system

Abstract

The system according to this embodiment aims to provide advice on saving and investing in household finances and to offer suggestions for enjoying the returns obtained. [Solution] The system according to the embodiment comprises a reception unit, a savings unit, an investment unit, and a benefit unit. The reception unit inputs income and expenditure information. The savings unit provides savings advice based on the information input by the reception unit. The investment unit makes investment recommendations based on the advice provided by the savings unit. The benefit unit makes suggestions for enjoying the returns obtained by the investment unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, although advice on savings and investments is provided in household management, there is a problem that proposals on how to enjoy the obtained returns are lacking.

[0005] The system according to the embodiment aims to provide advice on savings and investments in household management and make proposals for enjoying the obtained returns.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a savings unit, an investment unit, and a benefit unit. The reception unit inputs income and expenditure information. The savings unit provides savings advice based on the information input by the reception unit. The investment unit makes investment recommendations based on the advice provided by the savings unit. The benefit unit makes suggestions for enjoying the returns obtained by the investment unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide advice on saving and investing in household finances and make suggestions for enjoying the returns obtained. [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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F manages communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

[0018] [[ID=(18)]]The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26 . The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 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 system according to an embodiment of the present invention is a system that uses a household budgeting function as an entry point and supports three categories: saving, increasing, and enjoying. In this household finance management system, the user inputs income and expense information into the household budget, a generating AI analyzes that information and provides advice for saving, further recommends investments, and makes suggestions for "enjoying" the returns obtained. Conventional apps did not have a "enjoying" category, but the present invention also focuses on the part of returning the returns obtained from saving and investing. First, the user inputs income and expense information into the household budget. For example, the user inputs details of income and expenses. This information is input into the generating AI. Next, the generating AI analyzes the input information and provides advice for saving. For example, it suggests ways to reduce unnecessary expenses and efficient saving methods. This allows the user to save effectively. Furthermore, the generating AI also makes recommendations for investments. For example, it suggests appropriate investment destinations based on the user's income and expense information. This allows the user to make effective investments to increase their income. Finally, it makes suggestions for "enjoying" the returns obtained. For example, it suggests how to spend money that can be used for travel or hobbies. This allows users to effectively utilize the returns they earn from saving and investing. This system enables users to efficiently manage their household finances and provides balanced support across three categories: saving, increasing wealth, and enjoying life. Thus, the household finance management system can provide balanced support across these three categories based on the user's income and expenditure information.

[0029] The household finance management system according to this embodiment comprises a reception unit, a saving unit, an investment unit, and an entertainment unit. The reception unit allows the user to input income and expense information. This information includes, but is not limited to, details of income and expenses. The reception unit allows, for example, the user to manually input income and expense information. The reception unit can also automatically acquire bank account and credit card information and input it as income and expense information. Furthermore, the reception unit can input income and expense information using voice input or image recognition technology. For example, the user can take a picture of a receipt, and the image can be analyzed to automatically input income and expense information. The saving unit uses a generating AI to provide saving advice based on the income and expense information input by the reception unit. This saving advice includes, but is not limited to, methods for reducing unnecessary spending and efficient saving methods. For example, the generating AI can analyze the user's spending patterns, identify unnecessary spending, and suggest ways to reduce it. The generating AI can also suggest efficient saving methods based on the user's lifestyle. Furthermore, the generating AI can provide a specific action plan for saving based on the user's income and expense information. For example, the Generative AI suggests specific ways for users to reduce certain spending items. The Investment Department uses the Generative AI to make investment recommendations based on the advice provided by the Savings Department. Investment recommendations include, but are not limited to, suggesting appropriate investment targets and setting investment amounts. For example, the Generative AI analyzes the user's income and expenditure information and suggests investment targets that minimize risk. The Generative AI can also suggest the optimal investment strategy based on the user's investment goals. Furthermore, the Generative AI can analyze the user's past investment history and suggest the optimal investment targets. For example, the Generative AI suggests specific investment targets that minimize risk based on the user's past investment history. The Enjoyment Department uses the Generative AI to make suggestions for "enjoying" the returns obtained by the Investment Department. Suggestions for enjoyment include, but are not limited to, how to spend money on travel or hobbies. For example, the Generative AI analyzes the user's income and expenditure information and suggests ways to enjoy oneself within a budget.Furthermore, the generating AI can suggest the most suitable ways to enjoy oneself based on the user's hobbies and interests. In addition, the generating AI can suggest region-specific ways to enjoy oneself, taking into account the user's geographical location. For example, if the generating AI is in a specific region, it will suggest ways to enjoy oneself that are unique to that region. This allows the household management system according to this embodiment to provide balanced support across three categories—saving, investing, and enjoying oneself—based on the user's income and expenditure information.

[0030] The reception desk can analyze the user's past income and expense history and select the optimal input method. For example, the reception desk can automatically display income and expense items that the user has frequently entered in the past as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest income and expense items to be entered at specific time periods based on the user's past income and expense history. In this way, the optimal input method can be selected by analyzing the user's past income and expense history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past income and expense data into a generating AI and have the generating AI select the optimal input method.

[0031] The reception unit can filter income and expense information based on the user's current living situation and areas of interest when the user enters this information. For example, the reception unit can prioritize displaying relevant income and expense items according to the user's current living situation. The reception unit can also simplify input by filtering relevant income and expense items based on the user's areas of interest. Furthermore, the reception unit can adjust the input order of income and expense items based on the user's living situation and areas of interest. This simplifies input by filtering income and expense information based on the user's living situation and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's living situation data into a generating AI and have the generating AI perform the filtering of income and expense information.

