system

Abstract

Provide a system. [Solution] means for receiving financial information from a user; means for performing data analysis based on the received financial information; A means for generating appropriate asset management and service proposals based on the data analysis results; means for providing the generated suggestions to a user; means for storing the suggestions and user information in a database; A system including:

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] Traditionally, financial planner (FP) work has been performed by human experts, but by introducing AI technology with advanced information analysis and predictive capabilities, it is necessary to achieve better asset management and service proposals while reducing labor costs. There is also a need to streamline proposal work in a variety of fields, including securities, insurance, and real estate investment, and provide consistently high-quality services. However, because no system has previously existed that meets these requirements, progress in automating FP work has been slow, and there have been limitations on work efficiency and proposal accuracy. The purpose of this invention is to solve these problems. [Means for solving the problem]

[0005] To solve the above problems, the present invention provides the following means. A system is configured including means for receiving financial information from users, means for performing data analysis based on the received financial information, means for generating appropriate asset management and service proposals based on the results of the data analysis, means for providing the generated proposals to users, and means for storing the proposals and user information in a database. This enables traditional financial planning operations to be automated using AI, enabling sophisticated proposals to be realized through large-scale data analysis, improving operational efficiency and providing services of consistent quality. Furthermore, information from users is encrypted and securely transmitted using a secure communication protocol. Furthermore, by using a database that stores large amounts of financial data and training an AI model based on that data, more accurate analysis and proposals can be performed.

[0006] "User" means any individual or entity that utilizes the System to provide its financial information and receive proposals.

[0007] "Financial information" includes information such as the user's age, annual income, savings, basic financial situation, investment goals, and risk tolerance.

[0008] "Data analysis" refers to the process of using AI algorithms and models to conduct detailed analysis of input financial information to make future predictions and evaluate the current situation.

[0009] "Asset management" refers to the efficient allocation of funds by selecting financial products and investment targets such as stocks, bonds, and real estate in order to achieve a user's financial goals.

[0010] "Service Proposal" refers to specific investment, insurance product, and financial plan recommendations provided to users based on the results of data analysis.

[0011] "Database" refers to a large-capacity data storage system that accumulates historical market data, customer data, analytical results, etc., and uses them to train AI models and generate future proposals.

[0012] "Secure communication protocol" refers to a communication method that uses encryption technology to ensure confidentiality of communications in order to safely transfer financial information from users and proposals from the system.

[0013] An "AI model" is an algorithm trained using machine learning and deep learning techniques from large amounts of data, and is used to make future predictions and perform complex data analysis.

[0014] "Training" refers to the process by which an AI model uses large amounts of learning data to improve its pattern recognition capabilities and predictive accuracy.

[0015] "Financial products" refers to various products that are the subject of investment, such as stocks, bonds, insurance, derivatives, and real estate investment trusts. [Brief explanation of the drawings]

[0016] [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

[0017] 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.

[0018] First, the terms used in the following description will be explained.

[0019] 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).

[0020] 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.

[0021] 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.

[0022] 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.

[0023] 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."

[0024] [First embodiment]

[0025] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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."

[0037] This invention is a financial planner system that utilizes AI, which receives financial information from users, performs advanced data analysis based on that information, and generates and provides appropriate asset management and service proposals to users. The specific configuration and processing flow of the system are as follows:

[0038] System Configuration

[0039] This system consists of three main components: a user terminal, a server, and a database.

[0040] User terminal

[0041] A user terminal is a device through which a user enters their financial information and receives offers provided by the system, such as a smartphone, tablet, or PC.

[0042] server

[0043] The server is the main processing unit that receives the financial information submitted by users, performs data analysis, and generates proposals. The server has an AI model installed and is trained on the information retrieved from the database.

[0044] Database

[0045] The database is a large-capacity data storage system that stores historical market data, customer data, analytical results, etc. The server uses this database to train AI models and generate advanced recommendations.

[0046] Processing flow

[0047] The specific processing flow centered on the server, terminal, and user will be explained below.

[0048] User

[0049] Users access the system and enter their financial information, such as their age, annual income, savings amount, financial goals, etc. This information is encrypted using a secure communication protocol and sent from the user's terminal to the server.

[0050] Terminal

[0051] The user terminal plays a role in encrypting the entered financial information and sending it to the server. For example, if a user enters information such as "Age: 35," "Annual income: 6 million yen," "Savings: 3 million yen," and "Goal: Secure funds for children's education," the information is encrypted and sent to the server.

[0052] server

[0053] The server decodes the received financial information and inputs it into the AI ​​model, which is trained on historical market and customer data stored in a database and performs detailed analysis based on the received information. As a result of the analysis, appropriate asset management and service proposals are generated.

[0054] The generated proposals are expressed as recommendations for specific investments, insurance products, and financial plans in a variety of fields, including securities, insurance, and real estate investment. For example, based on the user's input information, the system may generate proposals such as "take out education insurance," "invest in a mutual fund that is expected to grow steadily," or "purchase a rental property."

[0055] Terminal

[0056] The server-generated proposals are then sent, again using a secure communication protocol, to the user terminal, which decrypts the proposals and visually displays them to the user.

[0057] User

[0058] The user can review the displayed suggestions and accept them if necessary. If the user accepts a suggestion, the information is fed back to the server and updated in the database. This feedback further improves the accuracy of future suggestions.

[0059] Specific examples

[0060] For example, if a user in their 30s wants to secure funds for their child's education in the near future, they input their age, annual income, savings amount, and goals into the system. The server uses this information to perform AI analysis and generates specific suggestions such as "enroll in education insurance," "invest in rental property," and "invest in mutual funds with stable growth potential." These suggestions are displayed on the user's device, and the user decides whether to accept them.

[0061] By automating financial planning tasks, this system can provide users with more efficient and accurate asset management proposals than conventional manual work, allowing users to receive proposals that are optimal for their financial situation and goals.

[0062] The processing flow will be explained below.

[0063] Step 1:

[0064] Users enter their financial information (age, annual income, savings amount, financial goals, etc.) into a dedicated form.

[0065] Step 2:

[0066] The terminal encrypts the input information using a secure communication protocol and transmits it to the server.

[0067] Step 3:

[0068] The server receives the encrypted data and decrypts it to obtain the user's financial information.

[0069] Step 4:

[0070] The server retrieves historical market and customer data from a database and inputs the user's financial information into a trained AI model.

[0071] Step 5:

[0072] The server's AI model performs detailed data analysis based on the user's financial information, including predictions that take into account the user's risk tolerance and market trends.

[0073] Step 6:

[0074] Based on the analysis results, the server generates asset management and service proposals that best suit the user's financial goals, including specific investment options, insurance products, real estate investments, and more.

[0075] Step 7:

[0076] The server encrypts the generated proposal again using a secure communication protocol and transmits it to the terminal.

[0077] Step 8:

[0078] The terminal receives the encrypted proposal, decrypts it and visually displays it to the user.

[0079] Step 9:

[0080] The user can then review the displayed proposal and decide whether to accept it. If necessary, the user can also request additional questions or a new proposal with different conditions.

[0081] Step 10:

[0082] If the user accepts the suggestion, the information is fed back to the server via the terminal.

[0083] Step 11:

[0084] The server stores the user's feedback information in a database and uses it as data to update the AI ​​model.

[0085] By following the steps above, this system can provide highly accurate advice based on the user's financial information, streamlining and automating financial planning operations.

[0086] Example 1

[0087] 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."

[0088] Conventional financial planner systems have had difficulty effectively collecting users' financial information and analyzing the data securely and efficiently to provide appropriate asset management proposals. In particular, ensuring the security of user information and performing advanced data analysis that effectively utilizes past market data are required. This has resulted in users being unable to receive reliable asset management proposals.

[0089] 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.

[0090] In this invention, the server includes means for receiving financial information from a user, means for encrypting the received financial information, means for transmitting the encrypted financial information to the server using a secure communication protocol, means for decrypting the received financial information and performing data analysis based on an AI model, means for generating appropriate asset management and service proposals based on the analysis results, means for re-encrypting the generated proposals and providing them to the user using a secure communication protocol, and means for storing the proposals and user information in a database. This allows users to input their financial information under high security and receive advanced data analysis by AI based on that information, thereby enabling them to receive optimal asset management proposals in real time.

[0091] "User Terminal" means an electronic device used by a User to input financial information and display proposals received from the System, including a smartphone, tablet, PC, etc.

[0092] The "server" is the main processing device that receives financial information sent from user terminals, analyzes the data based on that information, and generates appropriate asset management proposals.

[0093] The "database" is a large-capacity data storage system that stores historical market data, customer data, analytical results, etc., and the server uses this data to train AI models and generate recommendations.

[0094] "Financial information" refers to information related to a person's financial situation that is entered by the user, and specifically includes age, annual income, savings amount, financial goals, and the like.

[0095] "Encryption" is a technology that uses special algorithms to convert a user's financial information and proposals into a format that cannot be deciphered by third parties in order to ensure information security.

[0096] A "secure communication protocol" is a communication protocol for ensuring safety in data transmission, and specifically includes HTTPS (Hypertext Transfer Protocol Secure).

[0097] An "AI model" is an algorithm and its training results that uses machine learning technology to analyze data, and is used to generate asset management proposals based on past market data and customer data.

[0098] "Data analysis" is an analytical process carried out by an AI model using historical market data and customer data based on the user's financial information.

[0099] "Asset management proposals" are investment strategies and financial plan proposals generated by the server based on the results of data analysis, and include specific options such as securities, insurance, and real estate investments.

[0100] This invention is a financial planner system that utilizes AI, which receives financial information from users, performs advanced data analysis based on that information, and generates and provides appropriate asset management and service proposals to users. The specific configuration and processing flow of the system are as follows:

[0101] System Configuration

[0102] This system consists of three main components: a user terminal, a server, and a database.

[0103] User terminal

[0104] A user terminal is a device through which a user enters their financial information and receives offers provided by the system, such as a smartphone, tablet, or PC.

[0105] server

[0106] The server is the main processing unit that receives financial information submitted by users, analyzes the data, and generates proposals. An AI model is installed on the server and trained based on the information retrieved from the database. Specifically, machine learning libraries such as TENSORFLOW (registered trademark) and PyTorch can be used.

[0107] Database

[0108] The database is a large-capacity data storage system that stores historical market data, customer data, analytical results, etc. The server uses this database to train AI models and generate advanced recommendations.

[0109] User

[0110] Users access the system and enter their financial information, such as their age, annual income, savings amount, financial goals, etc. This information is encrypted using a secure communication protocol and sent from the user's terminal to the server.

[0111] Terminal

[0112] The user terminal plays a role in encrypting the entered financial information and sending it to the server. For example, if a user enters information such as "Age: 35," "Annual income: 6 million yen," "Savings: 3 million yen," and "Goal: Secure funds for children's education," the information is encrypted and sent to the server.

[0113] server

[0114] The server decodes the received financial information and inputs it into the AI ​​model. The AI ​​model is trained based on past market and customer data stored in a database and performs detailed analysis based on the received information. As a result of the analysis, appropriate asset management and service proposals are generated. The generated proposals are expressed as recommendations for specific investments, insurance products, and financial plans in a variety of fields, including securities, insurance, and real estate investment. For example, based on the information entered by the user, recommendations such as "take out education insurance," "invest in a mutual fund that is expected to grow steadily," and "purchase a rental property" are generated.

[0115] Terminal

[0116] The server-generated proposals are then sent, again using a secure communication protocol, to the user terminal, which decrypts the proposals and visually displays them to the user.

[0117] User

[0118] The user can review the displayed suggestions and accept them if necessary. If the user accepts a suggestion, the information is fed back to the server and updated in the database. This feedback further improves the accuracy of future suggestions.

[0119] Specific examples

[0120] For example, if a user in their 30s wants to secure funds for their child's education in the near future, they input their age, annual income, savings amount, and goals into the system. The server uses this information to perform AI analysis and generates specific suggestions such as "enrolling in education insurance," "investing in rental properties," and "investing in mutual funds with stable growth potential." These suggestions are displayed on the user's device, and the user decides whether to accept them.

[0121] Prompt Sentence Examples

[0122] "Enter your financial information: age, annual income, savings amount, goals."

[0123] "Generate asset management recommendations based on AI models."

[0124] This system automates the work of financial planners and provides users with efficient and accurate asset management proposals, allowing them to receive proposals that are optimal for their financial situation and goals.

[0125] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0126] Step 1:

[0127] Users access the system using a browser or a dedicated app and enter their login information to authenticate, which allows the system to recognize the user and provide an input screen for entering individual financial information.

[0128] Input: User authentication information (username, password)

[0129] Output: Financial information input screen

[0130] Step 2:

[0131] Users input financial information such as their age, annual income, savings amount, and financial goals. For example, they input information such as "Age: 35," "Annual income: 6 million yen," "Savings: 3 million yen," and "Goal: Secure funds for children's education."

[0132] Input: User's financial information (age, annual income, savings amount, financial goals)

[0133] Output: Financial information before encryption

[0134] Step 3:

[0135] The user terminal encrypts the entered financial information using the Advanced Encryption Standard (AES).

[0136] Input: User's financial information

[0137] Output: Encrypted financial information

[0138] Step 4:

[0139] The terminal sends encrypted financial information to the server using a secure communication protocol (HTTPS).

[0140] Input: Encrypted financial information

[0141] Output: Encrypted data sent to the server

[0142] Step 5:

[0143] The server receives and decrypts the encrypted financial information, which is then retrieved as the original financial information.

[0144] Input: Encrypted financial information

[0145] Output: Decrypted financial information

[0146] Step 6:

[0147] The server preprocesses the decrypted financial information, specifically normalizing numerical data such as age and annual income and converting it into a format compatible with AI models.

[0148] Input: Decrypted financial information

[0149] Output: Preprocessed data

[0150] Step 7:

[0151] The server inputs the pre-processed data into the AI ​​model, which is trained on historical market and customer data, and generates appropriate asset management and service proposals based on the analysis.

[0152] Input: Preprocessed data

[0153] Output: Analysis results (appropriate asset management and service proposals)

[0154] Step 8:

[0155] The server encrypts the generated proposal and transmits it to the user terminal using a secure communication protocol.

[0156] Input: Analysis results (service proposal)

[0157] Output: Encrypted service proposal

[0158] Step 9:

[0159] The user terminal receives and decrypts the encrypted proposal and visually displays it to the user.

[0160] Input: Encrypted service proposal

[0161] Output: Decoded service offer

[0162] Step 10:

[0163] The user checks the displayed suggestions and accepts them if necessary. If the suggestion is accepted, the information is fed back to the system.

[0164] Input: Decoded service offer

[0165] Output: User feedback information

[0166] Step 11:

[0167] The server stores the user's feedback information in a database and uses it to make updates to improve the accuracy of future suggestions.

[0168] Input: User feedback information

[0169] Output: Updated database

[0170] In this way, the system safely and efficiently handles financial information from users and provides appropriate asset management suggestions.

[0171] (Application example 1)

[0172] 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."

[0173] Conventional financial planner systems have difficulty reflecting users' spending patterns and daily income information in real time. This has resulted in asset management and service proposals that do not accurately reflect the user's latest situation, limiting their effectiveness. Furthermore, conventional systems require users to perform multiple operations on different platforms, resulting in poor usability.

[0174] 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.

[0175] In this invention, the server includes means for receiving financial information from a user, means for performing data analysis based on the received financial information, means for generating appropriate asset management and service proposals based on the results of the data analysis, means for providing the generated proposals to the user, means for storing the proposals and user information in a database, and means for updating the proposals in real time based on the user's spending patterns and income information. This enables asset management proposals that reflect the user's latest financial situation and spending patterns in a timely manner, further improving usability.

[0176] "Financial Information" is data about a user's income, expenses, savings, investments, etc.

[0177] "Data analytics" is the process of using algorithms and models to discover patterns and make predictions using collected financial information.

[0178] "Asset management" refers to proposing optimal investment methods, insurance contracts, etc. based on the user's financial information.

[0179] "Service proposal" refers to recommending services such as financial products and insurance products based on the user's financial information.

[0180] A "secure communication protocol" is a communication method that encrypts data when sending and receiving it to prevent unauthorized access by third parties.

[0181] "Encryption" is a technique for converting data so that it cannot be understood in its original form, and only the sender and receiver can decipher the data.

[0182] "Large-scale financial data" refers to large amounts of financial-related information, such as historical market data and customer data, that are used for data analysis and training artificial intelligence models.

[0183] An "AI model" is an algorithm or model built using artificial intelligence (AI) technology to extract and analyze useful information from input data.

[0184] "Spending patterns" refer to tendencies or rules that indicate how a user spends money.

[0185] "Income information" is data indicating the user's income such as salary and bonuses.

[0186] "Means for updating proposals in real time" refers to a system function that instantly analyzes the latest collected financial information and can always provide users with optimal asset management proposals.

[0187] This invention provides a financial planner system that utilizes AI. The detailed implementation method of the system that realizes the PonPon application example is explained below.

[0188] System configuration

[0189] This system consists of three main components: a user terminal, a server, and a database.

[0190] User terminal

[0191] The user terminal is a device such as a smartphone, tablet, or PC that allows the user to enter financial information and receive proposals. The user enters their own financial information through the application and receives proposals from the system.

[0192] server

[0193] The server is the main processing unit that receives financial information submitted by users, analyzes the data, and generates proposals. The server has a generative AI model installed and trained on the information retrieved from the database. The server encrypts the data using Python's cryptography library and communicates using the requests library.

[0194] Database

[0195] The database is a large-capacity data storage that stores historical market data, customer data, analytical results, etc. The server uses this database to train AI models and generate advanced recommendations.

[0196] Program processing

[0197] 1. User enters financial information:

[0198] The user inputs information such as age, annual income, savings amount, financial goals, etc. into the application. For example, the user inputs information such as "Age: 35," "Annual income: 6 million yen," "Savings: 3 million yen," and "Goal: Secure funds for children's education."

[0199] 2. Encryption and Transmission of Financial Information:

[0200] The user's terminal encrypts the entered financial information using Python's cryptography library before sending it to the server, ensuring the security of the information.

[0201] 3. Server-based data analysis and proposal generation:

[0202] The server decodes the received financial information and inputs it into the AI ​​model, which is trained on historical market and customer data stored in a database and performs detailed analysis based on the received information. As a result of the analysis, appropriate asset management and service proposals are generated.

[0203] 4. Providing Suggestions to Users:

[0204] The generated proposals are expressed as specific investment ideas, insurance products, and financial plans in various fields such as securities, insurance, and real estate investment. For example, they include proposals such as "take out education insurance," "invest in a mutual fund with stable growth prospects," and "purchase a rental property." These proposals are then sent to the user's device using a secure communication protocol.

[0205] 5. Displaying and Accepting Proposals:

[0206] The user's device decodes the proposals sent from the server and visually displays them. The user can then review the proposals and accept them as necessary. This allows asset management proposals to always reflect the user's latest financial situation and spending patterns.

[0207] Specific examples

[0208] For example, if a user in their 30s wants to secure funds for their child's education in the near future, they input their age, annual income, savings amount, and goals into the system. The server uses this information to perform AI analysis and generates specific suggestions such as "enrolling in education insurance," "investing in rental properties," and "investing in mutual funds with stable growth potential." These suggestions are displayed on the user's device, allowing the user to decide whether to accept them.

[0209] Prompt Sentence Examples

[0210] "I'm 35 years old, earn 6 million yen a year, and have 3 million yen saved up. My goal is to secure funds for my children's education. What is the best way to manage my assets?"

[0211] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0212] Step 1:

[0213] The user enters financial information.

[0214] Input: The user enters financial information such as their age, annual income, savings amount, and financial goals into the application. Specifically, the user enters information such as "Age: 35," "Annual income: 6 million yen," "Savings: 3 million yen," and "Goal: Secure funds for children's education."

[0215] Output: The entered financial information is temporarily stored on the user's terminal.

