System

Abstract

To provide a system in embodiments which manages income and expense of an individual and provides effective investment advice.SOLUTION: A system in embodiments comprises: a reception part; a budget making part; an expense tracking part; and an investment advising part. The reception part receives input of information of income and expense. The budget making part makes a budget on the basis of the information input on the reception part. The expense tracking part tracks the expense on the basis of the budget made by the budget making part. The investment advising part provides investment advice on the basis of the expense information tracked by the expense tracking part.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 a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, managing an individual's income and expenses was complicated, making it difficult to obtain effective investment advice.

[0005] The system according to the embodiment aims to manage an individual's income and expenditure and provide effective investment advice. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a budgeting unit, an expenditure tracking unit, and an investment advice unit. The reception unit inputs income and expenditure information. The budgeting unit prepares a budget based on the information input by the reception unit. The expenditure tracking unit tracks expenditure based on the budget prepared by the budgeting unit. The investment advice unit provides investment advice based on the expenditure information tracked by the expenditure tracking unit. Effect of the Invention

[0007] The system according to the embodiment can manage an individual's income and expenditure and provide effective investment advice. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[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 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. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The personal asset management system according to the embodiment of the present invention is an application that utilizes a generation AI to support personal asset management. In this system, a user accesses an app and inputs information interactively, thereby collecting information such as income, expenditure, and investment status. The collected information is input to the generation AI, which performs analysis. Based on the analysis results, the system provides budgeting, expenditure tracking, and investment advice suitable for the user. For example, a monthly budget is created based on the user's income and expenditure. In addition, the system analyzes spending trends and provides advice to reduce wasteful spending. In addition, the system analyzes investment information and suggests suitable investment destinations for the user. The generation AI provides advice and suggestions in real time through dialogue with the user. For example, if a user inputs "I want to save money because I'm spending a lot this month," the generation AI analyzes the details of the expenditure and provides specific advice for saving. In addition, if a user inputs "I'm looking for a new investment destination," the generation AI analyzes market trends and suggests suitable investment destinations. This application allows the user to manage money more effectively. For example, the system can maintain a balance between income and expenditure by budgeting, and can reduce wasteful spending by tracking expenditure. In addition, the system can find suitable investment destinations through investment advice. This supports users in building long-term assets. Furthermore, the generative AI learns based on user feedback to improve the accuracy of its advice and suggestions. For example, if a user rates a piece of advice provided as "useful," the generative AI will provide that advice to other users. Conversely, if the user rates the advice as "unhelpful," the generative AI will learn to improve the advice. In this way, a personal asset management application that utilizes generative AI becomes a powerful tool for users to manage their money more effectively and support long-term asset building. This allows the personal asset management system to help users balance their income and expenses, reduce waste, and find appropriate investment opportunities.

[0029] The personal asset management system according to the embodiment includes a reception unit, a budgeting unit, an expenditure tracking unit, and an investment advice unit. The reception unit is a unit for a user to input information on income and expenditure. The information on income and expenditure includes, but is not limited to, for example, salary, rent, and utility expenses. The reception unit enables, for example, a user to access an app and input information in an interactive format. The budgeting unit uses a generation AI to create a budget based on the information input by the reception unit. The budget is created based on, for example, the ratio of income to expenditure and a savings goal, but is not limited to such examples. For example, the budgeting unit creates a monthly budget based on the user's income and expenditure. The expenditure tracking unit uses a generation AI to track expenditure based on the budget created by the budgeting unit. Expenditure tracking is performed based on, for example, categorization of expenditure and frequency of tracking, but is not limited to such examples. For example, the expenditure tracking unit compares the budget with actual expenditure and provides advice to reduce waste. The investment advice unit uses a generation AI to provide investment advice based on the expenditure information tracked by the expenditure tracking unit. The investment advice is provided based on, for example, risk assessment and selection criteria for investment destinations, but is not limited to such examples. For example, the investment advice unit analyzes expenditure information and market trends and proposes investment destinations suitable for the user. In this way, the personal asset management system according to the embodiment allows the user to input income and expenditure information, create a budget, track expenditures, and receive investment advice. Some or all of the above-mentioned processing in the investment advice unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the investment advice unit can provide investment advice using a generation AI model that inputs expenditure information and market trends and outputs investment advice.

[0030] The reception unit is a part for a user to input information on income and expenditure. The information on income and expenditure includes, but is not limited to, for example, salary, rent, and utility expenses. The reception unit, for example, enables a user to access the app and input information in an interactive format. Specifically, a user uses a smartphone or a computer to log in to a dedicated application and input details of income and expenditure. The application provides an intuitive interface to make it easy for the user to input information, and includes a drop-down menu for selecting income and expenditure categories and a numeric input field for inputting amounts. Furthermore, the reception unit also provides a function for automatically acquiring transaction data and automatically inputting income and expenditure information by linking the user with bank account and credit card information. This allows the user to input accurate information without hassle. The reception unit also has a function for the user to check the history of past income and expenditure, which makes it easier for the user to grasp his or her own asset situation. Furthermore, the reception unit uses encryption technology to protect data and ensure privacy in order to safely manage the information input by the user. This allows the user to input income and expenditure information with peace of mind.

[0031] The budgeting unit uses the generation AI to create a budget based on the information input by the reception unit. The budget can be created based on, for example, the ratio of income to expenditure and the savings goal, but is not limited to such examples. For example, the budgeting unit creates a monthly budget based on the user's income and expenditure. Specifically, the generation AI analyzes the user's income and expenditure data, and proposes an optimal budget taking into account past expenditure patterns and fluctuations in income. The generation AI can flexibly adjust the budget according to the user's lifestyle and goals. For example, if the user is planning a trip, the generation AI takes into account the budget for that and makes a proposal to reduce other expenditures. In addition, the generation AI calculates the monthly savings amount based on the savings goal set by the user and incorporates it into the budget. This allows the user to continue saving without difficulty. Furthermore, the budgeting unit provides an alert function to help the user keep to the budget, and notifies the user when expenditure is about to exceed the budget. This allows the user to be conscious of spending within the budget and prevent wasteful spending. The budgeting unit can continuously improve the accuracy of the budget based on the user's feedback and propose a more appropriate budget.

[0032] The expenditure tracking unit uses the generation AI to track expenditures based on the budget set by the budgeting unit. Expenditure tracking is performed, for example, based on categorization of expenditures and frequency of tracking, but is not limited to such examples. For example, the expenditure tracking unit compares the budget with actual expenditures and provides advice to reduce wasteful spending. Specifically, the generation AI analyzes the user's expenditure data in real time to check whether the expenditure is within the budget. The generation AI performs a detailed analysis for each expenditure category and identifies which category has the most expenditure. For example, if expenditure exceeds the budget in a category such as food or entertainment, the generation AI analyzes the cause and provides specific advice to reduce wasteful spending. Furthermore, the expenditure tracking unit provides a function to visualize the progress of expenditures based on the goal set by the user. This allows the user to grasp his / her own expenditure situation at a glance and take action toward achieving the goal. The expenditure tracking unit also has a function to automatically acquire transaction data and track expenditures in order to save the user the trouble of recording expenditures. This allows the user to perform accurate expenditure management without hassle. The expense tracking section continuously improves the accuracy of expense tracking based on user feedback, supporting more effective expense management.

[0033] The investment advice unit uses the generation AI to provide investment advice based on the expenditure information tracked by the expenditure tracking unit. The investment advice is provided based on, for example, risk assessment and investment selection criteria, but is not limited to such examples. For example, the investment advice unit analyzes expenditure information and market trends to suggest investment destinations suitable for the user. Specifically, the generation AI considers the user's spending patterns and savings goals and suggests an investment strategy according to the user's risk tolerance. The generation AI analyzes the latest market data in real time to evaluate the risk and return of the investment destination. For example, it analyzes the trends of the stock market and real estate market and selects the optimal investment destination for the user. In addition, the generation AI learns the user's investment history and preferences and provides individually customized investment advice. This allows the user to make investments that match their own risk tolerance and goals. In addition, the investment advice unit also provides support when the user makes an investment. For example, it not only provides advice on the selection of investment destinations, but also on the timing of investment and the method of diversifying investments. This allows the user to efficiently increase assets while diversifying risk. The investment advice unit can continuously improve the accuracy of advice based on user feedback and suggest more appropriate investment strategies.

[0034] The budgeting unit can create a monthly budget based on the information on income and expenditure. The budgeting unit creates a monthly budget based on, for example, the information on income and expenditure. The monthly budget includes, for example, fixed expenses, variable expenses, and savings goals, but is not limited to such examples. For example, the budgeting unit creates a budget based on the ratio of income to expenditure. The budgeting unit can also set a savings goal and create a budget based on the goal. Furthermore, the budgeting unit can analyze the information on income and expenditure and propose an optimal budget. This makes it possible to create a monthly budget based on the information on income and expenditure. A part or all of the above-mentioned processing in the budgeting unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the budgeting unit can input the information on income and expenditure into the generation AI, and the generation AI can create a budget.