[0032] The reception unit can prioritize inputting highly relevant information based on the user's geographical location when inputting income and expense information. For example, if the user is in a specific region, the reception unit will prioritize displaying income and expense items related to that region. The reception unit can also filter relevant income and expense items based on the user's geographical location, simplifying the input process. Furthermore, the reception unit can adjust the input order of income and expense items based on the user's geographical location. This simplifies input by prioritizing the input of income and expense information based on the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location data into a generating AI and have the generating AI prioritize the input of highly relevant information.

[0033] The reception unit can analyze the user's social media activity and input relevant information when entering income and expense information. For example, the reception unit can automatically display relevant income and expense items as suggestions based on the user's social media activity. The reception unit can also filter relevant income and expense items based on the user's social media activity, simplifying the input process. Furthermore, the reception unit can adjust the input order of income and expense items based on the user's social media activity. This simplifies the input process by inputting income and expense information based on the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's social media data into a generating AI and have the generating AI input the relevant income and expense information.

[0034] The savings department, when providing savings advice, can analyze in detail how to reduce unnecessary spending and propose specific reduction methods. For example, the savings department can analyze the user's past spending history to identify unnecessary spending items and propose ways to reduce them. It can also analyze the user's current spending patterns and propose efficient reduction methods. Furthermore, the savings department can propose specific reduction methods based on the user's lifestyle. In this way, by analyzing in detail how to reduce unnecessary spending, it can propose specific reduction methods. Some or all of the above processes in the savings department may be performed using AI, for example, or not using AI. For example, the savings department can input the user's spending data into a generating AI and have the generating AI perform the analysis and proposal of ways to reduce unnecessary spending.

[0035] The savings unit can propose individually customized savings methods based on the user's income and expenditure information when providing savings advice. For example, the savings unit can propose individually customized savings methods based on the user's income and expenditure information. The savings unit can also analyze the user's income and expenditure information and propose the optimal savings method. Furthermore, the savings unit can propose specific savings measures based on the user's income and expenditure information. This makes it possible to save more effectively by proposing individually customized savings methods based on the user's income and expenditure information. Some or all of the above processing in the savings unit may be performed using AI, for example, or without AI. For example, the savings unit can input the user's income and expenditure data into a generating AI and have the generating AI execute the proposal of individually customized savings methods.

[0036] The savings unit can propose region-specific savings methods by considering the user's geographical location when providing savings advice. For example, if the user is in a specific region, the savings unit will propose region-specific savings methods. The savings unit can also filter and propose region-specific savings methods based on the user's geographical location. Furthermore, the savings unit can propose the optimal savings method based on the user's geographical location. This makes it possible to achieve more effective savings by proposing region-specific savings methods based on the user's geographical location. Some or all of the above processing in the savings unit may be performed using AI, for example, or without AI. For example, the savings unit can input the user's geographical location data into a generating AI and have the generating AI execute the proposal of region-specific savings methods.

[0037] The savings unit can analyze the user's social media activity and suggest relevant savings methods when providing savings advice. For example, the savings unit can automatically suggest relevant savings methods based on the user's social media activity. The savings unit can also filter and suggest relevant savings methods based on the user's social media activity. Furthermore, the savings unit can suggest the optimal savings method based on the user's social media activity. This makes it possible to save more effectively by suggesting relevant savings methods based on the user's social media activity. Some or all of the above processing in the savings unit may be performed using AI, for example, or without AI. For example, the savings unit can input the user's social media data into a generating AI and have the generating AI suggest relevant savings methods.

[0038] The investment department can propose investments that minimize risk based on the user's income and expense information when providing investment recommendations. For example, the investment department can analyze the user's income and expense information and propose investments that minimize risk. The investment department can also propose the optimal investment based on the user's income and expense information. Furthermore, the investment department can propose specific investments that minimize risk based on the user's income and expense information. This makes safer investment possible by proposing investments that minimize risk based on the user's income and expense information. Some or all of the above processing in the investment department may be performed using AI, for example, or without AI. For example, the investment department can input the user's income and expense data into a generating AI and have the generating AI execute a proposal for investments that minimize risk.

[0039] The investment department can analyze a user's past investment history and propose the optimal investment strategy when providing investment recommendations. For example, the investment department can analyze a user's past investment history and propose the optimal investment strategy. The investment department can also propose the optimal investment destination based on the user's past investment history. Furthermore, the investment department can propose the optimal investment strategy based on the user's past investment history. In this way, the investment department can propose the optimal investment strategy by analyzing the user's past investment history. Some or all of the above processes in the investment department may be performed using AI, for example, or not using AI. For example, the investment department can input the user's investment history data into a generating AI and have the generating AI execute the proposal of the optimal investment strategy.

[0040] The investment department can propose region-specific investments by considering the user's geographical location when providing investment recommendations. For example, if the user is in a specific region, the investment department will propose region-specific investments. The investment department can also filter and propose region-specific investments based on the user's geographical location. Furthermore, the investment department can propose the most suitable investments based on the user's geographical location. This enables more effective investment by proposing region-specific investments based on the user's geographical location. Some or all of the above processing in the investment department may be performed using AI, for example, or without AI. For example, the investment department can input the user's geographical location data into a generating AI and have the generating AI execute region-specific investment proposals.

[0041] The investment department can analyze a user's social media activity and suggest relevant investment opportunities when providing investment recommendations. For example, the investment department can automatically suggest relevant investment opportunities based on the user's social media activity. It can also filter and suggest relevant investment opportunities based on the user's social media activity. Furthermore, the investment department can suggest the most suitable investment opportunities based on the user's social media activity. This enables more effective investment by suggesting relevant investment opportunities based on the user's social media activity. Some or all of the above processing in the investment department may be performed using AI, for example, or without AI. For example, the investment department can input the user's social media data into a generating AI and have the generating AI suggest relevant investment opportunities.

