System

Abstract

A system is provided.SOLUTION: A system comprising: means for collecting basic information from a user; means for diagnosing an ideal money life plan on the basis of the basic information; means for analyzing a use history of an electronic payment service and proposing improvement of waste; means for receiving an additional condition or a change request from the user and generating a new plan; and means for transmitting a notification when extra expense or income occurs.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In modern society, many users have concerns and questions about their own financial life plans. Consulting with a specialist is often difficult due to time and cost. Furthermore, wasteful spending is common in everyday life, and effective ways to improve it are needed. A system is needed to solve these issues, allowing users to easily create their ideal financial life plan and receive ongoing support. [Means for solving the problem]

[0005] The present invention solves these problems with a system that includes a means for collecting basic information from a user, a means for assessing an ideal financial life plan based on the basic information, a means for analyzing the user's usage history of electronic payment services and making suggestions for improving wasteful spending, a means for accepting additional conditions or change requests from the user and generating a new plan, and a means for sending notifications when unexpected expenses or income occur. This allows users to create and update their own financial life plan with simple operations and receive specific savings suggestions, thereby leading to a healthier financial life.

[0006] "Basic information" refers to personal information necessary for diagnosing a financial life plan, such as the user's age, family structure, and annual income.

[0007] An "ideal money life plan" refers to an income and expenditure plan optimized to achieve the user's future financial goals.

[0008] "Electronic payment service" refers to a platform that enables users to make payments and transfer money digitally, and primarily keeps records of everyday spending and transactions.

[0009] "Suggestions to improve wasteful spending" refers to analyzing a user's spending history and providing specific advice to identify and reduce excessive spending.

[0010] An "additional condition or change request" is a request for modification to an existing financial life plan by a user to reflect changes in life stage or new goals.

[0011] "Unexpected expenses or income" refers to unexpected increases in expenses or income, temporary economic events that require adjustments to your plans.

[0012] "Means for sending notifications" refers to the system's ability to provide information or advice to users via digital communication means such as LINE messages. [Brief explanation of the drawings]

[0013] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0014] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0017] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0018] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0019] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0020] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0021] [First embodiment]

[0022] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0023] 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.

[0024] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. 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).

[0025] 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.

[0026] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. 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 acquires the data indicating the user input.

[0027] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0028] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0029] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0031] 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.

[0032] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0033] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0034] This invention is a system that allows users to easily diagnose their ideal financial life plan through messaging applications such as LINE, and provides suggestions for improving wasteful spending and adjusting the plan to accommodate changes in life stages.The system is designed around a server, and accomplishes the following steps through interactions with the user.

[0035] First, the server uses a messaging application to ask the user for basic information, such as age, family composition, and annual income. Based on this, the user sends a message back to the server with their basic information. The server receives this information and stores it in a database.

[0036] The server then uses the stored user information to diagnose an ideal financial life plan using an AI model, and the results are sent back to the user via a messaging app.

[0037] Furthermore, the server collects the user's usage history of electronic payment services, such as PayPay, and uses an AI model to analyze wasteful spending. This allows it to provide specific money-saving tips, such as "Your insurance premiums are too high, so we recommend you review them" or "You can save XX yen each month by eating out less." The resulting suggestions are sent to the user via a messaging app.

[0038] Users can submit additional conditions or change requests to the server based on changes in their life stage or new goals. The server receives these requests, re-diagnoses them using the AI ​​model, generates a new plan, and sends it back to the user.

[0039] Finally, the server promptly notifies the user when unexpected expenses or income arise, and periodically reassess the user's financial life plan, updating it as necessary and providing the results to the user.

[0040] As a concrete example, let's assume that the user is 50 years old, has an annual income of 5 million yen, and has a family consisting of a spouse and one child. After providing this basic information to the server, the AI ​​model proposes an ideal retirement asset formation plan and monthly spending review. It also points out that eating out expenses are high based on PayPay usage history and suggests ways to reduce expenses. If the user requests to "review the plan for when another child is born," the server regenerates a plan that reflects these new conditions and presents it to the user. Then, if, for example, an unexpected medical expense arises, the server promptly notifies the user and makes any necessary adjustments to the plan.

[0041] In this way, this system supports users' daily economic activities and provides them with concrete means to easily realize their ideal financial life plan.

[0042] The processing flow will be explained below.

[0043] Step 1:

[0044] The server sends a message to the user's LINE account asking for basic information, such as age, family composition, and annual income.

[0045] Step 2:

[0046] Users receive LINE messages and respond to each question by replying with their own information.

[0047] Step 3:

[0048] The server receives the user's response and stores information such as age, family composition, and annual income in a database.

[0049] Step 4:

[0050] The server retrieves the stored user information from the database and inputs it into the AI ​​model to diagnose the ideal financial life plan.

[0051] Step 5:

[0052] The server sends the diagnosis results of the ideal financial life plan generated by the AI ​​model to the user via LINE message.

[0053] Step 6:

[0054] The server uses the API of electronic payment services such as PayPay to collect user usage history.

[0055] Step 7:

[0056] The server inputs the collected usage history into an AI model and performs an analysis to identify areas for improvement in wasteful spending.

[0057] Step 8:

[0058] Based on the analysis results, the server sends specific money-saving tips and suggestions for reducing wasteful spending (e.g., reviewing insurance premiums, reducing eating out expenses, etc.) to the user via LINE messages.

[0059] Step 9:

[0060] Users can send additional conditions or change requests to the server via LINE messages depending on changes in their life stage (e.g., having more children) or new financial goals.

[0061] Step 10:

[0062] The server receives additional conditions and change requests from the user and inputs them back into the AI ​​model to generate a new financial life plan.

[0063] Step 11:

[0064] The server will send the diagnosis results of the new plan to the user via LINE message.

[0065] Step 12:

[0066] When unexpected expenses or income occur, the user sends that information to the server via LINE message.

[0067] Step 13:

[0068] The server receives information about unexpected expenses and income and makes necessary plan adjustments based on that information.

[0069] Step 14:

[0070] The server will send the new adjusted plan results and advice to the user via LINE message.

[0071] Step 15:

[0072] The server periodically re-evaluates the user information in the database, generates new diagnostic results, and sends them to the user via LINE message.

[0073] Example 1

[0074] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0075] Conventional economic and life planning support systems required users to input detailed information and create plans using specialized knowledge. This was time-consuming and difficult for average users to create appropriate plans. Furthermore, there were few ways to obtain real-time improvement suggestions based on current living environment and spending status. Furthermore, it was difficult to respond quickly to changes in life stages or unexpected expenses.

[0076] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0077] In this invention, the server includes means for collecting basic information from a user, means for diagnosing an ideal economic life plan using a generative artificial intelligence model based on the basic information, means for analyzing the usage history of electronic payment services and making suggestions for improving wasteful spending, means for accepting additional conditions and change requests from the user and generating a new economic life plan, and means for sending notifications when unexpected expenses or income occur and for reevaluating and updating the economic life plan as necessary. This allows the user to easily create an ideal economic life plan, and enables real-time expenditure analysis and improvement suggestions, as well as quick response to changes in life stages and unexpected expenses.

[0078] "Basic information" refers to information necessary for making a personal economic life plan, such as the user's age, family structure, and annual income.

[0079] A "generative artificial intelligence model" is an artificial intelligence technology that generates optimal economic and lifestyle plans based on a user's basic information and usage history data.

[0080] An "electronic payment service" is a service that provides digital payment methods that users can use for shopping and payments.

[0081] "Usage history" is a record of a user's transactions made through an electronic payment service.

[0082] "Suggestions for improving wasteful spending" are specific advice for reducing unnecessary or excessive spending detected through an analysis of usage history.

[0083] A "messaging application" is a communications application that allows for the exchange of text and media in real time over the Internet.

[0084] An "economic life plan" is a plan that sets ideal spending allocations and savings goals based on the user's income, expenses, asset type, etc.

[0085] A "notification" is an electronic message sent to inform a user of specific information.

[0086] "Life stage changes" are important events or changes in circumstances that occur in a user's life (e.g., marriage, childbirth, job change, etc.).

[0087] "Unexpected expenses and income" refers to sudden expenses that occur unexpectedly or income that is received temporarily at a specific time.

[0088] This invention is a system that allows users to easily diagnose their ideal financial life plan via a messaging application, and provides suggestions for reducing wasteful spending and adjusting the plan to accommodate changes in life stages. The system is designed around a server, and provides the ideal financial life plan through interactions with the user.

[0089] First, the server uses a messaging application to ask the user for basic information, such as age, family composition, and annual income. Based on this, the user then sends a message back to the server with their basic information. The server receives this information and stores it in a database.

[0090] The server then uses the stored user information to diagnose an ideal economic life plan using a generative artificial intelligence model, and the results are sent back to the user via a messaging app.

[0091] The server also collects the user's electronic payment service usage history and uses a generative artificial intelligence model to analyze wasteful spending. This allows the server to provide specific money-saving tips, such as "Your insurance premiums are too high, so we recommend you review them" or "You can save XX yen each month by eating out less." The resulting suggestions are sent to the user via a messaging application.

[0092] Users can submit additional conditions or change requests to the server depending on changes in their life stage or new goals. The server receives these requests, re-diagnoses them using a generative artificial intelligence model, generates a new plan, and sends it back to the user.

[0093] Finally, if unexpected expenses or income arise, the server will promptly notify the user. In addition, the server will periodically reassess the user's financial life plan, update it as necessary, and provide the results to the user.

[0094] As a concrete example, let's assume that the user is 50 years old, has an annual income of 5 million yen, and has a family consisting of a spouse and one child. After providing this basic information to the server, the generative AI model proposes an ideal retirement asset formation plan and monthly spending revisions. It also points out that dining out expenses are high based on the user's electronic payment service usage history and suggests ways to reduce them. If the user requests to "reconsider the plan for when another child is born," the server regenerates a plan that reflects these new conditions and presents it to the user. Then, if, for example, an unexpected medical expense arises, the server promptly notifies the user and makes any necessary adjustments to the plan.

[0095] An example of a prompt sentence is as follows:

[0096] "Age 50, annual income 5 million yen, family composition: spouse and one child. Please provide an ideal retirement asset formation plan based on this basic information. Also, based on the usage history of electronic payment services, please point out that eating out expenses are high and suggest ways to reduce them."

[0097] This system supports users' daily economic activities and provides concrete means for easily realizing their ideal economic life plan. It uses cloud servers (e.g., AWS, Google Cloud Platform) as hardware, and messaging applications (e.g., general messaging services), electronic payment services (e.g., general electronic payment systems), and generative artificial intelligence models (e.g., GPT-4, BERT) as software.

[0098] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0099] Step 1:

[0100] The server uses a messaging application to send a message to the user asking for basic information (age, family composition, annual income, etc.). The input is the message content sent from the server, and the output is the basic information replied by the user. Specifically, the server calls the API of the messaging service and sends the question in the specified format.

[0101] Step 2:

[0102] The user enters basic information through a messaging application and sends it back to the server. The input is the data the user enters into the messaging application, and the output is the basic information sent to the server. The specific operation is when the user answers a question in the messaging application and taps the send button.

[0103] Step 3:

[0104] The server stores the received basic information in a database. The input is the basic information received from the user, and the output is the data stored in the database. Specifically, the server issues an SQL query via the database middleware and stores the data.

[0105] Step 4:

[0106] The server uses a generative AI model to diagnose an ideal economic life plan based on the user's saved basic information. The input is the basic information obtained from the database, and the output is the diagnosis result returned by the generative AI model. Specifically, the server generates a prompt sentence from the basic information and sends a request to the AI ​​model's API.

[0107] Step 5:

[0108] The server sends the diagnosis results to the user via a messaging application. The input is the diagnosis results of the generative AI model, and the output is the message content sent to the user. Specifically, the server converts the generated diagnosis results into a text message and sends it using the messaging application's API.

[0109] Step 6:

[0110] The server collects the user's usage history of the electronic payment service. The input is authentication information obtained from the API of the electronic payment service, and the output is the obtained usage history data. Specifically, the server calls the API of the electronic payment service and obtains the user's usage history data.

[0111] Step 7:

[0112] The server uses a generative artificial intelligence model to analyze the collected usage history data. The input is the acquired usage history data, and the output is suggestions for improving wasteful spending. Specifically, the server inputs the usage history data into the AI ​​model and receives an analysis of wasteful spending and suggestions for improvement.

[0113] Step 8:

[0114] The server sends wasteful spending improvement suggestions to the user via a messaging application. The input is the suggestion generated by the generative artificial intelligence model, and the output is the improvement suggestion message sent to the user. Specifically, the server converts the suggestion content into a text message and sends it via the messaging application's API.

[0115] Step 9:

[0116] The user submits additional conditions or change requests according to changes in their life stage or new goals. The input is the request from the user, and the output is the new conditions sent to the server. Specifically, the user enters the change request in a messaging app and taps the send button.

[0117] Step 10:

[0118] Based on the received request, the server re-diagnoses the economic life plan using a generative artificial intelligence model. The input is the added conditions and changes, and the output is the regenerated economic life plan. Specifically, the server creates a prompt statement including the new conditions, sends it to the AI ​​model, and receives the new plan.

[0119] Step 11:

[0120] The server sends the regenerated economic life plan to the user through a messaging application. The input is the new plan from the generative artificial intelligence model, and the output is the new plan sent to the user. Specifically, the server converts the new plan into a text message and sends it using the messaging application's API.

[0121] Step 12:

[0122] When a user incurs unexpected expenses or income, the user notifies the server of that information. The input is the unexpected expense or income information, and the output is the notification content sent to the server. Specifically, the user enters the expense or income information in a messaging app and taps the send button.

[0123] Step 13:

[0124] The server receives the notification, adjusts the plan using a generative AI model, and resends it to the user. The input is non-recurring expenses and income information, and the output is the adjusted economic life plan. Specifically, the server inputs the received information into the AI ​​model, generates an adjusted plan, and sends it to the user via a messaging app.

[0125] Step 14:

[0126] The server periodically reevaluates the user's economic life plan and updates it as necessary. The input is the existing plan and data such as the latest spending history, and the output is the updated economic life plan. Specifically, the server periodically executes a scheduled task, retrieves the latest information from the database, reevaluates it using the generative artificial intelligence model, and saves the results.

[0127] (Application example 1)

[0128] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0129] Many people today use electronic payment services, and there is a demand for financial improvement proposals based on their usage history to reduce wasteful spending and realize efficient financial life plans. However, there is a lack of flexible planning systems that can accommodate changes in users' lifestyles and unexpected expenses. There is also a need for systems that periodically reevaluate users' financial situations and provide updated plans.

[0130] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0131] In this invention, the server includes means for collecting basic information from the user, means for diagnosing an ideal financial life plan based on the basic information, means for analyzing the usage history of electronic payment services and proposing improvements to wasteful spending, means for accepting additional conditions or change requests from the user and generating a new plan, means for sending notifications when unexpected expenses or income occur, and means for periodically reevaluating the user's financial situation using an AI model based on the user's basic information and usage history of electronic payment services and providing an updated plan. This supports the user's daily financial activities and makes it easy to realize their ideal financial life plan.

[0132] "User" means any individual or entity that uses the Service or System.

[0133] "Basic information" refers to personal information such as the user's age, family structure, and annual income.

[0134] A "Money Life Plan" is a plan for achieving an ideal financial life based on the user's income and expenses.

[0135] "Electronic payment services" are services that provide digital payment methods, including credit cards and mobile payments.

[0136] "Usage history" refers to the transaction records when a user uses an electronic payment service.

[0137] An "AI model" is an algorithm or system that uses artificial intelligence to analyze data and make predictions and suggestions.

[0138] "Suggestions to improve wasteful spending" are specific advice for reducing wasteful spending based on the user's spending data.

[0139] "Additional conditions" refer to new conditions or requests specified by the user.

[0140] A "change request" is a request sent by a user when they want to change an existing plan.

[0141] "Unexpected expenses" refers to unexpected expenses that occur.

[0142] "Notification" means that the system sends information to the user.

[0143] "Periodic reassessment" refers to reviewing and assessing the user's financial situation at regular intervals.

[0144] An "updated plan" is the latest financial life plan created based on new data and conditions.

[0145] The embodiment of the present invention is a software configuration using a server, a user terminal, and an AI model. The system allows users to provide personal information and generate, evaluate, and update a financial life plan based on their usage history of electronic payment services.

[0146] First, a messaging application such as LINE is installed on the user's device, and the user provides basic information to the server through this application. This basic information includes age, family composition, annual income, etc. This basic information is collected using the LINE API and sent to the server.

[0147] The server is built on a cloud service such as AWS EC2, and the collected basic information is stored in a database such as Amazon RDS. Next, an ideal financial life plan is generated using an AI model based on the stored basic information. The AI ​​model uses machine learning libraries such as TensorFlow and PyTorch.

[0148] The generated ideal financial life plan is sent to the user via LINE message, allowing them to receive specific suggestions for improvement based on their own financial situation. For example, advice such as "specific ways to reduce eating out expenses to 10,000 yen per month" is provided.

[0149] The server also periodically collects usage history for electronic payment services (e.g., electronic wallets) and stores it in a database. Based on the collected usage history, the AI ​​model makes suggestions for improving wasteful spending. For example, specific suggestions such as "Your eating out expenses are high, so we suggest you cut them down" are sent to the user via LINE messages.

[0150] Furthermore, the system accepts additional conditions and change requests from users via LINE messages. For example, if a user sends a request such as "I would like to review the plan for when we have another child," the server generates a plan that reflects the new conditions and notifies the user again. Furthermore, if unexpected expenses or income arise, the system promptly notifies the user and proposes a new plan.

[0151] As a specific example, if User A installs "Smart Money Advisor" on their smartphone and links it to their LINE account, a financial life plan will be generated after they enter their basic information. For example, based on information such as "age: 30, family composition: spouse and one child, annual income: 6 million yen," the AI ​​model will propose a post-retirement asset formation plan and a monthly spending review. Furthermore, because their electronic payment service usage history shows that "monthly dining out expenses: 20,000 yen" is high, they will receive a LINE message suggesting "specific ways to reduce dining out expenses to 10,000 yen per month."

[0152] An example of a prompt sentence to input to the generative AI model is as follows:

[0153] User Information:

[0154] Age: 30

[0155] Family: Spouse and one child

[0156] Annual income: 6 million yen

[0157] Usage history:

[0158] Electronic payment service: Monthly dining out expenses: 20,000 yen

[0159] request:

[0160] Reassessing your plan following the birth of a new child

[0161] Generate the plan:

[0162] Specific ways to reduce eating out expenses to 10,000 yen per month

[0163] Adjusting future asset formation plans

[0164] As described above, the present invention provides specific means for supporting users' daily economic activities and helping them realize their ideal financial life plan.

[0165] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0166] Step 1:

[0167] The user enters basic information (age, family composition, annual income, etc.) into the server using a messaging application such as LINE. The entered basic information is sent to the server via the LINE API and stored in a database.

[0168] Input: Basic information sent by the user via messaging applications (age, family composition, annual income, etc.)

[0169] Output: Basic user information stored in the database

[0170] Step 2:

[0171] The server uses the stored basic information to generate an ideal financial life plan using an AI model. The AI ​​model uses machine learning libraries such as TensorFlow and PyTorch. The generated plan is stored in a database on the server.

[0172] Input: User basic information stored in the database

[0173] Output: Generated ideal financial life plan (stored in database)

[0174] Step 3:

[0175] The server notifies the user of the generated ideal financial life plan via a LINE message. The notification is sent using the LINE Messaging API.

[0176] Input: Generated ideal financial life plan

[0177] Output: Money life plan notified via LINE message

[0178] Step 4:

[0179] The server periodically collects the user's usage history for the electronic payment service. The collected usage history is stored in a database. Usage history is obtained using the PayPay API, etc.

[0180] Input: Electronic payment service usage history (e.g., dining out expenses, shopping expenses, etc.)

[0181] Output: Usage history of electronic payment services stored in a database

[0182] Step 5:

[0183] The server uses the collected usage history to analyze wasteful spending using an AI model and generate improvement proposals. For example, if eating out expenses are too high, it will generate a reduction proposal.

[0184] Input: Electronic payment service usage history stored in the database

[0185] Output: Suggestions for improving waste

[0186] Step 6:

[0187] The server notifies the user of the generated suggestions for improving wasteful spending via LINE messages. The notifications are sent using the LINE Messaging API.

[0188] Input: Suggestions for improving wasteful spending

[0189] Output: Improvement suggestions notified via LINE message

[0190] Step 7:

[0191] Users can send additional conditions or change requests to the server via a messaging app, which then accepts the conditions, re-diagnoses them using the AI ​​model, and generates an updated plan.

[0192] Input: Additional terms and changes requested by the user

[0193] Output: Updated financial life plan (saved in database)

[0194] Step 8:

[0195] The server notifies the user of the generated update plan via a LINE message. The notification is sent using the LINE Messaging API.

