system

Abstract

Provide a system. A means for a user to input income and expenditure data and purchasing information; A means for transmitting input data from the terminal to a server; A server stores the received household accounting data and analyzes it using the generation AI. A means for the generation AI to identify the user's spending patterns and savings points based on the analysis results, and A means for the server to collect optimal product and service information from the company's database, A means for the generative AI to customize suggestions based on user attributes; and a means for the terminal to notify the user of the suggested information from the server; 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] Due to the recent rise in prices, many households are keenly interested in saving money and managing their finances, but they are unsure of what to do specifically and spend a lot of time and effort trying to find effective ways to save and invest. In addition, traditional household accounting apps only record income and expenses and are unable to provide proactive savings suggestions or investment advice. This makes it difficult for users to implement savings and financial strategies tailored to their individual circumstances. Furthermore, it is difficult for companies to accurately understand users' needs and provide products and services, resulting in a lack of optimal matching. [Means for solving the problem]

[0005] To solve the above problem, a system is provided that includes a means for a user to input income and expenditure data and purchasing information, a means for a terminal to transmit the input data to a server, a means for the server to store the received household accounting data and analyze it using a generation AI, a means for the generation AI to identify the user's spending patterns and savings points based on the analysis results, a means for the server to collect optimal product and service information from corporate databases, a means for the generation AI to customize proposals based on the user's attributes, and a means for the terminal to notify the user of the proposal information from the server. Furthermore, by including a means for the server to store the proposed investment advice for each user, a means for the generation AI to identify the user's surplus funds and guide their investment strategy, and a means for the terminal to notify the user of the investment advice from the server, the system enables the user to receive practical and specific savings and investment advice. Furthermore, by including a means for the server to regularly update the latest sales and service information from affiliated companies, a means for the generation AI to analyze this information and select the most suitable sales information for the user, and a means for the terminal to notify the user of the sales information, the system can achieve optimal matching between users and companies and further enhance the user's savings.

[0006] "Income and expenditure data" refers to all information about a user's income and expenses, including items such as salary, living expenses, utility bills, and food expenses.

[0007] "Purchase information" refers to detailed information about a product purchased by a user, and includes data such as the product name, purchase price, purchase date and time, and the store where the product was purchased.

[0008] "Terminal" refers to a device operated by a user, and specifically includes smartphones, tablets, personal computers, etc.

[0009] "Server" refers to a centralized management device that stores and analyzes data, and includes cloud servers and data centers.

[0010] "Generative AI" refers to systems that use artificial intelligence techniques to analyze data and make predictions, particularly to identify users' spending patterns, identify optimal savings opportunities, and generate investment advice.

[0011] "Analysis" refers to the process of extracting meaningful information and patterns from collected data and can include data mining and machine learning techniques.

[0012] "Savings points" refer to specific suggestions and methods that allow users to reduce their spending, including sale information and advice on cutting costs.

[0013] "Suggested information" refers to various advice and recommendations that the generating AI provides to users based on the analysis results, including suggestions for saving money and investment opportunities.

[0014] "Investment advice" refers to specific investment proposals for efficiently managing a user's excess funds, including advice on the purchase of investment trusts and stocks.

[0015] "Special sale information" refers to information about limited-time discounted products and services offered by partner companies, and includes information that allows users to purchase products at a price lower than the regular price.

[0016] "User attributes" refers to information about an individual user, and includes data such as age, gender, region, hobbies, and preferences. [Brief explanation of the drawings]

[0017] [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

[0018] 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.

[0019] First, the terms used in the following description will be explained.

[0020] 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).

[0021] 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.

[0022] 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.

[0023] 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.

[0024] 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."

[0025] [First embodiment]

[0026] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0027] 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.

[0028] 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).

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] 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."

[0038] This invention is a system that uses AI to provide optimal savings proposals and investment advice based on a user's income and expenditure data and purchasing information. The system includes a terminal operated by the user, a server that analyzes the data, and AI.

[0039] Users use a household accounting app to input income and expenditure data and purchase information. This data includes food expenses, utility bills, rent, income, etc. Purchase information can also be easily added by uploading photos of receipts. The device then sends the information entered by the user to the server.

[0040] The server stores the received data and analyzes it using the Generative AI. This analysis process clarifies the user's spending patterns and income trends. For example, if a user has a high monthly food budget, the server can suggest special sale information at a nearby supermarket. The server also collects special sale and service information from the company's database, and the Generative AI customizes the most appropriate suggestions based on the user's attributes.

[0041] The device receives the suggested information from the server and notifies the user. The notification includes specific savings points and details of special sales. For example, it may notify the user of discount information at a specific supermarket as "This week's food sales information." The user can also receive advice on how to invest the savings. The generating AI analyzes the user's surplus funds and suggests appropriate investment destinations and methods.

[0042] As a specific example, if a user's monthly food expenses are determined to be high, the server will collect sales information from nearby supermarkets, and the generation AI will analyze this information to provide optimal savings suggestions to the user. For example, the device will be notified of "This week's sales information" that vegetables are on sale at Supermarket A. In addition, a weekly shopping list and recommended purchase times will be presented as specific ways to reduce the user's food expenses.

[0043] Furthermore, when it comes to surplus funds, users can input their monthly savings amount and the AI ​​will use that information to provide appropriate investment advice. The server will provide the user with information on the most suitable investment trusts and stocks, which will then be notified to the user via the device. For example, information on investment trust products that promise stable returns will be displayed as "recommended investments for this month's surplus funds."

[0044] In summary, this system efficiently utilizes the user's income and expenditure data and purchasing information, and uses generative AI to individually suggest optimal savings and investments, enabling users to obtain specific action plans and implement more effective financial strategies.

[0045] The processing flow will be explained below.

[0046] Step 1:

[0047] The user opens the household accounting app and enters income and expenditure data and purchase information. Income and expenditure data includes salary, living expenses, utility bills, food expenses, etc., while purchase information includes product name, purchase price, purchase date and time, and purchase store.

[0048] Step 2:

[0049] The terminal temporarily stores the data entered by the user, checks the input, and then sends the data to the server at a specific timing or when the user operates the terminal.

[0050] Step 3:

[0051] The server stores the received household accounting data in a database. When storing the data, it checks its consistency and performs data cleaning if necessary.

[0052] Step 4:

[0053] The server periodically initiates a data analysis process, providing income and expenditure data and purchase information to the AI ​​generator, which then analyzes this data to clarify the user's spending patterns and income trends.

[0054] Step 5:

[0055] Based on the analysis results, the AI ​​will identify ways for users to save money. For users with high food expenses, it will provide sales information and suggestions on where to buy food, and for users with high energy consumption, it will generate advice on how to reduce energy consumption.

[0056] Step 6:

[0057] The AI ​​analyzes the user's surplus funds and derives an appropriate investment strategy. Based on the results of this analysis, the server generates optimal investment advice for each user and stores it in a database.

[0058] Step 7:

[0059] The server accesses the database of partner companies to collect the latest sales and service information. This information is updated regularly and used by the generating AI for analysis.

[0060] Step 8:

[0061] The generative AI analyzes corporate sales information and selects the most appropriate sales information and services based on the user's attributes (age, gender, region, hobbies, preferences, etc.). The selected information is customized for each user.

[0062] Step 9:

[0063] The server saves the generated suggestion information for each user and sends it to the device, which then notifies the user using the notification function.

[0064] Step 10:

[0065] The terminal notifies the user of the proposed information and investment advice received from the server. The notification includes specific savings points and detailed investment proposals. The user confirms the notification and initiates specific actions.

[0066] As a specific example, if a user is determined to have high food expenses, the server will provide the generation AI with sale information and a list of nearby supermarkets to identify the best shopping destination. For example, the device will be notified of discounted vegetables at Supermarket A as "This week's sale information." As a specific example of investment advice, the server will have the generation AI suggest appropriate investment trusts and stocks based on the user's savings amount, and the device will notify the user of product information on investment trusts with stable returns as "recommended investments for this month's surplus funds."

[0067] In this way, useful savings and investment advice is provided to the user through specific actions at each step.

[0068] Example 1

[0069] 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."

[0070] In modern life, users are required to efficiently manage their income and expenses, and make savings and investments. However, with conventional methods, users must manually enter data, making it extremely difficult to find appropriate savings methods and investment destinations. It is also difficult to effectively utilize sales and service information. Therefore, there is a need for a system that allows users to easily manage their income and expenses and receive optimal savings suggestions and investment advice.

[0071] 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.

[0072] In this invention, the server includes means for a user to input income and expenditure data and purchasing information, means for a terminal to transmit the input data to the server, means for the server to store the received data and analyze it using a generating AI, means for the generating AI to identify the user's spending patterns and savings points based on the analysis results, means for the server to collect optimal product and service information from a corporate database, means for the generating AI to customize proposals based on the user's attributes, and means for the terminal to notify the user of the proposal information from the server. This enables users to easily manage their income and expenditure and receive individually optimized savings proposals and investment advice.

[0073] "User" refers to an individual who uses the system to input income and expenditure data and purchasing information and receive savings suggestions and investment advice.

[0074] "Terminal" refers to an electronic device that a user uses to input income and expenditure data and purchase information and transmit that data to a server.

[0075] "Server" refers to a central processing unit that receives and stores data sent from a terminal and analyzes it using artificial intelligence.

[0076] "Generative AI" refers to artificial intelligence models that analyze collected data and generate recommendations based on users' spending patterns and attributes.

[0077] A "database" refers to a storage device within a system that stores users' income and expenditure data, purchasing information, and sale and service information collected from companies.

[0078] "Suggested information" refers to information such as savings methods and investment advice generated by the generating AI based on the analysis results.

[0079] "Special Offer Information" refers to information about discounts and sales collected from a company's database.

[0080] "Savings Points" refer to specific items or methods identified to reduce a user's expenses.

[0081] "Investment advice" refers to information in which the generating AI analyzes the user's surplus funds and suggests appropriate investment destinations and methods.

[0082] This invention is a system that uses AI to provide optimal savings proposals and investment advice based on a user's income and expenditure data and purchasing information. The system includes a terminal operated by the user, a server that analyzes the data, and AI.

[0083] Users use a household accounting app to input income and expenditure data and purchase information. This input data includes food expenses, utility bills, rent, income, etc. Furthermore, users can easily add purchase information by uploading photos of receipts. For example, a user can input "this month's food expenses" in the app and supplement the expenditure data by attaching a photo of the receipt. This information is sent to the server via the device.

[0084] The server stores the data received from the device in a database (e.g., MySQL (registered trademark) or MongoDB). The stored data is then analyzed by the Generative AI. In this analysis process, the Generative AI uses machine learning models such as TENSORFLOW (registered trademark) or PyTorch to analyze the user's spending patterns and income trends. For example, for a user who spends a lot on food, the Generative AI can suggest special sales information at a nearby supermarket.

[0085] Furthermore, the server collects sale and service information from the company's database. This is done using web scraping technology and API integration. The generation AI analyzes the collected sale information and customizes the most appropriate suggestions based on the user's attributes. For example, the generation AI might generate a suggestion such as, "User A has a high monthly food budget, so it would be best for him or her to purchase the vegetables on sale at Supermarket A this week."

[0086] The device receives the suggested information from the server and notifies the user. The notification includes specific savings points and details of special sales. For example, the app's push notification function could be used to display a message such as "This week's special sales information: Vegetables are 30% off at Supermarket A."

[0087] The generation AI also analyzes the user's surplus funds and suggests appropriate investment destinations and methods. For example, if a user enters 5,000 yen as their monthly savings amount, the generation AI will use that information to suggest that "it would be best to invest 5,000 yen in an investment trust that promises stable returns." The server provides information on investment trusts and stocks, and the terminal notifies the user of this. The notification will say, "We have product information for an investment trust that promises stable returns as a recommended investment destination for this month's surplus funds."

[0088] As a specific example, if a user's monthly food expenses are determined to be high, the server will collect sales information from nearby supermarkets, and the generation AI will analyze this information to provide optimal savings suggestions to the user. For example, the device will be notified of "This week's sales information" that vegetables are on sale at Supermarket A. In addition, a weekly shopping list and recommended purchase times will be presented as specific ways to reduce the user's food expenses.

[0089] Examples of prompts include:

[0090] "If a user's food budget is high, which supermarket sales should they be shown? Also, suggest a specific shopping list to help them save money on food."

[0091] "Please provide information on the most appropriate mutual funds and stocks for users to invest their surplus funds."

[0092] By implementing this system, users can efficiently manage their income and expenses and receive individually optimized savings proposals and investment advice.

[0093] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0094] Step 1: User enters income and expenditure data and purchasing information

[0095] Input: The user opens the household accounting app and enters income and expenditure data such as food expenses, utility bills, rent, and income. They also add purchase information by uploading photos of receipts.

[0096] Data processing: Converts text data entered on the terminal into JSON format, and compresses receipt images as needed to prepare them for transmission to the server.

[0097] Output: JSON format income and expenditure data and compressed receipt image data are generated on the terminal.

[0098] Specific operation: The user enters "This month's food expenses" in the app, selects "Upload receipt" and takes a photo with the camera.

[0099] Step 2: The device sends the entered data to the server

[0100] Input: JSON-formatted balance data and compressed receipt image data generated in Step 1.

[0101] Data processing: The terminal packages the income and expenditure data and receipt image as an HTTP POST request.

[0102] Output: An HTTP POST request is sent to a specific API endpoint on the server side.

[0103] Specific operation: When the user taps the "Send data" button, the device sends the data to the server.

[0104] Step 3: The server stores the received data and analyzes it using the generative AI.

[0105] Input: Income and expenditure data (JSON format) and receipt image data sent from the device.

[0106] Data processing: The server stores the data in a database and prepares it for input into the generative AI model. The database is MySQL or MongoDB.

[0107] Output: The analysis results from the generative AI are obtained.

[0108] Specific operation: The server saves the data in a database and launches a generative AI model (e.g., TensorFlow or PyTorch) to begin analysis.

[0109] Step 4: Generative AI identifies the user's spending patterns and savings points based on the analysis results

[0110] Input: User's income and expenditure data and purchasing information stored on the server.

[0111] Data processing: Generative AI uses machine learning algorithms to analyze data and identify users' spending patterns and savings opportunities.

[0112] Output: Analysis results that identify the user's spending patterns and savings points.

[0113] Specific operation: The generation AI identifies the pattern that "User A has high food expenses" and clearly indicates savings points by "suggesting information about special sales at Supermarket A."

[0114] Step 5: The server collects the best products and services from the company's database.

[0115] Input: Special offers and service information collected from company databases.

[0116] Data processing: The server periodically retrieves information via web scraping or API and stores it in a database.

[0117] Output: The latest sales and service information is saved in the server database.

[0118] What it does: The server periodically crawls websites that provide sale information and stores new information in a database.

[0119] Step 6: Generative AI customizes suggestions based on user attributes

[0120] Input: User spending patterns, savings points, and special offers.

[0121] Data processing: Generative AI analyzes this data and generates optimal savings proposals for users.

[0122] Output: User-optimized customization suggestions.

[0123] Specific operation: The generation AI generates a specific suggestion such as, "Suggest to user A the vegetables on sale at supermarket A this week."

[0124] Step 7: The device notifies the user of the suggested information from the server.

[0125] Input: Customized proposal information.

[0126] Data processing: The device provides information to the user via push notifications or in-app notifications.

[0127] Output: The notification message that the user receives.

[0128] Specific operation: The device sends a push notification saying, "This week's sale information: 30% off vegetables at Supermarket A."

[0129] Step 8: The generated AI analyzes the user's surplus funds and provides investment advice

[0130] Input: User savings data.

[0131] Data processing: Generative AI analyzes the user's savings data and generates optimal investment advice.

[0132] Output: Investment advice to the user.

[0133] Specific operation: The user enters "monthly savings amount of 5,000 yen," and the generation AI generates a suggestion to "invest 5,000 yen in an investment trust that is expected to provide stable returns."

[0134] Step 9: The terminal notifies the user of the investment advice from the server.

[0135] Input: Investment advice sent from the server.

[0136] Data processing: Prepare push notifications and in-app notifications so that the device can notify the user of investment advice.

[0137] Output: Investment advice notification received by the user.

[0138] Specific operation: The device sends a push notification with "Recommended investments for this month's surplus funds."

[0139] The above are the specific processing steps of the program of this system.

[0140] (Application example 1)

[0141] 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."

[0142] In modern society, individual consumption behavior is becoming more diverse, creating a need for efficient asset management and investment strategies. However, current household accounting apps and asset management systems face challenges, such as cumbersome data entry, lack of personalized savings and investment suggestions, and a lack of up-to-date sales information. In particular, the manual management of receipt information places a significant burden on users. A system that can solve these problems and provide effective savings and investment suggestions based on individual users' attributes and consumption patterns is needed.

[0143] 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.

[0144] In this invention, the server includes: a means for a user to input income and expenditure data and purchase information; a means for a terminal to transmit the input data to the server; a means for the server to store the received household accounting data and analyze it using a generation AI; a means for the generation AI to identify the user's spending patterns and savings points based on the analysis results; a means for the server to collect optimal product and service information from an external database; a means for the generation AI to customize proposals based on the user's attributes; a means for the terminal to notify the user of the proposal information from the server; a means for providing appropriate investment advice based on the savings amount calculated from the user's income and expenditure data; and a means for capturing an image of a receipt and extracting purchase information using optical character recognition technology. This allows users to easily manage their income and expenditure data and receive individually customized savings and investment proposals.

[0145] A "user" is an individual or corporation that inputs income and expenditure data and purchasing information into the system.

[0146] "Income and expenditure data" is information about income and expenditure entered by the user, and specifically includes information such as food expenses, utility expenses, rent, and income.

[0147] "Purchase information" refers to information about a purchase history that a user enters using a receipt or other method.

[0148] A "terminal" is a computer device that allows a user to input income and expenditure data and purchasing information and transmit them to a server, and includes smartphones, tablets, etc.

[0149] A "server" is a computer system that stores and analyzes income and expenditure data and purchasing information received from users.

[0150] "Household account book data" is a group of information including all income and expenditure data and purchasing information entered by the user.

[0151] "Generative AI" is artificial intelligence that analyzes users' income and expenditure data and purchasing information to generate personalized suggestions.

[0152] "Analysis results" are the results obtained by the generation AI analyzing the user's income and expenditure data and purchasing information.

[0153] A "spending pattern" is a tendency for a user to spend money over a period of time.

[0154] "Saving points" are items or ways in which a user can potentially reduce their expenses.

[0155] An "external database" is an external information source that the server accesses to obtain product and service information.

[0156] "Suggested information" refers to information such as products, services, and money-saving methods that are customized by the generating AI based on the user's attributes.

[0157] "Investment advice" refers to suggestions on investment destinations and investment methods provided by the generating AI based on the user's surplus funds.

[0158] "Excess funds" are the funds remaining after a user calculates their monthly income and expenses.

[0159] "Optical character recognition technology" is a technology that extracts character information from image data.

[0160] "Purchase information extraction" refers to the act of using optical character recognition technology to obtain purchase and payment information from receipt images.

[0161] This invention is a system that uses AI generation to provide optimal savings suggestions and investment advice based on a user's income and expenditure data and purchasing information. Each of these means will be described in detail below.

[0162] System configuration

[0163] The system includes the following components:

[0164] A device (such as a smartphone or tablet) on which users input income and expenditure data and purchasing information

[0165] A server that analyzes data and uses generative AI to generate proposal information

[0166] Network for sending notifications to users

[0167] User operation

[0168] Users enter income and expenditure data and purchase information using a dedicated app installed on their smartphone. This can be done manually or by uploading photos of receipts. The receipt information is converted into text data using optical character recognition technology (e.g., Google® Cloud Vision API) and extracted as purchase information.

[0169] Server-side processing

[0170] The server receives and stores the income and expenditure data and purchase information sent by the user. The stored data is analyzed using generative AI (e.g., OpenAI® GPT-4®). The analysis clarifies the user's spending patterns and income trends. Below are some examples of specific prompts:

[0171] Prompt Sentence Examples

[0172] "User A's income and expenditure data is shown below. Based on this data, please suggest the optimal savings and investment plan for User A. Monthly income is 300,000 yen, food expenses are 50,000 yen, rent is 100,000 yen, utility bills are 20,000 yen, and savings are 30,000 yen."

[0173] Proposal generation and notification

[0174] Based on the results of the analysis by the generation AI, optimal savings points, sale information, and investment advice are generated for the user. The server collects the latest sale information and service information from external databases (for example, databases of affiliated companies), and the generation AI customizes the proposals based on the user's attributes. The optimal proposals are notified to the device. This allows the user to easily implement specific savings action plans and investment strategies.

[0175] For example, if a user's monthly food expenses are determined to be high, the server will collect sales information from nearby supermarkets, and the generation AI will analyze this information and notify the user of the sales information. For example, the AI ​​may notify the user of "This week's sales information" by notifying them of discounts on vegetables at a specific supermarket. The user can also receive advice on how to invest the money they have saved.

[0176] The system allows users to easily manage their income and expenditure data and receive personalized savings and investment suggestions, leading to more efficient asset management and effective financial investment.

[0177] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0178] Step 1:

[0179] Users enter income and expenditure data and purchasing information. Users manually enter income and expenditure data (food expenses, utility bills, rent, income, etc.) via a dedicated smartphone app. Alternatively, purchasing information can be entered by uploading photos of receipts. The input data is saved on the device as input fields or image files.

[0180] Step 2:

[0181] The terminal sends the entered data to the server. The terminal uploads the income and expenditure data and image files to the server via the network. The data is sent using a protocol such as an HTTP request.

[0182] Step 3:

[0183] The server stores the received household accounting data and purchasing information. The server stores the data for each user in a database (e.g., Firebase or MongoDB). The stored data is used for later analysis.