[0042] The "Enjoyment Department" can suggest the most suitable way to enjoy oneself based on the user's financial information when providing suggestions for enjoyment. For example, the Enjoyment Department can analyze the user's financial information and suggest ways to enjoy oneself within their budget. Furthermore, the Enjoyment Department can also suggest cost-effective ways to enjoy oneself based on the user's financial information. In addition, the Enjoyment Department can suggest ways to enjoy oneself while avoiding unnecessary spending based on the user's financial information. This allows for more effective enjoyment by suggesting the most suitable way to enjoy oneself based on the user's financial information. Some or all of the above processing in the Enjoyment Department may be performed using AI, for example, or without AI. For example, the Enjoyment Department can input the user's financial data into a generating AI and have the generating AI generate suggestions for the most suitable way to enjoy oneself.

[0043] The "Enjoyment Department" can analyze the user's past hobbies and activity history when providing suggestions for enjoyment, and propose the most suitable way to enjoy oneself. For example, the "Enjoyment Department" can analyze the user's past hobbies and activity history and propose related ways to enjoy oneself. Furthermore, the "Enjoyment Department" can also propose new ways to enjoy oneself based on the user's past hobbies and activity history. In addition, the "Enjoyment Department" can propose the most suitable way to enjoy oneself based on the user's past hobbies and activity history. In this way, by analyzing the user's past hobbies and activity history, the "Enjoyment Department" can propose the most suitable way to enjoy oneself. Some or all of the above processing in the "Enjoyment Department" may be performed using AI, for example, or not using AI. For example, the "Enjoyment Department" can input the user's hobby and activity history data into a generating AI and have the generating AI execute suggestions for the most suitable way to enjoy oneself.

[0044] The "Enjoyment Department" can suggest region-specific ways to enjoy activities by considering the user's geographical location when providing suggestions for activities. For example, if the user is in a specific region, the "Enjoyment Department" can suggest region-specific ways to enjoy activities. The "Enjoyment Department" can also filter and suggest region-specific ways to enjoy activities based on the user's geographical location. Furthermore, the "Enjoyment Department" can suggest the most optimal way to enjoy activities based on the user's geographical location. This allows for more effective enjoyment by suggesting region-specific ways to enjoy activities based on the user's geographical location. Some or all of the above processing in the "Enjoyment Department" may be performed using AI, for example, or without AI. For example, the "Enjoyment Department" can input the user's geographical location data into a generating AI and have the generating AI generate suggestions for region-specific ways to enjoy activities.

[0045] The "Enjoyment Department" can analyze a user's social media activity and suggest relevant ways to enjoy themselves when providing suggestions. For example, the Enjoyment Department can automatically suggest relevant ways to enjoy themselves based on the user's social media activity. The Enjoyment Department can also filter and suggest relevant ways to enjoy themselves based on the user's social media activity. Furthermore, the Enjoyment Department can suggest the most optimal way to enjoy themselves based on the user's social media activity. This allows for more effective ways to enjoy oneself by suggesting relevant ways to enjoy oneself based on the user's social media activity. Some or all of the above processing in the Enjoyment Department may be performed using AI, for example, or without AI. For example, the Enjoyment Department can input the user's social media data into a generating AI and have the generating AI perform the task of suggesting relevant ways to enjoy oneself.

[0046] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0047] The savings feature can analyze users' purchase history and provide discount information for specific stores and brands. For example, it can offer discount coupons for stores users frequently visit or notify them when products from a particular brand are on sale. It can also suggest stores where similar products can be purchased at a lower price based on the user's purchase history. This allows users to save money efficiently.

[0048] The investment department can also propose long-term investment strategies based on the user's occupation and career plan. For example, if the user has a stable job, it will propose high-risk investments, and conversely, if they have an unstable job, it will propose low-risk investments. It can also propose investment strategies that anticipate future income growth based on the user's career plan. In this way, it can provide the optimal investment strategy tailored to the user's occupation and career plan.

[0049] The "Enjoyment" section can also customize suggestions for activities based on the user's family structure and life stage. For example, it can suggest activities that the whole family can enjoy together for families with children, and suggest hobbies and trips that can be enjoyed alone for single users. It can also suggest romantic travel plans for newlyweds, depending on the user's life stage. This allows the service to provide the most suitable way for users to enjoy themselves based on their family structure and life stage.

[0050] The energy conservation section can acquire user energy consumption data and suggest efficient energy-saving methods. For example, it can analyze the user's electricity and gas usage and suggest ways to reduce wasteful energy consumption. It can also suggest ways to save energy during specific time periods based on energy consumption data. Furthermore, it can recommend the purchase of energy-efficient home appliances. This allows users to efficiently save energy.

[0051] The reception desk can analyze users' purchase history and automate the input of income and expense information. For example, it can automatically input frequently purchased items and services as income and expense information, reducing manual input. It can also predict specific expenditure items based on purchase history, simplifying input. Furthermore, it can suggest stores where similar products can be purchased at a lower price based on the user's purchase history. In this way, the input of income and expense information can be made more efficient by utilizing the user's purchase history.

[0052] The following briefly describes the processing flow for example form 1.