[0216] How it works: Users enter financial information into a form provided on their smartphone, tablet, or computer screen. Once completed, the device passes the data to the next processing step.

[0217] Step 2:

[0218] The device encrypts the financial information and sends it to the server.

[0219] Input: Financial information entered by the user.

[0220] Output: Encrypted financial information is sent to the server.

[0221] How it works: The device encrypts financial information using Python's cryptography library, then uses the requests library to send the encrypted information to the server as an HTTP POST request. Before sending, the device performs a thorough encryption process to prevent information leaks.

[0222] Step 3:

[0223] The server interprets the received financial information and retrieves relevant information from a database.

[0224] Input: Encrypted financial information received by the server.

[0225] Output: Interpreted financial information and information from related databases.

[0226] How it works: The server first decrypts the encrypted data it receives, then retrieves historical market data and user data with the same attributes from a database to input the decrypted financial information into an AI model, preparing it for analysis.

[0227] Step 4:

[0228] The server analyzes the data and uses generative AI models to generate asset management and service proposals.

[0229] Input: Interpreted financial information and related information retrieved from databases.

[0230] Output: Generated asset management and service proposals.

[0231] How it works: The server inputs the interpreted financial information and related data into the AI ​​model. The model then performs advanced data analysis using predictive algorithms and historical data to generate optimal recommendations for the user. Specific recommendations include "take out education insurance," "invest in a mutual fund with stable growth potential," and "purchase a rental property."

[0232] Step 5:

[0233] The server transmits the generated proposal to the user terminal.

[0234] Input: Generated asset management and servicing proposals.

[0235] Output: Proposal data sent to the user device.

[0236] Specific operation: The server re-encrypts the generated proposal and sends it to the user device using a secure communication protocol, taking care to ensure the integrity and confidentiality of the information.

[0237] Step 6:

[0238] The terminal interprets the suggestions and displays them visually to the user.

[0239] Input: Encrypted proposal data sent by the server.

[0240] Output: The decoded proposal in a format that can be viewed by the user.

[0241] Specific operation: After the user device decrypts the received encrypted data, it visually displays the decrypted suggestions to the user. For example, the suggestions may be displayed on the smartphone app screen in the form of "Enroll in education insurance" or "Invest in mutual funds."

[0242] Step 7:

[0243] The user reviews the proposal and accepts it if necessary.

[0244] Input: Proposal data, decrypted and displayed.

[0245] Output: The user's selection.

[0246] What it does: The user reviews the suggestions and chooses whether to accept them. If accepted, the information is fed back from the device to the server and updated in the database. This feedback improves the accuracy of future suggestions.

[0247] 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.

[0248] This invention combines an emotion engine with an AI financial planner system that analyzes data based on a user's financial information and generates appropriate asset management and service proposals. Specifically, the goal is to provide more personalized services by recognizing the user's emotional state and utilizing that information in data analysis and proposal generation.

[0249] System Configuration

[0250] This system consists of four main components: a user terminal, a server, a database, and an emotion engine.

[0251] User terminal

[0252] A user terminal is a device through which a user enters their financial information and receives offers provided by the system, such as a smartphone, tablet, or PC.

[0253] server

[0254] The server is the main processing unit that receives the financial information submitted by users, performs data analysis, and generates proposals. The server has an AI model installed and is trained on the information retrieved from the database.

[0255] Database

[0256] The database is a large-capacity data storage system that stores historical market data, customer data, analytical results, etc. The server uses this database to train AI models and generate advanced recommendations.

[0257] Emotion Engine

[0258] The emotion engine is a system that recognizes the user's emotional state and reflects that information in data analysis and proposal generation. The emotion engine recognizes emotions by analyzing the user's facial expressions, voice, input patterns, etc.

[0259] Processing flow

[0260] The specific processing flow centered on the server, terminal, and user will be explained below.

[0261] User

[0262] Users access the system and enter their financial information, such as their age, annual income, savings amount, financial goals, etc. This information is encrypted using a secure communication protocol and sent from the user's terminal to the server.

[0263] Terminal

[0264] The user device encrypts the entered financial information and transmits it to the server. The device is also equipped with an emotion engine that analyzes the user's facial expressions and voice in real time to recognize their emotional state.

[0265] server

[0266] The server inputs the received financial information and the user's emotional state information into the AI ​​model. The AI ​​model is trained based on historical market and customer data stored in a database and performs detailed data analysis. As a result of the analysis, asset management and service proposals that are best suited to the user's financial goals and emotional state are generated.

[0267] The generated proposals are expressed as recommendations for specific investments, insurance products, and financial plans in various fields such as securities, insurance, real estate investment, etc. For example, if a user has an emotional state of "feeling anxious about risk," more stable investments will be suggested to alleviate that anxiety.

[0268] Terminal

[0269] The proposals generated by the server are again encrypted using a secure communication protocol and sent to the terminal, where they are decrypted and visually displayed to the user.

[0270] User

[0271] The user can review the displayed suggestions and accept them if necessary. For example, suggestions such as "take out education insurance," "invest in a mutual fund that is expected to grow steadily," and "purchase a rental property" are displayed. The user can decide whether to accept the suggestion that best suits their emotional state.

[0272] server

[0273] If the user accepts a suggestion, the information is fed back to the server via the device and stored in a database, allowing future suggestions to be more accurate.

[0274] Specific examples

[0275] For example, if a user in their 30s wants to secure funds for their children's future education, they can input their age, annual income, savings amount, and goals into the system. Furthermore, if the user expresses "anxiety about education expenses" through the emotion engine, the server will take this into consideration and suggest low-risk investment options and stable insurance products. This allows users to create an optimal asset management plan that suits their emotional state.

[0276] This system automates financial planning tasks while providing personalized proposals that take into account the user's emotions. This allows users to receive optimal proposals based on their financial situation and emotional state, allowing them to manage their assets with peace of mind.

[0277] The processing flow will be explained below.

[0278] Step 1:

[0279] Users enter their financial information (age, annual income, savings amount, financial goals, etc.) into a dedicated form. The emotion engine also collects the user's facial expressions and voice in real time.

[0280] Step 2:

[0281] The terminal encrypts the entered financial information and emotion data using a secure communication protocol and transmits it to the server.

[0282] Step 3:

[0283] The server receives the encrypted data and decrypts it to obtain the user's financial information and emotional state.

[0284] Step 4:

[0285] The server retrieves historical market and customer data from a database and inputs users' financial and emotional information into a trained AI model.

[0286] Step 5:

[0287] The server's AI model performs detailed data analysis based on the user's financial and emotional information, including predictions that take into account the user's financial goals, risk tolerance, and emotional state.

[0288] Step 6:

[0289] Based on the analysis results, the server generates asset management and service recommendations that best suit the user's financial goals and emotional state, including specific investment options, insurance products, real estate investments, and more.

[0290] Step 7:

[0291] The server encrypts the generated proposal again using a secure communication protocol and transmits it to the terminal.

[0292] Step 8:

[0293] The device receives the encrypted proposal, decrypts it, and visually displays it to the user, including details of recommended financial plans and insurance products.

[0294] Step 9:

[0295] The user can review the displayed proposal and decide whether to accept it. If necessary, they can also request additional questions or a new proposal with different conditions.

[0296] Step 10:

[0297] If the user accepts the suggestion, the information is fed back to the server via the terminal.

[0298] Step 11:

[0299] The server stores the user's feedback information in a database and uses it as data to update the AI ​​model.

[0300] Through the above steps, the system provides highly accurate advice based on the user's financial information, streamlining and automating financial planning work, while also using an emotion engine to make personalized proposals that adapt to the user's emotional state.

[0301] Example 2

[0302] 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."

[0303] Conventional financial planning systems can make asset management proposals based on a user's financial information, but they cannot make proposals that take into account the user's emotional state. As a result, personalized proposals based on the user's emotional state are not provided, making it difficult to realize proposals that fully reflect the user's anxiety and sensitivity to risk. The present invention aims to solve these problems and provide a system that makes financial proposals that take into account the user's emotional state.

[0304] 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.

[0305] In this invention, the server includes means for receiving financial information and an emotional state from a user, means for performing data analysis based on the received financial information and emotional state, and means for generating appropriate asset management and service proposals based on the results of the data analysis, thereby enabling personalized asset management and service proposals that take the user's emotional state into consideration.

[0306] A "user" is any person or entity that accesses the system to input financial information and emotional state and receive recommendations.

[0307] "Financial information" refers to economic information such as a user's age, annual income, savings, and financial goals.

[0308] "Emotional state" refers to the psychological state recognized from the user's facial expression, voice, input pattern, etc.

[0309] "Secure communication protocol" refers to technology (e.g., TLS and SSL) that encrypts data and enables secure communication.

[0310] "Data analytics" refers to the process of using AI models and algorithms to conduct detailed analysis based on received financial information and emotional state.

[0311] "AI model" refers to an artificial intelligence algorithm that is trained based on past data and can perform a specific task (e.g., data analysis, recommendation generation).

[0312] "Wealth Management and Service Recommendations" refers to recommended investment products, insurance, and other plans generated based on the user's financial information and emotional state.

[0313] "Database" refers to a data storage system for storing historical market data, customer data, analytical results, etc.

[0314] "Feedback" refers to data that is sent back to the server regarding the suggestions that the user has accepted, and that is used to improve the system.

[0315] This invention combines an emotion engine with an AI financial planner system that analyzes data based on a user's financial information and generates appropriate asset management and service proposals. Specifically, the goal is to provide more personalized services by recognizing the user's emotional state and utilizing that information in data analysis and proposal generation.

[0316] System Configuration

[0317] This system consists of four main components: a user terminal, a server, a database, and an emotion engine.

[0318] User terminal

[0319] The user terminal is a device where the user enters their financial information and receives proposals provided by the system. It can be a smartphone, tablet, or PC. The terminal is equipped with an emotion engine that analyzes the user's facial expressions and voice in real time to recognize their emotional state.

[0320] server

[0321] The server is the main processing unit that receives financial information and emotional states submitted by users, analyzes the data, and generates recommendations. AI models trained with deep learning frameworks such as TensorFlow and PyTorch are installed on the server. The server performs analysis based on the information retrieved from the database.

[0322] Database

[0323] The database is a large-capacity data storage system that stores historical market data, customer data, analytical results, etc. The database uses MySQL (registered trademark), PostgreSQL, or NoSQL databases such as MongoDB. The server uses this database to train AI models and generate advanced recommendations.

[0324] Emotion Engine

[0325] The emotion engine is a system that recognizes the user's emotional state and reflects that information in data analysis and proposal generation. The emotion engine analyzes the user's facial expressions, voice, input patterns, etc. to recognize emotions.

[0326] Specific examples

[0327] For example, if a user in their 30s wants to secure funds for their children's future education, they can input their age, annual income, savings amount, and goals into the system. Furthermore, if the user expresses "anxiety about education expenses" through the emotion engine, the server will take this into consideration and suggest low-risk investment options and stable insurance products. This allows users to create an optimal asset management plan according to their emotional state.

[0328] Prompt Sentence Examples

[0329] User age: 35

[0330] Annual income: 8 million yen

[0331] Savings: 5 million yen

[0332] Financial goal: I want to save 3 million yen for my children's education.

[0333] Emotional state: Anxiety about education costs

[0334] Recommendations: Low-risk investment options and stable insurance products

[0335] This system automates financial planning tasks while providing personalized proposals that take into account the user's emotions. This allows users to receive optimal proposals based on their financial situation and emotional state, allowing them to manage their assets with peace of mind.

[0336] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0337] System program processing flow

[0338] The processing flow of the system program will be explained below by dividing it into specific steps.

[0339] Step 1: Enter your user information

[0340] User

[0341] Users access the system using a device, launching a browser or a dedicated app and entering financial information such as age, annual income, savings amount, and financial goals.

[0342] input

[0343] Age, annual income, savings, financial goals

[0344] output

[0345] Financial Information Data

[0346] Step 2: Encrypt and send information

[0347] Terminal

[0348] The terminal encrypts the financial information entered by the user using the Transport Layer Security (TLS) protocol, and then sends the encrypted financial information to the server over a secure communication channel using an HTTP POST request.

[0349] input

[0350] Financial Information Data

[0351] output

[0352] Encrypted financial data

[0353] Step 3: Analyze emotional state

[0354] Terminal

[0355] The emotion engine installed in the device captures and analyzes the user's facial expressions and voice data in real time, and determines the user's emotional state using facial recognition and voice analysis technologies.

[0356] input

[0357] User's facial expression data, voice data

[0358] output

[0359] Emotional state data

[0360] Step 4: Receive data and prepare for analysis

[0361] server

[0362] The server receives the encrypted data sent from the terminal and decrypts it using the TLS protocol. The server retrieves historical market data and customer data from a database (e.g., MySQL, PostgreSQL).

[0363] input

[0364] Encrypted financial data

[0365] output

[0366] Decoded financial information data, historical data

[0367] Step 5: Data analysis and proposal generation

[0368] server

[0369] The server inputs the decoded financial information and emotional state data into an AI model, which then analyzes the data and generates optimal asset management and service recommendations. The AI ​​models used include TensorFlow and PyTorch.

[0370] input

[0371] Decoded financial information data, emotional state data, historical data

[0372] output

[0373] Asset management and service proposals

[0374] Step 6: Encrypt and send the proposal

[0375] server

[0376] The server encrypts the generated proposal again using the TLS protocol and sends the encrypted proposal data to the user's device using an HTTP POST request.

[0377] input

[0378] Asset management and service proposals

[0379] output

[0380] Encrypted proposal data

[0381] Step 7: Viewing Proposals

[0382] Terminal

[0383] The user device decrypts the received encrypted proposal and visually displays it to the user. Specifically, the proposal content is presented to the user in the form of graphs or charts using a JavaScript (registered trademark) library (e.g., D3.js, Chart.js).

[0384] input

[0385] Encrypted proposal data

[0386] output

[0387] Visualized proposal

[0388] Step 8: Review and feedback on proposals

[0389] User

[0390] The user checks the displayed proposals and selects whether to accept the proposals, such as "take out education insurance" or "invest in a mutual fund that is expected to grow steadily." The user's selection is sent to the server as feedback data.

[0391] input

[0392] Visualized proposal

[0393] output

[0394] Feedback Data

[0395] Step 9: Saving feedback and improving the model

[0396] server

[0397] The server receives feedback data from users and stores it in a database. The feedback information is used to retrain the AI ​​model and improve the accuracy of future suggestions.

[0398] input

[0399] Feedback Data

[0400] output

[0401] Updated database, improved AI models

[0402] (Application example 2)

[0403] 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."

[0404] While conventional financial planner systems can provide appropriate asset management and service proposals based on a user's financial information, they have a problem in that they cannot take into account the user's emotional state and therefore have a low level of personalization. In particular, when a user is feeling stressed or anxious, it is difficult to provide appropriate proposals according to that situation.

[0405] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving financial information from a user, means for recognizing the user's emotional state using an emotion engine, and means for performing data analysis based on the received financial information and emotional state information. This enables personalized asset management and service proposals that take the user's emotional state into consideration.

[0406] "User" refers to a person who uses the service and provides financial information and emotional state to the system.

[0407] "Financial Information" refers to financial data such as a user's age, annual income, savings, and financial goals.

[0408] An "emotion engine" is a system that analyzes a user's facial expressions, voice, input patterns, etc. to recognize their emotional state.

[0409] "Emotional state" refers to the psychological state that a user feels, such as stress, anxiety, relief, or joy.

[0410] "Data Analysis" refers to the process by which the system performs analysis based on the received financial and emotional state information.

[0411] "Asset management" refers to the process of carrying out financial plans, such as investing and purchasing financial products, to increase a user's wealth.

[0412] "Service proposal" refers to a proposal that provides the user with the most suitable financial products, investment plans, etc.

[0413] "Database" refers to a large-scale data storage system that stores historical market data, customer data, proposal data, etc.

[0414] A "generative AI model" refers to an artificial intelligence model that is trained based on past data and generates suggestions for users.

[0415] A "secure communication protocol" refers to a communication method for securely encrypting and sending and receiving information.

[0416] System Configuration

[0417] The system for implementing this invention consists of four main components: a user terminal, a server, a database, and an emotion engine.

[0418] User terminal

[0419] The user terminal is the device where the user enters financial information and receives proposals provided by the system. It can be a smartphone, tablet, or PC. The terminal is equipped with an emotion-recognition camera and microphone, which analyzes the user's facial expressions and voice in real time to recognize their emotional state.

[0420] server

[0421] The server is the main processing unit that receives the financial and emotional state information sent by the user and performs data analysis. The server is installed with a generative AI model that is trained based on the received information. The generated proposals are then sent back to the user's device.

[0422] Database

[0423] The database is a storage system for large amounts of financial data, including market data, customer data, and historical proposal data, on which generative AI models are trained.

[0424] Emotion Engine

[0425] The emotion engine is a system that recognizes the user's emotional state and reflects it in data analysis and proposal generation. It analyzes facial expressions, voice, and input patterns to determine the user's emotional state. This information is sent to the server along with the user's financial information.

[0426] Processing flow

[0427] The server performs the process in the following procedure.

[0428] 1. Entering financial information: Users access the system and enter their financial information such as age, annual income, savings amount, financial goals, etc. This information is encrypted using a secure communication protocol and sent to the server.

[0429] 2. Emotional state recognition: The emotion engine on the user device analyzes the user's facial expressions and voice in real time to recognize the user's emotional state. The results are also sent to the server.

[0430] 3. Data analysis and proposal generation: The server inputs the received financial information and emotional state information into the AI ​​model and performs data analysis. Based on the analysis results, appropriate asset management and service proposals are generated.

[0431] 4. Proposal transmission and display: The generated proposal is again encrypted using a secure communication protocol and sent to the user's device, which decrypts it and visually displays it.

[0432] 5. Feedback storage: If the user accepts the suggestion, the information is fed back to the server and stored in the database, which will improve the accuracy of future suggestions.

[0433] Specific examples

[0434] For example, a user accesses the system via their smartphone and enters financial information such as their age and income. If the user is feeling stressed, the emotion engine recognizes this and sends it to the server. The server then uses this information to suggest stable investment options that reduce risk. Taking the user's emotional state into account provides a more personalized financial management plan.

[0435] Prompt Sentence Examples

[0436] "Generate purchasing advice for users when they are stressed. Output suggestions based on the following criteria:

[0437] The user is feeling stressed.

[0438] I have a strong desire to buy, but I want to avoid waste.

[0439] Please give me some example sentences for the suggestions you would like to generate.

[0440] This enables highly personalized suggestions that reflect the user's emotional state.

[0441] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0442] Step 1:

[0443] Users access the system using devices such as smartphones or PCs and enter financial information such as their age, annual income, savings amount, financial goals, etc. This information is encrypted on the device and sent to the server using a secure communication protocol.

[0444] Input: User's financial information (age, annual income, savings, financial goals, etc.)

[0445] Output: Encrypted financial information sent to the server

[0446] Step 2:

[0447] The emotion engine installed in the device recognizes the user's emotional state by analyzing the user's real-time facial expressions and voice. For example, the device's camera captures the user's facial expressions, and the emotion engine analyzes the data to identify the user's emotional state.

[0448] Input: User's facial expression data and voice data

[0449] Output: Recognized emotional state data

[0450] Step 3:

[0451] The emotional state recognized by the emotion engine is encrypted and transmitted to the server.

[0452] Input: Emotional state data

[0453] Output: Encrypted emotional state data is sent to the server

[0454] Step 4:

[0455] The server inputs the received financial information and emotional state information into the generative AI model and performs data analysis. The generative AI model generates optimal asset management and service proposals based on the input data, taking into account the user's emotional state.

[0456] Input: Financial and emotional state information

[0457] Output: Generated asset management and service proposals

[0458] Step 5:

[0459] The server encrypts the generated proposal using a secure communication protocol and transmits it to the user terminal.