[0196] Enter: Updated Money Life Plan

[0197] Output: Renewal plan notified via LINE message

[0198] Step 9:

[0199] When unexpected expenses or income arise, the server receives information from the user and quickly re-diagnoses the plan using an AI model. Based on the results of the re-diagnosis, the plan is updated and the user is notified.

[0200] Input: User-submitted information about occasional expenses and income

[0201] Output: Reassessed financial life plan and notification

[0202] Through these steps, the system supports users' daily economic activities and enables them to realize their ideal financial life plan.

[0203] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0204] This invention is a system that allows users to easily diagnose their ideal financial life plan through messaging applications such as LINE, suggests ways to reduce wasteful spending, and adjusts the plan to accommodate changes in life stages, and also recognizes the user's emotions and adjusts the feedback content accordingly.This system is designed around a server, and achieves the following steps through interactions with the user.

[0205] First, the server uses a messaging application to ask the user for basic information, such as age, family composition, and annual income. Based on this, the user sends a message back to the server with their basic information. The server receives this information and stores it in a database.

[0206] The server then uses the stored user information to diagnose an ideal financial life plan using an AI model, and the results are sent back to the user via a messaging app.

[0207] Furthermore, the server collects the user's usage history of electronic payment services, such as PayPay, and uses an AI model to analyze wasteful spending. This allows it to provide specific money-saving tips, such as "Your insurance premiums are too high, so we recommend you review them" or "You can save XX yen each month by eating out less." The resulting suggestions are sent to the user via a messaging app.

[0208] Furthermore, by combining the emotion engine, the server analyzes the user's emotions in real time and provides feedback according to the user's emotional state. For example, if the user is feeling stressed, the server will provide specific advice on how to reduce stress. If the server recognizes that the user is in a positive emotional state, it will make proactive suggestions such as additional investments or savings plans.

[0209] Users can submit additional conditions or change requests to the server based on changes in their life stage or new goals. The server receives these requests, re-diagnoses them using the AI ​​model, generates a new plan, and sends it back to the user.

[0210] As a concrete example, let's assume that the user is 50 years old, has an annual income of 5 million yen, and has a family consisting of a spouse and one child. After providing this basic information to the server, the AI ​​model will propose an ideal retirement asset formation plan and a monthly spending review. It will also point out that eating out expenses are high based on PayPay usage history and suggest ways to reduce them. If the user requests to "review the plan for when we have another child," the server will regenerate a plan that reflects these new conditions and present it to the user.

[0211] Furthermore, if the user expresses negative emotions toward finances, for example, if the emotion engine detects "anxiety about investments," the server will propose a conservative investment plan to reduce risk. On the other hand, if the user expresses positive emotions, the server will propose an aggressive investment strategy, leveraging the user's positive emotions to promote further asset formation.

[0212] In this way, this system supports users' daily financial activities and provides concrete means for easily realizing their ideal financial life plan. It also recognizes the user's emotional state and provides feedback accordingly, enabling more personalized support.

[0213] The processing flow will be explained below.

[0214] Step 1:

[0215] The server sends a message to the user's LINE account asking for basic information, such as age, family composition, and annual income.

[0216] Step 2:

[0217] Users receive LINE messages and respond to each question by replying with their own information.

[0218] Step 3:

[0219] The server receives the user's response and stores information such as age, family composition, and annual income in a database.

[0220] Step 4:

[0221] The server retrieves the stored user information from the database and inputs it into the AI ​​model to diagnose the ideal financial life plan.

[0222] Step 5:

[0223] The server sends the diagnosis results of the ideal financial life plan generated by the AI ​​model to the user via LINE message.

[0224] Step 6:

[0225] The server uses the API of electronic payment services such as PayPay to collect user usage history.

[0226] Step 7:

[0227] The server inputs the collected usage history into an AI model and performs an analysis to identify areas for improvement in wasteful spending.

[0228] Step 8:

[0229] Based on the analysis results, the server sends specific money-saving tips and suggestions for reducing wasteful spending (e.g., reviewing insurance premiums, reducing eating out expenses, etc.) to the user via LINE messages.

[0230] Step 9:

[0231] The server uses an emotion engine to recognize the user's emotions based on the content of the user's LINE messages and the timing of the exchanges.

[0232] Step 10:

[0233] The server adjusts the feedback based on the user's emotional state, for example, by providing suggestions for stress reduction if the user is feeling stressed, or offering proactive investment plans if the user is feeling positive.

[0234] Step 11:

[0235] Depending on changes in their life stage or new financial goals, users can send additional conditions or change requests to the server via LINE messages.

[0236] Step 12:

[0237] The server receives additional conditions and change requests from the user and inputs them into the AI ​​model to generate a new financial life plan.

[0238] Step 13:

[0239] The server will send the diagnosis results of the new plan to the user via LINE message.

[0240] Step 14:

[0241] When unexpected expenses or income occur, the user sends that information to the server via LINE message.

[0242] Step 15:

[0243] The server receives information about unexpected expenses and income and makes necessary plan adjustments based on that information.

[0244] Step 16:

[0245] The server will send the new adjusted plan results and advice to the user via LINE message.

[0246] Step 17:

[0247] The server periodically re-evaluates the user information in the database, generates new diagnostic results, and sends them to the user via LINE message.

[0248] Example 2

[0249] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0250] In modern society, personal financial activities are becoming increasingly complex, requiring appropriate management. However, it is not easy for users to understand their own financial situation and implement efficient asset management or reduce wasteful spending. Furthermore, financial decisions are often influenced by changes in life stages and emotions, so flexible and personalized advice is required. To solve these challenges, a comprehensive management system is needed that not only provides a diagnosis based on the user's basic information, but also includes emotional analysis.

[0251] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0252] In this invention, the server includes means for collecting basic information from the user, means for diagnosing an ideal economic plan based on the basic information, means for analyzing the usage history of electronic payment services and proposing improvements to wasteful spending, means for analyzing the user's emotions and adjusting the feedback content, means for accepting additional conditions or change requests from the user and generating a new plan, and means for sending notifications when unexpected expenses or income occur. This makes it possible to comprehensively manage the user's economic activities and provide flexible and personalized advice.

[0253] "Means for collecting basic information from users" refers to functions for obtaining basic personal information such as the user's age, family composition, and annual income through messaging applications, web forms, etc.

[0254] "Means for diagnosing ideal economic plans based on basic information" is a function that uses collected basic information about users to automatically generate ideal asset management and spending plans using AI models and algorithms.

[0255] "Means for analyzing the usage history of electronic payment services and making suggestions for improving wasteful spending" is a function that analyzes the user's electronic payment history (for example, credit card statements and electronic money usage history), identifies wasteful spending items, and provides money-saving suggestions based on that.

[0256] "Means for analyzing user emotions and adjusting feedback content" refers to a function that uses an emotion analysis engine to determine the user's emotional state and adjusts advice and suggestions accordingly.

[0257] "Means for accepting additional conditions and change requests from users and generating new plans" refers to a function that provides users with the ability to input change requests such as new conditions and upcoming life events, and allows AI models, etc., to generate new economic plans based on these requests.

[0258] The "means for sending notifications when unexpected income or expenses occur" is a function that enables the system to automatically detect when the user has unexpected income or expenses and send appropriate notifications or advice to the user.

[0259] "Message application" refers to application software used by users to send and receive text messages and other data, specifically LINE and other chat applications.

[0260] An "ideal economic plan" refers to a comprehensive plan for achieving the most efficient and desirable state in terms of asset formation, expenditure management, etc.

[0261] An "emotion analysis engine" is an algorithm or system that analyzes a user's text messages or other input data to determine the emotions contained therein.

[0262] An "AI model" refers to an artificial intelligence algorithm or system that uses machine learning and deep learning to analyze data and make suggestions and diagnoses to users.

[0263] This invention is a system that allows users to easily diagnose their ideal financial plan through a messaging application, suggests ways to reduce wasteful spending, and adjusts the plan to accommodate changes in life stages, and also recognizes the user's emotions and adjusts the feedback content accordingly. This system is designed around a server, and specific processes are realized through interactions with the user.

[0264] Hardware and Software Configuration

[0265] The system is implemented using the following hardware and software:

[0266] Hardware: Server (e.g., Amazon Web Services EC2 instance)

[0267] Software: messaging applications (e.g., LINE API), databases (e.g., MySQL), AI models (e.g., OpenAI GPT-4), electronic payment data (e.g., electronic payment service API), sentiment analysis engines (e.g., Microsoft Azure Text Analytics API)

[0268] Specific flow of data processing and data calculation

[0269] The server sends a message to the user through a messaging application asking for basic information such as age, family composition, and annual income. The user enters this information and replies using the messaging application. The server stores the received basic information in a database (MySQL).

[0270] Once the basic information is saved, the server uses an AI model (OpenAI GPT-4) to diagnose an ideal economic plan based on the user's basic information, and the generated diagnosis results are sent to the user again via a messaging app.

[0271] The server also collects the user's electronic payment service usage history and uses an AI model to analyze wasteful spending. For example, if it determines that the user spends a lot of money eating out, it will generate specific suggestions such as "You can save XX yen each month by eating out less." These suggestions are also sent to the user via a messaging app.

[0272] By combining it with an emotion analysis engine, the server analyzes the user's emotions in real time and provides feedback according to the user's emotional state. If the server determines that the user is feeling stressed, it provides advice on how to reduce stress, and if the server recognizes that the user is in a positive emotional state, it suggests additional investment or savings plans.

[0273] Users can submit additional conditions or change requests to the server based on changes in their life stage or new goals. The server receives these requests, re-diagnoses them using the AI ​​model, generates a new plan, and presents it to the user again.

[0274] Examples of concrete examples and prompts

[0275] As a concrete example, let's assume that the user is 50 years old, has an annual income of 5 million yen, and has a family consisting of a spouse and one child. After providing this basic information to the server, the AI ​​model proposes an ideal retirement asset formation plan and a monthly spending review. It also points out that dining out expenses are high based on the user's electronic payment service usage history and suggests ways to reduce them. If the user requests to "review the plan for when we have another child," the server regenerates a plan that reflects these new conditions and presents it to the user. Furthermore, if the user expresses concerns about investing, the sentiment analysis engine detects this and suggests a conservative investment plan.

[0276] Example prompt sentence:

[0277] "Please suggest an ideal retirement asset formation plan for a user who is 50 years old, has an annual income of 5 million yen, and has a family structure of spouse and one child."

[0278] "Analyze the usage history of electronic payment services and provide specific advice to users to reduce wasteful spending."

[0279] "Please explain how to provide appropriate feedback when users express negative emotions."

[0280] As described above, this system supports users' daily economic activities and provides concrete means for realizing ideal economic plans. Furthermore, by recognizing the user's emotional state and providing feedback accordingly, it achieves more personalized support.

[0281] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0282] Step 1:

[0283] Collecting basic information

[0284] Input: The user sends basic information such as age, family composition, and annual income to the server via a messaging application.

[0285] Specific operation: The server sends a message via a messaging application, for example, "Please tell me your age, family composition, and annual income." The user replies using the messaging application, "50 years old, spouse and one child, annual income 5 million yen."

[0286] Data processing: The server processes the received information and converts it into the required format for storage in a database (e.g. MySQL).

[0287] Output: Basic information is saved in the database.

[0288] Step 2:

[0289] Diagnosis based on basic information

[0290] Input: The server retrieves the stored basic information.

[0291] Specific operation: The server retrieves the information "50 years old, spouse and one child, annual income of 5 million yen" from the MySQL database.

[0292] Data calculation: The server uses an AI model (e.g., OpenAI GPT-4) to prompt for basic information and generate the user's ideal economic plan.

[0293] Output: The server gets the generated economic plan.

[0294] Step 3:

[0295] Sending diagnostic results

[0296] Input: Server has economic plan generated by AI model.

[0297] Specific operation: The server sends the generated economic plan via a messaging application saying, "Here's your ideal retirement asset formation plan."

[0298] Output: The user receives the diagnosis through a messaging application.

[0299] Step 4:

[0300] Collection and analysis of electronic payment history

[0301] Input: With the user's consent, the server collects the user's usage history of electronic payment services (e.g., electronic payment service API).

[0302] Specific operation: The server calls the electronic payment service API and obtains the user's latest usage history data.

[0303] Data calculation: The server uses the acquired data to apply an AI model to analyze wasteful spending patterns, such as "high spending on eating out," and generate specific savings suggestions.

[0304] Output: Analysis results and savings recommendations are generated.

[0305] Step 5:

[0306] Submit an improvement suggestion

[0307] Input: Server has analysis results and savings suggestions.

[0308] Specific operation: The server sends the generated savings plan via a messaging application in the form of, for example, "If you eat out less, you can save XX yen each month."

[0309] Output: The user receives the savings offer through a messaging application.

[0310] Step 6:

[0311] Sentiment analysis and feedback adjustment

[0312] Input: The server receives the user's message and analyzes the sentiment using a sentiment analysis engine (e.g., Microsoft Azure Text Analytics API).

[0313] Specific operation: The server sends the user's input message to the sentiment analysis engine and obtains a sentiment result such as "I feel anxious about investing."

[0314] Data calculation: Based on the sentiment results, the server adjusts the feedback content, for example, suggesting a conservative investment plan.

[0315] Output: The adjusted feedback is generated.

[0316] Step 7:

[0317] Sending emotional suggestions

[0318] Input: The server has adjusted feedback.

[0319] What happens: The server sends tailored feedback via a messaging application, such as "Consider a more conservative investment plan."

[0320] Output: The user receives the suggestion through their messaging application.

[0321] Step 8:

[0322] Responding to changes in life stages

[0323] Input: The user sends new conditions or goals to the server via a messaging application.

[0324] Specific operation: For example, a user sends a request saying, "I would like to review my plans for having another child." The server receives this request.

[0325] Data calculation: The server inputs new conditions as prompts into the AI ​​model and generates the ideal economic plan again.

[0326] Output: A new economic plan is generated.

[0327] Step 9:

[0328] Sending re-diagnosis results

[0329] Input: The server has a new economic plan.

[0330] Specific operation: The server sends the generated new economic plan to the user via a messaging application.

[0331] Output: The user receives the new plan through the messaging application.

[0332] As a result, this system comprehensively supports users' daily economic activities and provides ideal economic plans. Furthermore, it flexibly responds to changes in the user's emotions and life stage, providing personalized support.

[0333] (Application example 2)

[0334] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0335] In modern society, efficient asset management and planned spending are extremely important. However, many users find it difficult to manage their own expenses and formulate future life plans. Furthermore, few systems take into account the impact of emotional states on asset management, making it difficult to provide appropriate feedback tailored to the user's emotions. Therefore, there is a need for a system that allows users to easily diagnose their ideal financial life plan, receive suggestions for improving wasteful spending, and provide feedback appropriate to their emotional state.

[0336] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0337] In this invention, the server includes means for collecting basic information from a user, means for diagnosing an ideal money life plan based on the basic information, means for analyzing the usage history of electronic payment methods and proposing improvements to wasteful spending, means for accepting additional conditions and change requests from the user and generating a new plan, means for sending notifications when unexpected expenses or income occur, means for analyzing the user's emotional state, and means for providing feedback according to the user's emotional state. This allows the user to efficiently manage their assets, improve wasteful spending, and achieve planned spending, and further allows them to manage their assets more appropriately by receiving feedback according to their emotional state.

[0338] A "user" is a person who uses the system and provides data on basic information and emotional state.

[0339] "Basic information" refers to information necessary for diagnosing a financial life plan, such as the user's age, income, and family composition.

[0340] The "Ideal Money Life Plan" is a plan that shows optimal spending, savings, and investment plans based on the user's financial goals and current situation.

[0341] "Electronic payment instrument" refers to a platform or method for making payments electronically, including, for example, mobile payment services.

[0342] "Suggestions for improving wasteful spending" are suggestions that analyze the user's spending patterns and provide specific advice for reducing unnecessary or excessive spending.

[0343] "Additional conditions and change requests" are new conditions and settings that users provide to the system in response to changes in their life stage or new goals.

[0344] The "means for generating a new plan" is a function for creating a new money life plan based on the user's additional conditions or change requests.

[0345] The "means for sending a notification when unexpected expenses or income occur" is a function for notifying the user when unexpected expenses or income occur.

[0346] A "means for analyzing emotional state" is a method for determining a user's emotions and using that information to provide appropriate feedback.

[0347] The "means for providing feedback according to emotional state" is a function that shows advice or suggestions customized to the user's emotional state.

[0348] In an embodiment of the present invention, the system includes a server, a user terminal, an electronic payment method, and a sentiment analysis engine as its main components. A user uses a smartphone to input basic information through a provided messaging application. The basic information includes data on economic status such as age, family composition, and annual income. The server receives this basic information and stores it in a database.

[0349] The server then uses the stored basic information to create a generative AI model that will diagnose an ideal financial life plan. This generative AI model will then suggest an optimal plan based on the user's financial situation and life stage. The results of the diagnosis will be sent to the user via a messaging app.

[0350] Furthermore, the server collects usage history of electronic payment methods (for example, mobile payment services) and analyzes spending data. Data science techniques are used to analyze the spending data and identify large amounts of spending and wasteful spending. Specific suggestions for improving wasteful spending, such as "You spend too much on eating out, so you need to cut back," are generated. These suggestions are also sent via a messaging application.

[0351] The sentiment analysis engine analyzes the user's emotions in messages in real time. This engine uses natural language processing technology to analyze messages from users and determine their emotional state. For example, if the user is feeling "anxious about investing," the server will suggest a conservative investment plan. On the other hand, if the user is expressing positive emotions, the server will suggest an aggressive investment strategy.

[0352] When the user provides additional conditions or change requests based on changes in their life stage or new goals, the server receives them and again uses the generative AI model to generate a new financial life plan, which is also sent to the user via a messaging app.

[0353] The primary hardware used includes smartphones and servers, while the primary software includes a messaging application, a sentiment analysis engine, and a generative AI model. Utilizing these components, users can efficiently manage their assets, reduce waste, and plan their spending.

[0354] As a specific example, if a user is 50 years old, has an annual income of 5 million yen, and has a family consisting of a spouse and one child, by providing basic information, the AI ​​model will propose an ideal post-retirement asset formation plan and monthly spending revisions. It will also point out that dining out expenses are high based on the user's electronic payment method usage history and suggest ways to reduce expenses. If the user requests to "review the plan for when we have another child," the server will regenerate a plan reflecting the new conditions and present it to the user.

[0355] Examples of prompts include:

[0356] "Please tell me your plan for a 50-year-old with an annual income of 5 million yen, a spouse, and one child."

[0357] "Please tell us how to improve wasteful spending based on your electronic payment usage history."

[0358] "I want to reassess my plans for having another child."

[0359] "I'm feeling anxious about my investment. What should I do?"

[0360] As a result, users can receive specific feedback tailored to their own financial situation and receive appropriate advice according to their emotional state.

[0361] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0362] Step 1:

[0363] A user opens a messaging application on their smartphone and enters basic information such as age, income, and family composition.

[0364] Input: User's basic information (age, income, family composition, etc.)

[0365] Output: Basic information data sent to the server

[0366] Step 2:

[0367] The server stores the received basic information in a database.

[0368] Input: Basic information data sent by the user

[0369] Output: Basic information stored in the database

[0370] Step 3:

[0371] Based on the stored basic information, the server uses a generative AI model to diagnose your ideal financial life plan.

[0372] Input: Basic information data

[0373] Output: Diagnosed ideal financial life plan

[0374] Step 4:

[0375] The server sends the diagnosis results to the user through a messaging application.

[0376] Input: Diagnosed ideal financial life plan

[0377] Output: Diagnostic results sent via messaging application

[0378] Step 5:

[0379] The server collects electronic payment transaction history and analyzes spending data, specifically using data science techniques to identify excessive and wasteful spending.

[0380] Input: Electronic payment method usage history

[0381] Output: Suggestions for improving waste

[0382] Step 6:

[0383] The server sends the improvement suggestions to the user through a messaging application.

[0384] Input: Suggestions for improving wasteful spending

[0385] Output: Improvement suggestions sent via messaging application

[0386] Step 7:

[0387] The user sends additional information to the server in response to new requests or changes in life stages.

[0388] Input: User additional conditions or change requests

[0389] Output: Additional conditions and change request data sent to the server

[0390] Step 8:

[0391] The server receives additional conditions and change requests and creates a new financial life plan using a generative AI model.

[0392] Input: Additional conditions and change request data

[0393] Output: The new financial life plan generated.

[0394] Step 9:

[0395] The server also sends the new plan to the user through the messaging application.

[0396] Input: Generated new financial life plan

[0397] Output: New plan sent through messaging application

[0398] Step 10:

[0399] The server uses a sentiment analysis engine to analyze the user's sentiment in the message in real time.