[0035] The expenditure tracking unit can compare the budget and actual expenditure and provide specific advice to reduce wasteful spending. The expenditure tracking unit, for example, compares the budget and actual expenditure and provides specific advice to reduce wasteful spending. Specific advice to reduce wasteful spending includes, for example, saving methods and review points for spending, but is not limited to such examples. For example, the expenditure tracking unit categorizes expenditures and suggests saving methods for each category. In addition, the expenditure tracking unit can also identify review points for spending and provide specific advice based on the points. Furthermore, the expenditure tracking unit can analyze spending trends and provide advice to reduce wasteful spending. This makes it possible to provide advice to reduce wasteful spending. Part or all of the above-mentioned processing in the expenditure tracking unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the expenditure tracking unit can input the budget and actual expenditure into the generation AI, and the generation AI can provide advice to reduce wasteful spending.

[0036] The investment advice unit can analyze the expenditure information and market trends and propose an investment destination suitable for the user. The investment advice unit can, for example, analyze the expenditure information and market trends and propose an investment destination suitable for the user. Market trends include, for example, stock price fluctuations and economic indicators, but are not limited to such examples. For example, the investment advice unit can evaluate the risk tolerance of the user based on the expenditure information and propose an appropriate investment destination based on the evaluation. The investment advice unit can also analyze market trends and predict future investment risks. Furthermore, the investment advice unit can also propose an optimal investment destination based on the investment goal of the user. This makes it possible to propose an investment destination suitable for the user. Part or all of the above-mentioned processing in the investment advice unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the investment advice unit can input the expenditure information and market trends into the generation AI, and the generation AI can propose an appropriate investment destination.

[0037] The investment advice unit can learn based on the user's feedback and improve the specific accuracy of the advice and proposal. The investment advice unit can learn based on, for example, the user's feedback and improve the specific accuracy of the advice and proposal. The specific accuracy of the advice and proposal includes, for example, a method of utilizing user feedback and an improvement of a machine learning algorithm, but is not limited to such examples. For example, when the user evaluates the provided advice as "useful", the investment advice unit will provide the advice to other users. Also, when the user evaluates the advice as "not useful", the investment advice unit can learn to improve the advice. Furthermore, the investment advice unit can improve the machine learning algorithm based on the user's feedback and improve the accuracy of the advice and proposal. This can improve the accuracy of the advice and proposal. A part or all of the above-mentioned processing in the investment advice unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the investment advice unit can input the user's feedback to the generation AI, and the generation AI can improve the accuracy of the advice and proposal.

[0038] The reception unit can analyze the user's past income and expenditure history and select an appropriate input method. The reception unit, for example, analyzes the user's past income and expenditure history and selects an appropriate input method. The appropriate input method includes, for example, voice input, manual input, scan input, etc., but is not limited to such examples. For example, the reception unit automatically displays as candidates the income and expenditure items that the user has frequently input in the past. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest income and expenditure items to be used in a specific time period from the user's past input history. This makes it possible to select an optimal input method based on the user's past income and expenditure history. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the user's past income and expenditure history into the generation AI, which can select an appropriate input method.

[0039] The reception unit can perform filtering based on the user's current living situation and field of interest when inputting income and expenditure information. For example, the reception unit performs filtering based on the user's current living situation and field of interest when inputting income and expenditure information. The current living situation and field of interest include, for example, family structure, hobbies, and occupation, but are not limited to such examples. For example, the reception unit preferentially displays related income and expenditure items based on the user's current living situation. In addition, the reception unit can also filter and display related income and expenditure items based on the user's field of interest. Furthermore, the reception unit can customize the input of income and expenditure information based on the user's living situation and field of interest. This makes it possible to filter income and expenditure information based on the user's current living situation and field of interest. Part or all of the above-mentioned processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can input data on the user's living situation and field of interest to the generation AI, and the generation AI can perform filtering.

[0040] The reception unit can preferentially input highly relevant information in consideration of the geographical location information of the user when inputting the information on income and expenditure. For example, the reception unit preferentially inputs highly relevant information in consideration of the geographical location information of the user when inputting the information on income and expenditure. The geographical location information includes, for example, GPS data, address information, and the like, but is not limited to such examples. For example, when the user is in a specific area, the reception unit preferentially displays income and expenditure items related to the area. In addition, the reception unit can also filter and display related income and expenditure information based on the geographical location information of the user. Furthermore, the reception unit can customize the input of income and expenditure information based on the user's current location. This makes it possible to preferentially input highly relevant information based on the geographical location information of the user. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the geographical location information of the user to the generation AI, and the generation AI can preferentially input highly relevant information.

[0041] The reception unit can analyze the social media activity of the user and input related information when the income and expenditure information is input. For example, the reception unit can analyze the social media activity of the user and input related information when the income and expenditure information is input. The social media activity includes, for example, the content of posts, the number of followers, the number of likes, etc., but is not limited to such examples. For example, the reception unit automatically suggests related income and expenditure items from the social media activity of the user. In addition, the reception unit can also analyze the social media activity of the user and filter the related income and expenditure information. Furthermore, the reception unit can customize the input of income and expenditure information based on the social media activity of the user. This makes it possible to input related information based on the social media activity of the user. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the reception unit can input data of the user's social media activity to the generation AI, and the generation AI can input the related information.

[0042] The budgeting unit can adjust the specific level of detail of the budget based on the importance of the income and expenditure when making a budget. For example, the budgeting unit adjusts the level of detail of the budget based on the importance of the income and expenditure when making a budget. The importance of the income and expenditure includes, for example, daily necessities, entertainment expenses, etc., but is not limited to such examples. For example, the budgeting unit makes a detailed budget for important income and expenditure items. In addition, the budgeting unit can also make a simplified budget for income and expenditure items with low importance. Furthermore, the budgeting unit can dynamically adjust the level of detail of the budget based on the importance of the income and expenditure. This makes it possible to adjust the level of detail of the budget based on the importance of the income and expenditure. A part or all of the above-mentioned processing in the budgeting unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the budgeting unit can input the importance of the income and expenditure to the generation AI, and the generation AI can adjust the level of detail of the budget.

[0043] The budgeting unit can apply different budgeting algorithms depending on the income and expenditure categories when making a budget. The budgeting unit, for example, applies different budgeting algorithms depending on the income and expenditure categories when making a budget. The budgeting algorithms include, but are not limited to, linear regression, decision trees, and the like. For example, the budgeting unit applies different budgeting algorithms depending on the income and expenditure categories. The budgeting unit can also select an optimal budgeting algorithm for each income and expenditure category. Furthermore, the budgeting unit can dynamically adjust the budgeting algorithm based on the income and expenditure categories. This makes it possible to apply different budgeting algorithms depending on the income and expenditure categories. Some or all of the above-mentioned processing in the budgeting unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the budgeting unit can input the income and expenditure categories into the generation AI, and the generation AI can apply the optimal budgeting algorithm.

[0044] The budget planning unit can determine the specific priority of the budget based on the submission timing of the income and the expenditure when planning the budget. For example, the budget planning unit determines the priority of the budget based on the submission timing of the income and the expenditure when planning the budget. The submission timing of the income and the expenditure includes, but is not limited to, the beginning of the month and the end of the month. For example, the budget planning unit determines the priority of the budget based on the submission timing of the income and the expenditure. The budget planning unit can also prioritize the budget for the income and the expenditure items that are close to the submission timing. Furthermore, the budget planning unit can dynamically adjust the priority of the budget based on the submission timing. This makes it possible to determine the priority of the budget based on the submission timing of the income and the expenditure. A part or all of the above-mentioned processing in the budget planning unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the budget planning unit can input the submission timing of the income and the expenditure to the generation AI, and the generation AI can determine the priority of the budget.

[0045] The budget planning unit can adjust the order of the budget based on the relevance between income and expenditure when planning the budget. The budget planning unit, for example, adjusts the order of the budget based on the relevance between income and expenditure when planning the budget. The relevance between income and expenditure includes, for example, the relevance between the source of income and the destination of expenditure, the purpose of expenditure, and the like, but is not limited to such examples. For example, the budget planning unit adjusts the order of the budget based on the relevance between income and expenditure. The budget planning unit can also preferentially incorporate items with a high relevance between income and expenditure into the budget. Furthermore, the budget planning unit can dynamically adjust the order of the budget based on the relevance between income and expenditure. This makes it possible to adjust the order of the budget based on the relevance between income and expenditure. A part or all of the above-mentioned processing in the budget planning unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the budget planning unit can input the relevance between income and expenditure into the generation AI, and the generation AI can adjust the order of the budget.

