System

Abstract

An object of a system according to an embodiment is to propose an asset management plan based on expenditure information and collectively manage assets.SOLUTION: A system according to an embodiment includes an input unit, an analysis unit, a proposal unit, and a management unit. The input unit inputs expense information. The analysis unit analyzes the expense information input by the input unit. The proposal unit proposes an asset management plan based on the expense information analyzed by the analysis unit. The management unit manages assets based on the asset management plan proposed by the proposal unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately propose or manage asset management plans based on expenditure information, and there is room for improvement.

[0005] The system according to the embodiment aims to propose an asset management plan based on expenditure information and to manage assets in a centralized manner. [Means for solving the problem]

[0006] The system according to the embodiment includes an input unit, an analysis unit, a proposal unit, and a management unit. The input unit inputs expenditure information. The analysis unit analyzes the expenditure information input by the input unit. The proposal unit proposes an asset management plan based on the expenditure information analyzed by the analysis unit. The management unit manages assets based on the asset management plan proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to the embodiment can propose an asset management plan based on expenditure information and manage assets in a centralized manner. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) In an embodiment of the present invention, an expense management and asset management integrated management system allows a user to input daily expense information, and a generation AI analyzes the information to identify spending trends and wasteful spending. Furthermore, the system grasps the user's asset status and proposes an optimal asset management plan. The expense management and asset management integrated management system provides efficient asset management by having the user input expense information, and the generation AI analyzes the information, proposes an asset management plan, and manages assets. For example, in the expense management and asset management integrated management system, the user inputs expense information by category, such as food, transportation, and entertainment. For example, the user inputs specific amounts, such as "Today's food expenses are 1,000 yen, and transportation expenses are 500 yen." This information is input into the generation AI. The expense management and asset management integrated management system then uses the generation AI to analyze the input expense information. Based on the input expense information, the generation AI identifies spending patterns, such as months with high food expenses or weeks with high transportation expenses. It also identifies wasteful spending trends and provides the user with advice on saving. Furthermore, the expense management and asset management integrated management system grasps the user's asset status using the generation AI. By inputting information about a user's bank accounts, investment trusts, stocks, and other assets, the Generator AI grasps the user's total assets. For example, a user can input specific asset information such as "1 million yen in a bank account, 500,000 yen in investment trusts, and 300,000 yen in stocks." Based on this information, the Generator AI proposes an asset management plan for the user. The Generator AI comprehensively analyzes the user's spending habits and asset status to propose an optimal asset management plan. For example, it provides plans tailored to the user's needs, such as a plan to reduce wasteful spending and increase savings, or an investment plan with reduced risk. This allows the expense management and asset management system to manage the user's expenses and asset management in one place. This allows the Generator AI to efficiently manage the user's expenses and asset management. For example, a user can review their monthly expenses and reduce wasteful spending to increase savings. Furthermore, by following the asset management plan proposed by the Generator AI, they can efficiently increase their assets while reducing risk. This allows users to manage their assets to achieve their future goals.

[0029] An expense management and asset management integrated management system according to an embodiment includes an input unit, an analysis unit, a proposal unit, and a management unit. The input unit allows a user to input daily expense information. The user inputs expense information by category, such as food, transportation, and entertainment. For example, the user can input specific amounts, such as "Today's food expenses are 1,000 yen, and transportation expenses are 500 yen." The analysis unit uses a generation AI to analyze the expense information input by the input unit. The analysis unit identifies the user's spending trends based on the input expense information. For example, the analysis unit identifies spending patterns, such as months with high food expenses or weeks with high transportation expenses. The analysis unit can also identify wasteful spending trends and provide the user with advice on saving. The proposal unit uses the generation AI to propose an asset management plan based on the expense information analyzed by the analysis unit. The proposal unit, for example, understands the user's asset status and proposes an optimal asset management plan. For example, the proposal unit provides plans tailored to the user's needs, such as plans to reduce wasteful spending and increase savings, or investment plans with reduced risk. The management unit manages assets based on the asset management plan proposed by the proposal unit. The management unit efficiently manages the user's assets, for example, in accordance with the proposed asset management plan. As a result, the expense management and asset management integrated management system according to the embodiment can efficiently manage the user's expenses and manage their assets. For example, the user can increase savings by reviewing monthly expenses and reducing wasteful spending. Furthermore, by following the asset management plan proposed by the generation AI, the user can efficiently increase assets while reducing risk. This enables the user to manage their assets to achieve their future goals.

[0030] The input unit allows the user to input expenditure information for each category of food, transportation, and entertainment. The categories of food, transportation, and entertainment include specific definitions and ranges, such as whether food expenses include eating out. The input unit allows the user to input expenditure information for each category of food, transportation, and entertainment. For example, the user can input specific amounts such as "Today's food expenses are 1,000 yen, and transportation expenses are 500 yen." This allows detailed expenditure management by inputting expenditure information for each category. Some or all of the above-described processing in the input unit may be performed using, or without, a generation AI. For example, the input unit may input the expenditure information entered by the user to a generation AI, which may then analyze the expenditure information.

[0031] The analysis unit can grasp the user's spending trends based on the input expenditure information. The spending trends include specific grasping methods and criteria, such as monthly spending patterns and the proportion of spending in specific categories. The analysis unit grasps the user's spending trends based on the input expenditure information, for example. For example, the analysis unit can identify spending patterns, such as months with high food expenses or weeks with high transportation expenses. This allows the user's spending trends to be grasped, making it possible to identify wasteful spending and provide advice on saving. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the input expenditure information into a generation AI, which can analyze the spending trends.

[0032] The analysis unit can identify wasteful spending trends and provide saving advice to the user. Examples of wasteful spending trends include specific definitions and methods for identifying unnecessary purchases and excessive spending. The analysis unit can, for example, identify wasteful spending trends and provide saving advice to the user. For example, the analysis unit can analyze the user's spending information and identify wasteful spending patterns. The analysis unit can also provide specific saving advice to the user. This allows the user's spending to be efficiently managed by identifying wasteful spending trends and providing saving advice. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input spending information to a generation AI to identify wasteful spending trends, and the generation AI can analyze the wasteful spending trends.

[0033] The proposal unit can grasp the user's asset status and propose an asset management plan. The asset status includes, for example, valuation methods for cash, investment assets, real estate, etc. The proposal unit, for example, grasps the user's asset status and proposes an optimal asset management plan. For example, the proposal unit can grasp the user's total assets by inputting information on the user's bank accounts, investment trusts, stocks, etc. The proposal unit comprehensively analyzes the user's spending habits and asset status and proposes an optimal asset management plan. For example, the proposal unit provides plans tailored to the user's needs, such as a plan to reduce wasteful spending and increase savings, or a low-risk investment plan. This enables efficient asset management by grasping the user's asset status and proposing an optimal asset management plan. Some or all of the above-described processing in the proposal unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the proposal unit can input the user's asset information into a generation AI, which then proposes an optimal asset management plan.

[0034] The management unit can manage assets based on the proposed asset management plan. Asset management includes, for example, specific methods and standards such as asset diversification and risk management. The management unit, for example, manages assets based on the proposed asset management plan. For example, the management unit can efficiently manage the user's assets in accordance with the proposed asset management plan. This enables efficient asset management by managing assets based on the proposed asset management plan. Some or all of the above-mentioned processing in the management unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the management unit can input the proposed asset management plan into the generation AI, and the generation AI can perform asset management.

[0035] The input unit can analyze the user's past spending history and select an input method. The past spending history includes, for example, specific content and analysis methods, such as spending data for the past year and spending trends by category. The input unit, for example, analyzes the user's past spending history and selects the optimal input method. For example, the input unit preferentially suggests input methods (such as voice and text) that the user has frequently used in the past. The input unit can also automatically input spending information for specific categories from the user's past spending history. The input unit can also provide templates to reduce the effort of inputting information based on the user's past spending history. This allows the optimal input method to be selected by analyzing the user's past spending history, thereby reducing the effort of inputting information. Some or all of the above-mentioned processing in the input unit can be performed, for example, using a generation AI or without a generation AI. For example, the input unit can input the user's past spending history into a generation AI, which can select the optimal input method.

