System

Abstract

A system is provided.SOLUTION: A system comprising: an artificial intelligence engine for recommending an action based on a user's transaction history and location information; means for collecting the user's transaction history and location information; and means having a graphical user interface for notifying a user of a recommended action generated by the artificial intelligence engine.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] While existing payment applications provide users with convenient transaction and campaign information, it is rare for this information to be integrated into user behavior in a timely and appropriate manner. This leaves users having to search for the information themselves, which limits the app's usefulness. Furthermore, insufficient balance status and special offer information are often overlooked and not used. This detracts from the user experience and hinders the use of payment apps. [Means for solving the problem]

[0005] This invention provides a system that has an artificial intelligence engine on a server that recommends actions based on a user's transaction history and location information, and generates optimal recommended actions based on the collected user transaction history and location information. Specifically, it provides a means for displaying wallet balances and top-up suggestions when a user launches the app, and also provides a means for generating special offers and campaign information based on specific times and location information and notifying the user via a graphical user interface. In this way, users can spend 3-4 hours with the payment app without even realizing it, improving convenience and promoting app usage.

[0006] "User transaction history" refers to data containing detailed information about payments made by a user in the past, including the date and time of purchase, transaction amount, store information, etc.

[0007] "Location information" refers to data about a user's current location and places they have visited in the past, including GPS data, Wi-Fi connection information, and base station information.

[0008] An "artificial intelligence engine" is a system that includes algorithms and computer programs that analyze patterns from collected data and generate optimal actions and suggestions for users.

[0009] A "graphical user interface" is a visual interface that allows a user to operate an application, and includes icons, buttons, menus, text displays, etc.

[0010] "Wallet Balance" refers to the balance information of a User's electronic money or points within the Payment Application, and indicates the amount of funds available to the User.

[0011] "Top-up suggestion" refers to a message or suggestion that encourages a user to add additional funds when their wallet balance is insufficient.

[0012] "Benefit information" refers to information about special offers such as discounts, campaigns, and point bonuses available to users.

[0013] "Campaign Information" refers to information about promotional activities that are carried out at specific times and places, and that offer special benefits or discounts to users. [Brief explanation of the drawings]

[0014] [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

[0015] 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.

[0016] First, the terms used in the following description will be explained.

[0017] 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).

[0018] 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.

[0019] 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.

[0020] 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.

[0021] 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."

[0022] [First embodiment]

[0023] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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."

[0035] The present invention is a system centered around an artificial intelligence (AI) engine that recommends actions based on a user's transaction history and location information. This system collects a user's transaction history and location information, and the AI ​​engine generates optimal recommended actions based on that data and notifies the user. Specific embodiments of this system are described below.

[0036] Overall structure

[0037] The system includes the following major components:

[0038] 1. Server - Collects user data and uses an AI engine to make recommendations.

[0039] 2. Terminal - The device that the user operates, such as a smartphone or tablet.

[0040] 3. User - An individual user of the system.

[0041] Initial setup and data acquisition

[0042] user

[0043] When users first install and launch the app, they enter their personal information (email address, phone number, etc.), credit card details, and agree to the terms of use. This information is necessary to obtain accurate user data.

[0044] Terminal

[0045] When users enter their information into the app, the data is sent to the server. After the initial setup is complete, the device will periodically communicate with the server to obtain the latest recommendation behavior and campaign information.

[0046] server

[0047] The server stores the user's transaction history and location information in a database, which is then used by the AI ​​engine to analyze the user's behavioral patterns and generate optimal recommendations.

[0048] Generate and notify recommended actions

[0049] server

[0050] By analyzing the user's transaction history and location information, the AI ​​engine runs an algorithm that recommends the best course of action for that day. For example, it selects information about a point-double campaign at a specific store or nearby special offers based on the user's current location and past purchase history.

[0051] Terminal

[0052] The device receives recommended actions and campaign information from the server and displays them to the user as pop-up notifications. For example, when the device is turned on in the morning, it displays the wallet balance and top-up suggestions, and when the device is out and about, it notifies the user of special offers at nearby stores.

[0053] Specific examples

[0054] Case 1: Morning wallet balance display and top-up suggestion

[0055] A user launches the app in the morning.

[0056] The terminal sends a request to the server and receives the wallet balance and a top-up offer.

