

[0039] The support department can provide customer support. For example, the support department can provide a chatbot that instantly answers user questions using natural language processing technology. For example, the support department can use generative AI to analyze the content of user questions and generate appropriate answers. For example, the support department can have the generative AI analyze the user's past question history and provide customized support. This can enhance support for household budget management and life planning by providing instant answers to user questions. Some or all of the above processes in the support department may be performed using generative AI or not. For example, the support department can input user question data into a generative AI, which can then analyze the data and generate answers.

[0040] The management department can analyze past communication charge data and select the optimal management method. For example, the management department can propose management methods to reduce unnecessary spending based on the user's past communication charge data. For example, the management department can also propose the most cost-effective plan based on the user's past communication charge data. For example, the management department can analyze the user's past communication charge data and select the optimal management method based on monthly spending patterns. This allows for the provision of the optimal communication charge management method based on past data. Some or all of the above processes in the management department may be performed using AI or not. For example, the management department can input past communication charge data into AI, which can analyze the data and select the optimal management method.

[0041] The management department can filter communication charges based on the user's contract plan. For example, the management department can propose the optimal communication charge management method based on the user's contract plan. The management department can also remove unnecessary options and manage only the necessary options based on the user's contract plan. The management department can also simplify management by filtering communication charges optimally based on the user's contract plan. This allows the management department to provide the optimal communication charge management method based on the user's contract plan. Some or all of the above processes in the management department may be performed using AI or not. For example, the management department can input user contract plan data into AI, which can analyze the data and select the optimal filtering method.

[0042] The management unit can prioritize the management of charges that are most relevant to the user's geographical location when managing communication charges. For example, if the user is in a specific region, the management unit can prioritize the management of communication charges in that region. For example, if the user is traveling, the management unit can also prioritize the management of communication charges at the travel destination. For example, if the user is at home, the management unit can also prioritize the management of communication charges at home. This allows the management unit to provide an optimal communication charge management method based on the user's geographical location. Some or all of the above processing in the management unit may be performed using AI or not. For example, the management unit can input the user's geographical location information into AI, which can analyze the data and select the optimal management method.

[0043] The management department can analyze users' social media activity and manage related charges when managing communication charges. For example, the management department can extract and manage information related to communication charge payments from users' social media activity. For example, the management department can analyze users' social media activity and determine priorities related to communication charge payments. For example, the management department can propose communication charge management methods based on users' social media activity. This allows the management department to provide the optimal communication charge management method based on users' social media activity. Some or all of the above processes in the management department may be performed using AI or not. For example, the management department can input user social media data into AI, which can analyze the data and select the optimal management method.

[0044] The points management unit can analyze a user's past point usage history to select the optimal management method when managing points. For example, the points management unit can propose the optimal points management method based on the user's past point usage history. For example, the points management unit can also propose a management method to reduce unnecessary point usage based on the user's past point usage history. For example, the points management unit can analyze a user's past point usage history and select the most efficient points management method. This allows the unit to provide the optimal points management method based on the user's past point usage history. Some or all of the above processes in the points management unit may be performed using AI or not. For example, the points management unit can input past point usage history data into AI, which can analyze the data and select the optimal management method.

[0045] The point management unit can filter points based on the user's current lifestyle and areas of interest during point management. For example, the point management unit can suggest the optimal point management method based on the user's current lifestyle. The point management unit can also suggest how to use points based on the user's areas of interest. For example, the point management unit can analyze the user's current lifestyle and areas of interest and select the optimal point management method. This allows the point management unit to provide the optimal point management method based on the user's current lifestyle and areas of interest. Some or all of the above processing in the point management unit may be performed using AI or not. For example, the point management unit can input user lifestyle and area of ​​interest data into AI, which can analyze the data and select the optimal filtering method.

[0046] The points management unit can prioritize the management of points that are highly relevant to the user, taking into account the user's geographical location information. For example, if the user is in a specific region, the points management unit can prioritize point usage in that region. For example, if the user is traveling, the points management unit can also prioritize point usage at the travel destination. For example, if the user is at home, the points management unit can also prioritize point usage at home. This allows the system to provide an optimal points management method based on the user's geographical location information. Some or all of the above processing in the points management unit may be performed using AI, or not. For example, the points management unit can input the user's geographical location information into an AI, which can analyze the data and select the optimal management method.

[0047] The points management unit can analyze users' social media activities and manage related points during point management. For example, the points management unit can extract and manage information related to point usage from users' social media activities. For example, the points management unit can analyze users' social media activities and determine priorities related to point usage. For example, the points management unit can propose point management methods based on users' social media activities. This allows the system to provide the optimal point management method based on users' social media activities. Some or all of the above processes in the points management unit may be performed using AI or not. For example, the points management unit can input user social media data into AI, which can analyze the data and select the optimal management method.

[0048] The optimization unit can analyze the user's past data usage history to select the optimal method when optimizing data utilization. For example, the optimization unit can propose the optimal data utilization method based on the user's past data usage history. For example, the optimization unit can also propose optimization methods to reduce unnecessary data utilization based on the user's past data usage history. For example, the optimization unit can analyze the user's past data usage history and select the most efficient data utilization method. This makes it possible to provide the optimal data utilization method based on the user's past data usage history. Some or all of the above processing in the optimization unit may be performed using AI or not. For example, the optimization unit can input past data usage history data into AI, which can analyze the data and select the optimal method.

[0049] The optimization unit can perform filtering based on the user's contract plan when optimizing data utilization. For example, the optimization unit can propose the optimal data utilization method based on the user's contract plan. The optimization unit can also perform filtering to reduce unnecessary data utilization based on the user's contract plan. For example, the optimization unit can perform optimal data utilization filtering based on the user's contract plan and simplify management. This allows the optimization unit to provide the optimal data utilization method based on the user's contract plan. Some or all of the above processing in the optimization unit may be performed using AI or not. For example, the optimization unit can input user contract plan data into AI, which can analyze the data and select the optimal filtering method.

[0050] The optimization unit can prioritize optimizing data usage by considering the user's geographical location information. For example, if the user is in a specific region, the optimization unit can prioritize optimizing data usage in that region. For example, if the user is traveling, the optimization unit can also prioritize optimizing data usage at the travel destination. For example, if the user is at home, the optimization unit can also prioritize optimizing data usage at home. This allows the system to provide the optimal data usage method based on the user's geographical location information. Some or all of the above processing in the optimization unit may be performed using AI or not. For example, the optimization unit can input the user's geographical location information into AI, which can then analyze the data and select the optimal method.

[0051] The optimization unit can analyze users' social media activities and optimize related data when optimizing data utilization. For example, the optimization unit can extract and optimize information related to data utilization from users' social media activities. The optimization unit can also analyze users' social media activities and determine priorities related to data utilization. For example, the optimization unit can propose methods for optimizing data utilization based on users' social media activities. This makes it possible to provide the optimal data utilization method based on users' social media activities. Some or all of the above processing in the optimization unit may be performed using AI or not. For example, the optimization unit can input user social media data into AI, which can analyze the data and select the optimal method.

[0052] The integration unit can analyze the user's past usage history to select the optimal integration method when integrating with smart home appliances. For example, the integration unit can propose the optimal integration method based on the user's past smart home appliance usage history. For example, the integration unit can also propose an integration method that reduces unnecessary operations based on the user's past usage history. For example, the integration unit can analyze the user's past usage history and select the most efficient integration method. This allows the system to provide the optimal smart home appliance integration method based on the user's past usage history. Some or all of the above processing in the integration unit may be performed using AI or not. For example, the integration unit can input past usage history data into AI, which can then analyze the data and select the optimal integration method.

[0053] The integration unit can perform filtering based on the user's current lifestyle and areas of interest when integrating with smart home appliances. For example, the integration unit can propose the optimal smart home appliance integration method based on the user's current lifestyle. The integration unit can also propose how to use smart home appliances based on the user's areas of interest. For example, the integration unit can analyze the user's current lifestyle and areas of interest and select the optimal smart home appliance integration method. This allows the system to provide the optimal smart home appliance integration method based on the user's current lifestyle and areas of interest. Some or all of the above processing in the integration unit may be performed using AI or not. For example, the integration unit can input user lifestyle and area of ​​interest data into AI, which can then analyze the data and select the optimal filtering method.

[0054] The integration unit can prioritize the integration of highly relevant smart home appliances by considering the user's geographical location when integrating with smart home appliances. For example, if the user is in a specific region, the integration unit can prioritize the integration of smart home appliances in that region. For example, if the user is traveling, the integration unit can also prioritize the integration of smart home appliances at the travel destination. For example, if the user is at home, the integration unit can also prioritize the integration of smart home appliances at home. This allows the system to provide the optimal smart home appliance integration method based on the user's geographical location. Some or all of the above processing in the integration unit may be performed using AI or not. For example, the integration unit can input the user's geographical location information into AI, which can analyze the data and select the optimal integration method.

[0055] The integration unit can analyze the user's social media activity when integrating with smart home appliances and integrate the relevant appliances. For example, the integration unit can extract information related to the operation of smart home appliances from the user's social media activity and integrate it. For example, the integration unit can analyze the user's social media activity and determine priorities related to the operation of smart home appliances. For example, the integration unit can propose a method for integrating smart home appliances based on the user's social media activity. This makes it possible to provide the optimal method for integrating smart home appliances based on the user's social media activity. Some or all of the above processing in the integration unit may be performed using AI or not. For example, the integration unit can input the user's social media data into AI, which can analyze the data and select the optimal integration method.

[0056] The planning department can analyze a user's past planning history to select the optimal method when planning life events. For example, the planning department can propose the optimal planning method based on the user's past life event planning history. For example, the planning department can also propose a planning method that reduces unnecessary steps based on the user's past planning history. For example, the planning department can analyze the user's past planning history and select the most efficient planning method. This allows the planning department to provide the optimal life event planning method based on the user's past planning history. 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 history data into AI, which can analyze the data and select the optimal method.

[0057] The planning unit can filter the user's current living situation and areas of interest when planning life events. For example, the planning unit can propose the optimal life event planning method based on the user's current living situation. The planning unit can also propose a life event planning method based on the user's areas of interest. For example, the planning unit can analyze the user's current living situation and areas of interest and select the optimal life event planning method. This allows the planning unit to provide the optimal life event planning method based on the user's current living situation and areas of interest. 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 the user's living situation and areas of interest data into the AI, which can then analyze the data and select the optimal filtering method.

[0058] The planning unit can prioritize highly relevant life events when planning life events, taking into account the user's geographical location. For example, if the user is in a specific region, the planning unit will prioritize life events in that region. For example, if the user is traveling, the planning unit can also prioritize life events at the travel destination. For example, if the user is at home, the planning unit can also prioritize life events at home. This allows the system to provide an optimal life event planning method based on the user's geographical location. 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 the user's geographical location into an AI, which can analyze the data and select the optimal planning method.