[0184] Step 4:

[0185] The server uses optical character recognition technology to extract purchase information from the receipt image. The analyzed image data is converted into text data, and each item (product name, price, etc.) is integrated into the household accounting data. This process uses OCR technology such as Google Cloud Vision API.

[0186] Step 5:

[0187] The server inputs the user's income and expenditure data and purchase information into the generation AI. The generation AI model (e.g., OpenAI GPT-4) receives input including the following prompt:

[0188] "User A's income and expenditure data is shown below. Based on this data, please suggest the optimal savings and investment plan for User A. Monthly income is 300,000 yen, food expenses are 50,000 yen, rent is 100,000 yen, utility bills are 20,000 yen, and savings are 30,000 yen."

[0189] Step 6:

[0190] The generating AI identifies the user's spending patterns and savings points and generates optimal proposals. Based on this data, the AI ​​analyzes the user's income and expenditure patterns and suggests effective ways to save and appropriate investments. The output is generated as savings proposals and investment advice and stored in a database on the server.

[0191] Step 7:

[0192] The server collects the latest sales and service information from external databases, and uses an automatically updated API to obtain the latest information from partner company databases. This data is also input into the generation AI.

[0193] Step 8:

[0194] Based on the information collected by the generation AI, the proposed information is customized according to the user's attributes. Specifically, individual sale information and money-saving advice are customized based on the user's area of ​​residence and purchase history. The output is generated as customized proposed information.

[0195] Step 9:

[0196] The device notifies the user of the proposed information received from the server. The information is delivered to the user in the form of a push notification via a dedicated app. The user receives the notification and can take action based on the content.

[0197] Step 10:

[0198] The server generates investment advice for each user and saves it for future recommendations. The advice is linked to the user's profile and used as data to improve the accuracy of future recommendations.

[0199] This allows users to easily input income and expenditure data and receive personalized savings and investment suggestions, leading to more efficient asset management and more effective financial investment.

[0200] 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.

[0201] This invention is a system that uses a generative AI and an emotion engine to provide optimal savings proposals and investment advice based on a user's income and expenditure data and purchasing information. This system includes a terminal operated by the user, a server that analyzes the data, a generative AI, and an emotion engine that recognizes the user's emotions.

[0202] Users use a household accounting app to input income and expenditure data and purchase information. This data includes food expenses, utility bills, rent, income, etc. Purchase information can also be easily added by uploading photos of receipts. The device then sends the information entered by the user to the server.

[0203] The server stores the received data and analyzes it using the Generative AI. This analysis process clarifies the user's spending patterns and income trends. For example, if a user has a high monthly food budget, the server can suggest special sale information at a nearby supermarket. The server also collects special sale and service information from the company's database, and the Generative AI customizes the most appropriate suggestions based on the user's attributes.

[0204] Here, a new emotion engine that recognizes the user's emotions is incorporated. The emotion engine analyzes emotions from the user's input data and behavior. For example, it can identify the user's emotional state, such as whether they are feeling stressed, based on the text they enter, trends in their income and expenditure data, and their app usage patterns.

[0205] The emotion data analyzed by the emotion engine is provided to the generative AI, which then takes this emotion data into account to customize suggestions. For example, if the user is feeling stressed, the generative AI will provide relaxing shopping suggestions or investment advice.

[0206] The device receives suggested information and investment advice from the server and notifies the user. The notification includes details of specific savings points and special sales information. For example, it may notify discount information at a specific supermarket as "This week's food sales information." The user can also receive advice on how to invest the savings. The generating AI analyzes the user's surplus funds and suggests appropriate investment destinations and methods.

[0207] As a specific example, if a user is determined to have high food expenses, the server will provide sales information and a list of nearby supermarkets to the generation AI to identify the best shopping destination. For example, the device will be notified of this week's sales information, such as a discount on vegetables at Supermarket A. Furthermore, if the user's emotions indicate a state of stress, the emotion engine will also suggest sales information for products with a relaxing effect (such as aroma oils).

[0208] To give a specific example of investment, when a user inputs the amount of savings, the AI ​​generator will suggest appropriate investment trusts and stocks based on that information. The server will generate optimal investment advice for the user, and the device will notify them of investment trust product information that promises stable returns as "recommended investments for this month's surplus funds." Furthermore, if the user's emotions indicate caution, low-risk investments will be prioritized.

[0209] In this way, by incorporating an emotion engine, it is possible to provide more personalized savings and investment advice based on the user's emotional state, allowing the user to manage their finances comfortably and effectively.

[0210] The processing flow will be explained below.

[0211] Step 1:

[0212] The user opens the household accounting app and enters income and expenditure data and purchase information. Income and expenditure data includes salary, living expenses, utility bills, food expenses, etc., while purchase information includes product name, purchase price, purchase date and time, and purchase store.

[0213] Step 2:

[0214] The terminal temporarily stores the data entered by the user, and after confirmation, transmits this data to the server.

[0215] Step 3:

[0216] The server stores the received household accounting data in a database. When storing the data, it checks its consistency and performs data cleaning if necessary.

[0217] Step 4:

[0218] The server provides data to the generation AI and emotion engine. The generation AI analyzes income and expenditure data and purchase information to clarify the user's spending patterns and income trends. The emotion engine analyzes emotions from the user's input data and behavior.

[0219] Step 5:

[0220] Based on the analysis results, the AI ​​will identify ways for users to save money. For example, it will provide sales information and shopping suggestions to users who spend a lot on food, and generate advice on how to reduce energy consumption to users who consume a lot of energy.

[0221] Step 6:

[0222] The emotion engine identifies the user's emotional state (stress, satisfaction, etc.) and provides that data to the generative AI.

[0223] Step 7:

[0224] Generative AI takes emotional data into account to customize suggestions, for example, offering relaxing shopping suggestions or investment advice if the user is feeling stressed.

[0225] Step 8:

[0226] The server accesses the company's database to collect the latest sales and service information. This information is updated regularly and used by the generating AI for analysis.

[0227] Step 9:

[0228] The generative AI analyzes corporate sales information and selects the most appropriate sales information and services based on the user's attributes (age, gender, region, hobbies, preferences, etc.). The selected information is customized for each user.

[0229] Step 10:

[0230] The server saves the generated suggestion information for each user and sends it to the device, which then notifies the user using the notification function.

[0231] Step 11:

[0232] The terminal notifies the user of the proposed information and investment advice received from the server. The notification includes specific savings points and detailed investment proposals. The user confirms the notification and initiates specific actions.

[0233] As a specific example, if a user is determined to have high food expenses, the server will provide sales information and a list of nearby supermarkets to the generation AI to identify the best shopping destination. For example, the device will be notified of this week's sales information, such as a discount on vegetables at Supermarket A. Furthermore, if the user's emotions indicate a state of stress, the emotion engine will also suggest sales information for products with a relaxing effect (such as aroma oils).

[0234] To give a specific example of investment, when a user inputs the amount of savings, the AI ​​generator will suggest appropriate investment trusts and stocks based on that information. The server will generate optimal investment advice for the user, and the device will notify them of investment trust product information that promises stable returns as "recommended investments for this month's surplus funds." Furthermore, if the user's emotions indicate caution, low-risk investments will be prioritized.

[0235] In this way, by incorporating an emotion engine, it becomes possible to make suggestions based on the user's emotional state, thereby providing more personalized savings and investment advice.

[0236] Example 2

[0237] 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."

[0238] Conventional household management systems and investment advice systems can make suggestions based on a user's income and expenditure data and purchasing information, but they cannot take into account the user's emotional state. This has the problem that they cannot provide flexible suggestions and advice that respond to the stress and emotional fluctuations that users face. As a result, users may not follow the suggested advice, and may not achieve the full effect of their savings or investments.

[0239] 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.

[0240] In this invention, the server includes means for an emotion engine to analyze the user's emotional state, means for a generation AI to identify the user's spending patterns and savings points based on the analysis results and the user's emotional state, and means for the generation AI to customize proposals based on the user's attributes and emotional state. This allows the server to provide proposals and advice that meet the individual needs of the user according to their emotional state, enabling the user to save and invest more effectively.

[0241] A "user" is a person who uses the system to enter income and expenditure data and purchasing information and receive savings and investment advice.

[0242] "Income and expenditure data" refers to information related to income and expenses entered by the user, including, for example, food expenses, utility expenses, rent, and income.

[0243] "Purchase information" is detailed information about a user's purchases, including photos of receipts and data about purchased items.

[0244] A "terminal" is a device that a user operates to input income and expenditure data and purchasing information, and to receive proposal information and investment advice.

[0245] A "server" is a computer system that receives, stores, and analyzes data sent from a terminal.

[0246] "Generative AI" is artificial intelligence that analyzes a user's income and expenditure data and purchasing information to identify and customize the user's spending patterns, savings points, and investment advice.

[0247] An "emotion engine" is a device or software that analyzes a user's input data and behavioral patterns to identify the emotional state the user is feeling.

[0248] The "analysis results" are information about the user's spending patterns and income trends obtained by the generation AI by analyzing income and expenditure data and purchasing information.

[0249] "Attributes" are characteristics and information specific to a user, and include data such as age, occupation, and family structure.

[0250] A "corporate database" is a database for collecting information on products and services offered by markets and companies.

[0251] "Special sale information" is information about specific products or services being offered at discounted prices.

[0252] "Investment advice" refers to investment suggestions presented based on a user's financial data and emotional state.

[0253] "Means to customize recommendations" refers to the generative AI's ability to individually tailor optimal savings and investment strategies based on a user's financial data, attributes, and emotional state.

[0254] A "notification" is information sent from a server and displayed on a user's terminal.

[0255] This invention is a system that uses a generative AI and an emotion engine to provide optimal savings proposals and investment advice based on a user's income and expenditure data and purchasing information. This system includes a terminal operated by the user, a server that analyzes the data, a generative AI, and an emotion engine that recognizes the user's emotions.

[0256] Users use a household accounting app to input income and expenditure data and purchase information. This data includes food expenses, utility bills, rent, income, etc. Purchase information can also be easily added by uploading photos of receipts. The device then sends the information entered by the user to the server.

[0257] The server stores the received data and analyzes it using the Generative AI. This analysis process clarifies the user's spending patterns and income trends. For example, if a user has a high monthly food budget, it can suggest special sale information at a nearby supermarket. The server also collects special sale and service information from the company's database, and the Generative AI customizes the most appropriate suggestions based on the user's attributes and emotional state.

[0258] Here, a new emotion engine that recognizes the user's emotions is incorporated. The emotion engine analyzes emotions from the user's input data and behavior. For example, it can identify the user's emotional state, such as whether they are feeling stressed, based on the text they enter, trends in their income and expenditure data, and their app usage patterns.

[0259] The emotion data analyzed by the emotion engine is provided to the generative AI, which then takes this emotion data into account to customize suggestions. For example, if the user is feeling stressed, the generative AI will provide relaxing shopping suggestions or investment advice.

[0260] The device receives suggested information and investment advice from the server and notifies the user. The notification includes details of specific savings points and special sales information. For example, it may notify discount information at a specific supermarket as "This week's food sales information." The user can also receive advice on how to invest the savings. The generating AI analyzes the user's surplus funds and suggests appropriate investment destinations and methods.

[0261] As a specific example, if a user is determined to have high food expenses, the server will provide sales information and a list of nearby supermarkets to the generation AI to identify the best shopping destination. For example, the device will be notified of this week's sales information, such as a discount on vegetables at Supermarket A. Furthermore, if the user's emotions indicate a state of stress, the emotion engine will also suggest sales information for products with a relaxing effect (such as aroma oils).

[0262] To give a specific example of investment, when a user inputs the amount of savings, the AI ​​generator will suggest appropriate investment trusts and stocks based on that information. The server will generate optimal investment advice for the user, and the device will notify them of investment trust product information that promises stable returns as "recommended investments for this month's surplus funds." Furthermore, if the user's emotions indicate caution, low-risk investments will be prioritized.

[0263] Examples of prompts include, "If the user's food expenses are high this month, suggest sales information for nearby supermarkets and also notify the user of products that will help the user relax." and "If the user enters the amount of savings, suggest low-risk investments based on that information. Prioritize low-risk options, especially when the user is emotionally cautious."

[0264] In this way, by incorporating an emotion engine, it is possible to provide more personalized savings and investment advice based on the user's emotional state, allowing the user to manage their finances comfortably and effectively.

[0265] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0266] Processing Steps

[0267] Step 1: Enter income and expenditure data and purchasing information

[0268] 1. The user opens the household accounting app on their device.

[0269] 2. The user enters income and expenditure data (e.g., food expenses, utility bills, rent, income, etc.) into the app.

[0270] Input: User manually enters "15,000 yen" as food expenses

[0271] Output: Data is temporarily saved on the device.

[0272] 3. Users add purchase information by uploading a photo of their receipt to the app.

[0273] Input: Upload a photo of your supermarket receipt

[0274] Output: The receipt details are recorded in the app as purchase information.

[0275] 4. The terminal sends these input data to the server.

[0276] Input: Income and expenditure data and purchasing information

[0277] Output: Sending data from the device to the server

[0278] Step 2: Receiving and storing data

[0279] 1. The server receives income and expenditure data and purchase information sent from the terminal.

[0280] Input: Income and expenditure data from the device "Food expenses: 15,000 yen", purchase information

[0281] Output: Stored as received data on the server

[0282] 2. The server stores the received data in a database.

[0283] Input: Income and expenditure data and purchasing information

[0284] Output: Income and expenditure data and purchasing information stored in the database

[0285] Step 3: Analysis by generative AI model

[0286] 1. The server provides the data stored in the database to the generative AI model.

[0287] Input: Saved income and expenditure data and purchasing information

[0288] Output: Data is provided to a generative AI model

[0289] 2. The generative AI model analyzes the provided data and derives the user's spending patterns and income trends.

[0290] Import: Provided data

[0291] Output: The analysis result is "The average monthly food cost exceeds 20,000 yen."

[0292] Step 4: Gather information about special offers and services

[0293] 1. The server collects the latest sales and service information from the company's database and online sources.

[0294] Inputs: Corporate databases and online sources

[0295] Output: Special sale information such as "Supermarket A's special sale this week: 30% off vegetables"

[0296] 2. The server provides the collected information to the generative AI model.

[0297] Input: Collected sale information

[0298] Output: Special offers provided to the generative AI model

[0299] Step 5: Customize optimal proposals with generative AI models

[0300] 1. The generative AI model customizes optimal offers based on income and expenditure data, sales information, and user attributes.

[0301] Input: Income and expenditure data, special sale information, user attributes

[0302] Output: Suggestions such as "You can get a good deal on vegetables at Supermarket A"

[0303] Step 6: Analyze user emotions with the emotion engine

[0304] 1. The emotion engine analyzes the user's input data and behavioral patterns to identify their emotional state.

[0305] Input: User input data, behavioral patterns

[0306] Output: User's emotional state (e.g., "I feel stressed")

[0307] 2. The emotion engine provides the analyzed emotion data to the generative AI model.

[0308] Input: User emotion data

[0309] Output: Emotion data fed to a generative AI model

[0310] Step 7: Re-customize suggestions with generative AI models, taking into account emotions

[0311] 1. The generative AI model then re-customizes the suggestions, taking into account sentiment data.

[0312] Input: Emotion data

[0313] Output: Additional suggestions, such as "Special offers on relaxing aroma oils"

[0314] Step 8: Server generates and sends proposals and investment advice

[0315] 1. The server generates customized proposal information and investment advice using generative AI.

[0316] Input: Generative AI model proposal

[0317] Output: Proposal information and investment advice for users

[0318] 2. The server sends the latest proposal information and investment advice to the terminal.

[0319] Input: Proposal information and investment advice

[0320] Output: Sending data from the server to the device

[0321] Step 9: Device Notifications and User Actions

[0322] 1. The terminal notifies the user of the received information.

[0323] Input: Proposal information and investment advice sent from the server

[0324] Output: Notifications displayed on the device (e.g., "This week's specials: 30% off vegetables at Supermarket A" or "Specials on relaxing aroma oils")

[0325] 2. The user checks the notification on their device and takes action based on the proposed information and investment advice.

[0326] Input: Device notifications

[0327] Output: Actual savings behavior and investment decisions

[0328] In this vein, the system of the present invention provides suggestions and advice tailored to the user's individual needs based on their emotional state, allowing them to save and invest more effectively.

[0329] (Application example 2)

[0330] 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."

[0331] In modern society, users face many challenges in managing their daily living expenses, saving money, and making investment decisions. In addition, because users' emotional state influences their purchasing and investment choices, it is difficult for conventional household management systems and investment advice tools to provide optimal recommendations for individual users. Therefore, there is a need for a system that provides personalized saving and investment advice that takes into account not only users' income and expenditure data but also their emotional state.

[0332] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0333] In this invention, the server includes: means for a user to input income and expenditure data and purchasing information; means for a terminal to transmit the input data to the server; means for the server to store the received household accounting data and analyze it using a generation AI; means for the generation AI to identify the user's spending patterns and savings points based on the analysis results; means for the server to collect optimal product and service information from corporate databases; means for the generation AI to customize proposals based on the user's attributes; means for the terminal to notify the user of the proposal information from the server; means for analyzing the user's emotions using an emotion engine and providing the results to the generation AI; and means for the generation AI to customize proposals taking the user's emotion data into consideration. This enables personalized proposals based on the user's emotional state, resulting in more effective savings and investment advice.

[0334] "Income and expenditure data" is a general term for information about a user's daily income and expenditure.

[0335] "Purchase information" refers to detailed information about products and services purchased by a user.

[0336] "Terminal" refers to a hardware device through which a user inputs information and communicates with a server.

[0337] A "server" is a computer system that stores and analyzes collected data.

[0338] "Household account data" refers to data that records a user's income and expenditure information and purchasing information.

[0339] "Generative AI" is a system that uses artificial intelligence technology to analyze data and generate optimal suggestions for users.

[0340] The "spending pattern" indicates the tendency or characteristics of a user regarding spending.

[0341] "Savings Points" are specific areas and ways in which users can reduce their spending.

[0342] A "corporate database" is a company-owned information resource that stores information about products and services.

[0343] "Customizing suggestions" means individually tailoring optimal suggestions based on the user's attributes and tendencies.

[0344] An "emotion engine" refers to a technology or system for analyzing a user's emotions.

[0345] "Emotion data" is information relating to the user's emotional state.

[0346] "Investment advice" is information that suggests optimal investment methods based on the user's financial situation.

[0347] "Excess funds" are the funds remaining after subtracting expenses from a user's income.

[0348] An "investment strategy" is a plan or guideline for how to invest excess funds.

[0349] "Special sale information" is information about products and services being sold at a price lower than the regular price.

[0350] "Notify" refers to a means of informing a user of specific information.

[0351] This invention is a system that uses a generative AI and an emotion engine to provide optimal savings proposals and investment advice based on a user's income and expenditure data and purchasing information. This system includes a terminal operated by the user, a server that analyzes the data, a generative AI, and an emotion engine that recognizes the user's emotions.

[0352] Users use a smartphone app to enter income and expenditure data and purchase information. This app has the function of automatically collecting expenditure data by linking with the electronic payment services that users use on a daily basis. Users can also enter data manually, and can easily add purchase information by uploading photos of receipts.

[0353] The device sends the information entered by the user to the server. The server stores the received data and analyzes it using generative AI. This analysis process clarifies the user's spending patterns and income trends, and suggests optimal savings points. For example, a user with high monthly food expenses will be notified of special sales at a nearby supermarket.

[0354] The server then collects sales and service information from the company's database, and the generative AI customizes the most appropriate proposals based on the user's attributes. An emotion engine that recognizes the user's emotions is then incorporated. The emotion engine analyzes the user's input data and behavior to determine their emotional state, such as whether they are feeling stressed.

[0355] The emotion data analyzed by the emotion engine is provided to the generative AI, which then takes this emotion data into account to customize suggestions. For example, if the user is feeling stressed, the generative AI will suggest products and services that will help them relax.

[0356] The device receives suggested information and investment advice from the server and notifies the user. The notification includes details of specific savings points and sales information. For example, a notification of discount information at a specific supermarket may be sent as "This week's food sales information." The user can also receive advice on how to invest the savings they have made, and the generating AI will analyze the user's surplus funds and suggest appropriate investment destinations and methods.

[0357] The hardware and software used are as follows:

[0358] Hardware: Smartphone (iOS, ANDROID (registered trademark))

[0359] Software: Python, TensorFlow, Emotion API (Microsoft® Azure®), Watson® Tone Analyzer (IBM), Firebase Cloud Messaging (Google)

[0360] For example, if user A's food expenses are judged to be high, the device will be notified of this week's special sale information, such as discounts on vegetables at a nearby supermarket. If user A's emotions indicate a state of stress, the emotion engine will also suggest sales information for products that have a relaxing effect.

[0361] Prompt Sentence Examples

[0362] If the user's food budget is high, suggest which supermarket has a sale on which product. If the user is feeling stressed, provide information on products that will help them relax.

[0363] This allows for personalized suggestions based on the user's emotional state, resulting in more effective savings and investment advice.

[0364] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0365] Step 1:

[0366] Users input their financial data and purchase information via a smartphone app. This information can be automatically obtained from electronic payment services, manually entered by the user, or extracted from photos of receipts. The app stores the financial data and purchase information in its internal database.

[0367] Step 2:

[0368] The terminal sends the collected income and expenditure data and purchase information to the server. It receives data from the internal database as input and generates the data sent to the server as output. This data transmission uses SSL / TLS encrypted communication.

[0369] Step 3:

[0370] The server stores the received data and analyzes it using generative AI. In this step, the income and expenditure data and purchasing information sent as input are received and stored in a database as output. The stored data is then input into a generative AI model, which analyzes spending patterns and income trends. Specifically, data analysis is performed using Python's pandas and TensorFlow.

[0371] Step 4:

[0372] The generative AI identifies the user's spending patterns and savings points based on the analysis results. It receives the analysis results as input and identifies optimal savings points as output. For example, if the user's food expenses are higher than other categories, advice on reducing food expenses will be suggested.