[0057] The server checks the user's wallet balance and sends data to the device, including a suggestion if a charge is needed.

[0058] The terminal displays the wallet balance and top-up suggestions on the screen, and the user can top up as needed.

[0059] Case 2: Notification of a nearby store's double points campaign

[0060] When a user reaches a specific time or location while out and about, the server uses that information to recommend point-double campaigns being held at nearby stores.

[0061] The device will notify the user of the recommended actions in real time.

[0062] Users can visit the notified store and make payments using the app to enjoy the double points benefit.

[0063] Case 3: Information about the next day's events before going to bed

[0064] When a user launches the app before going to bed, information about the next day's events and campaigns will be displayed on the device.

[0065] The server generates optimal event information based on the user's past behavioral history and the next day's schedule and sends it to the device.

[0066] The device will then notify the user of this information, allowing them to adjust their actions based on their schedule for the next day.

[0067] In this way, the present invention significantly improves user convenience, allowing the use of payment applications to become a natural part of everyday life.

[0068] The processing flow will be explained below.

[0069] Specific processing steps of the program

[0070] Case 1: Morning wallet balance display and top-up suggestion

[0071] Step 1:

[0072] A user launches the app in the morning.

[0073] Step 2:

[0074] The terminal sends a request to the server for the wallet balance and a top-up proposal.

[0075] Step 3:

[0076] The server retrieves the wallet balance from the database based on the user ID.

[0077] Step 4:

[0078] The server checks the wallet balance and determines if a charge is required.

[0079] Step 5:

[0080] The server generates the wallet balance and, if necessary, a top-up proposal and returns it to the terminal.

[0081] Step 6:

[0082] The terminal displays the received wallet balance and top-up proposal on the user interface.

[0083] Step 7:

[0084] If the user accepts the charge proposal, the charge is carried out.

[0085] ---

[0086] Case 2: Notification of a nearby store's double points campaign

[0087] Step 1:

[0088] The user launches the app while out and about.

[0089] Step 2:

[0090] The device acquires the current location information and sends it to the server.

[0091] Step 3:

[0092] The server uses the user's location information and past transaction history to search for point doubling campaigns currently being held at nearby stores.

[0093] Step 4:

[0094] The server generates optimal campaign information and returns it to the terminal.

[0095] Step 5:

[0096] The device notifies the user of campaign information received in real time.

[0097] Step 6:

[0098] The user checks the notification and takes action based on the campaign information.

[0099] Step 7:

[0100] Users visit participating stores and make payments using the app.

[0101] ---

[0102] Case 3: Information about the next day's events before going to bed

[0103] Step 1:

[0104] The user launches the app before going to bed.

[0105] Step 2:

[0106] The terminal sends a request for next day's event information to the server.

[0107] Step 3:

[0108] The server retrieves the user's past behavior history and the next day's schedule from the database.

[0109] Step 4:

[0110] The server generates optimal event and campaign information for the next day.

[0111] Step 5:

[0112] The server returns the generated event information to the terminal.

[0113] Step 6:

[0114] The event information received by the terminal is displayed on the user interface.

[0115] Step 7:

[0116] The user checks the schedule for the next day and adjusts the schedule if necessary.

[0117] These are the specific processing steps of the program, which allow users to effectively use the payment app and increase convenience in their daily lives.

[0118] Example 1

[0119] 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."

[0120] Users need to be recommended the most appropriate actions at the right time in their daily lives, but current systems have difficulty effectively utilizing transaction history and location information to provide users with the most useful information in real time. Another challenge is providing accurate and useful information while ensuring user privacy and data security.

[0121] 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.

[0122] In this invention, the server includes an artificial intelligence engine for recommending actions based on the user's transaction history and location information, means for collecting the user's transaction history and location information, display means for notifying the user of the recommended actions generated by the artificial intelligence engine, means for the terminal to encrypt the user's input information and transmit it to the server, means for the server to store the user information in a database and generate a user ID, means for the terminal to periodically acquire the user's location information and transaction history and transmit them to the server, means for the server to analyze the data using the AI ​​engine, and means for the server to generate optimal recommended actions based on the analysis results and transmit them to the terminal. This allows users to receive optimal recommended actions in real time in their daily lives, improving their purchasing experience and enabling efficient action planning. Data security and privacy protection are also ensured.

[0123] "User" refers to an individual entity that uses this system.