[0059] The planning department can analyze a user's social media activity when planning life events and plan related events. For example, the planning department can extract information relevant to life event planning from the user's social media activity and plan accordingly. The planning department can also analyze a user's social media activity and determine priorities related to life event planning. For example, the planning department can propose a life event planning method based on the user's social media activity. This allows the planning department to provide the optimal life event planning method based on the user's social media activity. 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 the user's social media data into an AI, which can analyze the data and select the optimal planning method.

[0060] The sharing function can analyze the user's past sharing history to select the optimal method when sharing with family. For example, the sharing function can suggest the optimal sharing method based on the user's past family sharing history. For example, the sharing function can also suggest a sharing method that reduces unnecessary steps based on the user's past sharing history. For example, the sharing function can analyze the user's past sharing history and select the most efficient sharing method. This allows the system to provide the optimal family sharing method based on the user's past sharing history. Some or all of the above processing in the sharing function may be performed using AI or not. For example, the sharing function can input past sharing history data into an AI, which can analyze the data and select the optimal method.

[0061] The sharing function can filter data based on the user's current lifestyle and areas of interest during family sharing. For example, the sharing function can suggest the optimal family sharing method based on the user's current lifestyle. The sharing function can also suggest a family sharing method based on the user's areas of interest. The sharing function can also analyze the user's current lifestyle and areas of interest and select the optimal family sharing method. This allows the sharing function to provide the optimal family sharing method based on the user's current lifestyle and areas of interest. Some or all of the above processing in the sharing function may be performed using AI or not. For example, the sharing function can input user lifestyle and area of ​​interest data into an AI, which can analyze the data and select the optimal filtering method.

[0062] The sharing function can prioritize sharing highly relevant information when sharing with family, taking into account the user's geographical location. For example, if the user is in a specific region, the sharing function will prioritize sharing family information for that region. For example, if the user is traveling, the sharing function can also prioritize sharing family information for the travel destination. For example, if the user is at home, the sharing function can also prioritize sharing family information for home. This allows for the provision of the optimal family sharing method based on the user's geographical location. Some or all of the above processing in the sharing function may be performed using AI or not. For example, the sharing function can input the user's geographical location into an AI, which can analyze the data and select the optimal sharing method.

[0063] The sharing function can analyze the user's social media activity and share relevant information when sharing with family. For example, the sharing function can extract and share information relevant to family sharing from the user's social media activity. The sharing function can also analyze the user's social media activity and determine priorities related to family sharing. For example, the sharing function can suggest methods of family sharing based on the user's social media activity. This allows the function to provide the optimal family sharing method based on the user's social media activity. Some or all of the above processing in the sharing function may be performed using AI or not. For example, the sharing function can input the user's social media data into an AI, which can analyze the data and select the optimal sharing method.

[0064] The analysis unit can analyze the user's past data history and select the optimal method during data analysis. For example, the analysis unit can propose the optimal data analysis method based on the user's past data history. For example, the analysis unit can also propose methods to reduce unnecessary data analysis based on the user's past data history. For example, the analysis unit can analyze the user's past data history and select the most efficient data analysis method. This allows the analysis unit to provide the optimal data analysis method based on the user's past data history. Some or all of the above-described processes in the analysis unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the analysis unit can input past data history data into a generation AI, which can analyze the data and select the optimal method.

[0065] The analysis unit can filter data based on the user's current lifestyle and areas of interest during data analysis. For example, the analysis unit can suggest the optimal data analysis method based on the user's current lifestyle. The analysis unit can also suggest a data analysis method based on the user's areas of interest. For example, the analysis unit can analyze the user's current lifestyle and areas of interest and select the optimal data analysis method. This allows the analysis unit to provide the optimal data analysis method based on the user's current lifestyle and areas of interest. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input the user's lifestyle and areas of interest data into a generation AI, which can then analyze the data and select the optimal filtering method.

[0066] The analysis unit can prioritize the analysis of highly relevant data by considering the user's geographical location information during data analysis. For example, if the user is in a specific region, the analysis unit will prioritize data analysis in that region. For example, if the user is traveling, the analysis unit can also prioritize data analysis at the travel destination. For example, if the user is at home, the analysis unit can also prioritize data analysis at home. This allows the analysis unit to provide the optimal data analysis method based on the user's geographical location information. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input the user's geographical location information into a generation AI, which can then analyze the data and select the optimal method.

[0067] The analysis unit can analyze users' social media activities and analyze related data during data analysis. For example, the analysis unit can extract and analyze information relevant to data analysis from users' social media activities. The analysis unit can also analyze users' social media activities and determine priorities related to data analysis. For example, the analysis unit can propose a data analysis method based on users' social media activities. This allows the analysis unit to provide the optimal data analysis method based on users' social media activities. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input user social media data into a generative AI, which can analyze the data and select the optimal method.

[0068] The notification unit can analyze the user's past notification history and select the optimal method when sending a notification. For example, the notification unit can propose the optimal notification method based on the user's past notification history. For example, the notification unit can also propose methods to reduce unnecessary notifications based on the user's past notification history. For example, the notification unit can analyze the user's past notification history and select the most efficient notification method. This allows the system to provide the optimal notification method based on the user's past notification history. Some or all of the above processing in the notification unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the notification unit can input past notification history data into a generation AI, which can then analyze the data and select the optimal method.

[0069] The notification unit can filter notifications based on the user's current living situation and areas of interest. For example, the notification unit can suggest the optimal notification method based on the user's current living situation. The notification unit can also suggest a notification method based on the user's areas of interest. The notification unit can also analyze the user's current living situation and areas of interest and select the optimal notification method. This allows the notification unit to provide the optimal notification method based on the user's current living situation and areas of interest. Some or all of the above processing in the notification unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the notification unit can input user living situation and area of ​​interest data into a generative AI, which can then analyze the data and select the optimal filtering method.

[0070] The notification unit can prioritize highly relevant notifications by considering the user's geographical location information when sending notifications. For example, if the user is in a specific region, the notification unit can prioritize notifications for that region. For example, if the user is traveling, the notification unit can prioritize notifications for their travel destination. For example, if the user is at home, the notification unit can prioritize notifications for their home. This allows the system to provide the most appropriate notification method based on the user's geographical location information. Some or all of the above processing in the notification unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the notification unit can input the user's geographical location information into a generative AI, which can analyze the data and select the optimal method.

[0071] The notification unit can analyze the user's social media activity and send relevant notifications at the time of notification. For example, the notification unit can extract information relevant to the notification from the user's social media activity and send the notification. The notification unit can also analyze the user's social media activity and determine the priority of notifications. The notification unit can also suggest notification methods based on the user's social media activity. This allows the system to provide the most suitable notification method based on the user's social media activity. Some or all of the above processing in the notification unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the notification unit can input the user's social media data into a generative AI, which can analyze the data and select the most suitable method.

[0072] The planning unit can analyze the user's past planning history and select the optimal method during the planning process. For example, the planning unit can propose the optimal planning method based on the user's past planning history. For example, the planning unit can also propose a planning method to reduce unnecessary steps based on the user's past planning history. For example, the planning unit can analyze the user's past planning history and select the most efficient planning method. This allows the system to provide the optimal planning method based on the user's past planning history. Some or all of the above-described processes in the planning unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the planning unit can input past planning history data into a generation AI, which can then analyze the data and select the optimal method.

[0073] The planning unit can perform filtering based on the user's current living situation and areas of interest during the planning process. For example, the planning unit can propose the optimal planning method based on the user's current living situation. The planning unit can also propose a planning method based on the user's areas of interest. For example, the planning unit can analyze the user's current living situation and areas of interest and select the optimal planning method. This allows the system to provide the optimal planning method based on the user's current living situation and areas of interest. Some or all of the above-described processes in the planning unit may be performed using a generative AI, or they may not be performed using a generative AI. For example, the planning unit can input user living situation and area of ​​interest data into a generative AI, which can then analyze the data and select the optimal filtering method.

[0074] The planning unit can prioritize highly relevant plans by considering the user's geographical location during the planning process. For example, if the user is in a specific region, the planning unit will prioritize planning for that region. If the user is traveling, the planning unit can also prioritize planning for their travel destination. If the user is at home, the planning unit can also prioritize planning for their home. This allows the system to provide the optimal planning method based on the user's geographical location. Some or all of the above processing in the planning unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the planning unit can input the user's geographical location information into a generative AI, which can then analyze the data and select the optimal method.

[0075] The planning unit can analyze the user's social media activity and create a relevant plan during the planning process. For example, the planning unit can extract information relevant to planning from the user's social media activity and create a plan. The planning unit can also analyze the user's social media activity and determine priorities relevant to planning. For example, the planning unit can propose a planning method based on the user's social media activity. This allows the planning unit to provide the optimal planning method based on the user's social media activity. Some or all of the above processes in the planning unit may be performed using generative AI, or they may not. For example, the planning unit can input the user's social media data into a generative AI, which can analyze the data and select the optimal method.

[0076] The support unit can analyze the user's past support history to select the optimal method during support. For example, the support unit can propose the optimal support method based on the user's past support history. For example, the support unit can also propose support methods to reduce unnecessary steps based on the user's past support history. For example, the support unit can analyze the user's past support history and select the most efficient support method. This allows the support unit to provide the optimal support method based on the user's past support history. Some or all of the above processes in the support unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the support unit can input past support history data into a generation AI, which can analyze the data and select the optimal method.

[0077] The support unit can filter data based on the user's current living situation and areas of interest during support. For example, the support unit can suggest the optimal support method based on the user's current living situation. The support unit can also suggest a support method based on the user's areas of interest. The support unit can also analyze the user's current living situation and areas of interest and select the optimal support method. This allows the support unit to provide the optimal support method based on the user's current living situation and areas of interest. Some or all of the above processing in the support unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the support unit can input data on the user's living situation and areas of interest into a generative AI, which can then analyze the data and select the optimal filtering method.

[0078] The support unit can prioritize highly relevant support by considering the user's geographical location during support. For example, if the user is in a specific region, the support unit will prioritize support in that region. If the user is traveling, the support unit can also prioritize support at their travel destination. If the user is at home, the support unit can also prioritize support at their home. This allows the support unit to provide the most appropriate support method based on the user's geographical location. Some or all of the above processing in the support unit may be performed using or without a generative AI. For example, the support unit can input the user's geographical location information into a generative AI, which can then analyze the data and select the optimal method.

[0079] The support unit can analyze the user's social media activity and provide relevant support during support sessions. For example, the support unit can extract support-related information from the user's social media activity and provide support. The support unit can also analyze the user's social media activity and determine support-related priorities. For example, the support unit can propose support methods based on the user's social media activity. This allows the support unit to provide the optimal support method based on the user's social media activity. Some or all of the above processes in the support unit may be performed using or without a generative AI. For example, the support unit can input the user's social media data into a generative AI, which can analyze the data and select the optimal method.

[0080] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0081] The household finance and life planning system may also include a health management section. This section can collect user health data and provide advice related to household finance and life planning. For example, it can analyze user exercise data and suggest a budget for maintaining health. It can also collect user dietary data and suggest ways to manage food expenses to maintain a healthy diet. Furthermore, based on the user's health checkup results, the health management section can predict future medical expenses and suggest appropriate insurance plans. This supports household finance and life planning based on the user's health status.