[0373] Step 5:

[0374] The server collects optimal product and service information from the company's database. It receives spending patterns and savings points as input and extracts suitable product and service information as output. Specific product information and special sale information are collected in this step.

[0375] Step 6:

[0376] The emotion engine analyzes emotions from the user's input data and behavior. In this step, user behavior data and input text are received as input, and emotion data is generated as output. Emotion analysis is performed using the Emotion API and Watson Tone Analyzer.

[0377] Step 7:

[0378] The generative AI takes emotional data into account to customize suggestions. It receives emotional data and collected product and service information as input, and generates optimal suggestions for the user as output. Specifically, a user in a stressed state will be offered information about special sales on products that have a relaxing effect.

[0379] Step 8:

[0380] The device notifies the user of the proposed information and investment advice from the server. It receives the customized proposals from the server as input and generates push notifications as output. This notification is generated using Firebase Cloud Messaging.

[0381] As a concrete example, consider the following prompt sentence:

[0382] If the user's food budget is high, suggest which supermarket has a sale on which product. If the user is feeling stressed, provide information on products that will help them relax.

[0383] 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.

[0384] 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.

[0385] 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.

[0386] [Second embodiment]

[0387] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0388] 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.

[0389] 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).

[0390] 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.

[0391] 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.

[0392] 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).

[0393] 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.

[0394] 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.

[0395] 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.

[0396] 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.

[0397] 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.

[0398] 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."

[0399] This invention is a system that uses AI to provide optimal savings proposals and investment advice based on a user's income and expenditure data and purchasing information. The system includes a terminal operated by the user, a server that analyzes the data, and AI.

[0400] Users use a household accounting app to input income and expenditure data and purchase information. This data includes food expenses, utility bills, rent, income, etc. Purchase information can also be easily added by uploading photos of receipts. The device then sends the information entered by the user to the server.

[0401] The server stores the received data and analyzes it using the Generative AI. This analysis process clarifies the user's spending patterns and income trends. For example, if a user has a high monthly food budget, the server can suggest special sale information at a nearby supermarket. The server also collects special sale and service information from the company's database, and the Generative AI customizes the most appropriate suggestions based on the user's attributes.

[0402] The device receives the suggested information from the server and notifies the user. The notification includes specific savings points and details of special sales. For example, it may notify the user of discount information at a specific supermarket as "This week's food sales information." The user can also receive advice on how to invest the savings. The generating AI analyzes the user's surplus funds and suggests appropriate investment destinations and methods.

[0403] As a specific example, if a user's monthly food expenses are determined to be high, the server will collect sales information from nearby supermarkets, and the generation AI will analyze this information to provide optimal savings suggestions to the user. For example, the device will be notified of "This week's sales information" that vegetables are on sale at Supermarket A. In addition, a weekly shopping list and recommended purchase times will be presented as specific ways to reduce the user's food expenses.

[0404] Furthermore, when it comes to surplus funds, users can input their monthly savings amount and the AI ​​will use that information to provide appropriate investment advice. The server will provide the user with information on the most suitable investment trusts and stocks, which will then be notified to the user via the device. For example, information on investment trust products that promise stable returns will be displayed as "recommended investments for this month's surplus funds."

[0405] In summary, this system efficiently utilizes the user's income and expenditure data and purchasing information, and uses generative AI to individually suggest optimal savings and investments, enabling users to obtain specific action plans and implement more effective financial strategies.

[0406] The processing flow will be explained below.

[0407] Step 1:

[0408] The user opens the household accounting app and enters income and expenditure data and purchase information. Income and expenditure data includes salary, living expenses, utility bills, food expenses, etc., while purchase information includes product name, purchase price, purchase date and time, and purchase store.

[0409] Step 2:

[0410] The terminal temporarily stores the data entered by the user, checks the input, and then sends the data to the server at a specific timing or when the user operates the terminal.

[0411] Step 3:

[0412] The server stores the received household accounting data in a database. When storing the data, it checks its consistency and performs data cleaning if necessary.

[0413] Step 4:

[0414] The server periodically initiates a data analysis process, providing income and expenditure data and purchase information to the AI ​​generator, which then analyzes this data to clarify the user's spending patterns and income trends.

[0415] Step 5:

[0416] Based on the analysis results, the AI ​​will identify ways for users to save money. For users with high food expenses, it will provide sales information and suggestions on where to buy food, and for users with high energy consumption, it will generate advice on how to reduce energy consumption.

[0417] Step 6:

[0418] The AI ​​analyzes the user's surplus funds and derives an appropriate investment strategy. Based on the results of this analysis, the server generates optimal investment advice for each user and stores it in a database.

[0419] Step 7:

[0420] The server accesses the database of partner companies to collect the latest sales and service information. This information is updated regularly and used by the generating AI for analysis.

[0421] Step 8:

[0422] The generative AI analyzes corporate sales information and selects the most appropriate sales information and services based on the user's attributes (age, gender, region, hobbies, preferences, etc.). The selected information is customized for each user.

[0423] Step 9:

[0424] The server saves the generated suggestion information for each user and sends it to the device, which then notifies the user using the notification function.

[0425] Step 10:

[0426] The terminal notifies the user of the proposed information and investment advice received from the server. The notification includes specific savings points and detailed investment proposals. The user confirms the notification and initiates specific actions.

[0427] As a specific example, if a user is determined to have high food expenses, the server will provide the generation AI with sale information and a list of nearby supermarkets to identify the best shopping destination. For example, the device will be notified of discounted vegetables at Supermarket A as "This week's sale information." As a specific example of investment advice, the server will have the generation AI suggest appropriate investment trusts and stocks based on the user's savings amount, and the device will notify the user of product information on investment trusts with stable returns as "recommended investments for this month's surplus funds."

[0428] In this way, useful savings and investment advice is provided to the user through specific actions at each step.

[0429] Example 1

[0430] 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."

[0431] In modern life, users are required to efficiently manage their income and expenses, and make savings and investments. However, with conventional methods, users must manually enter data, making it extremely difficult to find appropriate savings methods and investment destinations. It is also difficult to effectively utilize sales and service information. Therefore, there is a need for a system that allows users to easily manage their income and expenses and receive optimal savings suggestions and investment advice.

[0432] 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.

[0433] In this invention, the server includes means for a user to input income and expenditure data and purchasing information, means for a terminal to transmit the input data to the server, means for the server to store the received data and analyze it using a generating AI, means for the generating AI to identify the user's spending patterns and savings points based on the analysis results, means for the server to collect optimal product and service information from a corporate database, means for the generating AI to customize proposals based on the user's attributes, and means for the terminal to notify the user of the proposal information from the server. This enables users to easily manage their income and expenditure and receive individually optimized savings proposals and investment advice.

[0434] "User" refers to an individual who uses the system to input income and expenditure data and purchasing information and receive savings suggestions and investment advice.

[0435] "Terminal" refers to an electronic device that a user uses to input income and expenditure data and purchase information and transmit that data to a server.

[0436] "Server" refers to a central processing unit that receives and stores data sent from a terminal and analyzes it using artificial intelligence.

[0437] "Generative AI" refers to artificial intelligence models that analyze collected data and generate recommendations based on users' spending patterns and attributes.

[0438] A "database" refers to a storage device within a system that stores users' income and expenditure data, purchasing information, and sale and service information collected from companies.

[0439] "Suggested information" refers to information such as savings methods and investment advice generated by the generating AI based on the analysis results.

[0440] "Special Offer Information" refers to information about discounts and sales collected from a company's database.

[0441] "Savings Points" refer to specific items or methods identified to reduce a user's expenses.

[0442] "Investment advice" refers to information in which the generating AI analyzes the user's surplus funds and suggests appropriate investment destinations and methods.

[0443] This invention is a system that uses AI to provide optimal savings proposals and investment advice based on a user's income and expenditure data and purchasing information. The system includes a terminal operated by the user, a server that analyzes the data, and AI.

[0444] Users use a household accounting app to input income and expenditure data and purchase information. This input data includes food expenses, utility bills, rent, income, etc. Furthermore, users can easily add purchase information by uploading photos of receipts. For example, a user can input "this month's food expenses" in the app and supplement the expenditure data by attaching a photo of the receipt. This information is sent to the server via the device.

[0445] The server stores the data received from the device in a database (e.g., MySQL or MongoDB). The stored data is then analyzed by the Generative AI. In this analysis process, the Generative AI uses machine learning models such as TensorFlow or PyTorch to analyze the user's spending patterns and income trends. For example, for a user who spends a lot on food, the Generative AI can suggest special sales information at a nearby supermarket.

[0446] Furthermore, the server collects sale and service information from the company's database. This is done using web scraping technology and API integration. The generation AI analyzes the collected sale information and customizes the most appropriate suggestions based on the user's attributes. For example, the generation AI might generate a suggestion such as, "User A has a high monthly food budget, so it would be best for him or her to purchase the vegetables on sale at Supermarket A this week."

[0447] The device receives the suggested information from the server and notifies the user. The notification includes specific savings points and details of special sales. For example, the app's push notification function could be used to display a message such as "This week's special sales information: Vegetables are 30% off at Supermarket A."

[0448] The generation AI also analyzes the user's surplus funds and suggests appropriate investment destinations and methods. For example, if a user enters 5,000 yen as their monthly savings amount, the generation AI will use that information to suggest that "it would be best to invest 5,000 yen in an investment trust that promises stable returns." The server provides information on investment trusts and stocks, and the terminal notifies the user of this. The notification will say, "We have product information for an investment trust that promises stable returns as a recommended investment destination for this month's surplus funds."

[0449] As a specific example, if a user's monthly food expenses are determined to be high, the server will collect sales information from nearby supermarkets, and the generation AI will analyze this information to provide optimal savings suggestions to the user. For example, the device will be notified of "This week's sales information" that vegetables are on sale at Supermarket A. In addition, a weekly shopping list and recommended purchase times will be presented as specific ways to reduce the user's food expenses.

[0450] Examples of prompts include:

[0451] "If a user's food budget is high, which supermarket sales should they be shown? Also, suggest a specific shopping list to help them save money on food."

[0452] "Please provide information on the most appropriate mutual funds and stocks for users to invest their surplus funds."

[0453] By implementing this system, users can efficiently manage their income and expenses and receive individually optimized savings proposals and investment advice.

[0454] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0455] Step 1: User enters income and expenditure data and purchasing information

[0456] Input: The user opens the household accounting app and enters income and expenditure data such as food expenses, utility bills, rent, and income. They also add purchase information by uploading photos of receipts.

[0457] Data processing: Converts text data entered on the terminal into JSON format, and compresses receipt images as needed to prepare them for transmission to the server.

[0458] Output: JSON format income and expenditure data and compressed receipt image data are generated on the terminal.

[0459] Specific operation: The user enters "This month's food expenses" in the app, selects "Upload receipt" and takes a photo with the camera.

[0460] Step 2: The device sends the entered data to the server

[0461] Input: JSON-formatted balance data and compressed receipt image data generated in Step 1.

[0462] Data processing: The terminal packages the income and expenditure data and receipt image as an HTTP POST request.

[0463] Output: An HTTP POST request is sent to a specific API endpoint on the server side.

[0464] Specific operation: When the user taps the "Send data" button, the device sends the data to the server.

[0465] Step 3: The server stores the received data and analyzes it using the generative AI.

[0466] Input: Income and expenditure data (JSON format) and receipt image data sent from the device.

[0467] Data processing: The server stores the data in a database and prepares it for input into the generative AI model. The database is MySQL or MongoDB.

[0468] Output: The analysis results from the generative AI are obtained.

[0469] Specific operation: The server saves the data in a database and launches a generative AI model (e.g., TensorFlow or PyTorch) to begin analysis.

[0470] Step 4: Generative AI identifies the user's spending patterns and savings points based on the analysis results

[0471] Input: User's income and expenditure data and purchasing information stored on the server.

[0472] Data processing: Generative AI uses machine learning algorithms to analyze data and identify users' spending patterns and savings opportunities.

[0473] Output: Analysis results that identify the user's spending patterns and savings points.

[0474] Specific operation: The generation AI identifies the pattern that "User A has high food expenses" and clearly indicates savings points by "suggesting information about special sales at Supermarket A."

[0475] Step 5: The server collects the best products and services from the company's database.

[0476] Input: Special offers and service information collected from company databases.

[0477] Data processing: The server periodically retrieves information via web scraping or API and stores it in a database.

[0478] Output: The latest sales and service information is saved in the server database.

[0479] What it does: The server periodically crawls websites that provide sale information and stores new information in a database.

[0480] Step 6: Generative AI customizes suggestions based on user attributes

[0481] Input: User spending patterns, savings points, and special offers.

[0482] Data processing: Generative AI analyzes this data and generates optimal savings proposals for users.

[0483] Output: User-optimized customization suggestions.

[0484] Specific operation: The generation AI generates a specific suggestion such as, "Suggest to user A the vegetables on sale at supermarket A this week."

[0485] Step 7: The device notifies the user of the suggested information from the server.

[0486] Input: Customized proposal information.

[0487] Data processing: The device provides information to the user via push notifications or in-app notifications.

[0488] Output: The notification message that the user receives.

[0489] Specific operation: The device sends a push notification saying, "This week's sale information: 30% off vegetables at Supermarket A."

[0490] Step 8: The generated AI analyzes the user's surplus funds and provides investment advice

[0491] Input: User savings data.

[0492] Data processing: Generative AI analyzes the user's savings data and generates optimal investment advice.

[0493] Output: Investment advice to the user.

[0494] Specific operation: The user enters "monthly savings amount of 5,000 yen," and the generation AI generates a suggestion to "invest 5,000 yen in an investment trust that is expected to provide stable returns."

[0495] Step 9: The terminal notifies the user of the investment advice from the server.

[0496] Input: Investment advice sent from the server.

[0497] Data processing: Prepare push notifications and in-app notifications so that the device can notify the user of investment advice.

[0498] Output: Investment advice notification received by the user.

[0499] Specific operation: The device sends a push notification with "Recommended investments for this month's surplus funds."

[0500] The above are the specific processing steps of the program of this system.

[0501] (Application example 1)

[0502] 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."

[0503] In modern society, individual consumption behavior is becoming more diverse, creating a need for efficient asset management and investment strategies. However, current household accounting apps and asset management systems face challenges, such as cumbersome data entry, lack of personalized savings and investment suggestions, and a lack of up-to-date sales information. In particular, the manual management of receipt information places a significant burden on users. A system that can solve these problems and provide effective savings and investment suggestions based on individual users' attributes and consumption patterns is needed.

[0504] 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.

[0505] In this invention, the server includes: a means for a user to input income and expenditure data and purchase information; a means for a terminal to transmit the input data to the server; a means for the server to store the received household accounting data and analyze it using a generation AI; a means for the generation AI to identify the user's spending patterns and savings points based on the analysis results; a means for the server to collect optimal product and service information from an external database; a means for the generation AI to customize proposals based on the user's attributes; a means for the terminal to notify the user of the proposal information from the server; a means for providing appropriate investment advice based on the savings amount calculated from the user's income and expenditure data; and a means for capturing an image of a receipt and extracting purchase information using optical character recognition technology. This allows users to easily manage their income and expenditure data and receive individually customized savings and investment proposals.

[0506] A "user" is an individual or corporation that inputs income and expenditure data and purchasing information into the system.

[0507] "Income and expenditure data" is information about income and expenditure entered by the user, and specifically includes information such as food expenses, utility expenses, rent, and income.

[0508] "Purchase information" refers to information about a purchase history that a user enters using a receipt or other method.

[0509] A "terminal" is a computer device that allows a user to input income and expenditure data and purchasing information and transmit them to a server, and includes smartphones, tablets, etc.

[0510] A "server" is a computer system that stores and analyzes income and expenditure data and purchasing information received from users.

[0511] "Household account book data" is a group of information including all income and expenditure data and purchasing information entered by the user.

[0512] "Generative AI" is artificial intelligence that analyzes users' income and expenditure data and purchasing information to generate personalized suggestions.

[0513] "Analysis results" are the results obtained by the generation AI analyzing the user's income and expenditure data and purchasing information.

[0514] A "spending pattern" is a tendency for a user to spend money over a period of time.

[0515] "Saving points" are items or ways in which a user can potentially reduce their expenses.

[0516] An "external database" is an external information source that the server accesses to obtain product and service information.

[0517] "Suggested information" refers to information such as products, services, and money-saving methods that are customized by the generating AI based on the user's attributes.

[0518] "Investment advice" refers to suggestions on investment destinations and investment methods provided by the generating AI based on the user's surplus funds.

[0519] "Excess funds" are the funds remaining after a user calculates their monthly income and expenses.

[0520] "Optical character recognition technology" is a technology that extracts character information from image data.

[0521] "Purchase information extraction" refers to the act of using optical character recognition technology to obtain purchase and payment information from receipt images.

[0522] This invention is a system that uses AI generation to provide optimal savings suggestions and investment advice based on a user's income and expenditure data and purchasing information. Each of these means will be described in detail below.

[0523] System configuration

[0524] The system includes the following components:

[0525] A device (such as a smartphone or tablet) on which users input income and expenditure data and purchasing information

[0526] A server that analyzes data and uses generative AI to generate proposal information

[0527] Network for sending notifications to users

[0528] User operation

[0529] Users enter income and expenditure data and purchase information using a dedicated app installed on their smartphone. This can be done manually or by uploading photos of receipts. The receipt information is converted into text data using optical character recognition technology (e.g., Google Cloud Vision API) and extracted as purchase information.

[0530] Server-side processing

[0531] The server receives and stores the income and expenditure data and purchase information sent by the user. The stored data is analyzed using generative AI (e.g., OpenAI GPT-4). The analysis reveals the user's spending patterns and income trends. Below are some examples of specific prompts:

[0532] Prompt Sentence Examples

[0533] "User A's income and expenditure data is shown below. Based on this data, please suggest the optimal savings and investment plan for User A. Monthly income is 300,000 yen, food expenses are 50,000 yen, rent is 100,000 yen, utility bills are 20,000 yen, and savings are 30,000 yen."

[0534] Proposal generation and notification

[0535] Based on the results of the analysis by the generation AI, optimal savings points, sale information, and investment advice are generated for the user. The server collects the latest sale information and service information from external databases (for example, databases of affiliated companies), and the generation AI customizes the proposals based on the user's attributes. The optimal proposals are notified to the device. This allows the user to easily implement specific savings action plans and investment strategies.

[0536] For example, if a user's monthly food expenses are determined to be high, the server will collect sales information from nearby supermarkets, and the generation AI will analyze this information and notify the user of the sales information. For example, the AI ​​may notify the user of "This week's sales information" by notifying them of discounts on vegetables at a specific supermarket. The user can also receive advice on how to invest the money they have saved.

[0537] The system allows users to easily manage their income and expenditure data and receive personalized savings and investment suggestions, leading to more efficient asset management and effective financial investment.

[0538] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0539] Step 1:

[0540] Users enter income and expenditure data and purchasing information. Users manually enter income and expenditure data (food expenses, utility bills, rent, income, etc.) via a dedicated smartphone app. Alternatively, purchasing information can be entered by uploading photos of receipts. The input data is saved on the device as input fields or image files.

[0541] Step 2:

[0542] The terminal sends the entered data to the server. The terminal uploads the income and expenditure data and image files to the server via the network. The data is sent using a protocol such as an HTTP request.

[0543] Step 3:

[0544] The server stores the received household accounting data and purchasing information. The server stores the data for each user in a database (e.g., Firebase or MongoDB). The stored data is used for later analysis.

[0545] Step 4:

[0546] The server uses optical character recognition technology to extract purchase information from the receipt image. The analyzed image data is converted into text data, and each item (product name, price, etc.) is integrated into the household accounting data. This process uses OCR technology such as Google Cloud Vision API.

[0547] Step 5:

[0548] The server inputs the user's income and expenditure data and purchase information into the generation AI. The generation AI model (e.g., OpenAI GPT-4) receives input including the following prompt:

[0549] "User A's income and expenditure data is shown below. Based on this data, please suggest the optimal savings and investment plan for User A. Monthly income is 300,000 yen, food expenses are 50,000 yen, rent is 100,000 yen, utility bills are 20,000 yen, and savings are 30,000 yen."

[0550] Step 6:

[0551] The generating AI identifies the user's spending patterns and savings points and generates optimal proposals. Based on this data, the AI ​​analyzes the user's income and expenditure patterns and suggests effective ways to save and appropriate investments. The output is generated as savings proposals and investment advice and stored in a database on the server.

[0552] Step 7:

[0553] The server collects the latest sales and service information from external databases, and uses an automatically updated API to obtain the latest information from partner company databases. This data is also input into the generation AI.

[0554] Step 8:

[0555] Based on the information collected by the generation AI, the proposed information is customized according to the user's attributes. Specifically, individual sale information and money-saving advice are customized based on the user's area of ​​residence and purchase history. The output is generated as customized proposed information.

[0556] Step 9:

[0557] The device notifies the user of the proposed information received from the server. The information is delivered to the user in the form of a push notification via a dedicated app. The user receives the notification and can take action based on the content.

[0558] Step 10:

[0559] The server generates investment advice for each user and saves it for future recommendations. The advice is linked to the user's profile and used as data to improve the accuracy of future recommendations.

[0560] This allows users to easily input income and expenditure data and receive personalized savings and investment suggestions, leading to more efficient asset management and more effective financial investment.

[0561] 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.

[0562] This invention is a system that uses a generative AI and an emotion engine to provide optimal savings proposals and investment advice based on a user's income and expenditure data and purchasing information. This system includes a terminal operated by the user, a server that analyzes the data, a generative AI, and an emotion engine that recognizes the user's emotions.

[0563] Users use a household accounting app to input income and expenditure data and purchase information. This data includes food expenses, utility bills, rent, income, etc. Purchase information can also be easily added by uploading photos of receipts. The device then sends the information entered by the user to the server.