[0036] The input unit can filter the expenditure information based on the user's current living situation and areas of interest when inputting the expenditure information. The living situation includes specific definitions and filtering methods such as family structure, income status, and lifestyle. The input unit can filter the expenditure information based on the user's current living situation and areas of interest when inputting the expenditure information. For example, if the user is traveling, the input unit can prioritize inputting travel-related expenditure information. Furthermore, if the user is interested in health, the input unit can prioritize inputting health-related expenditure information. Furthermore, if the user is participating in a specific event, the input unit can prioritize inputting expenditure information related to the event. By filtering the expenditure information based on the user's living situation and areas of interest, more relevant expenditure information can be input. Some or all of the above-described processing in the input unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the input unit can input data on the user's living situation and areas of interest to the generation AI, which can then perform the filtering.

[0037] The input unit can select the optimal input means depending on the user's input method (voice, text, image, etc.) when inputting expenditure information. The input method includes specific types and selection criteria, such as voice input, text input, and image input. The input unit can select the optimal input means depending on the user's input method (voice, text, image, etc.) when inputting expenditure information. For example, if the user prefers voice input, the input unit can provide voice input preferentially. Also, if the user prefers text input, the input unit can provide text input preferentially. Furthermore, if the user uploads an image of a receipt, the input unit can automatically input expenditure information using image recognition technology. This improves input convenience by selecting the optimal input means depending on the user's input method. Some or all of the above-described processing in the input unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the input unit can input data on the user's input method into a generation AI, which can select the optimal input means.

[0038] When inputting expenditure information, the input unit can prioritize inputting highly relevant expenditure information in consideration of the user's geographical location information. Geographical location information includes, for example, specific acquisition methods and usage criteria, such as GPS data and location information services. When inputting expenditure information, the input unit, for example, prioritizes inputting highly relevant expenditure information in consideration of the user's geographical location information. For example, when the user is at a specific store, the input unit prioritizes inputting expenditure information at that store. Furthermore, when the user is traveling, the input unit can prioritize inputting expenditure information at the travel destination. Furthermore, when the user is at home, the input unit can prioritize inputting daily expenditure information. In this way, highly relevant expenditure information can be prioritized by considering the user's geographical location information. Some or all of the above-described processing in the input unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the input unit can input the user's geographical location information to a generation AI, which can then prioritize inputting highly relevant expenditure information.

[0039] The input unit can analyze the user's social media activity and input related expenditure information when inputting expenditure information. Social media activity includes specific analysis methods and usage criteria, such as the content of posts, the number of likes, and the number of followers. The input unit can analyze the user's social media activity and input related expenditure information when inputting expenditure information, for example. For example, the input unit can input expenditure information related to places the user checked in on social media. The input unit can also analyze the content of the user's social media posts and input related expenditure information. The input unit can also input related expenditure information based on the activities of the user's friends on social media. In this way, related expenditure information can be efficiently input by analyzing the user's social media activity. Some or all of the above-described processing in the input unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the input unit can input data on the user's social media activity to a generation AI, which then inputs the related expenditure information.

[0040] The input unit can customize the input method by reflecting the user's past feedback when inputting expenditure information. The past feedback includes, for example, specific content such as the user's evaluation comments and usage history, and the method of reflecting the feedback. The input unit can customize the input method by reflecting the user's past feedback when inputting expenditure information. For example, the input unit can preferentially provide input methods that the user has previously preferred. The input unit can also improve the input interface based on the user's past feedback. The input unit can also simplify the input procedure by referring to the user's past feedback. This customizes the input method by reflecting the user's past feedback, improving input convenience. Some or all of the above-described processing in the input unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the input unit can input the user's past feedback into the generation AI, which can then customize the input method.

[0041] The analysis unit can adjust the level of detail of the analysis based on the importance of the expenditure information during analysis. The importance of the expenditure information includes specific evaluation criteria and adjustment methods, such as high expenditures and frequent expenditures. The analysis unit can adjust the level of detail of the analysis based on the importance of the expenditure information during analysis, for example. For example, the analysis unit performs a detailed analysis on important expenditure categories. The analysis unit can also perform a brief analysis on less important expenditure categories. The analysis unit can also determine the priority of the analysis according to the importance of the expenditure information. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the expenditure information. Some or all of the above-mentioned processing in the analysis unit can be performed using, or without, a generation AI. For example, the analysis unit can input importance data of the expenditure information into the generation AI, which can then adjust the level of detail of the analysis.

[0042] The analysis unit can apply different analysis algorithms depending on the expenditure category during analysis. Expenditure categories include specific definitions and analysis algorithms, such as food expenses, transportation expenses, and entertainment expenses. The analysis unit can apply different analysis algorithms depending on the expenditure category during analysis. For example, the analysis unit can perform an analysis of food expenses taking into account price fluctuations of ingredients. The analysis unit can also perform an analysis of transportation expenses taking into account the frequency of transportation use. The analysis unit can also perform an analysis of entertainment expenses taking into account the frequency of event participation. By applying different analysis algorithms depending on the expenditure category, more accurate analysis is possible. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input expenditure category data into a generation AI, and the generation AI can apply different analysis algorithms.

[0043] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. Past analysis results include, for example, specific content and usage methods, such as past spending trends and savings effects. During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit adjusts the analysis algorithm based on the user's past analysis results. The analysis unit can also identify specific spending patterns from the user's past analysis results. The analysis unit can also improve the accuracy of the analysis by referring to the user's past analysis results. In this way, the accuracy of the analysis is improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit can be performed, for example, using a generation AI or can be performed without using a generation AI. For example, the analysis unit can input the user's past analysis result data into the generation AI, which can improve the accuracy of the analysis.

[0044] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of the expenditure information. The submission time includes specific evaluation criteria and usage methods, such as the end of the month or after payday. During analysis, the analysis unit can determine the priority of analysis based on the time of submission of the expenditure information. For example, the analysis unit prioritizes analysis of recently submitted expenditure information. The analysis unit can also postpone analysis of older submitted expenditure information. The analysis unit can also dynamically adjust the priority of analysis based on the time of submission. This enables efficient analysis by determining the priority of analysis based on the time of submission of the expenditure information. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input data on the time of submission of expenditure information into the generation AI, and the generation AI can determine the priority of analysis.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the expenditure information during analysis. The relevance includes specific evaluation criteria and adjustment methods, such as expenditures in the same category or expenditures during the same period. The analysis unit can adjust the order of analysis based on the relevance of the expenditure information during analysis, for example. For example, the analysis unit prioritizes analysis of highly relevant expenditure information. The analysis unit can also postpone analysis of less relevant expenditure information. The analysis unit can also dynamically adjust the order of analysis based on the relevance of the expenditure information. This enables efficient analysis by adjusting the order of analysis based on the relevance of the expenditure information. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input relevance data of the expenditure information into the generation AI, which can then adjust the order of analysis.

[0046] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. The level of expertise includes specific evaluation criteria and adjustment methods, such as beginner, intermediate, and advanced. During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, the analysis unit uses detailed technical terminology if the user has technical expertise. Furthermore, the analysis unit can explain the analysis results in simple terms if the user does not have technical expertise. Furthermore, the analysis unit can adjust the way the analysis results are presented according to the user's level of expertise. By adjusting the use of technical terminology in the analysis according to the user's level of expertise, more understandable analysis results can be provided. Some or all of the above-described processing in the analysis unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input the user's level of expertise data into the generation AI, which can then adjust the use of technical terminology.

[0047] The proposal unit can adjust the level of detail of the proposal based on the importance of the asset management plan when making a proposal. The importance includes, for example, specific evaluation criteria and adjustment methods, such as high-risk investments and low-risk investments. The proposal unit can adjust the level of detail of the proposal based on the importance of the asset management plan when making a proposal. For example, the proposal unit can make detailed proposals for important asset management plans. The proposal unit can also make brief proposals for less important asset management plans. The proposal unit can also determine the priority of proposals according to the importance of the asset management plans. This enables efficient proposals by adjusting the level of detail of the proposal based on the importance of the asset management plan. Some or all of the above-mentioned processing in the proposal unit can be performed using, or without, a generation AI. For example, the proposal unit can input importance data of the asset management plan into the generation AI, which can then adjust the level of detail of the proposal.