[0082] The household budgeting and life planning system can also include an entertainment management section. This section can manage the user's spending related to entertainment activities and suggest optimal entertainment plans. For example, it can analyze the user's movie viewing and music streaming service usage and suggest the most suitable subscription plan. It can also manage the user's travel plans and support the optimization of travel expenses. Furthermore, it can provide information on relevant events and activities based on the user's hobbies and interests. This allows for efficient management of spending related to the user's entertainment activities and supports a fulfilling lifestyle.

[0083] The household finance and life planning system can also include an education management section. This section can manage the user's education-related expenses and propose optimal education plans. For example, it can analyze the user's children's education costs and provide information on suitable education loans and scholarships. It can also manage the user's self-improvement and skill development expenses and provide information on suitable online courses and seminars. Furthermore, based on the user's educational goals, the education management section can create long-term education plans and predict future education costs. This allows for efficient management of the user's education-related expenses and supports the achievement of educational goals.

[0084] The household budgeting and life planning system may also include an environmental management section. This section can manage the user's environmentally related spending and support a sustainable lifestyle. For example, it can analyze the user's energy consumption data and propose an optimal energy-saving plan. It can also manage the user's recycling activities and optimize recycling-related spending. Furthermore, it can analyze the user's use of environmentally friendly products and services and suggest sustainable options. This allows for efficient management of the user's environmentally related spending and supports a sustainable lifestyle.

[0085] The household budgeting and life planning system can also include a community management section. This section can manage the user's community-related spending and support their engagement with the community. For example, it can manage the user's participation in local events and volunteer activities and suggest optimal participation plans. It can also manage the user's donations and support activities to the community and suggest optimal donation plans. Furthermore, it can provide information relevant to the user's community and strengthen their engagement with the community. This allows for efficient management of the user's community-related spending and supports their engagement with the community.

[0086] The following briefly describes the processing flow for example form 1.

[0087] Step 1: The management department manages communication charges. For example, it can centrally manage users' communication charges and have a detailed understanding of monthly communication expenses. It can also analyze communication service pricing plans and suggest the most suitable plan. Furthermore, it can manage communication payment history and provide advice to reduce unnecessary expenses. Step 2: The points management unit manages the points. For example, it integrates with electronic payment services to manage point usage history and balances. Furthermore, it can suggest advantageous ways to use points to support their effective utilization. It can also manage point expiration dates and send notifications to prevent them from expiring. Step 3: The optimization unit optimizes data usage. For example, it can analyze users' data usage and propose the optimal plan. Furthermore, it can provide information on how to save data usage and offer special promotions. It can also analyze data usage patterns and provide advice on reducing unnecessary data usage. Step 4: The integration unit connects with smart home appliances. For example, it connects with smart home appliances (smart speakers, IoT devices, etc.) to provide support for more convenient and efficient household budget management. Furthermore, it can provide an interface to simplify the operation of smart home appliances. It can also analyze the usage of smart home appliances and suggest the optimal way to use them. Step 5: The planning department plans life events. For example, it can collaborate with life planning services (insurance, mortgages, etc.) to create plans for future life events such as marriage, children's education, home purchase, and retirement finances. Furthermore, it can provide tools to support life event planning. It can also collect and provide users with the information necessary for life event planning. Step 6: The shared section enables family sharing. For example, the entire family can share an account and check the household finances in real time. Furthermore, it can provide tools to support budgeting and spending management for the entire family. It can also analyze the family's income and spending patterns and provide customized advice.

[0088] (Example of form 2) The household finance management and life planning system according to an embodiment of the present invention is a system that supports users' household finance management and life planning by linking with communication services and related services. This system allows for centralized management of daily expenses and income, and enables users to plan for future life events. For example, the household finance management and life planning system includes communication charge management, point linking, data usage optimization, smart home appliance integration, life event planning, and family sharing functions. The communication charge management function centrally manages mobile phone and internet charges for communication services, allowing users to understand their monthly communication expenses in detail. This helps reduce unnecessary spending. The point linking function links with electronic payment services to manage point usage history and balances. Furthermore, it supports effective use of points by suggesting advantageous ways to use them. The data usage optimization function analyzes the user's data usage and suggests the optimal plan. This allows for the provision of data saving methods and advantageous campaign information. The smart home appliance integration function links with smart home appliances (smart speakers, IoT devices, etc.) to provide support for more convenient and efficient household finance management. The life event planning function integrates with life planning services (insurance, mortgages, etc.) to allow users to plan for future life events such as marriage, children's education, home purchase, and retirement finances. The family sharing function allows all family members to share an account and check household finances in real time. This enables budget setting and expenditure management for the entire family. The household finance management and life planning system utilizes generative AI to analyze the user's income and expenditure patterns and provide customized saving advice and budget setting suggestions. It also uses generative AI to send timely notifications based on the user's financial activity. Furthermore, the generative AI can simulate financial plans for the user's future life events and set specific savings goals. A chatbot is provided that instantly answers user questions using natural language processing technology. In this way, the household finance management and life planning system can efficiently support the user's household finance management and life planning.

[0089] The household budget management and life planning system according to this embodiment comprises a management unit, a points management unit, an optimization unit, a linkage unit, a planning unit, and a sharing unit. The management unit manages communication charges. For example, the management unit can centrally manage users' communication charges and understand monthly communication expenses in detail. The management unit can also analyze communication service pricing plans and propose the optimal plan. For example, the management unit can manage the payment history of communication charges and provide advice to reduce unnecessary spending. The points management unit manages points. For example, the points management unit links with electronic payment services and manages point usage history and balances. For example, the points management unit can suggest advantageous ways to use points to support their effective use. For example, the points management unit can manage the expiration date of points and send notifications to prevent them from expiring. The optimization unit optimizes data usage. For example, the optimization unit analyzes users' data usage and proposes the optimal plan. For example, the optimization unit can provide information on how to save on data usage and advantageous campaign information. The optimization unit can, for example, analyze data usage patterns and provide advice to reduce wasteful data usage. The integration unit integrates with smart home appliances. The integration unit integrates with smart home appliances (smart speakers, IoT devices, etc.) to provide support for more convenient and efficient household budget management. The integration unit can also, for example, provide an interface to simplify the operation of smart home appliances. The integration unit can also, for example, analyze the usage of smart home appliances and suggest the optimal way to use them. The planning unit plans life events. The planning unit integrates with life planning services (insurance, mortgages, etc.) to create plans for future life events such as marriage, children's education, home purchase, and retirement financial planning. The planning unit can also, for example, provide tools to support life event planning. The planning unit can also, for example, collect and provide users with the information necessary for life event planning. The sharing unit facilitates family sharing. The sharing unit allows all family members to share an account and check the household budget in real time.The shared section can, for example, provide tools to support budgeting and expense management for the entire family. The shared section can also, for example, analyze family income and expense patterns and provide customized advice. This allows the household financial management and life planning system according to the embodiment to efficiently support the user's household financial management and life planning.

[0090] The management department manages communication charges. For example, the management department can centrally manage users' communication charges and have a detailed understanding of their monthly communication expenses. Specifically, the management department obtains data directly from users' communication service providers and automatically compiles monthly communication expenses. This allows users to see their communication expenses at a glance and easily identify unnecessary spending. The management department can also analyze communication service pricing plans and suggest the most suitable plan. For example, based on the user's past communication usage data, it can evaluate whether the current plan is optimal and suggest a more cost-effective plan. Furthermore, the management department can manage communication payment history and provide advice to reduce unnecessary spending. For example, it can analyze past payment history to identify unnecessary expenses such as late fees and excessive data usage charges and provide specific advice on how to avoid them. This allows users to efficiently manage and save on communication costs.

[0091] The Points Management Department manages points. For example, it integrates with electronic payment services to manage point usage history and balances. Specifically, the Points Management Department collects point data from multiple electronic payment services and manages it centrally. This allows users to view multiple point programs through a single interface, making it easier to effectively utilize points. The Points Management Department can also suggest advantageous ways to use points to support their effective use. For example, it can provide information on periods and campaigns with high point redemption rates at specific stores or services, enabling users to maximize their point utilization. Furthermore, the Points Management Department can manage point expiration dates and send notifications to prevent points from expiring. For example, it can send notifications to users when points are nearing expiration, urging them to use their points before they expire. This ensures that users can utilize their points to the fullest without wasting them.

[0092] The optimization unit optimizes data usage. For example, it analyzes a user's data usage and proposes the optimal plan. Specifically, the optimization unit analyzes a user's past data usage patterns and evaluates whether the current plan is optimal. For example, if a user exceeds their monthly data usage limit, it will propose a larger data plan. It can also provide information on how to save data usage and advantageous campaigns. For example, it will suggest ways to save data usage by restricting data usage during specific times or for specific applications. Furthermore, the optimization unit can analyze data usage patterns and provide advice to reduce unnecessary data usage. For example, if an application running in the background is consuming a large amount of data, it will advise changing the settings of that application. This allows users to efficiently manage their data usage and save on communication costs.

[0093] The integration unit connects with smart home appliances. For example, it connects with smart home appliances (smart speakers, IoT devices, etc.) to provide support for more convenient and efficient household budget management. Specifically, the integration unit collects data from smart home appliances and integrates it into the household budget management system. This allows users to centrally manage the usage of smart home appliances and understand energy consumption and appliance usage patterns. The integration unit can also provide an interface to simplify the operation of smart home appliances. For example, users can access the household budget management system via voice commands through a smart speaker to check their household budget in real time and record expenses. Furthermore, the integration unit can analyze the usage of smart home appliances and suggest optimal usage. For example, it can suggest ways to save on electricity bills by adjusting the usage time of energy-intensive appliances. This allows users to use smart home appliances efficiently and manage their household budget more effectively.

[0094] The planning department plans life events. For example, it can collaborate with life planning services (insurance, mortgages, etc.) to create plans for future life events such as marriage, children's education, home purchase, and retirement finances. Specifically, the planning department analyzes the user's current income, expenses, and savings to calculate the funds needed for future life events. For example, it considers expenses such as children's education, home purchase costs, and retirement living expenses to set necessary savings targets. The planning department can also provide tools to support life event planning. For example, it can use simulation tools to create financial plans based on different scenarios. Furthermore, the planning department can collect and provide users with the information necessary for life event planning. For example, it can provide comparative information on insurance products and mortgages to support users in making the best choices. This makes it easier for users to plan for future life events and live with peace of mind.

[0095] The shared section facilitates family sharing. For example, the shared section allows all family members to share an account and monitor household finances in real time. Specifically, the shared section centrally manages the income and expenses of all family members and provides a dashboard for understanding the overall household finances. This allows all family members to check the current financial situation in real time and prevent unnecessary spending. The shared section can also provide tools to support family-wide budgeting and spending management. For example, it can provide a shared budget sheet and spending log tool, making it easier to manage overall spending by allowing each member to record their own expenses. Furthermore, the shared section can analyze family income and spending patterns and provide customized advice. For example, if there is a tendency for spending to increase in a particular month, it can identify the cause and provide specific advice on how to reduce unnecessary spending. This allows all family members to cooperate in managing household finances and using funds efficiently.