[0046] The expenditure tracking unit can improve the specific accuracy of the expenditure tracking by considering the interrelationship between income and expenditure when tracking expenditure. The expenditure tracking unit, for example, improves the accuracy of tracking by considering the interrelationship between income and expenditure when tracking expenditure. The specific accuracy of tracking includes, for example, an allowable error range, data accuracy, and the like, but is not limited to such examples. For example, the expenditure tracking unit improves the accuracy of the expenditure tracking by considering the interrelationship between income and expenditure. In addition, the expenditure tracking unit can adjust the expenditure tracking algorithm based on the interrelationship between income and expenditure. Furthermore, the expenditure tracking unit can analyze the interrelationship between income and expenditure and dynamically improve the accuracy of the expenditure tracking. This makes it possible to improve the accuracy of the expenditure tracking by considering the interrelationship between income and expenditure. Part or all of the above-mentioned processing in the expenditure tracking unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the expenditure tracking unit inputs the interrelationship between income and expenditure into the generation AI, and the generation AI can improve the accuracy of the expenditure tracking.

[0047] The expenditure tracking unit can perform expenditure tracking while taking into consideration the attribute information of the submitter of the income and expenditure. For example, the expenditure tracking unit performs expenditure tracking while taking into consideration the attribute information of the submitter of the income and expenditure. The attribute information of the submitter includes, for example, age, occupation, income level, etc., but is not limited to such examples. For example, the expenditure tracking unit performs expenditure tracking while taking into consideration the attribute information of the submitter of the income and expenditure. In addition, the expenditure tracking unit can adjust the expenditure tracking algorithm based on the attribute information of the submitter. Furthermore, the expenditure tracking unit can analyze the attribute information of the submitter and improve the accuracy of expenditure tracking. In this way, expenditure tracking can be performed while taking into consideration the attribute information of the submitter. Part or all of the above-mentioned processing in the expenditure tracking unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the expenditure tracking unit inputs the attribute information of the submitter to the generation AI, and the generation AI can perform expenditure tracking.

[0048] The expenditure tracking unit can perform expenditure tracking while taking into consideration the geographical distribution of income and expenditure. The expenditure tracking unit performs expenditure tracking while taking into consideration the geographical distribution of income and expenditure, for example. The geographical distribution includes, but is not limited to, expenditure by region, comparison between cities and rural areas, and the like. For example, the expenditure tracking unit performs expenditure tracking while taking into consideration the geographical distribution of income and expenditure. The expenditure tracking unit can also adjust the expenditure tracking algorithm based on the geographical distribution. Furthermore, the expenditure tracking unit can analyze the geographical distribution and improve the accuracy of expenditure tracking. This allows expenditure tracking while taking into consideration the geographical distribution of income and expenditure. A part or all of the above-mentioned processing in the expenditure tracking unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the expenditure tracking unit inputs data on the geographical distribution into the generation AI, and the generation AI can perform expenditure tracking.

[0049] The expenditure tracking unit can improve the accuracy of tracking by referring to related literature on income and expenditure when tracking expenditure. The expenditure tracking unit can improve the accuracy of tracking by referring to related literature on income and expenditure when tracking expenditure, for example. The related literature includes, but is not limited to, academic papers, industry reports, and the like. For example, the expenditure tracking unit can improve the accuracy of expenditure tracking by referring to related literature on income and expenditure. In addition, the expenditure tracking unit can adjust the expenditure tracking algorithm based on the related literature. Furthermore, the expenditure tracking unit can analyze the related literature and dynamically improve the accuracy of expenditure tracking. This makes it possible to improve the accuracy of expenditure tracking by referring to related literature on income and expenditure. Part or all of the above-mentioned processing in the expenditure tracking unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the expenditure tracking unit can input data of related literature into the generation AI, and the generation AI can improve the accuracy of expenditure tracking.

[0050] The investment advice unit can provide appropriate investment advice by referring to past data on income and expenditure when providing investment advice. The investment advice unit can provide optimal investment advice by referring to past data on income and expenditure when providing investment advice, for example. The past data on income and expenditure includes, but is not limited to, data from the past year and a specific period. For example, the investment advice unit can suggest optimal investment destinations based on the past data on income and expenditure. The investment advice unit can also provide investment advice with minimized risk from the past data. Furthermore, the investment advice unit can analyze the past data on income and expenditure and provide the most efficient investment advice. This makes it possible to provide optimal investment advice based on the past data on income and expenditure. A part or all of the above-mentioned processing in the investment advice unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the investment advice unit can input the past data on income and expenditure into the generation AI, which can provide optimal investment advice.

[0051] The investment advice unit can customize the investment advice means based on the current status of income and expenditure when providing investment advice. The investment advice unit, for example, customizes the investment advice means based on the current status of income and expenditure when providing investment advice. The current status of income and expenditure includes, for example, the current income level, expenditure tendency, and the like, but is not limited to such examples. For example, the investment advice unit proposes an optimal investment means based on the current status of income and expenditure. The investment advice unit can also provide an investment means with minimized risk, taking into account the current status. Furthermore, the investment advice unit can analyze the current status of income and expenditure and propose the most efficient investment means. This makes it possible to propose the optimal investment means based on the current status of income and expenditure. Part or all of the above-mentioned processing in the investment advice unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the investment advice unit can input the current status of income and expenditure into the generation AI, and the generation AI can propose the optimal investment means.

[0052] The investment advice unit can provide appropriate investment advice in consideration of the geographic location information of income and expenditure when providing investment advice. The investment advice unit, for example, provides optimal investment advice in consideration of the geographic location information of income and expenditure when providing investment advice. The geographic location information includes, for example, GPS data, address information, and the like, but is not limited to such examples. For example, the investment advice unit proposes an optimal investment destination based on the geographic location information of income and expenditure. The investment advice unit can also provide investment advice with minimized risk based on the geographic location information. Furthermore, the investment advice unit can analyze the geographic location information of income and expenditure and provide the most efficient investment advice. This makes it possible to propose an optimal investment destination based on the geographic location information of income and expenditure. A part or all of the above-mentioned processing in the investment advice unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the investment advice unit inputs geographic location information into the generation AI, and the generation AI can provide optimal investment advice.

[0053] The investment advice unit can analyze the social media activity of income and expenditures and propose a means of investment advice when providing investment advice. For example, the investment advice unit can analyze the social media activity of income and expenditures and propose a means of investment advice when providing investment advice. Social media activity includes, for example, the content of posts, the number of followers, the number of likes, and the like, but is not limited to such examples. For example, the investment advice unit can propose an optimal investment means based on the social media activity of income and expenditure. In addition, the investment advice unit can provide an investment means with minimized risk based on the social media activity. Furthermore, the investment advice unit can analyze the social media activity of income and expenditures and propose the most efficient investment means. This makes it possible to propose an optimal investment means based on the social media activity of income and expenditure. A part or all of the above-mentioned processing in the investment advice unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the investment advice unit can input data of social media activity into the generation AI, and the generation AI can propose the optimal investment means.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The personal asset management system can further collect the user's health data and consider it as a factor affecting the balance between income and expenditure. For example, it can monitor the user's amount of exercise and sleep time, and if the user's health condition is deteriorating, it can predict an increase in medical expenses and adjust the budget. Also, if the user's health condition is good, it can recommend expenditures for maintaining health. Furthermore, it can suggest the optimal use of health insurance and medical expense deductions based on the user's health data. This allows the user to manage their assets more effectively while maintaining their health condition.

[0056] The budgeting unit can adjust the budget taking into consideration the user's life events (marriage, childbirth, moving, etc.). For example, for a user who is planning to get married, the budget can include expenses necessary for the wedding ceremony and the new life. Also, for a user who is about to give birth, a budget can be proposed that takes into consideration childcare supplies and medical expenses. Furthermore, for a user who is planning to move, moving expenses and initial costs for the new home can be reflected in the budget. This allows the user to create an appropriate budget according to life events.

[0057] The expenditure tracking unit can analyze the user's purchase history and track expenditures at specific brands and stores. For example, if the user frequently purchases products from a specific brand, the unit can provide sale information and discount coupons for that brand. Also, if the user spends a lot at a specific store, the unit can suggest ways to utilize the store's point program and member benefits. Furthermore, the unit can analyze the user's consumption trends based on the purchase history and provide specific advice on reducing wasteful spending. This allows the user to shop smarter and manage their expenditures.

[0058] The investment advice department can suggest investment destinations taking into account the user's hobbies and interests. For example, if the user is interested in environmental protection, it can suggest investment destinations in environmentally conscious companies and renewable energy-related companies. If the user is interested in technology, it can recommend investments in companies and startups developing the latest technology. Furthermore, if the user is interested in art and culture, it can suggest investments in art funds and cultural projects. This allows users to make investments that match their interests and values.

[0059] The investment advice unit can personalize the way in which investment advice is provided based on user feedback. For example, if the user prefers visual information, the investment advice unit can provide investment advice using graphs and charts. If the user requests detailed information, the investment advice unit can provide detailed text-based reports. Furthermore, if the user wants to obtain information quickly, the investment advice unit can provide concise advice that summarizes the main points. This allows the user to receive investment advice in a format that suits their preferences.

[0060] The process flow of the first embodiment will be briefly described below.