[0460] Input: Asset management and service proposals

[0461] Output: The encrypted proposal data is sent to the device.

[0462] Step 6:

[0463] The user device decrypts the encrypted proposal data received from the server and visually displays it to the user, for example, on the screen of a smartphone or PC.

[0464] Input: Encrypted proposal data

[0465] Output: A visual representation of the proposal

[0466] Step 7:

[0467] The user reviews the displayed suggestions and accepts them if necessary, and the feedback is sent back to the server using a secure communication protocol.

[0468] Input: User feedback

[0469] Output: Encrypted feedback data is sent to the server

[0470] Step 8:

[0471] The server receives user feedback and stores it in a database, which is used as training data to improve the accuracy of future suggestions.

[0472] Input: Feedback data

[0473] Output: Feedback data stored in a database

[0474] 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.

[0475] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (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.

[0476] 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.

[0477] [Second embodiment]

[0478] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0479] 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.

[0480] 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).

[0481] 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.

[0482] 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.

[0483] 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).

[0484] 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.

[0485] 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.

[0486] 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.

[0487] 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.

[0488] In the smart glasses 214, the 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.

[0489] 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."

[0490] This invention is a financial planner system that utilizes AI, which receives financial information from users, performs advanced data analysis based on that information, and generates and provides appropriate asset management and service proposals to users. The specific configuration and processing flow of the system are as follows:

[0491] System Configuration

[0492] This system consists of three main components: a user terminal, a server, and a database.

[0493] User terminal

[0494] A user terminal is a device through which a user enters their financial information and receives offers provided by the system, such as a smartphone, tablet, or PC.

[0495] server

[0496] The server is the main processing unit that receives the financial information submitted by users, performs data analysis, and generates proposals. The server has an AI model installed and is trained on the information retrieved from the database.

[0497] Database

[0498] The database is a large-capacity data storage system that stores historical market data, customer data, analytical results, etc. The server uses this database to train AI models and generate advanced recommendations.

[0499] Processing flow

[0500] The specific processing flow centered on the server, terminal, and user will be explained below.

[0501] User

[0502] Users access the system and enter their financial information, such as their age, annual income, savings amount, financial goals, etc. This information is encrypted using a secure communication protocol and sent from the user's terminal to the server.

[0503] Terminal

[0504] The user terminal plays a role in encrypting the entered financial information and sending it to the server. For example, if a user enters information such as "Age: 35," "Annual income: 6 million yen," "Savings: 3 million yen," and "Goal: Secure funds for children's education," the information is encrypted and sent to the server.

[0505] server

[0506] The server decodes the received financial information and inputs it into the AI ​​model, which is trained on historical market and customer data stored in a database and performs detailed analysis based on the received information. As a result of the analysis, appropriate asset management and service proposals are generated.

[0507] The generated proposals are expressed as recommendations for specific investments, insurance products, and financial plans in a variety of fields, including securities, insurance, and real estate investment. For example, based on the user's input information, the system may generate proposals such as "take out education insurance," "invest in a mutual fund that is expected to grow steadily," or "purchase a rental property."

[0508] Terminal

[0509] The server-generated proposals are then sent, again using a secure communication protocol, to the user terminal, which decrypts the proposals and visually displays them to the user.

[0510] User

[0511] The user can review the displayed suggestions and accept them if necessary. If the user accepts a suggestion, the information is fed back to the server and updated in the database. This feedback further improves the accuracy of future suggestions.

[0512] Specific examples

[0513] For example, if a user in their 30s wants to secure funds for their child's education in the near future, they input their age, annual income, savings amount, and goals into the system. The server uses this information to perform AI analysis and generates specific suggestions such as "enroll in education insurance," "invest in rental property," and "invest in mutual funds with stable growth potential." These suggestions are displayed on the user's device, and the user decides whether to accept them.

[0514] By automating financial planning tasks, this system can provide users with more efficient and accurate asset management proposals than conventional manual work, allowing users to receive proposals that are optimal for their financial situation and goals.

[0515] The processing flow will be explained below.

[0516] Step 1:

[0517] Users enter their financial information (age, annual income, savings amount, financial goals, etc.) into a dedicated form.

[0518] Step 2:

[0519] The terminal encrypts the input information using a secure communication protocol and transmits it to the server.

[0520] Step 3:

[0521] The server receives the encrypted data and decrypts it to obtain the user's financial information.

[0522] Step 4:

[0523] The server retrieves historical market and customer data from a database and inputs the user's financial information into a trained AI model.

[0524] Step 5:

[0525] The server's AI model performs detailed data analysis based on the user's financial information, including predictions that take into account the user's risk tolerance and market trends.

[0526] Step 6:

[0527] Based on the analysis results, the server generates asset management and service proposals that best suit the user's financial goals, including specific investment options, insurance products, real estate investments, and more.

[0528] Step 7:

[0529] The server encrypts the generated proposal again using a secure communication protocol and transmits it to the terminal.

[0530] Step 8:

[0531] The terminal receives the encrypted proposal, decrypts it and visually displays it to the user.

[0532] Step 9:

[0533] The user can then review the displayed proposal and decide whether to accept it. If necessary, the user can also request additional questions or a new proposal with different conditions.

[0534] Step 10:

[0535] If the user accepts the suggestion, the information is fed back to the server via the terminal.

[0536] Step 11:

[0537] The server stores the user's feedback information in a database and uses it as data to update the AI ​​model.

[0538] By following the steps above, this system can provide highly accurate advice based on the user's financial information, streamlining and automating financial planning operations.

[0539] Example 1

[0540] 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."

[0541] Conventional financial planner systems have had difficulty effectively collecting users' financial information and analyzing the data securely and efficiently to provide appropriate asset management proposals. In particular, ensuring the security of user information and performing advanced data analysis that effectively utilizes past market data are required. This has resulted in users being unable to receive reliable asset management proposals.

[0542] 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.

[0543] In this invention, the server includes means for receiving financial information from a user, means for encrypting the received financial information, means for transmitting the encrypted financial information to the server using a secure communication protocol, means for decrypting the received financial information and performing data analysis based on an AI model, means for generating appropriate asset management and service proposals based on the analysis results, means for re-encrypting the generated proposals and providing them to the user using a secure communication protocol, and means for storing the proposals and user information in a database. This allows users to input their financial information under high security and receive advanced data analysis by AI based on that information, thereby enabling them to receive optimal asset management proposals in real time.

[0544] "User Terminal" means an electronic device used by a User to input financial information and display proposals received from the System, including a smartphone, tablet, PC, etc.

[0545] The "server" is the main processing device that receives financial information sent from user terminals, analyzes the data based on that information, and generates appropriate asset management proposals.

[0546] The "database" is a large-capacity data storage system that stores historical market data, customer data, analytical results, etc., and the server uses this data to train AI models and generate recommendations.

[0547] "Financial information" refers to information related to a person's financial situation that is entered by the user, and specifically includes age, annual income, savings amount, financial goals, and the like.

[0548] "Encryption" is a technology that uses special algorithms to convert a user's financial information and proposals into a format that cannot be deciphered by third parties in order to ensure information security.

[0549] A "secure communication protocol" is a communication protocol for ensuring safety in data transmission, and specifically includes HTTPS (Hypertext Transfer Protocol Secure).

[0550] An "AI model" is an algorithm and its training results that uses machine learning technology to analyze data, and is used to generate asset management proposals based on past market data and customer data.

[0551] "Data analysis" is an analytical process carried out by an AI model using historical market data and customer data based on the user's financial information.

[0552] "Asset management proposals" are investment strategies and financial plan proposals generated by the server based on the results of data analysis, and include specific options such as securities, insurance, and real estate investments.

[0553] This invention is a financial planner system that utilizes AI, which receives financial information from users, performs advanced data analysis based on that information, and generates and provides appropriate asset management and service proposals to users. The specific configuration and processing flow of the system are as follows:

[0554] System Configuration

[0555] This system consists of three main components: a user terminal, a server, and a database.

[0556] User terminal

[0557] A user terminal is a device through which a user enters their financial information and receives offers provided by the system, such as a smartphone, tablet, or PC.

[0558] server

[0559] The server is the main processing unit that receives financial information submitted by users, analyzes the data, and generates proposals. An AI model is installed on the server and trained based on the information retrieved from the database. Specifically, machine learning libraries such as TensorFlow or PyTorch can be used.

[0560] Database

[0561] The database is a large-capacity data storage system that stores historical market data, customer data, analytical results, etc. The server uses this database to train AI models and generate advanced recommendations.

[0562] User

[0563] Users access the system and enter their financial information, such as their age, annual income, savings amount, financial goals, etc. This information is encrypted using a secure communication protocol and sent from the user's terminal to the server.

[0564] Terminal

[0565] The user terminal plays a role in encrypting the entered financial information and sending it to the server. For example, if a user enters information such as "Age: 35," "Annual income: 6 million yen," "Savings: 3 million yen," and "Goal: Secure funds for children's education," the information is encrypted and sent to the server.

[0566] server

[0567] The server decodes the received financial information and inputs it into the AI ​​model. The AI ​​model is trained based on past market and customer data stored in a database and performs detailed analysis based on the received information. As a result of the analysis, appropriate asset management and service proposals are generated. The generated proposals are expressed as recommendations for specific investments, insurance products, and financial plans in a variety of fields, including securities, insurance, and real estate investment. For example, based on the information entered by the user, recommendations such as "take out education insurance," "invest in a mutual fund that is expected to grow steadily," and "purchase a rental property" are generated.

[0568] Terminal

[0569] The server-generated proposals are then sent, again using a secure communication protocol, to the user terminal, which decrypts the proposals and visually displays them to the user.

[0570] User

[0571] The user can review the displayed suggestions and accept them if necessary. If the user accepts a suggestion, the information is fed back to the server and updated in the database. This feedback further improves the accuracy of future suggestions.

[0572] Specific examples

[0573] For example, if a user in their 30s wants to secure funds for their child's education in the near future, they input their age, annual income, savings amount, and goals into the system. The server uses this information to perform AI analysis and generates specific suggestions such as "enrolling in education insurance," "investing in rental properties," and "investing in mutual funds with stable growth potential." These suggestions are displayed on the user's device, and the user decides whether to accept them.

[0574] Prompt Sentence Examples

[0575] "Enter your financial information: age, annual income, savings amount, goals."

[0576] "Generate asset management recommendations based on AI models."

[0577] This system automates the work of financial planners and provides users with efficient and accurate asset management proposals, allowing them to receive proposals that are optimal for their financial situation and goals.

[0578] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0579] Step 1:

[0580] Users access the system using a browser or a dedicated app and enter their login information to authenticate, which allows the system to recognize the user and provide an input screen for entering individual financial information.

[0581] Input: User authentication information (username, password)

[0582] Output: Financial information input screen

[0583] Step 2:

[0584] Users input financial information such as their age, annual income, savings amount, and financial goals. For example, they input information such as "Age: 35," "Annual income: 6 million yen," "Savings: 3 million yen," and "Goal: Secure funds for children's education."

[0585] Input: User's financial information (age, annual income, savings amount, financial goals)

[0586] Output: Financial information before encryption

[0587] Step 3:

[0588] The user terminal encrypts the entered financial information using the Advanced Encryption Standard (AES).

[0589] Input: User's financial information

[0590] Output: Encrypted financial information

[0591] Step 4:

[0592] The terminal sends encrypted financial information to the server using a secure communication protocol (HTTPS).

[0593] Input: Encrypted financial information

[0594] Output: Encrypted data sent to the server

[0595] Step 5:

[0596] The server receives and decrypts the encrypted financial information, which is then retrieved as the original financial information.

[0597] Input: Encrypted financial information

[0598] Output: Decrypted financial information

[0599] Step 6:

[0600] The server preprocesses the decrypted financial information, specifically normalizing numerical data such as age and annual income and converting it into a format compatible with AI models.

[0601] Input: Decrypted financial information

[0602] Output: Preprocessed data

[0603] Step 7:

[0604] The server inputs the pre-processed data into the AI ​​model, which is trained on historical market and customer data, and generates appropriate asset management and service proposals based on the analysis.

[0605] Input: Preprocessed data

[0606] Output: Analysis results (appropriate asset management and service proposals)

[0607] Step 8:

[0608] The server encrypts the generated proposal and transmits it to the user terminal using a secure communication protocol.

[0609] Input: Analysis results (service proposal)

[0610] Output: Encrypted service proposal

[0611] Step 9:

[0612] The user terminal receives and decrypts the encrypted proposal and visually displays it to the user.

[0613] Input: Encrypted service proposal

[0614] Output: Decoded service offer

[0615] Step 10:

[0616] The user checks the displayed suggestions and accepts them if necessary. If the suggestion is accepted, the information is fed back to the system.

[0617] Input: Decoded service offer

[0618] Output: User feedback information

[0619] Step 11:

[0620] The server stores the user's feedback information in a database and uses it to make updates to improve the accuracy of future suggestions.

[0621] Input: User feedback information

[0622] Output: Updated database

[0623] In this way, the system safely and efficiently handles financial information from users and provides appropriate asset management suggestions.

[0624] (Application example 1)

[0625] 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."

[0626] Conventional financial planner systems have difficulty reflecting users' spending patterns and daily income information in real time. This has resulted in asset management and service proposals that do not accurately reflect the user's latest situation, limiting their effectiveness. Furthermore, conventional systems require users to perform multiple operations on different platforms, resulting in poor usability.

[0627] 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.

[0628] In this invention, the server includes means for receiving financial information from a user, means for performing data analysis based on the received financial information, means for generating appropriate asset management and service proposals based on the results of the data analysis, means for providing the generated proposals to the user, means for storing the proposals and user information in a database, and means for updating the proposals in real time based on the user's spending patterns and income information. This enables asset management proposals that reflect the user's latest financial situation and spending patterns in a timely manner, further improving usability.

[0629] "Financial Information" is data about a user's income, expenses, savings, investments, etc.

[0630] "Data analytics" is the process of using algorithms and models to discover patterns and make predictions using collected financial information.

[0631] "Asset management" refers to proposing optimal investment methods, insurance contracts, etc. based on the user's financial information.

[0632] "Service proposal" refers to recommending services such as financial products and insurance products based on the user's financial information.

[0633] A "secure communication protocol" is a communication method that encrypts data when sending and receiving it to prevent unauthorized access by third parties.

[0634] "Encryption" is a technique for converting data so that it cannot be understood in its original form, and only the sender and receiver can decipher the data.

[0635] "Large-scale financial data" refers to large amounts of financial-related information, such as historical market data and customer data, that are used for data analysis and training artificial intelligence models.

[0636] An "AI model" is an algorithm or model built using artificial intelligence (AI) technology to extract and analyze useful information from input data.

[0637] "Spending patterns" refer to tendencies or rules that indicate how a user spends money.

[0638] "Income information" is data indicating the user's income such as salary and bonuses.

[0639] "Means for updating proposals in real time" refers to a system function that instantly analyzes the latest collected financial information and can always provide users with optimal asset management proposals.

[0640] This invention provides a financial planner system that utilizes AI. The detailed implementation method of the system that realizes the PonPon application example is explained below.

[0641] System configuration

[0642] This system consists of three main components: a user terminal, a server, and a database.

[0643] User terminal

[0644] The user terminal is a device such as a smartphone, tablet, or PC that allows the user to enter financial information and receive proposals. The user enters their own financial information through the application and receives proposals from the system.

[0645] server

[0646] The server is the main processing unit that receives financial information submitted by users, analyzes the data, and generates proposals. The server has a generative AI model installed and trained on the information retrieved from the database. The server encrypts the data using Python's cryptography library and communicates using the requests library.

[0647] Database

[0648] The database is a large-capacity data storage that stores historical market data, customer data, analytical results, etc. The server uses this database to train AI models and generate advanced recommendations.

[0649] Program processing

[0650] 1. User enters financial information:

[0651] The user inputs information such as age, annual income, savings amount, financial goals, etc. into the application. For example, the user inputs information such as "Age: 35," "Annual income: 6 million yen," "Savings: 3 million yen," and "Goal: Secure funds for children's education."

[0652] 2. Encryption and Transmission of Financial Information:

[0653] The user's terminal encrypts the entered financial information using Python's cryptography library before sending it to the server, ensuring the security of the information.

[0654] 3. Server-based data analysis and proposal generation:

[0655] The server decodes the received financial information and inputs it into the AI ​​model, which is trained on historical market and customer data stored in a database and performs detailed analysis based on the received information. As a result of the analysis, appropriate asset management and service proposals are generated.

[0656] 4. Providing Suggestions to Users:

[0657] The generated proposals are expressed as specific investment ideas, insurance products, and financial plans in various fields such as securities, insurance, and real estate investment. For example, they include proposals such as "take out education insurance," "invest in a mutual fund with stable growth prospects," and "purchase a rental property." These proposals are then sent to the user's device using a secure communication protocol.

[0658] 5. Displaying and Accepting Proposals:

[0659] The user's device decodes the proposals sent from the server and visually displays them. The user can then review the proposals and accept them as necessary. This allows asset management proposals to always reflect the user's latest financial situation and spending patterns.

[0660] Specific examples

[0661] For example, if a user in their 30s wants to secure funds for their child's education in the near future, they input their age, annual income, savings amount, and goals into the system. The server uses this information to perform AI analysis and generates specific suggestions such as "enrolling in education insurance," "investing in rental properties," and "investing in mutual funds with stable growth potential." These suggestions are displayed on the user's device, allowing the user to decide whether to accept them.

[0662] Prompt Sentence Examples

[0663] "I'm 35 years old, earn 6 million yen a year, and have 3 million yen saved up. My goal is to secure funds for my children's education. What is the best way to manage my assets?"

[0664] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0665] Step 1:

[0666] The user enters financial information.

[0667] Input: The user enters financial information such as their age, annual income, savings amount, and financial goals into the application. Specifically, the user enters information such as "Age: 35," "Annual income: 6 million yen," "Savings: 3 million yen," and "Goal: Secure funds for children's education."

[0668] Output: The entered financial information is temporarily stored on the user's terminal.

[0669] How it works: Users enter financial information into a form provided on their smartphone, tablet, or computer screen. Once completed, the device passes the data to the next processing step.

[0670] Step 2:

[0671] The device encrypts the financial information and sends it to the server.

[0672] Input: Financial information entered by the user.

[0673] Output: Encrypted financial information is sent to the server.

[0674] How it works: The device encrypts financial information using Python's cryptography library, then uses the requests library to send the encrypted information to the server as an HTTP POST request. Before sending, the device performs a thorough encryption process to prevent information leaks.

[0675] Step 3:

[0676] The server interprets the received financial information and retrieves relevant information from a database.

[0677] Input: Encrypted financial information received by the server.

[0678] Output: Interpreted financial information and information from related databases.

[0679] How it works: The server first decrypts the encrypted data it receives, then retrieves historical market data and user data with the same attributes from a database to input the decrypted financial information into an AI model, preparing it for analysis.

[0680] Step 4:

[0681] The server analyzes the data and uses generative AI models to generate asset management and service proposals.

[0682] Input: Interpreted financial information and related information retrieved from databases.

[0683] Output: Generated asset management and service proposals.

[0684] How it works: The server inputs the interpreted financial information and related data into the AI ​​model. The model then performs advanced data analysis using predictive algorithms and historical data to generate optimal recommendations for the user. Specific recommendations include "take out education insurance," "invest in a mutual fund with stable growth potential," and "purchase a rental property."

[0685] Step 5:

[0686] The server transmits the generated proposal to the user terminal.

[0687] Input: Generated asset management and servicing proposals.

[0688] Output: Proposal data sent to the user device.

[0689] Specific operation: The server re-encrypts the generated proposal and sends it to the user device using a secure communication protocol, taking care to ensure the integrity and confidentiality of the information.

[0690] Step 6:

[0691] The terminal interprets the suggestions and displays them visually to the user.

[0692] Input: Encrypted proposal data sent by the server.

[0693] Output: The decoded proposal in a format that can be viewed by the user.