[0096] The analysis unit can perform data analysis and prediction. For example, the analysis unit can analyze a user's income and expenditure data and predict future income and expenditure. For example, the analysis unit can use generative AI to analyze a user's income and expenditure patterns and provide customized saving advice and budget setting suggestions. For example, the analysis unit can also make predictions so that the generative AI can send notifications at the appropriate time based on the user's financial activities. This improves the accuracy of the user's household financial management and life planning through data analysis and prediction. Some or all of the above processes in the analysis unit may be performed using generative AI or not. For example, the analysis unit can input user's income and expenditure data into the generative AI, which can analyze the data and output prediction results.

[0097] The notification unit can provide personalized notifications. For example, it can send notifications at the appropriate time based on the user's financial activity. For example, it can use generative AI to analyze the user's income and expenditure patterns and provide customized notifications. For example, the notification unit can use generative AI to simulate a financial plan for the user's future life events and provide notifications to set specific savings goals. This improves the efficiency of household management and life planning by providing users with timely notifications. Some or all of the above processes in the notification unit may be performed using generative AI or not. For example, the notification unit can input the user's financial activity data into generative AI, which can then analyze the data and generate notification content.

[0098] The Planning Department can perform life event planning. For example, the Planning Department can collaborate with life planning services (insurance, mortgages, etc.) to create plans for future life events such as marriage, children's education, home purchase, and retirement financial planning. For example, the Planning Department can use generative AI to simulate financial plans for the user's future life events and set specific savings goals. For example, the Planning Department can use generative AI to analyze the user's income and expenditure patterns and propose a customized life event plan. This can support the user's planning for life events. Some or all of the above processes in the Planning Department may be performed using generative AI or not. For example, the Planning Department can input the user's income and expenditure data into the generative AI, which can then analyze the data and generate a life event plan.

[0099] The support department can provide customer support. For example, the support department can provide a chatbot that instantly answers user questions using natural language processing technology. For example, the support department can use generative AI to analyze the content of user questions and generate appropriate answers. For example, the support department can have the generative AI analyze the user's past question history and provide customized support. This can enhance support for household budget management and life planning by providing instant answers to user questions. Some or all of the above processes in the support department may be performed using generative AI or not. For example, the support department can input user question data into a generative AI, which can then analyze the data and generate answers.

[0100] The management unit can estimate the user's emotions and adjust the communication charge management method based on the estimated emotions. For example, if the user is stressed, the management unit can provide a simple interface and minimize the steps for managing communication charges. For example, if the user is relaxed, the management unit can provide detailed management options and suggest a customizable management method. For example, if the user is in a hurry, the management unit can prioritize voice input to allow for quick management of communication charges. This allows for a communication charge management method that is tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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 management unit may be performed using AI or not. For example, the management unit can input user emotion data into a generative AI, which can estimate the emotions and adjust the communication charge management method based on the result.

[0101] The management department can analyze past communication charge data and select the optimal management method. For example, the management department can propose management methods to reduce unnecessary spending based on the user's past communication charge data. For example, the management department can also propose the most cost-effective plan based on the user's past communication charge data. For example, the management department can analyze the user's past communication charge data and select the optimal management method based on monthly spending patterns. This allows for the provision of the optimal communication charge management method based on past data. Some or all of the above processes in the management department may be performed using AI or not. For example, the management department can input past communication charge data into AI, which can analyze the data and select the optimal management method.

[0102] The management department can filter communication charges based on the user's contract plan. For example, the management department can propose the optimal communication charge management method based on the user's contract plan. The management department can also remove unnecessary options and manage only the necessary options based on the user's contract plan. The management department can also simplify management by filtering communication charges optimally based on the user's contract plan. This allows the management department to provide the optimal communication charge management method based on the user's contract plan. Some or all of the above processes in the management department may be performed using AI or not. For example, the management department can input user contract plan data into AI, which can analyze the data and select the optimal filtering method.

[0103] The management unit can estimate the user's emotions and determine the priority of communication charges based on the estimated emotions. For example, if the user is stressed, the management unit can prioritize displaying important communication charges. For example, if the user is relaxed, the management unit can also display a detailed breakdown of communication charges and determine their priority. For example, if the user is in a hurry, the management unit can prioritize displaying the most important communication charges. This provides communication charge prioritization 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 management unit may be performed using AI or not. For example, the management unit can input user emotion data into a generative AI, the generative AI can estimate the emotions, and the management unit can determine the priority of communication charges based on the results.

[0104] The management unit can prioritize the management of charges that are most relevant to the user's geographical location when managing communication charges. For example, if the user is in a specific region, the management unit can prioritize the management of communication charges in that region. For example, if the user is traveling, the management unit can also prioritize the management of communication charges at the travel destination. For example, if the user is at home, the management unit can also prioritize the management of communication charges at home. This allows the management unit to provide an optimal communication charge management method based on the user's geographical location. Some or all of the above processing in the management unit may be performed using AI or not. For example, the management unit can input the user's geographical location information into AI, which can analyze the data and select the optimal management method.

[0105] The management department can analyze users' social media activity and manage related charges when managing communication charges. For example, the management department can extract and manage information related to communication charge payments from users' social media activity. For example, the management department can analyze users' social media activity and determine priorities related to communication charge payments. For example, the management department can propose communication charge management methods based on users' social media activity. This allows the management department to provide the optimal communication charge management method based on users' social media activity. Some or all of the above processes in the management department may be performed using AI or not. For example, the management department can input user social media data into AI, which can analyze the data and select the optimal management method.

[0106] The point management unit can estimate the user's emotions and adjust the point management method based on the estimated emotions. For example, if the user is stressed, the point management unit can provide a simple interface and minimize the point management procedure. For example, if the user is relaxed, the point management unit can provide detailed management options and suggest a customizable management method. For example, if the user is in a hurry, the point management unit can prioritize voice input to allow for quick point management. This allows for a point management method that is tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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 point management unit may be performed using AI or not. For example, the point management unit can input user emotion data into a generative AI, which can estimate the emotions and adjust the point management method based on the result.

[0107] The points management unit can analyze a user's past point usage history to select the optimal management method when managing points. For example, the points management unit can propose the optimal points management method based on the user's past point usage history. For example, the points management unit can also propose a management method to reduce unnecessary point usage based on the user's past point usage history. For example, the points management unit can analyze a user's past point usage history and select the most efficient points management method. This allows the unit to provide the optimal points management method based on the user's past point usage history. Some or all of the above processes in the points management unit may be performed using AI or not. For example, the points management unit can input past point usage history data into AI, which can analyze the data and select the optimal management method.

[0108] The point management unit can filter points based on the user's current lifestyle and areas of interest during point management. For example, the point management unit can suggest the optimal point management method based on the user's current lifestyle. The point management unit can also suggest how to use points based on the user's areas of interest. For example, the point management unit can analyze the user's current lifestyle and areas of interest and select the optimal point management method. This allows the point management unit to provide the optimal point management method based on the user's current lifestyle and areas of interest. Some or all of the above processing in the point management unit may be performed using AI or not. For example, the point management unit can input user lifestyle and area of ​​interest data into AI, which can analyze the data and select the optimal filtering method.

[0109] The point management unit can estimate the user's emotions and determine the priority of points based on the estimated emotions. For example, if the user is stressed, the point management unit can prioritize the use of important points. For example, if the user is relaxed, the point management unit can also display a detailed breakdown of points and determine their priority. For example, if the user is in a hurry, the point management unit can prioritize the use of the most important points. This allows for the provision of point prioritization 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 point management unit may be performed using AI or not. For example, the point management unit can input user emotion data into a generative AI, the generative AI can estimate the emotions, and the point priority can be determined based on the result.

[0110] The points management unit can prioritize the management of points that are highly relevant to the user, taking into account the user's geographical location information. For example, if the user is in a specific region, the points management unit can prioritize point usage in that region. For example, if the user is traveling, the points management unit can also prioritize point usage at the travel destination. For example, if the user is at home, the points management unit can also prioritize point usage at home. This allows the system to provide an optimal points management method based on the user's geographical location information. Some or all of the above processing in the points management unit may be performed using AI, or not. For example, the points management unit can input the user's geographical location information into an AI, which can analyze the data and select the optimal management method.

[0111] The points management unit can analyze users' social media activities and manage related points during point management. For example, the points management unit can extract and manage information related to point usage from users' social media activities. For example, the points management unit can analyze users' social media activities and determine priorities related to point usage. For example, the points management unit can propose point management methods based on users' social media activities. This allows the system to provide the optimal point management method based on users' social media activities. Some or all of the above processes in the points management unit may be performed using AI or not. For example, the points management unit can input user social media data into AI, which can analyze the data and select the optimal management method.

[0112] The optimization unit can estimate the user's emotions and adjust the data utilization optimization method based on the estimated user emotions. For example, if the user is stressed, the optimization unit can provide a simple interface and minimize the data utilization optimization steps. For example, if the user is relaxed, the optimization unit can also provide detailed optimization options and suggest a customizable optimization method. For example, if the user is in a hurry, the optimization unit can prioritize voice input and enable rapid data utilization optimization. This allows for the provision of data utilization optimization methods that are tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the optimization unit may be performed using AI or not. For example, the optimization unit can input user emotion data into a generative AI, the generative AI can estimate the emotions, and the data utilization optimization method can be adjusted based on the results.

[0113] The optimization unit can analyze the user's past data usage history to select the optimal method when optimizing data utilization. For example, the optimization unit can propose the optimal data utilization method based on the user's past data usage history. For example, the optimization unit can also propose optimization methods to reduce unnecessary data utilization based on the user's past data usage history. For example, the optimization unit can analyze the user's past data usage history and select the most efficient data utilization method. This makes it possible to provide the optimal data utilization method based on the user's past data usage history. Some or all of the above processing in the optimization unit may be performed using AI or not. For example, the optimization unit can input past data usage history data into AI, which can analyze the data and select the optimal method.

[0114] The optimization unit can perform filtering based on the user's contract plan when optimizing data utilization. For example, the optimization unit can propose the optimal data utilization method based on the user's contract plan. The optimization unit can also perform filtering to reduce unnecessary data utilization based on the user's contract plan. For example, the optimization unit can perform optimal data utilization filtering based on the user's contract plan and simplify management. This allows the optimization unit to provide the optimal data utilization method based on the user's contract plan. Some or all of the above processing in the optimization unit may be performed using AI or not. For example, the optimization unit can input user contract plan data into AI, which can analyze the data and select the optimal filtering method.

[0115] The optimization unit can estimate the user's emotions and determine the priority of data usage based on the estimated emotions. For example, if the user is stressed, the optimization unit can prioritize displaying important data usage. For example, if the user is relaxed, the optimization unit can also display a detailed breakdown of data usage and determine its priority. For example, if the user is in a hurry, the optimization unit can prioritize displaying the most important data usage. This provides data usage priorities that correspond 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 optimization unit may be performed using AI or not. For example, the optimization unit can input user emotion data into a generative AI, the generative AI can estimate the emotions, and the optimization unit can determine the priority of data usage based on the results.