[0048] The proposal unit can apply different proposal algorithms depending on the asset category when making a proposal. Asset categories include specific definitions and proposal algorithms, such as stocks, bonds, and real estate. The proposal unit can apply different proposal algorithms depending on the asset category when making a proposal. For example, the proposal unit can propose a savings plan for a bank account. The proposal unit can also propose a low-risk investment plan for investment trusts. The proposal unit can also propose an investment plan that takes risk and return into consideration for stocks. This enables more accurate proposals by applying different proposal algorithms depending on the asset category. Some or all of the above-mentioned processing in the proposal unit can be performed using, or without, a generation AI, for example. For example, the proposal unit can input asset category data into a generation AI, and the generation AI can apply different proposal algorithms.

[0049] The suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results when making a suggestion. The past suggestion results include, for example, specific content and usage methods, such as past investment performance and user feedback. The suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results when making a suggestion. For example, the suggestion unit can adjust the suggestion algorithm based on the user's past suggestion results. The suggestion unit can also identify a specific asset management pattern from the user's past suggestion results. The suggestion unit can also improve the accuracy of the suggestion by referring to the user's past suggestion results. In this way, the accuracy of the suggestion is improved by referring to the user's past suggestion results. Some or all of the above-mentioned processing in the suggestion unit can be performed, for example, using a generation AI or without using a generation AI. For example, the suggestion unit can input the user's past suggestion result data into the generation AI, which can improve the accuracy of the suggestion.

[0050] The proposal unit, when making a proposal, can determine the priority of the proposal based on the submission date of the asset management plan. The submission date includes specific evaluation criteria and usage methods, such as the end of the month or after payday. The proposal unit, when making a proposal, can determine the priority of the proposal based on the submission date of the asset management plan. For example, the proposal unit can prioritize asset management plans that were submitted recently. The proposal unit can also postpone asset management plans that were submitted earlier. The proposal unit can also dynamically adjust the priority of the proposal based on the submission date. This enables efficient proposals by determining the priority of the proposal based on the submission date of the asset management plan. Some or all of the above-mentioned processing in the proposal unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the proposal unit can input data on the submission date of the asset management plan into the generation AI, which can then determine the priority of the proposal.

[0051] The proposal unit can adjust the order of proposals based on the relevance of the asset management plans when making a proposal. The relevance includes specific evaluation criteria and adjustment methods, such as assets in the same category or assets from the same period. The proposal unit can, for example, adjust the order of proposals based on the relevance of the asset management plans when making a proposal. For example, the proposal unit prioritizes proposing asset management plans with high relevance. The proposal unit can also postpone asset management plans with low relevance. The proposal unit can also dynamically adjust the order of proposals based on the relevance of the asset management plans. This enables efficient proposals by adjusting the order of proposals based on the relevance of the asset management plans. Some or all of the above-mentioned processing in the proposal unit can be performed using, or without, a generation AI. For example, the proposal unit can input relevance data of asset management plans into the generation AI, which can then adjust the order of proposals.

[0052] The suggestion unit may adjust the use of technical terminology in the proposal according to the user's level of expertise. The level of expertise may include specific evaluation criteria and adjustment methods, such as beginner, intermediate, or advanced. The suggestion unit may adjust the use of technical terminology in the proposal according to the user's level of expertise. For example, the suggestion unit may use detailed technical terminology if the user has technical expertise. The suggestion unit may also explain the proposal content in simple terms if the user does not have technical expertise. The suggestion unit may also adjust the way the proposal content is expressed according to the user's level of expertise. This allows for more understandable proposals by adjusting the use of technical terminology in the proposal according to the user's level of expertise. Some or all of the above-described processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit may input the user's level of expertise data into the generation AI, which may then adjust the use of technical terminology.

[0053] During asset management, the management unit can analyze the user's past asset management history to select the optimal management method. The past asset management history includes, for example, specific details such as past investment performance and asset increases / decreases, as well as analysis methods. During asset management, the management unit, for example, analyzes the user's past asset management history to select the optimal management method. For example, the management unit proposes the optimal management method based on the user's past asset management history. The management unit can also select a management method that reduces risk from the user's past asset management history. The management unit can also analyze the user's past asset management history to select the most efficient management method. This enables the optimal management method to be selected by analyzing the user's past asset management history, enabling efficient asset management. Some or all of the above-described processing in the management unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the management unit can input the user's past asset management history data into the generation AI, which then selects the optimal management method.

[0054] The management unit can customize the management means based on the user's current living situation during asset management. Living situations include specific definitions and customization methods for, for example, family composition, income status, and lifestyle. The management unit customizes the management means based on the user's current living situation during asset management. For example, if the user is traveling, the management unit can provide an asset management method for the user's travel destination. If the user is interested in health, the management unit can also provide a health-related asset management method. If the user is participating in a specific event, the management unit can also provide an asset management method related to the event. This enables more appropriate asset management by customizing the management means based on the user's current living situation. Some or all of the above-described processing in the management unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the management unit can input the user's living situation data into a generation AI, which can then customize the management means.

[0055] The management unit can improve the management method by reflecting user feedback during asset management. The feedback includes, for example, specific content such as user evaluation comments and usage history, and the method of reflection. The management unit can improve the management method by reflecting user feedback during asset management. For example, the management unit improves the management method based on the user's past feedback. The management unit can also simplify the management procedure by referring to the user's feedback. The management unit can also improve the management interface by reflecting user feedback. In this way, the management method can be improved by reflecting user feedback, enabling efficient asset management. Some or all of the above-mentioned processing in the management unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the management unit can input user feedback data into the generation AI, which can improve the management method.

[0056] The management unit can select the optimal management method by taking into account the user's geographical location information during asset management. Geographical location information includes, for example, specific acquisition methods and usage standards, such as GPS data and location information services. The management unit can select the optimal management method by taking into account the user's geographical location information during asset management. For example, when the user is in a specific area, the management unit can provide an asset management method appropriate for that area. Furthermore, when the user is traveling, the management unit can also provide an asset management method for the user's travel destination. Furthermore, when the user is at home, the management unit can also provide an everyday asset management method. This enables efficient asset management by taking into account the user's geographical location information. Some or all of the above-described processing in the management unit can be performed using, or without, a generation AI. For example, the management unit can input the user's geographical location information data into the generation AI, which can then select the optimal management method.

[0057] The management unit can analyze the user's social media activity during asset management to suggest management measures. Social media activity includes specific analysis methods and usage criteria, such as the content of posts, the number of likes, and the number of followers. For example, the management unit can analyze the user's social media activity during asset management to suggest management measures. For example, the management unit can suggest asset management methods related to places the user has checked in on social media. The management unit can also analyze the content of the user's social media posts to suggest related asset management methods. The management unit can also suggest related asset management methods based on the activities of the user's friends on social media. In this way, related asset management methods can be suggested by analyzing the user's social media activity. Some or all of the above-mentioned processing in the management unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the management unit can input the user's social media activity data into a generation AI, which can then suggest related asset management methods.

[0058] The management unit can customize the management method by reflecting the user's past feedback during asset management. The past feedback includes, for example, specific content such as the user's evaluation comments and usage history, and the method of reflecting the feedback. The management unit, for example, customizes the management method by reflecting the user's past feedback during asset management. For example, the management unit customizes the management method based on the user's past feedback. The management unit can also simplify the management procedure by referring to the user's past feedback. The management unit can also customize the management interface by reflecting the user's past feedback. This enables the management method to be customized and efficient asset management by reflecting the user's past feedback. Some or all of the above-described processing in the management unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the management unit can input the user's past feedback data into the generation AI, which can then customize the management method.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the expenditure information. For example, the analysis unit can perform a detailed analysis for important expenditure categories. The analysis unit can also perform a brief analysis for less important expenditure categories. The analysis unit can also determine the priority of the analysis based on the importance of the expenditure information. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the expenditure information. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input importance data of the expenditure information to the generation AI, which can then adjust the level of detail of the analysis.

[0061] During asset management, the management unit can analyze the user's past asset management history and select the optimal management method. For example, the management unit can propose the optimal management method based on the user's past asset management history. The management unit can also select a management method that reduces risk from the user's past asset management history. The management unit can also analyze the user's past asset management history and select the most efficient management method. This enables the optimal management method to be selected by analyzing the user's past asset management history, enabling efficient asset management. Some or all of the above-mentioned processing in the management unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the management unit can input the user's past asset management history data into the generation AI, which can select the optimal management method.