[0400] Input: User's message data

[0401] Output: Parsed emotional state data

[0402] Step 11:

[0403] The server generates feedback according to the emotional state and provides it to the user through a messaging application.

[0404] Input: Parsed emotional state data

[0405] Output: Feedback according to emotional state

[0406] The above is the flow of specific processing steps of the program for realizing the application example.

[0407] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0408] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) 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 voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0409] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0410] [Second embodiment]

[0411] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0412] 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.

[0413] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. 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).

[0414] 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.

[0415] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0416] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0417] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0418] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0419] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[0420] 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.

[0421] In the smart glasses 214, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0422] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0423] This invention is a system that allows users to easily diagnose their ideal financial life plan through messaging applications such as LINE, and provides suggestions for improving wasteful spending and adjusting the plan to accommodate changes in life stages.The system is designed around a server, and accomplishes the following steps through interactions with the user.

[0424] First, the server uses a messaging application to ask the user for basic information, such as age, family composition, and annual income. Based on this, the user sends a message back to the server with their basic information. The server receives this information and stores it in a database.

[0425] The server then uses the stored user information to diagnose an ideal financial life plan using an AI model, and the results are sent back to the user via a messaging app.

[0426] Furthermore, the server collects the user's usage history of electronic payment services, such as PayPay, and uses an AI model to analyze wasteful spending. This allows it to provide specific money-saving tips, such as "Your insurance premiums are too high, so we recommend you review them" or "You can save XX yen each month by eating out less." The resulting suggestions are sent to the user via a messaging app.

[0427] Users can submit additional conditions or change requests to the server based on changes in their life stage or new goals. The server receives these requests, re-diagnoses them using the AI ​​model, generates a new plan, and sends it back to the user.

[0428] Finally, the server promptly notifies the user when unexpected expenses or income arise, and periodically reassess the user's financial life plan, updating it as necessary and providing the results to the user.

[0429] As a concrete example, let's assume that the user is 50 years old, has an annual income of 5 million yen, and has a family consisting of a spouse and one child. After providing this basic information to the server, the AI ​​model proposes an ideal retirement asset formation plan and monthly spending review. It also points out that eating out expenses are high based on PayPay usage history and suggests ways to reduce expenses. If the user requests to "review the plan for when another child is born," the server regenerates a plan that reflects these new conditions and presents it to the user. Then, if, for example, an unexpected medical expense arises, the server promptly notifies the user and makes any necessary adjustments to the plan.

[0430] In this way, this system supports users' daily economic activities and provides them with concrete means to easily realize their ideal financial life plan.

[0431] The processing flow will be explained below.

[0432] Step 1:

[0433] The server sends a message to the user's LINE account asking for basic information, such as age, family composition, and annual income.

[0434] Step 2:

[0435] Users receive LINE messages and respond to each question by replying with their own information.

[0436] Step 3:

[0437] The server receives the user's response and stores information such as age, family composition, and annual income in a database.

[0438] Step 4:

[0439] The server retrieves the stored user information from the database and inputs it into the AI ​​model to diagnose the ideal financial life plan.

[0440] Step 5:

[0441] The server sends the diagnosis results of the ideal financial life plan generated by the AI ​​model to the user via LINE message.

[0442] Step 6:

[0443] The server uses the API of electronic payment services such as PayPay to collect user usage history.

[0444] Step 7:

[0445] The server inputs the collected usage history into an AI model and performs an analysis to identify areas for improvement in wasteful spending.

[0446] Step 8:

[0447] Based on the analysis results, the server sends specific money-saving tips and suggestions for reducing wasteful spending (e.g., reviewing insurance premiums, reducing eating out expenses, etc.) to the user via LINE messages.

[0448] Step 9:

[0449] Users can send additional conditions or change requests to the server via LINE messages depending on changes in their life stage (e.g., having more children) or new financial goals.

[0450] Step 10:

[0451] The server receives additional conditions and change requests from the user and inputs them back into the AI ​​model to generate a new financial life plan.

[0452] Step 11:

[0453] The server will send the diagnosis results of the new plan to the user via LINE message.

[0454] Step 12:

[0455] When unexpected expenses or income occur, the user sends that information to the server via LINE message.

[0456] Step 13:

[0457] The server receives information about unexpected expenses and income and makes necessary plan adjustments based on that information.

[0458] Step 14:

[0459] The server will send the new adjusted plan results and advice to the user via LINE message.

[0460] Step 15:

[0461] The server periodically re-evaluates the user information in the database, generates new diagnostic results, and sends them to the user via LINE message.

[0462] Example 1

[0463] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0464] Conventional economic and life planning support systems required users to input detailed information and create plans using specialized knowledge. This was time-consuming and difficult for average users to create appropriate plans. Furthermore, there were few ways to obtain real-time improvement suggestions based on current living environment and spending status. Furthermore, it was difficult to respond quickly to changes in life stages or unexpected expenses.

[0465] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0466] In this invention, the server includes means for collecting basic information from a user, means for diagnosing an ideal economic life plan using a generative artificial intelligence model based on the basic information, means for analyzing the usage history of electronic payment services and making suggestions for improving wasteful spending, means for accepting additional conditions and change requests from the user and generating a new economic life plan, and means for sending notifications when unexpected expenses or income occur and for reevaluating and updating the economic life plan as necessary. This allows the user to easily create an ideal economic life plan, and enables real-time expenditure analysis and improvement suggestions, as well as quick response to changes in life stages and unexpected expenses.

[0467] "Basic information" refers to information necessary for making a personal economic life plan, such as the user's age, family structure, and annual income.

[0468] A "generative artificial intelligence model" is an artificial intelligence technology that generates optimal economic and lifestyle plans based on a user's basic information and usage history data.

[0469] An "electronic payment service" is a service that provides digital payment methods that users can use for shopping and payments.

[0470] "Usage history" is a record of a user's transactions made through an electronic payment service.

[0471] "Suggestions for improving wasteful spending" are specific advice for reducing unnecessary or excessive spending detected through an analysis of usage history.

[0472] A "messaging application" is a communications application that allows for the exchange of text and media in real time over the Internet.

[0473] An "economic life plan" is a plan that sets ideal spending allocations and savings goals based on the user's income, expenses, asset type, etc.

[0474] A "notification" is an electronic message sent to inform a user of specific information.

[0475] "Life stage changes" are important events or changes in circumstances that occur in a user's life (e.g., marriage, childbirth, job change, etc.).

[0476] "Unexpected expenses and income" refers to sudden expenses that occur unexpectedly or income that is received temporarily at a specific time.

[0477] This invention is a system that allows users to easily diagnose their ideal financial life plan via a messaging application, and provides suggestions for reducing wasteful spending and adjusting the plan to accommodate changes in life stages. The system is designed around a server, and provides the ideal financial life plan through interactions with the user.

[0478] First, the server uses a messaging application to ask the user for basic information, such as age, family composition, and annual income. Based on this, the user then sends a message back to the server with their basic information. The server receives this information and stores it in a database.

[0479] The server then uses the stored user information to diagnose an ideal economic life plan using a generative artificial intelligence model, and the results are sent back to the user via a messaging app.

[0480] The server also collects the user's electronic payment service usage history and uses a generative artificial intelligence model to analyze wasteful spending. This allows the server to provide specific money-saving tips, such as "Your insurance premiums are too high, so we recommend you review them" or "You can save XX yen each month by eating out less." The resulting suggestions are sent to the user via a messaging application.

[0481] Users can submit additional conditions or change requests to the server depending on changes in their life stage or new goals. The server receives these requests, re-diagnoses them using a generative artificial intelligence model, generates a new plan, and sends it back to the user.

[0482] Finally, if unexpected expenses or income arise, the server will promptly notify the user. In addition, the server will periodically reassess the user's financial life plan, update it as necessary, and provide the results to the user.

[0483] As a concrete example, let's assume that the user is 50 years old, has an annual income of 5 million yen, and has a family consisting of a spouse and one child. After providing this basic information to the server, the generative AI model proposes an ideal retirement asset formation plan and monthly spending revisions. It also points out that dining out expenses are high based on the user's electronic payment service usage history and suggests ways to reduce them. If the user requests to "reconsider the plan for when another child is born," the server regenerates a plan that reflects these new conditions and presents it to the user. Then, if, for example, an unexpected medical expense arises, the server promptly notifies the user and makes any necessary adjustments to the plan.

[0484] An example of a prompt sentence is as follows:

[0485] "Age 50, annual income 5 million yen, family composition: spouse and one child. Please provide an ideal retirement asset formation plan based on this basic information. Also, based on the usage history of electronic payment services, please point out that eating out expenses are high and suggest ways to reduce them."

[0486] This system supports users' daily economic activities and provides concrete means for easily realizing their ideal economic life plan. It uses cloud servers (e.g., AWS, Google Cloud Platform) as hardware, and messaging applications (e.g., general messaging services), electronic payment services (e.g., general electronic payment systems), and generative artificial intelligence models (e.g., GPT-4, BERT) as software.

[0487] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0488] Step 1:

[0489] The server uses a messaging application to send a message to the user asking for basic information (age, family composition, annual income, etc.). The input is the message content sent from the server, and the output is the basic information replied by the user. Specifically, the server calls the API of the messaging service and sends the question in the specified format.

[0490] Step 2:

[0491] The user enters basic information through a messaging application and sends it back to the server. The input is the data the user enters into the messaging application, and the output is the basic information sent to the server. The specific operation is when the user answers a question in the messaging application and taps the send button.

[0492] Step 3:

[0493] The server stores the received basic information in a database. The input is the basic information received from the user, and the output is the data stored in the database. Specifically, the server issues an SQL query via the database middleware and stores the data.

[0494] Step 4:

[0495] The server uses a generative AI model to diagnose an ideal economic life plan based on the user's saved basic information. The input is the basic information obtained from the database, and the output is the diagnosis result returned by the generative AI model. Specifically, the server generates a prompt sentence from the basic information and sends a request to the AI ​​model's API.

[0496] Step 5:

[0497] The server sends the diagnosis results to the user via a messaging application. The input is the diagnosis results of the generative AI model, and the output is the message content sent to the user. Specifically, the server converts the generated diagnosis results into a text message and sends it using the messaging application's API.

[0498] Step 6:

[0499] The server collects the user's usage history of the electronic payment service. The input is authentication information obtained from the API of the electronic payment service, and the output is the obtained usage history data. Specifically, the server calls the API of the electronic payment service and obtains the user's usage history data.

[0500] Step 7:

[0501] The server uses a generative artificial intelligence model to analyze the collected usage history data. The input is the acquired usage history data, and the output is suggestions for improving wasteful spending. Specifically, the server inputs the usage history data into the AI ​​model and receives an analysis of wasteful spending and suggestions for improvement.

[0502] Step 8:

[0503] The server sends wasteful spending improvement suggestions to the user via a messaging application. The input is the suggestion generated by the generative artificial intelligence model, and the output is the improvement suggestion message sent to the user. Specifically, the server converts the suggestion content into a text message and sends it via the messaging application's API.

[0504] Step 9:

[0505] The user submits additional conditions or change requests according to changes in their life stage or new goals. The input is the request from the user, and the output is the new conditions sent to the server. Specifically, the user enters the change request in a messaging app and taps the send button.

[0506] Step 10:

[0507] Based on the received request, the server re-diagnoses the economic life plan using a generative artificial intelligence model. The input is the added conditions and changes, and the output is the regenerated economic life plan. Specifically, the server creates a prompt statement including the new conditions, sends it to the AI ​​model, and receives the new plan.

[0508] Step 11:

[0509] The server sends the regenerated economic life plan to the user through a messaging application. The input is the new plan from the generative artificial intelligence model, and the output is the new plan sent to the user. Specifically, the server converts the new plan into a text message and sends it using the messaging application's API.

[0510] Step 12:

[0511] When a user incurs unexpected expenses or income, the user notifies the server of that information. The input is the unexpected expense or income information, and the output is the notification content sent to the server. Specifically, the user enters the expense or income information in a messaging app and taps the send button.

[0512] Step 13:

[0513] The server receives the notification, adjusts the plan using a generative AI model, and resends it to the user. The input is non-recurring expenses and income information, and the output is the adjusted economic life plan. Specifically, the server inputs the received information into the AI ​​model, generates an adjusted plan, and sends it to the user via a messaging app.

[0514] Step 14:

[0515] The server periodically reevaluates the user's economic life plan and updates it as necessary. The input is the existing plan and data such as the latest spending history, and the output is the updated economic life plan. Specifically, the server periodically executes a scheduled task, retrieves the latest information from the database, reevaluates it using the generative artificial intelligence model, and saves the results.

[0516] (Application example 1)

[0517] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0518] Many people today use electronic payment services, and there is a demand for financial improvement proposals based on their usage history to reduce wasteful spending and realize efficient financial life plans. However, there is a lack of flexible planning systems that can accommodate changes in users' lifestyles and unexpected expenses. There is also a need for systems that periodically reevaluate users' financial situations and provide updated plans.

[0519] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0520] In this invention, the server includes means for collecting basic information from the user, means for diagnosing an ideal financial life plan based on the basic information, means for analyzing the usage history of electronic payment services and proposing improvements to wasteful spending, means for accepting additional conditions or change requests from the user and generating a new plan, means for sending notifications when unexpected expenses or income occur, and means for periodically reevaluating the user's financial situation using an AI model based on the user's basic information and usage history of electronic payment services and providing an updated plan. This supports the user's daily financial activities and makes it easy to realize their ideal financial life plan.

[0521] "User" means any individual or entity that uses the Service or System.

[0522] "Basic information" refers to personal information such as the user's age, family structure, and annual income.

[0523] A "Money Life Plan" is a plan for achieving an ideal financial life based on the user's income and expenses.

[0524] "Electronic payment services" are services that provide digital payment methods, including credit cards and mobile payments.

[0525] "Usage history" refers to the transaction records when a user uses an electronic payment service.

[0526] An "AI model" is an algorithm or system that uses artificial intelligence to analyze data and make predictions and suggestions.

[0527] "Suggestions to improve wasteful spending" are specific advice for reducing wasteful spending based on the user's spending data.

[0528] "Additional conditions" refer to new conditions or requests specified by the user.

[0529] A "change request" is a request sent by a user when they want to change an existing plan.

[0530] "Unexpected expenses" refers to unexpected expenses that occur.

[0531] "Notification" means that the system sends information to the user.

[0532] "Periodic reassessment" refers to reviewing and assessing the user's financial situation at regular intervals.

[0533] An "updated plan" is the latest financial life plan created based on new data and conditions.

[0534] The embodiment of the present invention is a software configuration using a server, a user terminal, and an AI model. The system allows users to provide personal information and generate, evaluate, and update a financial life plan based on their usage history of electronic payment services.

[0535] First, a messaging application such as LINE is installed on the user's device, and the user provides basic information to the server through this application. This basic information includes age, family composition, annual income, etc. This basic information is collected using the LINE API and sent to the server.

[0536] The server is built on a cloud service such as AWS EC2, and the collected basic information is stored in a database such as Amazon RDS. Next, an ideal financial life plan is generated using an AI model based on the stored basic information. The AI ​​model uses machine learning libraries such as TensorFlow and PyTorch.

[0537] The generated ideal financial life plan is sent to the user via LINE message, allowing them to receive specific suggestions for improvement based on their own financial situation. For example, advice such as "specific ways to reduce eating out expenses to 10,000 yen per month" is provided.

[0538] The server also periodically collects usage history for electronic payment services (e.g., electronic wallets) and stores it in a database. Based on the collected usage history, the AI ​​model makes suggestions for improving wasteful spending. For example, specific suggestions such as "Your eating out expenses are high, so we suggest you cut them down" are sent to the user via LINE messages.

[0539] Furthermore, the system accepts additional conditions and change requests from users via LINE messages. For example, if a user sends a request such as "I would like to review the plan for when we have another child," the server generates a plan that reflects the new conditions and notifies the user again. Furthermore, if unexpected expenses or income arise, the system promptly notifies the user and proposes a new plan.

[0540] As a specific example, if User A installs "Smart Money Advisor" on their smartphone and links it to their LINE account, a financial life plan will be generated after they enter their basic information. For example, based on information such as "age: 30, family composition: spouse and one child, annual income: 6 million yen," the AI ​​model will propose a post-retirement asset formation plan and a monthly spending review. Furthermore, because their electronic payment service usage history shows that "monthly dining out expenses: 20,000 yen" is high, they will receive a LINE message suggesting "specific ways to reduce dining out expenses to 10,000 yen per month."

[0541] An example of a prompt sentence to input to the generative AI model is as follows:

[0542] User Information:

[0543] Age: 30

[0544] Family: Spouse and one child

[0545] Annual income: 6 million yen

[0546] Usage history:

[0547] Electronic payment service: Monthly dining out expenses: 20,000 yen

[0548] request:

[0549] Reassessing your plan following the birth of a new child

[0550] Generate the plan:

[0551] Specific ways to reduce eating out expenses to 10,000 yen per month

[0552] Adjusting future asset formation plans

[0553] As described above, the present invention provides specific means for supporting users' daily economic activities and helping them realize their ideal financial life plan.

[0554] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0555] Step 1:

[0556] The user enters basic information (age, family composition, annual income, etc.) into the server using a messaging application such as LINE. The entered basic information is sent to the server via the LINE API and stored in a database.

[0557] Input: Basic information sent by the user via messaging applications (age, family composition, annual income, etc.)

[0558] Output: Basic user information stored in the database

[0559] Step 2:

[0560] The server uses the stored basic information to generate an ideal financial life plan using an AI model. The AI ​​model uses machine learning libraries such as TensorFlow and PyTorch. The generated plan is stored in a database on the server.

[0561] Input: User basic information stored in the database

[0562] Output: Generated ideal financial life plan (stored in database)

[0563] Step 3:

[0564] The server notifies the user of the generated ideal financial life plan via a LINE message. The notification is sent using the LINE Messaging API.

[0565] Input: Generated ideal financial life plan

[0566] Output: Money life plan notified via LINE message

[0567] Step 4:

[0568] The server periodically collects the user's usage history for the electronic payment service. The collected usage history is stored in a database. Usage history is obtained using the PayPay API, etc.

[0569] Input: Electronic payment service usage history (e.g., dining out expenses, shopping expenses, etc.)

[0570] Output: Usage history of electronic payment services stored in a database

[0571] Step 5:

[0572] The server uses the collected usage history to analyze wasteful spending using an AI model and generate improvement proposals. For example, if eating out expenses are too high, it will generate a reduction proposal.

[0573] Input: Electronic payment service usage history stored in the database

[0574] Output: Suggestions for improving waste

[0575] Step 6:

[0576] The server notifies the user of the generated suggestions for improving wasteful spending via LINE messages. The notifications are sent using the LINE Messaging API.

[0577] Input: Suggestions for improving wasteful spending

[0578] Output: Improvement suggestions notified via LINE message

[0579] Step 7:

[0580] Users can send additional conditions or change requests to the server via a messaging app, which then accepts the conditions, re-diagnoses them using the AI ​​model, and generates an updated plan.

[0581] Input: Additional terms and changes requested by the user

[0582] Output: Updated financial life plan (saved in database)

[0583] Step 8:

[0584] The server notifies the user of the generated update plan via a LINE message. The notification is sent using the LINE Messaging API.

[0585] Enter: Updated Money Life Plan

[0586] Output: Renewal plan notified via LINE message

[0587] Step 9:

[0588] When unexpected expenses or income arise, the server receives information from the user and quickly re-diagnoses the plan using an AI model. Based on the results of the re-diagnosis, the plan is updated and the user is notified.

[0589] Input: User-submitted information about occasional expenses and income

[0590] Output: Reassessed financial life plan and notification

[0591] Through these steps, the system supports users' daily economic activities and enables them to realize their ideal financial life plan.

[0592] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0593] This invention is a system that allows users to easily diagnose their ideal financial life plan through messaging applications such as LINE, suggests ways to reduce wasteful spending, and adjusts the plan to accommodate changes in life stages, and also recognizes the user's emotions and adjusts the feedback content accordingly.This system is designed around a server, and achieves the following steps through interactions with the user.

[0594] First, the server uses a messaging application to ask the user for basic information, such as age, family composition, and annual income. Based on this, the user sends a message back to the server with their basic information. The server receives this information and stores it in a database.

[0595] The server then uses the stored user information to diagnose an ideal financial life plan using an AI model, and the results are sent back to the user via a messaging app.

[0596] Furthermore, the server collects the user's usage history of electronic payment services, such as PayPay, and uses an AI model to analyze wasteful spending. This allows it to provide specific money-saving tips, such as "Your insurance premiums are too high, so we recommend you review them" or "You can save XX yen each month by eating out less." The resulting suggestions are sent to the user via a messaging app.

[0597] Furthermore, by combining the emotion engine, the server analyzes the user's emotions in real time and provides feedback according to the user's emotional state. For example, if the user is feeling stressed, the server will provide specific advice on how to reduce stress. If the server recognizes that the user is in a positive emotional state, it will make proactive suggestions such as additional investments or savings plans.