[0116] The optimization unit can prioritize optimizing data usage by considering the user's geographical location information. For example, if the user is in a specific region, the optimization unit can prioritize optimizing data usage in that region. For example, if the user is traveling, the optimization unit can also prioritize optimizing data usage at the travel destination. For example, if the user is at home, the optimization unit can also prioritize optimizing data usage at home. This allows the system to provide the optimal data usage method based on the user's geographical location information. Some or all of the above processing in the optimization unit may be performed using AI or not. For example, the optimization unit can input the user's geographical location information into AI, which can then analyze the data and select the optimal method.

[0117] The optimization unit can analyze users' social media activities and optimize related data when optimizing data utilization. For example, the optimization unit can extract and optimize information related to data utilization from users' social media activities. The optimization unit can also analyze users' social media activities and determine priorities related to data utilization. For example, the optimization unit can propose methods for optimizing data utilization based on users' social media activities. This makes it possible to provide the optimal data utilization method based on users' social media activities. Some or all of the above processing in the optimization unit may be performed using AI or not. For example, the optimization unit can input user social media data into AI, which can analyze the data and select the optimal method.

[0118] The integration unit can estimate the user's emotions and adjust the method of integration with smart home appliances based on the estimated emotions. For example, if the user is stressed, the integration unit can provide a simple interface and minimize the steps involved in integrating with smart home appliances. For example, if the user is relaxed, the integration unit can provide detailed integration options and suggest a customizable integration method. For example, if the user is in a hurry, the integration unit can prioritize voice input to enable quick integration with smart home appliances. This allows for the provision of smart home appliance integration methods that are tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the integration unit may be performed using AI or not. For example, the integration unit can input user emotion data into a generative AI, which can estimate the emotions and adjust the smart home appliance integration method based on the result.

[0119] The integration unit can analyze the user's past usage history to select the optimal integration method when integrating with smart home appliances. For example, the integration unit can propose the optimal integration method based on the user's past smart home appliance usage history. For example, the integration unit can also propose an integration method that reduces unnecessary operations based on the user's past usage history. For example, the integration unit can analyze the user's past usage history and select the most efficient integration method. This allows the system to provide the optimal smart home appliance integration method based on the user's past usage history. Some or all of the above processing in the integration unit may be performed using AI or not. For example, the integration unit can input past usage history data into AI, which can then analyze the data and select the optimal integration method.

[0120] The integration unit can perform filtering based on the user's current lifestyle and areas of interest when integrating with smart home appliances. For example, the integration unit can propose the optimal smart home appliance integration method based on the user's current lifestyle. The integration unit can also propose how to use smart home appliances based on the user's areas of interest. For example, the integration unit can analyze the user's current lifestyle and areas of interest and select the optimal smart home appliance integration method. This allows the system to provide the optimal smart home appliance integration method based on the user's current lifestyle and areas of interest. Some or all of the above processing in the integration unit may be performed using AI or not. For example, the integration unit can input user lifestyle and area of ​​interest data into AI, which can then analyze the data and select the optimal filtering method.

[0121] The integration unit can estimate the user's emotions and determine the priority of smart appliances based on the estimated emotions. For example, if the user is stressed, the integration unit can prioritize displaying the operation of important smart appliances. For example, if the user is relaxed, the integration unit can also display detailed instructions for operating smart appliances and determine their priority. For example, if the user is in a hurry, the integration unit can prioritize displaying the operation of the most important smart appliances. This provides smart appliance prioritization 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 integration unit may be performed using AI or not. For example, the integration unit can input user emotion data into a generative AI, the generative AI can estimate the emotions, and the smart appliance priority can be determined based on the result.

[0122] The integration unit can prioritize the integration of highly relevant smart home appliances by considering the user's geographical location when integrating with smart home appliances. For example, if the user is in a specific region, the integration unit can prioritize the integration of smart home appliances in that region. For example, if the user is traveling, the integration unit can also prioritize the integration of smart home appliances at the travel destination. For example, if the user is at home, the integration unit can also prioritize the integration of smart home appliances at home. This allows the system to provide the optimal smart home appliance integration method based on the user's geographical location. Some or all of the above processing in the integration unit may be performed using AI or not. For example, the integration unit can input the user's geographical location information into AI, which can analyze the data and select the optimal integration method.

[0123] The integration unit can analyze the user's social media activity when integrating with smart home appliances and integrate the relevant appliances. For example, the integration unit can extract information related to the operation of smart home appliances from the user's social media activity and integrate it. For example, the integration unit can analyze the user's social media activity and determine priorities related to the operation of smart home appliances. For example, the integration unit can propose a method for integrating smart home appliances based on the user's social media activity. This makes it possible to provide the optimal method for integrating smart home appliances based on the user's social media activity. Some or all of the above processing in the integration unit may be performed using AI or not. For example, the integration unit can input the user's social media data into AI, which can analyze the data and select the optimal integration method.

[0124] The planning unit can estimate the user's emotions and adjust the life event planning method based on the estimated user emotions. For example, if the user is stressed, the planning unit can provide a simple interface and minimize the life event planning steps. For example, if the user is relaxed, the planning unit can also provide detailed planning options and suggest a customizable planning method. For example, if the user is in a hurry, the planning unit can prioritize voice input to allow for quick life event planning. This allows for the provision of a life event planning method that is tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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 a generative AI, which can estimate the emotions and adjust the life event planning method based on the results.

[0125] The planning department can analyze a user's past planning history to select the optimal method when planning life events. For example, the planning department can propose the optimal planning method based on the user's past life event planning history. For example, the planning department can also propose a planning method that reduces unnecessary steps based on the user's past planning history. For example, the planning department can analyze the user's past planning history and select the most efficient planning method. This allows the planning department to provide the optimal life event planning method based on the user's past planning history. 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 history data into AI, which can analyze the data and select the optimal method.

[0126] The planning unit can filter the user's current living situation and areas of interest when planning life events. For example, the planning unit can propose the optimal life event planning method based on the user's current living situation. The planning unit can also propose a life event planning method based on the user's areas of interest. For example, the planning unit can analyze the user's current living situation and areas of interest and select the optimal life event planning method. This allows the planning unit to provide the optimal life event planning method based on the user's current living situation and areas of interest. 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 the user's living situation and areas of interest data into the AI, which can then analyze the data and select the optimal filtering method.

[0127] The planning unit can estimate the user's emotions and determine the priority of life events based on the estimated emotions. For example, if the user is stressed, the planning unit will prioritize displaying important life event plans. For example, if the user is relaxed, the planning unit can also display detailed life event planning methods and determine their priorities. For example, if the user is in a hurry, the planning unit can also prioritize displaying the most important life event plans. This provides life event priorities that correspond 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 a generative AI, the generative AI can estimate the emotions, and the prioritization of life events can be determined based on the results.

[0128] The planning unit can prioritize highly relevant life events when planning life events, taking into account the user's geographical location. For example, if the user is in a specific region, the planning unit will prioritize life events in that region. For example, if the user is traveling, the planning unit can also prioritize life events at the travel destination. For example, if the user is at home, the planning unit can also prioritize life events at home. This allows the system to provide an optimal life event planning method based on the user's geographical location. 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 the user's geographical location into an AI, which can analyze the data and select the optimal planning method.

[0129] The planning department can analyze a user's social media activity when planning life events and plan related events. For example, the planning department can extract information relevant to life event planning from the user's social media activity and plan accordingly. The planning department can also analyze a user's social media activity and determine priorities related to life event planning. For example, the planning department can propose a life event planning method based on the user's social media activity. This allows the planning department to provide the optimal life event planning method based on the user's social media activity. 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 the user's social media data into an AI, which can analyze the data and select the optimal planning method.

[0130] The sharing function can estimate the user's emotions and adjust the family sharing method based on the estimated emotions. For example, if the user is stressed, the sharing function can provide a simple interface and minimize the family sharing procedure. For example, if the user is relaxed, the sharing function can also provide detailed sharing options and suggest a customizable sharing method. For example, if the user is in a hurry, the sharing function can prioritize voice input to enable quick family sharing. This allows for providing a family sharing method that is tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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 sharing function may be performed using AI or not. For example, the sharing function can input user emotion data into a generative AI, which can estimate the emotions and adjust the family sharing method based on the result.

[0131] The sharing function can analyze the user's past sharing history to select the optimal method when sharing with family. For example, the sharing function can suggest the optimal sharing method based on the user's past family sharing history. For example, the sharing function can also suggest a sharing method that reduces unnecessary steps based on the user's past sharing history. For example, the sharing function can analyze the user's past sharing history and select the most efficient sharing method. This allows the system to provide the optimal family sharing method based on the user's past sharing history. Some or all of the above processing in the sharing function may be performed using AI or not. For example, the sharing function can input past sharing history data into an AI, which can analyze the data and select the optimal method.

[0132] The sharing function can filter data based on the user's current lifestyle and areas of interest during family sharing. For example, the sharing function can suggest the optimal family sharing method based on the user's current lifestyle. The sharing function can also suggest a family sharing method based on the user's areas of interest. The sharing function can also analyze the user's current lifestyle and areas of interest and select the optimal family sharing method. This allows the sharing function to provide the optimal family sharing method based on the user's current lifestyle and areas of interest. Some or all of the above processing in the sharing function may be performed using AI or not. For example, the sharing function can input user lifestyle and area of ​​interest data into an AI, which can analyze the data and select the optimal filtering method.

[0133] The sharing section can estimate the user's emotions and determine the priority of family sharing based on the estimated emotions. For example, if the user is stressed, the sharing section can prioritize displaying important family sharing information. For example, if the user is relaxed, the sharing section can also display detailed family sharing information and determine its priority. For example, if the user is in a hurry, the sharing section can prioritize displaying the most important family sharing information. This provides a family sharing priority that corresponds 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 sharing section may be performed using AI or not. For example, the sharing section can input user emotion data into a generative AI, the generative AI can estimate the emotions, and the family sharing priority can be determined based on the result.

[0134] The sharing function can prioritize sharing highly relevant information when sharing with family, taking into account the user's geographical location. For example, if the user is in a specific region, the sharing function will prioritize sharing family information for that region. For example, if the user is traveling, the sharing function can also prioritize sharing family information for the travel destination. For example, if the user is at home, the sharing function can also prioritize sharing family information for home. This allows for the provision of the optimal family sharing method based on the user's geographical location. Some or all of the above processing in the sharing function may be performed using AI or not. For example, the sharing function can input the user's geographical location into an AI, which can analyze the data and select the optimal sharing method.

[0135] The sharing function can analyze the user's social media activity and share relevant information when sharing with family. For example, the sharing function can extract and share information relevant to family sharing from the user's social media activity. The sharing function can also analyze the user's social media activity and determine priorities related to family sharing. For example, the sharing function can suggest methods of family sharing based on the user's social media activity. This allows the function to provide the optimal family sharing method based on the user's social media activity. Some or all of the above processing in the sharing function may be performed using AI or not. For example, the sharing function can input the user's social media data into an AI, which can analyze the data and select the optimal sharing method.