[0053] Step 1: The reception desk receives the user's income and expense information. This information includes details of income and expenses. The reception desk allows users to manually enter this information, or it can automatically retrieve bank account and credit card information and input it as income and expense information. It is also possible to input income and expense information using voice input or image recognition technology. For example, a user can take a picture of a receipt, and the system can analyze the image to automatically input the income and expense information. Step 2: The savings department uses a generating AI to provide savings advice based on the income and expense information entered by the reception department. This advice includes ways to reduce unnecessary spending and efficient saving methods. The generating AI analyzes the user's spending patterns, identifies unnecessary spending, and suggests ways to reduce it. It can also suggest efficient saving methods based on the user's lifestyle and provide concrete action plans. Step 3: The investment department uses a generative AI to provide investment recommendations based on the advice provided by the savings department. These recommendations include suggesting appropriate investment targets and setting investment amounts. The generative AI analyzes the user's income and expense information and suggests investment targets that minimize risk. It can also suggest the optimal investment strategy based on the user's investment goals and analyze past investment history to suggest the best investment targets. Step 4: The Enjoyment Unit uses a Generative AI to make suggestions for enjoying the returns obtained by the Investment Unit. These suggestions include how to spend money on travel and hobbies. The Generative AI analyzes the user's income and expenditure information and suggests ways to enjoy themselves within their budget. It can also suggest the most suitable ways to enjoy themselves based on the user's hobbies and interests, and can even suggest region-specific ways to enjoy themselves by considering geographical location information.

[0054] (Example of form 2) The household finance management system according to an embodiment of the present invention is a system that uses a household budgeting function as an entry point and supports three categories: saving, increasing, and enjoying. In this household finance management system, the user inputs income and expense information into the household budget, a generating AI analyzes that information and provides advice for saving, further recommends investments, and makes suggestions for "enjoying" the returns obtained. Conventional apps did not have a "enjoying" category, but the present invention also focuses on the part of returning the returns obtained from saving and investing. First, the user inputs income and expense information into the household budget. For example, the user inputs details of income and expenses. This information is input into the generating AI. Next, the generating AI analyzes the input information and provides advice for saving. For example, it suggests ways to reduce unnecessary expenses and efficient saving methods. This allows the user to save effectively. Furthermore, the generating AI also makes recommendations for investments. For example, it suggests appropriate investment destinations based on the user's income and expense information. This allows the user to make effective investments to increase their income. Finally, it makes suggestions for "enjoying" the returns obtained. For example, it suggests how to spend money that can be used for travel or hobbies. This allows users to effectively utilize the returns they earn from saving and investing. This system enables users to efficiently manage their household finances and provides balanced support across three categories: saving, increasing wealth, and enjoying life. Thus, the household finance management system can provide balanced support across these three categories based on the user's income and expenditure information.

[0055] The household finance management system according to this embodiment comprises a reception unit, a saving unit, an investment unit, and an entertainment unit. The reception unit allows the user to input income and expense information. This information includes, but is not limited to, details of income and expenses. The reception unit allows, for example, the user to manually input income and expense information. The reception unit can also automatically acquire bank account and credit card information and input it as income and expense information. Furthermore, the reception unit can input income and expense information using voice input or image recognition technology. For example, the user can take a picture of a receipt, and the image can be analyzed to automatically input income and expense information. The saving unit uses a generating AI to provide saving advice based on the income and expense information input by the reception unit. This saving advice includes, but is not limited to, methods for reducing unnecessary spending and efficient saving methods. For example, the generating AI can analyze the user's spending patterns, identify unnecessary spending, and suggest ways to reduce it. The generating AI can also suggest efficient saving methods based on the user's lifestyle. Furthermore, the generating AI can provide a specific action plan for saving based on the user's income and expense information. For example, the Generative AI suggests specific ways for users to reduce certain spending items. The Investment Department uses the Generative AI to make investment recommendations based on the advice provided by the Savings Department. Investment recommendations include, but are not limited to, suggesting appropriate investment targets and setting investment amounts. For example, the Generative AI analyzes the user's income and expenditure information and suggests investment targets that minimize risk. The Generative AI can also suggest the optimal investment strategy based on the user's investment goals. Furthermore, the Generative AI can analyze the user's past investment history and suggest the optimal investment targets. For example, the Generative AI suggests specific investment targets that minimize risk based on the user's past investment history. The Enjoyment Department uses the Generative AI to make suggestions for "enjoying" the returns obtained by the Investment Department. Suggestions for enjoyment include, but are not limited to, how to spend money on travel or hobbies. For example, the Generative AI analyzes the user's income and expenditure information and suggests ways to enjoy oneself within a budget.Furthermore, the generating AI can suggest the most suitable ways to enjoy oneself based on the user's hobbies and interests. In addition, the generating AI can suggest region-specific ways to enjoy oneself, taking into account the user's geographical location. For example, if the generating AI is in a specific region, it will suggest ways to enjoy oneself that are unique to that region. This allows the household management system according to this embodiment to provide balanced support across three categories—saving, investing, and enjoying oneself—based on the user's income and expenditure information.

[0056] The reception desk can estimate the user's emotions and adjust the timing of inputting financial information based on the estimated emotions. For example, if the user is stressed, the reception desk may postpone inputting financial information and prompt the user to input it when they are relaxed. Conversely, if the user is relaxed, the reception desk may actively encourage inputting financial information and prompt the user to enter detailed information. Furthermore, if the user is in a hurry, the reception desk may provide a simplified input form to allow for quick input of financial information. This allows for inputting financial information at a more appropriate time by adjusting the timing of input according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI, or not using AI. For example, the reception desk may input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0057] The reception desk can analyze the user's past income and expense history and select the optimal input method. For example, the reception desk can automatically display income and expense items that the user has frequently entered in the past as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest income and expense items to be entered at specific time periods based on the user's past income and expense history. In this way, the optimal input method can be selected by analyzing the user's past income and expense history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past income and expense data into a generating AI and have the generating AI select the optimal input method.