[0564] The server stores the received data and analyzes it using the Generative AI. This analysis process clarifies the user's spending patterns and income trends. For example, if a user has a high monthly food budget, the server can suggest special sale information at a nearby supermarket. The server also collects special sale and service information from the company's database, and the Generative AI customizes the most appropriate suggestions based on the user's attributes.

[0565] Here, a new emotion engine that recognizes the user's emotions is incorporated. The emotion engine analyzes emotions from the user's input data and behavior. For example, it can identify the user's emotional state, such as whether they are feeling stressed, based on the text they enter, trends in their income and expenditure data, and their app usage patterns.

[0566] The emotion data analyzed by the emotion engine is provided to the generative AI, which then takes this emotion data into account to customize suggestions. For example, if the user is feeling stressed, the generative AI will provide relaxing shopping suggestions or investment advice.

[0567] The device receives suggested information and investment advice from the server and notifies the user. The notification includes details of specific savings points and special sales information. For example, it may notify discount information at a specific supermarket as "This week's food sales information." The user can also receive advice on how to invest the savings. The generating AI analyzes the user's surplus funds and suggests appropriate investment destinations and methods.

[0568] As a specific example, if a user is determined to have high food expenses, the server will provide sales information and a list of nearby supermarkets to the generation AI to identify the best shopping destination. For example, the device will be notified of this week's sales information, such as a discount on vegetables at Supermarket A. Furthermore, if the user's emotions indicate a state of stress, the emotion engine will also suggest sales information for products with a relaxing effect (such as aroma oils).

[0569] To give a specific example of investment, when a user inputs the amount of savings, the AI ​​generator will suggest appropriate investment trusts and stocks based on that information. The server will generate optimal investment advice for the user, and the device will notify them of investment trust product information that promises stable returns as "recommended investments for this month's surplus funds." Furthermore, if the user's emotions indicate caution, low-risk investments will be prioritized.

[0570] In this way, by incorporating an emotion engine, it is possible to provide more personalized savings and investment advice based on the user's emotional state, allowing the user to manage their finances comfortably and effectively.

[0571] The processing flow will be explained below.

[0572] Step 1:

[0573] The user opens the household accounting app and enters income and expenditure data and purchase information. Income and expenditure data includes salary, living expenses, utility bills, food expenses, etc., while purchase information includes product name, purchase price, purchase date and time, and purchase store.

[0574] Step 2:

[0575] The terminal temporarily stores the data entered by the user, and after confirmation, transmits this data to the server.

[0576] Step 3:

[0577] The server stores the received household accounting data in a database. When storing the data, it checks its consistency and performs data cleaning if necessary.

[0578] Step 4:

[0579] The server provides data to the generation AI and emotion engine. The generation AI analyzes income and expenditure data and purchase information to clarify the user's spending patterns and income trends. The emotion engine analyzes emotions from the user's input data and behavior.

[0580] Step 5:

[0581] Based on the analysis results, the AI ​​will identify ways for users to save money. For example, it will provide sales information and shopping suggestions to users who spend a lot on food, and generate advice on how to reduce energy consumption to users who consume a lot of energy.

[0582] Step 6:

[0583] The emotion engine identifies the user's emotional state (stress, satisfaction, etc.) and provides that data to the generative AI.

[0584] Step 7:

[0585] Generative AI takes emotional data into account to customize suggestions, for example, offering relaxing shopping suggestions or investment advice if the user is feeling stressed.

[0586] Step 8:

[0587] The server accesses the company's database to collect the latest sales and service information. This information is updated regularly and used by the generating AI for analysis.

[0588] Step 9:

[0589] The generative AI analyzes corporate sales information and selects the most appropriate sales information and services based on the user's attributes (age, gender, region, hobbies, preferences, etc.). The selected information is customized for each user.

[0590] Step 10:

[0591] The server saves the generated suggestion information for each user and sends it to the device, which then notifies the user using the notification function.

[0592] Step 11:

[0593] The terminal notifies the user of the proposed information and investment advice received from the server. The notification includes specific savings points and detailed investment proposals. The user confirms the notification and initiates specific actions.

[0594] As a specific example, if a user is determined to have high food expenses, the server will provide sales information and a list of nearby supermarkets to the generation AI to identify the best shopping destination. For example, the device will be notified of this week's sales information, such as a discount on vegetables at Supermarket A. Furthermore, if the user's emotions indicate a state of stress, the emotion engine will also suggest sales information for products with a relaxing effect (such as aroma oils).

[0595] To give a specific example of investment, when a user inputs the amount of savings, the AI ​​generator will suggest appropriate investment trusts and stocks based on that information. The server will generate optimal investment advice for the user, and the device will notify them of investment trust product information that promises stable returns as "recommended investments for this month's surplus funds." Furthermore, if the user's emotions indicate caution, low-risk investments will be prioritized.

[0596] In this way, by incorporating an emotion engine, it becomes possible to make suggestions based on the user's emotional state, thereby providing more personalized savings and investment advice.

[0597] Example 2

[0598] 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."

[0599] Conventional household management systems and investment advice systems can make suggestions based on a user's income and expenditure data and purchasing information, but they cannot take into account the user's emotional state. This has the problem that they cannot provide flexible suggestions and advice that respond to the stress and emotional fluctuations that users face. As a result, users may not follow the suggested advice, and may not achieve the full effect of their savings or investments.

[0600] 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.

[0601] In this invention, the server includes means for an emotion engine to analyze the user's emotional state, means for a generation AI to identify the user's spending patterns and savings points based on the analysis results and the user's emotional state, and means for the generation AI to customize proposals based on the user's attributes and emotional state. This allows the server to provide proposals and advice that meet the individual needs of the user according to their emotional state, enabling the user to save and invest more effectively.

[0602] A "user" is a person who uses the system to enter income and expenditure data and purchasing information and receive savings and investment advice.

[0603] "Income and expenditure data" refers to information related to income and expenses entered by the user, including, for example, food expenses, utility expenses, rent, and income.

[0604] "Purchase information" is detailed information about a user's purchases, including photos of receipts and data about purchased items.

[0605] A "terminal" is a device that a user operates to input income and expenditure data and purchasing information, and to receive proposal information and investment advice.

[0606] A "server" is a computer system that receives, stores, and analyzes data sent from a terminal.

[0607] "Generative AI" is artificial intelligence that analyzes a user's income and expenditure data and purchasing information to identify and customize the user's spending patterns, savings points, and investment advice.

[0608] An "emotion engine" is a device or software that analyzes a user's input data and behavioral patterns to identify the emotional state the user is feeling.

[0609] The "analysis results" are information about the user's spending patterns and income trends obtained by the generation AI by analyzing income and expenditure data and purchasing information.

[0610] "Attributes" are characteristics and information specific to a user, and include data such as age, occupation, and family structure.

[0611] A "corporate database" is a database for collecting information on products and services offered by markets and companies.

[0612] "Special sale information" is information about specific products or services being offered at discounted prices.

[0613] "Investment advice" refers to investment suggestions presented based on a user's financial data and emotional state.

[0614] "Means to customize recommendations" refers to the generative AI's ability to individually tailor optimal savings and investment strategies based on a user's financial data, attributes, and emotional state.

[0615] A "notification" is information sent from a server and displayed on a user's terminal.

[0616] This invention is a system that uses a generative AI and an emotion engine to provide optimal savings proposals and investment advice based on a user's income and expenditure data and purchasing information. This system includes a terminal operated by the user, a server that analyzes the data, a generative AI, and an emotion engine that recognizes the user's emotions.

[0617] Users use a household accounting app to input income and expenditure data and purchase information. This data includes food expenses, utility bills, rent, income, etc. Purchase information can also be easily added by uploading photos of receipts. The device then sends the information entered by the user to the server.

[0618] The server stores the received data and analyzes it using the Generative AI. This analysis process clarifies the user's spending patterns and income trends. For example, if a user has a high monthly food budget, it can suggest special sale information at a nearby supermarket. The server also collects special sale and service information from the company's database, and the Generative AI customizes the most appropriate suggestions based on the user's attributes and emotional state.

[0619] Here, a new emotion engine that recognizes the user's emotions is incorporated. The emotion engine analyzes emotions from the user's input data and behavior. For example, it can identify the user's emotional state, such as whether they are feeling stressed, based on the text they enter, trends in their income and expenditure data, and their app usage patterns.

[0620] The emotion data analyzed by the emotion engine is provided to the generative AI, which then takes this emotion data into account to customize suggestions. For example, if the user is feeling stressed, the generative AI will provide relaxing shopping suggestions or investment advice.

[0621] The device receives suggested information and investment advice from the server and notifies the user. The notification includes details of specific savings points and special sales information. For example, it may notify discount information at a specific supermarket as "This week's food sales information." The user can also receive advice on how to invest the savings. The generating AI analyzes the user's surplus funds and suggests appropriate investment destinations and methods.

[0622] As a specific example, if a user is determined to have high food expenses, the server will provide sales information and a list of nearby supermarkets to the generation AI to identify the best shopping destination. For example, the device will be notified of this week's sales information, such as a discount on vegetables at Supermarket A. Furthermore, if the user's emotions indicate a state of stress, the emotion engine will also suggest sales information for products with a relaxing effect (such as aroma oils).

[0623] To give a specific example of investment, when a user inputs the amount of savings, the AI ​​generator will suggest appropriate investment trusts and stocks based on that information. The server will generate optimal investment advice for the user, and the device will notify them of investment trust product information that promises stable returns as "recommended investments for this month's surplus funds." Furthermore, if the user's emotions indicate caution, low-risk investments will be prioritized.

[0624] Examples of prompts include, "If the user's food expenses are high this month, suggest sales information for nearby supermarkets and also notify the user of products that will help the user relax." and "If the user enters the amount of savings, suggest low-risk investments based on that information. Prioritize low-risk options, especially when the user is emotionally cautious."

[0625] In this way, by incorporating an emotion engine, it is possible to provide more personalized savings and investment advice based on the user's emotional state, allowing the user to manage their finances comfortably and effectively.

[0626] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0627] Processing Steps

[0628] Step 1: Enter income and expenditure data and purchasing information

[0629] 1. The user opens the household accounting app on their device.

[0630] 2. The user enters income and expenditure data (e.g., food expenses, utility bills, rent, income, etc.) into the app.

[0631] Input: User manually enters "15,000 yen" as food expenses

[0632] Output: Data is temporarily saved on the device.

[0633] 3. Users add purchase information by uploading a photo of their receipt to the app.

[0634] Input: Upload a photo of your supermarket receipt

[0635] Output: The receipt details are recorded in the app as purchase information.

[0636] 4. The terminal sends these input data to the server.

[0637] Input: Income and expenditure data and purchasing information

[0638] Output: Sending data from the device to the server

[0639] Step 2: Receiving and storing data

[0640] 1. The server receives income and expenditure data and purchase information sent from the terminal.

[0641] Input: Income and expenditure data from the device "Food expenses: 15,000 yen", purchase information

[0642] Output: Stored as received data on the server

[0643] 2. The server stores the received data in a database.

[0644] Input: Income and expenditure data and purchasing information

[0645] Output: Income and expenditure data and purchasing information stored in the database

[0646] Step 3: Analysis by generative AI model

[0647] 1. The server provides the data stored in the database to the generative AI model.

[0648] Input: Saved income and expenditure data and purchasing information

[0649] Output: Data is provided to a generative AI model

[0650] 2. The generative AI model analyzes the provided data and derives the user's spending patterns and income trends.

[0651] Import: Provided data

[0652] Output: The analysis result is "The average monthly food cost exceeds 20,000 yen."

[0653] Step 4: Gather information about special offers and services

[0654] 1. The server collects the latest sales and service information from the company's database and online sources.

[0655] Inputs: Corporate databases and online sources

[0656] Output: Special sale information such as "Supermarket A's special sale this week: 30% off vegetables"

[0657] 2. The server provides the collected information to the generative AI model.

[0658] Input: Collected sale information

[0659] Output: Special offers provided to the generative AI model

[0660] Step 5: Customize optimal proposals with generative AI models

[0661] 1. The generative AI model customizes optimal offers based on income and expenditure data, sales information, and user attributes.

[0662] Input: Income and expenditure data, special sale information, user attributes

[0663] Output: Suggestions such as "You can get a good deal on vegetables at Supermarket A"

[0664] Step 6: Analyze user emotions with the emotion engine

[0665] 1. The emotion engine analyzes the user's input data and behavioral patterns to identify their emotional state.

[0666] Input: User input data, behavioral patterns

[0667] Output: User's emotional state (e.g., "I feel stressed")

[0668] 2. The emotion engine provides the analyzed emotion data to the generative AI model.

[0669] Input: User emotion data

[0670] Output: Emotion data fed to a generative AI model

[0671] Step 7: Re-customize suggestions with generative AI models, taking into account emotions

[0672] 1. The generative AI model then re-customizes the suggestions, taking into account sentiment data.

[0673] Input: Emotion data

[0674] Output: Additional suggestions, such as "Special offers on relaxing aroma oils"

[0675] Step 8: Server generates and sends proposals and investment advice

[0676] 1. The server generates customized proposal information and investment advice using generative AI.

[0677] Input: Generative AI model proposal

[0678] Output: Proposal information and investment advice for users

[0679] 2. The server sends the latest proposal information and investment advice to the terminal.

[0680] Input: Proposal information and investment advice

[0681] Output: Sending data from the server to the device

[0682] Step 9: Device Notifications and User Actions

[0683] 1. The terminal notifies the user of the received information.

[0684] Input: Proposal information and investment advice sent from the server

[0685] Output: Notifications displayed on the device (e.g., "This week's specials: 30% off vegetables at Supermarket A" or "Specials on relaxing aroma oils")

[0686] 2. The user checks the notification on their device and takes action based on the proposed information and investment advice.

[0687] Input: Device notifications

[0688] Output: Actual savings behavior and investment decisions

[0689] In this vein, the system of the present invention provides suggestions and advice tailored to the user's individual needs based on their emotional state, allowing them to save and invest more effectively.

[0690] (Application example 2)

[0691] 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."

[0692] In modern society, users face many challenges in managing their daily living expenses, saving money, and making investment decisions. In addition, because users' emotional state influences their purchasing and investment choices, it is difficult for conventional household management systems and investment advice tools to provide optimal recommendations for individual users. Therefore, there is a need for a system that provides personalized saving and investment advice that takes into account not only users' income and expenditure data but also their emotional state.

[0693] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0694] In this invention, the server includes: means for a user to input income and expenditure data and purchasing information; means for a terminal to transmit the input data to the server; means for the server to store the received household accounting data and analyze it using a generation AI; means for the generation AI to identify the user's spending patterns and savings points based on the analysis results; means for the server to collect optimal product and service information from corporate databases; means for the generation AI to customize proposals based on the user's attributes; means for the terminal to notify the user of the proposal information from the server; means for analyzing the user's emotions using an emotion engine and providing the results to the generation AI; and means for the generation AI to customize proposals taking the user's emotion data into consideration. This enables personalized proposals based on the user's emotional state, resulting in more effective savings and investment advice.

[0695] "Income and expenditure data" is a general term for information about a user's daily income and expenditure.

[0696] "Purchase information" refers to detailed information about products and services purchased by a user.

[0697] "Terminal" refers to a hardware device through which a user inputs information and communicates with a server.

[0698] A "server" is a computer system that stores and analyzes collected data.

[0699] "Household account data" refers to data that records a user's income and expenditure information and purchasing information.

[0700] "Generative AI" is a system that uses artificial intelligence technology to analyze data and generate optimal suggestions for users.

[0701] The "spending pattern" indicates the tendency or characteristics of a user regarding spending.

[0702] "Savings Points" are specific areas and ways in which users can reduce their spending.

[0703] A "corporate database" is a company-owned information resource that stores information about products and services.

[0704] "Customizing suggestions" means individually tailoring optimal suggestions based on the user's attributes and tendencies.

[0705] An "emotion engine" refers to a technology or system for analyzing a user's emotions.

[0706] "Emotion data" is information relating to the user's emotional state.

[0707] "Investment advice" is information that suggests optimal investment methods based on the user's financial situation.

[0708] "Excess funds" are the funds remaining after subtracting expenses from a user's income.

[0709] An "investment strategy" is a plan or guideline for how to invest excess funds.

[0710] "Special sale information" is information about products and services being sold at a price lower than the regular price.

[0711] "Notify" refers to a means of informing a user of specific information.

[0712] This invention is a system that uses a generative AI and an emotion engine to provide optimal savings proposals and investment advice based on a user's income and expenditure data and purchasing information. This system includes a terminal operated by the user, a server that analyzes the data, a generative AI, and an emotion engine that recognizes the user's emotions.

[0713] Users use a smartphone app to enter income and expenditure data and purchase information. This app has the function of automatically collecting expenditure data by linking with the electronic payment services that users use on a daily basis. Users can also enter data manually, and can easily add purchase information by uploading photos of receipts.

[0714] The device sends the information entered by the user to the server. The server stores the received data and analyzes it using generative AI. This analysis process clarifies the user's spending patterns and income trends, and suggests optimal savings points. For example, a user with high monthly food expenses will be notified of special sales at a nearby supermarket.

[0715] The server then collects sales and service information from the company's database, and the generative AI customizes the most appropriate proposals based on the user's attributes. An emotion engine that recognizes the user's emotions is then incorporated. The emotion engine analyzes the user's input data and behavior to determine their emotional state, such as whether they are feeling stressed.

[0716] The emotion data analyzed by the emotion engine is provided to the generative AI, which then takes this emotion data into account to customize suggestions. For example, if the user is feeling stressed, the generative AI will suggest products and services that will help them relax.

[0717] The device receives suggested information and investment advice from the server and notifies the user. The notification includes details of specific savings points and sales information. For example, a notification of discount information at a specific supermarket may be sent as "This week's food sales information." The user can also receive advice on how to invest the savings they have made, and the generating AI will analyze the user's surplus funds and suggest appropriate investment destinations and methods.

[0718] The hardware and software used are as follows:

[0719] Hardware: Smartphone (iOS, Android)

[0720] Software: Python, TensorFlow, Emotion API (Microsoft Azure), Watson Tone Analyzer (IBM), Firebase Cloud Messaging (Google)

[0721] For example, if user A's food expenses are judged to be high, the device will be notified of this week's special sale information, such as discounts on vegetables at a nearby supermarket. If user A's emotions indicate a state of stress, the emotion engine will also suggest sales information for products that have a relaxing effect.

[0722] Prompt Sentence Examples

[0723] If the user's food budget is high, suggest which supermarket has a sale on which product. If the user is feeling stressed, provide information on products that will help them relax.

[0724] This allows for personalized suggestions based on the user's emotional state, resulting in more effective savings and investment advice.

[0725] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0726] Step 1:

[0727] Users input their financial data and purchase information via a smartphone app. This information can be automatically obtained from electronic payment services, manually entered by the user, or extracted from photos of receipts. The app stores the financial data and purchase information in its internal database.

[0728] Step 2:

[0729] The terminal sends the collected income and expenditure data and purchase information to the server. It receives data from the internal database as input and generates the data sent to the server as output. This data transmission uses SSL / TLS encrypted communication.

[0730] Step 3:

[0731] The server stores the received data and analyzes it using generative AI. In this step, the income and expenditure data and purchasing information sent as input are received and stored in a database as output. The stored data is then input into a generative AI model, which analyzes spending patterns and income trends. Specifically, data analysis is performed using Python's pandas and TensorFlow.

[0732] Step 4:

[0733] The generative AI identifies the user's spending patterns and savings points based on the analysis results. It receives the analysis results as input and identifies optimal savings points as output. For example, if the user's food expenses are higher than other categories, advice on reducing food expenses will be suggested.

[0734] Step 5:

[0735] The server collects optimal product and service information from the company's database. It receives spending patterns and savings points as input and extracts suitable product and service information as output. Specific product information and special sale information are collected in this step.

[0736] Step 6:

[0737] The emotion engine analyzes emotions from the user's input data and behavior. In this step, user behavior data and input text are received as input, and emotion data is generated as output. Emotion analysis is performed using the Emotion API and Watson Tone Analyzer.

[0738] Step 7:

[0739] The generative AI takes emotional data into account to customize suggestions. It receives emotional data and collected product and service information as input, and generates optimal suggestions for the user as output. Specifically, a user in a stressed state will be offered information about special sales on products that have a relaxing effect.

[0740] Step 8:

[0741] The device notifies the user of the proposed information and investment advice from the server. It receives the customized proposals from the server as input and generates push notifications as output. This notification is generated using Firebase Cloud Messaging.

[0742] As a concrete example, consider the following prompt sentence:

[0743] If the user's food budget is high, suggest which supermarket has a sale on which product. If the user is feeling stressed, provide information on products that will help them relax.

[0744] 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.

[0745] 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.

[0746] 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.

[0747] [Third embodiment]

[0748] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0749] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0750] 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).

[0751] 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.

[0752] 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.

[0753] 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).

[0754] 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.

[0755] 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.

[0756] 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.

[0757] 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.

[0758] 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.

[0759] 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."

[0760] This invention is a system that uses AI to provide optimal savings proposals and investment advice based on a user's income and expenditure data and purchasing information. The system includes a terminal operated by the user, a server that analyzes the data, and AI.

[0761] Users use a household accounting app to input income and expenditure data and purchase information. This data includes food expenses, utility bills, rent, income, etc. Purchase information can also be easily added by uploading photos of receipts. The device then sends the information entered by the user to the server.

[0762] The server stores the received data and analyzes it using the Generative AI. This analysis process clarifies the user's spending patterns and income trends. For example, if a user has a high monthly food budget, the server can suggest special sale information at a nearby supermarket. The server also collects special sale and service information from the company's database, and the Generative AI customizes the most appropriate suggestions based on the user's attributes.