[0136] The analysis unit can estimate the user's emotions and adjust the data analysis method based on the estimated user emotions. For example, if the user is stressed, the analysis unit can provide a simple interface and minimize the data analysis procedure. For example, if the user is relaxed, the analysis unit can also provide detailed analysis options and suggest a customizable analysis method. For example, if the user is in a hurry, the analysis unit can prioritize voice input and enable rapid data analysis. This allows for the provision of a data analysis method tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using or without a generative AI. For example, the analysis unit can input user emotion data into a generative AI, the generative AI can estimate the emotions, and the data analysis method can be adjusted based on the results.

[0137] The analysis unit can analyze the user's past data history and select the optimal method during data analysis. For example, the analysis unit can propose the optimal data analysis method based on the user's past data history. For example, the analysis unit can also propose methods to reduce unnecessary data analysis based on the user's past data history. For example, the analysis unit can analyze the user's past data history and select the most efficient data analysis method. This allows the analysis unit to provide the optimal data analysis method based on the user's past data history. Some or all of the above-described processes in the analysis unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the analysis unit can input past data history data into a generation AI, which can analyze the data and select the optimal method.

[0138] The analysis unit can filter data based on the user's current lifestyle and areas of interest during data analysis. For example, the analysis unit can suggest the optimal data analysis method based on the user's current lifestyle. The analysis unit can also suggest a data analysis method based on the user's areas of interest. For example, the analysis unit can analyze the user's current lifestyle and areas of interest and select the optimal data analysis method. This allows the analysis unit to provide the optimal data analysis method based on the user's current lifestyle and areas of interest. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input the user's lifestyle and areas of interest data into a generation AI, which can then analyze the data and select the optimal filtering method.

[0139] The analysis unit can estimate the user's emotions and determine the priority of data analysis based on the estimated user emotions. For example, if the user is stressed, the analysis unit can prioritize displaying important data analysis. For example, if the user is relaxed, the analysis unit can also display a detailed breakdown of data analysis and determine its priority. For example, if the user is in a hurry, the analysis unit can prioritize displaying the most important data analysis. This provides data analysis prioritization 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 analysis unit may be performed using or without a generative AI. For example, the analysis unit can input user emotion data into a generative AI, the generative AI can estimate the emotions, and the analysis unit can determine the priority of data analysis based on the results.

[0140] The analysis unit can prioritize the analysis of highly relevant data by considering the user's geographical location information during data analysis. For example, if the user is in a specific region, the analysis unit will prioritize data analysis in that region. For example, if the user is traveling, the analysis unit can also prioritize data analysis at the travel destination. For example, if the user is at home, the analysis unit can also prioritize data analysis at home. This allows the analysis unit to provide the optimal data analysis method based on the user's geographical location information. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input the user's geographical location information into a generation AI, which can then analyze the data and select the optimal method.

[0141] The analysis unit can analyze users' social media activities and analyze related data during data analysis. For example, the analysis unit can extract and analyze information relevant to data analysis from users' social media activities. The analysis unit can also analyze users' social media activities and determine priorities related to data analysis. For example, the analysis unit can propose a data analysis method based on users' social media activities. This allows the analysis unit to provide the optimal data analysis method based on users' social media activities. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input user social media data into a generative AI, which can analyze the data and select the optimal method.

[0142] The notification unit can estimate the user's emotions and adjust the notification method based on the estimated emotions. For example, if the user is stressed, the notification unit can provide a simple interface and minimize the notification procedure. For example, if the user is relaxed, the notification unit can also provide detailed notification options and suggest a customizable notification method. For example, if the user is in a hurry, the notification unit can prioritize voice input and provide a quick notification. This allows for a notification method that is tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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 or without generative AI. For example, the notification unit can input user emotion data into a generative AI, which can estimate the emotions and adjust the notification method based on the result.

[0143] The notification unit can analyze the user's past notification history and select the optimal method when sending a notification. For example, the notification unit can propose the optimal notification method based on the user's past notification history. For example, the notification unit can also propose methods to reduce unnecessary notifications based on the user's past notification history. For example, the notification unit can analyze the user's past notification history and select the most efficient notification method. This allows the system to provide the optimal notification method based on the user's past notification history. Some or all of the above processing in the notification unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the notification unit can input past notification history data into a generation AI, which can then analyze the data and select the optimal method.

[0144] The notification unit can filter notifications based on the user's current living situation and areas of interest. For example, the notification unit can suggest the optimal notification method based on the user's current living situation. The notification unit can also suggest a notification method based on the user's areas of interest. The notification unit can also analyze the user's current living situation and areas of interest and select the optimal notification method. This allows the notification unit to provide the optimal notification method based on the user's current living situation and areas of interest. Some or all of the above processing in the notification unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the notification unit can input user living situation and area of ​​interest data into a generative AI, which can then analyze the data and select the optimal filtering method.

[0145] The notification unit can estimate the user's emotions and determine the priority of notifications based on the estimated emotions. For example, if the user is stressed, the notification unit will prioritize important notifications. If the user is relaxed, the notification unit can also display a detailed breakdown of notifications and determine their priority. If the user is in a hurry, the notification unit can also prioritize the most important notifications. This provides notification prioritization 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 or without a generative AI. For example, the notification unit can input user emotion data into a generative AI, the generative AI can estimate the emotions, and the notification priority can be determined based on the result.

[0146] The notification unit can prioritize highly relevant notifications by considering the user's geographical location information when sending notifications. For example, if the user is in a specific region, the notification unit can prioritize notifications for that region. For example, if the user is traveling, the notification unit can prioritize notifications for their travel destination. For example, if the user is at home, the notification unit can prioritize notifications for their home. This allows the system to provide the most appropriate notification method based on the user's geographical location information. Some or all of the above processing in the notification unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the notification unit can input the user's geographical location information into a generative AI, which can analyze the data and select the optimal method.

[0147] The notification unit can analyze the user's social media activity and send relevant notifications at the time of notification. For example, the notification unit can extract information relevant to the notification from the user's social media activity and send the notification. The notification unit can also analyze the user's social media activity and determine the priority of notifications. The notification unit can also suggest notification methods based on the user's social media activity. This allows the system to provide the most suitable notification method based on the user's social media activity. Some or all of the above processing in the notification unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the notification unit can input the user's social media data into a generative AI, which can analyze the data and select the most suitable method.

[0148] The planning unit can estimate the user's emotions and adjust the planning method based on the estimated emotions. For example, if the user is stressed, the planning unit can provide a simple interface and minimize the planning steps. For example, if the user is relaxed, the planning unit can provide detailed planning options and suggest a customizable planning method. For example, if the user is in a hurry, the planning unit can prioritize voice input to enable rapid planning. This allows for the provision of a planning method that is tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the planning unit may be performed using or without generative AI. For example, the planning unit can input user emotion data into a generative AI, which can estimate the emotions and adjust the planning method based on the results.

[0149] The planning unit can analyze the user's past planning history and select the optimal method during the planning process. For example, the planning unit can propose the optimal planning method based on the user's past planning history. For example, the planning unit can also propose a planning method to reduce unnecessary steps based on the user's past planning history. For example, the planning unit can analyze the user's past planning history and select the most efficient planning method. This allows the system to provide the optimal planning method based on the user's past planning history. Some or all of the above-described processes in the planning unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the planning unit can input past planning history data into a generation AI, which can then analyze the data and select the optimal method.

[0150] The planning unit can perform filtering based on the user's current living situation and areas of interest during the planning process. For example, the planning unit can propose the optimal planning method based on the user's current living situation. The planning unit can also propose a planning method based on the user's areas of interest. For example, the planning unit can analyze the user's current living situation and areas of interest and select the optimal planning method. This allows the system to provide the optimal planning method based on the user's current living situation and areas of interest. Some or all of the above-described processes in the planning unit may be performed using a generative AI, or they may not be performed using a generative AI. For example, the planning unit can input user living situation and area of ​​interest data into a generative AI, which can then analyze the data and select the optimal filtering method.

[0151] The planning unit can estimate the user's emotions and determine the priority of the planning based on the estimated emotions. For example, if the user is stressed, the planning unit will prioritize displaying important planning items. For example, if the user is relaxed, the planning unit can also display a detailed breakdown of the planning items and determine their priority. For example, if the user is in a hurry, the planning unit can also prioritize displaying the most important planning items. This allows for prioritizing planning according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI 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 generative AI or not. For example, the planning unit can input user emotion data into a generative AI, the generative AI can estimate the emotions, and the planning priority can be determined based on the results.

[0152] The planning unit can prioritize highly relevant plans by considering the user's geographical location during the planning process. For example, if the user is in a specific region, the planning unit will prioritize planning for that region. If the user is traveling, the planning unit can also prioritize planning for their travel destination. If the user is at home, the planning unit can also prioritize planning for their home. This allows the system to provide the optimal planning method based on the user's geographical location. Some or all of the above processing in the planning unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the planning unit can input the user's geographical location information into a generative AI, which can then analyze the data and select the optimal method.

[0153] The planning unit can analyze the user's social media activity and create a relevant plan during the planning process. For example, the planning unit can extract information relevant to planning from the user's social media activity and create a plan. The planning unit can also analyze the user's social media activity and determine priorities relevant to planning. For example, the planning unit can propose a planning method based on the user's social media activity. This allows the planning unit to provide the optimal planning method based on the user's social media activity. Some or all of the above processes in the planning unit may be performed using generative AI, or they may not. For example, the planning unit can input the user's social media data into a generative AI, which can analyze the data and select the optimal method.

[0154] The support unit can estimate the user's emotions and adjust its support methods based on the estimated emotions. For example, if the user is stressed, the support unit can provide a simple interface and minimize the support procedure. If the user is relaxed, the support unit can also provide detailed support options and suggest customizable support methods. If the user is in a hurry, the support unit can prioritize voice input to provide support quickly. This allows for the provision of support methods tailored to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the support unit may be performed using or without generative AI. For example, the support unit can input user emotion data into a generative AI, which can estimate the emotions and adjust its support methods based on the results.

[0155] The support unit can analyze the user's past support history to select the optimal method during support. For example, the support unit can propose the optimal support method based on the user's past support history. For example, the support unit can also propose support methods to reduce unnecessary steps based on the user's past support history. For example, the support unit can analyze the user's past support history and select the most efficient support method. This allows the support unit to provide the optimal support method based on the user's past support history. Some or all of the above processes in the support unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the support unit can input past support history data into a generation AI, which can analyze the data and select the optimal method.

[0156] The support unit can filter data based on the user's current living situation and areas of interest during support. For example, the support unit can suggest the optimal support method based on the user's current living situation. The support unit can also suggest a support method based on the user's areas of interest. The support unit can also analyze the user's current living situation and areas of interest and select the optimal support method. This allows the support unit to provide the optimal support method based on the user's current living situation and areas of interest. Some or all of the above processing in the support unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the support unit can input data on the user's living situation and areas of interest into a generative AI, which can then analyze the data and select the optimal filtering method.