[0061] Step 1: The reception unit is a section where the user inputs income and expense information. The income and expense information includes, for example, salary, rent, and utility bills. The reception unit allows the user to access the app and input information interactively. Step 2: The budgeting unit uses a generative AI to create a budget based on the information entered by the reception unit. The budget is created based on the income-to-expense ratio and savings goals. For example, the budgeting unit creates a monthly budget based on the user's income and expenses. Step 3: The expense tracker uses generative AI to track expenses based on the budget set by the budgeting unit. Expense tracking is based on expense categorization and tracking frequency. For example, the expense tracker compares budgeted and actual expenses and provides advice on reducing waste. Step 4: The investment advice unit uses the generation AI to provide investment advice based on the expenditure information tracked by the expenditure tracking unit. The investment advice is based on risk assessment and investment selection criteria. For example, the investment advice unit analyzes expenditure information and market trends to suggest investments suitable for the user.

[0062] (Example 2) The personal asset management system according to the embodiment of the present invention is an application that utilizes a generation AI to support personal asset management. In this system, a user accesses an app and inputs information interactively, thereby collecting information such as income, expenditure, and investment status. The collected information is input to the generation AI, which performs analysis. Based on the analysis results, the system provides budgeting, expenditure tracking, and investment advice suitable for the user. For example, a monthly budget is created based on the user's income and expenditure. In addition, the system analyzes spending trends and provides advice to reduce wasteful spending. In addition, the system analyzes investment information and suggests suitable investment destinations for the user. The generation AI provides advice and suggestions in real time through dialogue with the user. For example, if a user inputs "I want to save money because I'm spending a lot this month," the generation AI analyzes the details of the expenditure and provides specific advice for saving. In addition, if a user inputs "I'm looking for a new investment destination," the generation AI analyzes market trends and suggests suitable investment destinations. This application allows the user to manage money more effectively. For example, the system can maintain a balance between income and expenditure by budgeting, and can reduce wasteful spending by tracking expenditure. In addition, the system can find suitable investment destinations through investment advice. This supports users in building long-term assets. Furthermore, the generative AI learns based on user feedback to improve the accuracy of its advice and suggestions. For example, if a user rates a piece of advice provided as "useful," the generative AI will provide that advice to other users. Conversely, if the user rates the advice as "unhelpful," the generative AI will learn to improve the advice. In this way, a personal asset management application that utilizes generative AI becomes a powerful tool for users to manage their money more effectively and support long-term asset building. This allows the personal asset management system to help users balance their income and expenses, reduce waste, and find appropriate investment opportunities.

[0063] The personal asset management system according to the embodiment includes a reception unit, a budgeting unit, an expenditure tracking unit, and an investment advice unit. The reception unit is a unit for a user to input information on income and expenditure. The information on income and expenditure includes, but is not limited to, for example, salary, rent, and utility expenses. The reception unit enables, for example, a user to access an app and input information in an interactive format. The budgeting unit uses a generation AI to create a budget based on the information input by the reception unit. The budget is created based on, for example, the ratio of income to expenditure and a savings goal, but is not limited to such examples. For example, the budgeting unit creates a monthly budget based on the user's income and expenditure. The expenditure tracking unit uses a generation AI to track expenditure based on the budget created by the budgeting unit. Expenditure tracking is performed based on, for example, categorization of expenditure and frequency of tracking, but is not limited to such examples. For example, the expenditure tracking unit compares the budget with actual expenditure and provides advice to reduce waste. The investment advice unit uses a generation AI to provide investment advice based on the expenditure information tracked by the expenditure tracking unit. The investment advice is provided based on, for example, risk assessment and selection criteria for investment destinations, but is not limited to such examples. For example, the investment advice unit analyzes expenditure information and market trends and proposes investment destinations suitable for the user. In this way, the personal asset management system according to the embodiment allows the user to input income and expenditure information, create a budget, track expenditures, and receive investment advice. Some or all of the above-mentioned processing in the investment advice unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the investment advice unit can provide investment advice using a generation AI model that inputs expenditure information and market trends and outputs investment advice.

[0064] The reception unit is a part for a user to input information on income and expenditure. The information on income and expenditure includes, but is not limited to, for example, salary, rent, and utility expenses. The reception unit, for example, enables a user to access the app and input information in an interactive format. Specifically, a user uses a smartphone or a computer to log in to a dedicated application and input details of income and expenditure. The application provides an intuitive interface to make it easy for the user to input information, and includes a drop-down menu for selecting income and expenditure categories and a numeric input field for inputting amounts. Furthermore, the reception unit also provides a function for automatically acquiring transaction data and automatically inputting income and expenditure information by linking the user with bank account and credit card information. This allows the user to input accurate information without hassle. The reception unit also has a function for the user to check the history of past income and expenditure, which makes it easier for the user to grasp his or her own asset situation. Furthermore, the reception unit uses encryption technology to protect data and ensure privacy in order to safely manage the information input by the user. This allows the user to input income and expenditure information with peace of mind.

[0065] The budgeting unit uses the generation AI to create a budget based on the information input by the reception unit. The budget can be created based on, for example, the ratio of income to expenditure and the savings goal, but is not limited to such examples. For example, the budgeting unit creates a monthly budget based on the user's income and expenditure. Specifically, the generation AI analyzes the user's income and expenditure data, and proposes an optimal budget taking into account past expenditure patterns and fluctuations in income. The generation AI can flexibly adjust the budget according to the user's lifestyle and goals. For example, if the user is planning a trip, the generation AI takes into account the budget for that and makes a proposal to reduce other expenditures. In addition, the generation AI calculates the monthly savings amount based on the savings goal set by the user and incorporates it into the budget. This allows the user to continue saving without difficulty. Furthermore, the budgeting unit provides an alert function to help the user keep to the budget, and notifies the user when expenditure is about to exceed the budget. This allows the user to be conscious of spending within the budget and prevent wasteful spending. The budgeting unit can continuously improve the accuracy of the budget based on the user's feedback and propose a more appropriate budget.

[0066] The expenditure tracking unit uses the generation AI to track expenditures based on the budget set by the budgeting unit. Expenditure tracking is performed, for example, based on categorization of expenditures and frequency of tracking, but is not limited to such examples. For example, the expenditure tracking unit compares the budget with actual expenditures and provides advice to reduce wasteful spending. Specifically, the generation AI analyzes the user's expenditure data in real time to check whether the expenditure is within the budget. The generation AI performs a detailed analysis for each expenditure category and identifies which category has the most expenditure. For example, if expenditure exceeds the budget in a category such as food or entertainment, the generation AI analyzes the cause and provides specific advice to reduce wasteful spending. Furthermore, the expenditure tracking unit provides a function to visualize the progress of expenditures based on the goal set by the user. This allows the user to grasp his / her own expenditure situation at a glance and take action toward achieving the goal. The expenditure tracking unit also has a function to automatically acquire transaction data and track expenditures in order to save the user the trouble of recording expenditures. This allows the user to perform accurate expenditure management without hassle. The expense tracking section continuously improves the accuracy of expense tracking based on user feedback, supporting more effective expense management.

[0067] The investment advice unit uses the generation AI to provide investment advice based on the expenditure information tracked by the expenditure tracking unit. The investment advice is provided based on, for example, risk assessment and investment selection criteria, but is not limited to such examples. For example, the investment advice unit analyzes expenditure information and market trends to suggest investment destinations suitable for the user. Specifically, the generation AI considers the user's spending patterns and savings goals and suggests an investment strategy according to the user's risk tolerance. The generation AI analyzes the latest market data in real time to evaluate the risk and return of the investment destination. For example, it analyzes the trends of the stock market and real estate market and selects the optimal investment destination for the user. In addition, the generation AI learns the user's investment history and preferences and provides individually customized investment advice. This allows the user to make investments that match their own risk tolerance and goals. In addition, the investment advice unit also provides support when the user makes an investment. For example, it not only provides advice on the selection of investment destinations, but also on the timing of investment and the method of diversifying investments. This allows the user to efficiently increase assets while diversifying risk. The investment advice unit can continuously improve the accuracy of advice based on user feedback and suggest more appropriate investment strategies.

[0068] The budgeting unit can create a monthly budget based on the information on income and expenditure. The budgeting unit creates a monthly budget based on, for example, the information on income and expenditure. The monthly budget includes, for example, fixed expenses, variable expenses, and savings goals, but is not limited to such examples. For example, the budgeting unit creates a budget based on the ratio of income to expenditure. The budgeting unit can also set a savings goal and create a budget based on the goal. Furthermore, the budgeting unit can analyze the information on income and expenditure and propose an optimal budget. This makes it possible to create a monthly budget based on the information on income and expenditure. A part or all of the above-mentioned processing in the budgeting unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the budgeting unit can input the information on income and expenditure into the generation AI, and the generation AI can create a budget.