[0763] The device receives the suggested information from the server and notifies the user. The notification includes specific savings points and details of special sales. For example, it may notify the user of discount information at a specific supermarket as "This week's food sales information." The user can also receive advice on how to invest the savings. The generating AI analyzes the user's surplus funds and suggests appropriate investment destinations and methods.

[0764] As a specific example, if a user's monthly food expenses are determined to be high, the server will collect sales information from nearby supermarkets, and the generation AI will analyze this information to provide optimal savings suggestions to the user. For example, the device will be notified of "This week's sales information" that vegetables are on sale at Supermarket A. In addition, a weekly shopping list and recommended purchase times will be presented as specific ways to reduce the user's food expenses.

[0765] Furthermore, when it comes to surplus funds, users can input their monthly savings amount and the AI ​​will use that information to provide appropriate investment advice. The server will provide the user with information on the most suitable investment trusts and stocks, which will then be notified to the user via the device. For example, information on investment trust products that promise stable returns will be displayed as "recommended investments for this month's surplus funds."

[0766] In summary, this system efficiently utilizes the user's income and expenditure data and purchasing information, and uses generative AI to individually suggest optimal savings and investments, enabling users to obtain specific action plans and implement more effective financial strategies.

[0767] The processing flow will be explained below.

[0768] Step 1:

[0769] The user opens the household accounting app and enters income and expenditure data and purchase information. Income and expenditure data includes salary, living expenses, utility bills, food expenses, etc., while purchase information includes product name, purchase price, purchase date and time, and purchase store.

[0770] Step 2:

[0771] The terminal temporarily stores the data entered by the user, checks the input, and then sends the data to the server at a specific timing or when the user operates the terminal.

[0772] Step 3:

[0773] The server stores the received household accounting data in a database. When storing the data, it checks its consistency and performs data cleaning if necessary.

[0774] Step 4:

[0775] The server periodically initiates a data analysis process, providing income and expenditure data and purchase information to the AI ​​generator, which then analyzes this data to clarify the user's spending patterns and income trends.

[0776] Step 5:

[0777] Based on the analysis results, the AI ​​will identify ways for users to save money. For users with high food expenses, it will provide sales information and suggestions on where to buy food, and for users with high energy consumption, it will generate advice on how to reduce energy consumption.

[0778] Step 6:

[0779] The AI ​​analyzes the user's surplus funds and derives an appropriate investment strategy. Based on the results of this analysis, the server generates optimal investment advice for each user and stores it in a database.

[0780] Step 7:

[0781] The server accesses the database of partner companies to collect the latest sales and service information. This information is updated regularly and used by the generating AI for analysis.

[0782] Step 8:

[0783] The generative AI analyzes corporate sales information and selects the most appropriate sales information and services based on the user's attributes (age, gender, region, hobbies, preferences, etc.). The selected information is customized for each user.

[0784] Step 9:

[0785] The server saves the generated suggestion information for each user and sends it to the device, which then notifies the user using the notification function.

[0786] Step 10:

[0787] The terminal notifies the user of the proposed information and investment advice received from the server. The notification includes specific savings points and detailed investment proposals. The user confirms the notification and initiates specific actions.

[0788] As a specific example, if a user is determined to have high food expenses, the server will provide the generation AI with sale information and a list of nearby supermarkets to identify the best shopping destination. For example, the device will be notified of discounted vegetables at Supermarket A as "This week's sale information." As a specific example of investment advice, the server will have the generation AI suggest appropriate investment trusts and stocks based on the user's savings amount, and the device will notify the user of product information on investment trusts with stable returns as "recommended investments for this month's surplus funds."

[0789] In this way, useful savings and investment advice is provided to the user through specific actions at each step.

[0790] Example 1

[0791] 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."

[0792] In modern life, users are required to efficiently manage their income and expenses, and make savings and investments. However, with conventional methods, users must manually enter data, making it extremely difficult to find appropriate savings methods and investment destinations. It is also difficult to effectively utilize sales and service information. Therefore, there is a need for a system that allows users to easily manage their income and expenses and receive optimal savings suggestions and investment advice.

[0793] 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.

[0794] In this invention, the server includes means for a user to input income and expenditure data and purchasing information, means for a terminal to transmit the input data to the server, means for the server to store the received data and analyze it using a generating AI, means for the generating AI to identify the user's spending patterns and savings points based on the analysis results, means for the server to collect optimal product and service information from a corporate database, means for the generating AI to customize proposals based on the user's attributes, and means for the terminal to notify the user of the proposal information from the server. This enables users to easily manage their income and expenditure and receive individually optimized savings proposals and investment advice.

[0795] "User" refers to an individual who uses the system to input income and expenditure data and purchasing information and receive savings suggestions and investment advice.

[0796] "Terminal" refers to an electronic device that a user uses to input income and expenditure data and purchase information and transmit that data to a server.

[0797] "Server" refers to a central processing unit that receives and stores data sent from a terminal and analyzes it using artificial intelligence.

[0798] "Generative AI" refers to artificial intelligence models that analyze collected data and generate recommendations based on users' spending patterns and attributes.

[0799] A "database" refers to a storage device within a system that stores users' income and expenditure data, purchasing information, and sale and service information collected from companies.

[0800] "Suggested information" refers to information such as savings methods and investment advice generated by the generating AI based on the analysis results.

[0801] "Special Offer Information" refers to information about discounts and sales collected from a company's database.

[0802] "Savings Points" refer to specific items or methods identified to reduce a user's expenses.

[0803] "Investment advice" refers to information in which the generating AI analyzes the user's surplus funds and suggests appropriate investment destinations and methods.

[0804] This invention is a system that uses AI to provide optimal savings proposals and investment advice based on a user's income and expenditure data and purchasing information. The system includes a terminal operated by the user, a server that analyzes the data, and AI.

[0805] Users use a household accounting app to input income and expenditure data and purchase information. This input data includes food expenses, utility bills, rent, income, etc. Furthermore, users can easily add purchase information by uploading photos of receipts. For example, a user can input "this month's food expenses" in the app and supplement the expenditure data by attaching a photo of the receipt. This information is sent to the server via the device.

[0806] The server stores the data received from the device in a database (e.g., MySQL or MongoDB). The stored data is then analyzed by the Generative AI. In this analysis process, the Generative AI uses machine learning models such as TensorFlow or PyTorch to analyze the user's spending patterns and income trends. For example, for a user who spends a lot on food, the Generative AI can suggest special sales information at a nearby supermarket.

[0807] Furthermore, the server collects sale and service information from the company's database. This is done using web scraping technology and API integration. The generation AI analyzes the collected sale information and customizes the most appropriate suggestions based on the user's attributes. For example, the generation AI might generate a suggestion such as, "User A has a high monthly food budget, so it would be best for him or her to purchase the vegetables on sale at Supermarket A this week."

[0808] The device receives the suggested information from the server and notifies the user. The notification includes specific savings points and details of special sales. For example, the app's push notification function could be used to display a message such as "This week's special sales information: Vegetables are 30% off at Supermarket A."

[0809] The generation AI also analyzes the user's surplus funds and suggests appropriate investment destinations and methods. For example, if a user enters 5,000 yen as their monthly savings amount, the generation AI will use that information to suggest that "it would be best to invest 5,000 yen in an investment trust that promises stable returns." The server provides information on investment trusts and stocks, and the terminal notifies the user of this. The notification will say, "We have product information for an investment trust that promises stable returns as a recommended investment destination for this month's surplus funds."

[0810] As a specific example, if a user's monthly food expenses are determined to be high, the server will collect sales information from nearby supermarkets, and the generation AI will analyze this information to provide optimal savings suggestions to the user. For example, the device will be notified of "This week's sales information" that vegetables are on sale at Supermarket A. In addition, a weekly shopping list and recommended purchase times will be presented as specific ways to reduce the user's food expenses.

[0811] Examples of prompts include:

[0812] "If a user's food budget is high, which supermarket sales should they be shown? Also, suggest a specific shopping list to help them save money on food."

[0813] "Please provide information on the most appropriate mutual funds and stocks for users to invest their surplus funds."

[0814] By implementing this system, users can efficiently manage their income and expenses and receive individually optimized savings proposals and investment advice.

[0815] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0816] Step 1: User enters income and expenditure data and purchasing information

[0817] Input: The user opens the household accounting app and enters income and expenditure data such as food expenses, utility bills, rent, and income. They also add purchase information by uploading photos of receipts.

[0818] Data processing: Converts text data entered on the terminal into JSON format, and compresses receipt images as needed to prepare them for transmission to the server.

[0819] Output: JSON format income and expenditure data and compressed receipt image data are generated on the terminal.

[0820] Specific operation: The user enters "This month's food expenses" in the app, selects "Upload receipt" and takes a photo with the camera.

[0821] Step 2: The device sends the entered data to the server

[0822] Input: JSON-formatted balance data and compressed receipt image data generated in Step 1.

[0823] Data processing: The terminal packages the income and expenditure data and receipt image as an HTTP POST request.

[0824] Output: An HTTP POST request is sent to a specific API endpoint on the server side.

[0825] Specific operation: When the user taps the "Send data" button, the device sends the data to the server.

[0826] Step 3: The server stores the received data and analyzes it using the generative AI.

[0827] Input: Income and expenditure data (JSON format) and receipt image data sent from the device.

[0828] Data processing: The server stores the data in a database and prepares it for input into the generative AI model. The database is MySQL or MongoDB.

[0829] Output: The analysis results from the generative AI are obtained.

[0830] Specific operation: The server saves the data in a database and launches a generative AI model (e.g., TensorFlow or PyTorch) to begin analysis.

[0831] Step 4: Generative AI identifies the user's spending patterns and savings points based on the analysis results

[0832] Input: User's income and expenditure data and purchasing information stored on the server.

[0833] Data processing: Generative AI uses machine learning algorithms to analyze data and identify users' spending patterns and savings opportunities.

[0834] Output: Analysis results that identify the user's spending patterns and savings points.

[0835] Specific operation: The generation AI identifies the pattern that "User A has high food expenses" and clearly indicates savings points by "suggesting information about special sales at Supermarket A."

[0836] Step 5: The server collects the best products and services from the company's database.

[0837] Input: Special offers and service information collected from company databases.

[0838] Data processing: The server periodically retrieves information via web scraping or API and stores it in a database.

[0839] Output: The latest sales and service information is saved in the server database.

[0840] What it does: The server periodically crawls websites that provide sale information and stores new information in a database.

[0841] Step 6: Generative AI customizes suggestions based on user attributes

[0842] Input: User spending patterns, savings points, and special offers.

[0843] Data processing: Generative AI analyzes this data and generates optimal savings proposals for users.

[0844] Output: User-optimized customization suggestions.

[0845] Specific operation: The generation AI generates a specific suggestion such as, "Suggest to user A the vegetables on sale at supermarket A this week."

[0846] Step 7: The device notifies the user of the suggested information from the server.

[0847] Input: Customized proposal information.

[0848] Data processing: The device provides information to the user via push notifications or in-app notifications.

[0849] Output: The notification message that the user receives.

[0850] Specific operation: The device sends a push notification saying, "This week's sale information: 30% off vegetables at Supermarket A."

[0851] Step 8: The generated AI analyzes the user's surplus funds and provides investment advice

[0852] Input: User savings data.

[0853] Data processing: Generative AI analyzes the user's savings data and generates optimal investment advice.

[0854] Output: Investment advice to the user.

[0855] Specific operation: The user enters "monthly savings amount of 5,000 yen," and the generation AI generates a suggestion to "invest 5,000 yen in an investment trust that is expected to provide stable returns."

[0856] Step 9: The terminal notifies the user of the investment advice from the server.

[0857] Input: Investment advice sent from the server.

[0858] Data processing: Prepare push notifications and in-app notifications so that the device can notify the user of investment advice.

[0859] Output: Investment advice notification received by the user.

[0860] Specific operation: The device sends a push notification with "Recommended investments for this month's surplus funds."

[0861] The above are the specific processing steps of the program of this system.

[0862] (Application example 1)

[0863] 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."

[0864] In modern society, individual consumption behavior is becoming more diverse, creating a need for efficient asset management and investment strategies. However, current household accounting apps and asset management systems face challenges, such as cumbersome data entry, lack of personalized savings and investment suggestions, and a lack of up-to-date sales information. In particular, the manual management of receipt information places a significant burden on users. A system that can solve these problems and provide effective savings and investment suggestions based on individual users' attributes and consumption patterns is needed.

[0865] 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.

[0866] In this invention, the server includes: a means for a user to input income and expenditure data and purchase information; a means for a terminal to transmit the input data to the server; a means for the server to store the received household accounting data and analyze it using a generation AI; a means for the generation AI to identify the user's spending patterns and savings points based on the analysis results; a means for the server to collect optimal product and service information from an external database; a means for the generation AI to customize proposals based on the user's attributes; a means for the terminal to notify the user of the proposal information from the server; a means for providing appropriate investment advice based on the savings amount calculated from the user's income and expenditure data; and a means for capturing an image of a receipt and extracting purchase information using optical character recognition technology. This allows users to easily manage their income and expenditure data and receive individually customized savings and investment proposals.

[0867] A "user" is an individual or corporation that inputs income and expenditure data and purchasing information into the system.

[0868] "Income and expenditure data" is information about income and expenditure entered by the user, and specifically includes information such as food expenses, utility expenses, rent, and income.

[0869] "Purchase information" refers to information about a purchase history that a user enters using a receipt or other method.

[0870] A "terminal" is a computer device that allows a user to input income and expenditure data and purchasing information and transmit them to a server, and includes smartphones, tablets, etc.

[0871] A "server" is a computer system that stores and analyzes income and expenditure data and purchasing information received from users.

[0872] "Household account book data" is a group of information including all income and expenditure data and purchasing information entered by the user.

[0873] "Generative AI" is artificial intelligence that analyzes users' income and expenditure data and purchasing information to generate personalized suggestions.

[0874] "Analysis results" are the results obtained by the generation AI analyzing the user's income and expenditure data and purchasing information.

[0875] A "spending pattern" is a tendency for a user to spend money over a period of time.

[0876] "Saving points" are items or ways in which a user can potentially reduce their expenses.

[0877] An "external database" is an external information source that the server accesses to obtain product and service information.

[0878] "Suggested information" refers to information such as products, services, and money-saving methods that are customized by the generating AI based on the user's attributes.

[0879] "Investment advice" refers to suggestions on investment destinations and investment methods provided by the generating AI based on the user's surplus funds.

[0880] "Excess funds" are the funds remaining after a user calculates their monthly income and expenses.

[0881] "Optical character recognition technology" is a technology that extracts character information from image data.

[0882] "Purchase information extraction" refers to the act of using optical character recognition technology to obtain purchase and payment information from receipt images.

[0883] This invention is a system that uses AI generation to provide optimal savings suggestions and investment advice based on a user's income and expenditure data and purchasing information. Each of these means will be described in detail below.

[0884] System configuration

[0885] The system includes the following components:

[0886] A device (such as a smartphone or tablet) on which users input income and expenditure data and purchasing information

[0887] A server that analyzes data and uses generative AI to generate proposal information

[0888] Network for sending notifications to users

[0889] User operation

[0890] Users enter income and expenditure data and purchase information using a dedicated app installed on their smartphone. This can be done manually or by uploading photos of receipts. The receipt information is converted into text data using optical character recognition technology (e.g., Google Cloud Vision API) and extracted as purchase information.

[0891] Server-side processing

[0892] The server receives and stores the income and expenditure data and purchase information sent by the user. The stored data is analyzed using generative AI (e.g., OpenAI GPT-4). The analysis reveals the user's spending patterns and income trends. Below are some examples of specific prompts:

[0893] Prompt Sentence Examples

[0894] "User A's income and expenditure data is shown below. Based on this data, please suggest the optimal savings and investment plan for User A. Monthly income is 300,000 yen, food expenses are 50,000 yen, rent is 100,000 yen, utility bills are 20,000 yen, and savings are 30,000 yen."

[0895] Proposal generation and notification

[0896] Based on the results of the analysis by the generation AI, optimal savings points, sale information, and investment advice are generated for the user. The server collects the latest sale information and service information from external databases (for example, databases of affiliated companies), and the generation AI customizes the proposals based on the user's attributes. The optimal proposals are notified to the device. This allows the user to easily implement specific savings action plans and investment strategies.

[0897] For example, if a user's monthly food expenses are determined to be high, the server will collect sales information from nearby supermarkets, and the generation AI will analyze this information and notify the user of the sales information. For example, the AI ​​may notify the user of "This week's sales information" by notifying them of discounts on vegetables at a specific supermarket. The user can also receive advice on how to invest the money they have saved.

[0898] The system allows users to easily manage their income and expenditure data and receive personalized savings and investment suggestions, leading to more efficient asset management and effective financial investment.

[0899] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0900] Step 1:

[0901] Users enter income and expenditure data and purchasing information. Users manually enter income and expenditure data (food expenses, utility bills, rent, income, etc.) via a dedicated smartphone app. Alternatively, purchasing information can be entered by uploading photos of receipts. The input data is saved on the device as input fields or image files.

[0902] Step 2:

[0903] The terminal sends the entered data to the server. The terminal uploads the income and expenditure data and image files to the server via the network. The data is sent using a protocol such as an HTTP request.

[0904] Step 3:

[0905] The server stores the received household accounting data and purchasing information. The server stores the data for each user in a database (e.g., Firebase or MongoDB). The stored data is used for later analysis.

[0906] Step 4:

[0907] The server uses optical character recognition technology to extract purchase information from the receipt image. The analyzed image data is converted into text data, and each item (product name, price, etc.) is integrated into the household accounting data. This process uses OCR technology such as Google Cloud Vision API.

[0908] Step 5:

[0909] The server inputs the user's income and expenditure data and purchase information into the generation AI. The generation AI model (e.g., OpenAI GPT-4) receives input including the following prompt:

[0910] "User A's income and expenditure data is shown below. Based on this data, please suggest the optimal savings and investment plan for User A. Monthly income is 300,000 yen, food expenses are 50,000 yen, rent is 100,000 yen, utility bills are 20,000 yen, and savings are 30,000 yen."

[0911] Step 6:

[0912] The generating AI identifies the user's spending patterns and savings points and generates optimal proposals. Based on this data, the AI ​​analyzes the user's income and expenditure patterns and suggests effective ways to save and appropriate investments. The output is generated as savings proposals and investment advice and stored in a database on the server.

[0913] Step 7:

[0914] The server collects the latest sales and service information from external databases, and uses an automatically updated API to obtain the latest information from partner company databases. This data is also input into the generation AI.

[0915] Step 8:

[0916] Based on the information collected by the generation AI, the proposed information is customized according to the user's attributes. Specifically, individual sale information and money-saving advice are customized based on the user's area of ​​residence and purchase history. The output is generated as customized proposed information.

[0917] Step 9:

[0918] The device notifies the user of the proposed information received from the server. The information is delivered to the user in the form of a push notification via a dedicated app. The user receives the notification and can take action based on the content.

[0919] Step 10:

[0920] The server generates investment advice for each user and saves it for future recommendations. The advice is linked to the user's profile and used as data to improve the accuracy of future recommendations.

[0921] This allows users to easily input income and expenditure data and receive personalized savings and investment suggestions, leading to more efficient asset management and more effective financial investment.

[0922] 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.

[0923] This invention is a system that uses a generative AI and an emotion engine to provide optimal savings proposals and investment advice based on a user's income and expenditure data and purchasing information. This system includes a terminal operated by the user, a server that analyzes the data, a generative AI, and an emotion engine that recognizes the user's emotions.

[0924] Users use a household accounting app to input income and expenditure data and purchase information. This data includes food expenses, utility bills, rent, income, etc. Purchase information can also be easily added by uploading photos of receipts. The device then sends the information entered by the user to the server.

[0925] The server stores the received data and analyzes it using the Generative AI. This analysis process clarifies the user's spending patterns and income trends. For example, if a user has a high monthly food budget, the server can suggest special sale information at a nearby supermarket. The server also collects special sale and service information from the company's database, and the Generative AI customizes the most appropriate suggestions based on the user's attributes.

[0926] Here, a new emotion engine that recognizes the user's emotions is incorporated. The emotion engine analyzes emotions from the user's input data and behavior. For example, it can identify the user's emotional state, such as whether they are feeling stressed, based on the text they enter, trends in their income and expenditure data, and their app usage patterns.

[0927] The emotion data analyzed by the emotion engine is provided to the generative AI, which then takes this emotion data into account to customize suggestions. For example, if the user is feeling stressed, the generative AI will provide relaxing shopping suggestions or investment advice.

[0928] The device receives suggested information and investment advice from the server and notifies the user. The notification includes details of specific savings points and special sales information. For example, it may notify discount information at a specific supermarket as "This week's food sales information." The user can also receive advice on how to invest the savings. The generating AI analyzes the user's surplus funds and suggests appropriate investment destinations and methods.

[0929] As a specific example, if a user is determined to have high food expenses, the server will provide sales information and a list of nearby supermarkets to the generation AI to identify the best shopping destination. For example, the device will be notified of this week's sales information, such as a discount on vegetables at Supermarket A. Furthermore, if the user's emotions indicate a state of stress, the emotion engine will also suggest sales information for products with a relaxing effect (such as aroma oils).

[0930] To give a specific example of investment, when a user inputs the amount of savings, the AI ​​generator will suggest appropriate investment trusts and stocks based on that information. The server will generate optimal investment advice for the user, and the device will notify them of investment trust product information that promises stable returns as "recommended investments for this month's surplus funds." Furthermore, if the user's emotions indicate caution, low-risk investments will be prioritized.

[0931] In this way, by incorporating an emotion engine, it is possible to provide more personalized savings and investment advice based on the user's emotional state, allowing the user to manage their finances comfortably and effectively.

[0932] The processing flow will be explained below.

[0933] Step 1:

[0934] The user opens the household accounting app and enters income and expenditure data and purchase information. Income and expenditure data includes salary, living expenses, utility bills, food expenses, etc., while purchase information includes product name, purchase price, purchase date and time, and purchase store.