[0124] "Transaction History" means historical information about purchases and payments made by a User.

[0125] "Location information" refers to data that indicates a user's current location and movement history.

[0126] "Artificial Intelligence Engine" refers to software or a system that contains algorithms for recommending optimal actions based on a user's transaction history and location information.

[0127] "Means for collection" means a method or device for incorporating a user's transaction history and location information into the system.

[0128] "Means for notifying" refers to means for informing the user of the generated recommendation action.

[0129] "Display Means" means a device or software feature for visually presenting information to a user.

[0130] "Device" refers to an electronic device such as a smartphone or tablet operated by a user.

[0131] "Encryption" means a technique or process that transforms data so that it cannot be read by third parties.

[0132] "Server" refers to the remote computer system that stores user information and performs data analysis using an AI engine.

[0133] "Database" means a system for systematically storing data such as user information, transaction history, and location information.

[0134] "User ID" refers to an identifier that uniquely identifies a user.

[0135] "Periodic" means repeated at regular intervals of time.

[0136] "Means of analysis" refers to the methods and technologies used to analyze collected data using an AI engine.

[0137] "Optimal recommended action" means the action or option that is most beneficial to the user and chosen based on specific criteria.

[0138] The present invention is a system centered around an artificial intelligence engine that recommends actions based on a user's transaction history and location information. The system includes the following main components:

[0139] Overall structure

[0140] The system includes the following major components:

[0141] 1. Server - Collects user data and uses an AI engine to make recommendations.

[0142] 2. Device - An electronic device that a user operates, such as a smartphone or tablet.

[0143] 3. User - An individual entity that uses the System.

[0144] Initial setup and data acquisition

[0145] user

[0146] When a user installs and launches the app for the first time, they enter their email address, phone number, credit card information, etc., and agree to the terms of use. This series of operations ensures that the necessary data is accurately collected.

[0147] Terminal

[0148] The information entered by the user is sent from the device to the server. The information is encrypted before being sent. After the initial setup is complete, the device will periodically communicate with the server to obtain the latest recommendation behavior and campaign information.

[0149] server

[0150] The server stores the received user information in a database, and also generates a user ID and creates an account for the user.

[0151] Generate recommended actions

[0152] Terminal

[0153] The device periodically collects the user's location information and records the history of transactions as they occur.

[0154] user

[0155] When a user makes a transaction using the app, details of the transaction (such as date, time, location, and amount) are recorded.

[0156] server

[0157] Transaction history and location information sent from the terminal are continuously received and stored in a database.

[0158] The server runs an AI engine that analyzes users' transaction history and location information, for example, using machine learning models to analyze their purchasing and travel patterns.

[0159] server

[0160] Based on the analysis results, the AI ​​engine generates optimal recommendations, such as double points campaigns at specific stores or events to visit on that day.

[0161] Recommended Action Notifications

[0162] server

[0163] The server transmits the generated recommended actions and campaign information to the terminal.

[0164] Terminal

[0165] The device processes the recommended actions received from the server and presents them to the user as a pop-up notification.

[0166] Specific examples

[0167] Case 1: Morning wallet balance display and top-up suggestion

[0168] user

[0169] Start the app in the morning.

[0170] Terminal

[0171] The terminal sends a request to the server and receives the wallet balance and a top-up offer.

[0172] server

[0173] The server checks the user's wallet balance and, if a charge is required, sends the suggested charge data.

[0174] Terminal

[0175] The terminal will display the wallet balance and top-up suggestions as a pop-up on the screen.

[0176] user

[0177] If necessary, tap the "Charge" button to perform the charging operation.

[0178] Case 2: Notification of a nearby store's double points campaign

[0179] user

[0180] Reach a specific location while out and about.

[0181] Terminal

[0182] The current location is obtained using the GPS function and the location information is sent to the server.

[0183] server

[0184] Analyze and identify point doubling campaigns at nearby stores based on the user's location information and past transaction history.

[0185] server

[0186] Send recommended actions (points double campaign information) to the device.

[0187] Terminal

[0188] The device will notify the user of this information in real time as a pop-up notification.

[0189] user

[0190] Check the notification and head to the store to redeem the offer.

[0191] Case 3: Information about the next day's events before going to bed

[0192] user

[0193] Start the app before you go to bed.

[0194] Terminal

[0195] A request is sent to the server to receive event information and campaign information for the next day.