[0069] The expenditure tracking unit can compare the budget and actual expenditure and provide specific advice to reduce wasteful spending. The expenditure tracking unit, for example, compares the budget and actual expenditure and provides specific advice to reduce wasteful spending. Specific advice to reduce wasteful spending includes, for example, saving methods and review points for spending, but is not limited to such examples. For example, the expenditure tracking unit categorizes expenditures and suggests saving methods for each category. In addition, the expenditure tracking unit can also identify review points for spending and provide specific advice based on the points. Furthermore, the expenditure tracking unit can analyze spending trends and provide advice to reduce wasteful spending. This makes it possible to provide advice to reduce wasteful spending. Part or all of the above-mentioned processing in the expenditure tracking unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the expenditure tracking unit can input the budget and actual expenditure into the generation AI, and the generation AI can provide advice to reduce wasteful spending.

[0070] The investment advice unit can analyze the expenditure information and market trends and propose an investment destination suitable for the user. The investment advice unit can, for example, analyze the expenditure information and market trends and propose an investment destination suitable for the user. Market trends include, for example, stock price fluctuations and economic indicators, but are not limited to such examples. For example, the investment advice unit can evaluate the risk tolerance of the user based on the expenditure information and propose an appropriate investment destination based on the evaluation. The investment advice unit can also analyze market trends and predict future investment risks. Furthermore, the investment advice unit can also propose an optimal investment destination based on the investment goal of the user. This makes it possible to propose an investment destination suitable for the user. Part or all of the above-mentioned processing in the investment advice unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the investment advice unit can input the expenditure information and market trends into the generation AI, and the generation AI can propose an appropriate investment destination.

[0071] The investment advice unit can learn based on the user's feedback and improve the specific accuracy of the advice and proposal. The investment advice unit can learn based on, for example, the user's feedback and improve the specific accuracy of the advice and proposal. The specific accuracy of the advice and proposal includes, for example, a method of utilizing user feedback and an improvement of a machine learning algorithm, but is not limited to such examples. For example, when the user evaluates the provided advice as "useful", the investment advice unit will provide the advice to other users. Also, when the user evaluates the advice as "not useful", the investment advice unit can learn to improve the advice. Furthermore, the investment advice unit can improve the machine learning algorithm based on the user's feedback and improve the accuracy of the advice and proposal. This can improve the accuracy of the advice and proposal. A part or all of the above-mentioned processing in the investment advice unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the investment advice unit can input the user's feedback to the generation AI, and the generation AI can improve the accuracy of the advice and proposal.

[0072] The reception unit can estimate the user's emotion and adjust the timing of the input of the income and expense information based on the estimated user's emotion. The reception unit, for example, estimates the user's emotion and adjusts the timing of the input of the income and expense information based on the estimated user's emotion. The user's emotion includes, for example, stress, relaxation, and hurry, but is not limited to such examples. For example, when the user is feeling stressed, the reception unit postpones the input of the income and expense information and prompts the user to input it at a time when the user is relaxed. In addition, when the user is relaxed, it is also possible to prompt the user to input detailed information and collect more accurate data. Furthermore, when the user is in a hurry, it is also possible to provide a simplified input form so that the user can input information quickly. This makes it possible to adjust the timing of the input of the income and expense information according to the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the reception unit can input the user's emotion data to the generation AI, which can then estimate the emotion.

[0073] The reception unit can analyze the user's past income and expenditure history and select an appropriate input method. The reception unit, for example, analyzes the user's past income and expenditure history and selects an appropriate input method. The appropriate input method includes, for example, voice input, manual input, scan input, etc., but is not limited to such examples. For example, the reception unit automatically displays as candidates the income and expenditure items that the user has frequently input in the past. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest income and expenditure items to be used in a specific time period from the user's past input history. This makes it possible to select an optimal input method based on the user's past income and expenditure history. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the user's past income and expenditure history into the generation AI, which can select an appropriate input method.

[0074] The reception unit can perform filtering based on the user's current living situation and field of interest when inputting income and expenditure information. For example, the reception unit performs filtering based on the user's current living situation and field of interest when inputting income and expenditure information. The current living situation and field of interest include, for example, family structure, hobbies, and occupation, but are not limited to such examples. For example, the reception unit preferentially displays related income and expenditure items based on the user's current living situation. In addition, the reception unit can also filter and display related income and expenditure items based on the user's field of interest. Furthermore, the reception unit can customize the input of income and expenditure information based on the user's living situation and field of interest. This makes it possible to filter income and expenditure information based on the user's current living situation and field of interest. Part or all of the above-mentioned processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can input data on the user's living situation and field of interest to the generation AI, and the generation AI can perform filtering.

[0075] The reception unit can estimate the user's emotion and determine the priority of the information to be input based on the estimated user's emotion. The reception unit, for example, estimates the user's emotion and determines the priority of the information to be input based on the estimated user's emotion. The priority of the information to be input includes, for example, importance and urgency, but is not limited to such examples. For example, when the user is stressed, the reception unit allows only important information to be input preferentially. In addition, when the user is relaxed, the reception unit can also allow detailed information to be input preferentially. Furthermore, when the user is in a hurry, the reception unit can also allow the most important information to be input preferentially. In this way, the priority of the information to be input can be determined according to the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the reception unit can input the user's emotion data to the generation AI, and the generation AI can determine the priority of the information.

[0076] The reception unit can preferentially input highly relevant information in consideration of the geographical location information of the user when inputting the information on income and expenditure. For example, the reception unit preferentially inputs highly relevant information in consideration of the geographical location information of the user when inputting the information on income and expenditure. The geographical location information includes, for example, GPS data, address information, and the like, but is not limited to such examples. For example, when the user is in a specific area, the reception unit preferentially displays income and expenditure items related to the area. In addition, the reception unit can also filter and display related income and expenditure information based on the geographical location information of the user. Furthermore, the reception unit can customize the input of income and expenditure information based on the user's current location. This makes it possible to preferentially input highly relevant information based on the geographical location information of the user. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the geographical location information of the user to the generation AI, and the generation AI can preferentially input highly relevant information.

[0077] The reception unit can analyze the social media activity of the user and input related information when the income and expenditure information is input. For example, the reception unit can analyze the social media activity of the user and input related information when the income and expenditure information is input. The social media activity includes, for example, the content of posts, the number of followers, the number of likes, etc., but is not limited to such examples. For example, the reception unit automatically suggests related income and expenditure items from the social media activity of the user. In addition, the reception unit can also analyze the social media activity of the user and filter the related income and expenditure information. Furthermore, the reception unit can customize the input of income and expenditure information based on the social media activity of the user. This makes it possible to input related information based on the social media activity of the user. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the reception unit can input data of the user's social media activity to the generation AI, and the generation AI can input the related information.

[0078] The budgeting unit can estimate the user's emotions and adjust the way of expressing the budget based on the estimated user's emotions. The budgeting unit, for example, estimates the user's emotions and adjusts the way of expressing the budget based on the estimated user's emotions. The way of expressing the budget includes, for example, a graph display, a text display, and the like, but is not limited to such examples. For example, the budgeting unit provides a simple and visually easy-to-understand budget table when the user is stressed. Also, when the user is relaxed, a detailed budget table can be provided and customizable options can be added. Furthermore, when the user is in a hurry, a concise budget table with the main points can be provided. This makes it possible to adjust the way of expressing the budget according to the user's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the budgeting unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the budgeting unit can input a user's emotional data into the generation AI, which can then adjust the way the budget is presented.

[0079] The budgeting unit can adjust the specific level of detail of the budget based on the importance of the income and expenditure when making a budget. For example, the budgeting unit adjusts the level of detail of the budget based on the importance of the income and expenditure when making a budget. The importance of the income and expenditure includes, for example, daily necessities, entertainment expenses, etc., but is not limited to such examples. For example, the budgeting unit makes a detailed budget for important income and expenditure items. In addition, the budgeting unit can also make a simplified budget for income and expenditure items with low importance. Furthermore, the budgeting unit can dynamically adjust the level of detail of the budget based on the importance of the income and expenditure. This makes it possible to adjust the level of detail of the budget based on the importance of the income and expenditure. A part or all of the above-mentioned processing in the budgeting unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the budgeting unit can input the importance of the income and expenditure to the generation AI, and the generation AI can adjust the level of detail of the budget.

[0080] The budgeting unit can apply different budgeting algorithms depending on the income and expenditure categories when making a budget. The budgeting unit, for example, applies different budgeting algorithms depending on the income and expenditure categories when making a budget. The budgeting algorithms include, but are not limited to, linear regression, decision trees, and the like. For example, the budgeting unit applies different budgeting algorithms depending on the income and expenditure categories. The budgeting unit can also select an optimal budgeting algorithm for each income and expenditure category. Furthermore, the budgeting unit can dynamically adjust the budgeting algorithm based on the income and expenditure categories. This makes it possible to apply different budgeting algorithms depending on the income and expenditure categories. Some or all of the above-mentioned processing in the budgeting unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the budgeting unit can input the income and expenditure categories into the generation AI, and the generation AI can apply the optimal budgeting algorithm.