[0935] Step 2:

[0936] The terminal temporarily stores the data entered by the user, and after confirmation, transmits this data to the server.

[0937] Step 3:

[0938] The server stores the received household accounting data in a database. When storing the data, it checks its consistency and performs data cleaning if necessary.

[0939] Step 4:

[0940] The server provides data to the generation AI and emotion engine. The generation AI analyzes income and expenditure data and purchase information to clarify the user's spending patterns and income trends. The emotion engine analyzes emotions from the user's input data and behavior.

[0941] Step 5:

[0942] Based on the analysis results, the AI ​​will identify ways for users to save money. For example, it will provide sales information and shopping suggestions to users who spend a lot on food, and generate advice on how to reduce energy consumption to users who consume a lot of energy.

[0943] Step 6:

[0944] The emotion engine identifies the user's emotional state (stress, satisfaction, etc.) and provides that data to the generative AI.

[0945] Step 7:

[0946] Generative AI takes emotional data into account to customize suggestions, for example, offering relaxing shopping suggestions or investment advice if the user is feeling stressed.

[0947] Step 8:

[0948] The server accesses the company's database to collect the latest sales and service information. This information is updated regularly and used by the generating AI for analysis.

[0949] Step 9:

[0950] The generative AI analyzes corporate sales information and selects the most appropriate sales information and services based on the user's attributes (age, gender, region, hobbies, preferences, etc.). The selected information is customized for each user.

[0951] Step 10:

[0952] The server saves the generated suggestion information for each user and sends it to the device, which then notifies the user using the notification function.

[0953] Step 11:

[0954] The terminal notifies the user of the proposed information and investment advice received from the server. The notification includes specific savings points and detailed investment proposals. The user confirms the notification and initiates specific actions.

[0955] As a specific example, if a user is determined to have high food expenses, the server will provide sales information and a list of nearby supermarkets to the generation AI to identify the best shopping destination. For example, the device will be notified of this week's sales information, such as a discount on vegetables at Supermarket A. Furthermore, if the user's emotions indicate a state of stress, the emotion engine will also suggest sales information for products with a relaxing effect (such as aroma oils).

[0956] To give a specific example of investment, when a user inputs the amount of savings, the AI ​​generator will suggest appropriate investment trusts and stocks based on that information. The server will generate optimal investment advice for the user, and the device will notify them of investment trust product information that promises stable returns as "recommended investments for this month's surplus funds." Furthermore, if the user's emotions indicate caution, low-risk investments will be prioritized.

[0957] In this way, by incorporating an emotion engine, it becomes possible to make suggestions based on the user's emotional state, thereby providing more personalized savings and investment advice.

[0958] Example 2

[0959] 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."

[0960] Conventional household management systems and investment advice systems can make suggestions based on a user's income and expenditure data and purchasing information, but they cannot take into account the user's emotional state. This has the problem that they cannot provide flexible suggestions and advice that respond to the stress and emotional fluctuations that users face. As a result, users may not follow the suggested advice, and may not achieve the full effect of their savings or investments.

[0961] 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.

[0962] In this invention, the server includes means for an emotion engine to analyze the user's emotional state, means for a generation AI to identify the user's spending patterns and savings points based on the analysis results and the user's emotional state, and means for the generation AI to customize proposals based on the user's attributes and emotional state. This allows the server to provide proposals and advice that meet the individual needs of the user according to their emotional state, enabling the user to save and invest more effectively.

[0963] A "user" is a person who uses the system to enter income and expenditure data and purchasing information and receive savings and investment advice.

[0964] "Income and expenditure data" refers to information related to income and expenses entered by the user, including, for example, food expenses, utility expenses, rent, and income.

[0965] "Purchase information" is detailed information about a user's purchases, including photos of receipts and data about purchased items.

[0966] A "terminal" is a device that a user operates to input income and expenditure data and purchasing information, and to receive proposal information and investment advice.

[0967] A "server" is a computer system that receives, stores, and analyzes data sent from a terminal.

[0968] "Generative AI" is artificial intelligence that analyzes a user's income and expenditure data and purchasing information to identify and customize the user's spending patterns, savings points, and investment advice.

[0969] An "emotion engine" is a device or software that analyzes a user's input data and behavioral patterns to identify the emotional state the user is feeling.

[0970] The "analysis results" are information about the user's spending patterns and income trends obtained by the generation AI by analyzing income and expenditure data and purchasing information.

[0971] "Attributes" are characteristics and information specific to a user, and include data such as age, occupation, and family structure.

[0972] A "corporate database" is a database for collecting information on products and services offered by markets and companies.

[0973] "Special sale information" is information about specific products or services being offered at discounted prices.

[0974] "Investment advice" refers to investment suggestions presented based on a user's financial data and emotional state.

[0975] "Means to customize recommendations" refers to the generative AI's ability to individually tailor optimal savings and investment strategies based on a user's financial data, attributes, and emotional state.

[0976] A "notification" is information sent from a server and displayed on a user's terminal.

[0977] This invention is a system that uses a generative AI and an emotion engine to provide optimal savings proposals and investment advice based on a user's income and expenditure data and purchasing information. This system includes a terminal operated by the user, a server that analyzes the data, a generative AI, and an emotion engine that recognizes the user's emotions.

[0978] Users use a household accounting app to input income and expenditure data and purchase information. This data includes food expenses, utility bills, rent, income, etc. Purchase information can also be easily added by uploading photos of receipts. The device then sends the information entered by the user to the server.

[0979] The server stores the received data and analyzes it using the Generative AI. This analysis process clarifies the user's spending patterns and income trends. For example, if a user has a high monthly food budget, it can suggest special sale information at a nearby supermarket. The server also collects special sale and service information from the company's database, and the Generative AI customizes the most appropriate suggestions based on the user's attributes and emotional state.

[0980] Here, a new emotion engine that recognizes the user's emotions is incorporated. The emotion engine analyzes emotions from the user's input data and behavior. For example, it can identify the user's emotional state, such as whether they are feeling stressed, based on the text they enter, trends in their income and expenditure data, and their app usage patterns.

[0981] The emotion data analyzed by the emotion engine is provided to the generative AI, which then takes this emotion data into account to customize suggestions. For example, if the user is feeling stressed, the generative AI will provide relaxing shopping suggestions or investment advice.

[0982] The device receives suggested information and investment advice from the server and notifies the user. The notification includes details of specific savings points and special sales information. For example, it may notify discount information at a specific supermarket as "This week's food sales information." The user can also receive advice on how to invest the savings. The generating AI analyzes the user's surplus funds and suggests appropriate investment destinations and methods.

[0983] As a specific example, if a user is determined to have high food expenses, the server will provide sales information and a list of nearby supermarkets to the generation AI to identify the best shopping destination. For example, the device will be notified of this week's sales information, such as a discount on vegetables at Supermarket A. Furthermore, if the user's emotions indicate a state of stress, the emotion engine will also suggest sales information for products with a relaxing effect (such as aroma oils).

[0984] To give a specific example of investment, when a user inputs the amount of savings, the AI ​​generator will suggest appropriate investment trusts and stocks based on that information. The server will generate optimal investment advice for the user, and the device will notify them of investment trust product information that promises stable returns as "recommended investments for this month's surplus funds." Furthermore, if the user's emotions indicate caution, low-risk investments will be prioritized.

[0985] Examples of prompts include, "If the user's food expenses are high this month, suggest sales information for nearby supermarkets and also notify the user of products that will help the user relax." and "If the user enters the amount of savings, suggest low-risk investments based on that information. Prioritize low-risk options, especially when the user is emotionally cautious."

[0986] In this way, by incorporating an emotion engine, it is possible to provide more personalized savings and investment advice based on the user's emotional state, allowing the user to manage their finances comfortably and effectively.

[0987] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0988] Processing Steps

[0989] Step 1: Enter income and expenditure data and purchasing information

[0990] 1. The user opens the household accounting app on their device.

[0991] 2. The user enters income and expenditure data (e.g., food expenses, utility bills, rent, income, etc.) into the app.

[0992] Input: User manually enters "15,000 yen" as food expenses

[0993] Output: Data is temporarily saved on the device.

[0994] 3. Users add purchase information by uploading a photo of their receipt to the app.

[0995] Input: Upload a photo of your supermarket receipt

[0996] Output: The receipt details are recorded in the app as purchase information.

[0997] 4. The terminal sends these input data to the server.

[0998] Input: Income and expenditure data and purchasing information

[0999] Output: Sending data from the device to the server

[1000] Step 2: Receiving and storing data

[1001] 1. The server receives income and expenditure data and purchase information sent from the terminal.

[1002] Input: Income and expenditure data from the device "Food expenses: 15,000 yen", purchase information

[1003] Output: Stored as received data on the server

[1004] 2. The server stores the received data in a database.

[1005] Input: Income and expenditure data and purchasing information

[1006] Output: Income and expenditure data and purchasing information stored in the database

[1007] Step 3: Analysis by generative AI model

[1008] 1. The server provides the data stored in the database to the generative AI model.

[1009] Input: Saved income and expenditure data and purchasing information

[1010] Output: Data is provided to a generative AI model

[1011] 2. The generative AI model analyzes the provided data and derives the user's spending patterns and income trends.

[1012] Import: Provided data

[1013] Output: The analysis result is "The average monthly food cost exceeds 20,000 yen."

[1014] Step 4: Gather information about special offers and services

[1015] 1. The server collects the latest sales and service information from the company's database and online sources.

[1016] Inputs: Corporate databases and online sources

[1017] Output: Special sale information such as "Supermarket A's special sale this week: 30% off vegetables"

[1018] 2. The server provides the collected information to the generative AI model.

[1019] Input: Collected sale information

[1020] Output: Special offers provided to the generative AI model

[1021] Step 5: Customize optimal proposals with generative AI models

[1022] 1. The generative AI model customizes optimal offers based on income and expenditure data, sales information, and user attributes.

[1023] Input: Income and expenditure data, special sale information, user attributes

[1024] Output: Suggestions such as "You can get a good deal on vegetables at Supermarket A"

[1025] Step 6: Analyze user emotions with the emotion engine

[1026] 1. The emotion engine analyzes the user's input data and behavioral patterns to identify their emotional state.

[1027] Input: User input data, behavioral patterns

[1028] Output: User's emotional state (e.g., "I feel stressed")

[1029] 2. The emotion engine provides the analyzed emotion data to the generative AI model.

[1030] Input: User emotion data

[1031] Output: Emotion data fed to a generative AI model

[1032] Step 7: Re-customize suggestions with generative AI models, taking into account emotions

[1033] 1. The generative AI model then re-customizes the suggestions, taking into account sentiment data.

[1034] Input: Emotion data

[1035] Output: Additional suggestions, such as "Special offers on relaxing aroma oils"

[1036] Step 8: Server generates and sends proposals and investment advice

[1037] 1. The server generates customized proposal information and investment advice using generative AI.

[1038] Input: Generative AI model proposal

[1039] Output: Proposal information and investment advice for users

[1040] 2. The server sends the latest proposal information and investment advice to the terminal.

[1041] Input: Proposal information and investment advice

[1042] Output: Sending data from the server to the device

[1043] Step 9: Device Notifications and User Actions

[1044] 1. The terminal notifies the user of the received information.

[1045] Input: Proposal information and investment advice sent from the server

[1046] Output: Notifications displayed on the device (e.g., "This week's specials: 30% off vegetables at Supermarket A" or "Specials on relaxing aroma oils")

[1047] 2. The user checks the notification on their device and takes action based on the proposed information and investment advice.

[1048] Input: Device notifications

[1049] Output: Actual savings behavior and investment decisions

[1050] In this vein, the system of the present invention provides suggestions and advice tailored to the user's individual needs based on their emotional state, allowing them to save and invest more effectively.

[1051] (Application example 2)

[1052] 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."

[1053] In modern society, users face many challenges in managing their daily living expenses, saving money, and making investment decisions. In addition, because users' emotional state influences their purchasing and investment choices, it is difficult for conventional household management systems and investment advice tools to provide optimal recommendations for individual users. Therefore, there is a need for a system that provides personalized saving and investment advice that takes into account not only users' income and expenditure data but also their emotional state.

[1054] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1055] In this invention, the server includes: means for a user to input income and expenditure data and purchasing information; means for a terminal to transmit the input data to the server; means for the server to store the received household accounting data and analyze it using a generation AI; means for the generation AI to identify the user's spending patterns and savings points based on the analysis results; means for the server to collect optimal product and service information from corporate databases; means for the generation AI to customize proposals based on the user's attributes; means for the terminal to notify the user of the proposal information from the server; means for analyzing the user's emotions using an emotion engine and providing the results to the generation AI; and means for the generation AI to customize proposals taking the user's emotion data into consideration. This enables personalized proposals based on the user's emotional state, resulting in more effective savings and investment advice.

[1056] "Income and expenditure data" is a general term for information about a user's daily income and expenditure.

[1057] "Purchase information" refers to detailed information about products and services purchased by a user.

[1058] "Terminal" refers to a hardware device through which a user inputs information and communicates with a server.

[1059] A "server" is a computer system that stores and analyzes collected data.

[1060] "Household account data" refers to data that records a user's income and expenditure information and purchasing information.

[1061] "Generative AI" is a system that uses artificial intelligence technology to analyze data and generate optimal suggestions for users.

[1062] The "spending pattern" indicates the tendency or characteristics of a user regarding spending.

[1063] "Savings Points" are specific areas and ways in which users can reduce their spending.

[1064] A "corporate database" is a company-owned information resource that stores information about products and services.

[1065] "Customizing suggestions" means individually tailoring optimal suggestions based on the user's attributes and tendencies.

[1066] An "emotion engine" refers to a technology or system for analyzing a user's emotions.

[1067] "Emotion data" is information relating to the user's emotional state.

[1068] "Investment advice" is information that suggests optimal investment methods based on the user's financial situation.

[1069] "Excess funds" are the funds remaining after subtracting expenses from a user's income.

[1070] An "investment strategy" is a plan or guideline for how to invest excess funds.

[1071] "Special sale information" is information about products and services being sold at a price lower than the regular price.

[1072] "Notify" refers to a means of informing a user of specific information.

[1073] This invention is a system that uses a generative AI and an emotion engine to provide optimal savings proposals and investment advice based on a user's income and expenditure data and purchasing information. This system includes a terminal operated by the user, a server that analyzes the data, a generative AI, and an emotion engine that recognizes the user's emotions.

[1074] Users use a smartphone app to enter income and expenditure data and purchase information. This app has the function of automatically collecting expenditure data by linking with the electronic payment services that users use on a daily basis. Users can also enter data manually, and can easily add purchase information by uploading photos of receipts.

[1075] The device sends the information entered by the user to the server. The server stores the received data and analyzes it using generative AI. This analysis process clarifies the user's spending patterns and income trends, and suggests optimal savings points. For example, a user with high monthly food expenses will be notified of special sales at a nearby supermarket.

[1076] The server then collects sales and service information from the company's database, and the generative AI customizes the most appropriate proposals based on the user's attributes. An emotion engine that recognizes the user's emotions is then incorporated. The emotion engine analyzes the user's input data and behavior to determine their emotional state, such as whether they are feeling stressed.

[1077] The emotion data analyzed by the emotion engine is provided to the generative AI, which then takes this emotion data into account to customize suggestions. For example, if the user is feeling stressed, the generative AI will suggest products and services that will help them relax.

[1078] The device receives suggested information and investment advice from the server and notifies the user. The notification includes details of specific savings points and sales information. For example, a notification of discount information at a specific supermarket may be sent as "This week's food sales information." The user can also receive advice on how to invest the savings they have made, and the generating AI will analyze the user's surplus funds and suggest appropriate investment destinations and methods.

[1079] The hardware and software used are as follows:

[1080] Hardware: Smartphone (iOS, Android)

[1081] Software: Python, TensorFlow, Emotion API (Microsoft Azure), Watson Tone Analyzer (IBM), Firebase Cloud Messaging (Google)

[1082] For example, if user A's food expenses are judged to be high, the device will be notified of this week's special sale information, such as discounts on vegetables at a nearby supermarket. If user A's emotions indicate a state of stress, the emotion engine will also suggest sales information for products that have a relaxing effect.

[1083] Prompt Sentence Examples

[1084] If the user's food budget is high, suggest which supermarket has a sale on which product. If the user is feeling stressed, provide information on products that will help them relax.

[1085] This allows for personalized suggestions based on the user's emotional state, resulting in more effective savings and investment advice.

[1086] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1087] Step 1:

[1088] Users input their financial data and purchase information via a smartphone app. This information can be automatically obtained from electronic payment services, manually entered by the user, or extracted from photos of receipts. The app stores the financial data and purchase information in its internal database.

[1089] Step 2:

[1090] The terminal sends the collected income and expenditure data and purchase information to the server. It receives data from the internal database as input and generates the data sent to the server as output. This data transmission uses SSL / TLS encrypted communication.

[1091] Step 3:

[1092] The server stores the received data and analyzes it using generative AI. In this step, the income and expenditure data and purchasing information sent as input are received and stored in a database as output. The stored data is then input into a generative AI model, which analyzes spending patterns and income trends. Specifically, data analysis is performed using Python's pandas and TensorFlow.

[1093] Step 4:

[1094] The generative AI identifies the user's spending patterns and savings points based on the analysis results. It receives the analysis results as input and identifies optimal savings points as output. For example, if the user's food expenses are higher than other categories, advice on reducing food expenses will be suggested.

[1095] Step 5:

[1096] The server collects optimal product and service information from the company's database. It receives spending patterns and savings points as input and extracts suitable product and service information as output. Specific product information and special sale information are collected in this step.

[1097] Step 6:

[1098] The emotion engine analyzes emotions from the user's input data and behavior. In this step, user behavior data and input text are received as input, and emotion data is generated as output. Emotion analysis is performed using the Emotion API and Watson Tone Analyzer.

[1099] Step 7:

[1100] The generative AI takes emotional data into account to customize suggestions. It receives emotional data and collected product and service information as input, and generates optimal suggestions for the user as output. Specifically, a user in a stressed state will be offered information about special sales on products that have a relaxing effect.

[1101] Step 8:

[1102] The device notifies the user of the proposed information and investment advice from the server. It receives the customized proposals from the server as input and generates push notifications as output. This notification is generated using Firebase Cloud Messaging.

[1103] As a concrete example, consider the following prompt sentence:

[1104] If the user's food budget is high, suggest which supermarket has a sale on which product. If the user is feeling stressed, provide information on products that will help them relax.

[1105] 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.

[1106] 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.

[1107] 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.

[1108] [Fourth embodiment]

[1109] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1110] 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.

[1111] 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).

[1112] 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.

[1113] 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.

[1114] 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).

[1115] 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.

[1116] 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.

[1117] 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.

[1118] 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.

[1119] 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.

[1120] 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.

[1121] 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."

[1122] This invention is a system that uses AI to provide optimal savings proposals and investment advice based on a user's income and expenditure data and purchasing information. The system includes a terminal operated by the user, a server that analyzes the data, and AI.

[1123] Users use a household accounting app to input income and expenditure data and purchase information. This data includes food expenses, utility bills, rent, income, etc. Purchase information can also be easily added by uploading photos of receipts. The device then sends the information entered by the user to the server.

[1124] The server stores the received data and analyzes it using the Generative AI. This analysis process clarifies the user's spending patterns and income trends. For example, if a user has a high monthly food budget, the server can suggest special sale information at a nearby supermarket. The server also collects special sale and service information from the company's database, and the Generative AI customizes the most appropriate suggestions based on the user's attributes.

[1125] The device receives the suggested information from the server and notifies the user. The notification includes specific savings points and details of special sales. For example, it may notify the user of discount information at a specific supermarket as "This week's food sales information." The user can also receive advice on how to invest the savings. The generating AI analyzes the user's surplus funds and suggests appropriate investment destinations and methods.

[1126] As a specific example, if a user's monthly food expenses are determined to be high, the server will collect sales information from nearby supermarkets, and the generation AI will analyze this information to provide optimal savings suggestions to the user. For example, the device will be notified of "This week's sales information" that vegetables are on sale at Supermarket A. In addition, a weekly shopping list and recommended purchase times will be presented as specific ways to reduce the user's food expenses.

[1127] Furthermore, when it comes to surplus funds, users can input their monthly savings amount and the AI ​​will use that information to provide appropriate investment advice. The server will provide the user with information on the most suitable investment trusts and stocks, which will then be notified to the user via the device. For example, information on investment trust products that promise stable returns will be displayed as "recommended investments for this month's surplus funds."

[1128] In summary, this system efficiently utilizes the user's income and expenditure data and purchasing information, and uses generative AI to individually suggest optimal savings and investments, enabling users to obtain specific action plans and implement more effective financial strategies.

[1129] The processing flow will be explained below.

[1130] Step 1:

[1131] The user opens the household accounting app and enters income and expenditure data and purchase information. Income and expenditure data includes salary, living expenses, utility bills, food expenses, etc., while purchase information includes product name, purchase price, purchase date and time, and purchase store.

[1132] Step 2:

[1133] The terminal temporarily stores the data entered by the user, checks the input, and then sends the data to the server at a specific timing or when the user operates the terminal.

[1134] Step 3:

[1135] The server stores the received household accounting data in a database. When storing the data, it checks its consistency and performs data cleaning if necessary.

[1136] Step 4:

[1137] The server periodically initiates a data analysis process, providing income and expenditure data and purchase information to the AI ​​generator, which then analyzes this data to clarify the user's spending patterns and income trends.

[1138] Step 5:

[1139] Based on the analysis results, the AI ​​will identify ways for users to save money. For users with high food expenses, it will provide sales information and suggestions on where to buy food, and for users with high energy consumption, it will generate advice on how to reduce energy consumption.

[1140] Step 6:

[1141] The AI ​​analyzes the user's surplus funds and derives an appropriate investment strategy. Based on the results of this analysis, the server generates optimal investment advice for each user and stores it in a database.