[0694] Specific operation: After the user device decrypts the received encrypted data, it visually displays the decrypted suggestions to the user. For example, the suggestions may be displayed on the smartphone app screen in the form of "Enroll in education insurance" or "Invest in mutual funds."

[0695] Step 7:

[0696] The user reviews the proposal and accepts it if necessary.

[0697] Input: Proposal data, decrypted and displayed.

[0698] Output: The user's selection.

[0699] What it does: The user reviews the suggestions and chooses whether to accept them. If accepted, the information is fed back from the device to the server and updated in the database. This feedback improves the accuracy of future suggestions.

[0700] 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.

[0701] This invention combines an emotion engine with an AI financial planner system that analyzes data based on a user's financial information and generates appropriate asset management and service proposals. Specifically, the goal is to provide more personalized services by recognizing the user's emotional state and utilizing that information in data analysis and proposal generation.

[0702] System Configuration

[0703] This system consists of four main components: a user terminal, a server, a database, and an emotion engine.

[0704] User terminal

[0705] A user terminal is a device through which a user enters their financial information and receives offers provided by the system, such as a smartphone, tablet, or PC.

[0706] server

[0707] The server is the main processing unit that receives the financial information submitted by users, performs data analysis, and generates proposals. The server has an AI model installed and is trained on the information retrieved from the database.

[0708] Database

[0709] The database is a large-capacity data storage system that stores historical market data, customer data, analytical results, etc. The server uses this database to train AI models and generate advanced recommendations.

[0710] Emotion Engine

[0711] The emotion engine is a system that recognizes the user's emotional state and reflects that information in data analysis and proposal generation. The emotion engine recognizes emotions by analyzing the user's facial expressions, voice, input patterns, etc.

[0712] Processing flow

[0713] The specific processing flow centered on the server, terminal, and user will be explained below.

[0714] User

[0715] Users access the system and enter their financial information, such as their age, annual income, savings amount, financial goals, etc. This information is encrypted using a secure communication protocol and sent from the user's terminal to the server.

[0716] Terminal

[0717] The user device encrypts the entered financial information and transmits it to the server. The device is also equipped with an emotion engine that analyzes the user's facial expressions and voice in real time to recognize their emotional state.

[0718] server

[0719] The server inputs the received financial information and the user's emotional state information into the AI ​​model. The AI ​​model is trained based on historical market and customer data stored in a database and performs detailed data analysis. As a result of the analysis, asset management and service proposals that are best suited to the user's financial goals and emotional state are generated.

[0720] The generated proposals are expressed as recommendations for specific investments, insurance products, and financial plans in various fields such as securities, insurance, real estate investment, etc. For example, if a user has an emotional state of "feeling anxious about risk," more stable investments will be suggested to alleviate that anxiety.

[0721] Terminal

[0722] The proposals generated by the server are again encrypted using a secure communication protocol and sent to the terminal, where they are decrypted and visually displayed to the user.

[0723] User

[0724] The user can review the displayed suggestions and accept them if necessary. For example, suggestions such as "take out education insurance," "invest in a mutual fund that is expected to grow steadily," and "purchase a rental property" are displayed. The user can decide whether to accept the suggestion that best suits their emotional state.

[0725] server

[0726] If the user accepts a suggestion, the information is fed back to the server via the device and stored in a database, allowing future suggestions to be more accurate.

[0727] Specific examples

[0728] For example, if a user in their 30s wants to secure funds for their children's future education, they can input their age, annual income, savings amount, and goals into the system. Furthermore, if the user expresses "anxiety about education expenses" through the emotion engine, the server will take this into consideration and suggest low-risk investment options and stable insurance products. This allows users to create an optimal asset management plan that suits their emotional state.

[0729] This system automates financial planning tasks while providing personalized proposals that take into account the user's emotions. This allows users to receive optimal proposals based on their financial situation and emotional state, allowing them to manage their assets with peace of mind.

[0730] The processing flow will be explained below.

[0731] Step 1:

[0732] Users enter their financial information (age, annual income, savings amount, financial goals, etc.) into a dedicated form. The emotion engine also collects the user's facial expressions and voice in real time.

[0733] Step 2:

[0734] The terminal encrypts the entered financial information and emotion data using a secure communication protocol and transmits it to the server.

[0735] Step 3:

[0736] The server receives the encrypted data and decrypts it to obtain the user's financial information and emotional state.

[0737] Step 4:

[0738] The server retrieves historical market and customer data from a database and inputs users' financial and emotional information into a trained AI model.

[0739] Step 5:

[0740] The server's AI model performs detailed data analysis based on the user's financial and emotional information, including predictions that take into account the user's financial goals, risk tolerance, and emotional state.

[0741] Step 6:

[0742] Based on the analysis results, the server generates asset management and service recommendations that best suit the user's financial goals and emotional state, including specific investment options, insurance products, real estate investments, and more.

[0743] Step 7:

[0744] The server encrypts the generated proposal again using a secure communication protocol and transmits it to the terminal.

[0745] Step 8:

[0746] The device receives the encrypted proposal, decrypts it, and visually displays it to the user, including details of recommended financial plans and insurance products.

[0747] Step 9:

[0748] The user can review the displayed proposal and decide whether to accept it. If necessary, they can also request additional questions or a new proposal with different conditions.

[0749] Step 10:

[0750] If the user accepts the suggestion, the information is fed back to the server via the terminal.

[0751] Step 11:

[0752] The server stores the user's feedback information in a database and uses it as data to update the AI ​​model.

[0753] Through the above steps, the system provides highly accurate advice based on the user's financial information, streamlining and automating financial planning work, while also using an emotion engine to make personalized proposals that adapt to the user's emotional state.

[0754] Example 2

[0755] 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."

[0756] Conventional financial planning systems can make asset management proposals based on a user's financial information, but they cannot make proposals that take into account the user's emotional state. As a result, personalized proposals based on the user's emotional state are not provided, making it difficult to realize proposals that fully reflect the user's anxiety and sensitivity to risk. The present invention aims to solve these problems and provide a system that makes financial proposals that take into account the user's emotional state.

[0757] 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.

[0758] In this invention, the server includes means for receiving financial information and an emotional state from a user, means for performing data analysis based on the received financial information and emotional state, and means for generating appropriate asset management and service proposals based on the results of the data analysis, thereby enabling personalized asset management and service proposals that take the user's emotional state into consideration.

[0759] A "user" is any person or entity that accesses the system to input financial information and emotional state and receive recommendations.

[0760] "Financial information" refers to economic information such as a user's age, annual income, savings, and financial goals.

[0761] "Emotional state" refers to the psychological state recognized from the user's facial expression, voice, input pattern, etc.

[0762] "Secure communication protocol" refers to technology (e.g., TLS and SSL) that encrypts data and enables secure communication.

[0763] "Data analytics" refers to the process of using AI models and algorithms to conduct detailed analysis based on received financial information and emotional state.

[0764] "AI model" refers to an artificial intelligence algorithm that is trained based on past data and can perform a specific task (e.g., data analysis, recommendation generation).

[0765] "Wealth Management and Service Recommendations" refers to recommended investment products, insurance, and other plans generated based on the user's financial information and emotional state.

[0766] "Database" refers to a data storage system for storing historical market data, customer data, analytical results, etc.

[0767] "Feedback" refers to data that is sent back to the server regarding the suggestions that the user has accepted, and that is used to improve the system.

[0768] This invention combines an emotion engine with an AI financial planner system that analyzes data based on a user's financial information and generates appropriate asset management and service proposals. Specifically, the goal is to provide more personalized services by recognizing the user's emotional state and utilizing that information in data analysis and proposal generation.

[0769] System Configuration

[0770] This system consists of four main components: a user terminal, a server, a database, and an emotion engine.

[0771] User terminal

[0772] The user terminal is a device where the user enters their financial information and receives proposals provided by the system. It can be a smartphone, tablet, or PC. The terminal is equipped with an emotion engine that analyzes the user's facial expressions and voice in real time to recognize their emotional state.

[0773] server

[0774] The server is the main processing unit that receives financial information and emotional states submitted by users, analyzes the data, and generates recommendations. AI models trained with deep learning frameworks such as TensorFlow and PyTorch are installed on the server. The server performs analysis based on the information retrieved from the database.

[0775] Database

[0776] The database is a large-capacity data storage system that stores historical market data, customer data, analytical results, etc. The database may be a NoSQL database such as MySQL, PostgreSQL, or MongoDB. The server uses this database to train AI models and generate advanced recommendations.

[0777] Emotion Engine

[0778] The emotion engine is a system that recognizes the user's emotional state and reflects that information in data analysis and proposal generation. The emotion engine analyzes the user's facial expressions, voice, input patterns, etc. to recognize emotions.

[0779] Specific examples

[0780] For example, if a user in their 30s wants to secure funds for their children's future education, they can input their age, annual income, savings amount, and goals into the system. Furthermore, if the user expresses "anxiety about education expenses" through the emotion engine, the server will take this into consideration and suggest low-risk investment options and stable insurance products. This allows users to create an optimal asset management plan according to their emotional state.

[0781] Prompt Sentence Examples

[0782] User age: 35

[0783] Annual income: 8 million yen

[0784] Savings: 5 million yen

[0785] Financial goal: I want to save 3 million yen for my children's education.

[0786] Emotional state: Anxiety about education costs

[0787] Recommendations: Low-risk investment options and stable insurance products

[0788] This system automates financial planning tasks while providing personalized proposals that take into account the user's emotions. This allows users to receive optimal proposals based on their financial situation and emotional state, allowing them to manage their assets with peace of mind.

[0789] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0790] System program processing flow

[0791] The processing flow of the system program will be explained below by dividing it into specific steps.

[0792] Step 1: Enter your user information

[0793] User

[0794] Users access the system using a device, launching a browser or a dedicated app and entering financial information such as age, annual income, savings amount, and financial goals.

[0795] input

[0796] Age, annual income, savings, financial goals

[0797] output

[0798] Financial Information Data

[0799] Step 2: Encrypt and send information

[0800] Terminal

[0801] The terminal encrypts the financial information entered by the user using the Transport Layer Security (TLS) protocol, and then sends the encrypted financial information to the server over a secure communication channel using an HTTP POST request.

[0802] input

[0803] Financial Information Data

[0804] output

[0805] Encrypted financial data

[0806] Step 3: Analyze emotional state

[0807] Terminal

[0808] The emotion engine installed in the device captures and analyzes the user's facial expressions and voice data in real time, and determines the user's emotional state using facial recognition and voice analysis technologies.

[0809] input

[0810] User's facial expression data, voice data

[0811] output

[0812] Emotional state data

[0813] Step 4: Receive data and prepare for analysis

[0814] server

[0815] The server receives the encrypted data sent from the terminal and decrypts it using the TLS protocol. The server retrieves historical market data and customer data from a database (e.g., MySQL, PostgreSQL).

[0816] input

[0817] Encrypted financial data

[0818] output

[0819] Decoded financial information data, historical data

[0820] Step 5: Data analysis and proposal generation

[0821] server

[0822] The server inputs the decoded financial information and emotional state data into an AI model, which then analyzes the data and generates optimal asset management and service recommendations. The AI ​​models used include TensorFlow and PyTorch.

[0823] input

[0824] Decoded financial information data, emotional state data, historical data

[0825] output

[0826] Asset management and service proposals

[0827] Step 6: Encrypt and send the proposal

[0828] server

[0829] The server encrypts the generated proposal again using the TLS protocol and sends the encrypted proposal data to the user's device using an HTTP POST request.

[0830] input

[0831] Asset management and service proposals

[0832] output

[0833] Encrypted proposal data

[0834] Step 7: Viewing Proposals

[0835] Terminal

[0836] The user device decrypts the received encrypted proposal and visually displays it. Specifically, it uses JavaScript libraries (e.g., D3.js, Chart.js) to present the proposal to the user in the form of graphs or charts.

[0837] input

[0838] Encrypted proposal data

[0839] output

[0840] Visualized proposal

[0841] Step 8: Review and feedback on proposals

[0842] User

[0843] The user checks the displayed proposals and selects whether to accept the proposals, such as "take out education insurance" or "invest in a mutual fund that is expected to grow steadily." The user's selection is sent to the server as feedback data.

[0844] input

[0845] Visualized proposal

[0846] output

[0847] Feedback Data

[0848] Step 9: Saving feedback and improving the model

[0849] server

[0850] The server receives feedback data from users and stores it in a database. The feedback information is used to retrain the AI ​​model and improve the accuracy of future suggestions.

[0851] input

[0852] Feedback Data

[0853] output

[0854] Updated database, improved AI models

[0855] (Application example 2)

[0856] 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."

[0857] While conventional financial planner systems can provide appropriate asset management and service proposals based on a user's financial information, they have a problem in that they cannot take into account the user's emotional state and therefore have a low level of personalization. In particular, when a user is feeling stressed or anxious, it is difficult to provide appropriate proposals according to that situation.

[0858] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving financial information from a user, means for recognizing the user's emotional state using an emotion engine, and means for performing data analysis based on the received financial information and emotional state information. This enables personalized asset management and service proposals that take the user's emotional state into consideration.

[0859] "User" refers to a person who uses the service and provides financial information and emotional state to the system.

[0860] "Financial Information" refers to financial data such as a user's age, annual income, savings, and financial goals.

[0861] An "emotion engine" is a system that analyzes a user's facial expressions, voice, input patterns, etc. to recognize their emotional state.

[0862] "Emotional state" refers to the psychological state that a user feels, such as stress, anxiety, relief, or joy.

[0863] "Data Analysis" refers to the process by which the system performs analysis based on the received financial and emotional state information.

[0864] "Asset management" refers to the process of carrying out financial plans, such as investing and purchasing financial products, to increase a user's wealth.

[0865] "Service proposal" refers to a proposal that provides the user with the most suitable financial products, investment plans, etc.

[0866] "Database" refers to a large-scale data storage system that stores historical market data, customer data, proposal data, etc.

[0867] A "generative AI model" refers to an artificial intelligence model that is trained based on past data and generates suggestions for users.

[0868] A "secure communication protocol" refers to a communication method for securely encrypting and sending and receiving information.

[0869] System Configuration

[0870] The system for implementing this invention consists of four main components: a user terminal, a server, a database, and an emotion engine.

[0871] User terminal

[0872] The user terminal is the device where the user enters financial information and receives proposals provided by the system. It can be a smartphone, tablet, or PC. The terminal is equipped with an emotion-recognition camera and microphone, which analyzes the user's facial expressions and voice in real time to recognize their emotional state.

[0873] server

[0874] The server is the main processing unit that receives the financial and emotional state information sent by the user and performs data analysis. The server is installed with a generative AI model that is trained based on the received information. The generated proposals are then sent back to the user's device.

[0875] Database

[0876] The database is a storage system for large amounts of financial data, including market data, customer data, and historical proposal data, on which generative AI models are trained.

[0877] Emotion Engine

[0878] The emotion engine is a system that recognizes the user's emotional state and reflects it in data analysis and proposal generation. It analyzes facial expressions, voice, and input patterns to determine the user's emotional state. This information is sent to the server along with the user's financial information.

[0879] Processing flow

[0880] The server performs the process in the following procedure.

[0881] 1. Entering financial information: Users access the system and enter their financial information such as age, annual income, savings amount, financial goals, etc. This information is encrypted using a secure communication protocol and sent to the server.

[0882] 2. Emotional state recognition: The emotion engine on the user device analyzes the user's facial expressions and voice in real time to recognize the user's emotional state. The results are also sent to the server.

[0883] 3. Data analysis and proposal generation: The server inputs the received financial information and emotional state information into the AI ​​model and performs data analysis. Based on the analysis results, appropriate asset management and service proposals are generated.

[0884] 4. Proposal transmission and display: The generated proposal is again encrypted using a secure communication protocol and sent to the user's device, which decrypts it and visually displays it.

[0885] 5. Feedback storage: If the user accepts the suggestion, the information is fed back to the server and stored in the database, which will improve the accuracy of future suggestions.

[0886] Specific examples

[0887] For example, a user accesses the system via their smartphone and enters financial information such as their age and income. If the user is feeling stressed, the emotion engine recognizes this and sends it to the server. The server then uses this information to suggest stable investment options that reduce risk. Taking the user's emotional state into account provides a more personalized financial management plan.

[0888] Prompt Sentence Examples

[0889] "Generate purchasing advice for users when they are stressed. Output suggestions based on the following criteria:

[0890] The user is feeling stressed.

[0891] I have a strong desire to buy, but I want to avoid waste.

[0892] Please give me some example sentences for the suggestions you would like to generate.

[0893] This enables highly personalized suggestions that reflect the user's emotional state.

[0894] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0895] Step 1:

[0896] Users access the system using devices such as smartphones or PCs and enter financial information such as their age, annual income, savings amount, financial goals, etc. This information is encrypted on the device and sent to the server using a secure communication protocol.

[0897] Input: User's financial information (age, annual income, savings, financial goals, etc.)

[0898] Output: Encrypted financial information sent to the server

[0899] Step 2:

[0900] The emotion engine installed in the device recognizes the user's emotional state by analyzing the user's real-time facial expressions and voice. For example, the device's camera captures the user's facial expressions, and the emotion engine analyzes the data to identify the user's emotional state.

[0901] Input: User's facial expression data and voice data

[0902] Output: Recognized emotional state data

[0903] Step 3:

[0904] The emotional state recognized by the emotion engine is encrypted and transmitted to the server.

[0905] Input: Emotional state data

[0906] Output: Encrypted emotional state data is sent to the server

[0907] Step 4:

[0908] The server inputs the received financial information and emotional state information into the generative AI model and performs data analysis. The generative AI model generates optimal asset management and service proposals based on the input data, taking into account the user's emotional state.

[0909] Input: Financial and emotional state information

[0910] Output: Generated asset management and service proposals

[0911] Step 5:

[0912] The server encrypts the generated proposal using a secure communication protocol and transmits it to the user terminal.

[0913] Input: Asset management and service proposals

[0914] Output: The encrypted proposal data is sent to the device.

[0915] Step 6:

[0916] The user device decrypts the encrypted proposal data received from the server and visually displays it to the user, for example, on the screen of a smartphone or PC.

[0917] Input: Encrypted proposal data

[0918] Output: A visual representation of the proposal

[0919] Step 7:

[0920] The user reviews the displayed suggestions and accepts them if necessary, and the feedback is sent back to the server using a secure communication protocol.

[0921] Input: User feedback

[0922] Output: Encrypted feedback data is sent to the server

[0923] Step 8:

[0924] The server receives user feedback and stores it in a database, which is used as training data to improve the accuracy of future suggestions.

[0925] Input: Feedback data

[0926] Output: Feedback data stored in a database

[0927] 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.

[0928] 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.

[0929] 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.

[0930] [Third embodiment]

[0931] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0932] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0933] 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).

[0934] 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.

[0935] 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.

[0936] 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).

[0937] 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.

[0938] 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.

[0939] 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.

[0940] 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.

[0941] 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.

[0942] 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."

[0943] This invention is a financial planner system that utilizes AI, which receives financial information from users, performs advanced data analysis based on that information, and generates and provides appropriate asset management and service proposals to users. The specific configuration and processing flow of the system are as follows:

[0944] System Configuration

[0945] This system consists of three main components: a user terminal, a server, and a database.

[0946] User terminal

[0947] A user terminal is a device through which a user enters their financial information and receives offers provided by the system, such as a smartphone, tablet, or PC.

[0948] server

[0949] The server is the main processing unit that receives the financial information submitted by users, performs data analysis, and generates proposals. The server has an AI model installed and is trained on the information retrieved from the database.

[0950] Database

[0951] The database is a large-capacity data storage system that stores historical market data, customer data, analytical results, etc. The server uses this database to train AI models and generate advanced recommendations.

[0952] Processing flow

[0953] The specific processing flow centered on the server, terminal, and user will be explained below.