[0081] The budgeting unit can estimate the user's emotions and adjust the length of the budget based on the estimated user's emotions. The budgeting unit can, for example, estimate the user's emotions and adjust the length of the budget based on the estimated user's emotions. The length of the budget includes, for example, a monthly budget, an annual budget, and the like, but is not limited to such examples. For example, the budgeting unit can propose a short-term budget when the user is stressed. Also, when the user is relaxed, it can also propose a long-term budget. Furthermore, when the user is in a hurry, it can also propose a budget that covers the main points in a short period of time. This makes it possible to adjust the length of the budget according to the user's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the budgeting unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the budgeting unit can input the user's emotion data into the generation AI, and the generation AI can adjust the length of the budget.

[0082] The budget planning unit can determine the specific priority of the budget based on the submission timing of the income and the expenditure when planning the budget. For example, the budget planning unit determines the priority of the budget based on the submission timing of the income and the expenditure when planning the budget. The submission timing of the income and the expenditure includes, but is not limited to, the beginning of the month and the end of the month. For example, the budget planning unit determines the priority of the budget based on the submission timing of the income and the expenditure. The budget planning unit can also prioritize the budget for the income and the expenditure items that are close to the submission timing. Furthermore, the budget planning unit can dynamically adjust the priority of the budget based on the submission timing. This makes it possible to determine the priority of the budget based on the submission timing of the income and the expenditure. A part or all of the above-mentioned processing in the budget planning unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the budget planning unit can input the submission timing of the income and the expenditure to the generation AI, and the generation AI can determine the priority of the budget.

[0083] The budget planning unit can adjust the order of the budget based on the relevance between income and expenditure when planning the budget. The budget planning unit, for example, adjusts the order of the budget based on the relevance between income and expenditure when planning the budget. The relevance between income and expenditure includes, for example, the relevance between the source of income and the destination of expenditure, the purpose of expenditure, and the like, but is not limited to such examples. For example, the budget planning unit adjusts the order of the budget based on the relevance between income and expenditure. The budget planning unit can also preferentially incorporate items with a high relevance between income and expenditure into the budget. Furthermore, the budget planning unit can dynamically adjust the order of the budget based on the relevance between income and expenditure. This makes it possible to adjust the order of the budget based on the relevance between income and expenditure. A part or all of the above-mentioned processing in the budget planning unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the budget planning unit can input the relevance between income and expenditure into the generation AI, and the generation AI can adjust the order of the budget.

[0084] The expenditure tracking unit can estimate the user's emotion and adjust the expenditure tracking criteria based on the estimated user's emotion. The expenditure tracking unit, for example, estimates the user's emotion and adjusts the expenditure tracking criteria based on the estimated user's emotion. The expenditure tracking criteria include, for example, the category of expenditure, the frequency of tracking, and the like, but are not limited to such examples. For example, the expenditure tracking unit provides simplified expenditure tracking criteria when the user is stressed. Also, detailed expenditure tracking criteria can be provided when the user is relaxed. Furthermore, expenditure tracking criteria with the main points can be provided when the user is in a hurry. This allows the expenditure tracking criteria to be adjusted according to the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the expenditure tracking unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the expenditure tracking unit can input user emotional data into the generation AI, which can then adjust its expenditure tracking criteria.

[0085] The expenditure tracking unit can improve the specific accuracy of the expenditure tracking by considering the interrelationship between income and expenditure when tracking expenditure. The expenditure tracking unit, for example, improves the accuracy of tracking by considering the interrelationship between income and expenditure when tracking expenditure. The specific accuracy of tracking includes, for example, an allowable error range, data accuracy, and the like, but is not limited to such examples. For example, the expenditure tracking unit improves the accuracy of the expenditure tracking by considering the interrelationship between income and expenditure. In addition, the expenditure tracking unit can adjust the expenditure tracking algorithm based on the interrelationship between income and expenditure. Furthermore, the expenditure tracking unit can analyze the interrelationship between income and expenditure and dynamically improve the accuracy of the expenditure tracking. This makes it possible to improve the accuracy of the expenditure tracking by considering the interrelationship between income and expenditure. Part or all of the above-mentioned processing in the expenditure tracking unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the expenditure tracking unit inputs the interrelationship between income and expenditure into the generation AI, and the generation AI can improve the accuracy of the expenditure tracking.

[0086] The expenditure tracking unit can perform expenditure tracking while taking into consideration the attribute information of the submitter of the income and expenditure. For example, the expenditure tracking unit performs expenditure tracking while taking into consideration the attribute information of the submitter of the income and expenditure. The attribute information of the submitter includes, for example, age, occupation, income level, etc., but is not limited to such examples. For example, the expenditure tracking unit performs expenditure tracking while taking into consideration the attribute information of the submitter of the income and expenditure. In addition, the expenditure tracking unit can adjust the expenditure tracking algorithm based on the attribute information of the submitter. Furthermore, the expenditure tracking unit can analyze the attribute information of the submitter and improve the accuracy of expenditure tracking. In this way, expenditure tracking can be performed while taking into consideration the attribute information of the submitter. Part or all of the above-mentioned processing in the expenditure tracking unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the expenditure tracking unit inputs the attribute information of the submitter to the generation AI, and the generation AI can perform expenditure tracking.

[0087] The expenditure tracking unit can estimate the user's emotion and adjust the order of displaying the expenditure tracking results based on the estimated user's emotion. The expenditure tracking unit, for example, estimates the user's emotion and adjusts the order of displaying the expenditure tracking results based on the estimated user's emotion. The order of displaying the expenditure tracking results includes, for example, importance, chronological order, and the like, but is not limited to such examples. For example, the expenditure tracking unit preferentially displays important expenditure items when the user is stressed. In addition, detailed expenditure items can also be displayed when the user is relaxed. Furthermore, when the user is in a hurry, expenditure items with the main points can also be displayed preferentially. This makes it possible to adjust the order of displaying the expenditure tracking results according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the expenditure tracking unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the expenditure tracking unit can input user emotional data to the generation AI and adjust the order in which the generation AI displays the expenditure tracking results.

[0088] The expenditure tracking unit can perform expenditure tracking while taking into consideration the geographical distribution of income and expenditure. The expenditure tracking unit performs expenditure tracking while taking into consideration the geographical distribution of income and expenditure, for example. The geographical distribution includes, but is not limited to, expenditure by region, comparison between cities and rural areas, and the like. For example, the expenditure tracking unit performs expenditure tracking while taking into consideration the geographical distribution of income and expenditure. The expenditure tracking unit can also adjust the expenditure tracking algorithm based on the geographical distribution. Furthermore, the expenditure tracking unit can analyze the geographical distribution and improve the accuracy of expenditure tracking. This allows expenditure tracking while taking into consideration the geographical distribution of income and expenditure. A part or all of the above-mentioned processing in the expenditure tracking unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the expenditure tracking unit inputs data on the geographical distribution into the generation AI, and the generation AI can perform expenditure tracking.

[0089] The expenditure tracking unit can improve the accuracy of tracking by referring to related literature on income and expenditure when tracking expenditure. The expenditure tracking unit can improve the accuracy of tracking by referring to related literature on income and expenditure when tracking expenditure, for example. The related literature includes, but is not limited to, academic papers, industry reports, and the like. For example, the expenditure tracking unit can improve the accuracy of expenditure tracking by referring to related literature on income and expenditure. In addition, the expenditure tracking unit can adjust the expenditure tracking algorithm based on the related literature. Furthermore, the expenditure tracking unit can analyze the related literature and dynamically improve the accuracy of expenditure tracking. This makes it possible to improve the accuracy of expenditure tracking by referring to related literature on income and expenditure. Part or all of the above-mentioned processing in the expenditure tracking unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the expenditure tracking unit can input data of related literature into the generation AI, and the generation AI can improve the accuracy of expenditure tracking.

[0090] The investment advice unit can estimate the user's emotions and adjust the investment advice method based on the estimated user's emotions. The investment advice unit can estimate the user's emotions and adjust the investment advice method based on the estimated user's emotions. The investment advice method includes, for example, risk assessment, investment strategy, and the like, but is not limited to such examples. For example, the investment advice unit can suggest a low-risk investment destination when the user is stressed. In addition, when the user is relaxed, it can also suggest an investment destination with a good balance between risk and return. Furthermore, when the user is excited, it can also suggest a high-risk investment destination. This makes it possible to adjust the investment advice method according to the user's emotions. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the investment advice unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the investment advice unit can input the user's emotion data into the generation AI, and the generation AI can adjust the investment advice method.

[0091] The investment advice unit can provide appropriate investment advice by referring to past data on income and expenditure when providing investment advice. The investment advice unit can provide optimal investment advice by referring to past data on income and expenditure when providing investment advice, for example. The past data on income and expenditure includes, but is not limited to, data from the past year and a specific period. For example, the investment advice unit can suggest optimal investment destinations based on the past data on income and expenditure. The investment advice unit can also provide investment advice with minimized risk from the past data. Furthermore, the investment advice unit can analyze the past data on income and expenditure and provide the most efficient investment advice. This makes it possible to provide optimal investment advice based on the past data on income and expenditure. A part or all of the above-mentioned processing in the investment advice unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the investment advice unit can input the past data on income and expenditure into the generation AI, which can provide optimal investment advice.