[0157] The support unit can estimate the user's emotions and determine the priority of support based on the estimated emotions. For example, if the user is stressed, the support unit will prioritize displaying important support. For example, if the user is relaxed, the support unit can also display a detailed breakdown of support and determine its priority. For example, if the user is in a hurry, the support unit can also prioritize displaying the most important support. This allows for the provision of support prioritization 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 support unit may be performed using generative AI or not. For example, the support unit can input user emotion data into a generative AI, the generative AI can estimate the emotions, and the support priority can be determined based on the result.

[0158] The support unit can prioritize highly relevant support by considering the user's geographical location during support. For example, if the user is in a specific region, the support unit will prioritize support in that region. If the user is traveling, the support unit can also prioritize support at their travel destination. If the user is at home, the support unit can also prioritize support at their home. This allows the support unit to provide the most appropriate support method based on the user's geographical location. Some or all of the above processing in the support unit may be performed using or without a generative AI. For example, the support unit can input the user's geographical location information into a generative AI, which can then analyze the data and select the optimal method.

[0159] The support unit can analyze the user's social media activity and provide relevant support during support sessions. For example, the support unit can extract support-related information from the user's social media activity and provide support. The support unit can also analyze the user's social media activity and determine support-related priorities. For example, the support unit can propose support methods based on the user's social media activity. This allows the support unit to provide the optimal support method based on the user's social media activity. Some or all of the above processes in the support unit may be performed using or without a generative AI. For example, the support unit can input the user's social media data into a generative AI, which can analyze the data and select the optimal method.

[0160] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0161] The household finance and life planning system may also include a health management section. This section can collect user health data and provide advice related to household finance and life planning. For example, it can analyze user exercise data and suggest a budget for maintaining health. It can also collect user dietary data and suggest ways to manage food expenses to maintain a healthy diet. Furthermore, based on the user's health checkup results, the health management section can predict future medical expenses and suggest appropriate insurance plans. This supports household finance and life planning based on the user's health status.

[0162] The household budgeting and life planning system can also include an entertainment management section. This section can manage the user's spending related to entertainment activities and suggest optimal entertainment plans. For example, it can analyze the user's movie viewing and music streaming service usage and suggest the most suitable subscription plan. It can also manage the user's travel plans and support the optimization of travel expenses. Furthermore, it can provide information on relevant events and activities based on the user's hobbies and interests. This allows for efficient management of spending related to the user's entertainment activities and supports a fulfilling lifestyle.

[0163] The household finance and life planning system can also include an education management section. This section can manage the user's education-related expenses and propose optimal education plans. For example, it can analyze the user's children's education costs and provide information on suitable education loans and scholarships. It can also manage the user's self-improvement and skill development expenses and provide information on suitable online courses and seminars. Furthermore, based on the user's educational goals, the education management section can create long-term education plans and predict future education costs. This allows for efficient management of the user's education-related expenses and supports the achievement of educational goals.

[0164] The household budgeting and life planning system may also include an environmental management section. This section can manage the user's environmentally related spending and support a sustainable lifestyle. For example, it can analyze the user's energy consumption data and propose an optimal energy-saving plan. It can also manage the user's recycling activities and optimize recycling-related spending. Furthermore, it can analyze the user's use of environmentally friendly products and services and suggest sustainable options. This allows for efficient management of the user's environmentally related spending and supports a sustainable lifestyle.

[0165] The household budgeting and life planning system can also include a community management section. This section can manage the user's community-related spending and support their engagement with the community. For example, it can manage the user's participation in local events and volunteer activities and suggest optimal participation plans. It can also manage the user's donations and support activities to the community and suggest optimal donation plans. Furthermore, it can provide information relevant to the user's community and strengthen their engagement with the community. This allows for efficient management of the user's community-related spending and supports their engagement with the community.

[0166] The household finance management and life planning system may also include an emotion estimation unit. This unit can estimate the user's emotions and adjust the household finance management and life planning methods based on those emotions. For example, if the user is stressed, the emotion estimation unit can provide a simple interface and minimize the steps involved in household finance management and life planning. If the user is relaxed, the emotion estimation unit can also provide detailed management options and suggest customizable methods. Furthermore, if the user is in a hurry, the emotion estimation unit can prioritize voice input to enable quick household finance management and life planning. This allows for the provision of household finance management and life planning methods tailored to the user's emotions.

[0167] The household finance management and life planning system may also include an emotion estimation unit. This unit can estimate the user's emotions and adjust the notification method based on the estimated emotion. For example, if the user is stressed, the emotion estimation unit can provide a simple interface and minimize the notification process. If the user is relaxed, it can also provide detailed notification options and suggest customizable notification methods. Furthermore, if the user is in a hurry, the emotion estimation unit can prioritize voice input to provide quick notifications. This allows for notification methods tailored to the user's emotions.

[0168] The household budgeting and life planning system may also include an emotion estimation unit. This unit can estimate the user's emotions and adjust the point management method based on those emotions. For example, if the user is stressed, the emotion estimation unit can provide a simple interface and minimize the point management steps. If the user is relaxed, the emotion estimation unit can also provide detailed management options and suggest customizable methods. Furthermore, if the user is in a hurry, the emotion estimation unit can prioritize voice input to enable quick point management. This allows for a point management method tailored to the user's emotions.

[0169] The household finance management and life planning system may also include an emotion estimation unit. This unit can estimate the user's emotions and adjust the data utilization optimization method based on the estimated emotions. For example, if the user is stressed, the emotion estimation unit can provide a simple interface and minimize the data utilization optimization steps. If the user is relaxed, the emotion estimation unit can also provide detailed optimization options and suggest customizable methods. Furthermore, if the user is in a hurry, the emotion estimation unit can prioritize voice input to quickly optimize data utilization. This allows for the provision of data utilization optimization methods tailored to the user's emotions.

[0170] The household finance management and life planning system may also include an emotion estimation unit. This unit can estimate the user's emotions and adjust the life event planning method based on those emotions. For example, if the user is stressed, the emotion estimation unit can provide a simple interface and minimize the life event planning steps. If the user is relaxed, the emotion estimation unit can also provide detailed planning options and suggest customizable methods. Furthermore, if the user is in a hurry, the emotion estimation unit can prioritize voice input to allow for quick life event planning. This allows for a life event planning method tailored to the user's emotions.

[0171] The following briefly describes the processing flow for example form 2.

[0172] Step 1: The management department manages communication charges. For example, it can centrally manage users' communication charges and have a detailed understanding of monthly communication expenses. It can also analyze communication service pricing plans and suggest the most suitable plan. Furthermore, it can manage communication payment history and provide advice to reduce unnecessary expenses. Step 2: The points management unit manages the points. For example, it integrates with electronic payment services to manage point usage history and balances. Furthermore, it can suggest advantageous ways to use points to support their effective utilization. It can also manage point expiration dates and send notifications to prevent them from expiring. Step 3: The optimization unit optimizes data usage. For example, it can analyze users' data usage and propose the optimal plan. Furthermore, it can provide information on how to save data usage and offer special promotions. It can also analyze data usage patterns and provide advice on reducing unnecessary data usage. Step 4: The integration unit connects with smart home appliances. For example, it connects with smart home appliances (smart speakers, IoT devices, etc.) to provide support for more convenient and efficient household budget management. Furthermore, it can provide an interface to simplify the operation of smart home appliances. It can also analyze the usage of smart home appliances and suggest the optimal way to use them. Step 5: The planning department plans life events. For example, it can collaborate with life planning services (insurance, mortgages, etc.) to create plans for future life events such as marriage, children's education, home purchase, and retirement finances. Furthermore, it can provide tools to support life event planning. It can also collect and provide users with the information necessary for life event planning. Step 6: The shared section enables family sharing. For example, the entire family can share an account and check the household finances in real time. Furthermore, it can provide tools to support budgeting and spending management for the entire family. It can also analyze the family's income and spending patterns and provide customized advice.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] Each of the multiple elements mentioned above, including the management unit, point management unit, optimization unit, collaboration unit, planning unit, sharing unit, analysis unit, notification unit, planning unit, and support unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the management unit manages communication charges using the control unit 46A of the smart device 14 and proposes the optimal plan using the specific processing unit 290 of the data processing unit 12. The point management unit manages point usage history and balance using the control unit 46A of the smart device 14 and proposes advantageous usage using the specific processing unit 290 of the data processing unit 12. The optimization unit analyzes data usage using the control unit 46A of the smart device 14 and proposes the optimal plan using the specific processing unit 290 of the data processing unit 12. The collaboration unit supports household budget management by collaborating with smart home appliances using the control unit 46A of the smart device 14. The planning unit plans life events using the specific processing unit 290 of the data processing unit 12. The sharing unit allows all family members to share an account using the control unit 46A of the smart device 14 and check the household budget status in real time. The analysis unit analyzes the user's income and expenditure data using the specific processing unit 290 of the data processing device 12 and predicts future income and expenses. The notification unit sends notifications based on the user's financial activities using the specific processing unit 290 of the data processing device 12. The planning unit generates a life event plan using the specific processing unit 290 of the data processing device 12. The support unit provides a chatbot that answers user questions using the control unit 46A of the smart device 14. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0177] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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).

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.).

[0189] 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.

[0190] 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.

[0191] 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.

[0192] Each of the multiple elements mentioned above, including the management unit, point management unit, optimization unit, collaboration unit, planning unit, sharing unit, analysis unit, notification unit, planning unit, and support unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the management unit manages communication charges using the control unit 46A of the smart glasses 214 and proposes the optimal plan using the specific processing unit 290 of the data processing unit 12. The point management unit manages point usage history and balance using the control unit 46A of the smart glasses 214 and proposes advantageous usage using the specific processing unit 290 of the data processing unit 12. The optimization unit analyzes data usage using the control unit 46A of the smart glasses 214 and proposes the optimal plan using the specific processing unit 290 of the data processing unit 12. The collaboration unit collaborates with smart home appliances using the control unit 46A of the smart glasses 214 to support household budget management. The planning unit plans life events using the specific processing unit 290 of the data processing unit 12. The sharing unit allows all family members to share an account via the control unit 46A of the smart glasses 214 and check the household financial situation in real time. The analysis unit analyzes the user's income and expenditure data via the specific processing unit 290 of the data processing device 12 and predicts future income and expenditure. The notification unit sends notifications based on the user's financial activities via the specific processing unit 290 of the data processing device 12. The planning unit generates a life event plan via the specific processing unit 290 of the data processing device 12. The support unit provides a chatbot that answers user questions via the control unit 46A of the smart glasses 214. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0193] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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).

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.).

[0205] 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.

[0206] 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.

[0207] 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.