[0062] When making a proposal, the proposal unit can adjust the level of detail of the proposal based on the importance of the asset management plan. For example, the proposal unit can make a detailed proposal for an important asset management plan. The proposal unit can also make a concise proposal for an asset management plan with low importance. The proposal unit can also determine the priority of the proposal according to the importance of the asset management plan. This enables efficient proposals by adjusting the level of detail of the proposal based on the importance of the asset management plan. Some or all of the above-mentioned processing in the proposal unit may be performed using, or without, a generation AI, for example. For example, the proposal unit can input importance data of the asset management plan to the generation AI, which can then adjust the level of detail of the proposal.

[0063] During analysis, the analysis unit can apply different analysis algorithms depending on the expenditure category. For example, the analysis unit can perform an analysis of food expenses taking into account price fluctuations of ingredients. The analysis unit can also perform an analysis of transportation expenses taking into account the frequency of transportation use. The analysis unit can also perform an analysis of entertainment expenses taking into account the frequency of event participation. By applying different analysis algorithms depending on the expenditure category, more accurate analysis is possible. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input expenditure category data into a generation AI, which then applies different analysis algorithms.

[0064] When making a proposal, the proposal unit can determine the priority of the proposal based on the submission date of the asset management plan. For example, the proposal unit can prioritize asset management plans that were submitted recently. The proposal unit can also postpone asset management plans that were submitted earlier. The proposal unit can also dynamically adjust the priority of the proposal based on the submission date. This enables efficient proposals by determining the priority of the proposal based on the submission date of the asset management plan. Some or all of the above-mentioned processing in the proposal unit may be performed using, or without, the generation AI, for example. For example, the proposal unit can input data on the submission date of the asset management plan into the generation AI, and the generation AI can determine the priority of the proposal.

[0065] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit adjusts the analysis algorithm based on the user's past analysis results. The analysis unit can also identify specific spending patterns from the user's past analysis results. The analysis unit can also improve the accuracy of the analysis by referring to the user's past analysis results. In this way, the accuracy of the analysis is improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input the user's past analysis result data into the generation AI, which can improve the accuracy of the analysis.

[0066] The processing flow of the first embodiment will be briefly explained below.

[0067] Step 1: The input unit allows the user to input daily expenditure information. The user inputs expenditure information by category, such as food, transportation, and entertainment. For example, the user can input specific amounts such as "Today's food expenses are 1,000 yen, and transportation expenses are 500 yen." Step 2: The analysis unit uses the generation AI to analyze the expenditure information input by the input unit. The analysis unit, for example, understands the user's spending trends based on the input expenditure information. For example, the analysis unit identifies spending patterns, such as months with high food expenses or weeks with high transportation expenses. The analysis unit can also identify wasteful spending trends and provide the user with advice on saving money. Step 3: The proposal unit uses the generation AI to propose an asset management plan based on the expenditure information analyzed by the analysis unit. The proposal unit, for example, grasps the user's asset status and proposes an optimal asset management plan. For example, the proposal unit provides plans tailored to the user's needs, such as a plan to reduce wasteful spending and increase savings, or an investment plan with reduced risk. Step 4: The management unit manages the assets based on the asset management plan proposed by the proposal unit. The management unit efficiently manages the user's assets, for example, in accordance with the proposed asset management plan. As a result, the expense management and asset management integrated management system according to the embodiment can efficiently manage the user's expenses and manage their assets.

[0068] (Example 2) In an embodiment of the present invention, an expense management and asset management integrated management system allows a user to input daily expense information, and a generation AI analyzes the information to identify spending trends and wasteful spending. Furthermore, the system grasps the user's asset status and proposes an optimal asset management plan. The expense management and asset management integrated management system provides efficient asset management by having the user input expense information, and the generation AI analyzes the information, proposes an asset management plan, and manages assets. For example, in the expense management and asset management integrated management system, the user inputs expense information by category, such as food, transportation, and entertainment. For example, the user inputs specific amounts, such as "Today's food expenses are 1,000 yen, and transportation expenses are 500 yen." This information is input into the generation AI. The expense management and asset management integrated management system then uses the generation AI to analyze the input expense information. Based on the input expense information, the generation AI identifies spending patterns, such as months with high food expenses or weeks with high transportation expenses. It also identifies wasteful spending trends and provides the user with advice on saving. Furthermore, the expense management and asset management integrated management system grasps the user's asset status using the generation AI. By inputting information about a user's bank accounts, investment trusts, stocks, and other assets, the Generator AI grasps the user's total assets. For example, a user can input specific asset information such as "1 million yen in a bank account, 500,000 yen in investment trusts, and 300,000 yen in stocks." Based on this information, the Generator AI proposes an asset management plan for the user. The Generator AI comprehensively analyzes the user's spending habits and asset status to propose an optimal asset management plan. For example, it provides plans tailored to the user's needs, such as a plan to reduce wasteful spending and increase savings, or an investment plan with reduced risk. This allows the expense management and asset management system to manage the user's expenses and asset management in one place. This allows the Generator AI to efficiently manage the user's expenses and asset management. For example, a user can review their monthly expenses and reduce wasteful spending to increase savings. Furthermore, by following the asset management plan proposed by the Generator AI, they can efficiently increase their assets while reducing risk. This allows users to manage their assets to achieve their future goals.

[0069] An expense management and asset management integrated management system according to an embodiment includes an input unit, an analysis unit, a proposal unit, and a management unit. The input unit allows a user to input daily expense information. The user inputs expense information by category, such as food, transportation, and entertainment. For example, the user can input specific amounts, such as "Today's food expenses are 1,000 yen, and transportation expenses are 500 yen." The analysis unit uses a generation AI to analyze the expense information input by the input unit. The analysis unit identifies the user's spending trends based on the input expense information. For example, the analysis unit identifies spending patterns, such as months with high food expenses or weeks with high transportation expenses. The analysis unit can also identify wasteful spending trends and provide the user with advice on saving. The proposal unit uses the generation AI to propose an asset management plan based on the expense information analyzed by the analysis unit. The proposal unit, for example, understands the user's asset status and proposes an optimal asset management plan. For example, the proposal unit provides plans tailored to the user's needs, such as plans to reduce wasteful spending and increase savings, or investment plans with reduced risk. The management unit manages assets based on the asset management plan proposed by the proposal unit. The management unit efficiently manages the user's assets, for example, in accordance with the proposed asset management plan. As a result, the expense management and asset management integrated management system according to the embodiment can efficiently manage the user's expenses and manage their assets. For example, the user can increase savings by reviewing monthly expenses and reducing wasteful spending. Furthermore, by following the asset management plan proposed by the generation AI, the user can efficiently increase assets while reducing risk. This enables the user to manage their assets to achieve their future goals.

[0070] The input unit allows the user to input expenditure information for each category of food, transportation, and entertainment. The categories of food, transportation, and entertainment include specific definitions and ranges, such as whether food expenses include eating out. The input unit allows the user to input expenditure information for each category of food, transportation, and entertainment. For example, the user can input specific amounts such as "Today's food expenses are 1,000 yen, and transportation expenses are 500 yen." This allows detailed expenditure management by inputting expenditure information for each category. Some or all of the above-described processing in the input unit may be performed using, or without, a generation AI. For example, the input unit may input the expenditure information entered by the user to a generation AI, which may then analyze the expenditure information.

[0071] The analysis unit can grasp the user's spending trends based on the input expenditure information. The spending trends include specific grasping methods and criteria, such as monthly spending patterns and the proportion of spending in specific categories. The analysis unit grasps the user's spending trends based on the input expenditure information, for example. For example, the analysis unit can identify spending patterns, such as months with high food expenses or weeks with high transportation expenses. This allows the user's spending trends to be grasped, making it possible to identify wasteful spending and provide advice on saving. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the input expenditure information into a generation AI, which can analyze the spending trends.

[0072] The analysis unit can identify wasteful spending trends and provide saving advice to the user. Examples of wasteful spending trends include specific definitions and methods for identifying unnecessary purchases and excessive spending. The analysis unit can, for example, identify wasteful spending trends and provide saving advice to the user. For example, the analysis unit can analyze the user's spending information and identify wasteful spending patterns. The analysis unit can also provide specific saving advice to the user. This allows the user's spending to be efficiently managed by identifying wasteful spending trends and providing saving advice. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input spending information to a generation AI to identify wasteful spending trends, and the generation AI can analyze the wasteful spending trends.