[1142] Step 7:

[1143] The server accesses the database of partner companies to collect the latest sales and service information. This information is updated regularly and used by the generating AI for analysis.

[1144] Step 8:

[1145] The generative AI analyzes corporate sales information and selects the most appropriate sales information and services based on the user's attributes (age, gender, region, hobbies, preferences, etc.). The selected information is customized for each user.

[1146] Step 9:

[1147] The server saves the generated suggestion information for each user and sends it to the device, which then notifies the user using the notification function.

[1148] Step 10:

[1149] The terminal notifies the user of the proposed information and investment advice received from the server. The notification includes specific savings points and detailed investment proposals. The user confirms the notification and initiates specific actions.

[1150] As a specific example, if a user is determined to have high food expenses, the server will provide the generation AI with sale information and a list of nearby supermarkets to identify the best shopping destination. For example, the device will be notified of discounted vegetables at Supermarket A as "This week's sale information." As a specific example of investment advice, the server will have the generation AI suggest appropriate investment trusts and stocks based on the user's savings amount, and the device will notify the user of product information on investment trusts with stable returns as "recommended investments for this month's surplus funds."

[1151] In this way, useful savings and investment advice is provided to the user through specific actions at each step.

[1152] Example 1

[1153] 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."

[1154] In modern life, users are required to efficiently manage their income and expenses, and make savings and investments. However, with conventional methods, users must manually enter data, making it extremely difficult to find appropriate savings methods and investment destinations. It is also difficult to effectively utilize sales and service information. Therefore, there is a need for a system that allows users to easily manage their income and expenses and receive optimal savings suggestions and investment advice.

[1155] 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.

[1156] In this invention, the server includes means for a user to input income and expenditure data and purchasing information, means for a terminal to transmit the input data to the server, means for the server to store the received data and analyze it using a generating AI, means for the generating AI to identify the user's spending patterns and savings points based on the analysis results, means for the server to collect optimal product and service information from a corporate database, means for the generating AI to customize proposals based on the user's attributes, and means for the terminal to notify the user of the proposal information from the server. This enables users to easily manage their income and expenditure and receive individually optimized savings proposals and investment advice.

[1157] "User" refers to an individual who uses the system to input income and expenditure data and purchasing information and receive savings suggestions and investment advice.

[1158] "Terminal" refers to an electronic device that a user uses to input income and expenditure data and purchase information and transmit that data to a server.

[1159] "Server" refers to a central processing unit that receives and stores data sent from a terminal and analyzes it using artificial intelligence.

[1160] "Generative AI" refers to artificial intelligence models that analyze collected data and generate recommendations based on users' spending patterns and attributes.

[1161] A "database" refers to a storage device within a system that stores users' income and expenditure data, purchasing information, and sale and service information collected from companies.

[1162] "Suggested information" refers to information such as savings methods and investment advice generated by the generating AI based on the analysis results.

[1163] "Special Offer Information" refers to information about discounts and sales collected from a company's database.

[1164] "Savings Points" refer to specific items or methods identified to reduce a user's expenses.

[1165] "Investment advice" refers to information in which the generating AI analyzes the user's surplus funds and suggests appropriate investment destinations and methods.

[1166] This invention is a system that uses AI to provide optimal savings proposals and investment advice based on a user's income and expenditure data and purchasing information. The system includes a terminal operated by the user, a server that analyzes the data, and AI.

[1167] Users use a household accounting app to input income and expenditure data and purchase information. This input data includes food expenses, utility bills, rent, income, etc. Furthermore, users can easily add purchase information by uploading photos of receipts. For example, a user can input "this month's food expenses" in the app and supplement the expenditure data by attaching a photo of the receipt. This information is sent to the server via the device.

[1168] The server stores the data received from the device in a database (e.g., MySQL or MongoDB). The stored data is then analyzed by the Generative AI. In this analysis process, the Generative AI uses machine learning models such as TensorFlow or PyTorch to analyze the user's spending patterns and income trends. For example, for a user who spends a lot on food, the Generative AI can suggest special sales information at a nearby supermarket.

[1169] Furthermore, the server collects sale and service information from the company's database. This is done using web scraping technology and API integration. The generation AI analyzes the collected sale information and customizes the most appropriate suggestions based on the user's attributes. For example, the generation AI might generate a suggestion such as, "User A has a high monthly food budget, so it would be best for him or her to purchase the vegetables on sale at Supermarket A this week."

[1170] The device receives the suggested information from the server and notifies the user. The notification includes specific savings points and details of special sales. For example, the app's push notification function could be used to display a message such as "This week's special sales information: Vegetables are 30% off at Supermarket A."

[1171] The generation AI also analyzes the user's surplus funds and suggests appropriate investment destinations and methods. For example, if a user enters 5,000 yen as their monthly savings amount, the generation AI will use that information to suggest that "it would be best to invest 5,000 yen in an investment trust that promises stable returns." The server provides information on investment trusts and stocks, and the terminal notifies the user of this. The notification will say, "We have product information for an investment trust that promises stable returns as a recommended investment destination for this month's surplus funds."

[1172] As a specific example, if a user's monthly food expenses are determined to be high, the server will collect sales information from nearby supermarkets, and the generation AI will analyze this information to provide optimal savings suggestions to the user. For example, the device will be notified of "This week's sales information" that vegetables are on sale at Supermarket A. In addition, a weekly shopping list and recommended purchase times will be presented as specific ways to reduce the user's food expenses.

[1173] Examples of prompts include:

[1174] "If a user's food budget is high, which supermarket sales should they be shown? Also, suggest a specific shopping list to help them save money on food."

[1175] "Please provide information on the most appropriate mutual funds and stocks for users to invest their surplus funds."

[1176] By implementing this system, users can efficiently manage their income and expenses and receive individually optimized savings proposals and investment advice.

[1177] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1178] Step 1: User enters income and expenditure data and purchasing information

[1179] Input: The user opens the household accounting app and enters income and expenditure data such as food expenses, utility bills, rent, and income. They also add purchase information by uploading photos of receipts.

[1180] Data processing: Converts text data entered on the terminal into JSON format, and compresses receipt images as needed to prepare them for transmission to the server.

[1181] Output: JSON format income and expenditure data and compressed receipt image data are generated on the terminal.

[1182] Specific operation: The user enters "This month's food expenses" in the app, selects "Upload receipt" and takes a photo with the camera.

[1183] Step 2: The device sends the entered data to the server

[1184] Input: JSON-formatted balance data and compressed receipt image data generated in Step 1.

[1185] Data processing: The terminal packages the income and expenditure data and receipt image as an HTTP POST request.

[1186] Output: An HTTP POST request is sent to a specific API endpoint on the server side.

[1187] Specific operation: When the user taps the "Send data" button, the device sends the data to the server.

[1188] Step 3: The server stores the received data and analyzes it using the generative AI.

[1189] Input: Income and expenditure data (JSON format) and receipt image data sent from the device.

[1190] Data processing: The server stores the data in a database and prepares it for input into the generative AI model. The database is MySQL or MongoDB.

[1191] Output: The analysis results from the generative AI are obtained.

[1192] Specific operation: The server saves the data in a database and launches a generative AI model (e.g., TensorFlow or PyTorch) to begin analysis.

[1193] Step 4: Generative AI identifies the user's spending patterns and savings points based on the analysis results

[1194] Input: User's income and expenditure data and purchasing information stored on the server.

[1195] Data processing: Generative AI uses machine learning algorithms to analyze data and identify users' spending patterns and savings opportunities.

[1196] Output: Analysis results that identify the user's spending patterns and savings points.

[1197] Specific operation: The generation AI identifies the pattern that "User A has high food expenses" and clearly indicates savings points by "suggesting information about special sales at Supermarket A."

[1198] Step 5: The server collects the best products and services from the company's database.

[1199] Input: Special offers and service information collected from company databases.

[1200] Data processing: The server periodically retrieves information via web scraping or API and stores it in a database.

[1201] Output: The latest sales and service information is saved in the server database.

[1202] What it does: The server periodically crawls websites that provide sale information and stores new information in a database.

[1203] Step 6: Generative AI customizes suggestions based on user attributes

[1204] Input: User spending patterns, savings points, and special offers.

[1205] Data processing: Generative AI analyzes this data and generates optimal savings proposals for users.

[1206] Output: User-optimized customization suggestions.

[1207] Specific operation: The generation AI generates a specific suggestion such as, "Suggest to user A the vegetables on sale at supermarket A this week."

[1208] Step 7: The device notifies the user of the suggested information from the server.

[1209] Input: Customized proposal information.

[1210] Data processing: The device provides information to the user via push notifications or in-app notifications.

[1211] Output: The notification message that the user receives.

[1212] Specific operation: The device sends a push notification saying, "This week's sale information: 30% off vegetables at Supermarket A."

[1213] Step 8: The generated AI analyzes the user's surplus funds and provides investment advice

[1214] Input: User savings data.

[1215] Data processing: Generative AI analyzes the user's savings data and generates optimal investment advice.

[1216] Output: Investment advice to the user.

[1217] Specific operation: The user enters "monthly savings amount of 5,000 yen," and the generation AI generates a suggestion to "invest 5,000 yen in an investment trust that is expected to provide stable returns."

[1218] Step 9: The terminal notifies the user of the investment advice from the server.

[1219] Input: Investment advice sent from the server.

[1220] Data processing: Prepare push notifications and in-app notifications so that the device can notify the user of investment advice.

[1221] Output: Investment advice notification received by the user.

[1222] Specific operation: The device sends a push notification with "Recommended investments for this month's surplus funds."

[1223] The above are the specific processing steps of the program of this system.

[1224] (Application example 1)

[1225] 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."

[1226] In modern society, individual consumption behavior is becoming more diverse, creating a need for efficient asset management and investment strategies. However, current household accounting apps and asset management systems face challenges, such as cumbersome data entry, lack of personalized savings and investment suggestions, and a lack of up-to-date sales information. In particular, the manual management of receipt information places a significant burden on users. A system that can solve these problems and provide effective savings and investment suggestions based on individual users' attributes and consumption patterns is needed.

[1227] 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.

[1228] In this invention, the server includes: a means for a user to input income and expenditure data and purchase information; a means for a terminal to transmit the input data to the server; a means for the server to store the received household accounting data and analyze it using a generation AI; a means for the generation AI to identify the user's spending patterns and savings points based on the analysis results; a means for the server to collect optimal product and service information from an external database; a means for the generation AI to customize proposals based on the user's attributes; a means for the terminal to notify the user of the proposal information from the server; a means for providing appropriate investment advice based on the savings amount calculated from the user's income and expenditure data; and a means for capturing an image of a receipt and extracting purchase information using optical character recognition technology. This allows users to easily manage their income and expenditure data and receive individually customized savings and investment proposals.

[1229] A "user" is an individual or corporation that inputs income and expenditure data and purchasing information into the system.

[1230] "Income and expenditure data" is information about income and expenditure entered by the user, and specifically includes information such as food expenses, utility expenses, rent, and income.

[1231] "Purchase information" refers to information about a purchase history that a user enters using a receipt or other method.

[1232] A "terminal" is a computer device that allows a user to input income and expenditure data and purchasing information and transmit them to a server, and includes smartphones, tablets, etc.

[1233] A "server" is a computer system that stores and analyzes income and expenditure data and purchasing information received from users.

[1234] "Household account book data" is a group of information including all income and expenditure data and purchasing information entered by the user.

[1235] "Generative AI" is artificial intelligence that analyzes users' income and expenditure data and purchasing information to generate personalized suggestions.

[1236] "Analysis results" are the results obtained by the generation AI analyzing the user's income and expenditure data and purchasing information.

[1237] A "spending pattern" is a tendency for a user to spend money over a period of time.

[1238] "Saving points" are items or ways in which a user can potentially reduce their expenses.

[1239] An "external database" is an external information source that the server accesses to obtain product and service information.

[1240] "Suggested information" refers to information such as products, services, and money-saving methods that are customized by the generating AI based on the user's attributes.

[1241] "Investment advice" refers to suggestions on investment destinations and investment methods provided by the generating AI based on the user's surplus funds.

[1242] "Excess funds" are the funds remaining after a user calculates their monthly income and expenses.

[1243] "Optical character recognition technology" is a technology that extracts character information from image data.

[1244] "Purchase information extraction" refers to the act of using optical character recognition technology to obtain purchase and payment information from receipt images.

[1245] This invention is a system that uses AI generation to provide optimal savings suggestions and investment advice based on a user's income and expenditure data and purchasing information. Each of these means will be described in detail below.

[1246] System configuration

[1247] The system includes the following components:

[1248] A device (such as a smartphone or tablet) on which users input income and expenditure data and purchasing information

[1249] A server that analyzes data and uses generative AI to generate proposal information

[1250] Network for sending notifications to users

[1251] User operation

[1252] Users enter income and expenditure data and purchase information using a dedicated app installed on their smartphone. This can be done manually or by uploading photos of receipts. The receipt information is converted into text data using optical character recognition technology (e.g., Google Cloud Vision API) and extracted as purchase information.

[1253] Server-side processing

[1254] The server receives and stores the income and expenditure data and purchase information sent by the user. The stored data is analyzed using generative AI (e.g., OpenAI GPT-4). The analysis reveals the user's spending patterns and income trends. Below are some examples of specific prompts:

[1255] Prompt Sentence Examples

[1256] "User A's income and expenditure data is shown below. Based on this data, please suggest the optimal savings and investment plan for User A. Monthly income is 300,000 yen, food expenses are 50,000 yen, rent is 100,000 yen, utility bills are 20,000 yen, and savings are 30,000 yen."

[1257] Proposal generation and notification

[1258] Based on the results of the analysis by the generation AI, optimal savings points, sale information, and investment advice are generated for the user. The server collects the latest sale information and service information from external databases (for example, databases of affiliated companies), and the generation AI customizes the proposals based on the user's attributes. The optimal proposals are notified to the device. This allows the user to easily implement specific savings action plans and investment strategies.

[1259] For example, if a user's monthly food expenses are determined to be high, the server will collect sales information from nearby supermarkets, and the generation AI will analyze this information and notify the user of the sales information. For example, the AI ​​may notify the user of "This week's sales information" by notifying them of discounts on vegetables at a specific supermarket. The user can also receive advice on how to invest the money they have saved.

[1260] The system allows users to easily manage their income and expenditure data and receive personalized savings and investment suggestions, leading to more efficient asset management and effective financial investment.

[1261] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1262] Step 1:

[1263] Users enter income and expenditure data and purchasing information. Users manually enter income and expenditure data (food expenses, utility bills, rent, income, etc.) via a dedicated smartphone app. Alternatively, purchasing information can be entered by uploading photos of receipts. The input data is saved on the device as input fields or image files.

[1264] Step 2:

[1265] The terminal sends the entered data to the server. The terminal uploads the income and expenditure data and image files to the server via the network. The data is sent using a protocol such as an HTTP request.

[1266] Step 3:

[1267] The server stores the received household accounting data and purchasing information. The server stores the data for each user in a database (e.g., Firebase or MongoDB). The stored data is used for later analysis.

[1268] Step 4:

[1269] The server uses optical character recognition technology to extract purchase information from the receipt image. The analyzed image data is converted into text data, and each item (product name, price, etc.) is integrated into the household accounting data. This process uses OCR technology such as Google Cloud Vision API.

[1270] Step 5:

[1271] The server inputs the user's income and expenditure data and purchase information into the generation AI. The generation AI model (e.g., OpenAI GPT-4) receives input including the following prompt:

[1272] "User A's income and expenditure data is shown below. Based on this data, please suggest the optimal savings and investment plan for User A. Monthly income is 300,000 yen, food expenses are 50,000 yen, rent is 100,000 yen, utility bills are 20,000 yen, and savings are 30,000 yen."

[1273] Step 6:

[1274] The generating AI identifies the user's spending patterns and savings points and generates optimal proposals. Based on this data, the AI ​​analyzes the user's income and expenditure patterns and suggests effective ways to save and appropriate investments. The output is generated as savings proposals and investment advice and stored in a database on the server.

[1275] Step 7:

[1276] The server collects the latest sales and service information from external databases, and uses an automatically updated API to obtain the latest information from partner company databases. This data is also input into the generation AI.

[1277] Step 8:

[1278] Based on the information collected by the generation AI, the proposed information is customized according to the user's attributes. Specifically, individual sale information and money-saving advice are customized based on the user's area of ​​residence and purchase history. The output is generated as customized proposed information.

[1279] Step 9:

[1280] The device notifies the user of the proposed information received from the server. The information is delivered to the user in the form of a push notification via a dedicated app. The user receives the notification and can take action based on the content.

[1281] Step 10:

[1282] The server generates investment advice for each user and saves it for future recommendations. The advice is linked to the user's profile and used as data to improve the accuracy of future recommendations.

[1283] This allows users to easily input income and expenditure data and receive personalized savings and investment suggestions, leading to more efficient asset management and more effective financial investment.

[1284] 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.

[1285] This invention is a system that uses a generative AI and an emotion engine to provide optimal savings proposals and investment advice based on a user's income and expenditure data and purchasing information. This system includes a terminal operated by the user, a server that analyzes the data, a generative AI, and an emotion engine that recognizes the user's emotions.

[1286] Users use a household accounting app to input income and expenditure data and purchase information. This data includes food expenses, utility bills, rent, income, etc. Purchase information can also be easily added by uploading photos of receipts. The device then sends the information entered by the user to the server.

[1287] The server stores the received data and analyzes it using the Generative AI. This analysis process clarifies the user's spending patterns and income trends. For example, if a user has a high monthly food budget, the server can suggest special sale information at a nearby supermarket. The server also collects special sale and service information from the company's database, and the Generative AI customizes the most appropriate suggestions based on the user's attributes.

[1288] Here, a new emotion engine that recognizes the user's emotions is incorporated. The emotion engine analyzes emotions from the user's input data and behavior. For example, it can identify the user's emotional state, such as whether they are feeling stressed, based on the text they enter, trends in their income and expenditure data, and their app usage patterns.

[1289] The emotion data analyzed by the emotion engine is provided to the generative AI, which then takes this emotion data into account to customize suggestions. For example, if the user is feeling stressed, the generative AI will provide relaxing shopping suggestions or investment advice.

[1290] The device receives suggested information and investment advice from the server and notifies the user. The notification includes details of specific savings points and special sales information. For example, it may notify discount information at a specific supermarket as "This week's food sales information." The user can also receive advice on how to invest the savings. The generating AI analyzes the user's surplus funds and suggests appropriate investment destinations and methods.

[1291] As a specific example, if a user is determined to have high food expenses, the server will provide sales information and a list of nearby supermarkets to the generation AI to identify the best shopping destination. For example, the device will be notified of this week's sales information, such as a discount on vegetables at Supermarket A. Furthermore, if the user's emotions indicate a state of stress, the emotion engine will also suggest sales information for products with a relaxing effect (such as aroma oils).

[1292] To give a specific example of investment, when a user inputs the amount of savings, the AI ​​generator will suggest appropriate investment trusts and stocks based on that information. The server will generate optimal investment advice for the user, and the device will notify them of investment trust product information that promises stable returns as "recommended investments for this month's surplus funds." Furthermore, if the user's emotions indicate caution, low-risk investments will be prioritized.

[1293] In this way, by incorporating an emotion engine, it is possible to provide more personalized savings and investment advice based on the user's emotional state, allowing the user to manage their finances comfortably and effectively.

[1294] The processing flow will be explained below.

[1295] Step 1:

[1296] The user opens the household accounting app and enters income and expenditure data and purchase information. Income and expenditure data includes salary, living expenses, utility bills, food expenses, etc., while purchase information includes product name, purchase price, purchase date and time, and purchase store.

[1297] Step 2:

[1298] The terminal temporarily stores the data entered by the user, and after confirmation, transmits this data to the server.

[1299] Step 3:

[1300] The server stores the received household accounting data in a database. When storing the data, it checks its consistency and performs data cleaning if necessary.

[1301] Step 4:

[1302] The server provides data to the generation AI and emotion engine. The generation AI analyzes income and expenditure data and purchase information to clarify the user's spending patterns and income trends. The emotion engine analyzes emotions from the user's input data and behavior.

[1303] Step 5:

[1304] Based on the analysis results, the AI ​​will identify ways for users to save money. For example, it will provide sales information and shopping suggestions to users who spend a lot on food, and generate advice on how to reduce energy consumption to users who consume a lot of energy.

[1305] Step 6:

[1306] The emotion engine identifies the user's emotional state (stress, satisfaction, etc.) and provides that data to the generative AI.

[1307] Step 7:

[1308] Generative AI takes emotional data into account to customize suggestions, for example, offering relaxing shopping suggestions or investment advice if the user is feeling stressed.

[1309] Step 8:

[1310] The server accesses the company's database to collect the latest sales and service information. This information is updated regularly and used by the generating AI for analysis.

[1311] Step 9:

[1312] The generative AI analyzes corporate sales information and selects the most appropriate sales information and services based on the user's attributes (age, gender, region, hobbies, preferences, etc.). The selected information is customized for each user.

[1313] Step 10:

[1314] The server saves the generated suggestion information for each user and sends it to the device, which then notifies the user using the notification function.

[1315] Step 11:

[1316] The terminal notifies the user of the proposed information and investment advice received from the server. The notification includes specific savings points and detailed investment proposals. The user confirms the notification and initiates specific actions.

[1317] As a specific example, if a user is determined to have high food expenses, the server will provide sales information and a list of nearby supermarkets to the generation AI to identify the best shopping destination. For example, the device will be notified of this week's sales information, such as a discount on vegetables at Supermarket A. Furthermore, if the user's emotions indicate a state of stress, the emotion engine will also suggest sales information for products with a relaxing effect (such as aroma oils).

[1318] To give a specific example of investment, when a user inputs the amount of savings, the AI ​​generator will suggest appropriate investment trusts and stocks based on that information. The server will generate optimal investment advice for the user, and the device will notify them of investment trust product information that promises stable returns as "recommended investments for this month's surplus funds." Furthermore, if the user's emotions indicate caution, low-risk investments will be prioritized.