[0598] Users can submit additional conditions or change requests to the server based on changes in their life stage or new goals. The server receives these requests, re-diagnoses them using the AI ​​model, generates a new plan, and sends it back to the user.

[0599] As a concrete example, let's assume that the user is 50 years old, has an annual income of 5 million yen, and has a family consisting of a spouse and one child. After providing this basic information to the server, the AI ​​model will propose an ideal retirement asset formation plan and a monthly spending review. It will also point out that eating out expenses are high based on PayPay usage history and suggest ways to reduce them. If the user requests to "review the plan for when we have another child," the server will regenerate a plan that reflects these new conditions and present it to the user.

[0600] Furthermore, if the user expresses negative emotions toward finances, for example, if the emotion engine detects "anxiety about investments," the server will propose a conservative investment plan to reduce risk. On the other hand, if the user expresses positive emotions, the server will propose an aggressive investment strategy, leveraging the user's positive emotions to promote further asset formation.

[0601] In this way, this system supports users' daily financial activities and provides concrete means for easily realizing their ideal financial life plan. It also recognizes the user's emotional state and provides feedback accordingly, enabling more personalized support.

[0602] The processing flow will be explained below.

[0603] Step 1:

[0604] The server sends a message to the user's LINE account asking for basic information, such as age, family composition, and annual income.

[0605] Step 2:

[0606] Users receive LINE messages and respond to each question by replying with their own information.

[0607] Step 3:

[0608] The server receives the user's response and stores information such as age, family composition, and annual income in a database.

[0609] Step 4:

[0610] The server retrieves the stored user information from the database and inputs it into the AI ​​model to diagnose the ideal financial life plan.

[0611] Step 5:

[0612] The server sends the diagnosis results of the ideal financial life plan generated by the AI ​​model to the user via LINE message.

[0613] Step 6:

[0614] The server uses the API of electronic payment services such as PayPay to collect user usage history.

[0615] Step 7:

[0616] The server inputs the collected usage history into an AI model and performs an analysis to identify areas for improvement in wasteful spending.

[0617] Step 8:

[0618] Based on the analysis results, the server sends specific money-saving tips and suggestions for reducing wasteful spending (e.g., reviewing insurance premiums, reducing eating out expenses, etc.) to the user via LINE messages.

[0619] Step 9:

[0620] The server uses an emotion engine to recognize the user's emotions based on the content of the user's LINE messages and the timing of the exchanges.

[0621] Step 10:

[0622] The server adjusts the feedback based on the user's emotional state, for example, by providing suggestions for stress reduction if the user is feeling stressed, or offering proactive investment plans if the user is feeling positive.

[0623] Step 11:

[0624] Depending on changes in their life stage or new financial goals, users can send additional conditions or change requests to the server via LINE messages.

[0625] Step 12:

[0626] The server receives additional conditions and change requests from the user and inputs them into the AI ​​model to generate a new financial life plan.

[0627] Step 13:

[0628] The server will send the diagnosis results of the new plan to the user via LINE message.

[0629] Step 14:

[0630] When unexpected expenses or income occur, the user sends that information to the server via LINE message.

[0631] Step 15:

[0632] The server receives information about unexpected expenses and income and makes necessary plan adjustments based on that information.

[0633] Step 16:

[0634] The server will send the new adjusted plan results and advice to the user via LINE message.

[0635] Step 17:

[0636] The server periodically re-evaluates the user information in the database, generates new diagnostic results, and sends them to the user via LINE message.

[0637] Example 2

[0638] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0639] In modern society, personal financial activities are becoming increasingly complex, requiring appropriate management. However, it is not easy for users to understand their own financial situation and implement efficient asset management or reduce wasteful spending. Furthermore, financial decisions are often influenced by changes in life stages and emotions, so flexible and personalized advice is required. To solve these challenges, a comprehensive management system is needed that not only provides a diagnosis based on the user's basic information, but also includes emotional analysis.

[0640] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0641] In this invention, the server includes means for collecting basic information from the user, means for diagnosing an ideal economic plan based on the basic information, means for analyzing the usage history of electronic payment services and proposing improvements to wasteful spending, means for analyzing the user's emotions and adjusting the feedback content, means for accepting additional conditions or change requests from the user and generating a new plan, and means for sending notifications when unexpected expenses or income occur. This makes it possible to comprehensively manage the user's economic activities and provide flexible and personalized advice.

[0642] "Means for collecting basic information from users" refers to functions for obtaining basic personal information such as the user's age, family composition, and annual income through messaging applications, web forms, etc.

[0643] "Means for diagnosing ideal economic plans based on basic information" is a function that uses collected basic information about users to automatically generate ideal asset management and spending plans using AI models and algorithms.

[0644] "Means for analyzing the usage history of electronic payment services and making suggestions for improving wasteful spending" is a function that analyzes the user's electronic payment history (for example, credit card statements and electronic money usage history), identifies wasteful spending items, and provides money-saving suggestions based on that.

[0645] "Means for analyzing user emotions and adjusting feedback content" refers to a function that uses an emotion analysis engine to determine the user's emotional state and adjusts advice and suggestions accordingly.

[0646] "Means for accepting additional conditions and change requests from users and generating new plans" refers to a function that provides users with the ability to input change requests such as new conditions and upcoming life events, and allows AI models, etc., to generate new economic plans based on these requests.

[0647] The "means for sending notifications when unexpected income or expenses occur" is a function that enables the system to automatically detect when the user has unexpected income or expenses and send appropriate notifications or advice to the user.

[0648] "Message application" refers to application software used by users to send and receive text messages and other data, specifically LINE and other chat applications.

[0649] An "ideal economic plan" refers to a comprehensive plan for achieving the most efficient and desirable state in terms of asset formation, expenditure management, etc.

[0650] An "emotion analysis engine" is an algorithm or system that analyzes a user's text messages or other input data to determine the emotions contained therein.

[0651] An "AI model" refers to an artificial intelligence algorithm or system that uses machine learning and deep learning to analyze data and make suggestions and diagnoses to users.

[0652] This invention is a system that allows users to easily diagnose their ideal financial plan through a messaging application, suggests ways to reduce wasteful spending, and adjusts the plan to accommodate changes in life stages, and also recognizes the user's emotions and adjusts the feedback content accordingly. This system is designed around a server, and specific processes are realized through interactions with the user.

[0653] Hardware and Software Configuration

[0654] The system is implemented using the following hardware and software:

[0655] Hardware: Server (e.g., Amazon Web Services EC2 instance)

[0656] Software: messaging applications (e.g., LINE API), databases (e.g., MySQL), AI models (e.g., OpenAI GPT-4), electronic payment data (e.g., electronic payment service API), sentiment analysis engines (e.g., Microsoft Azure Text Analytics API)

[0657] Specific flow of data processing and data calculation

[0658] The server sends a message to the user through a messaging application asking for basic information such as age, family composition, and annual income. The user enters this information and replies using the messaging application. The server stores the received basic information in a database (MySQL).

[0659] Once the basic information is saved, the server uses an AI model (OpenAI GPT-4) to diagnose an ideal economic plan based on the user's basic information, and the generated diagnosis results are sent to the user again via a messaging app.

[0660] The server also collects the user's electronic payment service usage history and uses an AI model to analyze wasteful spending. For example, if it determines that the user spends a lot of money eating out, it will generate specific suggestions such as "You can save XX yen each month by eating out less." These suggestions are also sent to the user via a messaging app.

[0661] By combining it with an emotion analysis engine, the server analyzes the user's emotions in real time and provides feedback according to the user's emotional state. If the server determines that the user is feeling stressed, it provides advice on how to reduce stress, and if the server recognizes that the user is in a positive emotional state, it suggests additional investment or savings plans.

[0662] Users can submit additional conditions or change requests to the server based on changes in their life stage or new goals. The server receives these requests, re-diagnoses them using the AI ​​model, generates a new plan, and presents it to the user again.

[0663] Examples of concrete examples and prompts

[0664] As a concrete example, let's assume that the user is 50 years old, has an annual income of 5 million yen, and has a family consisting of a spouse and one child. After providing this basic information to the server, the AI ​​model proposes an ideal retirement asset formation plan and a monthly spending review. It also points out that dining out expenses are high based on the user's electronic payment service usage history and suggests ways to reduce them. If the user requests to "review the plan for when we have another child," the server regenerates a plan that reflects these new conditions and presents it to the user. Furthermore, if the user expresses concerns about investing, the sentiment analysis engine detects this and suggests a conservative investment plan.

[0665] Example prompt sentence:

[0666] "Please suggest an ideal retirement asset formation plan for a user who is 50 years old, has an annual income of 5 million yen, and has a family structure of spouse and one child."

[0667] "Analyze the usage history of electronic payment services and provide specific advice to users to reduce wasteful spending."

[0668] "Please explain how to provide appropriate feedback when users express negative emotions."

[0669] As described above, this system supports users' daily economic activities and provides concrete means for realizing ideal economic plans. Furthermore, by recognizing the user's emotional state and providing feedback accordingly, it achieves more personalized support.

[0670] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0671] Step 1:

[0672] Collecting basic information

[0673] Input: The user sends basic information such as age, family composition, and annual income to the server via a messaging application.

[0674] Specific operation: The server sends a message via a messaging application, for example, "Please tell me your age, family composition, and annual income." The user replies using the messaging application, "50 years old, spouse and one child, annual income 5 million yen."

[0675] Data processing: The server processes the received information and converts it into the required format for storage in a database (e.g. MySQL).

[0676] Output: Basic information is saved in the database.

[0677] Step 2:

[0678] Diagnosis based on basic information

[0679] Input: The server retrieves the stored basic information.

[0680] Specific operation: The server retrieves the information "50 years old, spouse and one child, annual income of 5 million yen" from the MySQL database.

[0681] Data calculation: The server uses an AI model (e.g., OpenAI GPT-4) to prompt for basic information and generate the user's ideal economic plan.

[0682] Output: The server gets the generated economic plan.

[0683] Step 3:

[0684] Sending diagnostic results

[0685] Input: Server has economic plan generated by AI model.

[0686] Specific operation: The server sends the generated economic plan via a messaging application saying, "Here's your ideal retirement asset formation plan."

[0687] Output: The user receives the diagnosis through a messaging application.

[0688] Step 4:

[0689] Collection and analysis of electronic payment history

[0690] Input: With the user's consent, the server collects the user's usage history of electronic payment services (e.g., electronic payment service API).

[0691] Specific operation: The server calls the electronic payment service API and obtains the user's latest usage history data.

[0692] Data calculation: The server uses the acquired data to apply an AI model to analyze wasteful spending patterns, such as "high spending on eating out," and generate specific savings suggestions.

[0693] Output: Analysis results and savings recommendations are generated.

[0694] Step 5:

[0695] Submit an improvement suggestion

[0696] Input: Server has analysis results and savings suggestions.

[0697] Specific operation: The server sends the generated savings plan via a messaging application in the form of, for example, "If you eat out less, you can save XX yen each month."

[0698] Output: The user receives the savings offer through a messaging application.

[0699] Step 6:

[0700] Sentiment analysis and feedback adjustment

[0701] Input: The server receives the user's message and analyzes the sentiment using a sentiment analysis engine (e.g., Microsoft Azure Text Analytics API).

[0702] Specific operation: The server sends the user's input message to the sentiment analysis engine and obtains a sentiment result such as "I feel anxious about investing."

[0703] Data calculation: Based on the sentiment results, the server adjusts the feedback content, for example, suggesting a conservative investment plan.

[0704] Output: The adjusted feedback is generated.

[0705] Step 7:

[0706] Sending emotional suggestions

[0707] Input: The server has adjusted feedback.

[0708] What happens: The server sends tailored feedback via a messaging application, such as "Consider a more conservative investment plan."

[0709] Output: The user receives the suggestion through their messaging application.

[0710] Step 8:

[0711] Responding to changes in life stages

[0712] Input: The user sends new conditions or goals to the server via a messaging application.

[0713] Specific operation: For example, a user sends a request saying, "I would like to review my plans for having another child." The server receives this request.

[0714] Data calculation: The server inputs new conditions as prompts into the AI ​​model and generates the ideal economic plan again.

[0715] Output: A new economic plan is generated.

[0716] Step 9:

[0717] Sending re-diagnosis results

[0718] Input: The server has a new economic plan.

[0719] Specific operation: The server sends the generated new economic plan to the user via a messaging application.

[0720] Output: The user receives the new plan through the messaging application.

[0721] As a result, this system comprehensively supports users' daily economic activities and provides ideal economic plans. Furthermore, it flexibly responds to changes in the user's emotions and life stage, providing personalized support.

[0722] (Application example 2)

[0723] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0724] In modern society, efficient asset management and planned spending are extremely important. However, many users find it difficult to manage their own expenses and formulate future life plans. Furthermore, few systems take into account the impact of emotional states on asset management, making it difficult to provide appropriate feedback tailored to the user's emotions. Therefore, there is a need for a system that allows users to easily diagnose their ideal financial life plan, receive suggestions for improving wasteful spending, and provide feedback appropriate to their emotional state.

[0725] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0726] In this invention, the server includes means for collecting basic information from a user, means for diagnosing an ideal money life plan based on the basic information, means for analyzing the usage history of electronic payment methods and proposing improvements to wasteful spending, means for accepting additional conditions and change requests from the user and generating a new plan, means for sending notifications when unexpected expenses or income occur, means for analyzing the user's emotional state, and means for providing feedback according to the user's emotional state. This allows the user to efficiently manage their assets, improve wasteful spending, and achieve planned spending, and further allows them to manage their assets more appropriately by receiving feedback according to their emotional state.

[0727] A "user" is a person who uses the system and provides data on basic information and emotional state.

[0728] "Basic information" refers to information necessary for diagnosing a financial life plan, such as the user's age, income, and family composition.

[0729] The "Ideal Money Life Plan" is a plan that shows optimal spending, savings, and investment plans based on the user's financial goals and current situation.

[0730] "Electronic payment instrument" refers to a platform or method for making payments electronically, including, for example, mobile payment services.

[0731] "Suggestions for improving wasteful spending" are suggestions that analyze the user's spending patterns and provide specific advice for reducing unnecessary or excessive spending.

[0732] "Additional conditions and change requests" are new conditions and settings that users provide to the system in response to changes in their life stage or new goals.

[0733] The "means for generating a new plan" is a function for creating a new money life plan based on the user's additional conditions or change requests.

[0734] The "means for sending a notification when unexpected expenses or income occur" is a function for notifying the user when unexpected expenses or income occur.

[0735] A "means for analyzing emotional state" is a method for determining a user's emotions and using that information to provide appropriate feedback.

[0736] The "means for providing feedback according to emotional state" is a function that shows advice or suggestions customized to the user's emotional state.

[0737] In an embodiment of the present invention, the system includes a server, a user terminal, an electronic payment method, and a sentiment analysis engine as its main components. A user uses a smartphone to input basic information through a provided messaging application. The basic information includes data on economic status such as age, family composition, and annual income. The server receives this basic information and stores it in a database.

[0738] The server then uses the stored basic information to create a generative AI model that will diagnose an ideal financial life plan. This generative AI model will then suggest an optimal plan based on the user's financial situation and life stage. The results of the diagnosis will be sent to the user via a messaging app.

[0739] Furthermore, the server collects usage history of electronic payment methods (for example, mobile payment services) and analyzes spending data. Data science techniques are used to analyze the spending data and identify large amounts of spending and wasteful spending. Specific suggestions for improving wasteful spending, such as "You spend too much on eating out, so you need to cut back," are generated. These suggestions are also sent via a messaging application.

[0740] The sentiment analysis engine analyzes the user's emotions in messages in real time. This engine uses natural language processing technology to analyze messages from users and determine their emotional state. For example, if the user is feeling "anxious about investing," the server will suggest a conservative investment plan. On the other hand, if the user is expressing positive emotions, the server will suggest an aggressive investment strategy.

[0741] When the user provides additional conditions or change requests based on changes in their life stage or new goals, the server receives them and again uses the generative AI model to generate a new financial life plan, which is also sent to the user via a messaging app.

[0742] The primary hardware used includes smartphones and servers, while the primary software includes a messaging application, a sentiment analysis engine, and a generative AI model. Utilizing these components, users can efficiently manage their assets, reduce waste, and plan their spending.

[0743] As a specific example, if a user is 50 years old, has an annual income of 5 million yen, and has a family consisting of a spouse and one child, by providing basic information, the AI ​​model will propose an ideal post-retirement asset formation plan and monthly spending revisions. It will also point out that dining out expenses are high based on the user's electronic payment method usage history and suggest ways to reduce expenses. If the user requests to "review the plan for when we have another child," the server will regenerate a plan reflecting the new conditions and present it to the user.

[0744] Examples of prompts include:

[0745] "Please tell me your plan for a 50-year-old with an annual income of 5 million yen, a spouse, and one child."

[0746] "Please tell us how to improve wasteful spending based on your electronic payment usage history."

[0747] "I want to reassess my plans for having another child."

[0748] "I'm feeling anxious about my investment. What should I do?"

[0749] As a result, users can receive specific feedback tailored to their own financial situation and receive appropriate advice according to their emotional state.

[0750] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0751] Step 1:

[0752] A user opens a messaging application on their smartphone and enters basic information such as age, income, and family composition.

[0753] Input: User's basic information (age, income, family composition, etc.)

[0754] Output: Basic information data sent to the server

[0755] Step 2:

[0756] The server stores the received basic information in a database.

[0757] Input: Basic information data sent by the user

[0758] Output: Basic information stored in the database

[0759] Step 3:

[0760] Based on the stored basic information, the server uses a generative AI model to diagnose your ideal financial life plan.

[0761] Input: Basic information data

[0762] Output: Diagnosed ideal financial life plan

[0763] Step 4:

[0764] The server sends the diagnosis results to the user through a messaging application.

[0765] Input: Diagnosed ideal financial life plan

[0766] Output: Diagnostic results sent via messaging application

[0767] Step 5:

[0768] The server collects electronic payment transaction history and analyzes spending data, specifically using data science techniques to identify excessive and wasteful spending.

[0769] Input: Electronic payment method usage history

[0770] Output: Suggestions for improving waste

[0771] Step 6:

[0772] The server sends the improvement suggestions to the user through a messaging application.

[0773] Input: Suggestions for improving wasteful spending

[0774] Output: Improvement suggestions sent via messaging application

[0775] Step 7:

[0776] The user sends additional information to the server in response to new requests or changes in life stages.

[0777] Input: User additional conditions or change requests

[0778] Output: Additional conditions and change request data sent to the server

[0779] Step 8:

[0780] The server receives additional conditions and change requests and creates a new financial life plan using a generative AI model.

[0781] Input: Additional conditions and change request data

[0782] Output: The new financial life plan generated.

[0783] Step 9:

[0784] The server also sends the new plan to the user through the messaging application.

[0785] Input: Generated new financial life plan

[0786] Output: New plan sent through messaging application

[0787] Step 10:

[0788] The server uses a sentiment analysis engine to analyze the user's sentiment in the message in real time.

[0789] Input: User's message data

[0790] Output: Parsed emotional state data

[0791] Step 11:

[0792] The server generates feedback according to the emotional state and provides it to the user through a messaging application.

[0793] Input: Parsed emotional state data

[0794] Output: Feedback according to emotional state

[0795] The above is the flow of specific processing steps of the program for realizing the application example.

[0796] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0797] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) 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 voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0798] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0799] [Third embodiment]

[0800] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0801] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0802] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. 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).

[0803] 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.

[0804] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0805] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0806] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0807] 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.

[0808] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[0809] 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.

[0810] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0811] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0812] This invention is a system that allows users to easily diagnose their ideal financial life plan through messaging applications such as LINE, and provides suggestions for improving wasteful spending and adjusting the plan to accommodate changes in life stages.The system is designed around a server, and accomplishes the following steps through interactions with the user.

[0813] First, the server uses a messaging application to ask the user for basic information, such as age, family composition, and annual income. Based on this, the user sends a message back to the server with their basic information. The server receives this information and stores it in a database.

[0814] The server then uses the stored user information to diagnose an ideal financial life plan using an AI model, and the results are sent back to the user via a messaging app.

[0815] Furthermore, the server collects the user's usage history of electronic payment services, such as PayPay, and uses an AI model to analyze wasteful spending. This allows it to provide specific money-saving tips, such as "Your insurance premiums are too high, so we recommend you review them" or "You can save XX yen each month by eating out less." The resulting suggestions are sent to the user via a messaging app.

[0816] Users can submit additional conditions or change requests to the server based on changes in their life stage or new goals. The server receives these requests, re-diagnoses them using the AI ​​model, generates a new plan, and sends it back to the user.

[0817] Finally, the server promptly notifies the user when unexpected expenses or income arise, and periodically reassess the user's financial life plan, updating it as necessary and providing the results to the user.