[0073] The proposal unit can grasp the user's asset status and propose an asset management plan. The asset status includes, for example, valuation methods for cash, investment assets, real estate, etc. The proposal unit, for example, grasps the user's asset status and proposes an optimal asset management plan. For example, the proposal unit can grasp the user's total assets by inputting information on the user's bank accounts, investment trusts, stocks, etc. The proposal unit comprehensively analyzes the user's spending habits and asset status and proposes an optimal asset management plan. For example, the proposal unit provides plans tailored to the user's needs, such as a plan to reduce wasteful spending and increase savings, or a low-risk investment plan. This enables efficient asset management by grasping the user's asset status and proposing an optimal asset management plan. Some or all of the above-described processing in the proposal unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the proposal unit can input the user's asset information into a generation AI, which then proposes an optimal asset management plan.

[0074] The management unit can manage assets based on the proposed asset management plan. Asset management includes, for example, specific methods and standards such as asset diversification and risk management. The management unit, for example, manages assets based on the proposed asset management plan. For example, the management unit can efficiently manage the user's assets in accordance with the proposed asset management plan. This enables efficient asset management by managing assets based on the proposed asset management plan. Some or all of the above-mentioned processing in the management unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the management unit can input the proposed asset management plan into the generation AI, and the generation AI can perform asset management.

[0075] The input unit can estimate the user's emotions and adjust the timing of inputting expenditure information based on the estimated user emotions. Emotion estimation includes specific methods and criteria such as facial expression recognition, voice analysis, and text analysis. The input unit can estimate the user's emotions and adjust the timing of inputting expenditure information based on the estimated user emotions. For example, if the user is feeling stressed, the input unit can prompt the user to input expenditure information during a time when the user is able to relax. The input unit can also send a notification prompting the user to input expenditure information when the user is relaxed. The input unit can also adjust the input of expenditure information to be postponed if the user is busy. This allows the input of expenditure information at a more appropriate time by adjusting the input timing of expenditure information according to the user's emotions. Emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the input unit can be performed using, for example, a generation AI, or without a generation AI. For example, the input unit can input the user's emotional data into the generation AI, which can then estimate the emotion and adjust the input timing based on the results.

[0076] The input unit can analyze the user's past spending history and select an input method. The past spending history includes, for example, specific content and analysis methods, such as spending data for the past year and spending trends by category. The input unit, for example, analyzes the user's past spending history and selects the optimal input method. For example, the input unit preferentially suggests input methods (such as voice and text) that the user has frequently used in the past. The input unit can also automatically input spending information for specific categories from the user's past spending history. The input unit can also provide templates to reduce the effort of inputting information based on the user's past spending history. This allows the optimal input method to be selected by analyzing the user's past spending history, thereby reducing the effort of inputting information. Some or all of the above-mentioned processing in the input unit can be performed, for example, using a generation AI or without a generation AI. For example, the input unit can input the user's past spending history into a generation AI, which can select the optimal input method.

[0077] The input unit can filter the expenditure information based on the user's current living situation and areas of interest when inputting the expenditure information. The living situation includes specific definitions and filtering methods such as family structure, income status, and lifestyle. The input unit can filter the expenditure information based on the user's current living situation and areas of interest when inputting the expenditure information. For example, if the user is traveling, the input unit can prioritize inputting travel-related expenditure information. Furthermore, if the user is interested in health, the input unit can prioritize inputting health-related expenditure information. Furthermore, if the user is participating in a specific event, the input unit can prioritize inputting expenditure information related to the event. By filtering the expenditure information based on the user's living situation and areas of interest, more relevant expenditure information can be input. Some or all of the above-described processing in the input unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the input unit can input data on the user's living situation and areas of interest to the generation AI, which can then perform the filtering.

[0078] The input unit can select the optimal input means depending on the user's input method (voice, text, image, etc.) when inputting expenditure information. The input method includes specific types and selection criteria, such as voice input, text input, and image input. The input unit can select the optimal input means depending on the user's input method (voice, text, image, etc.) when inputting expenditure information. For example, if the user prefers voice input, the input unit can provide voice input preferentially. Also, if the user prefers text input, the input unit can provide text input preferentially. Furthermore, if the user uploads an image of a receipt, the input unit can automatically input expenditure information using image recognition technology. This improves input convenience by selecting the optimal input means depending on the user's input method. Some or all of the above-described processing in the input unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the input unit can input data on the user's input method into a generation AI, which can select the optimal input means.

[0079] The input unit can estimate the user's emotions and prioritize the expense information to be input based on the estimated user emotions. Emotion estimation includes specific methods and criteria such as facial expression recognition, voice analysis, and text analysis. The input unit can estimate the user's emotions and prioritize the expense information to be input based on the estimated user emotions. For example, the input unit prioritizes input of important expense information when the user is stressed. The input unit can also prompt the user to input detailed expense information when the user is relaxed. The input unit can also adjust the input so that only simple expense information is input when the user is busy. Thus, by prioritizing the expense information according to the user's emotions, important expense information can be prioritized. Emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the input unit can be performed using, for example, a generation AI, or without a generation AI. For example, the input unit can input the user's emotional data into the generation AI, which can then estimate the emotion and determine the priority of the expenditure information based on the result.

[0080] When inputting expenditure information, the input unit can prioritize inputting highly relevant expenditure information in consideration of the user's geographical location information. Geographical location information includes, for example, specific acquisition methods and usage criteria, such as GPS data and location information services. When inputting expenditure information, the input unit, for example, prioritizes inputting highly relevant expenditure information in consideration of the user's geographical location information. For example, when the user is at a specific store, the input unit prioritizes inputting expenditure information at that store. Furthermore, when the user is traveling, the input unit can prioritize inputting expenditure information at the travel destination. Furthermore, when the user is at home, the input unit can prioritize inputting daily expenditure information. In this way, highly relevant expenditure information can be prioritized by considering the user's geographical location information. Some or all of the above-described processing in the input unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the input unit can input the user's geographical location information to a generation AI, which can then prioritize inputting highly relevant expenditure information.

[0081] The input unit can analyze the user's social media activity and input related expenditure information when inputting expenditure information. Social media activity includes specific analysis methods and usage criteria, such as the content of posts, the number of likes, and the number of followers. The input unit can analyze the user's social media activity and input related expenditure information when inputting expenditure information, for example. For example, the input unit can input expenditure information related to places the user checked in on social media. The input unit can also analyze the content of the user's social media posts and input related expenditure information. The input unit can also input related expenditure information based on the activities of the user's friends on social media. In this way, related expenditure information can be efficiently input by analyzing the user's social media activity. Some or all of the above-described processing in the input unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the input unit can input data on the user's social media activity to a generation AI, which then inputs the related expenditure information.

[0082] The input unit can customize the input method by reflecting the user's past feedback when inputting expenditure information. The past feedback includes, for example, specific content such as the user's evaluation comments and usage history, and the method of reflecting the feedback. The input unit can customize the input method by reflecting the user's past feedback when inputting expenditure information. For example, the input unit can preferentially provide input methods that the user has previously preferred. The input unit can also improve the input interface based on the user's past feedback. The input unit can also simplify the input procedure by referring to the user's past feedback. This customizes the input method by reflecting the user's past feedback, improving input convenience. Some or all of the above-described processing in the input unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the input unit can input the user's past feedback into the generation AI, which can then customize the input method.

[0083] The analysis unit can estimate the user's emotions and adjust the analysis method of the spending habits based on the estimated user emotions. Emotion estimation includes specific methods and standards, such as facial expression recognition, voice analysis, and text analysis. The analysis unit can estimate the user's emotions and adjust the analysis method of the spending habits based on the estimated user emotions. For example, the analysis unit can provide a concise analysis result when the user is stressed. The analysis unit can also provide a detailed analysis result when the user is relaxed. The analysis unit can also provide a visually appealing analysis result when the user is excited. This allows for more appropriate analysis results to be provided by adjusting the analysis method of the spending habits according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, the generation AI, or without the generation AI. For example, the analysis unit can input the user's emotional data into the generation AI, which can then infer the emotion and adjust the analysis method of spending habits based on the results.