[1319] In this way, by incorporating an emotion engine, it becomes possible to make suggestions based on the user's emotional state, thereby providing more personalized savings and investment advice.

[1320] Example 2

[1321] 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."

[1322] Conventional household management systems and investment advice systems can make suggestions based on a user's income and expenditure data and purchasing information, but they cannot take into account the user's emotional state. This has the problem that they cannot provide flexible suggestions and advice that respond to the stress and emotional fluctuations that users face. As a result, users may not follow the suggested advice, and may not achieve the full effect of their savings or investments.

[1323] 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.

[1324] In this invention, the server includes means for an emotion engine to analyze the user's emotional state, means for a generation AI to identify the user's spending patterns and savings points based on the analysis results and the user's emotional state, and means for the generation AI to customize proposals based on the user's attributes and emotional state. This allows the server to provide proposals and advice that meet the individual needs of the user according to their emotional state, enabling the user to save and invest more effectively.

[1325] A "user" is a person who uses the system to enter income and expenditure data and purchasing information and receive savings and investment advice.

[1326] "Income and expenditure data" refers to information related to income and expenses entered by the user, including, for example, food expenses, utility expenses, rent, and income.

[1327] "Purchase information" is detailed information about a user's purchases, including photos of receipts and data about purchased items.

[1328] A "terminal" is a device that a user operates to input income and expenditure data and purchasing information, and to receive proposal information and investment advice.

[1329] A "server" is a computer system that receives, stores, and analyzes data sent from a terminal.

[1330] "Generative AI" is artificial intelligence that analyzes a user's income and expenditure data and purchasing information to identify and customize the user's spending patterns, savings points, and investment advice.

[1331] An "emotion engine" is a device or software that analyzes a user's input data and behavioral patterns to identify the emotional state the user is feeling.

[1332] The "analysis results" are information about the user's spending patterns and income trends obtained by the generation AI by analyzing income and expenditure data and purchasing information.

[1333] "Attributes" are characteristics and information specific to a user, and include data such as age, occupation, and family structure.

[1334] A "corporate database" is a database for collecting information on products and services offered by markets and companies.

[1335] "Special sale information" is information about specific products or services being offered at discounted prices.

[1336] "Investment advice" refers to investment suggestions presented based on a user's financial data and emotional state.

[1337] "Means to customize recommendations" refers to the generative AI's ability to individually tailor optimal savings and investment strategies based on a user's financial data, attributes, and emotional state.

[1338] A "notification" is information sent from a server and displayed on a user's terminal.

[1339] This invention is a system that uses a generative AI and an emotion engine to provide optimal savings proposals and investment advice based on a user's income and expenditure data and purchasing information. This system includes a terminal operated by the user, a server that analyzes the data, a generative AI, and an emotion engine that recognizes the user's emotions.

[1340] Users use a household accounting app to input income and expenditure data and purchase information. This data includes food expenses, utility bills, rent, income, etc. Purchase information can also be easily added by uploading photos of receipts. The device then sends the information entered by the user to the server.

[1341] The server stores the received data and analyzes it using the Generative AI. This analysis process clarifies the user's spending patterns and income trends. For example, if a user has a high monthly food budget, it can suggest special sale information at a nearby supermarket. The server also collects special sale and service information from the company's database, and the Generative AI customizes the most appropriate suggestions based on the user's attributes and emotional state.

[1342] Here, a new emotion engine that recognizes the user's emotions is incorporated. The emotion engine analyzes emotions from the user's input data and behavior. For example, it can identify the user's emotional state, such as whether they are feeling stressed, based on the text they enter, trends in their income and expenditure data, and their app usage patterns.

[1343] The emotion data analyzed by the emotion engine is provided to the generative AI, which then takes this emotion data into account to customize suggestions. For example, if the user is feeling stressed, the generative AI will provide relaxing shopping suggestions or investment advice.

[1344] The device receives suggested information and investment advice from the server and notifies the user. The notification includes details of specific savings points and special sales information. For example, it may notify discount information at a specific supermarket as "This week's food sales information." The user can also receive advice on how to invest the savings. The generating AI analyzes the user's surplus funds and suggests appropriate investment destinations and methods.

[1345] As a specific example, if a user is determined to have high food expenses, the server will provide sales information and a list of nearby supermarkets to the generation AI to identify the best shopping destination. For example, the device will be notified of this week's sales information, such as a discount on vegetables at Supermarket A. Furthermore, if the user's emotions indicate a state of stress, the emotion engine will also suggest sales information for products with a relaxing effect (such as aroma oils).

[1346] To give a specific example of investment, when a user inputs the amount of savings, the AI ​​generator will suggest appropriate investment trusts and stocks based on that information. The server will generate optimal investment advice for the user, and the device will notify them of investment trust product information that promises stable returns as "recommended investments for this month's surplus funds." Furthermore, if the user's emotions indicate caution, low-risk investments will be prioritized.

[1347] Examples of prompts include, "If the user's food expenses are high this month, suggest sales information for nearby supermarkets and also notify the user of products that will help the user relax." and "If the user enters the amount of savings, suggest low-risk investments based on that information. Prioritize low-risk options, especially when the user is emotionally cautious."

[1348] In this way, by incorporating an emotion engine, it is possible to provide more personalized savings and investment advice based on the user's emotional state, allowing the user to manage their finances comfortably and effectively.

[1349] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1350] Processing Steps

[1351] Step 1: Enter income and expenditure data and purchasing information

[1352] 1. The user opens the household accounting app on their device.

[1353] 2. The user enters income and expenditure data (e.g., food expenses, utility bills, rent, income, etc.) into the app.

[1354] Input: User manually enters "15,000 yen" as food expenses

[1355] Output: Data is temporarily saved on the device.

[1356] 3. Users add purchase information by uploading a photo of their receipt to the app.

[1357] Input: Upload a photo of your supermarket receipt

[1358] Output: The receipt details are recorded in the app as purchase information.

[1359] 4. The terminal sends these input data to the server.

[1360] Input: Income and expenditure data and purchasing information

[1361] Output: Sending data from the device to the server

[1362] Step 2: Receiving and storing data

[1363] 1. The server receives income and expenditure data and purchase information sent from the terminal.

[1364] Input: Income and expenditure data from the device "Food expenses: 15,000 yen", purchase information

[1365] Output: Stored as received data on the server

[1366] 2. The server stores the received data in a database.

[1367] Input: Income and expenditure data and purchasing information

[1368] Output: Income and expenditure data and purchasing information stored in the database

[1369] Step 3: Analysis by generative AI model

[1370] 1. The server provides the data stored in the database to the generative AI model.

[1371] Input: Saved income and expenditure data and purchasing information

[1372] Output: Data is provided to a generative AI model

[1373] 2. The generative AI model analyzes the provided data and derives the user's spending patterns and income trends.

[1374] Import: Provided data

[1375] Output: The analysis result is "The average monthly food cost exceeds 20,000 yen."

[1376] Step 4: Gather information about special offers and services

[1377] 1. The server collects the latest sales and service information from the company's database and online sources.

[1378] Inputs: Corporate databases and online sources

[1379] Output: Special sale information such as "Supermarket A's special sale this week: 30% off vegetables"

[1380] 2. The server provides the collected information to the generative AI model.

[1381] Input: Collected sale information

[1382] Output: Special offers provided to the generative AI model

[1383] Step 5: Customize optimal proposals with generative AI models

[1384] 1. The generative AI model customizes optimal offers based on income and expenditure data, sales information, and user attributes.

[1385] Input: Income and expenditure data, special sale information, user attributes

[1386] Output: Suggestions such as "You can get a good deal on vegetables at Supermarket A"

[1387] Step 6: Analyze user emotions with the emotion engine

[1388] 1. The emotion engine analyzes the user's input data and behavioral patterns to identify their emotional state.

[1389] Input: User input data, behavioral patterns

[1390] Output: User's emotional state (e.g., "I feel stressed")

[1391] 2. The emotion engine provides the analyzed emotion data to the generative AI model.

[1392] Input: User emotion data

[1393] Output: Emotion data fed to a generative AI model

[1394] Step 7: Re-customize suggestions with generative AI models, taking into account emotions

[1395] 1. The generative AI model then re-customizes the suggestions, taking into account sentiment data.

[1396] Input: Emotion data

[1397] Output: Additional suggestions, such as "Special offers on relaxing aroma oils"

[1398] Step 8: Server generates and sends proposals and investment advice

[1399] 1. The server generates customized proposal information and investment advice using generative AI.

[1400] Input: Generative AI model proposal

[1401] Output: Proposal information and investment advice for users

[1402] 2. The server sends the latest proposal information and investment advice to the terminal.

[1403] Input: Proposal information and investment advice

[1404] Output: Sending data from the server to the device

[1405] Step 9: Device Notifications and User Actions

[1406] 1. The terminal notifies the user of the received information.

[1407] Input: Proposal information and investment advice sent from the server

[1408] Output: Notifications displayed on the device (e.g., "This week's specials: 30% off vegetables at Supermarket A" or "Specials on relaxing aroma oils")

[1409] 2. The user checks the notification on their device and takes action based on the proposed information and investment advice.

[1410] Input: Device notifications

[1411] Output: Actual savings behavior and investment decisions

[1412] In this vein, the system of the present invention provides suggestions and advice tailored to the user's individual needs based on their emotional state, allowing them to save and invest more effectively.

[1413] (Application example 2)

[1414] 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."

[1415] In modern society, users face many challenges in managing their daily living expenses, saving money, and making investment decisions. In addition, because users' emotional state influences their purchasing and investment choices, it is difficult for conventional household management systems and investment advice tools to provide optimal recommendations for individual users. Therefore, there is a need for a system that provides personalized saving and investment advice that takes into account not only users' income and expenditure data but also their emotional state.

[1416] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1417] In this invention, the server includes: means for a user to input income and expenditure data and purchasing information; means for a terminal to transmit the input data to the server; means for the server to store the received household accounting data and analyze it using a generation AI; means for the generation AI to identify the user's spending patterns and savings points based on the analysis results; means for the server to collect optimal product and service information from corporate databases; means for the generation AI to customize proposals based on the user's attributes; means for the terminal to notify the user of the proposal information from the server; means for analyzing the user's emotions using an emotion engine and providing the results to the generation AI; and means for the generation AI to customize proposals taking the user's emotion data into consideration. This enables personalized proposals based on the user's emotional state, resulting in more effective savings and investment advice.

[1418] "Income and expenditure data" is a general term for information about a user's daily income and expenditure.

[1419] "Purchase information" refers to detailed information about products and services purchased by a user.

[1420] "Terminal" refers to a hardware device through which a user inputs information and communicates with a server.

[1421] A "server" is a computer system that stores and analyzes collected data.

[1422] "Household account data" refers to data that records a user's income and expenditure information and purchasing information.

[1423] "Generative AI" is a system that uses artificial intelligence technology to analyze data and generate optimal suggestions for users.

[1424] The "spending pattern" indicates the tendency or characteristics of a user regarding spending.

[1425] "Savings Points" are specific areas and ways in which users can reduce their spending.

[1426] A "corporate database" is a company-owned information resource that stores information about products and services.

[1427] "Customizing suggestions" means individually tailoring optimal suggestions based on the user's attributes and tendencies.

[1428] An "emotion engine" refers to a technology or system for analyzing a user's emotions.

[1429] "Emotion data" is information relating to the user's emotional state.

[1430] "Investment advice" is information that suggests optimal investment methods based on the user's financial situation.

[1431] "Excess funds" are the funds remaining after subtracting expenses from a user's income.

[1432] An "investment strategy" is a plan or guideline for how to invest excess funds.

[1433] "Special sale information" is information about products and services being sold at a price lower than the regular price.

[1434] "Notify" refers to a means of informing a user of specific information.

[1435] This invention is a system that uses a generative AI and an emotion engine to provide optimal savings proposals and investment advice based on a user's income and expenditure data and purchasing information. This system includes a terminal operated by the user, a server that analyzes the data, a generative AI, and an emotion engine that recognizes the user's emotions.

[1436] Users use a smartphone app to enter income and expenditure data and purchase information. This app has the function of automatically collecting expenditure data by linking with the electronic payment services that users use on a daily basis. Users can also enter data manually, and can easily add purchase information by uploading photos of receipts.

[1437] The device sends the information entered by the user to the server. The server stores the received data and analyzes it using generative AI. This analysis process clarifies the user's spending patterns and income trends, and suggests optimal savings points. For example, a user with high monthly food expenses will be notified of special sales at a nearby supermarket.

[1438] The server then collects sales and service information from the company's database, and the generative AI customizes the most appropriate proposals based on the user's attributes. An emotion engine that recognizes the user's emotions is then incorporated. The emotion engine analyzes the user's input data and behavior to determine their emotional state, such as whether they are feeling stressed.

[1439] The emotion data analyzed by the emotion engine is provided to the generative AI, which then takes this emotion data into account to customize suggestions. For example, if the user is feeling stressed, the generative AI will suggest products and services that will help them relax.

[1440] The device receives suggested information and investment advice from the server and notifies the user. The notification includes details of specific savings points and sales information. For example, a notification of discount information at a specific supermarket may be sent as "This week's food sales information." The user can also receive advice on how to invest the savings they have made, and the generating AI will analyze the user's surplus funds and suggest appropriate investment destinations and methods.

[1441] The hardware and software used are as follows:

[1442] Hardware: Smartphone (iOS, Android)

[1443] Software: Python, TensorFlow, Emotion API (Microsoft Azure), Watson Tone Analyzer (IBM), Firebase Cloud Messaging (Google)

[1444] For example, if user A's food expenses are judged to be high, the device will be notified of this week's special sale information, such as discounts on vegetables at a nearby supermarket. If user A's emotions indicate a state of stress, the emotion engine will also suggest sales information for products that have a relaxing effect.

[1445] Prompt Sentence Examples

[1446] If the user's food budget is high, suggest which supermarket has a sale on which product. If the user is feeling stressed, provide information on products that will help them relax.

[1447] This allows for personalized suggestions based on the user's emotional state, resulting in more effective savings and investment advice.

[1448] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1449] Step 1:

[1450] Users input their financial data and purchase information via a smartphone app. This information can be automatically obtained from electronic payment services, manually entered by the user, or extracted from photos of receipts. The app stores the financial data and purchase information in its internal database.

[1451] Step 2:

[1452] The terminal sends the collected income and expenditure data and purchase information to the server. It receives data from the internal database as input and generates the data sent to the server as output. This data transmission uses SSL / TLS encrypted communication.

[1453] Step 3:

[1454] The server stores the received data and analyzes it using generative AI. In this step, the income and expenditure data and purchasing information sent as input are received and stored in a database as output. The stored data is then input into a generative AI model, which analyzes spending patterns and income trends. Specifically, data analysis is performed using Python's pandas and TensorFlow.

[1455] Step 4:

[1456] The generative AI identifies the user's spending patterns and savings points based on the analysis results. It receives the analysis results as input and identifies optimal savings points as output. For example, if the user's food expenses are higher than other categories, advice on reducing food expenses will be suggested.

[1457] Step 5:

[1458] The server collects optimal product and service information from the company's database. It receives spending patterns and savings points as input and extracts suitable product and service information as output. Specific product information and special sale information are collected in this step.

[1459] Step 6:

[1460] The emotion engine analyzes emotions from the user's input data and behavior. In this step, user behavior data and input text are received as input, and emotion data is generated as output. Emotion analysis is performed using the Emotion API and Watson Tone Analyzer.

[1461] Step 7:

[1462] The generative AI takes emotional data into account to customize suggestions. It receives emotional data and collected product and service information as input, and generates optimal suggestions for the user as output. Specifically, a user in a stressed state will be offered information about special sales on products that have a relaxing effect.

[1463] Step 8:

[1464] The device notifies the user of the proposed information and investment advice from the server. It receives the customized proposals from the server as input and generates push notifications as output. This notification is generated using Firebase Cloud Messaging.

[1465] As a concrete example, consider the following prompt sentence:

[1466] If the user's food budget is high, suggest which supermarket has a sale on which product. If the user is feeling stressed, provide information on products that will help them relax.

[1467] 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.

[1468] 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.

[1469] 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.

[1470] 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.

[1471] 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.

[1472] 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.

[1473] 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).

[1474] 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.

[1475] 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."

[1476] 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.

[1477] 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).

[1478] 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.

[1479] 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.

[1480] 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.

[1481] 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.

[1482] 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.

[1483] 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.

[1484] 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.

[1485] 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.

[1486] 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.

[1487] 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.

[1488] The following is further disclosed regarding the above embodiment.

[1489] (Claim 1)

[1490] a means for users to input income and expenditure data and purchasing information;

[1491] A means for transmitting input data from the terminal to a server;

[1492] A server stores the received household accounting data and analyzes it using the generation AI.

[1493] A means for the generation AI to identify the user's spending patterns and savings points based on the analysis results, and

[1494] A means for the server to collect optimal product and service information from the company's database,

[1495] A means for the generative AI to customize suggestions based on user attributes; and

[1496] a means for the terminal to notify the user of the suggested information from the server;

[1497] A system including:

[1498] (Claim 2)

[1499] a means for storing the investment advice proposed by the server for each user;

[1500] The means by which the Generative AI identifies the user's surplus funds and guides their investment strategy;

[1501] A means for the terminal to notify the investment advice from the server;

[1502] 2. The system of claim 1,

[1503] (Claim 3)

[1504] The server periodically updates the latest sales and service information of affiliated companies;

[1505] The generation AI analyzes this information and selects the best sale information for the user.

[1506] a means for the terminal to notify the user of sale information;

[1507] 2. The system of claim 1,

[1508] "Example 1"

[1509] (Claim 1)

[1510] a means for users to input income and expenditure data and purchasing information;

[1511] A means for transmitting input data from the terminal to a server;

[1512] A means for storing the received data by the server and analyzing it using a generating artificial intelligence;

[1513] A means for the generating artificial intelligence to identify the user's spending patterns and savings points based on the analysis results;

[1514] A means for the server to collect optimal product and service information from the company's database,

[1515] a means for the generative artificial intelligence to customize suggestions based on user attributes;

[1516] a means for the terminal to notify the user of the suggested information from the server;

[1517] Economic management systems, including

[1518] (Claim 2)

[1519] a means for storing the investment advice proposed by the server for each user;

[1520] A means by which the generative artificial intelligence identifies the user's excess funds and guides their investment strategy;

[1521] A means for the terminal to notify the investment advice from the server;

[1522] 2. The economic management system according to claim 1,

[1523] (Claim 3)

[1524] The server periodically updates the latest sales and service information of affiliated companies;

[1525] A means for the artificial intelligence to analyze this information and select the best sale information for the user;

[1526] a means for the terminal to notify the user of sale information;

[1527] 2. The economic management system according to claim 1,

[1528] "Application Example 1"

[1529] (Claim 1)

[1530] a means for users to input income and expenditure data and purchasing information;

[1531] A means for transmitting input data from the terminal to a server;

[1532] A server stores the received household accounting data and analyzes it using the generation AI.

[1533] A means for the generation AI to identify the user's spending patterns and savings points based on the analysis results, and

[1534] A means for the server to collect optimal product and service information from external databases;

[1535] A means for the generative AI to customize suggestions based on user attributes; and

[1536] a means for the terminal to notify the user of the suggested information from the server;

[1537] A means for providing appropriate investment advice based on the amount of savings calculated from the user's income and expenditure data;

[1538] means for capturing an image of the receipt and extracting purchase information using optical character recognition technology;

[1539] A system including:

[1540] (Claim 2)

[1541] a means for storing the investment advice proposed by the server for each user;

[1542] The means by which the Generative AI identifies the user's surplus funds and guides their investment strategy;

[1543] A means for the terminal to notify the investment advice from the server;

[1544] 10. The system of claim 1, wherein purchasing information extracted from receipts is integrated with household accounting data.

[1545] (Claim 3)

[1546] The server periodically updates the latest sales and service information of affiliated companies;

[1547] The generation AI analyzes this information and selects the best sale information for the user.

[1548] a means for the terminal to notify the user of sale information;

[1549] 10. The system of claim 1, which utilizes OCR technology to efficiently capture purchasing information.

[1550] "Example 2: Combining Emotion Engines"

[1551] (Claim 1)

[1552] a means for users to input income and expenditure data and purchasing information;

[1553] A means for transmitting input data from the terminal to a server;

[1554] A server stores the received household accounting data and analyzes it using the generation AI.

[1555] a means for the emotion engine to analyze the user's emotional state;

[1556] A means for the generative AI to identify the user's spending patterns and savings points based on the analysis results and emotional state;

[1557] A means for the server to collect optimal product and service information from the company's database,

[1558] A means for the generative AI to customize suggestions based on the user's attributes and emotional state; and

[1559] a means for the terminal to notify the user of the suggested information from the server;

[1560] A system including:

[1561] (Claim 2)

[1562] a means for storing the investment advice proposed by the server for each user;

[1563] A means for ...

Claims

1. a means for users to input income and expenditure data and purchasing information; A means for transmitting input data from the terminal to a server; A server stores the received household accounting data and analyzes it using the generation AI. A means for the generation AI to identify the user's spending patterns and savings points based on the analysis results, and A means for the server to collect optimal product and service information from the company's database, A means for the generative AI to customize suggestions based on user attributes; and a means for the terminal to notify the user of the suggested information from the server; A system including:

2. a means for storing the investment advice proposed by the server for each user; The means by which the Generative AI identifies the user's surplus funds and guides their investment strategy; A means for the terminal to notify the investment advice from the server; 2. The system of claim 1, wherein:

3. The server periodically updates the latest sales and service information of affiliated companies; The generation AI analyzes this information and selects the best sale information for the user. a means for the terminal to notify the user of sale information; 2. The system of claim 1, wherein:

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