[0058] The reception unit can filter income and expense information based on the user's current living situation and areas of interest when the user enters this information. For example, the reception unit can prioritize displaying relevant income and expense items according to the user's current living situation. The reception unit can also simplify input by filtering relevant income and expense items based on the user's areas of interest. Furthermore, the reception unit can adjust the input order of income and expense items based on the user's living situation and areas of interest. This simplifies input by filtering income and expense information based on the user's living situation and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's living situation data into a generating AI and have the generating AI perform the filtering of income and expense information.

[0059] The reception desk can estimate the user's emotions and determine the priority of the financial information to be entered based on the estimated emotions. For example, if the user is stressed, the reception desk may prioritize the input of only important financial information. If the user is relaxed, the reception desk may also prompt the user to enter detailed financial information. Furthermore, if the user is in a hurry, the reception desk may prioritize the input of simplified financial information. This ensures that important information is entered preferentially by prioritizing financial information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk may input user emotion data into a generative AI and have the generative AI determine the priority of financial information.

[0060] The reception unit can prioritize inputting highly relevant information based on the user's geographical location when inputting income and expense information. For example, if the user is in a specific region, the reception unit will prioritize displaying income and expense items related to that region. The reception unit can also filter relevant income and expense items based on the user's geographical location, simplifying the input process. Furthermore, the reception unit can adjust the input order of income and expense items based on the user's geographical location. This simplifies input by prioritizing the input of income and expense information based on the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location data into a generating AI and have the generating AI prioritize the input of highly relevant information.

[0061] The reception unit can analyze the user's social media activity and input relevant information when entering income and expense information. For example, the reception unit can automatically display relevant income and expense items as suggestions based on the user's social media activity. The reception unit can also filter relevant income and expense items based on the user's social media activity, simplifying the input process. Furthermore, the reception unit can adjust the input order of income and expense items based on the user's social media activity. This simplifies the input process by inputting income and expense information based on the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's social media data into a generating AI and have the generating AI input the relevant income and expense information.

[0062] The savings unit can estimate the user's emotions and adjust the way savings advice is presented based on those emotions. For example, if the user is stressed, the savings unit can provide simple and easy-to-understand savings advice. If the user is relaxed, it can also provide detailed savings advice. Furthermore, if the user is in a hurry, it can provide concise and to-the-point savings advice. By adjusting the way savings advice is presented according to the user's emotions, more effective advice can be provided. 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 savings unit may be performed using AI or not. For example, the savings unit can input user emotion data into a generative AI and have the generative AI adjust the way savings advice is presented.

[0063] The savings department, when providing savings advice, can analyze in detail how to reduce unnecessary spending and propose specific reduction methods. For example, the savings department can analyze the user's past spending history to identify unnecessary spending items and propose ways to reduce them. It can also analyze the user's current spending patterns and propose efficient reduction methods. Furthermore, the savings department can propose specific reduction methods based on the user's lifestyle. In this way, by analyzing in detail how to reduce unnecessary spending, it can propose specific reduction methods. Some or all of the above processes in the savings department may be performed using AI, for example, or not using AI. For example, the savings department can input the user's spending data into a generating AI and have the generating AI perform the analysis and proposal of ways to reduce unnecessary spending.

[0064] The savings unit can propose individually customized savings methods based on the user's income and expenditure information when providing savings advice. For example, the savings unit can propose individually customized savings methods based on the user's income and expenditure information. The savings unit can also analyze the user's income and expenditure information and propose the optimal savings method. Furthermore, the savings unit can propose specific savings measures based on the user's income and expenditure information. This makes it possible to save more effectively by proposing individually customized savings methods based on the user's income and expenditure information. Some or all of the above processing in the savings unit may be performed using AI, for example, or without AI. For example, the savings unit can input the user's income and expenditure data into a generating AI and have the generating AI execute the proposal of individually customized savings methods.

[0065] The savings unit can estimate the user's emotions and prioritize savings advice based on those emotions. For example, if the user is stressed, the savings unit will prioritize providing only important savings advice. It can also prioritize providing detailed savings advice if the user is relaxed. Furthermore, if the user is in a hurry, it can prioritize providing concise savings advice. This allows for the prioritization of important advice by determining the priority of savings advice according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the savings unit may be performed using AI or not. For example, the savings unit can input user emotion data into a generative AI and have the generative AI determine the priority of savings advice.

[0066] The savings unit can propose region-specific savings methods by considering the user's geographical location when providing savings advice. For example, if the user is in a specific region, the savings unit will propose region-specific savings methods. The savings unit can also filter and propose region-specific savings methods based on the user's geographical location. Furthermore, the savings unit can propose the optimal savings method based on the user's geographical location. This makes it possible to achieve more effective savings by proposing region-specific savings methods based on the user's geographical location. Some or all of the above processing in the savings unit may be performed using AI, for example, or without AI. For example, the savings unit can input the user's geographical location data into a generating AI and have the generating AI execute the proposal of region-specific savings methods.

[0067] The savings unit can analyze the user's social media activity and suggest relevant savings methods when providing savings advice. For example, the savings unit can automatically suggest relevant savings methods based on the user's social media activity. The savings unit can also filter and suggest relevant savings methods based on the user's social media activity. Furthermore, the savings unit can suggest the optimal savings method based on the user's social media activity. This makes it possible to save more effectively by suggesting relevant savings methods based on the user's social media activity. Some or all of the above processing in the savings unit may be performed using AI, for example, or without AI. For example, the savings unit can input the user's social media data into a generating AI and have the generating AI suggest relevant savings methods.

[0068] The investment unit can estimate the user's emotions and adjust the presentation of investment recommendations based on those emotions. For example, if the user is stressed, the investment unit can provide simple and easy-to-understand investment recommendations. If the user is relaxed, the investment unit can also provide detailed investment recommendations. Furthermore, if the user is in a hurry, the investment unit can provide concise and to-the-point investment recommendations. By adjusting the presentation of investment recommendations according to the user's emotions, more effective recommendations can be provided. 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 investment unit may be performed using AI or not. For example, the investment unit can input user emotion data into a generative AI and have the generative AI adjust the presentation of investment recommendations.