[0084] The analysis unit can adjust the level of detail of the analysis based on the importance of the expenditure information during analysis. The importance of the expenditure information includes specific evaluation criteria and adjustment methods, such as high expenditures and frequent expenditures. The analysis unit can adjust the level of detail of the analysis based on the importance of the expenditure information during analysis, for example. For example, the analysis unit performs a detailed analysis on important expenditure categories. The analysis unit can also perform a brief analysis on less important expenditure categories. The analysis unit can also determine the priority of the analysis according to the importance of the expenditure information. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the expenditure information. Some or all of the above-mentioned processing in the analysis unit can be performed using, or without, a generation AI. For example, the analysis unit can input importance data of the expenditure information into the generation AI, which can then adjust the level of detail of the analysis.

[0085] The analysis unit can apply different analysis algorithms depending on the expenditure category during analysis. Expenditure categories include specific definitions and analysis algorithms, such as food expenses, transportation expenses, and entertainment expenses. The analysis unit can apply different analysis algorithms depending on the expenditure category during analysis. For example, the analysis unit can perform an analysis of food expenses taking into account price fluctuations of ingredients. The analysis unit can also perform an analysis of transportation expenses taking into account the frequency of transportation use. The analysis unit can also perform an analysis of entertainment expenses taking into account the frequency of event participation. By applying different analysis algorithms depending on the expenditure category, more accurate analysis is possible. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input expenditure category data into a generation AI, and the generation AI can apply different analysis algorithms.

[0086] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. Past analysis results include, for example, specific content and usage methods, such as past spending trends and savings effects. During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit adjusts the analysis algorithm based on the user's past analysis results. The analysis unit can also identify specific spending patterns from the user's past analysis results. The analysis unit can also improve the accuracy of the analysis by referring to the user's past analysis results. In this way, the accuracy of the analysis is improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit can be performed, for example, using a generation AI or can be performed without using a generation AI. For example, the analysis unit can input the user's past analysis result data into the generation AI, which can improve the accuracy of the analysis.

[0087] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. Emotion estimation includes specific methods and criteria, such as facial expression recognition, voice analysis, and text analysis. The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit provides a simple, highly visible display method. If the user is relaxed, the analysis unit can provide a display method that includes detailed information. If the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. This allows for more appropriate display by adjusting the display method of the analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, the generation AI, or without the generation AI. For example, the analysis unit can input the user's emotional data into the generation AI, which can then estimate the emotion and adjust the way the analysis results are displayed based on that estimate.

[0088] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of the expenditure information. The submission time includes specific evaluation criteria and usage methods, such as the end of the month or after payday. During analysis, the analysis unit can determine the priority of analysis based on the time of submission of the expenditure information. For example, the analysis unit prioritizes analysis of recently submitted expenditure information. The analysis unit can also postpone analysis of older submitted expenditure information. The analysis unit can also dynamically adjust the priority of analysis based on the time of submission. This enables efficient analysis by determining the priority of analysis based on the time of submission of the expenditure information. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input data on the time of submission of expenditure information into the generation AI, and the generation AI can determine the priority of analysis.

[0089] The analysis unit can adjust the order of analysis based on the relevance of the expenditure information during analysis. The relevance includes specific evaluation criteria and adjustment methods, such as expenditures in the same category or expenditures during the same period. The analysis unit can adjust the order of analysis based on the relevance of the expenditure information during analysis, for example. For example, the analysis unit prioritizes analysis of highly relevant expenditure information. The analysis unit can also postpone analysis of less relevant expenditure information. The analysis unit can also dynamically adjust the order of analysis based on the relevance of the expenditure information. This enables efficient analysis by adjusting the order of analysis based on the relevance of the expenditure information. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input relevance data of the expenditure information into the generation AI, which can then adjust the order of analysis.

[0090] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. The level of expertise includes specific evaluation criteria and adjustment methods, such as beginner, intermediate, and advanced. During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, the analysis unit uses detailed technical terminology if the user has technical expertise. Furthermore, the analysis unit can explain the analysis results in simple terms if the user does not have technical expertise. Furthermore, the analysis unit can adjust the way the analysis results are presented according to the user's level of expertise. By adjusting the use of technical terminology in the analysis according to the user's level of expertise, more understandable analysis results can be provided. Some or all of the above-described processing in the analysis unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input the user's level of expertise data into the generation AI, which can then adjust the use of technical terminology.

[0091] The suggestion unit can estimate the user's emotions and adjust the method for proposing an asset management plan based on the estimated user's emotions. Emotion estimation includes specific methods and criteria, such as facial expression recognition, voice analysis, and text analysis. The suggestion unit can estimate the user's emotions and adjust the method for proposing an asset management plan based on the estimated user's emotions. For example, the suggestion unit can propose a detailed asset management plan when the user is relaxed. The suggestion unit can also propose a concise asset management plan when the user is in a hurry. The suggestion unit can also propose a visually appealing asset management plan when the user is excited. This enables more appropriate proposals by adjusting the method for proposing an asset management plan based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit can be performed using, for example, the generation AI, or without the generation AI. For example, the proposal unit can input the user's emotional data into the generation AI, which can then estimate the emotion and adjust the method of proposing an asset management plan based on the results.

[0092] The proposal unit can adjust the level of detail of the proposal based on the importance of the asset management plan when making a proposal. The importance includes, for example, specific evaluation criteria and adjustment methods, such as high-risk investments and low-risk investments. The proposal unit can adjust the level of detail of the proposal based on the importance of the asset management plan when making a proposal. For example, the proposal unit can make detailed proposals for important asset management plans. The proposal unit can also make brief proposals for less important asset management plans. The proposal unit can also determine the priority of proposals according to the importance of the asset management plans. This enables efficient proposals by adjusting the level of detail of the proposal based on the importance of the asset management plan. Some or all of the above-mentioned processing in the proposal unit can be performed using, or without, a generation AI. For example, the proposal unit can input importance data of the asset management plan into the generation AI, which can then adjust the level of detail of the proposal.

[0093] The proposal unit can apply different proposal algorithms depending on the asset category when making a proposal. Asset categories include specific definitions and proposal algorithms, such as stocks, bonds, and real estate. The proposal unit can apply different proposal algorithms depending on the asset category when making a proposal. For example, the proposal unit can propose a savings plan for a bank account. The proposal unit can also propose a low-risk investment plan for investment trusts. The proposal unit can also propose an investment plan that takes risk and return into consideration for stocks. This enables more accurate proposals by applying different proposal algorithms depending on the asset category. Some or all of the above-mentioned processing in the proposal unit can be performed using, or without, a generation AI, for example. For example, the proposal unit can input asset category data into a generation AI, and the generation AI can apply different proposal algorithms.

[0094] The suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results when making a suggestion. The past suggestion results include, for example, specific content and usage methods, such as past investment performance and user feedback. The suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results when making a suggestion. For example, the suggestion unit can adjust the suggestion algorithm based on the user's past suggestion results. The suggestion unit can also identify a specific asset management pattern from the user's past suggestion results. The suggestion unit can also improve the accuracy of the suggestion by referring to the user's past suggestion results. In this way, the accuracy of the suggestion is improved by referring to the user's past suggestion results. Some or all of the above-mentioned processing in the suggestion unit can be performed, for example, using a generation AI or without using a generation AI. For example, the suggestion unit can input the user's past suggestion result data into the generation AI, which can improve the accuracy of the suggestion.

[0095] The suggestion unit can estimate the user's emotion and adjust the length of the suggestion based on the estimated user's emotion. Emotion estimation includes specific methods and criteria, such as facial expression recognition, voice analysis, and text analysis. The suggestion unit can estimate the user's emotion and adjust the length of the suggestion based on the estimated user's emotion. For example, if the user is in a hurry, the suggestion unit can provide a short, to-the-point suggestion. If the user is relaxed, the suggestion unit can provide a longer suggestion with detailed explanations. If the user is excited, the suggestion unit can provide a suggestion with visually stimulating effects. This allows for more appropriate suggestions by adjusting the length of the suggestion based on the user's emotion. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit can be performed using, for example, the generation AI, or without the generation AI. For example, the suggestion unit can input the user's emotional data into the generation AI, which can then estimate the emotion and adjust the length of the suggestion based on the results.

[0096] The proposal unit, when making a proposal, can determine the priority of the proposal based on the submission date of the asset management plan. The submission date includes specific evaluation criteria and usage methods, such as the end of the month or after payday. The proposal unit, when making a proposal, can determine the priority of the proposal based on the submission date of the asset management plan. For example, the proposal unit can prioritize asset management plans that were submitted recently. The proposal unit can also postpone asset management plans that were submitted earlier. The proposal unit can also dynamically adjust the priority of the proposal based on the submission date. This enables efficient proposals by determining the priority of the proposal based on the submission date of the asset management plan. Some or all of the above-mentioned processing in the proposal unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the proposal unit can input data on the submission date of the asset management plan into the generation AI, which can then determine the priority of the proposal.