[0092] The investment advice unit can customize the investment advice means based on the current status of income and expenditure when providing investment advice. The investment advice unit, for example, customizes the investment advice means based on the current status of income and expenditure when providing investment advice. The current status of income and expenditure includes, for example, the current income level, expenditure tendency, and the like, but is not limited to such examples. For example, the investment advice unit proposes an optimal investment means based on the current status of income and expenditure. The investment advice unit can also provide an investment means with minimized risk, taking into account the current status. Furthermore, the investment advice unit can analyze the current status of income and expenditure and propose the most efficient investment means. This makes it possible to propose the optimal investment means based on the current status of income and expenditure. Part or all of the above-mentioned processing in the investment advice unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the investment advice unit can input the current status of income and expenditure into the generation AI, and the generation AI can propose the optimal investment means.

[0093] The investment advice unit can estimate the user's emotions and determine the priority of the investment advice based on the estimated user's emotions. The investment advice unit can estimate the user's emotions and determine the priority of the investment advice based on the estimated user's emotions. The priority of the investment advice includes, for example, high or low risk and large or small investment amount, but is not limited to such examples. For example, when the user is stressed, the investment advice unit preferentially suggests a low-risk investment. In addition, when the user is relaxed, it can also preferentially suggest an investment with a good balance between risk and return. Furthermore, when the user is excited, it can also preferentially suggest a high-risk investment. This makes it possible to determine the priority of the investment advice according to the user's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the investment advice unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the investment advice unit can input the user's emotional data into the generation AI, which can then determine the priority of the investment advice.

[0094] The investment advice unit can provide appropriate investment advice in consideration of the geographic location information of income and expenditure when providing investment advice. The investment advice unit, for example, provides optimal investment advice in consideration of the geographic location information of income and expenditure when providing investment advice. The geographic location information includes, for example, GPS data, address information, and the like, but is not limited to such examples. For example, the investment advice unit proposes an optimal investment destination based on the geographic location information of income and expenditure. The investment advice unit can also provide investment advice with minimized risk based on the geographic location information. Furthermore, the investment advice unit can analyze the geographic location information of income and expenditure and provide the most efficient investment advice. This makes it possible to propose an optimal investment destination based on the geographic location information of income and expenditure. A part or all of the above-mentioned processing in the investment advice unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the investment advice unit inputs geographic location information into the generation AI, and the generation AI can provide optimal investment advice.

[0095] The investment advice unit can analyze the social media activity of income and expenditures and propose a means of investment advice when providing investment advice. For example, the investment advice unit can analyze the social media activity of income and expenditures and propose a means of investment advice when providing investment advice. Social media activity includes, for example, the content of posts, the number of followers, the number of likes, and the like, but is not limited to such examples. For example, the investment advice unit can propose an optimal investment means based on the social media activity of income and expenditure. In addition, the investment advice unit can provide an investment means with minimized risk based on the social media activity. Furthermore, the investment advice unit can analyze the social media activity of income and expenditures and propose the most efficient investment means. This makes it possible to propose an optimal investment means based on the social media activity of income and expenditure. A part or all of the above-mentioned processing in the investment advice unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the investment advice unit can input data of social media activity into the generation AI, and the generation AI can propose the optimal investment means.

[0096] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0097] The personal asset management system can further collect the user's health data and consider it as a factor affecting the balance between income and expenditure. For example, it can monitor the user's amount of exercise and sleep time, and if the user's health condition is deteriorating, it can predict an increase in medical expenses and adjust the budget. Also, if the user's health condition is good, it can recommend expenditures for maintaining health. Furthermore, it can suggest the optimal use of health insurance and medical expense deductions based on the user's health data. This allows the user to manage their assets more effectively while maintaining their health condition.

[0098] The budgeting unit can adjust the budget taking into consideration the user's life events (marriage, childbirth, moving, etc.). For example, for a user who is planning to get married, the budget can include expenses necessary for the wedding ceremony and the new life. Also, for a user who is about to give birth, a budget can be proposed that takes into consideration childcare supplies and medical expenses. Furthermore, for a user who is planning to move, moving expenses and initial costs for the new home can be reflected in the budget. This allows the user to create an appropriate budget according to life events.

[0099] The expenditure tracking unit can analyze the user's purchase history and track expenditures at specific brands and stores. For example, if the user frequently purchases products from a specific brand, the unit can provide sale information and discount coupons for that brand. Also, if the user spends a lot at a specific store, the unit can suggest ways to utilize the store's point program and member benefits. Furthermore, the unit can analyze the user's consumption trends based on the purchase history and provide specific advice on reducing wasteful spending. This allows the user to shop smarter and manage their expenditures.

[0100] The investment advice department can suggest investment destinations taking into account the user's hobbies and interests. For example, if the user is interested in environmental protection, it can suggest investment destinations in environmentally conscious companies and renewable energy-related companies. If the user is interested in technology, it can recommend investments in companies and startups developing the latest technology. Furthermore, if the user is interested in art and culture, it can suggest investments in art funds and cultural projects. This allows users to make investments that match their interests and values.

[0101] The investment advice unit can personalize the way in which investment advice is provided based on user feedback. For example, if the user prefers visual information, the investment advice unit can provide investment advice using graphs and charts. If the user requests detailed information, the investment advice unit can provide detailed text-based reports. Furthermore, if the user wants to obtain information quickly, the investment advice unit can provide concise advice that summarizes the main points. This allows the user to receive investment advice in a format that suits their preferences.

[0102] The reception unit can estimate the user's emotions and customize an interface for inputting income and expense information based on the estimated user's emotions. For example, if the user is feeling stressed, a simple and intuitive interface can be provided to reduce the burden of input. Also, if the user is relaxed, detailed input options can be provided to collect more accurate data. Furthermore, if the user is in a hurry, a simplified interface for inputting only the most important information can be provided. In this way, an optimal interface for inputting information can be provided according to the user's emotions.

[0103] The expenditure tracking unit can estimate the user's emotions and reorganize the categories of expenditures based on the estimated user's emotions. For example, when the user is stressed, the categories of expenditures can be simplified and only major expenditure items can be displayed. When the user is relaxed, detailed categorization can be performed and the expenditures for each category can be displayed in detail. Furthermore, when the user is in a hurry, the most important expenditure items can be displayed with priority so that they can be checked quickly. In this way, the categories of expenditures can be optimally reorganized according to the user's emotions.

[0104] The budgeting unit can estimate the user's emotions and adjust the budget goal based on the estimated user's emotions. For example, if the user is feeling stressed, a realistic and achievable goal can be set and a reasonable budget can be proposed. Also, if the user is relaxed, a challenging goal can be set and active savings and investment can be encouraged. Furthermore, if the user is in a hurry, a goal that can be achieved in a short period of time can be set and a budget that can produce results quickly can be proposed. In this way, an optimal budget goal can be set according to the user's emotions.

[0105] The investment advice unit can estimate the user's emotions and adjust the evaluation criteria for investment risk based on the estimated user's emotions. For example, if the user is feeling stressed, low-risk investments are suggested preferentially. Also, if the user is relaxed, investments with a good balance between risk and return can be suggested. Furthermore, if the user is excited, high-risk investments can be suggested. In this way, it is possible to set optimal evaluation criteria for investment risk according to the user's emotions.

[0106] The expenditure tracking unit can estimate the user's emotions and suggest points for reviewing expenditures based on the estimated user's emotions. For example, if the user is feeling stressed, the expenditure tracking unit can briefly present points for reviewing expenditures and suggest a method for rapid improvement. If the user is relaxed, the expenditure tracking unit can present detailed points for review and suggest long-term improvement measures. Furthermore, if the user is in a hurry, the expenditure tracking unit can preferentially present the most effective points for review and suggest a method for achieving results in a short period of time. In this way, the expenditure tracking unit can suggest optimal points for reviewing expenditures according to the user's emotions.

[0107] The process flow of the second embodiment will be briefly described below.

[0108] Step 1: The reception unit is a section where the user inputs income and expense information. The income and expense information includes, for example, salary, rent, and utility bills. The reception unit allows the user to access the app and input information interactively. Step 2: The budgeting unit uses a generative AI to create a budget based on the information entered by the reception unit. The budget is created based on the income-to-expense ratio and savings goals. For example, the budgeting unit creates a monthly budget based on the user's income and expenses. Step 3: The expense tracker uses generative AI to track expenses based on the budget set by the budgeting unit. Expense tracking is based on expense categorization and tracking frequency. For example, the expense tracker compares budgeted and actual expenses and provides advice on reducing waste. Step 4: The investment advice unit uses the generation AI to provide investment advice based on the expenditure information tracked by the expenditure tracking unit. The investment advice is based on risk assessment and investment selection criteria. For example, the investment advice unit analyzes expenditure information and market trends to suggest investments suitable for the user.