[0069] The investment department can propose investments that minimize risk based on the user's income and expense information when providing investment recommendations. For example, the investment department can analyze the user's income and expense information and propose investments that minimize risk. The investment department can also propose the optimal investment based on the user's income and expense information. Furthermore, the investment department can propose specific investments that minimize risk based on the user's income and expense information. This makes safer investment possible by proposing investments that minimize risk based on the user's income and expense information. Some or all of the above processing in the investment department may be performed using AI, for example, or without AI. For example, the investment department can input the user's income and expense data into a generating AI and have the generating AI execute a proposal for investments that minimize risk.

[0070] The investment department can analyze a user's past investment history and propose the optimal investment strategy when providing investment recommendations. For example, the investment department can analyze a user's past investment history and propose the optimal investment strategy. The investment department can also propose the optimal investment destination based on the user's past investment history. Furthermore, the investment department can propose the optimal investment strategy based on the user's past investment history. In this way, the investment department can propose the optimal investment strategy by analyzing the user's past investment history. Some or all of the above processes in the investment department may be performed using AI, for example, or not using AI. For example, the investment department can input the user's investment history data into a generating AI and have the generating AI execute the proposal of the optimal investment strategy.

[0071] The investment department can estimate the user's emotions and prioritize investment recommendations based on those emotions. For example, if the user is stressed, the investment department will prioritize providing only important investment recommendations. It can also prioritize detailed investment recommendations if the user is relaxed. Furthermore, if the user is in a hurry, it can prioritize concise investment recommendations. This allows for the prioritization of important recommendations based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the investment department may be performed using AI or not. For example, the investment department can input user emotion data into a generative AI and have the generative AI determine the priority of investment recommendations.

[0072] The investment department can propose region-specific investments by considering the user's geographical location when providing investment recommendations. For example, if the user is in a specific region, the investment department will propose region-specific investments. The investment department can also filter and propose region-specific investments based on the user's geographical location. Furthermore, the investment department can propose the most suitable investments based on the user's geographical location. This enables more effective investment by proposing region-specific investments based on the user's geographical location. Some or all of the above processing in the investment department may be performed using AI, for example, or without AI. For example, the investment department can input the user's geographical location data into a generating AI and have the generating AI execute region-specific investment proposals.

[0073] The investment department can analyze a user's social media activity and suggest relevant investment opportunities when providing investment recommendations. For example, the investment department can automatically suggest relevant investment opportunities based on the user's social media activity. It can also filter and suggest relevant investment opportunities based on the user's social media activity. Furthermore, the investment department can suggest the most suitable investment opportunities based on the user's social media activity. This enables more effective investment by suggesting relevant investment opportunities based on the user's social media activity. Some or all of the above processing in the investment department may be performed using AI, for example, or without AI. For example, the investment department can input the user's social media data into a generating AI and have the generating AI suggest relevant investment opportunities.

[0074] The "Enjoyment" section can estimate the user's emotions and adjust the way enjoyment suggestions are presented based on those emotions. For example, if the user is feeling stressed, the "Enjoyment" section can suggest relaxing ways to have fun. If the user is relaxed, it can also suggest more active ways to have fun. Furthermore, if the user is in a hurry, it can suggest ways to have fun in a short amount of time. By adjusting the way enjoyment suggestions are presented according to the user's emotions, more effective suggestions can be provided. 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 "Enjoyment" section may be performed using AI or not. For example, the "Enjoyment" section can input user emotion data into a generative AI and have the generative AI adjust the way enjoyment suggestions are presented.

[0075] The "Enjoyment Department" can suggest the most suitable way to enjoy oneself based on the user's financial information when providing suggestions for enjoyment. For example, the Enjoyment Department can analyze the user's financial information and suggest ways to enjoy oneself within their budget. Furthermore, the Enjoyment Department can also suggest cost-effective ways to enjoy oneself based on the user's financial information. In addition, the Enjoyment Department can suggest ways to enjoy oneself while avoiding unnecessary spending based on the user's financial information. This allows for more effective enjoyment by suggesting the most suitable way to enjoy oneself based on the user's financial information. Some or all of the above processing in the Enjoyment Department may be performed using AI, for example, or without AI. For example, the Enjoyment Department can input the user's financial data into a generating AI and have the generating AI generate suggestions for the most suitable way to enjoy oneself.

[0076] The "Enjoyment Department" can analyze the user's past hobbies and activity history when providing suggestions for enjoyment, and propose the most suitable way to enjoy oneself. For example, the "Enjoyment Department" can analyze the user's past hobbies and activity history and propose related ways to enjoy oneself. Furthermore, the "Enjoyment Department" can also propose new ways to enjoy oneself based on the user's past hobbies and activity history. In addition, the "Enjoyment Department" can propose the most suitable way to enjoy oneself based on the user's past hobbies and activity history. In this way, by analyzing the user's past hobbies and activity history, the "Enjoyment Department" can propose the most suitable way to enjoy oneself. Some or all of the above processing in the "Enjoyment Department" may be performed using AI, for example, or not using AI. For example, the "Enjoyment Department" can input the user's hobby and activity history data into a generating AI and have the generating AI execute suggestions for the most suitable way to enjoy oneself.