[0097] The proposal unit can adjust the order of proposals based on the relevance of the asset management plans when making a proposal. The relevance includes specific evaluation criteria and adjustment methods, such as assets in the same category or assets from the same period. The proposal unit can, for example, adjust the order of proposals based on the relevance of the asset management plans when making a proposal. For example, the proposal unit prioritizes proposing asset management plans with high relevance. The proposal unit can also postpone asset management plans with low relevance. The proposal unit can also dynamically adjust the order of proposals based on the relevance of the asset management plans. This enables efficient proposals by adjusting the order of proposals based on the relevance of the asset management plans. Some or all of the above-mentioned processing in the proposal unit can be performed using, or without, a generation AI. For example, the proposal unit can input relevance data of asset management plans into the generation AI, which can then adjust the order of proposals.

[0098] The suggestion unit may adjust the use of technical terminology in the proposal according to the user's level of expertise. The level of expertise may include specific evaluation criteria and adjustment methods, such as beginner, intermediate, or advanced. The suggestion unit may adjust the use of technical terminology in the proposal according to the user's level of expertise. For example, the suggestion unit may use detailed technical terminology if the user has technical expertise. The suggestion unit may also explain the proposal content in simple terms if the user does not have technical expertise. The suggestion unit may also adjust the way the proposal content is expressed according to the user's level of expertise. This allows for more understandable proposals by adjusting the use of technical terminology in the proposal according to the user's level of expertise. Some or all of the above-described processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit may input the user's level of expertise data into the generation AI, which may then adjust the use of technical terminology.

[0099] The management unit can estimate the user's emotions and adjust the asset management method based on the estimated user emotions. Emotion estimation includes specific methods and criteria, such as facial expression recognition, voice analysis, and text analysis. The management unit, for example, estimates the user's emotions and adjusts the asset management method based on the estimated user emotions. For example, the management unit can provide a concise asset management method when the user is stressed. The management unit can also provide a detailed asset management method when the user is relaxed. The management unit can also provide a visually appealing asset management method when the user is excited. This enables more appropriate asset management by adjusting the asset management method according to the user's emotions. Emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the management unit can be performed using, for example, the generation AI, or without the generation AI. For example, the management department can input the user's emotional data into the generation AI, which can then infer the emotion and adjust the asset management method based on the results.

[0100] During asset management, the management unit can analyze the user's past asset management history to select the optimal management method. The past asset management history includes, for example, specific details such as past investment performance and asset increases / decreases, as well as analysis methods. During asset management, the management unit, for example, analyzes the user's past asset management history to select the optimal management method. For example, the management unit proposes the optimal management method based on the user's past asset management history. The management unit can also select a management method that reduces risk from the user's past asset management history. The management unit can also analyze the user's past asset management history to select the most efficient management method. This enables the optimal management method to be selected by analyzing the user's past asset management history, enabling efficient asset management. Some or all of the above-described processing in the management unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the management unit can input the user's past asset management history data into the generation AI, which then selects the optimal management method.

[0101] The management unit can customize the management means based on the user's current living situation during asset management. Living situations include specific definitions and customization methods for, for example, family composition, income status, and lifestyle. The management unit customizes the management means based on the user's current living situation during asset management. For example, if the user is traveling, the management unit can provide an asset management method for the user's travel destination. If the user is interested in health, the management unit can also provide a health-related asset management method. If the user is participating in a specific event, the management unit can also provide an asset management method related to the event. This enables more appropriate asset management by customizing the management means based on the user's current living situation. Some or all of the above-described processing in the management unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the management unit can input the user's living situation data into a generation AI, which can then customize the management means.

[0102] The management unit can improve the management method by reflecting user feedback during asset management. The feedback includes, for example, specific content such as user evaluation comments and usage history, and the method of reflection. The management unit can improve the management method by reflecting user feedback during asset management. For example, the management unit improves the management method based on the user's past feedback. The management unit can also simplify the management procedure by referring to the user's feedback. The management unit can also improve the management interface by reflecting user feedback. In this way, the management method can be improved by reflecting user feedback, enabling efficient asset management. Some or all of the above-mentioned processing in the management unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the management unit can input user feedback data into the generation AI, which can improve the management method.

[0103] The management unit can estimate the user's emotions and determine asset management priorities based on the estimated user emotions. Emotion estimation can include specific methods and criteria, such as facial expression recognition, voice analysis, and text analysis. The management unit can estimate the user's emotions and determine asset management priorities based on the estimated user emotions. For example, the management unit can prioritize important asset management tasks when the user is stressed. The management unit can also perform detailed asset management tasks when the user is relaxed. The management unit can also perform only simple asset management tasks when the user is busy. This allows asset management priorities to be determined based on the user's emotions, thereby prioritizing important asset management tasks. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the management unit can be performed using, for example, the generation AI, or without the generation AI. For example, the management department can input the user's emotional data into the generation AI, which can then infer the emotions and determine asset management priorities based on the results.

[0104] The management unit can select the optimal management method by taking into account the user's geographical location information during asset management. Geographical location information includes, for example, specific acquisition methods and usage standards, such as GPS data and location information services. The management unit can select the optimal management method by taking into account the user's geographical location information during asset management. For example, when the user is in a specific area, the management unit can provide an asset management method appropriate for that area. Furthermore, when the user is traveling, the management unit can also provide an asset management method for the user's travel destination. Furthermore, when the user is at home, the management unit can also provide an everyday asset management method. This enables efficient asset management by taking into account the user's geographical location information. Some or all of the above-described processing in the management unit can be performed using, or without, a generation AI. For example, the management unit can input the user's geographical location information data into the generation AI, which can then select the optimal management method.

[0105] The management unit can analyze the user's social media activity during asset management to suggest management measures. Social media activity includes specific analysis methods and usage criteria, such as the content of posts, the number of likes, and the number of followers. For example, the management unit can analyze the user's social media activity during asset management to suggest management measures. For example, the management unit can suggest asset management methods related to places the user has checked in on social media. The management unit can also analyze the content of the user's social media posts to suggest related asset management methods. The management unit can also suggest related asset management methods based on the activities of the user's friends on social media. In this way, related asset management methods can be suggested by analyzing the user's social media activity. Some or all of the above-mentioned processing in the management unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the management unit can input the user's social media activity data into a generation AI, which can then suggest related asset management methods.

[0106] The management unit can customize the management method by reflecting the user's past feedback during asset management. The past feedback includes, for example, specific content such as the user's evaluation comments and usage history, and the method of reflecting the feedback. The management unit, for example, customizes the management method by reflecting the user's past feedback during asset management. For example, the management unit customizes the management method based on the user's past feedback. The management unit can also simplify the management procedure by referring to the user's past feedback. The management unit can also customize the management interface by reflecting the user's past feedback. This enables the management method to be customized and efficient asset management by reflecting the user's past feedback. Some or all of the above-described processing in the management unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the management unit can input the user's past feedback data into the generation AI, which can then customize the management method. === Hard Collateral 1-1 === Each of the multiple elements including the input unit, analysis unit, proposal unit, and management unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the input unit is realized by the reception device 38 of the smart device 14, and a user inputs daily expenditure information. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the input expenditure information using a generation AI. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and proposes an asset management plan based on the analyzed expenditure information. The management unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and manages assets based on the proposed asset management plan. === Hard Collateral 1-2 === Each of the multiple elements including the input unit, analysis unit, proposal unit, and management unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the input unit is realized by the microphone 238 of the smart glasses 214, and the user inputs daily expenditure information by voice. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the input expenditure information using a generation AI. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and proposes an asset management plan based on the analyzed expenditure information. The management unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and manages assets based on the proposed asset management plan. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned input unit, analysis unit, proposal unit, and management unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the input unit is realized by the microphone 238 of the headset-type terminal 314, and the user inputs daily expenditure information by voice. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the input expenditure information using a generation AI. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and proposes an asset management plan based on the analyzed expenditure information. The management unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and manages assets based on the proposed asset management plan. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned input unit, analysis unit, proposal unit, and management unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the input unit is realized by the microphone 238 of the robot 414, and the user inputs daily expenditure information by voice. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the input expenditure information using a generation AI. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and proposes an asset management plan based on the analyzed expenditure information. The management unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and manages assets based on the proposed asset management plan.