[0196] server

[0197] Generates optimal event information based on the user's past behavioral history and the next day's schedule.

[0198] server

[0199] The generated event information is sent to the terminal.

[0200] Terminal

[0201] The device notifies the user of the event information.

[0202] user

[0203] Schedule the event for the next day.

[0204] Prompt Sentence Examples

[0205] Describe how when a user launches the app in the morning, the device receives the wallet balance and top-up suggestions from the server.

[0206]

[0207] Please explain the procedure for when a user reaches a specific point, the server sends information about a point double campaign at a nearby store, and the terminal notifies the user of this.

[0208] Such prompts can be used to input specific cases into the generative AI model.

[0209] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0210] Step 1:

[0211] A user launches the app for the first time, enters their email address, phone number, and credit card information, and agrees to the terms of use.

[0212] The terminal encrypts the entered information and sends it to the server.

[0213] Input: User's email address, phone number, credit card information, and agreement to the terms of use.

[0214] Output: Encrypted user information is sent to the server.

[0215] Specific operations: Receives information input through a user interface, encrypts it, and sends an HTTP request to the server.

[0216] Step 2:

[0217] The server stores the received user information in a database and generates a user ID.

[0218] Input: Encrypted user information.

[0219] Output: User information stored in the database and the generated user ID.

[0220] What it does: Stores the information in a database and generates and records a unique user ID.

[0221] Step 3:

[0222] The device periodically collects the user's location information and transaction history and sends them to the server.

[0223] Input: GPS location, details of when the transaction occurred.

[0224] Output: Location information and transaction history are sent to the server.

[0225] Specific operation: The device's GPS function is used to obtain location information, and history information is sent to the server each time a transaction is completed.

[0226] Step 4:

[0227] The server stores the received location information and transaction history in a database.

[0228] Input: Location information and transaction history received from the device.

[0229] Output: Location information and transaction history stored in a database.

[0230] Specific operation: Accurately record the received data in a database and organize it by user.

[0231] Step 5:

[0232] The server runs an AI engine that analyzes the user's transaction history and location information.

[0233] Input: Transaction history and location information stored in a database.

[0234] Output: Action recommendations generated as a result of the analysis.

[0235] What it does: It uses machine learning algorithms to analyze data and identify patterns in user behavior.

[0236] Step 6:

[0237] Based on the analysis results, the server generates optimal recommended actions and sends them to the device.

[0238] Input: Analysis results of the AI ​​engine.

[0239] Output: The recommended action data is sent to the device.

[0240] Specific operation: Based on the analysis results, an optimal action plan is created and sent to the terminal in JSON format.

[0241] Step 7:

[0242] The device processes the recommended actions received from the server and displays them to the user as a pop-up notification.

[0243] Input: Recommended action data sent from the server.

[0244] Output: Recommended action displayed as a popup notification.

[0245] Specific behavior: Analyzes received data and presents it visually through a user interface.

[0246] Step 8:

[0247] The user views the notification and acts on the suggested action displayed.

[0248] Input: The suggested action displayed on the device.

[0249] Output: User actions.

[0250] Specific action: The user checks the notification and performs a specific action, such as visiting a store or topping up.

[0251] (Application example 1)

[0252] 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."

[0253] In today's brick-and-mortar stores, users have limited access to useful information on special offers and campaigns in real time, creating a need for improved user purchasing experiences. There is also a need for systems that effectively utilize users' location information and transaction history to provide optimal recommendations for each individual user. Systems that can solve these issues and improve user convenience are anticipated.

[0254] 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.

[0255] In this invention, the server includes an artificial intelligence engine for recommending actions based on a user's transaction history and location information, means for collecting the user's transaction history and location information, means having a graphical user interface for notifying the user of the recommended actions generated by the artificial intelligence engine, and means for recommending information on benefits and campaigns at physical stores in real time based on the user's current location. This allows the user to obtain optimal information on benefits and campaigns in real time, which is expected to improve the purchasing experience.

[0256] "User" means an individual consumer who uses this system.

[0257] "Transaction history" refers to a record of a user's past purchases and transactions.

[0258] "Location information" refers to data about a user's current location and past travel routes.

[0259] The "artificial intelligence engine for recommending actions" is a system that uses algorithms to analyze transaction history and location information and suggest optimal actions.