[0077] The "Enjoyment" section can estimate the user's emotions and prioritize enjoyment suggestions based on those emotions. For example, if the user is stressed, the "Enjoyment" section will prioritize suggesting relaxing ways to have fun. If the user is relaxed, the "Enjoyment" section can also prioritize suggesting active ways to have fun. Furthermore, if the user is in a hurry, the "Enjoyment" section can prioritize suggesting ways to have fun in a short amount of time. This allows for the prioritization of important suggestions by determining the priority of enjoyment suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the "Enjoyment" section may be performed using AI or not. For example, the "Enjoyment" section can input user emotion data into a generative AI and have the generative AI determine the priority of enjoyment suggestions.

[0078] The "Enjoyment Department" can suggest region-specific ways to enjoy activities by considering the user's geographical location when providing suggestions for activities. For example, if the user is in a specific region, the "Enjoyment Department" can suggest region-specific ways to enjoy activities. The "Enjoyment Department" can also filter and suggest region-specific ways to enjoy activities based on the user's geographical location. Furthermore, the "Enjoyment Department" can suggest the most optimal way to enjoy activities based on the user's geographical location. This allows for more effective enjoyment by suggesting region-specific ways to enjoy activities based on the user's geographical location. Some or all of the above processing in the "Enjoyment Department" may be performed using AI, for example, or without AI. For example, the "Enjoyment Department" can input the user's geographical location data into a generating AI and have the generating AI generate suggestions for region-specific ways to enjoy activities.

[0079] The "Enjoyment Department" can analyze a user's social media activity and suggest relevant ways to enjoy themselves when providing suggestions. For example, the Enjoyment Department can automatically suggest relevant ways to enjoy themselves based on the user's social media activity. The Enjoyment Department can also filter and suggest relevant ways to enjoy themselves based on the user's social media activity. Furthermore, the Enjoyment Department can suggest the most optimal way to enjoy themselves based on the user's social media activity. This allows for more effective ways to enjoy oneself by suggesting relevant ways to enjoy oneself based on the user's social media activity. Some or all of the above processing in the Enjoyment Department may be performed using AI, for example, or without AI. For example, the Enjoyment Department can input the user's social media data into a generating AI and have the generating AI perform the task of suggesting relevant ways to enjoy oneself. === Hard Collateral 1-1 === Each of the multiple elements described above, including the reception unit, savings unit, investment unit, and enjoyment unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14, allowing the user to input income and expense information. The savings unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, providing savings advice using generating AI. The investment unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, providing investment recommendations using generating AI. The enjoyment unit is implemented, for example, by the control unit 46A of the smart device 14, offering suggestions for "enjoying" the obtained returns. The reception unit can, for example, estimate the user's emotions and adjust the timing of income and expense information input based on the estimated emotions. The savings unit can, for example, estimate the user's emotions and adjust the way savings advice is expressed based on the estimated emotions. === Hard Collateral 1-2 === Each of the multiple elements described above, including the reception unit, savings unit, investment unit, and entertainment unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214, allowing the user to input income and expense information. The savings unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, providing savings advice using generating AI. The investment unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, providing investment recommendations using generating AI. The entertainment unit is implemented, for example, by the control unit 46A of the smart glasses 214, offering suggestions for "enjoying" the obtained returns. The reception unit can, for example, estimate the user's emotions and adjust the timing of income and expense information input based on the estimated emotions. The savings unit can, for example, estimate the user's emotions and adjust the way savings advice is expressed based on the estimated emotions. === Hard Collateral 1-3 === Each of the multiple elements described above, including the reception unit, savings unit, investment unit, and entertainment unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314, allowing the user to input income and expense information. The savings unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, providing savings advice using generating AI. The investment unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, providing investment recommendations using generating AI. The entertainment unit is implemented by, for example, the control unit 46A of the headset terminal 314, offering suggestions for "enjoying" the obtained returns. The reception unit can, for example, estimate the user's emotions and adjust the timing of income and expense information input based on the estimated emotions. The savings unit can, for example, estimate the user's emotions and adjust the way savings advice is expressed based on the estimated emotions. === Hard Collateral 1-4 === Each of the multiple elements described above, including the reception unit, savings unit, investment unit, and entertainment unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414, allowing the user to input income and expense information. The savings unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, providing savings advice using generating AI. The investment unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, providing investment recommendations using generating AI. The entertainment unit is implemented by, for example, the control unit 46A of the robot 414, offering suggestions for "enjoying" the obtained returns. The reception unit can, for example, estimate the user's emotions and adjust the timing of income and expense information input based on the estimated emotions. The savings unit can, for example, estimate the user's emotions and adjust the way savings advice is expressed based on the estimated emotions.

[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 reception desk can also acquire user health data and adjust the timing of income and expense information entry. For example, based on the user's heart rate and sleep data, if it is determined that the user is under high stress, the entry will be postponed, and the user will be prompted to enter the information when they are relaxed. Also, based on health data, if the user is tired, a simplified input form can be provided to allow for quick entry of income and expense information. In this way, by adjusting the timing of income and expense information entry according to the user's health status, the information can be entered at a more appropriate time.

[0082] The savings feature can analyze users' purchase history and provide discount information for specific stores and brands. For example, it can offer discount coupons for stores users frequently visit or notify them when products from a particular brand are on sale. It can also suggest stores where similar products can be purchased at a lower price based on the user's purchase history. This allows users to save money efficiently.

[0083] The investment department can also propose long-term investment strategies based on the user's occupation and career plan. For example, if the user has a stable job, it will propose high-risk investments, and conversely, if they have an unstable job, it will propose low-risk investments. It can also propose investment strategies that anticipate future income growth based on the user's career plan. In this way, it can provide the optimal investment strategy tailored to the user's occupation and career plan.