[0954] User

[0955] Users access the system and enter their financial information, such as their age, annual income, savings amount, financial goals, etc. This information is encrypted using a secure communication protocol and sent from the user's terminal to the server.

[0956] Terminal

[0957] The user terminal plays a role in encrypting the entered financial information and sending it to the server. For example, if a user enters information such as "Age: 35," "Annual income: 6 million yen," "Savings: 3 million yen," and "Goal: Secure funds for children's education," the information is encrypted and sent to the server.

[0958] server

[0959] The server decodes the received financial information and inputs it into the AI ​​model, which is trained on historical market and customer data stored in a database and performs detailed analysis based on the received information. As a result of the analysis, appropriate asset management and service proposals are generated.

[0960] The generated proposals are expressed as recommendations for specific investments, insurance products, and financial plans in a variety of fields, including securities, insurance, and real estate investment. For example, based on the user's input information, the system may generate proposals such as "take out education insurance," "invest in a mutual fund that is expected to grow steadily," or "purchase a rental property."

[0961] Terminal

[0962] The server-generated proposals are then sent, again using a secure communication protocol, to the user terminal, which decrypts the proposals and visually displays them to the user.

[0963] User

[0964] The user can review the displayed suggestions and accept them if necessary. If the user accepts a suggestion, the information is fed back to the server and updated in the database. This feedback further improves the accuracy of future suggestions.

[0965] Specific examples

[0966] For example, if a user in their 30s wants to secure funds for their child's education in the near future, they input their age, annual income, savings amount, and goals into the system. The server uses this information to perform AI analysis and generates specific suggestions such as "enroll in education insurance," "invest in rental property," and "invest in mutual funds with stable growth potential." These suggestions are displayed on the user's device, and the user decides whether to accept them.

[0967] By automating financial planning tasks, this system can provide users with more efficient and accurate asset management proposals than conventional manual work, allowing users to receive proposals that are optimal for their financial situation and goals.

[0968] The processing flow will be explained below.

[0969] Step 1:

[0970] Users enter their financial information (age, annual income, savings amount, financial goals, etc.) into a dedicated form.

[0971] Step 2:

[0972] The terminal encrypts the input information using a secure communication protocol and transmits it to the server.

[0973] Step 3:

[0974] The server receives the encrypted data and decrypts it to obtain the user's financial information.

[0975] Step 4:

[0976] The server retrieves historical market and customer data from a database and inputs the user's financial information into a trained AI model.

[0977] Step 5:

[0978] The server's AI model performs detailed data analysis based on the user's financial information, including predictions that take into account the user's risk tolerance and market trends.

[0979] Step 6:

[0980] Based on the analysis results, the server generates asset management and service proposals that best suit the user's financial goals, including specific investment options, insurance products, real estate investments, and more.

[0981] Step 7:

[0982] The server encrypts the generated proposal again using a secure communication protocol and transmits it to the terminal.

[0983] Step 8:

[0984] The terminal receives the encrypted proposal, decrypts it and visually displays it to the user.

[0985] Step 9:

[0986] The user can then review the displayed proposal and decide whether to accept it. If necessary, the user can also request additional questions or a new proposal with different conditions.

[0987] Step 10:

[0988] If the user accepts the suggestion, the information is fed back to the server via the terminal.

[0989] Step 11:

[0990] The server stores the user's feedback information in a database and uses it as data to update the AI ​​model.

[0991] By following the steps above, this system can provide highly accurate advice based on the user's financial information, streamlining and automating financial planning operations.

[0992] Example 1

[0993] 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."

[0994] Conventional financial planner systems have had difficulty effectively collecting users' financial information and analyzing the data securely and efficiently to provide appropriate asset management proposals. In particular, ensuring the security of user information and performing advanced data analysis that effectively utilizes past market data are required. This has resulted in users being unable to receive reliable asset management proposals.

[0995] 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.

[0996] In this invention, the server includes means for receiving financial information from a user, means for encrypting the received financial information, means for transmitting the encrypted financial information to the server using a secure communication protocol, means for decrypting the received financial information and performing data analysis based on an AI model, means for generating appropriate asset management and service proposals based on the analysis results, means for re-encrypting the generated proposals and providing them to the user using a secure communication protocol, and means for storing the proposals and user information in a database. This allows users to input their financial information under high security and receive advanced data analysis by AI based on that information, thereby enabling them to receive optimal asset management proposals in real time.

[0997] "User Terminal" means an electronic device used by a User to input financial information and display proposals received from the System, including a smartphone, tablet, PC, etc.

[0998] The "server" is the main processing device that receives financial information sent from user terminals, analyzes the data based on that information, and generates appropriate asset management proposals.

[0999] The "database" is a large-capacity data storage system that stores historical market data, customer data, analytical results, etc., and the server uses this data to train AI models and generate recommendations.

[1000] "Financial information" refers to information related to a person's financial situation that is entered by the user, and specifically includes age, annual income, savings amount, financial goals, and the like.

[1001] "Encryption" is a technology that uses special algorithms to convert a user's financial information and proposals into a format that cannot be deciphered by third parties in order to ensure information security.

[1002] A "secure communication protocol" is a communication protocol for ensuring safety in data transmission, and specifically includes HTTPS (Hypertext Transfer Protocol Secure).

[1003] An "AI model" is an algorithm and its training results that uses machine learning technology to analyze data, and is used to generate asset management proposals based on past market data and customer data.

[1004] "Data analysis" is an analytical process carried out by an AI model using historical market data and customer data based on the user's financial information.

[1005] "Asset management proposals" are investment strategies and financial plan proposals generated by the server based on the results of data analysis, and include specific options such as securities, insurance, and real estate investments.

[1006] This invention is a financial planner system that utilizes AI, which receives financial information from users, performs advanced data analysis based on that information, and generates and provides appropriate asset management and service proposals to users. The specific configuration and processing flow of the system are as follows:

[1007] System Configuration

[1008] This system consists of three main components: a user terminal, a server, and a database.

[1009] User terminal

[1010] A user terminal is a device through which a user enters their financial information and receives offers provided by the system, such as a smartphone, tablet, or PC.

[1011] server

[1012] The server is the main processing unit that receives financial information submitted by users, analyzes the data, and generates proposals. An AI model is installed on the server and trained based on the information retrieved from the database. Specifically, machine learning libraries such as TensorFlow or PyTorch can be used.

[1013] Database

[1014] The database is a large-capacity data storage system that stores historical market data, customer data, analytical results, etc. The server uses this database to train AI models and generate advanced recommendations.

[1015] User

[1016] Users access the system and enter their financial information, such as their age, annual income, savings amount, financial goals, etc. This information is encrypted using a secure communication protocol and sent from the user's terminal to the server.

[1017] Terminal

[1018] The user terminal plays a role in encrypting the entered financial information and sending it to the server. For example, if a user enters information such as "Age: 35," "Annual income: 6 million yen," "Savings: 3 million yen," and "Goal: Secure funds for children's education," the information is encrypted and sent to the server.

[1019] server

[1020] The server decodes the received financial information and inputs it into the AI ​​model. The AI ​​model is trained based on past market and customer data stored in a database and performs detailed analysis based on the received information. As a result of the analysis, appropriate asset management and service proposals are generated. The generated proposals are expressed as recommendations for specific investments, insurance products, and financial plans in a variety of fields, including securities, insurance, and real estate investment. For example, based on the information entered by the user, recommendations such as "take out education insurance," "invest in a mutual fund that is expected to grow steadily," and "purchase a rental property" are generated.

[1021] Terminal

[1022] The server-generated proposals are then sent, again using a secure communication protocol, to the user terminal, which decrypts the proposals and visually displays them to the user.

[1023] User

[1024] The user can review the displayed suggestions and accept them if necessary. If the user accepts a suggestion, the information is fed back to the server and updated in the database. This feedback further improves the accuracy of future suggestions.

[1025] Specific examples

[1026] For example, if a user in their 30s wants to secure funds for their child's education in the near future, they input their age, annual income, savings amount, and goals into the system. The server uses this information to perform AI analysis and generates specific suggestions such as "enrolling in education insurance," "investing in rental properties," and "investing in mutual funds with stable growth potential." These suggestions are displayed on the user's device, and the user decides whether to accept them.

[1027] Prompt Sentence Examples

[1028] "Enter your financial information: age, annual income, savings amount, goals."

[1029] "Generate asset management recommendations based on AI models."

[1030] This system automates the work of financial planners and provides users with efficient and accurate asset management proposals, allowing them to receive proposals that are optimal for their financial situation and goals.

[1031] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1032] Step 1:

[1033] Users access the system using a browser or a dedicated app and enter their login information to authenticate, which allows the system to recognize the user and provide an input screen for entering individual financial information.

[1034] Input: User authentication information (username, password)

[1035] Output: Financial information input screen

[1036] Step 2:

[1037] Users input financial information such as their age, annual income, savings amount, and financial goals. For example, they input information such as "Age: 35," "Annual income: 6 million yen," "Savings: 3 million yen," and "Goal: Secure funds for children's education."

[1038] Input: User's financial information (age, annual income, savings amount, financial goals)

[1039] Output: Financial information before encryption

[1040] Step 3:

[1041] The user terminal encrypts the entered financial information using the Advanced Encryption Standard (AES).

[1042] Input: User's financial information

[1043] Output: Encrypted financial information

[1044] Step 4:

[1045] The terminal sends encrypted financial information to the server using a secure communication protocol (HTTPS).

[1046] Input: Encrypted financial information

[1047] Output: Encrypted data sent to the server

[1048] Step 5:

[1049] The server receives and decrypts the encrypted financial information, which is then retrieved as the original financial information.

[1050] Input: Encrypted financial information

[1051] Output: Decrypted financial information

[1052] Step 6:

[1053] The server preprocesses the decrypted financial information, specifically normalizing numerical data such as age and annual income and converting it into a format compatible with AI models.

[1054] Input: Decrypted financial information

[1055] Output: Preprocessed data

[1056] Step 7:

[1057] The server inputs the pre-processed data into the AI ​​model, which is trained on historical market and customer data, and generates appropriate asset management and service proposals based on the analysis.

[1058] Input: Preprocessed data

[1059] Output: Analysis results (appropriate asset management and service proposals)

[1060] Step 8:

[1061] The server encrypts the generated proposal and transmits it to the user terminal using a secure communication protocol.

[1062] Input: Analysis results (service proposal)

[1063] Output: Encrypted service proposal

[1064] Step 9:

[1065] The user terminal receives and decrypts the encrypted proposal and visually displays it to the user.

[1066] Input: Encrypted service proposal

[1067] Output: Decoded service offer

[1068] Step 10:

[1069] The user checks the displayed suggestions and accepts them if necessary. If the suggestion is accepted, the information is fed back to the system.

[1070] Input: Decoded service offer

[1071] Output: User feedback information

[1072] Step 11:

[1073] The server stores the user's feedback information in a database and uses it to make updates to improve the accuracy of future suggestions.

[1074] Input: User feedback information

[1075] Output: Updated database

[1076] In this way, the system safely and efficiently handles financial information from users and provides appropriate asset management suggestions.

[1077] (Application example 1)

[1078] 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."

[1079] Conventional financial planner systems have difficulty reflecting users' spending patterns and daily income information in real time. This has resulted in asset management and service proposals that do not accurately reflect the user's latest situation, limiting their effectiveness. Furthermore, conventional systems require users to perform multiple operations on different platforms, resulting in poor usability.

[1080] 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.

[1081] In this invention, the server includes means for receiving financial information from a user, means for performing data analysis based on the received financial information, means for generating appropriate asset management and service proposals based on the results of the data analysis, means for providing the generated proposals to the user, means for storing the proposals and user information in a database, and means for updating the proposals in real time based on the user's spending patterns and income information. This enables asset management proposals that reflect the user's latest financial situation and spending patterns in a timely manner, further improving usability.

[1082] "Financial Information" is data about a user's income, expenses, savings, investments, etc.

[1083] "Data analytics" is the process of using algorithms and models to discover patterns and make predictions using collected financial information.

[1084] "Asset management" refers to proposing optimal investment methods, insurance contracts, etc. based on the user's financial information.

[1085] "Service proposal" refers to recommending services such as financial products and insurance products based on the user's financial information.

[1086] A "secure communication protocol" is a communication method that encrypts data when sending and receiving it to prevent unauthorized access by third parties.

[1087] "Encryption" is a technique for converting data so that it cannot be understood in its original form, and only the sender and receiver can decipher the data.

[1088] "Large-scale financial data" refers to large amounts of financial-related information, such as historical market data and customer data, that are used for data analysis and training artificial intelligence models.

[1089] An "AI model" is an algorithm or model built using artificial intelligence (AI) technology to extract and analyze useful information from input data.

[1090] "Spending patterns" refer to tendencies or rules that indicate how a user spends money.

[1091] "Income information" is data indicating the user's income such as salary and bonuses.

[1092] "Means for updating proposals in real time" refers to a system function that instantly analyzes the latest collected financial information and can always provide users with optimal asset management proposals.

[1093] This invention provides a financial planner system that utilizes AI. The detailed implementation method of the system that realizes the PonPon application example is explained below.

[1094] System configuration

[1095] This system consists of three main components: a user terminal, a server, and a database.

[1096] User terminal

[1097] The user terminal is a device such as a smartphone, tablet, or PC that allows the user to enter financial information and receive proposals. The user enters their own financial information through the application and receives proposals from the system.

[1098] server

[1099] The server is the main processing unit that receives financial information submitted by users, analyzes the data, and generates proposals. The server has a generative AI model installed and trained on the information retrieved from the database. The server encrypts the data using Python's cryptography library and communicates using the requests library.

[1100] Database

[1101] The database is a large-capacity data storage that stores historical market data, customer data, analytical results, etc. The server uses this database to train AI models and generate advanced recommendations.

[1102] Program processing

[1103] 1. User enters financial information:

[1104] The user inputs information such as age, annual income, savings amount, financial goals, etc. into the application. For example, the user inputs information such as "Age: 35," "Annual income: 6 million yen," "Savings: 3 million yen," and "Goal: Secure funds for children's education."

[1105] 2. Encryption and Transmission of Financial Information:

[1106] The user's terminal encrypts the entered financial information using Python's cryptography library before sending it to the server, ensuring the security of the information.

[1107] 3. Server-based data analysis and proposal generation:

[1108] The server decodes the received financial information and inputs it into the AI ​​model, which is trained on historical market and customer data stored in a database and performs detailed analysis based on the received information. As a result of the analysis, appropriate asset management and service proposals are generated.

[1109] 4. Providing Suggestions to Users:

[1110] The generated proposals are expressed as specific investment ideas, insurance products, and financial plans in various fields such as securities, insurance, and real estate investment. For example, they include proposals such as "take out education insurance," "invest in a mutual fund with stable growth prospects," and "purchase a rental property." These proposals are then sent to the user's device using a secure communication protocol.

[1111] 5. Displaying and Accepting Proposals:

[1112] The user's device decodes the proposals sent from the server and visually displays them. The user can then review the proposals and accept them as necessary. This allows asset management proposals to always reflect the user's latest financial situation and spending patterns.

[1113] Specific examples

[1114] For example, if a user in their 30s wants to secure funds for their child's education in the near future, they input their age, annual income, savings amount, and goals into the system. The server uses this information to perform AI analysis and generates specific suggestions such as "enrolling in education insurance," "investing in rental properties," and "investing in mutual funds with stable growth potential." These suggestions are displayed on the user's device, allowing the user to decide whether to accept them.

[1115] Prompt Sentence Examples

[1116] "I'm 35 years old, earn 6 million yen a year, and have 3 million yen saved up. My goal is to secure funds for my children's education. What is the best way to manage my assets?"

[1117] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1118] Step 1:

[1119] The user enters financial information.

[1120] Input: The user enters financial information such as their age, annual income, savings amount, and financial goals into the application. Specifically, the user enters information such as "Age: 35," "Annual income: 6 million yen," "Savings: 3 million yen," and "Goal: Secure funds for children's education."

[1121] Output: The entered financial information is temporarily stored on the user's terminal.

[1122] How it works: Users enter financial information into a form provided on their smartphone, tablet, or computer screen. Once completed, the device passes the data to the next processing step.

[1123] Step 2:

[1124] The device encrypts the financial information and sends it to the server.

[1125] Input: Financial information entered by the user.

[1126] Output: Encrypted financial information is sent to the server.

[1127] How it works: The device encrypts financial information using Python's cryptography library, then uses the requests library to send the encrypted information to the server as an HTTP POST request. Before sending, the device performs a thorough encryption process to prevent information leaks.

[1128] Step 3:

[1129] The server interprets the received financial information and retrieves relevant information from a database.

[1130] Input: Encrypted financial information received by the server.

[1131] Output: Interpreted financial information and information from related databases.

[1132] How it works: The server first decrypts the encrypted data it receives, then retrieves historical market data and user data with the same attributes from a database to input the decrypted financial information into an AI model, preparing it for analysis.

[1133] Step 4:

[1134] The server analyzes the data and uses generative AI models to generate asset management and service proposals.

[1135] Input: Interpreted financial information and related information retrieved from databases.

[1136] Output: Generated asset management and service proposals.

[1137] How it works: The server inputs the interpreted financial information and related data into the AI ​​model. The model then performs advanced data analysis using predictive algorithms and historical data to generate optimal recommendations for the user. Specific recommendations include "take out education insurance," "invest in a mutual fund with stable growth potential," and "purchase a rental property."

[1138] Step 5:

[1139] The server transmits the generated proposal to the user terminal.

[1140] Input: Generated asset management and servicing proposals.

[1141] Output: Proposal data sent to the user device.

[1142] Specific operation: The server re-encrypts the generated proposal and sends it to the user device using a secure communication protocol, taking care to ensure the integrity and confidentiality of the information.

[1143] Step 6:

[1144] The terminal interprets the suggestions and displays them visually to the user.

[1145] Input: Encrypted proposal data sent by the server.

[1146] Output: The decoded proposal in a format that can be viewed by the user.

[1147] Specific operation: After the user device decrypts the received encrypted data, it visually displays the decrypted suggestions to the user. For example, the suggestions may be displayed on the smartphone app screen in the form of "Enroll in education insurance" or "Invest in mutual funds."

[1148] Step 7:

[1149] The user reviews the proposal and accepts it if necessary.

[1150] Input: Proposal data, decrypted and displayed.

[1151] Output: The user's selection.

[1152] What it does: The user reviews the suggestions and chooses whether to accept them. If accepted, the information is fed back from the device to the server and updated in the database. This feedback improves the accuracy of future suggestions.

[1153] 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.

[1154] This invention combines an emotion engine with an AI financial planner system that analyzes data based on a user's financial information and generates appropriate asset management and service proposals. Specifically, the goal is to provide more personalized services by recognizing the user's emotional state and utilizing that information in data analysis and proposal generation.

[1155] System Configuration

[1156] This system consists of four main components: a user terminal, a server, a database, and an emotion engine.

[1157] User terminal

[1158] A user terminal is a device through which a user enters their financial information and receives offers provided by the system, such as a smartphone, tablet, or PC.

[1159] server

[1160] The server is the main processing unit that receives the financial information submitted by users, performs data analysis, and generates proposals. The server has an AI model installed and is trained on the information retrieved from the database.

[1161] Database

[1162] The database is a large-capacity data storage system that stores historical market data, customer data, analytical results, etc. The server uses this database to train AI models and generate advanced recommendations.

[1163] Emotion Engine

[1164] The emotion engine is a system that recognizes the user's emotional state and reflects that information in data analysis and proposal generation. The emotion engine recognizes emotions by analyzing the user's facial expressions, voice, input patterns, etc.

[1165] Processing flow

[1166] The specific processing flow centered on the server, terminal, and user will be explained below.

[1167] User

[1168] Users access the system and enter their financial information, such as their age, annual income, savings amount, financial goals, etc. This information is encrypted using a secure communication protocol and sent from the user's terminal to the server.