[0208] Each of the multiple elements mentioned above, including the management unit, point management unit, optimization unit, collaboration unit, planning unit, sharing unit, analysis unit, notification unit, planning unit, and support unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the management unit manages communication charges using the control unit 46A of the headset terminal 314 and proposes the optimal plan using the specific processing unit 290 of the data processing unit 12. The point management unit manages point usage history and balance using the control unit 46A of the headset terminal 314 and proposes advantageous usage using the specific processing unit 290 of the data processing unit 12. The optimization unit analyzes data usage using the control unit 46A of the headset terminal 314 and proposes the optimal plan using the specific processing unit 290 of the data processing unit 12. The collaboration unit collaborates with smart home appliances using the control unit 46A of the headset terminal 314 to support household budget management. The planning unit plans life events using the specific processing unit 290 of the data processing unit 12. The sharing unit allows all family members to share an account via the control unit 46A of the headset terminal 314 and check the household financial situation in real time. The analysis unit analyzes the user's income and expenditure data via the specific processing unit 290 of the data processing device 12 and predicts future income and expenditure. The notification unit sends notifications based on the user's financial activities via the specific processing unit 290 of the data processing device 12. The planning unit generates a life event plan via the specific processing unit 290 of the data processing device 12. The support unit provides a chatbot that answers user questions via the control unit 46A of the headset terminal 314. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0209] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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.

[0214] 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).

[0215] 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.

[0216] 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.

[0217] 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.

[0218] 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.

[0219] 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.

[0220] 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.

[0221] 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.).

[0222] 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.

[0223] 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.

[0224] 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.

[0225] Each of the multiple elements mentioned above, including the management unit, point management unit, optimization unit, collaboration unit, planning unit, sharing unit, analysis unit, notification unit, planning unit, and support unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the management unit manages communication charges using the control unit 46A of the robot 414 and proposes the optimal plan using the specific processing unit 290 of the data processing unit 12. The point management unit manages point usage history and balance using the control unit 46A of the robot 414 and proposes advantageous usage using the specific processing unit 290 of the data processing unit 12. The optimization unit analyzes data usage using the control unit 46A of the robot 414 and proposes the optimal plan using the specific processing unit 290 of the data processing unit 12. The collaboration unit supports household budget management by collaborating with smart home appliances using the control unit 46A of the robot 414. The planning unit plans life events using the specific processing unit 290 of the data processing unit 12. The sharing unit allows all family members to share an account using the control unit 46A of the robot 414 and check the household budget status in real time. The analysis unit analyzes the user's income and expenditure data using the specific processing unit 290 of the data processing device 12 and predicts future income and expenditure. The notification unit sends notifications based on the user's financial activities using the specific processing unit 290 of the data processing device 12. The planning unit generates a life event plan using the specific processing unit 290 of the data processing device 12. The support unit provides a chatbot that answers user questions using the control unit 46A of the robot 414. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0226] 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.

[0227] 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.

[0228] 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.

[0229] 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.

[0230] 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.

[0231] 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."

[0232] 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.

[0233] 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.

[0234] 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.

[0235] 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.

[0236] 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.

[0237] 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.

[0238] 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.

[0239] 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.

[0240] 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.

[0241] 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.

[0242] 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.

[0243] 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.

[0244] (Note 1) The management department that manages communication charges, A point management unit that manages the points managed by the aforementioned management unit, An optimization unit that optimizes the use of data managed by the aforementioned point management unit, A collaboration unit that works in conjunction with smart home appliances optimized by the aforementioned optimization unit, The Planning Department plans life events coordinated by the aforementioned Collaboration Department, The system includes a shared area for family use as planned by the planning unit. A system characterized by the following features. (Note 2) It is equipped with an analysis unit that performs data analysis and prediction. The system described in Appendix 1, characterized by the features described herein. (Note 3) It includes a notification unit that provides personalized notifications. The system described in Appendix 1, characterized by the features described herein. (Note 4) We have a planning department that handles life event planning. The system described in Appendix 1, characterized by the features described herein. (Note 5) It has a support department that provides customer support. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned management department, The system estimates the user's emotions and adjusts the communication fee management method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned management department, Analyze past communication charge data to select the optimal management method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned management department, When managing communication charges, filtering is performed based on the user's contract plan. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned management department, The system estimates the user's emotions and determines the priority of communication charges based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned management department, When managing communication charges, the system prioritizes and manages charges that are highly relevant to the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned management department, When managing communication charges, analyze users' social media activity and manage related charges. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned point management unit, The system estimates the user's emotions and adjusts the point management method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned point management unit, When managing points, the system analyzes the user's past point usage history to select the optimal management method. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned point management unit, When managing points, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned point management unit, The system estimates the user's emotions and determines the priority of points based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned point management unit, When managing points, the system prioritizes managing points that are highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned point management unit, When managing points, analyze users' social media activity and manage the points related to that activity. The system described in Appendix 1, characterized by the features described herein. (Note 18) The optimization unit, We estimate user sentiment and adjust how data usage is optimized based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 19) The optimization unit, When optimizing data usage, we analyze the user's past data usage history to select the most suitable method. The system described in Appendix 1, characterized by the features described herein. (Note 20) The optimization unit, When optimizing data usage, filtering is performed based on the user's contract plan. The system described in Appendix 1, characterized by the features described herein. (Note 21) The optimization unit, We estimate user sentiment and prioritize data usage based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 22) The optimization unit, When optimizing data usage, the system prioritizes optimizing highly relevant data by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 23) The optimization unit, When optimizing data usage, we analyze users' social media activity and optimize relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned linkage unit is, It estimates the user's emotions and adjusts the method of interaction with smart home appliances based on the estimated user emotions. The system according to appended claim 1, characterized in that... (Appended claim 25) When the cooperation unit... Analyzes the user's past usage history to select an optimal cooperation method when cooperating with smart home appliances. The system according to appended claim 1, characterized in that... (Appended claim 26) When the cooperation unit... Performs filtering based on the user's current living situation and areas of interest when cooperating with smart home appliances. The system according to appended claim 1, characterized in that... (Appended claim 27) When the cooperation unit... Estimates the user's emotions and determines the priority of smart home appliances based on the estimated emotions of the user. The system according to appended claim 1, characterized in that... (Appended claim 28) When the cooperation unit... Considers the user's geographical location information and preferentially cooperates with highly relevant home appliances when cooperating with smart home appliances. The system according to appended claim 1, characterized in that... (Appended claim 29) When the cooperation unit... Analyzes the user's social media activities and cooperates with relevant home appliances when cooperating with smart home appliances. The system according to appended claim 1, characterized in that... (Appended claim 30) When the planning unit... Estimates the user's emotions and adjusts the planning method of life events based on the estimated emotions of the user. The system according to appended claim 1, characterized in that... (Appended claim 31) When the planning unit... Analyzes the user's past planning history to select an optimal method when planning life events. The system according to appended claim 1, characterized in that... (Appended claim 32) When the planning unit... When planning life events, filtering is performed based on the user's current living situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned planning department, It estimates the user's emotions and determines the priority of life events based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned planning department, When planning life events, the system prioritizes highly relevant events by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned planning department, When planning life events, analyze users' social media activity and plan relevant events. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned shared portion is, It estimates the user's emotions and adjusts the way family sharing is done based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned shared portion is, When sharing with family members, the system analyzes the user's past sharing history to select the optimal method. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned shared portion is, When sharing with family members, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned shared portion is, It estimates the user's emotions and determines the priority of family sharing based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned shared portion is, When sharing within a family, prioritize sharing highly relevant information by considering the user's geographical location information The system according to Appendix 1, characterized by this (Appendix 41) The sharing unit When sharing within a family, analyze the user's social media activities and share relevant information The system according to Appendix 1, characterized by this (Appendix 42) The analysis unit Estimate the user's emotion and adjust the method of data analysis based on the estimated user emotion The system according to Appendix 1, characterized by this (Appendix 43) The analysis unit When performing data analysis, analyze the user's past data history to select an optimal method The system according to Appendix 1, characterized by this (Appendix 44) The analysis unit When performing data analysis, perform filtering based on the user's current living situation and areas of interest The system according to Appendix 1, characterized by this (Appendix 45) The analysis unit Estimate the user's emotion and determine the priority of data analysis based on the estimated user emotion The system according to Appendix 1, characterized by this (Appendix 46) The analysis unit When performing data analysis, preferentially analyze highly relevant data by considering the user's geographical location information The system according to Appendix 1, characterized by this (Appendix 47) The analysis unit When performing data analysis, analyze the user's social media activities and analyze relevant data The system according to Appendix 1, characterized by this (Appendix 48) The notification unit It estimates the user's emotions and adjusts the notification method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 49) The aforementioned notification unit, When sending a notification, the system analyzes the user's past notification history to select the most suitable method. The system described in Appendix 1, characterized by the features described herein. (Note 50) The aforementioned notification unit, When sending notifications, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 51) The aforementioned notification unit, It estimates the user's emotions and prioritizes notifications based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 52) The aforementioned notification unit, When sending notifications, the system prioritizes relevant notifications by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 53) The aforementioned notification unit, When sending notifications, the system analyzes the user's social media activity and sends relevant notifications. The system described in Appendix 1, characterized by the features described herein. (Note 54) The aforementioned planning unit, It estimates the user's emotions and adjusts the planning method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 55) The aforementioned planning unit, During the planning stage, we analyze the user's past planning history to select the optimal method. The system described in Appendix 1, characterized by the features described herein. (Note 56) The aforementioned planning unit, During the planning stage, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 57) The aforementioned planning unit, The system estimates user emotions and determines planning priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 58) The aforementioned planning unit, During the planning stage, we prioritize highly relevant plans by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 59) The aforementioned planning unit, During the planning stage, we analyze users' social media activity and develop relevant plans. The system described in Appendix 1, characterized by the features described herein. (Note 60) The aforementioned support unit is It estimates the user's emotions and adjusts the support method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 61) The aforementioned support unit is During support, we analyze the user's past support history to select the most appropriate method. The system described in Appendix 1, characterized by the features described herein. (Note 62) The aforementioned support unit is During support, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 63) The aforementioned support unit is The system estimates the user's emotions and determines support priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 64) The aforementioned support unit is When providing support, we prioritize relevant support by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 65) The aforementioned support unit is During support, we analyze the user's social media activity and provide relevant support. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0245] 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. The management department that manages communication charges, A point management unit that manages the points managed by the aforementioned management unit, An optimization unit that optimizes the use of data managed by the aforementioned point management unit, A collaboration unit that works in conjunction with smart home appliances optimized by the aforementioned optimization unit, The Planning Department plans life events coordinated by the aforementioned Collaboration Department, The system includes a shared area for family use as planned by the planning unit. A system characterized by the following features.

2. It is equipped with an analysis unit that performs data analysis and prediction. The system according to feature 1.

3. It includes a notification unit that provides personalized notifications. The system according to feature 1.

4. We have a planning department that handles life event planning. The system according to feature 1.

5. It has a support department that provides customer support. The system according to feature 1.

6. The aforementioned management department, The system estimates the user's emotions and adjusts the communication fee management method based on the estimated user emotions. The system according to feature 1.

7. The aforementioned management department, Analyze past communication charge data to select the optimal management method. The system according to feature 1.

8. The aforementioned management department, When managing communication charges, filtering is performed based on the user's contract plan. The system according to feature 1.

9. The aforementioned management department, The system estimates the user's emotions and determines the priority of communication charges based on those estimated emotions. The system according to feature 1.

10. The aforementioned management department, When managing communication charges, the system prioritizes and manages charges that are highly relevant to the user's geographical location. The system according to feature 1.

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