[0818] As a concrete example, let's assume that the user is 50 years old, has an annual income of 5 million yen, and has a family consisting of a spouse and one child. After providing this basic information to the server, the AI ​​model proposes an ideal retirement asset formation plan and monthly spending review. It also points out that eating out expenses are high based on PayPay usage history and suggests ways to reduce expenses. If the user requests to "review the plan for when another child is born," the server regenerates a plan that reflects these new conditions and presents it to the user. Then, if, for example, an unexpected medical expense arises, the server promptly notifies the user and makes any necessary adjustments to the plan.

[0819] In this way, this system supports users' daily economic activities and provides them with concrete means to easily realize their ideal financial life plan.

[0820] The processing flow will be explained below.

[0821] Step 1:

[0822] The server sends a message to the user's LINE account asking for basic information, such as age, family composition, and annual income.

[0823] Step 2:

[0824] Users receive LINE messages and respond to each question by replying with their own information.

[0825] Step 3:

[0826] The server receives the user's response and stores information such as age, family composition, and annual income in a database.

[0827] Step 4:

[0828] The server retrieves the stored user information from the database and inputs it into the AI ​​model to diagnose the ideal financial life plan.

[0829] Step 5:

[0830] The server sends the diagnosis results of the ideal financial life plan generated by the AI ​​model to the user via LINE message.

[0831] Step 6:

[0832] The server uses the API of electronic payment services such as PayPay to collect user usage history.

[0833] Step 7:

[0834] The server inputs the collected usage history into an AI model and performs an analysis to identify areas for improvement in wasteful spending.

[0835] Step 8:

[0836] Based on the analysis results, the server sends specific money-saving tips and suggestions for reducing wasteful spending (e.g., reviewing insurance premiums, reducing eating out expenses, etc.) to the user via LINE messages.

[0837] Step 9:

[0838] Users can send additional conditions or change requests to the server via LINE messages depending on changes in their life stage (e.g., having more children) or new financial goals.

[0839] Step 10:

[0840] The server receives additional conditions and change requests from the user and inputs them back into the AI ​​model to generate a new financial life plan.

[0841] Step 11:

[0842] The server will send the diagnosis results of the new plan to the user via LINE message.

[0843] Step 12:

[0844] When unexpected expenses or income occur, the user sends that information to the server via LINE message.

[0845] Step 13:

[0846] The server receives information about unexpected expenses and income and makes necessary plan adjustments based on that information.

[0847] Step 14:

[0848] The server will send the new adjusted plan results and advice to the user via LINE message.

[0849] Step 15:

[0850] The server periodically re-evaluates the user information in the database, generates new diagnostic results, and sends them to the user via LINE message.

[0851] Example 1

[0852] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0853] Conventional economic and life planning support systems required users to input detailed information and create plans using specialized knowledge. This was time-consuming and difficult for average users to create appropriate plans. Furthermore, there were few ways to obtain real-time improvement suggestions based on current living environment and spending status. Furthermore, it was difficult to respond quickly to changes in life stages or unexpected expenses.

[0854] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0855] In this invention, the server includes means for collecting basic information from a user, means for diagnosing an ideal economic life plan using a generative artificial intelligence model based on the basic information, means for analyzing the usage history of electronic payment services and making suggestions for improving wasteful spending, means for accepting additional conditions and change requests from the user and generating a new economic life plan, and means for sending notifications when unexpected expenses or income occur and for reevaluating and updating the economic life plan as necessary. This allows the user to easily create an ideal economic life plan, and enables real-time expenditure analysis and improvement suggestions, as well as quick response to changes in life stages and unexpected expenses.

[0856] "Basic information" refers to information necessary for making a personal economic life plan, such as the user's age, family structure, and annual income.

[0857] A "generative artificial intelligence model" is an artificial intelligence technology that generates optimal economic and lifestyle plans based on a user's basic information and usage history data.

[0858] An "electronic payment service" is a service that provides digital payment methods that users can use for shopping and payments.

[0859] "Usage history" is a record of a user's transactions made through an electronic payment service.

[0860] "Suggestions for improving wasteful spending" are specific advice for reducing unnecessary or excessive spending detected through an analysis of usage history.

[0861] A "messaging application" is a communications application that allows for the exchange of text and media in real time over the Internet.

[0862] An "economic life plan" is a plan that sets ideal spending allocations and savings goals based on the user's income, expenses, asset type, etc.

[0863] A "notification" is an electronic message sent to inform a user of specific information.

[0864] "Life stage changes" are important events or changes in circumstances that occur in a user's life (e.g., marriage, childbirth, job change, etc.).

[0865] "Unexpected expenses and income" refers to sudden expenses that occur unexpectedly or income that is received temporarily at a specific time.

[0866] This invention is a system that allows users to easily diagnose their ideal financial life plan via a messaging application, and provides suggestions for reducing wasteful spending and adjusting the plan to accommodate changes in life stages. The system is designed around a server, and provides the ideal financial life plan through interactions with the user.

[0867] First, the server uses a messaging application to ask the user for basic information, such as age, family composition, and annual income. Based on this, the user then sends a message back to the server with their basic information. The server receives this information and stores it in a database.

[0868] The server then uses the stored user information to diagnose an ideal economic life plan using a generative artificial intelligence model, and the results are sent back to the user via a messaging app.

[0869] The server also collects the user's electronic payment service usage history and uses a generative artificial intelligence model to analyze wasteful spending. This allows the server to provide specific money-saving tips, such as "Your insurance premiums are too high, so we recommend you review them" or "You can save XX yen each month by eating out less." The resulting suggestions are sent to the user via a messaging application.

[0870] Users can submit additional conditions or change requests to the server depending on changes in their life stage or new goals. The server receives these requests, re-diagnoses them using a generative artificial intelligence model, generates a new plan, and sends it back to the user.

[0871] Finally, if unexpected expenses or income arise, the server will promptly notify the user. In addition, the server will periodically reassess the user's financial life plan, update it as necessary, and provide the results to the user.

[0872] As a concrete example, let's assume that the user is 50 years old, has an annual income of 5 million yen, and has a family consisting of a spouse and one child. After providing this basic information to the server, the generative AI model proposes an ideal retirement asset formation plan and monthly spending revisions. It also points out that dining out expenses are high based on the user's electronic payment service usage history and suggests ways to reduce them. If the user requests to "reconsider the plan for when another child is born," the server regenerates a plan that reflects these new conditions and presents it to the user. Then, if, for example, an unexpected medical expense arises, the server promptly notifies the user and makes any necessary adjustments to the plan.

[0873] An example of a prompt sentence is as follows:

[0874] "Age 50, annual income 5 million yen, family composition: spouse and one child. Please provide an ideal retirement asset formation plan based on this basic information. Also, based on the usage history of electronic payment services, please point out that eating out expenses are high and suggest ways to reduce them."

[0875] This system supports users' daily economic activities and provides concrete means for easily realizing their ideal economic life plan. It uses cloud servers (e.g., AWS, Google Cloud Platform) as hardware, and messaging applications (e.g., general messaging services), electronic payment services (e.g., general electronic payment systems), and generative artificial intelligence models (e.g., GPT-4, BERT) as software.

[0876] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0877] Step 1:

[0878] The server uses a messaging application to send a message to the user asking for basic information (age, family composition, annual income, etc.). The input is the message content sent from the server, and the output is the basic information replied by the user. Specifically, the server calls the API of the messaging service and sends the question in the specified format.

[0879] Step 2:

[0880] The user enters basic information through a messaging application and sends it back to the server. The input is the data the user enters into the messaging application, and the output is the basic information sent to the server. The specific operation is when the user answers a question in the messaging application and taps the send button.

[0881] Step 3:

[0882] The server stores the received basic information in a database. The input is the basic information received from the user, and the output is the data stored in the database. Specifically, the server issues an SQL query via the database middleware and stores the data.

[0883] Step 4:

[0884] The server uses a generative AI model to diagnose an ideal economic life plan based on the user's saved basic information. The input is the basic information obtained from the database, and the output is the diagnosis result returned by the generative AI model. Specifically, the server generates a prompt sentence from the basic information and sends a request to the AI ​​model's API.

[0885] Step 5:

[0886] The server sends the diagnosis results to the user via a messaging application. The input is the diagnosis results of the generative AI model, and the output is the message content sent to the user. Specifically, the server converts the generated diagnosis results into a text message and sends it using the messaging application's API.

[0887] Step 6:

[0888] The server collects the user's usage history of the electronic payment service. The input is authentication information obtained from the API of the electronic payment service, and the output is the obtained usage history data. Specifically, the server calls the API of the electronic payment service and obtains the user's usage history data.

[0889] Step 7:

[0890] The server uses a generative artificial intelligence model to analyze the collected usage history data. The input is the acquired usage history data, and the output is suggestions for improving wasteful spending. Specifically, the server inputs the usage history data into the AI ​​model and receives an analysis of wasteful spending and suggestions for improvement.

[0891] Step 8:

[0892] The server sends wasteful spending improvement suggestions to the user via a messaging application. The input is the suggestion generated by the generative artificial intelligence model, and the output is the improvement suggestion message sent to the user. Specifically, the server converts the suggestion content into a text message and sends it via the messaging application's API.

[0893] Step 9:

[0894] The user submits additional conditions or change requests according to changes in their life stage or new goals. The input is the request from the user, and the output is the new conditions sent to the server. Specifically, the user enters the change request in a messaging app and taps the send button.

[0895] Step 10:

[0896] Based on the received request, the server re-diagnoses the economic life plan using a generative artificial intelligence model. The input is the added conditions and changes, and the output is the regenerated economic life plan. Specifically, the server creates a prompt statement including the new conditions, sends it to the AI ​​model, and receives the new plan.

[0897] Step 11:

[0898] The server sends the regenerated economic life plan to the user through a messaging application. The input is the new plan from the generative artificial intelligence model, and the output is the new plan sent to the user. Specifically, the server converts the new plan into a text message and sends it using the messaging application's API.

[0899] Step 12:

[0900] When a user incurs unexpected expenses or income, the user notifies the server of that information. The input is the unexpected expense or income information, and the output is the notification content sent to the server. Specifically, the user enters the expense or income information in a messaging app and taps the send button.

[0901] Step 13:

[0902] The server receives the notification, adjusts the plan using a generative AI model, and resends it to the user. The input is non-recurring expenses and income information, and the output is the adjusted economic life plan. Specifically, the server inputs the received information into the AI ​​model, generates an adjusted plan, and sends it to the user via a messaging app.

[0903] Step 14:

[0904] The server periodically reevaluates the user's economic life plan and updates it as necessary. The input is the existing plan and data such as the latest spending history, and the output is the updated economic life plan. Specifically, the server periodically executes a scheduled task, retrieves the latest information from the database, reevaluates it using the generative artificial intelligence model, and saves the results.

[0905] (Application example 1)

[0906] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0907] Many people today use electronic payment services, and there is a demand for financial improvement proposals based on their usage history to reduce wasteful spending and realize efficient financial life plans. However, there is a lack of flexible planning systems that can accommodate changes in users' lifestyles and unexpected expenses. There is also a need for systems that periodically reevaluate users' financial situations and provide updated plans.

[0908] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0909] In this invention, the server includes means for collecting basic information from the user, means for diagnosing an ideal financial life plan based on the basic information, means for analyzing the usage history of electronic payment services and proposing improvements to wasteful spending, means for accepting additional conditions or change requests from the user and generating a new plan, means for sending notifications when unexpected expenses or income occur, and means for periodically reevaluating the user's financial situation using an AI model based on the user's basic information and usage history of electronic payment services and providing an updated plan. This supports the user's daily financial activities and makes it easy to realize their ideal financial life plan.

[0910] "User" means any individual or entity that uses the Service or System.

[0911] "Basic information" refers to personal information such as the user's age, family structure, and annual income.

[0912] A "Money Life Plan" is a plan for achieving an ideal financial life based on the user's income and expenses.

[0913] "Electronic payment services" are services that provide digital payment methods, including credit cards and mobile payments.

[0914] "Usage history" refers to the transaction records when a user uses an electronic payment service.

[0915] An "AI model" is an algorithm or system that uses artificial intelligence to analyze data and make predictions and suggestions.

[0916] "Suggestions to improve wasteful spending" are specific advice for reducing wasteful spending based on the user's spending data.

[0917] "Additional conditions" refer to new conditions or requests specified by the user.

[0918] A "change request" is a request sent by a user when they want to change an existing plan.

[0919] "Unexpected expenses" refers to unexpected expenses that occur.

[0920] "Notification" means that the system sends information to the user.

[0921] "Periodic reassessment" refers to reviewing and assessing the user's financial situation at regular intervals.

[0922] An "updated plan" is the latest financial life plan created based on new data and conditions.

[0923] The embodiment of the present invention is a software configuration using a server, a user terminal, and an AI model. The system allows users to provide personal information and generate, evaluate, and update a financial life plan based on their usage history of electronic payment services.

[0924] First, a messaging application such as LINE is installed on the user's device, and the user provides basic information to the server through this application. This basic information includes age, family composition, annual income, etc. This basic information is collected using the LINE API and sent to the server.

[0925] The server is built on a cloud service such as AWS EC2, and the collected basic information is stored in a database such as Amazon RDS. Next, an ideal financial life plan is generated using an AI model based on the stored basic information. The AI ​​model uses machine learning libraries such as TensorFlow and PyTorch.

[0926] The generated ideal financial life plan is sent to the user via LINE message, allowing them to receive specific suggestions for improvement based on their own financial situation. For example, advice such as "specific ways to reduce eating out expenses to 10,000 yen per month" is provided.

[0927] The server also periodically collects usage history for electronic payment services (e.g., electronic wallets) and stores it in a database. Based on the collected usage history, the AI ​​model makes suggestions for improving wasteful spending. For example, specific suggestions such as "Your eating out expenses are high, so we suggest you cut them down" are sent to the user via LINE messages.

[0928] Furthermore, the system accepts additional conditions and change requests from users via LINE messages. For example, if a user sends a request such as "I would like to review the plan for when we have another child," the server generates a plan that reflects the new conditions and notifies the user again. Furthermore, if unexpected expenses or income arise, the system promptly notifies the user and proposes a new plan.

[0929] As a specific example, if User A installs "Smart Money Advisor" on their smartphone and links it to their LINE account, a financial life plan will be generated after they enter their basic information. For example, based on information such as "age: 30, family composition: spouse and one child, annual income: 6 million yen," the AI ​​model will propose a post-retirement asset formation plan and a monthly spending review. Furthermore, because their electronic payment service usage history shows that "monthly dining out expenses: 20,000 yen" is high, they will receive a LINE message suggesting "specific ways to reduce dining out expenses to 10,000 yen per month."

[0930] An example of a prompt sentence to input to the generative AI model is as follows:

[0931] User Information:

[0932] Age: 30

[0933] Family: Spouse and one child

[0934] Annual income: 6 million yen

[0935] Usage history:

[0936] Electronic payment service: Monthly dining out expenses: 20,000 yen

[0937] request:

[0938] Reassessing your plan following the birth of a new child

[0939] Generate the plan:

[0940] Specific ways to reduce eating out expenses to 10,000 yen per month

[0941] Adjusting future asset formation plans

[0942] As described above, the present invention provides specific means for supporting users' daily economic activities and helping them realize their ideal financial life plan.

[0943] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0944] Step 1:

[0945] The user enters basic information (age, family composition, annual income, etc.) into the server using a messaging application such as LINE. The entered basic information is sent to the server via the LINE API and stored in a database.

[0946] Input: Basic information sent by the user via messaging applications (age, family composition, annual income, etc.)

[0947] Output: Basic user information stored in the database

[0948] Step 2:

[0949] The server uses the stored basic information to generate an ideal financial life plan using an AI model. The AI ​​model uses machine learning libraries such as TensorFlow and PyTorch. The generated plan is stored in a database on the server.

[0950] Input: User basic information stored in the database

[0951] Output: Generated ideal financial life plan (stored in database)

[0952] Step 3:

[0953] The server notifies the user of the generated ideal financial life plan via a LINE message. The notification is sent using the LINE Messaging API.

[0954] Input: Generated ideal financial life plan

[0955] Output: Money life plan notified via LINE message

[0956] Step 4:

[0957] The server periodically collects the user's usage history for the electronic payment service. The collected usage history is stored in a database. Usage history is obtained using the PayPay API, etc.

[0958] Input: Electronic payment service usage history (e.g., dining out expenses, shopping expenses, etc.)

[0959] Output: Usage history of electronic payment services stored in a database

[0960] Step 5:

[0961] The server uses the collected usage history to analyze wasteful spending using an AI model and generate improvement proposals. For example, if eating out expenses are too high, it will generate a reduction proposal.

[0962] Input: Electronic payment service usage history stored in the database

[0963] Output: Suggestions for improving waste

[0964] Step 6:

[0965] The server notifies the user of the generated suggestions for improving wasteful spending via LINE messages. The notifications are sent using the LINE Messaging API.

[0966] Input: Suggestions for improving wasteful spending

[0967] Output: Improvement suggestions notified via LINE message

[0968] Step 7:

[0969] Users can send additional conditions or change requests to the server via a messaging app, which then accepts the conditions, re-diagnoses them using the AI ​​model, and generates an updated plan.

[0970] Input: Additional terms and changes requested by the user

[0971] Output: Updated financial life plan (saved in database)

[0972] Step 8:

[0973] The server notifies the user of the generated update plan via a LINE message. The notification is sent using the LINE Messaging API.

[0974] Enter: Updated Money Life Plan

[0975] Output: Renewal plan notified via LINE message

[0976] Step 9:

[0977] When unexpected expenses or income arise, the server receives information from the user and quickly re-diagnoses the plan using an AI model. Based on the results of the re-diagnosis, the plan is updated and the user is notified.

[0978] Input: User-submitted information about occasional expenses and income

[0979] Output: Reassessed financial life plan and notification

[0980] Through these steps, the system supports users' daily economic activities and enables them to realize their ideal financial life plan.

[0981] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0982] This invention is a system that allows users to easily diagnose their ideal financial life plan through messaging applications such as LINE, suggests ways to reduce wasteful spending, and adjusts the plan to accommodate changes in life stages, and also recognizes the user's emotions and adjusts the feedback content accordingly.This system is designed around a server, and achieves the following steps through interactions with the user.

[0983] First, the server uses a messaging application to ask the user for basic information, such as age, family composition, and annual income. Based on this, the user sends a message back to the server with their basic information. The server receives this information and stores it in a database.

[0984] The server then uses the stored user information to diagnose an ideal financial life plan using an AI model, and the results are sent back to the user via a messaging app.

[0985] Furthermore, the server collects the user's usage history of electronic payment services, such as PayPay, and uses an AI model to analyze wasteful spending. This allows it to provide specific money-saving tips, such as "Your insurance premiums are too high, so we recommend you review them" or "You can save XX yen each month by eating out less." The resulting suggestions are sent to the user via a messaging app.

[0986] Furthermore, by combining the emotion engine, the server analyzes the user's emotions in real time and provides feedback according to the user's emotional state. For example, if the user is feeling stressed, the server will provide specific advice on how to reduce stress. If the server recognizes that the user is in a positive emotional state, it will make proactive suggestions such as additional investments or savings plans.

[0987] Users can submit additional conditions or change requests to the server based on changes in their life stage or new goals. The server receives these requests, re-diagnoses them using the AI ​​model, generates a new plan, and sends it back to the user.

[0988] As a concrete example, let's assume that the user is 50 years old, has an annual income of 5 million yen, and has a family consisting of a spouse and one child. After providing this basic information to the server, the AI ​​model will propose an ideal retirement asset formation plan and a monthly spending review. It will also point out that eating out expenses are high based on PayPay usage history and suggest ways to reduce them. If the user requests to "review the plan for when we have another child," the server will regenerate a plan that reflects these new conditions and present it to the user.

[0989] Furthermore, if the user expresses negative emotions toward finances, for example, if the emotion engine detects "anxiety about investments," the server will propose a conservative investment plan to reduce risk. On the other hand, if the user expresses positive emotions, the server will propose an aggressive investment strategy, leveraging the user's positive emotions to promote further asset formation.

[0990] In this way, this system supports users' daily financial activities and provides concrete means for easily realizing their ideal financial life plan. It also recognizes the user's emotional state and provides feedback accordingly, enabling more personalized support.

[0991] The processing flow will be explained below.

[0992] Step 1:

[0993] The server sends a message to the user's LINE account asking for basic information, such as age, family composition, and annual income.

[0994] Step 2:

[0995] Users receive LINE messages and respond to each question by replying with their own information.

[0996] Step 3:

[0997] The server receives the user's response and stores information such as age, family composition, and annual income in a database.

[0998] Step 4:

[0999] The server retrieves the stored user information from the database and inputs it into the AI ​​model to diagnose the ideal financial life plan.