[1169] Terminal

[1170] The user device encrypts the entered financial information and transmits it to the server. The device is also equipped with an emotion engine that analyzes the user's facial expressions and voice in real time to recognize their emotional state.

[1171] server

[1172] The server inputs the received financial information and the user's emotional state information into the AI ​​model. The AI ​​model is trained based on historical market and customer data stored in a database and performs detailed data analysis. As a result of the analysis, asset management and service proposals that are best suited to the user's financial goals and emotional state are generated.

[1173] The generated proposals are expressed as recommendations for specific investments, insurance products, and financial plans in various fields such as securities, insurance, real estate investment, etc. For example, if a user has an emotional state of "feeling anxious about risk," more stable investments will be suggested to alleviate that anxiety.

[1174] Terminal

[1175] The proposals generated by the server are again encrypted using a secure communication protocol and sent to the terminal, where they are decrypted and visually displayed to the user.

[1176] User

[1177] The user can review the displayed suggestions and accept them if necessary. For example, suggestions such as "take out education insurance," "invest in a mutual fund that is expected to grow steadily," and "purchase a rental property" are displayed. The user can decide whether to accept the suggestion that best suits their emotional state.

[1178] server

[1179] If the user accepts a suggestion, the information is fed back to the server via the device and stored in a database, allowing future suggestions to be more accurate.

[1180] Specific examples

[1181] For example, if a user in their 30s wants to secure funds for their children's future education, they can input their age, annual income, savings amount, and goals into the system. Furthermore, if the user expresses "anxiety about education expenses" through the emotion engine, the server will take this into consideration and suggest low-risk investment options and stable insurance products. This allows users to create an optimal asset management plan that suits their emotional state.

[1182] This system automates financial planning tasks while providing personalized proposals that take into account the user's emotions. This allows users to receive optimal proposals based on their financial situation and emotional state, allowing them to manage their assets with peace of mind.

[1183] The processing flow will be explained below.

[1184] Step 1:

[1185] Users enter their financial information (age, annual income, savings amount, financial goals, etc.) into a dedicated form. The emotion engine also collects the user's facial expressions and voice in real time.

[1186] Step 2:

[1187] The terminal encrypts the entered financial information and emotion data using a secure communication protocol and transmits it to the server.

[1188] Step 3:

[1189] The server receives the encrypted data and decrypts it to obtain the user's financial information and emotional state.

[1190] Step 4:

[1191] The server retrieves historical market and customer data from a database and inputs users' financial and emotional information into a trained AI model.

[1192] Step 5:

[1193] The server's AI model performs detailed data analysis based on the user's financial and emotional information, including predictions that take into account the user's financial goals, risk tolerance, and emotional state.

[1194] Step 6:

[1195] Based on the analysis results, the server generates asset management and service recommendations that best suit the user's financial goals and emotional state, including specific investment options, insurance products, real estate investments, and more.

[1196] Step 7:

[1197] The server encrypts the generated proposal again using a secure communication protocol and transmits it to the terminal.

[1198] Step 8:

[1199] The device receives the encrypted proposal, decrypts it, and visually displays it to the user, including details of recommended financial plans and insurance products.

[1200] Step 9:

[1201] The user can review the displayed proposal and decide whether to accept it. If necessary, they can also request additional questions or a new proposal with different conditions.

[1202] Step 10:

[1203] If the user accepts the suggestion, the information is fed back to the server via the terminal.

[1204] Step 11:

[1205] The server stores the user's feedback information in a database and uses it as data to update the AI ​​model.

[1206] Through the above steps, the system provides highly accurate advice based on the user's financial information, streamlining and automating financial planning work, while also using an emotion engine to make personalized proposals that adapt to the user's emotional state.

[1207] Example 2

[1208] 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."

[1209] Conventional financial planning systems can make asset management proposals based on a user's financial information, but they cannot make proposals that take into account the user's emotional state. As a result, personalized proposals based on the user's emotional state are not provided, making it difficult to realize proposals that fully reflect the user's anxiety and sensitivity to risk. The present invention aims to solve these problems and provide a system that makes financial proposals that take into account the user's emotional state.

[1210] 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.

[1211] In this invention, the server includes means for receiving financial information and an emotional state from a user, means for performing data analysis based on the received financial information and emotional state, and means for generating appropriate asset management and service proposals based on the results of the data analysis, thereby enabling personalized asset management and service proposals that take the user's emotional state into consideration.

[1212] A "user" is any person or entity that accesses the system to input financial information and emotional state and receive recommendations.

[1213] "Financial information" refers to economic information such as a user's age, annual income, savings, and financial goals.

[1214] "Emotional state" refers to the psychological state recognized from the user's facial expression, voice, input pattern, etc.

[1215] "Secure communication protocol" refers to technology (e.g., TLS and SSL) that encrypts data and enables secure communication.

[1216] "Data analytics" refers to the process of using AI models and algorithms to conduct detailed analysis based on received financial information and emotional state.

[1217] "AI model" refers to an artificial intelligence algorithm that is trained based on past data and can perform a specific task (e.g., data analysis, recommendation generation).

[1218] "Wealth Management and Service Recommendations" refers to recommended investment products, insurance, and other plans generated based on the user's financial information and emotional state.

[1219] "Database" refers to a data storage system for storing historical market data, customer data, analytical results, etc.

[1220] "Feedback" refers to data that is sent back to the server regarding the suggestions that the user has accepted, and that is used to improve the system.

[1221] This invention combines an emotion engine with an AI financial planner system that analyzes data based on a user's financial information and generates appropriate asset management and service proposals. Specifically, the goal is to provide more personalized services by recognizing the user's emotional state and utilizing that information in data analysis and proposal generation.

[1222] System Configuration

[1223] This system consists of four main components: a user terminal, a server, a database, and an emotion engine.

[1224] User terminal

[1225] The user terminal is a device where the user enters their financial information and receives proposals provided by the system. It can be a smartphone, tablet, or PC. The terminal is equipped with an emotion engine that analyzes the user's facial expressions and voice in real time to recognize their emotional state.

[1226] server

[1227] The server is the main processing unit that receives financial information and emotional states submitted by users, analyzes the data, and generates recommendations. AI models trained with deep learning frameworks such as TensorFlow and PyTorch are installed on the server. The server performs analysis based on the information retrieved from the database.

[1228] Database

[1229] The database is a large-capacity data storage system that stores historical market data, customer data, analytical results, etc. The database may be a NoSQL database such as MySQL, PostgreSQL, or MongoDB. The server uses this database to train AI models and generate advanced recommendations.

[1230] Emotion Engine

[1231] The emotion engine is a system that recognizes the user's emotional state and reflects that information in data analysis and proposal generation. The emotion engine analyzes the user's facial expressions, voice, input patterns, etc. to recognize emotions.

[1232] Specific examples

[1233] For example, if a user in their 30s wants to secure funds for their children's future education, they can input their age, annual income, savings amount, and goals into the system. Furthermore, if the user expresses "anxiety about education expenses" through the emotion engine, the server will take this into consideration and suggest low-risk investment options and stable insurance products. This allows users to create an optimal asset management plan according to their emotional state.

[1234] Prompt Sentence Examples

[1235] User age: 35

[1236] Annual income: 8 million yen

[1237] Savings: 5 million yen

[1238] Financial goal: I want to save 3 million yen for my children's education.

[1239] Emotional state: Anxiety about education costs

[1240] Recommendations: Low-risk investment options and stable insurance products

[1241] This system automates financial planning tasks while providing personalized proposals that take into account the user's emotions. This allows users to receive optimal proposals based on their financial situation and emotional state, allowing them to manage their assets with peace of mind.

[1242] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1243] System program processing flow

[1244] The processing flow of the system program will be explained below by dividing it into specific steps.

[1245] Step 1: Enter your user information

[1246] User

[1247] Users access the system using a device, launching a browser or a dedicated app and entering financial information such as age, annual income, savings amount, and financial goals.

[1248] input

[1249] Age, annual income, savings, financial goals

[1250] output

[1251] Financial Information Data

[1252] Step 2: Encrypt and send information

[1253] Terminal

[1254] The terminal encrypts the financial information entered by the user using the Transport Layer Security (TLS) protocol, and then sends the encrypted financial information to the server over a secure communication channel using an HTTP POST request.

[1255] input

[1256] Financial Information Data

[1257] output

[1258] Encrypted financial data

[1259] Step 3: Analyze emotional state

[1260] Terminal

[1261] The emotion engine installed in the device captures and analyzes the user's facial expressions and voice data in real time, and determines the user's emotional state using facial recognition and voice analysis technologies.

[1262] input

[1263] User's facial expression data, voice data

[1264] output

[1265] Emotional state data

[1266] Step 4: Receive data and prepare for analysis

[1267] server

[1268] The server receives the encrypted data sent from the terminal and decrypts it using the TLS protocol. The server retrieves historical market data and customer data from a database (e.g., MySQL, PostgreSQL).

[1269] input

[1270] Encrypted financial data

[1271] output

[1272] Decoded financial information data, historical data

[1273] Step 5: Data analysis and proposal generation

[1274] server

[1275] The server inputs the decoded financial information and emotional state data into an AI model, which then analyzes the data and generates optimal asset management and service recommendations. The AI ​​models used include TensorFlow and PyTorch.

[1276] input

[1277] Decoded financial information data, emotional state data, historical data

[1278] output

[1279] Asset management and service proposals

[1280] Step 6: Encrypt and send the proposal

[1281] server

[1282] The server encrypts the generated proposal again using the TLS protocol and sends the encrypted proposal data to the user's device using an HTTP POST request.

[1283] input

[1284] Asset management and service proposals

[1285] output

[1286] Encrypted proposal data

[1287] Step 7: Viewing Proposals

[1288] Terminal

[1289] The user device decrypts the received encrypted proposal and visually displays it. Specifically, it uses JavaScript libraries (e.g., D3.js, Chart.js) to present the proposal to the user in the form of graphs or charts.

[1290] input

[1291] Encrypted proposal data

[1292] output

[1293] Visualized proposal

[1294] Step 8: Review and feedback on proposals

[1295] User

[1296] The user checks the displayed proposals and selects whether to accept the proposals, such as "take out education insurance" or "invest in a mutual fund that is expected to grow steadily." The user's selection is sent to the server as feedback data.

[1297] input

[1298] Visualized proposal

[1299] output

[1300] Feedback Data

[1301] Step 9: Saving feedback and improving the model

[1302] server

[1303] The server receives feedback data from users and stores it in a database. The feedback information is used to retrain the AI ​​model and improve the accuracy of future suggestions.

[1304] input

[1305] Feedback Data

[1306] output

[1307] Updated database, improved AI models

[1308] (Application example 2)

[1309] 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."

[1310] While conventional financial planner systems can provide appropriate asset management and service proposals based on a user's financial information, they have a problem in that they cannot take into account the user's emotional state and therefore have a low level of personalization. In particular, when a user is feeling stressed or anxious, it is difficult to provide appropriate proposals according to that situation.

[1311] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving financial information from a user, means for recognizing the user's emotional state using an emotion engine, and means for performing data analysis based on the received financial information and emotional state information. This enables personalized asset management and service proposals that take the user's emotional state into consideration.

[1312] "User" refers to a person who uses the service and provides financial information and emotional state to the system.

[1313] "Financial Information" refers to financial data such as a user's age, annual income, savings, and financial goals.

[1314] An "emotion engine" is a system that analyzes a user's facial expressions, voice, input patterns, etc. to recognize their emotional state.

[1315] "Emotional state" refers to the psychological state that a user feels, such as stress, anxiety, relief, or joy.

[1316] "Data Analysis" refers to the process by which the system performs analysis based on the received financial and emotional state information.

[1317] "Asset management" refers to the process of carrying out financial plans, such as investing and purchasing financial products, to increase a user's wealth.

[1318] "Service proposal" refers to a proposal that provides the user with the most suitable financial products, investment plans, etc.

[1319] "Database" refers to a large-scale data storage system that stores historical market data, customer data, proposal data, etc.

[1320] A "generative AI model" refers to an artificial intelligence model that is trained based on past data and generates suggestions for users.

[1321] A "secure communication protocol" refers to a communication method for securely encrypting and sending and receiving information.

[1322] System Configuration

[1323] The system for implementing this invention consists of four main components: a user terminal, a server, a database, and an emotion engine.

[1324] User terminal

[1325] The user terminal is the device where the user enters financial information and receives proposals provided by the system. It can be a smartphone, tablet, or PC. The terminal is equipped with an emotion-recognition camera and microphone, which analyzes the user's facial expressions and voice in real time to recognize their emotional state.

[1326] server

[1327] The server is the main processing unit that receives the financial and emotional state information sent by the user and performs data analysis. The server is installed with a generative AI model that is trained based on the received information. The generated proposals are then sent back to the user's device.

[1328] Database

[1329] The database is a storage system for large amounts of financial data, including market data, customer data, and historical proposal data, on which generative AI models are trained.

[1330] Emotion Engine

[1331] The emotion engine is a system that recognizes the user's emotional state and reflects it in data analysis and proposal generation. It analyzes facial expressions, voice, and input patterns to determine the user's emotional state. This information is sent to the server along with the user's financial information.

[1332] Processing flow

[1333] The server performs the process in the following procedure.

[1334] 1. Entering financial information: Users access the system and enter their financial information such as age, annual income, savings amount, financial goals, etc. This information is encrypted using a secure communication protocol and sent to the server.

[1335] 2. Emotional state recognition: The emotion engine on the user device analyzes the user's facial expressions and voice in real time to recognize the user's emotional state. The results are also sent to the server.

[1336] 3. Data analysis and proposal generation: The server inputs the received financial information and emotional state information into the AI ​​model and performs data analysis. Based on the analysis results, appropriate asset management and service proposals are generated.

[1337] 4. Proposal transmission and display: The generated proposal is again encrypted using a secure communication protocol and sent to the user's device, which decrypts it and visually displays it.

[1338] 5. Feedback storage: If the user accepts the suggestion, the information is fed back to the server and stored in the database, which will improve the accuracy of future suggestions.

[1339] Specific examples

[1340] For example, a user accesses the system via their smartphone and enters financial information such as their age and income. If the user is feeling stressed, the emotion engine recognizes this and sends it to the server. The server then uses this information to suggest stable investment options that reduce risk. Taking the user's emotional state into account provides a more personalized financial management plan.

[1341] Prompt Sentence Examples

[1342] "Generate purchasing advice for users when they are stressed. Output suggestions based on the following criteria:

[1343] The user is feeling stressed.

[1344] I have a strong desire to buy, but I want to avoid waste.

[1345] Please give me some example sentences for the suggestions you would like to generate.

[1346] This enables highly personalized suggestions that reflect the user's emotional state.

[1347] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1348] Step 1:

[1349] Users access the system using devices such as smartphones or PCs and enter financial information such as their age, annual income, savings amount, financial goals, etc. This information is encrypted on the device and sent to the server using a secure communication protocol.

[1350] Input: User's financial information (age, annual income, savings, financial goals, etc.)

[1351] Output: Encrypted financial information sent to the server

[1352] Step 2:

[1353] The emotion engine installed in the device recognizes the user's emotional state by analyzing the user's real-time facial expressions and voice. For example, the device's camera captures the user's facial expressions, and the emotion engine analyzes the data to identify the user's emotional state.

[1354] Input: User's facial expression data and voice data

[1355] Output: Recognized emotional state data

[1356] Step 3:

[1357] The emotional state recognized by the emotion engine is encrypted and transmitted to the server.

[1358] Input: Emotional state data

[1359] Output: Encrypted emotional state data is sent to the server

[1360] Step 4:

[1361] The server inputs the received financial information and emotional state information into the generative AI model and performs data analysis. The generative AI model generates optimal asset management and service proposals based on the input data, taking into account the user's emotional state.

[1362] Input: Financial and emotional state information

[1363] Output: Generated asset management and service proposals

[1364] Step 5:

[1365] The server encrypts the generated proposal using a secure communication protocol and transmits it to the user terminal.

[1366] Input: Asset management and service proposals

[1367] Output: The encrypted proposal data is sent to the device.

[1368] Step 6:

[1369] The user device decrypts the encrypted proposal data received from the server and visually displays it to the user, for example, on the screen of a smartphone or PC.

[1370] Input: Encrypted proposal data

[1371] Output: A visual representation of the proposal

[1372] Step 7:

[1373] The user reviews the displayed suggestions and accepts them if necessary, and the feedback is sent back to the server using a secure communication protocol.

[1374] Input: User feedback

[1375] Output: Encrypted feedback data is sent to the server

[1376] Step 8:

[1377] The server receives user feedback and stores it in a database, which is used as training data to improve the accuracy of future suggestions.

[1378] Input: Feedback data

[1379] Output: Feedback data stored in a database

[1380] 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.

[1381] 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.

[1382] 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.

[1383] [Fourth embodiment]

[1384] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1385] 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.

[1386] 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).

[1387] 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.

[1388] 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.

[1389] 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).

[1390] 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.

[1391] 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.

[1392] 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.

[1393] 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.

[1394] 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.

[1395] 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.

[1396] 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."

[1397] This invention is a financial planner system that utilizes AI, which receives financial information from users, performs advanced data analysis based on that information, and generates and provides appropriate asset management and service proposals to users. The specific configuration and processing flow of the system are as follows:

[1398] System Configuration

[1399] This system consists of three main components: a user terminal, a server, and a database.

[1400] User terminal

[1401] A user terminal is a device through which a user enters their financial information and receives offers provided by the system, such as a smartphone, tablet, or PC.

[1402] server

[1403] The server is the main processing unit that receives the financial information submitted by users, performs data analysis, and generates proposals. The server has an AI model installed and is trained on the information retrieved from the database.

[1404] Database

[1405] The database is a large-capacity data storage system that stores historical market data, customer data, analytical results, etc. The server uses this database to train AI models and generate advanced recommendations.

[1406] Processing flow

[1407] The specific processing flow centered on the server, terminal, and user will be explained below.

[1408] User

[1409] Users access the system and enter their financial information, such as their age, annual income, savings amount, financial goals, etc. This information is encrypted using a secure communication protocol and sent from the user's terminal to the server.

[1410] Terminal

[1411] The user terminal plays a role in encrypting the entered financial information and sending it to the server. For example, if a user enters information such as "Age: 35," "Annual income: 6 million yen," "Savings: 3 million yen," and "Goal: Secure funds for children's education," the information is encrypted and sent to the server.

[1412] server

[1413] The server decodes the received financial information and inputs it into the AI ​​model, which is trained on historical market and customer data stored in a database and performs detailed analysis based on the received information. As a result of the analysis, appropriate asset management and service proposals are generated.

[1414] The generated proposals are expressed as recommendations for specific investments, insurance products, and financial plans in a variety of fields, including securities, insurance, and real estate investment. For example, based on the user's input information, the system may generate proposals such as "take out education insurance," "invest in a mutual fund that is expected to grow steadily," or "purchase a rental property."

[1415] Terminal

[1416] The server-generated proposals are then sent, again using a secure communication protocol, to the user terminal, which decrypts the proposals and visually displays them to the user.

[1417] User

[1418] The user can review the displayed suggestions and accept them if necessary. If the user accepts a suggestion, the information is fed back to the server and updated in the database. This feedback further improves the accuracy of future suggestions.

[1419] Specific examples

[1420] For example, if a user in their 30s wants to secure funds for their child's education in the near future, they input their age, annual income, savings amount, and goals into the system. The server uses this information to perform AI analysis and generates specific suggestions such as "enroll in education insurance," "invest in rental property," and "invest in mutual funds with stable growth potential." These suggestions are displayed on the user's device, and the user decides whether to accept them.

[1421] By automating financial planning tasks, this system can provide users with more efficient and accurate asset management proposals than conventional manual work, allowing users to receive proposals that are optimal for their financial situation and goals.

[1422] The processing flow will be explained below.

[1423] Step 1:

[1424] Users enter their financial information (age, annual income, savings amount, financial goals, etc.) into a dedicated form.