[0107] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0108] The suggestion unit can estimate the user's emotions and adjust the method of proposing an asset management plan based on the estimated user emotions. For example, if the user is relaxed, a detailed asset management plan can be proposed. If the user is in a hurry, a concise asset management plan can be proposed. If the user is excited, a visually appealing asset management plan can be proposed. This allows for more appropriate proposals by adjusting the method of proposing an asset management plan according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the suggestion unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the suggestion unit can input the user's emotion data into the generation AI, which can estimate the emotion and adjust the method of proposing an asset management plan based on the result.

[0109] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the expenditure information. For example, the analysis unit can perform a detailed analysis for important expenditure categories. The analysis unit can also perform a brief analysis for less important expenditure categories. The analysis unit can also determine the priority of the analysis based on the importance of the expenditure information. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the expenditure information. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input importance data of the expenditure information to the generation AI, which can then adjust the level of detail of the analysis.

[0110] The input unit can estimate the user's emotions and prioritize the expense information to be input based on the estimated user emotions. For example, when the user is feeling stressed, the input unit can prioritize inputting important expense information. Furthermore, when the user is relaxed, the input unit can also prompt the user to input detailed expense information. Furthermore, when the user is busy, the input unit can adjust the input so that only simple expense information is input. This allows the priority of expense information to be prioritized according to the user's emotions, thereby allowing important expense information to be input preferentially. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the input unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the input unit can input the user's emotion data into the generation AI, which can then estimate the emotion and prioritize the expense information based on the estimation result.

[0111] During asset management, the management unit can analyze the user's past asset management history and select the optimal management method. For example, the management unit can propose the optimal management method based on the user's past asset management history. The management unit can also select a management method that reduces risk from the user's past asset management history. The management unit can also analyze the user's past asset management history and select the most efficient management method. This enables the optimal management method to be selected by analyzing the user's past asset management history, enabling efficient asset management. Some or all of the above-mentioned processing in the management unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the management unit can input the user's past asset management history data into the generation AI, which can select the optimal management method.

[0112] When making a proposal, the proposal unit can adjust the level of detail of the proposal based on the importance of the asset management plan. For example, the proposal unit can make a detailed proposal for an important asset management plan. The proposal unit can also make a concise proposal for an asset management plan with low importance. The proposal unit can also determine the priority of the proposal according to the importance of the asset management plan. This enables efficient proposals by adjusting the level of detail of the proposal based on the importance of the asset management plan. Some or all of the above-mentioned processing in the proposal unit may be performed using, or without, a generation AI, for example. For example, the proposal unit can input importance data of the asset management plan to the generation AI, which can then adjust the level of detail of the proposal.

[0113] During analysis, the analysis unit can apply different analysis algorithms depending on the expenditure category. For example, the analysis unit can perform an analysis of food expenses taking into account price fluctuations of ingredients. The analysis unit can also perform an analysis of transportation expenses taking into account the frequency of transportation use. The analysis unit can also perform an analysis of entertainment expenses taking into account the frequency of event participation. By applying different analysis algorithms depending on the expenditure category, more accurate analysis is possible. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input expenditure category data into a generation AI, which then applies different analysis algorithms.

[0114] The management unit can estimate the user's emotions and adjust the asset management method based on the estimated user emotions. For example, the management unit can provide a concise asset management method when the user is stressed. The management unit can also provide a detailed asset management method when the user is relaxed. The management unit can also provide a visually appealing asset management method when the user is excited. This enables more appropriate asset management by adjusting the asset management method according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the management unit can be performed using, for example, the generation AI, or without the generation AI. For example, the management unit can input the user's emotion data into the generation AI, which can estimate the emotion and adjust the asset management method based on the result.

[0115] When making a proposal, the proposal unit can determine the priority of the proposal based on the submission date of the asset management plan. For example, the proposal unit can prioritize asset management plans that were submitted recently. The proposal unit can also postpone asset management plans that were submitted earlier. The proposal unit can also dynamically adjust the priority of the proposal based on the submission date. This enables efficient proposals by determining the priority of the proposal based on the submission date of the asset management plan. Some or all of the above-mentioned processing in the proposal unit may be performed using, or without, the generation AI, for example. For example, the proposal unit can input data on the submission date of the asset management plan into the generation AI, and the generation AI can determine the priority of the proposal.

[0116] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit adjusts the analysis algorithm based on the user's past analysis results. The analysis unit can also identify specific spending patterns from the user's past analysis results. The analysis unit can also improve the accuracy of the analysis by referring to the user's past analysis results. In this way, the accuracy of the analysis is improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input the user's past analysis result data into the generation AI, which can improve the accuracy of the analysis.

[0117] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated user emotions. For example, if the user is in a hurry, the suggestion unit can provide short, to-the-point suggestions. If the user is relaxed, the suggestion unit can provide longer suggestions with detailed explanations. If the user is excited, the suggestion unit can provide suggestions with visually stimulating effects. This allows for more appropriate suggestions by adjusting the length of the suggestions according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit can be performed using, for example, the generation AI, or without the generation AI. For example, the suggestion unit can input the user's emotion data into the generation AI, which can estimate the emotion and adjust the length of the suggestions based on the estimation result.

[0118] The processing flow of the second embodiment will be briefly explained below.

[0119] Step 1: The input unit allows the user to input daily expenditure information. The user inputs expenditure information by category, such as food, transportation, and entertainment. For example, the user can input specific amounts such as "Today's food expenses are 1,000 yen, and transportation expenses are 500 yen." Step 2: The analysis unit uses the generation AI to analyze the expenditure information input by the input unit. The analysis unit, for example, understands the user's spending trends based on the input expenditure information. For example, the analysis unit identifies spending patterns, such as months with high food expenses or weeks with high transportation expenses. The analysis unit can also identify wasteful spending trends and provide the user with advice on saving money. Step 3: The proposal unit uses the generation AI to propose an asset management plan based on the expenditure information analyzed by the analysis unit. The proposal unit, for example, grasps the user's asset status and proposes an optimal asset management plan. For example, the proposal unit provides plans tailored to the user's needs, such as a plan to reduce wasteful spending and increase savings, or an investment plan with reduced risk. Step 4: The management unit manages the assets based on the asset management plan proposed by the proposal unit. The management unit efficiently manages the user's assets, for example, in accordance with the proposed asset management plan. As a result, the expense management and asset management integrated management system according to the embodiment can efficiently manage the user's expenses and manage their assets.

[0120] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0121] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0122] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0123] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0124] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0125] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0126] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0127] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0128] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0129] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0130] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0131] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0132] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0133] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0134] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0135] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0136] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0137] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0138] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0139] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0140] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0141] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0142] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0143] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0144] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0145] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0146] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0147] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0148] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0149] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0150] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0151] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0152] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0153] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0154] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0157] 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.

[0158] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0159] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0160] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0161] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0162] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0163] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0164] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0165] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0166] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0167] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0168] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0169] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0170] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0171] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0172] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0173] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0174] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0175] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0176] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0177] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0178] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0179] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0180] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0181] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0182] 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.

[0183] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0184] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0185] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0186] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0187] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0188] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0189] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0190] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0191] [Explanation of symbols]

[0192] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an input section for inputting expenditure information; an analysis unit that analyzes the expenditure information input by the input unit; a proposal unit that proposes an asset management plan based on the expenditure information analyzed by the analysis unit; a management unit that manages assets based on the asset management plan proposed by the proposal unit; Equipped with A system characterized by:

2. The input unit Users enter spending information by food, transportation, and entertainment categories 2. The system of claim 1.

3. The analysis unit Understand user spending habits based on entered spending information 2. The system of claim 1.

4. The analysis unit Identify wasteful spending patterns and provide users with money-saving advice 2. The system of claim 1.

5. The proposal unit Understand the user's asset status and propose asset management plans 2. The system of claim 1.

6. The management unit Manage assets based on proposed investment plans 2. The system of claim 1.

7. The input unit The system estimates the user's emotions and adjusts the timing of inputting expenditure information based on the estimated user emotions.

2. The system of claim 1.

8. The input unit Analyze the user's past spending history and select the input method 2. The system of claim 1.

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