[1000] Step 5:

[1001] The server sends the diagnosis results of the ideal financial life plan generated by the AI ​​model to the user via LINE message.

[1002] Step 6:

[1003] The server uses the API of electronic payment services such as PayPay to collect user usage history.

[1004] Step 7:

[1005] The server inputs the collected usage history into an AI model and performs an analysis to identify areas for improvement in wasteful spending.

[1006] Step 8:

[1007] Based on the analysis results, the server sends specific money-saving tips and suggestions for reducing wasteful spending (e.g., reviewing insurance premiums, reducing eating out expenses, etc.) to the user via LINE messages.

[1008] Step 9:

[1009] The server uses an emotion engine to recognize the user's emotions based on the content of the user's LINE messages and the timing of the exchanges.

[1010] Step 10:

[1011] The server adjusts the feedback based on the user's emotional state, for example, by providing suggestions for stress reduction if the user is feeling stressed, or offering proactive investment plans if the user is feeling positive.

[1012] Step 11:

[1013] Depending on changes in their life stage or new financial goals, users can send additional conditions or change requests to the server via LINE messages.

[1014] Step 12:

[1015] The server receives additional conditions and change requests from the user and inputs them into the AI ​​model to generate a new financial life plan.

[1016] Step 13:

[1017] The server will send the diagnosis results of the new plan to the user via LINE message.

[1018] Step 14:

[1019] When unexpected expenses or income occur, the user sends that information to the server via LINE message.

[1020] Step 15:

[1021] The server receives information about unexpected expenses and income and makes necessary plan adjustments based on that information.

[1022] Step 16:

[1023] The server will send the new adjusted plan results and advice to the user via LINE message.

[1024] Step 17:

[1025] The server periodically re-evaluates the user information in the database, generates new diagnostic results, and sends them to the user via LINE message.

[1026] Example 2

[1027] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1028] In modern society, personal financial activities are becoming increasingly complex, requiring appropriate management. However, it is not easy for users to understand their own financial situation and implement efficient asset management or reduce wasteful spending. Furthermore, financial decisions are often influenced by changes in life stages and emotions, so flexible and personalized advice is required. To solve these challenges, a comprehensive management system is needed that not only provides a diagnosis based on the user's basic information, but also includes emotional analysis.

[1029] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1030] In this invention, the server includes means for collecting basic information from the user, means for diagnosing an ideal economic plan based on the basic information, means for analyzing the usage history of electronic payment services and proposing improvements to wasteful spending, means for analyzing the user's emotions and adjusting the feedback content, means for accepting additional conditions or change requests from the user and generating a new plan, and means for sending notifications when unexpected expenses or income occur. This makes it possible to comprehensively manage the user's economic activities and provide flexible and personalized advice.

[1031] "Means for collecting basic information from users" refers to functions for obtaining basic personal information such as the user's age, family composition, and annual income through messaging applications, web forms, etc.

[1032] "Means for diagnosing ideal economic plans based on basic information" is a function that uses collected basic information about users to automatically generate ideal asset management and spending plans using AI models and algorithms.

[1033] "Means for analyzing the usage history of electronic payment services and making suggestions for improving wasteful spending" is a function that analyzes the user's electronic payment history (for example, credit card statements and electronic money usage history), identifies wasteful spending items, and provides money-saving suggestions based on that.

[1034] "Means for analyzing user emotions and adjusting feedback content" refers to a function that uses an emotion analysis engine to determine the user's emotional state and adjusts advice and suggestions accordingly.

[1035] "Means for accepting additional conditions and change requests from users and generating new plans" refers to a function that provides users with the ability to input change requests such as new conditions and upcoming life events, and allows AI models, etc., to generate new economic plans based on these requests.

[1036] The "means for sending notifications when unexpected income or expenses occur" is a function that enables the system to automatically detect when the user has unexpected income or expenses and send appropriate notifications or advice to the user.

[1037] "Message application" refers to application software used by users to send and receive text messages and other data, specifically LINE and other chat applications.

[1038] An "ideal economic plan" refers to a comprehensive plan for achieving the most efficient and desirable state in terms of asset formation, expenditure management, etc.

[1039] An "emotion analysis engine" is an algorithm or system that analyzes a user's text messages or other input data to determine the emotions contained therein.

[1040] An "AI model" refers to an artificial intelligence algorithm or system that uses machine learning and deep learning to analyze data and make suggestions and diagnoses to users.

[1041] This invention is a system that allows users to easily diagnose their ideal financial plan through a messaging application, suggests ways to reduce wasteful spending, and adjusts the plan to accommodate changes in life stages, and also recognizes the user's emotions and adjusts the feedback content accordingly. This system is designed around a server, and specific processes are realized through interactions with the user.

[1042] Hardware and Software Configuration

[1043] The system is implemented using the following hardware and software:

[1044] Hardware: Server (e.g., Amazon Web Services EC2 instance)

[1045] Software: messaging applications (e.g., LINE API), databases (e.g., MySQL), AI models (e.g., OpenAI GPT-4), electronic payment data (e.g., electronic payment service API), sentiment analysis engines (e.g., Microsoft Azure Text Analytics API)

[1046] Specific flow of data processing and data calculation

[1047] The server sends a message to the user through a messaging application asking for basic information such as age, family composition, and annual income. The user enters this information and replies using the messaging application. The server stores the received basic information in a database (MySQL).

[1048] Once the basic information is saved, the server uses an AI model (OpenAI GPT-4) to diagnose an ideal economic plan based on the user's basic information, and the generated diagnosis results are sent to the user again via a messaging app.

[1049] The server also collects the user's electronic payment service usage history and uses an AI model to analyze wasteful spending. For example, if it determines that the user spends a lot of money eating out, it will generate specific suggestions such as "You can save XX yen each month by eating out less." These suggestions are also sent to the user via a messaging app.

[1050] By combining it with an emotion analysis engine, the server analyzes the user's emotions in real time and provides feedback according to the user's emotional state. If the server determines that the user is feeling stressed, it provides advice on how to reduce stress, and if the server recognizes that the user is in a positive emotional state, it suggests additional investment or savings plans.

[1051] Users can submit additional conditions or change requests to the server based on changes in their life stage or new goals. The server receives these requests, re-diagnoses them using the AI ​​model, generates a new plan, and presents it to the user again.

[1052] Examples of concrete examples and prompts

[1053] As a concrete example, let's assume that the user is 50 years old, has an annual income of 5 million yen, and has a family consisting of a spouse and one child. After providing this basic information to the server, the AI ​​model proposes an ideal retirement asset formation plan and a monthly spending review. It also points out that dining out expenses are high based on the user's electronic payment service usage history and suggests ways to reduce them. If the user requests to "review the plan for when we have another child," the server regenerates a plan that reflects these new conditions and presents it to the user. Furthermore, if the user expresses concerns about investing, the sentiment analysis engine detects this and suggests a conservative investment plan.

[1054] Example prompt sentence:

[1055] "Please suggest an ideal retirement asset formation plan for a user who is 50 years old, has an annual income of 5 million yen, and has a family structure of spouse and one child."

[1056] "Analyze the usage history of electronic payment services and provide specific advice to users to reduce wasteful spending."

[1057] "Please explain how to provide appropriate feedback when users express negative emotions."

[1058] As described above, this system supports users' daily economic activities and provides concrete means for realizing ideal economic plans. Furthermore, by recognizing the user's emotional state and providing feedback accordingly, it achieves more personalized support.

[1059] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1060] Step 1:

[1061] Collecting basic information

[1062] Input: The user sends basic information such as age, family composition, and annual income to the server via a messaging application.

[1063] Specific operation: The server sends a message via a messaging application, for example, "Please tell me your age, family composition, and annual income." The user replies using the messaging application, "50 years old, spouse and one child, annual income 5 million yen."

[1064] Data processing: The server processes the received information and converts it into the required format for storage in a database (e.g. MySQL).

[1065] Output: Basic information is saved in the database.

[1066] Step 2:

[1067] Diagnosis based on basic information

[1068] Input: The server retrieves the stored basic information.

[1069] Specific operation: The server retrieves the information "50 years old, spouse and one child, annual income of 5 million yen" from the MySQL database.

[1070] Data calculation: The server uses an AI model (e.g., OpenAI GPT-4) to prompt for basic information and generate the user's ideal economic plan.

[1071] Output: The server gets the generated economic plan.

[1072] Step 3:

[1073] Sending diagnostic results

[1074] Input: Server has economic plan generated by AI model.

[1075] Specific operation: The server sends the generated economic plan via a messaging application saying, "Here's your ideal retirement asset formation plan."

[1076] Output: The user receives the diagnosis through a messaging application.

[1077] Step 4:

[1078] Collection and analysis of electronic payment history

[1079] Input: With the user's consent, the server collects the user's usage history of electronic payment services (e.g., electronic payment service API).

[1080] Specific operation: The server calls the electronic payment service API and obtains the user's latest usage history data.

[1081] Data calculation: The server uses the acquired data to apply an AI model to analyze wasteful spending patterns, such as "high spending on eating out," and generate specific savings suggestions.

[1082] Output: Analysis results and savings recommendations are generated.

[1083] Step 5:

[1084] Submit an improvement suggestion

[1085] Input: Server has analysis results and savings suggestions.

[1086] Specific operation: The server sends the generated savings plan via a messaging application in the form of, for example, "If you eat out less, you can save XX yen each month."

[1087] Output: The user receives the savings offer through a messaging application.

[1088] Step 6:

[1089] Sentiment analysis and feedback adjustment

[1090] Input: The server receives the user's message and analyzes the sentiment using a sentiment analysis engine (e.g., Microsoft Azure Text Analytics API).

[1091] Specific operation: The server sends the user's input message to the sentiment analysis engine and obtains a sentiment result such as "I feel anxious about investing."

[1092] Data calculation: Based on the sentiment results, the server adjusts the feedback content, for example, suggesting a conservative investment plan.

[1093] Output: The adjusted feedback is generated.

[1094] Step 7:

[1095] Sending emotional suggestions

[1096] Input: The server has adjusted feedback.

[1097] What happens: The server sends tailored feedback via a messaging application, such as "Consider a more conservative investment plan."

[1098] Output: The user receives the suggestion through their messaging application.

[1099] Step 8:

[1100] Responding to changes in life stages

[1101] Input: The user sends new conditions or goals to the server via a messaging application.

[1102] Specific operation: For example, a user sends a request saying, "I would like to review my plans for having another child." The server receives this request.

[1103] Data calculation: The server inputs new conditions as prompts into the AI ​​model and generates the ideal economic plan again.

[1104] Output: A new economic plan is generated.

[1105] Step 9:

[1106] Sending re-diagnosis results

[1107] Input: The server has a new economic plan.

[1108] Specific operation: The server sends the generated new economic plan to the user via a messaging application.

[1109] Output: The user receives the new plan through the messaging application.

[1110] As a result, this system comprehensively supports users' daily economic activities and provides ideal economic plans. Furthermore, it flexibly responds to changes in the user's emotions and life stage, providing personalized support.

[1111] (Application example 2)

[1112] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1113] In modern society, efficient asset management and planned spending are extremely important. However, many users find it difficult to manage their own expenses and formulate future life plans. Furthermore, few systems take into account the impact of emotional states on asset management, making it difficult to provide appropriate feedback tailored to the user's emotions. Therefore, there is a need for a system that allows users to easily diagnose their ideal financial life plan, receive suggestions for improving wasteful spending, and provide feedback appropriate to their emotional state.

[1114] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1115] In this invention, the server includes means for collecting basic information from a user, means for diagnosing an ideal money life plan based on the basic information, means for analyzing the usage history of electronic payment methods and proposing improvements to wasteful spending, means for accepting additional conditions and change requests from the user and generating a new plan, means for sending notifications when unexpected expenses or income occur, means for analyzing the user's emotional state, and means for providing feedback according to the user's emotional state. This allows the user to efficiently manage their assets, improve wasteful spending, and achieve planned spending, and further allows them to manage their assets more appropriately by receiving feedback according to their emotional state.

[1116] A "user" is a person who uses the system and provides data on basic information and emotional state.

[1117] "Basic information" refers to information necessary for diagnosing a financial life plan, such as the user's age, income, and family composition.

[1118] The "Ideal Money Life Plan" is a plan that shows optimal spending, savings, and investment plans based on the user's financial goals and current situation.

[1119] "Electronic payment instrument" refers to a platform or method for making payments electronically, including, for example, mobile payment services.

[1120] "Suggestions for improving wasteful spending" are suggestions that analyze the user's spending patterns and provide specific advice for reducing unnecessary or excessive spending.

[1121] "Additional conditions and change requests" are new conditions and settings that users provide to the system in response to changes in their life stage or new goals.

[1122] The "means for generating a new plan" is a function for creating a new money life plan based on the user's additional conditions or change requests.

[1123] The "means for sending a notification when unexpected expenses or income occur" is a function for notifying the user when unexpected expenses or income occur.

[1124] A "means for analyzing emotional state" is a method for determining a user's emotions and using that information to provide appropriate feedback.

[1125] The "means for providing feedback according to emotional state" is a function that shows advice or suggestions customized to the user's emotional state.

[1126] In an embodiment of the present invention, the system includes a server, a user terminal, an electronic payment method, and a sentiment analysis engine as its main components. A user uses a smartphone to input basic information through a provided messaging application. The basic information includes data on economic status such as age, family composition, and annual income. The server receives this basic information and stores it in a database.

[1127] The server then uses the stored basic information to create a generative AI model that diagnoses an ideal financial life plan. This generative AI model then proposes an optimal plan based on the user's financial situation and life stage. The results are then sent to the user via a messaging app.

[1128] Furthermore, the server collects usage history of electronic payment methods (for example, mobile payment services) and analyzes spending data. Data science techniques are used to analyze the spending data and identify large amounts of spending and wasteful spending. Specific suggestions for improving wasteful spending, such as "You spend too much on eating out, so you need to cut back," are generated. These suggestions are also sent via a messaging application.

[1129] The sentiment analysis engine analyzes the user's emotions in messages in real time. This engine uses natural language processing technology to analyze messages from users and determine their emotional state. For example, if the user is feeling "anxious about investing," the server will suggest a conservative investment plan. On the other hand, if the user is expressing positive emotions, the server will suggest an aggressive investment strategy.

[1130] When the user provides additional conditions or change requests based on changes in their life stage or new goals, the server receives them and again uses the generative AI model to generate a new financial life plan, which is also sent to the user via a messaging app.

[1131] The primary hardware used includes smartphones and servers, while the primary software includes a messaging application, a sentiment analysis engine, and a generative AI model. Utilizing these components, users can efficiently manage their assets, reduce waste, and plan their spending.

[1132] As a specific example, if a user is 50 years old, has an annual income of 5 million yen, and has a family consisting of a spouse and one child, by providing basic information, the AI ​​model will propose an ideal post-retirement asset formation plan and monthly spending revisions. It will also point out that dining out expenses are high based on the user's electronic payment method usage history and suggest ways to reduce expenses. If the user requests to "review the plan for when we have another child," the server will regenerate a plan reflecting the new conditions and present it to the user.

[1133] Examples of prompts include:

[1134] "Please tell me your plan for a 50-year-old with an annual income of 5 million yen, a spouse, and one child."

[1135] "Please tell us how to improve wasteful spending based on your electronic payment usage history."

[1136] "I want to reassess my plans for having another child."

[1137] "I'm feeling anxious about my investment. What should I do?"

[1138] As a result, users can receive specific feedback tailored to their own financial situation and receive appropriate advice according to their emotional state.

[1139] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1140] Step 1:

[1141] A user opens a messaging application on their smartphone and enters basic information such as age, income, and family composition.

[1142] Input: User's basic information (age, income, family composition, etc.)

[1143] Output: Basic information data sent to the server

[1144] Step 2:

[1145] The server stores the received basic information in a database.

[1146] Input: Basic information data sent by the user

[1147] Output: Basic information stored in the database

[1148] Step 3:

[1149] Based on the stored basic information, the server uses a generative AI model to diagnose your ideal financial life plan.

[1150] Input: Basic information data

[1151] Output: Diagnosed ideal financial life plan

[1152] Step 4:

[1153] The server sends the diagnosis results to the user through a messaging application.

[1154] Input: Diagnosed ideal financial life plan

[1155] Output: Diagnostic results sent via messaging application

[1156] Step 5:

[1157] The server collects electronic payment transaction history and analyzes spending data, specifically using data science techniques to identify excessive and wasteful spending.

[1158] Input: Electronic payment method usage history

[1159] Output: Suggestions for improving waste

[1160] Step 6:

[1161] The server sends the improvement suggestions to the user through a messaging application.

[1162] Input: Suggestions for improving wasteful spending

[1163] Output: Improvement suggestions sent via messaging application

[1164] Step 7:

[1165] The user sends additional information to the server in response to new requests or changes in life stages.

[1166] Input: User additional conditions or change requests

[1167] Output: Additional conditions and change request data sent to the server

[1168] Step 8:

[1169] The server receives additional conditions and change requests and creates a new financial life plan using a generative AI model.

[1170] Input: Additional conditions and change request data

[1171] Output: The new financial life plan generated.

[1172] Step 9:

[1173] The server also sends the new plan to the user through the messaging application.

[1174] Input: Generated new financial life plan

[1175] Output: New plan sent through messaging application

[1176] Step 10:

[1177] The server uses a sentiment analysis engine to analyze the user's sentiment in the message in real time.

[1178] Input: User's message data

[1179] Output: Parsed emotional state data

[1180] Step 11:

[1181] The server generates feedback according to the emotional state and provides it to the user through a messaging application.

[1182] Input: Parsed emotional state data

[1183] Output: Feedback according to emotional state

[1184] The above is the flow of specific processing steps of the program for realizing the application example.

[1185] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1186] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) 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 voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1187] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1188] [Fourth embodiment]

[1189] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1190] 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.

[1191] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. 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).

[1192] 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.

[1193] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1194] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1195] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1196] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1197] 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.

[1198] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[1199] 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.

[1200] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1201] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1202] This invention is a system that allows users to easily diagnose their ideal financial life plan through messaging applications such as LINE, and provides suggestions for improving wasteful spending and adjusting the plan to accommodate changes in life stages.The system is designed around a server, and accomplishes the following steps through interactions with the user.

[1203] First, the server uses a messaging application to ask the user for basic information, such as age, family composition, and annual income. Based on this, the user sends a message back to the server with their basic information. The server receives this information and stores it in a database.

[1204] The server then uses the stored user information to diagnose an ideal financial life plan using an AI model, and the results are sent back to the user via a messaging app.

[1205] Furthermore, the server collects the user's usage history of electronic payment services, such as PayPay, and uses an AI model to analyze wasteful spending. This allows it to provide specific money-saving tips, such as "Your insurance premiums are too high, so we recommend you review them" or "You can save XX yen each month by eating out less." The resulting suggestions are sent to the user via a messaging app.

[1206] Users can submit additional conditions or change requests to the server based on changes in their life stage or new goals. The server receives these requests, re-diagnoses them using the AI ​​model, generates a new plan, and sends it back to the user.

[1207] Finally, the server promptly notifies the user when unexpected expenses or income arise, and periodically reassess the user's financial life plan, updating it as necessary and providing the results to the user.

[1208] As a concrete example, let's assume that the user is 50 years old, has an annual income of 5 million yen, and has a family consisting of a spouse and one child. After providing this basic information to the server, the AI ​​model proposes an ideal retirement asset formation plan and monthly spending review. It also points out that eating out expenses are high based on PayPay usage history and suggests ways to reduce expenses. If the user requests to "review the plan for when another child is born," the server regenerates a plan that reflects these new conditions and presents it to the user. Then, if, for example, an unexpected medical expense arises, the server promptly notifies the user and makes any necessary adjustments to the plan.

[1209] In this way, this system supports users' daily economic activities and provides them with concrete means to easily realize their ideal financial life plan.

[1210] The processing flow will be explained below.

[1211] Step 1:

[1212] The server sends a message to the user's LINE account asking for basic information, such as age, family composition, and annual income.

[1213] Step 2:

[1214] Users receive LINE messages and respond to each question by replying with their own information.

[1215] Step 3:

[1216] The server receives the user's response and stores information such as age, family composition, and annual income in a database.

[1217] Step 4:

[1218] The server retrieves the stored user information from the database and inputs it into the AI ​​model to diagnose the ideal financial life plan.

[1219] Step 5:

[1220] The server sends the diagnosis results of the ideal financial life plan generated by the AI ​​model to the user via LINE message.

[1221] Step 6:

[1222] The server uses the API of electronic payment services such as PayPay to collect user usage history.

[1223] Step 7:

[1224] The server inputs the collected usage history into an AI model and performs an analysis to identify areas for improvement in wasteful spending.

[1225] Step 8:

[1226] Based on the analysis results, the server sends specific money-saving tips and suggestions for reducing wasteful spending (e.g., reviewing insurance premiums, reducing eating out expenses, etc.) to the user via LINE messages.