[1425] Step 2:

[1426] The terminal encrypts the input information using a secure communication protocol and transmits it to the server.

[1427] Step 3:

[1428] The server receives the encrypted data and decrypts it to obtain the user's financial information.

[1429] Step 4:

[1430] The server retrieves historical market and customer data from a database and inputs the user's financial information into a trained AI model.

[1431] Step 5:

[1432] The server's AI model performs detailed data analysis based on the user's financial information, including predictions that take into account the user's risk tolerance and market trends.

[1433] Step 6:

[1434] Based on the analysis results, the server generates asset management and service proposals that best suit the user's financial goals, including specific investment options, insurance products, real estate investments, and more.

[1435] Step 7:

[1436] The server encrypts the generated proposal again using a secure communication protocol and transmits it to the terminal.

[1437] Step 8:

[1438] The terminal receives the encrypted proposal, decrypts it and visually displays it to the user.

[1439] Step 9:

[1440] The user can then review the displayed proposal and decide whether to accept it. If necessary, the user can also request additional questions or a new proposal with different conditions.

[1441] Step 10:

[1442] If the user accepts the suggestion, the information is fed back to the server via the terminal.

[1443] Step 11:

[1444] The server stores the user's feedback information in a database and uses it as data to update the AI ​​model.

[1445] By following the steps above, this system can provide highly accurate advice based on the user's financial information, streamlining and automating financial planning operations.

[1446] Example 1

[1447] 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."

[1448] Conventional financial planner systems have had difficulty effectively collecting users' financial information and analyzing the data securely and efficiently to provide appropriate asset management proposals. In particular, ensuring the security of user information and performing advanced data analysis that effectively utilizes past market data are required. This has resulted in users being unable to receive reliable asset management proposals.

[1449] 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.

[1450] In this invention, the server includes means for receiving financial information from a user, means for encrypting the received financial information, means for transmitting the encrypted financial information to the server using a secure communication protocol, means for decrypting the received financial information and performing data analysis based on an AI model, means for generating appropriate asset management and service proposals based on the analysis results, means for re-encrypting the generated proposals and providing them to the user using a secure communication protocol, and means for storing the proposals and user information in a database. This allows users to input their financial information under high security and receive advanced data analysis by AI based on that information, thereby enabling them to receive optimal asset management proposals in real time.

[1451] "User Terminal" means an electronic device used by a User to input financial information and display proposals received from the System, including a smartphone, tablet, PC, etc.

[1452] The "server" is the main processing device that receives financial information sent from user terminals, analyzes the data based on that information, and generates appropriate asset management proposals.

[1453] The "database" is a large-capacity data storage system that stores historical market data, customer data, analytical results, etc., and the server uses this data to train AI models and generate recommendations.

[1454] "Financial information" refers to information related to a person's financial situation that is entered by the user, and specifically includes age, annual income, savings amount, financial goals, and the like.

[1455] "Encryption" is a technology that uses special algorithms to convert a user's financial information and proposals into a format that cannot be deciphered by third parties in order to ensure information security.

[1456] A "secure communication protocol" is a communication protocol for ensuring safety in data transmission, and specifically includes HTTPS (Hypertext Transfer Protocol Secure).

[1457] An "AI model" is an algorithm and its training results that uses machine learning technology to analyze data, and is used to generate asset management proposals based on past market data and customer data.

[1458] "Data analysis" is an analytical process carried out by an AI model using historical market data and customer data based on the user's financial information.

[1459] "Asset management proposals" are investment strategies and financial plan proposals generated by the server based on the results of data analysis, and include specific options such as securities, insurance, and real estate investments.

[1460] This invention is a financial planner system that utilizes AI, which receives financial information from users, performs advanced data analysis based on that information, and generates and provides appropriate asset management and service proposals to users. The specific configuration and processing flow of the system are as follows:

[1461] System Configuration

[1462] This system consists of three main components: a user terminal, a server, and a database.

[1463] User terminal

[1464] A user terminal is a device through which a user enters their financial information and receives offers provided by the system, such as a smartphone, tablet, or PC.

[1465] server

[1466] The server is the main processing unit that receives financial information submitted by users, analyzes the data, and generates proposals. An AI model is installed on the server and trained based on the information retrieved from the database. Specifically, machine learning libraries such as TensorFlow or PyTorch can be used.

[1467] Database

[1468] The database is a large-capacity data storage system that stores historical market data, customer data, analytical results, etc. The server uses this database to train AI models and generate advanced recommendations.

[1469] User

[1470] Users access the system and enter their financial information, such as their age, annual income, savings amount, financial goals, etc. This information is encrypted using a secure communication protocol and sent from the user's terminal to the server.

[1471] Terminal

[1472] The user terminal plays a role in encrypting the entered financial information and sending it to the server. For example, if a user enters information such as "Age: 35," "Annual income: 6 million yen," "Savings: 3 million yen," and "Goal: Secure funds for children's education," the information is encrypted and sent to the server.

[1473] server

[1474] The server decodes the received financial information and inputs it into the AI ​​model. The AI ​​model is trained based on past market and customer data stored in a database and performs detailed analysis based on the received information. As a result of the analysis, appropriate asset management and service proposals are generated. The generated proposals are expressed as recommendations for specific investments, insurance products, and financial plans in a variety of fields, including securities, insurance, and real estate investment. For example, based on the information entered by the user, recommendations such as "take out education insurance," "invest in a mutual fund that is expected to grow steadily," and "purchase a rental property" are generated.

[1475] Terminal

[1476] The server-generated proposals are then sent, again using a secure communication protocol, to the user terminal, which decrypts the proposals and visually displays them to the user.

[1477] User

[1478] The user can review the displayed suggestions and accept them if necessary. If the user accepts a suggestion, the information is fed back to the server and updated in the database. This feedback further improves the accuracy of future suggestions.

[1479] Specific examples

[1480] For example, if a user in their 30s wants to secure funds for their child's education in the near future, they input their age, annual income, savings amount, and goals into the system. The server uses this information to perform AI analysis and generates specific suggestions such as "enrolling in education insurance," "investing in rental properties," and "investing in mutual funds with stable growth potential." These suggestions are displayed on the user's device, and the user decides whether to accept them.

[1481] Prompt Sentence Examples

[1482] "Enter your financial information: age, annual income, savings amount, goals."

[1483] "Generate asset management recommendations based on AI models."

[1484] This system automates the work of financial planners and provides users with efficient and accurate asset management proposals, allowing them to receive proposals that are optimal for their financial situation and goals.

[1485] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1486] Step 1:

[1487] Users access the system using a browser or a dedicated app and enter their login information to authenticate, which allows the system to recognize the user and provide an input screen for entering individual financial information.

[1488] Input: User authentication information (username, password)

[1489] Output: Financial information input screen

[1490] Step 2:

[1491] Users input financial information such as their age, annual income, savings amount, and financial goals. For example, they input information such as "Age: 35," "Annual income: 6 million yen," "Savings: 3 million yen," and "Goal: Secure funds for children's education."

[1492] Input: User's financial information (age, annual income, savings amount, financial goals)

[1493] Output: Financial information before encryption

[1494] Step 3:

[1495] The user terminal encrypts the entered financial information using the Advanced Encryption Standard (AES).

[1496] Input: User's financial information

[1497] Output: Encrypted financial information

[1498] Step 4:

[1499] The terminal sends encrypted financial information to the server using a secure communication protocol (HTTPS).

[1500] Input: Encrypted financial information

[1501] Output: Encrypted data sent to the server

[1502] Step 5:

[1503] The server receives and decrypts the encrypted financial information, which is then retrieved as the original financial information.

[1504] Input: Encrypted financial information

[1505] Output: Decrypted financial information

[1506] Step 6:

[1507] The server preprocesses the decrypted financial information, specifically normalizing numerical data such as age and annual income and converting it into a format compatible with AI models.

[1508] Input: Decrypted financial information

[1509] Output: Preprocessed data

[1510] Step 7:

[1511] The server inputs the pre-processed data into the AI ​​model, which is trained on historical market and customer data, and generates appropriate asset management and service proposals based on the analysis.

[1512] Input: Preprocessed data

[1513] Output: Analysis results (appropriate asset management and service proposals)

[1514] Step 8:

[1515] The server encrypts the generated proposal and transmits it to the user terminal using a secure communication protocol.

[1516] Input: Analysis results (service proposal)

[1517] Output: Encrypted service proposal

[1518] Step 9:

[1519] The user terminal receives and decrypts the encrypted proposal and visually displays it to the user.

[1520] Input: Encrypted service proposal

[1521] Output: Decoded service offer

[1522] Step 10:

[1523] The user checks the displayed suggestions and accepts them if necessary. If the suggestion is accepted, the information is fed back to the system.

[1524] Input: Decoded service offer

[1525] Output: User feedback information

[1526] Step 11:

[1527] The server stores the user's feedback information in a database and uses it to make updates to improve the accuracy of future suggestions.

[1528] Input: User feedback information

[1529] Output: Updated database

[1530] In this way, the system safely and efficiently handles financial information from users and provides appropriate asset management suggestions.

[1531] (Application example 1)

[1532] 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."

[1533] Conventional financial planner systems have difficulty reflecting users' spending patterns and daily income information in real time. This has resulted in asset management and service proposals that do not accurately reflect the user's latest situation, limiting their effectiveness. Furthermore, conventional systems require users to perform multiple operations on different platforms, resulting in poor usability.

[1534] 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.

[1535] In this invention, the server includes means for receiving financial information from a user, means for performing data analysis based on the received financial information, means for generating appropriate asset management and service proposals based on the results of the data analysis, means for providing the generated proposals to the user, means for storing the proposals and user information in a database, and means for updating the proposals in real time based on the user's spending patterns and income information. This enables asset management proposals that reflect the user's latest financial situation and spending patterns in a timely manner, further improving usability.

[1536] "Financial Information" is data about a user's income, expenses, savings, investments, etc.

[1537] "Data analytics" is the process of using algorithms and models to discover patterns and make predictions using collected financial information.

[1538] "Asset management" refers to proposing optimal investment methods, insurance contracts, etc. based on the user's financial information.

[1539] "Service proposal" refers to recommending services such as financial products and insurance products based on the user's financial information.

[1540] A "secure communication protocol" is a communication method that encrypts data when sending and receiving it to prevent unauthorized access by third parties.

[1541] "Encryption" is a technique for converting data so that it cannot be understood in its original form, and only the sender and receiver can decipher the data.

[1542] "Large-scale financial data" refers to large amounts of financial-related information, such as historical market data and customer data, that are used for data analysis and training artificial intelligence models.

[1543] An "AI model" is an algorithm or model built using artificial intelligence (AI) technology to extract and analyze useful information from input data.

[1544] "Spending patterns" refer to tendencies or rules that indicate how a user spends money.

[1545] "Income information" is data indicating the user's income such as salary and bonuses.

[1546] "Means for updating proposals in real time" refers to a system function that instantly analyzes the latest collected financial information and can always provide users with optimal asset management proposals.

[1547] This invention provides a financial planner system that utilizes AI. The detailed implementation method of the system that realizes the PonPon application example is explained below.

[1548] System configuration

[1549] This system consists of three main components: a user terminal, a server, and a database.

[1550] User terminal

[1551] The user terminal is a device such as a smartphone, tablet, or PC that allows the user to enter financial information and receive proposals. The user enters their own financial information through the application and receives proposals from the system.

[1552] server

[1553] The server is the main processing unit that receives financial information submitted by users, analyzes the data, and generates proposals. The server has a generative AI model installed and trained on the information retrieved from the database. The server encrypts the data using Python's cryptography library and communicates using the requests library.

[1554] Database

[1555] The database is a large-capacity data storage that stores historical market data, customer data, analytical results, etc. The server uses this database to train AI models and generate advanced recommendations.

[1556] Program processing

[1557] 1. User enters financial information:

[1558] The user inputs information such as age, annual income, savings amount, financial goals, etc. into the application. For example, the user inputs information such as "Age: 35," "Annual income: 6 million yen," "Savings: 3 million yen," and "Goal: Secure funds for children's education."

[1559] 2. Encryption and Transmission of Financial Information:

[1560] The user's terminal encrypts the entered financial information using Python's cryptography library before sending it to the server, ensuring the security of the information.

[1561] 3. Server-based data analysis and proposal generation:

[1562] The server decodes the received financial information and inputs it into the AI ​​model, which is trained on historical market and customer data stored in a database and performs detailed analysis based on the received information. As a result of the analysis, appropriate asset management and service proposals are generated.

[1563] 4. Providing Suggestions to Users:

[1564] The generated proposals are expressed as specific investment ideas, insurance products, and financial plans in various fields such as securities, insurance, and real estate investment. For example, they include proposals such as "take out education insurance," "invest in a mutual fund with stable growth prospects," and "purchase a rental property." These proposals are then sent to the user's device using a secure communication protocol.

[1565] 5. Displaying and Accepting Proposals:

[1566] The user's device decodes the proposals sent from the server and visually displays them. The user can then review the proposals and accept them as necessary. This allows asset management proposals to always reflect the user's latest financial situation and spending patterns.

[1567] Specific examples

[1568] For example, if a user in their 30s wants to secure funds for their child's education in the near future, they input their age, annual income, savings amount, and goals into the system. The server uses this information to perform AI analysis and generates specific suggestions such as "enrolling in education insurance," "investing in rental properties," and "investing in mutual funds with stable growth potential." These suggestions are displayed on the user's device, allowing the user to decide whether to accept them.

[1569] Prompt Sentence Examples

[1570] "I'm 35 years old, earn 6 million yen a year, and have 3 million yen saved up. My goal is to secure funds for my children's education. What is the best way to manage my assets?"

[1571] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1572] Step 1:

[1573] The user enters financial information.

[1574] Input: The user enters financial information such as their age, annual income, savings amount, and financial goals into the application. Specifically, the user enters information such as "Age: 35," "Annual income: 6 million yen," "Savings: 3 million yen," and "Goal: Secure funds for children's education."

[1575] Output: The entered financial information is temporarily stored on the user's terminal.

[1576] How it works: Users enter financial information into a form provided on their smartphone, tablet, or computer screen. Once completed, the device passes the data to the next processing step.

[1577] Step 2:

[1578] The device encrypts the financial information and sends it to the server.

[1579] Input: Financial information entered by the user.

[1580] Output: Encrypted financial information is sent to the server.

[1581] How it works: The device encrypts financial information using Python's cryptography library, then uses the requests library to send the encrypted information to the server as an HTTP POST request. Before sending, the device performs a thorough encryption process to prevent information leaks.

[1582] Step 3:

[1583] The server interprets the received financial information and retrieves relevant information from a database.

[1584] Input: Encrypted financial information received by the server.

[1585] Output: Interpreted financial information and information from related databases.

[1586] How it works: The server first decrypts the encrypted data it receives, then retrieves historical market data and user data with the same attributes from a database to input the decrypted financial information into an AI model, preparing it for analysis.

[1587] Step 4:

[1588] The server analyzes the data and uses generative AI models to generate asset management and service proposals.

[1589] Input: Interpreted financial information and related information retrieved from databases.

[1590] Output: Generated asset management and service proposals.

[1591] How it works: The server inputs the interpreted financial information and related data into the AI ​​model. The model then performs advanced data analysis using predictive algorithms and historical data to generate optimal recommendations for the user. Specific recommendations include "take out education insurance," "invest in a mutual fund with stable growth potential," and "purchase a rental property."

[1592] Step 5:

[1593] The server transmits the generated proposal to the user terminal.

[1594] Input: Generated asset management and servicing proposals.

[1595] Output: Proposal data sent to the user device.

[1596] Specific operation: The server re-encrypts the generated proposal and sends it to the user device using a secure communication protocol, taking care to ensure the integrity and confidentiality of the information.

[1597] Step 6:

[1598] The terminal interprets the suggestions and displays them visually to the user.

[1599] Input: Encrypted proposal data sent by the server.

[1600] Output: The decoded proposal in a format that can be viewed by the user.

[1601] Specific operation: After the user device decrypts the received encrypted data, it visually displays the decrypted suggestions to the user. For example, the suggestions may be displayed on the smartphone app screen in the form of "Enroll in education insurance" or "Invest in mutual funds."

[1602] Step 7:

[1603] The user reviews the proposal and accepts it if necessary.

[1604] Input: Proposal data, decrypted and displayed.

[1605] Output: The user's selection.

[1606] What it does: The user reviews the suggestions and chooses whether to accept them. If accepted, the information is fed back from the device to the server and updated in the database. This feedback improves the accuracy of future suggestions.

[1607] 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.

[1608] This invention combines an emotion engine with an AI financial planner system that analyzes data based on a user's financial information and generates appropriate asset management and service proposals. Specifically, the goal is to provide more personalized services by recognizing the user's emotional state and utilizing that information in data analysis and proposal generation.

[1609] System Configuration

[1610] This system consists of four main components: a user terminal, a server, a database, and an emotion engine.

[1611] User terminal

[1612] A user terminal is a device through which a user enters their financial information and receives offers provided by the system, such as a smartphone, tablet, or PC.

[1613] server

[1614] The server is the main processing unit that receives the financial information submitted by users, performs data analysis, and generates proposals. The server has an AI model installed and is trained on the information retrieved from the database.

[1615] Database

[1616] The database is a large-capacity data storage system that stores historical market data, customer data, analytical results, etc. The server uses this database to train AI models and generate advanced recommendations.

[1617] Emotion Engine

[1618] The emotion engine is a system that recognizes the user's emotional state and reflects that information in data analysis and proposal generation. The emotion engine recognizes emotions by analyzing the user's facial expressions, voice, input patterns, etc.

[1619] Processing flow

[1620] The specific processing flow centered on the server, terminal, and user will be explained below.

[1621] User

[1622] Users access the system and enter their financial information, such as their age, annual income, savings amount, financial goals, etc. This information is encrypted using a secure communication protocol and sent from the user's terminal to the server.

[1623] Terminal

[1624] The user device encrypts the entered financial information and transmits it to the server. The device is also equipped with an emotion engine that analyzes the user's facial expressions and voice in real time to recognize their emotional state.

[1625] server

[1626] The server inputs the received financial information and the user's emotional state information into the AI ​​model. The AI ​​model is trained based on historical market and customer data stored in a database and performs detailed data analysis. As a result of the analysis, asset management and service proposals that are best suited to the user's financial goals and emotional state are generated.

[1627] The generated proposals are expressed as recommendations for specific investments, insurance products, and financial plans in various fields such as securities, insurance, real estate investment, etc. For example, if a user has an emotional state of "feeling anxious about risk," more stable investments will be suggested to alleviate that anxiety.

[1628] Terminal

[1629] The proposals generated by the server are again encrypted using a secure communication protocol and sent to the terminal, where they are decrypted and visually displayed to the user.

[1630] User

[1631] The user can review the displayed suggestions and accept them if necessary. For example, suggestions such as "take out education insurance," "invest in a mutual fund that is expected to grow steadily," and "purchase a rental property" are displayed. The user can decide whether to accept the suggestion that best suits their emotional state.

[1632] server

[1633] If the user accepts a suggestion, the information is fed back to the server via the device and stored in a database, allowing future suggestions to be more accurate.

[1634] Specific examples

[1635] For example, if a user in their 30s wants to secure funds for their children's future education, they can input their age, annual income, savings amount, and goals into the system. Furthermore, if the user expresses "anxiety about education expenses" through the emotion engine, the server will take this into consideration and suggest low-risk investment options and stable insurance products. This allows users to create an optimal asset management plan that suits their emotional state.

[1636] This system automates financial planning tasks while providing personalized proposals that take into account the user's emotions. This allows users to receive optimal proposals based on their financial situation and emotional state, allowing them to manage their assets with peace of mind.

[1637] The processing flow will be explained below.

[1638] Step 1:

[1639] Users enter their financial information (age, annual income, savings amount, financial goals, etc.) into a dedicated form. The emotion engine also collects the user's facial expressions and voice in real time.