[0109] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0110] 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<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. 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 a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0111] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0112] Each of the multiple elements including the reception unit, the budgeting unit, the expenditure tracking unit, and the investment advice 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 reception unit is realized by the control unit 46A of the smart device 14, and enables the user to access the app and input information in an interactive format. The budgeting unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and creates a budget using a generating AI. The expenditure tracking unit is realized, for example, by the control unit 46A of the smart device 14, and compares the budget with actual expenditure and provides advice to reduce wasteful spending. The investment advice unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes expenditure information and market trends, and suggests investment destinations suitable for the user. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0113] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0114] 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.

[0115] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0116] 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.

[0117] 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 the voice according to instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0119] 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 performed in a secure state.

[0120] Fig. 4 shows an example of 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.

[0121] 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.

[0122] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0123] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 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 also perform processing similar to that of the specific processing unit 290 using these models.

[0124] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0125] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0126] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0127] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0128] Each of the multiple elements including the reception unit, the budgeting unit, the expenditure tracking unit, and the investment advice 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 reception unit is realized by the control unit 46A of the smart glasses 214, and enables the user to access the app and input information in an interactive format. The budgeting unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and creates a budget using a generating AI. The expenditure tracking unit is realized, for example, by the control unit 46A of the smart glasses 214, and compares the budget with actual expenditure and provides advice to reduce wasteful spending. The investment advice unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes expenditure information and market trends to suggest investment destinations suitable for the user. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0129] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0130] 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.

[0131] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0132] 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.

[0133] 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 the voice according to instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0135] 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 performed in a secure state.

[0136] 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.

[0137] 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.

[0138] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0139] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific 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.

[0140] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0141] 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 voice indicating a user input for 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.

[0142] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0143] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0144] Each of the multiple elements including the reception unit, the budgeting unit, the expenditure tracking unit, and the investment advice unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset type terminal 314, and enables the user to access the app and input information in an interactive format. The budgeting unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and creates a budget using a generation AI. The expenditure tracking unit is realized, for example, by the control unit 46A of the headset type terminal 314, and compares the budget with actual expenditure and provides advice to reduce wasteful spending. The investment advice unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes expenditure information and market trends, and proposes investment destinations suitable for the user. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0145] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0146] 7, a 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.

[0147] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0148] 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.

[0149] 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 the voice according to instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0151] 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 performed in a secure state.

[0152] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0153] 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.

[0154] 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.

[0155] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0156] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 program 60 executed on the RAM 48. The robot 414 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.

[0157] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0158] 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 a voice indicating a user input for 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.

[0159] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0160] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information 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.

[0161] Each of the multiple elements including the reception unit, the budgeting unit, the expenditure tracking unit, and the investment advice unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414, and enables the user to access the app and input information in an interactive format. The budgeting unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and creates a budget using a generation AI. The expenditure tracking unit is realized, for example, by the control unit 46A of the robot 414, and compares the budget with actual expenditure and provides advice to reduce wasteful spending. The investment advice unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes expenditure information and market trends, and suggests investment destinations suitable for the user. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0162] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0163] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0164] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually 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.

[0165] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0166] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0167] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0168] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0169] 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.

[0170] 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 Universal Serial Bus (USB) 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.

[0171] In addition, 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 upon request from the data processing device 12.

[0172] 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.

[0173] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0174] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0175] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[0176] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0177] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0178] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0179] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0180] (Appendix 1) A reception desk where income and expenditure information is entered; a budget planning unit that plans a budget based on the information input by the reception unit; an expenditure tracking unit that tracks expenditures based on the budget prepared by the budget preparation unit; and an investment advice unit that provides investment advice based on the expenditure information tracked by the expenditure tracking unit. A system characterized by: (Appendix 2) The budgeting department: Create a monthly budget based on your income and expenses 2. The system of claim 1, (Appendix 3) The expenditure tracking unit: Compare your budget to your actual spending and give you practical advice on how to reduce waste 2. The system of claim 1, (Appendix 4) The investment advice department: Analyze spending information and market trends to suggest suitable investment options to users 2. The system of claim 1, (Appendix 5) The investment advice department: Learns from user feedback to improve the specificity of advice and suggestions 2. The system of claim 1, (Appendix 6) The reception unit is To estimate a user's emotions and adjust the timing of inputting income and expenditure information based on the estimated user's emotions. 2. The system of claim 1, (Appendix 7) The reception unit is Analyze the user's past income and expenditure history and select the appropriate input method 2. The system of claim 1, (Appendix 8) The reception unit is Filtering income and expense information based on the user's current life situation and interests 2. The system of claim 1, (Appendix 9) The reception unit is Estimate the user's emotions and prioritize the information to be input based on the estimated user's emotions. 2. The system of claim 1, (Appendix 10) The reception unit is When entering income and expense information, the system takes into account the user's geographic location and prioritizes relevant information. 2. The system of claim 1, (Appendix 11) The reception unit is Analyze users' social media activity and populate relevant information when entering income and expense information 2. The system of claim 1, (Appendix 12) The budgeting department: Infer user sentiment and adjust the way the budget is presented based on the inferred user sentiment 2. The system of claim 1, (Appendix 13) The budgeting department: When creating a budget, adjust the level of specificity of your budget based on the importance of your income and expenses. 2. The system of claim 1, (Appendix 14) The budgeting department: When budgeting, apply different budgeting algorithms depending on your income and expense categories 2. The system of claim 1, (Appendix 15) The budgeting department: Infer user sentiment and adjust the length of the budget based on the estimated user sentiment. 2. The system of claim 1, (Appendix 16) The budgeting department: When preparing a budget, determine specific budget priorities based on the timing of revenue and expense submissions. 2. The system of claim 1, (Appendix 17) The budgeting department: When budgeting, order your budgets based on how your income and expenses relate to each other. 2. The system of claim 1, (Appendix 18) The expenditure tracking unit: Inferring user sentiment and adjusting spending tracking criteria based on the inferred user sentiment 2. The system of claim 1, (Appendix 19) The expenditure tracking unit: When tracking expenses, consider the correlation between income and expenses to improve the specificity of tracking. 2. The system of claim 1, (Appendix 20) The expenditure tracking unit: When tracking expenses, consider the attributes of the person submitting the income and expenses. 2. The system of claim 1, (Appendix 21) The expenditure tracking unit: Inferring user sentiment and adjusting the order in which expense tracking results are displayed based on the inferred user sentiment 2. The system of claim 1, (Appendix 22) The expenditure tracking unit: When tracking expenses, consider the geographic distribution of income and expenses. 2. The system of claim 1, (Appendix 23) The expenditure tracking unit: When tracking expenses, refer to related documents for income and expenses to improve the accuracy of tracking. 2. The system of claim 1, (Appendix 24) The investment advice department: To estimate a user's emotions and adjust a method of providing investment advice based on the estimated user's emotions. 2. The system of claim 1, (Appendix 25) The investment advice department: When making investment recommendations, the company will provide appropriate investment advice by referring to past income and expenditure data. 2. The system of claim 1, (Appendix 26) The investment advice department: When making investment advice, customize the investment advice measures based on your current income and expenditure situation. 2. The system of claim 1, (Appendix 27) The investment advice department: Inferring user sentiment and prioritizing investment advice based on the estimated user sentiment 2. The system of claim 1, (Appendix 28) The investment advice department: When making investment recommendations, geographic location information of income and expenditure is taken into account to provide appropriate investment advice 2. The system of claim 1, (Appendix 29) The investment advice department: When making investment recommendations, analyze social media activity of income and expenses to suggest investment advice measures 2. The system of claim 1, [Explanation of symbols]

[0181] 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. A reception desk where income and expenditure information is entered; a budget planning unit that plans a budget based on the information input by the reception unit; an expenditure tracking unit that tracks expenditures based on the budget prepared by the budget preparation unit; and an investment advice unit that provides investment advice based on the expenditure information tracked by the expenditure tracking unit. A system characterized by:

2. The budgeting department: Create a monthly budget based on your income and expenses 2. The system of claim 1 .

3. The expenditure tracking unit: Compare your budget to your actual spending and give you practical advice on how to reduce waste 2. The system of claim 1 .

4. The investment advice department: Analyze spending information and market trends to suggest suitable investment options to users 2. The system of claim 1 .

5. The investment advice department: Learns from user feedback to improve the specificity of advice and suggestions 2. The system of claim 1 .

6. The reception unit is To estimate a user's emotions and adjust the timing of inputting income and expenditure information based on the estimated user's emotions.

2. The system of claim 1 .

7. The reception unit is Analyze the user's past income and expenditure history and select the appropriate input method 2. The system of claim 1 .

8. The reception unit is Filtering income and expense information based on the user's current life situation and interests 2. The system of claim 1 .

9. The reception unit is Estimate the user's emotions and prioritize the information to be input based on the estimated user's emotions.

2. The system of claim 1 .

10. The reception unit is When entering income and expense information, the system takes into account the user's geographic location and prioritizes relevant information.

2. The system of claim 1 .

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