[1227] Step 9:

[1228] Users can send additional conditions or change requests to the server via LINE messages depending on changes in their life stage (e.g., having more children) or new financial goals.

[1229] Step 10:

[1230] The server receives additional conditions and change requests from the user and inputs them back into the AI ​​model to generate a new financial life plan.

[1231] Step 11:

[1232] The server will send the diagnosis results of the new plan to the user via LINE message.

[1233] Step 12:

[1234] When unexpected expenses or income occur, the user sends that information to the server via LINE message.

[1235] Step 13:

[1236] The server receives information about unexpected expenses and income and makes necessary plan adjustments based on that information.

[1237] Step 14:

[1238] The server will send the new adjusted plan results and advice to the user via LINE message.

[1239] Step 15:

[1240] The server periodically re-evaluates the user information in the database, generates new diagnostic results, and sends them to the user via LINE message.

[1241] Example 1

[1242] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1243] Conventional economic and life planning support systems required users to input detailed information and create plans using specialized knowledge. This was time-consuming and difficult for average users to create appropriate plans. Furthermore, there were few ways to obtain real-time improvement suggestions based on current living environment and spending status. Furthermore, it was difficult to respond quickly to changes in life stages or unexpected expenses.

[1244] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1245] In this invention, the server includes means for collecting basic information from a user, means for diagnosing an ideal economic life plan using a generative artificial intelligence model based on the basic information, means for analyzing the usage history of electronic payment services and making suggestions for improving wasteful spending, means for accepting additional conditions and change requests from the user and generating a new economic life plan, and means for sending notifications when unexpected expenses or income occur and for reevaluating and updating the economic life plan as necessary. This allows the user to easily create an ideal economic life plan, and enables real-time expenditure analysis and improvement suggestions, as well as quick response to changes in life stages and unexpected expenses.

[1246] "Basic information" refers to information necessary for making a personal economic life plan, such as the user's age, family structure, and annual income.

[1247] A "generative artificial intelligence model" is an artificial intelligence technology that generates optimal economic and lifestyle plans based on a user's basic information and usage history data.

[1248] An "electronic payment service" is a service that provides digital payment methods that users can use for shopping and payments.

[1249] "Usage history" is a record of a user's transactions made through an electronic payment service.

[1250] "Suggestions for improving wasteful spending" are specific advice for reducing unnecessary or excessive spending detected through an analysis of usage history.

[1251] A "messaging application" is a communications application that allows for the exchange of text and media in real time over the Internet.

[1252] An "economic life plan" is a plan that sets ideal spending allocations and savings goals based on the user's income, expenses, asset type, etc.

[1253] A "notification" is an electronic message sent to inform a user of specific information.

[1254] "Life stage changes" are important events or changes in circumstances that occur in a user's life (e.g., marriage, childbirth, job change, etc.).

[1255] "Unexpected expenses and income" refers to sudden expenses that occur unexpectedly or income that is received temporarily at a specific time.

[1256] This invention is a system that allows users to easily diagnose their ideal financial life plan via a messaging application, and provides suggestions for reducing wasteful spending and adjusting the plan to accommodate changes in life stages. The system is designed around a server, and provides the ideal financial life plan through interactions with the user.

[1257] First, the server uses a messaging application to ask the user for basic information, such as age, family composition, and annual income. Based on this, the user then sends a message back to the server with their basic information. The server receives this information and stores it in a database.

[1258] The server then uses the stored user information to diagnose an ideal economic life plan using a generative artificial intelligence model, and the results are sent back to the user via a messaging app.

[1259] The server also collects the user's electronic payment service usage history and uses a generative artificial intelligence model to analyze wasteful spending. This allows the server to provide specific money-saving tips, such as "Your insurance premiums are too high, so we recommend you review them" or "You can save XX yen each month by eating out less." The resulting suggestions are sent to the user via a messaging application.

[1260] Users can submit additional conditions or change requests to the server depending on changes in their life stage or new goals. The server receives these requests, re-diagnoses them using a generative artificial intelligence model, generates a new plan, and sends it back to the user.

[1261] Finally, if unexpected expenses or income arise, the server will promptly notify the user. In addition, the server will periodically reassess the user's financial life plan, update it as necessary, and provide the results to the user.

[1262] As a concrete example, let's assume that the user is 50 years old, has an annual income of 5 million yen, and has a family consisting of a spouse and one child. After providing this basic information to the server, the generative AI model proposes an ideal retirement asset formation plan and monthly spending revisions. It also points out that dining out expenses are high based on the user's electronic payment service usage history and suggests ways to reduce them. If the user requests to "reconsider the plan for when another child is born," the server regenerates a plan that reflects these new conditions and presents it to the user. Then, if, for example, an unexpected medical expense arises, the server promptly notifies the user and makes any necessary adjustments to the plan.

[1263] An example of a prompt sentence is as follows:

[1264] "Age 50, annual income 5 million yen, family composition: spouse and one child. Please provide an ideal retirement asset formation plan based on this basic information. Also, based on the usage history of electronic payment services, please point out that eating out expenses are high and suggest ways to reduce them."

[1265] This system supports users' daily economic activities and provides concrete means for easily realizing their ideal economic life plan. It uses cloud servers (e.g., AWS, Google Cloud Platform) as hardware, and messaging applications (e.g., general messaging services), electronic payment services (e.g., general electronic payment systems), and generative artificial intelligence models (e.g., GPT-4, BERT) as software.

[1266] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1267] Step 1:

[1268] The server uses a messaging application to send a message to the user asking for basic information (age, family composition, annual income, etc.). The input is the message content sent from the server, and the output is the basic information replied by the user. Specifically, the server calls the API of the messaging service and sends the question in the specified format.

[1269] Step 2:

[1270] The user enters basic information through a messaging application and sends it back to the server. The input is the data the user enters into the messaging application, and the output is the basic information sent to the server. The specific operation is when the user answers a question in the messaging application and taps the send button.

[1271] Step 3:

[1272] The server stores the received basic information in a database. The input is the basic information received from the user, and the output is the data stored in the database. Specifically, the server issues an SQL query via the database middleware and stores the data.

[1273] Step 4:

[1274] The server uses a generative AI model to diagnose an ideal economic life plan based on the user's saved basic information. The input is the basic information obtained from the database, and the output is the diagnosis result returned by the generative AI model. Specifically, the server generates a prompt sentence from the basic information and sends a request to the AI ​​model's API.

[1275] Step 5:

[1276] The server sends the diagnosis results to the user via a messaging application. The input is the diagnosis results of the generative AI model, and the output is the message content sent to the user. Specifically, the server converts the generated diagnosis results into a text message and sends it using the messaging application's API.

[1277] Step 6:

[1278] The server collects the user's usage history of the electronic payment service. The input is authentication information obtained from the API of the electronic payment service, and the output is the obtained usage history data. Specifically, the server calls the API of the electronic payment service and obtains the user's usage history data.

[1279] Step 7:

[1280] The server uses a generative artificial intelligence model to analyze the collected usage history data. The input is the acquired usage history data, and the output is suggestions for improving wasteful spending. Specifically, the server inputs the usage history data into the AI ​​model and receives an analysis of wasteful spending and suggestions for improvement.

[1281] Step 8:

[1282] The server sends wasteful spending improvement suggestions to the user via a messaging application. The input is the suggestion generated by the generative artificial intelligence model, and the output is the improvement suggestion message sent to the user. Specifically, the server converts the suggestion content into a text message and sends it via the messaging application's API.

[1283] Step 9:

[1284] The user submits additional conditions or change requests according to changes in their life stage or new goals. The input is the request from the user, and the output is the new conditions sent to the server. Specifically, the user enters the change request in a messaging app and taps the send button.

[1285] Step 10:

[1286] Based on the received request, the server re-diagnoses the economic life plan using a generative artificial intelligence model. The input is the added conditions and changes, and the output is the regenerated economic life plan. Specifically, the server creates a prompt statement including the new conditions, sends it to the AI ​​model, and receives the new plan.

[1287] Step 11:

[1288] The server sends the regenerated economic life plan to the user through a messaging application. The input is the new plan from the generative artificial intelligence model, and the output is the new plan sent to the user. Specifically, the server converts the new plan into a text message and sends it using the messaging application's API.

[1289] Step 12:

[1290] When a user incurs unexpected expenses or income, the user notifies the server of that information. The input is the unexpected expense or income information, and the output is the notification content sent to the server. Specifically, the user enters the expense or income information in a messaging app and taps the send button.

[1291] Step 13:

[1292] The server receives the notification, adjusts the plan using a generative AI model, and resends it to the user. The input is non-recurring expenses and income information, and the output is the adjusted economic life plan. Specifically, the server inputs the received information into the AI ​​model, generates an adjusted plan, and sends it to the user via a messaging app.

[1293] Step 14:

[1294] The server periodically reevaluates the user's economic life plan and updates it as necessary. The input is the existing plan and data such as the latest spending history, and the output is the updated economic life plan. Specifically, the server periodically executes a scheduled task, retrieves the latest information from the database, reevaluates it using the generative artificial intelligence model, and saves the results.

[1295] (Application example 1)

[1296] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1297] Many people today use electronic payment services, and there is a demand for financial improvement proposals based on their usage history to reduce wasteful spending and realize efficient financial life plans. However, there is a lack of flexible planning systems that can accommodate changes in users' lifestyles and unexpected expenses. There is also a need for systems that periodically reevaluate users' financial situations and provide updated plans.

[1298] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1299] In this invention, the server includes means for collecting basic information from the user, means for diagnosing an ideal financial life plan based on the basic information, means for analyzing the usage history of electronic payment services and proposing improvements to wasteful spending, means for accepting additional conditions or change requests from the user and generating a new plan, means for sending notifications when unexpected expenses or income occur, and means for periodically reevaluating the user's financial situation using an AI model based on the user's basic information and usage history of electronic payment services and providing an updated plan. This supports the user's daily financial activities and makes it easy to realize their ideal financial life plan.

[1300] "User" means any individual or entity that uses the Service or System.

[1301] "Basic information" refers to personal information such as the user's age, family structure, and annual income.

[1302] A "Money Life Plan" is a plan for achieving an ideal financial life based on the user's income and expenses.

[1303] "Electronic payment services" are services that provide digital payment methods, including credit cards and mobile payments.

[1304] "Usage history" refers to the transaction records when a user uses an electronic payment service.

[1305] An "AI model" is an algorithm or system that uses artificial intelligence to analyze data and make predictions and suggestions.

[1306] "Suggestions to improve wasteful spending" are specific advice for reducing wasteful spending based on the user's spending data.

[1307] "Additional conditions" refer to new conditions or requests specified by the user.

[1308] A "change request" is a request sent by a user when they want to change an existing plan.

[1309] "Unexpected expenses" refers to unexpected expenses that occur.

[1310] "Notification" means that the system sends information to the user.

[1311] "Periodic reassessment" refers to reviewing and assessing the user's financial situation at regular intervals.

[1312] An "updated plan" is the latest financial life plan created based on new data and conditions.

[1313] The embodiment of the present invention is a software configuration using a server, a user terminal, and an AI model. The system allows users to provide personal information and generate, evaluate, and update a financial life plan based on their usage history of electronic payment services.

[1314] First, a messaging application such as LINE is installed on the user's device, and the user provides basic information to the server through this application. This basic information includes age, family composition, annual income, etc. This basic information is collected using the LINE API and sent to the server.

[1315] The server is built on a cloud service such as AWS EC2, and the collected basic information is stored in a database such as Amazon RDS. Next, an ideal financial life plan is generated using an AI model based on the stored basic information. The AI ​​model uses machine learning libraries such as TensorFlow and PyTorch.

[1316] The generated ideal financial life plan is sent to the user via LINE message, allowing them to receive specific suggestions for improvement based on their own financial situation. For example, advice such as "specific ways to reduce eating out expenses to 10,000 yen per month" is provided.

[1317] The server also periodically collects usage history for electronic payment services (e.g., electronic wallets) and stores it in a database. Based on the collected usage history, the AI ​​model makes suggestions for improving wasteful spending. For example, specific suggestions such as "Your eating out expenses are high, so we suggest you cut them down" are sent to the user via LINE messages.

[1318] Furthermore, the system accepts additional conditions and change requests from users via LINE messages. For example, if a user sends a request such as "I would like to review the plan for when we have another child," the server generates a plan that reflects the new conditions and notifies the user again. Furthermore, if unexpected expenses or income arise, the system promptly notifies the user and proposes a new plan.

[1319] As a specific example, if User A installs "Smart Money Advisor" on their smartphone and links it to their LINE account, a financial life plan will be generated after they enter their basic information. For example, based on information such as "age: 30, family composition: spouse and one child, annual income: 6 million yen," the AI ​​model will propose a post-retirement asset formation plan and a monthly spending review. Furthermore, because their electronic payment service usage history shows that "monthly dining out expenses: 20,000 yen" is high, they will receive a LINE message suggesting "specific ways to reduce dining out expenses to 10,000 yen per month."

[1320] An example of a prompt sentence to input to the generative AI model is as follows:

[1321] User Information:

[1322] Age: 30

[1323] Family: Spouse and one child

[1324] Annual income: 6 million yen

[1325] Usage history:

[1326] Electronic payment service: Monthly dining out expenses: 20,000 yen

[1327] request:

[1328] Reassessing your plan following the birth of a new child

[1329] Generate the plan:

[1330] Specific ways to reduce eating out expenses to 10,000 yen per month

[1331] Adjusting future asset formation plans

[1332] As described above, the present invention provides specific means for supporting users' daily economic activities and helping them realize their ideal financial life plan.

[1333] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1334] Step 1:

[1335] The user enters basic information (age, family composition, annual income, etc.) into the server using a messaging application such as LINE. The entered basic information is sent to the server via the LINE API and stored in a database.

[1336] Input: Basic information sent by the user via messaging applications (age, family composition, annual income, etc.)

[1337] Output: Basic user information stored in the database

[1338] Step 2:

[1339] The server uses the stored basic information to generate an ideal financial life plan using an AI model. The AI ​​model uses machine learning libraries such as TensorFlow and PyTorch. The generated plan is stored in a database on the server.

[1340] Input: User basic information stored in the database

[1341] Output: Generated ideal financial life plan (stored in database)

[1342] Step 3:

[1343] The server notifies the user of the generated ideal financial life plan via a LINE message. The notification is sent using the LINE Messaging API.

[1344] Input: Generated ideal financial life plan

[1345] Output: Money life plan notified via LINE message

[1346] Step 4:

[1347] The server periodically collects the user's usage history for the electronic payment service. The collected usage history is stored in a database. Usage history is obtained using the PayPay API, etc.

[1348] Input: Electronic payment service usage history (e.g., dining out expenses, shopping expenses, etc.)

[1349] Output: Usage history of electronic payment services stored in a database

[1350] Step 5:

[1351] The server uses the collected usage history to analyze wasteful spending using an AI model and generate improvement proposals. For example, if eating out expenses are too high, it will generate a reduction proposal.

[1352] Input: Electronic payment service usage history stored in the database

[1353] Output: Suggestions for improving waste

[1354] Step 6:

[1355] The server notifies the user of the generated suggestions for improving wasteful spending via LINE messages. The notifications are sent using the LINE Messaging API.

[1356] Input: Suggestions for improving wasteful spending

[1357] Output: Improvement suggestions notified via LINE message

[1358] Step 7:

[1359] Users can send additional conditions or change requests to the server via a messaging app, which then accepts the conditions, re-diagnoses them using the AI ​​model, and generates an updated plan.

[1360] Input: Additional terms and changes requested by the user

[1361] Output: Updated financial life plan (saved in database)

[1362] Step 8:

[1363] The server notifies the user of the generated update plan via a LINE message. The notification is sent using the LINE Messaging API.

[1364] Enter: Updated Money Life Plan

[1365] Output: Renewal plan notified via LINE message

[1366] Step 9:

[1367] When unexpected expenses or income arise, the server receives information from the user and quickly re-diagnoses the plan using an AI model. Based on the results of the re-diagnosis, the plan is updated and the user is notified.

[1368] Input: User-submitted information about occasional expenses and income

[1369] Output: Reassessed financial life plan and notification

[1370] Through these steps, the system supports users' daily economic activities and enables them to realize their ideal financial life plan.

[1371] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1372] This invention is a system that allows users to easily diagnose their ideal financial life plan through messaging applications such as LINE, suggests ways to reduce wasteful spending, and adjusts the plan to accommodate changes in life stages, and also recognizes the user's emotions and adjusts the feedback content accordingly.This system is designed around a server, and achieves the following steps through interactions with the user.

[1373] First, the server uses a messaging application to ask the user for basic information, such as age, family composition, and annual income. Based on this, the user sends a message back to the server with their basic information. The server receives this information and stores it in a database.

[1374] The server then uses the stored user information to diagnose an ideal financial life plan using an AI model, and the results are sent back to the user via a messaging app.

[1375] Furthermore, the server collects the user's usage history of electronic payment services, such as PayPay, and uses an AI model to analyze wasteful spending. This allows it to provide specific money-saving tips, such as "Your insurance premiums are too high, so we recommend you review them" or "You can save XX yen each month by eating out less." The resulting suggestions are sent to the user via a messaging app.

[1376] Furthermore, by combining the emotion engine, the server analyzes the user's emotions in real time and provides feedback according to the user's emotional state. For example, if the user is feeling stressed, the server will provide specific advice on how to reduce stress. If the server recognizes that the user is in a positive emotional state, it will make proactive suggestions such as additional investments or savings plans.

[1377] Users can submit additional conditions or change requests to the server based on changes in their life stage or new goals. The server receives these requests, re-diagnoses them using the AI ​​model, generates a new plan, and sends it back to the user.

[1378] As a concrete example, let's assume that the user is 50 years old, has an annual income of 5 million yen, and has a family consisting of a spouse and one child. After providing this basic information to the server, the AI ​​model will propose an ideal retirement asset formation plan and a monthly spending review. It will also point out that eating out expenses are high based on PayPay usage history and suggest ways to reduce them. If the user requests to "review the plan for when we have another child," the server will regenerate a plan that reflects these new conditions and present it to the user.

[1379] Furthermore, if the user expresses negative emotions toward finances, for example, if the emotion engine detects "anxiety about investments," the server will propose a conservative investment plan to reduce risk. On the other hand, if the user expresses positive emotions, the server will propose an aggressive investment strategy, leveraging the user's positive emotions to promote further asset formation.

[1380] In this way, this system supports users' daily financial activities and provides concrete means for easily realizing their ideal financial life plan. It also recognizes the user's emotional state and provides feedback accordingly, enabling more personalized support.

[1381] The processing flow will be explained below.

[1382] Step 1:

[1383] The server sends a message to the user's LINE account asking for basic information, such as age, family composition, and annual income.

[1384] Step 2:

[1385] Users receive LINE messages and respond to each question by replying with their own information.

[1386] Step 3:

[1387] The server receives the user's response and stores information such as age, family composition, and annual income in a database.

[1388] Step 4:

[1389] The server retrieves the stored user information from the database and inputs it into the AI ​​model to diagnose the ideal financial life plan.

[1390] Step 5:

[1391] The server sends the diagnosis results of the ideal financial life plan generated by the AI ​​model to the user via LINE message.

[1392] Step 6:

[1393] The server uses the API of electronic payment services such as PayPay to collect user usage history.

[1394] Step 7:

[1395] The server inputs the collected usage history into an AI model and performs an analysis to identify areas for improvement in wasteful spending.

[1396] Step 8:

[1397] Based on the analysis results, the server sends specific money-saving tips and suggestions for reducing wasteful spending (e.g., reviewing insurance premiums, reducing eating out expenses, etc.) to the user via LINE messages.

[1398] Step 9:

[1399] The server uses an emotion engine to recognize the user's emotions based on the content of the user's LINE messages and the timing of the exchange.

[1400] Step 10:

[1401] The server adjusts the feedback based on the user's emotional state, for example, by providing suggestions for stress reduction if the user is feeling stressed, or offering proactive investment plans if the user is feeling positive.

[1402] Step 11:

[1403] Depending on changes in their life stage or new financial goals, users can send additional conditions or change requests to the server via LINE messages.

[1404] Step 12:

[1405] The server receives additional conditions and change requests from the user and inputs them into the AI ​​model to generate a new financial life plan.

[1406] Step 13:

[1407] The server will send the diagnosis results of the new plan to the user via LINE message.

[1408] Step 14:

[1409] When unexpected expenses or income occur, the user sends that information to the server via LINE message.

[1410] Step 15:

[1411] The server receives information about unexpected expenses and income and makes necessary plan adjustments based on that information.

[1412] Step 16:

[1413] The server will send the new adjusted plan results and advice to the user via LINE message.

[1414] Step 17:

[1415] The server periodically re-evaluates the user information in the database, generates new diagnostic results, and sends them to the user via LINE message.