[1640] Step 2:

[1641] The terminal encrypts the entered financial information and emotion data using a secure communication protocol and transmits it to the server.

[1642] Step 3:

[1643] The server receives the encrypted data and decrypts it to obtain the user's financial information and emotional state.

[1644] Step 4:

[1645] The server retrieves historical market and customer data from a database and inputs users' financial and emotional information into a trained AI model.

[1646] Step 5:

[1647] The server's AI model performs detailed data analysis based on the user's financial and emotional information, including predictions that take into account the user's financial goals, risk tolerance, and emotional state.

[1648] Step 6:

[1649] Based on the analysis results, the server generates asset management and service recommendations that best suit the user's financial goals and emotional state, including specific investment options, insurance products, real estate investments, and more.

[1650] Step 7:

[1651] The server encrypts the generated proposal again using a secure communication protocol and transmits it to the terminal.

[1652] Step 8:

[1653] The device receives the encrypted proposal, decrypts it, and visually displays it to the user, including details of recommended financial plans and insurance products.

[1654] Step 9:

[1655] The user can review the displayed proposal and decide whether to accept it. If necessary, they can also request additional questions or a new proposal with different conditions.

[1656] Step 10:

[1657] If the user accepts the suggestion, the information is fed back to the server via the terminal.

[1658] Step 11:

[1659] The server stores the user's feedback information in a database and uses it as data to update the AI ​​model.

[1660] Through the above steps, the system provides highly accurate advice based on the user's financial information, streamlining and automating financial planning work, while also using an emotion engine to make personalized proposals that adapt to the user's emotional state.

[1661] Example 2

[1662] 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."

[1663] Conventional financial planning systems can make asset management proposals based on a user's financial information, but they cannot make proposals that take into account the user's emotional state. As a result, personalized proposals based on the user's emotional state are not provided, making it difficult to realize proposals that fully reflect the user's anxiety and sensitivity to risk. The present invention aims to solve these problems and provide a system that makes financial proposals that take into account the user's emotional state.

[1664] 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.

[1665] In this invention, the server includes means for receiving financial information and an emotional state from a user, means for performing data analysis based on the received financial information and emotional state, and means for generating appropriate asset management and service proposals based on the results of the data analysis, thereby enabling personalized asset management and service proposals that take the user's emotional state into consideration.

[1666] A "user" is any person or entity that accesses the system to input financial information and emotional state and receive recommendations.

[1667] "Financial information" refers to economic information such as a user's age, annual income, savings, and financial goals.

[1668] "Emotional state" refers to the psychological state recognized from the user's facial expression, voice, input pattern, etc.

[1669] "Secure communication protocol" refers to technology (e.g., TLS and SSL) that encrypts data and enables secure communication.

[1670] "Data analytics" refers to the process of using AI models and algorithms to conduct detailed analysis based on received financial information and emotional state.

[1671] "AI model" refers to an artificial intelligence algorithm that is trained based on past data and can perform a specific task (e.g., data analysis, recommendation generation).

[1672] "Wealth Management and Service Recommendations" refers to recommended investment products, insurance, and other plans generated based on the user's financial information and emotional state.

[1673] "Database" refers to a data storage system for storing historical market data, customer data, analytical results, etc.

[1674] "Feedback" refers to data that is sent back to the server regarding the suggestions that the user has accepted, and that is used to improve the system.

[1675] This invention combines an emotion engine with an AI financial planner system that analyzes data based on a user's financial information and generates appropriate asset management and service proposals. Specifically, the goal is to provide more personalized services by recognizing the user's emotional state and utilizing that information in data analysis and proposal generation.

[1676] System Configuration

[1677] This system consists of four main components: a user terminal, a server, a database, and an emotion engine.

[1678] User terminal

[1679] The user terminal is a device where the user enters their financial information and receives proposals provided by the system. It can be a smartphone, tablet, or PC. The terminal is equipped with an emotion engine that analyzes the user's facial expressions and voice in real time to recognize their emotional state.

[1680] server

[1681] The server is the main processing unit that receives financial information and emotional states submitted by users, analyzes the data, and generates recommendations. AI models trained with deep learning frameworks such as TensorFlow and PyTorch are installed on the server. The server performs analysis based on the information retrieved from the database.

[1682] Database

[1683] The database is a large-capacity data storage system that stores historical market data, customer data, analytical results, etc. The database may be a NoSQL database such as MySQL, PostgreSQL, or MongoDB. The server uses this database to train AI models and generate advanced recommendations.

[1684] Emotion Engine

[1685] The emotion engine is a system that recognizes the user's emotional state and reflects that information in data analysis and proposal generation. The emotion engine analyzes the user's facial expressions, voice, input patterns, etc. to recognize emotions.

[1686] Specific examples

[1687] For example, if a user in their 30s wants to secure funds for their children's future education, they can input their age, annual income, savings amount, and goals into the system. Furthermore, if the user expresses "anxiety about education expenses" through the emotion engine, the server will take this into consideration and suggest low-risk investment options and stable insurance products. This allows users to create an optimal asset management plan according to their emotional state.

[1688] Prompt Sentence Examples

[1689] User age: 35

[1690] Annual income: 8 million yen

[1691] Savings: 5 million yen

[1692] Financial goal: I want to save 3 million yen for my children's education.

[1693] Emotional state: Anxiety about education costs

[1694] Recommendations: Low-risk investment options and stable insurance products

[1695] This system automates financial planning tasks while providing personalized proposals that take into account the user's emotions. This allows users to receive optimal proposals based on their financial situation and emotional state, allowing them to manage their assets with peace of mind.

[1696] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1697] System program processing flow

[1698] The processing flow of the system program will be explained below by dividing it into specific steps.

[1699] Step 1: Enter your user information

[1700] User

[1701] Users access the system using a device, launching a browser or a dedicated app and entering financial information such as age, annual income, savings amount, and financial goals.

[1702] input

[1703] Age, annual income, savings, financial goals

[1704] output

[1705] Financial Information Data

[1706] Step 2: Encrypt and send information

[1707] Terminal

[1708] The terminal encrypts the financial information entered by the user using the Transport Layer Security (TLS) protocol, and then sends the encrypted financial information to the server over a secure communication channel using an HTTP POST request.

[1709] input

[1710] Financial Information Data

[1711] output

[1712] Encrypted financial data

[1713] Step 3: Analyze emotional state

[1714] Terminal

[1715] The emotion engine installed in the device captures and analyzes the user's facial expressions and voice data in real time, and determines the user's emotional state using facial recognition and voice analysis technologies.

[1716] input

[1717] User's facial expression data, voice data

[1718] output

[1719] Emotional state data

[1720] Step 4: Receive data and prepare for analysis

[1721] server

[1722] The server receives the encrypted data sent from the terminal and decrypts it using the TLS protocol. The server retrieves historical market data and customer data from a database (e.g., MySQL, PostgreSQL).

[1723] input

[1724] Encrypted financial data

[1725] output

[1726] Decoded financial information data, historical data

[1727] Step 5: Data analysis and proposal generation

[1728] server

[1729] The server inputs the decoded financial information and emotional state data into an AI model, which then analyzes the data and generates optimal asset management and service recommendations. The AI ​​models used include TensorFlow and PyTorch.

[1730] input

[1731] Decoded financial information data, emotional state data, historical data

[1732] output

[1733] Asset management and service proposals

[1734] Step 6: Encrypt and send the proposal

[1735] server

[1736] The server encrypts the generated proposal again using the TLS protocol and sends the encrypted proposal data to the user's device using an HTTP POST request.

[1737] input

[1738] Asset management and service proposals

[1739] output

[1740] Encrypted proposal data

[1741] Step 7: Viewing Proposals

[1742] Terminal

[1743] The user device decrypts the received encrypted proposal and visually displays it. Specifically, it uses JavaScript libraries (e.g., D3.js, Chart.js) to present the proposal to the user in the form of graphs or charts.

[1744] input

[1745] Encrypted proposal data

[1746] output

[1747] Visualized proposal

[1748] Step 8: Review and feedback on proposals

[1749] User

[1750] The user checks the displayed proposals and selects whether to accept the proposals, such as "take out education insurance" or "invest in a mutual fund that is expected to grow steadily." The user's selection is sent to the server as feedback data.

[1751] input

[1752] Visualized proposal

[1753] output

[1754] Feedback Data

[1755] Step 9: Saving feedback and improving the model

[1756] server

[1757] The server receives feedback data from users and stores it in a database. The feedback information is used to retrain the AI ​​model and improve the accuracy of future suggestions.

[1758] input

[1759] Feedback Data

[1760] output

[1761] Updated database, improved AI models

[1762] (Application example 2)

[1763] 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."

[1764] While conventional financial planner systems can provide appropriate asset management and service proposals based on a user's financial information, they have a problem in that they cannot take into account the user's emotional state and therefore have a low level of personalization. In particular, when a user is feeling stressed or anxious, it is difficult to provide appropriate proposals according to that situation.

[1765] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving financial information from a user, means for recognizing the user's emotional state using an emotion engine, and means for performing data analysis based on the received financial information and emotional state information. This enables personalized asset management and service proposals that take the user's emotional state into consideration.

[1766] "User" refers to a person who uses the service and provides financial information and emotional state to the system.

[1767] "Financial Information" refers to financial data such as a user's age, annual income, savings, and financial goals.

[1768] An "emotion engine" is a system that analyzes a user's facial expressions, voice, input patterns, etc. to recognize their emotional state.

[1769] "Emotional state" refers to the psychological state that a user feels, such as stress, anxiety, relief, or joy.

[1770] "Data Analysis" refers to the process by which the system performs analysis based on the received financial and emotional state information.

[1771] "Asset management" refers to the process of carrying out financial plans, such as investing and purchasing financial products, to increase a user's wealth.

[1772] "Service proposal" refers to a proposal that provides the user with the most suitable financial products, investment plans, etc.

[1773] "Database" refers to a large-scale data storage system that stores historical market data, customer data, proposal data, etc.

[1774] A "generative AI model" refers to an artificial intelligence model that is trained based on past data and generates suggestions for users.

[1775] A "secure communication protocol" refers to a communication method for securely encrypting and sending and receiving information.

[1776] System Configuration

[1777] The system for implementing this invention consists of four main components: a user terminal, a server, a database, and an emotion engine.

[1778] User terminal

[1779] The user terminal is the device where the user enters financial information and receives proposals provided by the system. It can be a smartphone, tablet, or PC. The terminal is equipped with an emotion-recognition camera and microphone, which analyzes the user's facial expressions and voice in real time to recognize their emotional state.

[1780] server

[1781] The server is the main processing unit that receives the financial and emotional state information sent by the user and performs data analysis. The server is installed with a generative AI model that is trained based on the received information. The generated proposals are then sent back to the user's device.

[1782] Database

[1783] The database is a storage system for large amounts of financial data, including market data, customer data, and historical proposal data, on which generative AI models are trained.

[1784] Emotion Engine

[1785] The emotion engine is a system that recognizes the user's emotional state and reflects it in data analysis and proposal generation. It analyzes facial expressions, voice, and input patterns to determine the user's emotional state. This information is sent to the server along with the user's financial information.

[1786] Processing flow

[1787] The server performs the process in the following procedure.

[1788] 1. Entering financial information: Users access the system and enter their financial information such as age, annual income, savings amount, financial goals, etc. This information is encrypted using a secure communication protocol and sent to the server.

[1789] 2. Emotional state recognition: The emotion engine on the user device analyzes the user's facial expressions and voice in real time to recognize the user's emotional state. The results are also sent to the server.

[1790] 3. Data analysis and proposal generation: The server inputs the received financial information and emotional state information into the AI ​​model and performs data analysis. Based on the analysis results, appropriate asset management and service proposals are generated.

[1791] 4. Proposal transmission and display: The generated proposal is again encrypted using a secure communication protocol and sent to the user's device, which decrypts it and visually displays it.

[1792] 5. Feedback storage: If the user accepts the suggestion, the information is fed back to the server and stored in the database, which will improve the accuracy of future suggestions.

[1793] Specific examples

[1794] For example, a user accesses the system via their smartphone and enters financial information such as their age and income. If the user is feeling stressed, the emotion engine recognizes this and sends it to the server. The server then uses this information to suggest stable investment options that reduce risk. Taking the user's emotional state into account provides a more personalized financial management plan.

[1795] Prompt Sentence Examples

[1796] "Generate purchasing advice for users when they are stressed. Output suggestions based on the following criteria:

[1797] The user is feeling stressed.

[1798] I have a strong desire to buy, but I want to avoid waste.

[1799] Please give me some example sentences for the suggestions you would like to generate.

[1800] This enables highly personalized suggestions that reflect the user's emotional state.

[1801] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1802] Step 1:

[1803] Users access the system using devices such as smartphones or PCs and enter financial information such as their age, annual income, savings amount, financial goals, etc. This information is encrypted on the device and sent to the server using a secure communication protocol.

[1804] Input: User's financial information (age, annual income, savings, financial goals, etc.)

[1805] Output: Encrypted financial information sent to the server

[1806] Step 2:

[1807] The emotion engine installed in the device recognizes the user's emotional state by analyzing the user's real-time facial expressions and voice. For example, the device's camera captures the user's facial expressions, and the emotion engine analyzes the data to identify the user's emotional state.

[1808] Input: User's facial expression data and voice data

[1809] Output: Recognized emotional state data

[1810] Step 3:

[1811] The emotional state recognized by the emotion engine is encrypted and transmitted to the server.

[1812] Input: Emotional state data

[1813] Output: Encrypted emotional state data is sent to the server

[1814] Step 4:

[1815] The server inputs the received financial information and emotional state information into the generative AI model and performs data analysis. The generative AI model generates optimal asset management and service proposals based on the input data, taking into account the user's emotional state.

[1816] Input: Financial and emotional state information

[1817] Output: Generated asset management and service proposals

[1818] Step 5:

[1819] The server encrypts the generated proposal using a secure communication protocol and transmits it to the user terminal.

[1820] Input: Asset management and service proposals

[1821] Output: The encrypted proposal data is sent to the device.

[1822] Step 6:

[1823] The user device decrypts the encrypted proposal data received from the server and visually displays it to the user, for example, on the screen of a smartphone or PC.

[1824] Input: Encrypted proposal data

[1825] Output: A visual representation of the proposal

[1826] Step 7:

[1827] The user reviews the displayed suggestions and accepts them if necessary, and the feedback is sent back to the server using a secure communication protocol.

[1828] Input: User feedback

[1829] Output: Encrypted feedback data is sent to the server

[1830] Step 8:

[1831] The server receives user feedback and stores it in a database, which is used as training data to improve the accuracy of future suggestions.

[1832] Input: Feedback data

[1833] Output: Feedback data stored in a database

[1834] 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.

[1835] 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.

[1836] 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.

[1837] 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.

[1838] 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.

[1839] 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.

[1840] 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).

[1841] 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.

[1842] 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."

[1843] 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.

[1844] 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).

[1845] 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.

[1846] 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.

[1847] 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.

[1848] 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.

[1849] 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.

[1850] 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.

[1851] 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.

[1852] 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.

[1853] 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.

[1854] 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.

[1855] The following is further disclosed regarding the above embodiment.

[1856] (Claim 1)

[1857] means for receiving financial information from a user;

[1858] means for performing data analysis based on the received financial information;

[1859] A means for generating appropriate asset management and service proposals based on the data analysis results;

[1860] means for providing the generated suggestions to a user;

[1861] means for storing the suggestions and user information in a database;

[1862] A system including:

[1863] (Claim 2)

[1864] 10. The system of claim 1, wherein the means for receiving financial information from the user encrypts and transmits the information using a secure communication protocol.

[1865] (Claim 3)

[1866] 10. The system of claim 1, wherein the database stores large amounts of financial data, including historical market data and customer data, on which the AI ​​model is trained.

[1867] (Claim 4)

[1868] 2. The system of claim 1, wherein the means for performing data analysis analyzes the user's financial information using a deep learning model.

[1869] (Claim 5)

[1870] 2. The system of claim 1, wherein the means for generating proposals generates specific recommendations for each of securities, insurance, and real estate investments.

[1871] "Example 1"

[1872] (Claim 1)

[1873] means for receiving financial information from a user;

[1874] a means for encrypting received financial information;

[1875] means for transmitting the encrypted financial information to a server using a secure communications protocol;

[1876] A means for decoding the received financial information and performing data analysis based on an AI model;

[1877] a means for generating appropriate asset management and service proposals based on the analysis results;

[1878] means for re-encrypting the generated proposal and providing it to the user using a secure communications protocol;

[1879] means for storing the suggestions and user information in a database;

[1880] A system including:

[1881] (Claim 2)

[1882] The system of claim 1, characterized in that it uses the AI ​​model to generate multiple investment strategies and asset management proposals based on the analysis results.

[1883] (Claim 3)

[1884] 10. The system of claim 1, wherein the database stores large-scale financial data, including historical market data and customer data, on which the AI ​​model is trained.

[1885] "Application Example 1"

[1886] (Claim 1)

[1887] means for receiving financial information from a user;

[1888] means for performing data analysis based on the received financial information;

[1889] A means for generating appropriate asset management and service proposals based on the data analysis results;

[1890] means for providing the generated suggestions to a user;

[1891] means for storing the suggestions and user information in a database;

[1892] The system includes a means for updating offers in real time based on a user's spending patterns and income information.

[1893] (Claim 2)

[1894] 10. The system of claim 1, wherein the means for receiving financial information from the user encrypts and transmits the information using a secure communication protocol.

[1895] (Claim 3)

[1896] 10. The system of claim 1, wherein the database stores large amounts of financial data, including historical market data and customer data, on which the AI ​​model is trained.

[1897] "Example 2: Combining Emotion Engines"

[1898] (Claim 1)

[1899] means for receiving financial information and emotional state from a user;

[1900] means for performing data analysis based on the received financial information and emotional state;

[1901] A means for generating appropriate asset management and service proposals based on the data analysis results;

[1902] means for providing the generated suggestions to a user;

[1903] means for storing the suggestions and user information in a database;

[1904] A system including:

[1905] (Claim 2)

[1906] 10. The system of claim 1, wherein the means for receiving financial information and emotional state from the user encrypts and transmits the information using a secure communication protocol.

[1907] (Claim 3)

[1908] 10. The system of claim 1, wherein the database stores a large dataset including historical market data and customer data on which the AI ​​model is trained.

[1909] "Application example 2 when combining emotion engines"

[1910] (Claim 1)

[1911] means for receiving financial information from a user;

[1912] means for recognizing an emotional state of a user utilizing an emotion engine;

[1913] means for performing data analysis based on the received financial information and emotional state information;

[1914] A means for generating appropriate asset management and service proposals based on the data analysis results;

[1915] means for providing the generated suggestions to a user;

[1916] means for storing the suggestions and user information in a database;

[1917] A system including:

[1918] (Claim 2)

[1919] 10. The system of claim 1, wherein the means for receiving financial information from the user encrypts and transmits the information using a secure communication protocol.

[1920] (Claim 3)

[1921] 10. The system of claim 1, wherein the database stores large amounts of financial data, including historical market data and customer data, based on which the generative AI model is trained. [Explanation of symbols]

[1922] 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. means for receiving financial information from a user; means for performing data analysis based on the received financial information; A means for generating appropriate asset management and service proposals based on the data analysis results; means for providing the generated suggestions to a user; means for storing the suggestions and user information in a database; A system including:

2. 2. The system of claim 1, wherein the means for receiving financial information from the user encrypts and transmits the information using a secure communication protocol.

3. 10. The system of claim 1, wherein the database stores large amounts of financial data, including historical market data and customer data, and trains the AI ​​model based thereon.

4. 10. The system of claim 1, wherein the means for performing data analysis analyzes the user's financial information using a deep learning model.

5. 2. The system of claim 1, wherein the means for generating proposals generates specific recommendations for each of securities, insurance, and real estate investments.

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