[0260] A "graphical user interface" is an application's screen display that allows a user to visually receive information.

[0261] "Benefits and campaign information" refers to information such as discounts, points, and limited-edition products for the purpose of sales promotion.

[0262] A "physical store" is a physical store where users actually visit and purchase products.

[0263] "Real-time recommendation methods" are technologies and mechanisms that instantly provide users with information on special offers and campaigns that are tailored to their current situation.

[0264] Overall structure

[0265] This invention is a system that recommends actions based on a user's transaction history and location information. The main components include a server, a terminal, and a user.

[0266] 1. Server:

[0267] Collection of transaction history and location information: Transaction history and location information are periodically received from users' smartphones and tablets and stored in a database.

[0268] Artificial Intelligence Engine: Analyzes collected data and learns user behavior patterns. This engine uses AI algorithms to generate optimal recommended actions.

[0269] Generate recommended actions: Based on the user's current location, the system processes information on special offers and campaigns in physical stores in real time.

[0270] 2. Terminal:

[0271] Location information acquisition: The user's current location is measured using the GPS sensor built into smartphones and tablets.

[0272] Communication with the server: Sends the user's transaction history and location information to the server, and receives notifications of recommended actions from the server.

[0273] Graphical User Interface: Visually displays recommended actions, rewards information, wallet balance and top-up suggestions to the user.

[0274] 3. User:

[0275] Initial Setup: After installing the application, enter your identity and credit card information.

[0276] Receive and act on notifications: Receive special offers and campaign information and use it to make in-store purchases.

[0277] Explanation of program processing

[0278] 1. Hardware and Software:

[0279] Hardware: Smartphone (with GPS sensor), server

[0280] software:

[0281] geopy: A Python library for obtaining user location information.

[0282] requests: An HTTP request library for communicating with servers.

[0283] 2. Data processing and calculation:

[0284] Server: Based on the user's transaction history and location information, the AI ​​engine generates optimal recommendations, especially for determining special offers and campaign information near the user's current location in real time.

[0285] Device: Receives recommended actions and campaign information sent from the server and notifies the user. Location information is also periodically acquired and sent to the server.

[0286] Specific examples

[0287] For example, if a user is shopping in Tokyo, the smartphone's GPS sensor will acquire their current location and send it to the server. Based on that location information and past transaction history, the server will determine that the user is at a nearby convenience store and send a real-time notification to the device recommending a double points campaign. By receiving this notification, the user can enjoy shopping at a physical store and get great deals.

[0288] Example prompt sentence:

[0289] "You are currently developing an AI assistant that recommends optimal actions based on a user's transaction history and location information. This AI assistant recommends products and campaign information to users in real time while they are shopping in a physical store. Build a scenario in which, while the user is shopping in Tokyo, they are notified of a points double campaign currently being held at a nearby convenience store."

[0290] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0291] Step 1:

[0292] The device receives the user's initial setup information (identity verification information, credit card information). By inputting this initial setup information and sending it to the server, a user account is created and the application can be used. After the initial setup information is sent, an account ID is output.

[0293] Step 2:

[0294] The device acquires the user's location information. In this step, the smartphone's GPS sensor is used to measure the current location and send that location information to the server. The acquired location information is used as input, and location data is output.

[0295] Step 3:

[0296] The server receives the user's transaction history and location information and stores them in a database. The stored transaction history and location information are used as inputs, and behavioral pattern data for each user is output.

[0297] Step 4:

[0298] The AI ​​engine on the server analyzes the stored data and learns each user's behavioral patterns. At this stage, transaction history and location information are used as input, and behavioral recommendations are output as the analysis result.

[0299] Step 5:

[0300] The server's artificial intelligence engine generates information about special offers and campaigns based on the user's current location. In this step, the server inputs the user's current location information and past behavioral pattern data, and outputs the most suitable special offer information.

[0301] Step 6:

[0302] The server sends the generated bonus information to the terminal in real time. This notification information (bonus information) is input and sent to the terminal, which outputs a bonus notification that is displayed to the user.

[0303] Step 7:

[0304] The device notifies the user of the received reward information through a graphical user interface. In this step, the reward information is input and a notification is output to be displayed on the user's smartphone screen.

[0305] Step 8:

[0306] The user receives the notified benefit information and makes a purchase at the physical store based on it. In this step, the notified benefit information is used as input and the purchasing behavior at the physical store is output.