[1416] Example 2

[1417] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1418] In modern society, personal financial activities are becoming increasingly complex, requiring appropriate management. However, it is not easy for users to understand their own financial situation and implement efficient asset management or reduce wasteful spending. Furthermore, financial decisions are often influenced by changes in life stages and emotions, so flexible and personalized advice is required. To solve these challenges, a comprehensive management system is needed that not only provides a diagnosis based on the user's basic information, but also includes emotional analysis.

[1419] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1420] In this invention, the server includes means for collecting basic information from the user, means for diagnosing an ideal economic plan based on the basic information, means for analyzing the usage history of electronic payment services and proposing improvements to wasteful spending, means for analyzing the user's emotions and adjusting the feedback content, means for accepting additional conditions or change requests from the user and generating a new plan, and means for sending notifications when unexpected expenses or income occur. This makes it possible to comprehensively manage the user's economic activities and provide flexible and personalized advice.

[1421] "Means for collecting basic information from users" refers to functions for obtaining basic personal information such as the user's age, family composition, and annual income through messaging applications, web forms, etc.

[1422] "Means for diagnosing ideal economic plans based on basic information" is a function that uses collected basic information about users to automatically generate ideal asset management and spending plans using AI models and algorithms.

[1423] "Means for analyzing the usage history of electronic payment services and making suggestions for improving wasteful spending" is a function that analyzes the user's electronic payment history (for example, credit card statements and electronic money usage history), identifies wasteful spending items, and provides money-saving suggestions based on that.

[1424] "Means for analyzing user emotions and adjusting feedback content" refers to a function that uses an emotion analysis engine to determine the user's emotional state and adjusts advice and suggestions accordingly.

[1425] "Means for accepting additional conditions and change requests from users and generating new plans" refers to a function that provides users with the ability to input change requests such as new conditions and upcoming life events, and allows AI models, etc., to generate new economic plans based on these requests.

[1426] The "means for sending notifications when unexpected income or expenses occur" is a function that enables the system to automatically detect when the user has unexpected income or expenses and send appropriate notifications or advice to the user.

[1427] "Message application" refers to application software used by users to send and receive text messages and other data, specifically LINE and other chat applications.

[1428] An "ideal economic plan" refers to a comprehensive plan for achieving the most efficient and desirable state in terms of asset formation, expenditure management, etc.

[1429] An "emotion analysis engine" is an algorithm or system that analyzes a user's text messages or other input data to determine the emotions contained therein.

[1430] An "AI model" refers to an artificial intelligence algorithm or system that uses machine learning and deep learning to analyze data and make suggestions and diagnoses to users.

[1431] This invention is a system that allows users to easily diagnose their ideal financial plan through a messaging application, suggests ways to reduce wasteful spending, and adjusts the plan to accommodate changes in life stages, and also recognizes the user's emotions and adjusts the feedback content accordingly. This system is designed around a server, and specific processes are realized through interactions with the user.

[1432] Hardware and Software Configuration

[1433] The system is implemented using the following hardware and software:

[1434] Hardware: Server (e.g., Amazon Web Services EC2 instance)

[1435] Software: messaging applications (e.g., LINE API), databases (e.g., MySQL), AI models (e.g., OpenAI GPT-4), electronic payment data (e.g., electronic payment service API), sentiment analysis engines (e.g., Microsoft Azure Text Analytics API)

[1436] Specific flow of data processing and data calculation

[1437] The server sends a message to the user through a messaging application asking for basic information such as age, family composition, and annual income. The user enters this information and replies using the messaging application. The server stores the received basic information in a database (MySQL).

[1438] Once the basic information is saved, the server uses an AI model (OpenAI GPT-4) to diagnose an ideal economic plan based on the user's basic information, and the generated diagnosis results are sent to the user again via a messaging app.

[1439] The server also collects the user's electronic payment service usage history and uses an AI model to analyze wasteful spending. For example, if it determines that the user spends a lot of money eating out, it will generate specific suggestions such as "You can save XX yen each month by eating out less." These suggestions are also sent to the user via a messaging app.

[1440] By combining it with an emotion analysis engine, the server analyzes the user's emotions in real time and provides feedback according to the user's emotional state. If the server determines that the user is feeling stressed, it provides advice on how to reduce stress, and if the server recognizes that the user is in a positive emotional state, it suggests additional investment or savings plans.

[1441] Users can submit additional conditions or change requests to the server based on changes in their life stage or new goals. The server receives these requests, re-diagnoses them using the AI ​​model, generates a new plan, and presents it to the user again.

[1442] Examples of concrete examples and prompts

[1443] As a concrete example, let's assume that the user is 50 years old, has an annual income of 5 million yen, and has a family consisting of a spouse and one child. After providing this basic information to the server, the AI ​​model proposes an ideal retirement asset formation plan and a monthly spending review. It also points out that dining out expenses are high based on the user's electronic payment service usage history and suggests ways to reduce them. If the user requests to "review the plan for when we have another child," the server regenerates a plan that reflects these new conditions and presents it to the user. Furthermore, if the user expresses concerns about investing, the sentiment analysis engine detects this and suggests a conservative investment plan.

[1444] Example prompt sentence:

[1445] "Please suggest an ideal retirement asset formation plan for a user who is 50 years old, has an annual income of 5 million yen, and has a family structure of spouse and one child."

[1446] "Analyze the usage history of electronic payment services and provide specific advice to users to reduce wasteful spending."

[1447] "Please explain how to provide appropriate feedback when users express negative emotions."

[1448] As described above, this system supports users' daily economic activities and provides concrete means for realizing ideal economic plans. Furthermore, by recognizing the user's emotional state and providing feedback accordingly, it achieves more personalized support.

[1449] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1450] Step 1:

[1451] Collecting basic information

[1452] Input: The user sends basic information such as age, family composition, and annual income to the server via a messaging application.

[1453] Specific operation: The server sends a message via a messaging application, for example, "Please tell me your age, family composition, and annual income." The user replies using the messaging application, "50 years old, spouse and one child, annual income 5 million yen."

[1454] Data processing: The server processes the received information and converts it into the required format for storage in a database (e.g. MySQL).

[1455] Output: Basic information is saved in the database.

[1456] Step 2:

[1457] Diagnosis based on basic information

[1458] Input: The server retrieves the stored basic information.

[1459] Specific operation: The server retrieves the information "50 years old, spouse and one child, annual income of 5 million yen" from the MySQL database.

[1460] Data calculation: The server uses an AI model (e.g., OpenAI GPT-4) to prompt for basic information and generate the user's ideal economic plan.

[1461] Output: The server gets the generated economic plan.

[1462] Step 3:

[1463] Sending diagnostic results

[1464] Input: Server has economic plan generated by AI model.

[1465] Specific operation: The server sends the generated economic plan via a messaging application saying, "Here's your ideal retirement asset formation plan."

[1466] Output: The user receives the diagnosis through a messaging application.

[1467] Step 4:

[1468] Collection and analysis of electronic payment history

[1469] Input: With the user's consent, the server collects the user's usage history of electronic payment services (e.g., electronic payment service API).

[1470] Specific operation: The server calls the electronic payment service API and obtains the user's latest usage history data.

[1471] Data calculation: The server uses the acquired data to apply an AI model to analyze wasteful spending patterns, such as "high spending on eating out," and generate specific savings suggestions.

[1472] Output: Analysis results and savings recommendations are generated.

[1473] Step 5:

[1474] Submit an improvement suggestion

[1475] Input: Server has analysis results and savings suggestions.

[1476] Specific operation: The server sends the generated savings plan via a messaging application in the form of, for example, "If you eat out less, you can save XX yen each month."

[1477] Output: The user receives the savings offer through a messaging application.

[1478] Step 6:

[1479] Sentiment analysis and feedback adjustment

[1480] Input: The server receives the user's message and analyzes the sentiment using a sentiment analysis engine (e.g., Microsoft Azure Text Analytics API).

[1481] Specific operation: The server sends the user's input message to the sentiment analysis engine and obtains a sentiment result such as "I feel anxious about investing."

[1482] Data calculation: Based on the sentiment results, the server adjusts the feedback content, for example, suggesting a conservative investment plan.

[1483] Output: The adjusted feedback is generated.

[1484] Step 7:

[1485] Sending emotional suggestions

[1486] Input: The server has adjusted feedback.

[1487] What happens: The server sends tailored feedback via a messaging application, such as "Consider a more conservative investment plan."

[1488] Output: The user receives the suggestion through their messaging application.

[1489] Step 8:

[1490] Responding to changes in life stages

[1491] Input: The user sends new conditions or goals to the server via a messaging application.

[1492] Specific operation: For example, a user sends a request saying, "I would like to review my plans for having another child." The server receives this request.

[1493] Data calculation: The server inputs new conditions as prompts into the AI ​​model and generates the ideal economic plan again.

[1494] Output: A new economic plan is generated.

[1495] Step 9:

[1496] Sending re-diagnosis results

[1497] Input: The server has a new economic plan.

[1498] Specific operation: The server sends the generated new economic plan to the user via a messaging application.

[1499] Output: The user receives the new plan through the messaging application.

[1500] As a result, this system comprehensively supports users' daily economic activities and provides ideal economic plans. Furthermore, it flexibly responds to changes in the user's emotions and life stage, providing personalized support.

[1501] (Application example 2)

[1502] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1503] In modern society, efficient asset management and planned spending are extremely important. However, many users find it difficult to manage their own expenses and formulate future life plans. Furthermore, few systems take into account the impact of emotional states on asset management, making it difficult to provide appropriate feedback tailored to the user's emotions. Therefore, there is a need for a system that allows users to easily diagnose their ideal financial life plan, receive suggestions for improving wasteful spending, and provide feedback appropriate to their emotional state.

[1504] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1505] In this invention, the server includes means for collecting basic information from a user, means for diagnosing an ideal money life plan based on the basic information, means for analyzing the usage history of electronic payment methods and proposing improvements to wasteful spending, means for accepting additional conditions and change requests from the user and generating a new plan, means for sending notifications when unexpected expenses or income occur, means for analyzing the user's emotional state, and means for providing feedback according to the user's emotional state. This allows the user to efficiently manage their assets, improve wasteful spending, and achieve planned spending, and further allows them to manage their assets more appropriately by receiving feedback according to their emotional state.

[1506] A "user" is a person who uses the system and provides data on basic information and emotional state.

[1507] "Basic information" refers to information necessary for diagnosing a financial life plan, such as the user's age, income, and family composition.

[1508] The "Ideal Money Life Plan" is a plan that shows optimal spending, savings, and investment plans based on the user's financial goals and current situation.

[1509] "Electronic payment instrument" refers to a platform or method for making payments electronically, including, for example, mobile payment services.

[1510] "Suggestions for improving wasteful spending" are suggestions that analyze the user's spending patterns and provide specific advice for reducing unnecessary or excessive spending.

[1511] "Additional conditions and change requests" are new conditions and settings that users provide to the system in response to changes in their life stage or new goals.

[1512] The "means for generating a new plan" is a function for creating a new money life plan based on the user's additional conditions or change requests.

[1513] The "means for sending a notification when unexpected expenses or income occur" is a function for notifying the user when unexpected expenses or income occur.

[1514] A "means for analyzing emotional state" is a method for determining a user's emotions and using that information to provide appropriate feedback.

[1515] The "means for providing feedback according to emotional state" is a function that shows advice or suggestions customized to the user's emotional state.

[1516] In an embodiment of the present invention, the system includes a server, a user terminal, an electronic payment method, and a sentiment analysis engine as its main components. A user uses a smartphone to input basic information through a provided messaging application. The basic information includes data on economic status such as age, family composition, and annual income. The server receives this basic information and stores it in a database.

[1517] The server then uses the stored basic information to create a generative AI model that will diagnose an ideal financial life plan. This generative AI model will then suggest an optimal plan based on the user's financial situation and life stage. The results of the diagnosis will be sent to the user via a messaging app.

[1518] Furthermore, the server collects usage history of electronic payment methods (for example, mobile payment services) and analyzes spending data. Data science techniques are used to analyze the spending data and identify large amounts of spending and wasteful spending. Specific suggestions for improving wasteful spending, such as "You spend too much on eating out, so you need to cut back," are generated. These suggestions are also sent via a messaging application.

[1519] The sentiment analysis engine analyzes the user's emotions in messages in real time. This engine uses natural language processing technology to analyze messages from users and determine their emotional state. For example, if the user is feeling "anxious about investing," the server will suggest a conservative investment plan. On the other hand, if the user is expressing positive emotions, the server will suggest an aggressive investment strategy.

[1520] When the user provides additional conditions or change requests based on changes in their life stage or new goals, the server receives them and again uses the generative AI model to generate a new financial life plan, which is also sent to the user via a messaging app.

[1521] The primary hardware used includes smartphones and servers, while the primary software includes a messaging application, a sentiment analysis engine, and a generative AI model. Utilizing these components, users can efficiently manage their assets, reduce waste, and plan their spending.

[1522] As a specific example, if a user is 50 years old, has an annual income of 5 million yen, and has a family consisting of a spouse and one child, by providing basic information, the AI ​​model will propose an ideal post-retirement asset formation plan and monthly spending revisions. It will also point out that dining out expenses are high based on the user's electronic payment method usage history and suggest ways to reduce expenses. If the user requests to "review the plan for when we have another child," the server will regenerate a plan reflecting the new conditions and present it to the user.

[1523] Examples of prompts include:

[1524] "Please tell me your plan for a 50-year-old with an annual income of 5 million yen, a spouse, and one child."

[1525] "Please tell us how to improve wasteful spending based on your electronic payment usage history."

[1526] "I want to reassess my plans for having another child."

[1527] "I'm feeling anxious about my investment. What should I do?"

[1528] As a result, users can receive specific feedback tailored to their own financial situation and receive appropriate advice according to their emotional state.

[1529] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1530] Step 1:

[1531] A user opens a messaging application on their smartphone and enters basic information such as age, income, and family composition.

[1532] Input: User's basic information (age, income, family composition, etc.)

[1533] Output: Basic information data sent to the server

[1534] Step 2:

[1535] The server stores the received basic information in a database.

[1536] Input: Basic information data sent by the user

[1537] Output: Basic information stored in the database

[1538] Step 3:

[1539] Based on the stored basic information, the server uses a generative AI model to diagnose your ideal financial life plan.

[1540] Input: Basic information data

[1541] Output: Diagnosed ideal financial life plan

[1542] Step 4:

[1543] The server sends the diagnosis results to the user through a messaging application.

[1544] Input: Diagnosed ideal financial life plan

[1545] Output: Diagnostic results sent via messaging application

[1546] Step 5:

[1547] The server collects electronic payment transaction history and analyzes spending data, specifically using data science techniques to identify excessive and wasteful spending.

[1548] Input: Electronic payment method usage history

[1549] Output: Suggestions for improving waste

[1550] Step 6:

[1551] The server sends the improvement suggestions to the user through a messaging application.

[1552] Input: Suggestions for improving wasteful spending

[1553] Output: Improvement suggestions sent via messaging application

[1554] Step 7:

[1555] The user sends additional information to the server in response to new requests or changes in life stages.

[1556] Input: User additional conditions or change requests

[1557] Output: Additional conditions and change request data sent to the server

[1558] Step 8:

[1559] The server receives additional conditions and change requests and creates a new financial life plan using a generative AI model.

[1560] Input: Additional conditions and change request data

[1561] Output: The new financial life plan generated.

[1562] Step 9:

[1563] The server also sends the new plan to the user through the messaging application.

[1564] Input: Generated new financial life plan

[1565] Output: New plan sent through messaging application

[1566] Step 10:

[1567] The server uses a sentiment analysis engine to analyze the user's sentiment in the message in real time.

[1568] Input: User's message data

[1569] Output: Parsed emotional state data

[1570] Step 11:

[1571] The server generates feedback according to the emotional state and provides it to the user through a messaging application.

[1572] Input: Parsed emotional state data

[1573] Output: Feedback according to emotional state

[1574] The above is the flow of specific processing steps of the program for realizing the application example.

[1575] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1576] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) 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 voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1577] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1578] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1579] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1580] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1581] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1582] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1583] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1584] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1585] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1586] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1587] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1588] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1589] 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.

[1590] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1591] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1592] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1593] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1594] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1595] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1596] The following is further disclosed regarding the above embodiment.

[1597] (Claim 1)

[1598] a means for collecting basic information from users;

[1599] A means for diagnosing an ideal financial life plan based on the basic information;

[1600] A method for analyzing the usage history of electronic payment services and making suggestions for improving wasteful spending;

[1601] A means for accepting additional conditions or change requests from users to generate new plans;

[1602] A means of sending notifications when unexpected expenses or income arise;

[1603] A system including:

[1604] (Claim 2)

[1605] 10. The system of claim 1, wherein the system uses a messaging application to collect basic information from the user.

[1606] (Claim 3)

[1607] The system according to claim 1, wherein the diagnosed ideal financial life plan and suggestions for improving wasteful spending are sent to the user via a messaging application.

[1608] "Example 1"

[1609] (Claim 1)

[1610] a means for collecting basic information from users;

[1611] A means for diagnosing an ideal economic life plan using a generative artificial intelligence model based on the basic information;

[1612] A method for analyzing the usage history of electronic payment services and making suggestions for improving wasteful spending;

[1613] a means for accepting additional conditions or change requests from a user to generate a new economic life plan;

[1614] Notifications of unexpected expenses or incomes and a means to reassess and update your economic life plan as needed;

[1615] A system including:

[1616] (Claim 2)

[1617] 10. The system of claim 1, wherein the system uses a messaging application to collect basic information from the user.

[1618] (Claim 3)

[1619] The system according to claim 1, wherein the system transmits to the user via a messaging application an ideal economic life plan diagnosed based on the generative artificial intelligence model and suggestions for improving wasteful spending.

[1620] "Application Example 1"

[1621] (Claim 1)

[1622] a means for collecting basic information from users;

[1623] A means for diagnosing an ideal financial life plan based on the basic information;

[1624] A method for analyzing the usage history of electronic payment services and making suggestions for improving wasteful spending;

[1625] A means for accepting additional conditions or change requests from users to generate new plans;

[1626] A means of sending notifications when unexpected expenses or income arise;

[1627] A means for periodically reassessing the user's financial situation using an AI model based on the user's basic information and usage history of electronic payment services, and providing an updated plan;

[1628] A system including:

[1629] (Claim 2)

[1630] 10. The system of claim 1, wherein the system uses a messaging application to collect basic information from the user.

[1631] (Claim 3)

[1632] The system according to claim 1, wherein the diagnosed ideal financial life plan and suggestions for improving wasteful spending are sent to the user via a messaging application.

[1633] "Example 2: Combining Emotion Engines"

[1634] (Claim 1)

[1635] a means for collecting basic information from users;

[1636] A means for diagnosing an ideal economic plan based on the basic information;

[1637] A method for analyzing the usage history of electronic payment services and making suggestions for improving wasteful spending;

[1638] A means for analyzing user emotions and adjusting the feedback content;

[1639] A means for accepting additional conditions or change requests from the user to generate a new plan;

[1640] A means of sending notifications when unexpected expenses or income arise;

[1641] A system including:

[1642] (Claim 2)

[1643] 10. The system of claim 1, wherein the system uses a messaging application to collect basic information from the user.

[1644] (Claim 3)

[1645] 2. The system according to claim 1, wherein the diagnosed ideal economic plan and suggestions for improving wasteful spending are sent to the user via a messaging application.

[1646] "Application example 2 when combining emotion engines"

[1647] (Claim 1)

[1648] a means for collecting basic information from users;

[1649] A means for diagnosing an ideal financial life plan based on the basic information;

[1650] A method for analyzing the usage history of electronic payment methods and making suggestions for improving wasteful spending;

[1651] A means for accepting additional conditions or change requests from users to generate new plans;

[1652] A means of sending notifications when unexpected expenses or income arise;

[1653] means for analyzing the emotional state of a user;

[1654] a means for providing feedback according to an emotional state;

[1655] A system including:

[1656] (Claim 2)

[1657] 10. The system of claim 1, wherein the system uses a messaging application to collect basic information from the user.

[1658] (Claim 3)

[1659] The system of claim 1 sends the diagnosed ideal financial life plan and suggestions for improving wasteful spending to the user via a messaging application, and provides feedback according to the user's emotional state. [Explanation of symbols]

[1660] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a means for collecting basic information from users; A means for diagnosing an ideal financial life plan based on the basic information; A method for analyzing the usage history of electronic payment services and making suggestions for improving wasteful spending; A means for accepting additional conditions or change requests from users to generate new plans; A means of sending notifications when unexpected expenses or income arise; A system including:

2. The system of claim 1 , wherein the system collects basic information from the user using a messaging application.

3. 2. The system according to claim 1, wherein the diagnosed ideal money life plan and suggestions for improving wasteful spending are sent to the user via a messaging application.

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