[0084] The "Enjoyment" section can also customize suggestions for activities based on the user's family structure and life stage. For example, it can suggest activities that the whole family can enjoy together for families with children, and suggest hobbies and trips that can be enjoyed alone for single users. It can also suggest romantic travel plans for newlyweds, depending on the user's life stage. This allows the service to provide the most suitable way for users to enjoy themselves based on their family structure and life stage.

[0085] The reception desk can analyze the user's voice input, estimate their emotions, and adjust the way they input income and expense information. For example, if it determines from the tone and speed of the user's voice that they are stressed, it will provide a simplified input form; if they are relaxed, it will prompt for more detailed input. It can also estimate emotions from the content of the voice input and prompt for income and expense information input at the appropriate time. In this way, by estimating emotions from the user's voice and adjusting the way they input income and expense information, a more appropriate input experience can be provided.

[0086] The energy conservation section can acquire user energy consumption data and suggest efficient energy-saving methods. For example, it can analyze the user's electricity and gas usage and suggest ways to reduce wasteful energy consumption. It can also suggest ways to save energy during specific time periods based on energy consumption data. Furthermore, it can recommend the purchase of energy-efficient home appliances. This allows users to efficiently save energy.

[0087] The investment department can estimate the user's emotions and adjust the acceptable level of investment risk based on those emotions. For example, if the user is stressed, it can suggest low-risk investments, and if they are relaxed, it can suggest high-risk investments. It can also adjust the investment amount according to the user's emotions, recommending small investments if the user is highly stressed. This enables optimal investment risk management tailored to the user's emotions.

[0088] The "Enjoyment" section can estimate the user's emotions and adjust the timing of enjoyment suggestions based on those emotions. For example, if the user is stressed, it can suggest relaxing activities; if they are relaxed, it can suggest active activities. It can also suggest quick and enjoyable activities if the user is in a hurry. This allows for providing enjoyment suggestions at the optimal time, tailored to the user's emotions.

[0089] The reception desk can analyze users' purchase history and automate the input of income and expense information. For example, it can automatically input frequently purchased items and services as income and expense information, reducing manual input. It can also predict specific expenditure items based on purchase history, simplifying input. Furthermore, it can suggest stores where similar products can be purchased at a lower price based on the user's purchase history. In this way, the input of income and expense information can be made more efficient by utilizing the user's purchase history.

[0090] The savings function can also estimate the user's emotions and adjust the timing of savings advice based on those emotions. For example, it can provide simple savings advice when the user is stressed and detailed advice when they are relaxed. It can also provide concise savings advice to the user when they are in a hurry. This allows for the delivery of savings advice at the optimal time according to the user's emotions.

[0091] The following briefly describes the processing flow for example form 2.

[0092] Step 1: The reception desk receives the user's income and expense information. This information includes details of income and expenses. The reception desk allows users to manually enter this information, or it can automatically retrieve bank account and credit card information and input it as income and expense information. It is also possible to input income and expense information using voice input or image recognition technology. For example, a user can take a picture of a receipt, and the system can analyze the image to automatically input the income and expense information. Step 2: The savings department uses a generating AI to provide savings advice based on the income and expense information entered by the reception department. This advice includes ways to reduce unnecessary spending and efficient saving methods. The generating AI analyzes the user's spending patterns, identifies unnecessary spending, and suggests ways to reduce it. It can also suggest efficient saving methods based on the user's lifestyle and provide concrete action plans. Step 3: The investment department uses a generative AI to provide investment recommendations based on the advice provided by the savings department. These recommendations include suggesting appropriate investment targets and setting investment amounts. The generative AI analyzes the user's income and expense information and suggests investment targets that minimize risk. It can also suggest the optimal investment strategy based on the user's investment goals and analyze past investment history to suggest the best investment targets. Step 4: The Enjoyment Unit uses a Generative AI to make suggestions for enjoying the returns obtained by the Investment Unit. These suggestions include how to spend money on travel and hobbies. The Generative AI analyzes the user's income and expenditure information and suggests ways to enjoy themselves within their budget. It can also suggest the most suitable ways to enjoy themselves based on the user's hobbies and interests, and can even suggest region-specific ways to enjoy themselves by considering geographical location information.

[0093] 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.

[0094] 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 the following. 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 (for example, 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. 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 a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

[0095] 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.

[0096] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0097] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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).

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.).

[0109] 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.

[0110] 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. 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.

[0111] 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.

[0112] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0113] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.).

[0125] 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.

[0126] 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. 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.

[0127] 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.

[0128] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0129] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.).

[0142] 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.

[0143] 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. 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.

[0144] 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.

[0145] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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."

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] [Explanation of Symbols]

[0165] 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 reception area where income and expenditure information is entered, A savings unit provides savings advice based on the information entered by the reception unit, The Investment Department makes investment recommendations based on the advice provided by the Savings Department, The system includes a receiving unit that makes suggestions for enjoying the returns obtained by the investment unit. A system characterized by the following features.

2. The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of inputting income and expense information based on the estimated emotions. The system according to feature 1.

3. The aforementioned reception unit is Analyze the user's past income and expense history and select the appropriate input method. The system according to feature 1.

4. The aforementioned reception unit is When entering income and expense information, filtering is performed based on the user's current lifestyle and areas of interest. The system according to feature 1.

5. The aforementioned reception unit is The system estimates the user's emotions and determines the priority of the financial information to be entered based on those estimated emotions. The system according to feature 1.

6. The aforementioned reception unit is When entering income and expense information, the system prioritizes inputting information that is highly relevant based on the user's geographical location. The system according to feature 1.

7. The aforementioned reception unit is When entering income and expense information, the system analyzes the user's social media activity and inputs relevant information. The system according to feature 1.

8. The aforementioned energy-saving section is, The system estimates the user's emotions and adjusts the way savings advice is presented based on those estimated emotions. The system according to feature 1.

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