[0307] Step 9:

[0308] After the user finishes shopping, the terminal sends a new transaction history to the server. In this step, the post-purchase transaction data is used as input and the new transaction history to be sent to the server is used as output.

[0309] Step 10:

[0310] The server again saves the new transaction history in the database, and the AI ​​engine updates the data. In this step, the new transaction history is used as input and updated behavioral pattern data is output.

[0311] By repeating this process, the system can improve user convenience.

[0312] 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.

[0313] The present invention is a system that includes an artificial intelligence (AI) engine that recommends actions based on a user's transaction history and location information, and also incorporates an emotion engine that recognizes the user's emotions. This system uses the AI ​​engine to generate optimal recommended actions based on the collected user transaction history, location information, and emotion data, and notifies the user. Specific embodiments of this system are described below.

[0314] Overall structure

[0315] The system includes the following major components:

[0316] 1. Server - Collects user data and makes recommendations using AI and emotion engines.

[0317] 2. Terminal - The device that the user operates, such as a smartphone or tablet.

[0318] 3. User - An individual user of the system.

[0319] Initial setup and data acquisition

[0320] user

[0321] When users first install and launch the app, they enter their personal information (email address, phone number, etc.), credit card details, and agree to the terms of use. This information is necessary to obtain accurate user data.

[0322] Terminal

[0323] When users enter their information into the app, the data is sent to the server. After the initial setup is complete, the device will periodically communicate with the server to obtain the latest recommendation behavior and campaign information.

[0324] server

[0325] The server stores the user's transaction history and location information in a database. It also records the user's real-time emotional state using an emotion engine that recognizes emotions from the user's facial expressions and voice data. Based on this, the AI ​​engine analyzes the user's behavioral patterns and generates optimal recommendations.

[0326] Generate and notify recommended actions

[0327] server

[0328] By analyzing a user's transaction history, location information, and emotional data, the AI ​​engine runs an algorithm that recommends the best course of action for that day. For example, it selects information about a point-double campaign at a specific store or nearby special offers based on the user's current location, past purchase history, and emotional state.

[0329] Terminal

[0330] The device receives recommended actions and campaign information from the server and displays them to the user as pop-up notifications. For example, when the device is turned on in the morning, it displays the wallet balance and top-up suggestions, and when the device is out and about, it notifies the user of special offers at nearby stores. The content and display method of notifications may also be adjusted depending on the user's emotional state.

[0331] Specific examples

[0332] Case 1: Morning wallet balance display and top-up suggestion

[0333] A user launches the app in the morning.

[0334] The terminal sends a request to the server and receives the wallet balance and a top-up offer.

[0335] The server checks the user's wallet balance and sends data to the device, including a suggestion if a charge is needed.

[0336] The terminal displays the wallet balance and top-up suggestions on the screen, and the user can top up as needed.

[0337] Case 2: Notification of a nearby store's double points campaign

[0338] When a user reaches a specific time or location while out and about, the server uses that information to recommend point-double campaigns being held at nearby stores.

[0339] The device will notify the user of the recommended actions in real time, and the notification will be adjusted based on the user's emotions as recognized by the emotion engine. For example, if the user is feeling happy, the notification will be displayed in a friendly tone.

[0340] Users can visit the notified store and make payments using the app to enjoy the double points benefit.

[0341] Case 3: Information about the next day's events before going to bed

[0342] When a user launches the app before going to bed, information about the next day's events and campaigns will be displayed on the device.

[0343] The server generates optimal event information based on the user's past behavior history, the next day's schedule, and data collected by the emotion engine, and sends it to the device. This information is also customized according to the user's emotions.

[0344] The device will then notify the user of this information, allowing them to adjust their actions based on their schedule for the next day.

[0345] In this way, the present invention significantly improves user convenience, integrates the use of payment applications into everyday life, and provides appropriate feedback based on the user's emotional state.

[0346] The processing flow will be explained below.

[0347] Specific processing steps of the program

[0348] Case 1: Morning wallet balance display and top-up suggestion

[0349] Step 1:

[0350] A user launches the app in the morning.

[0351] Step 2:

[0352] The terminal sends a request to the server for the wallet balance and a top-up proposal.

[0353] Step 3:

[0354] The server retrieves the wallet balance from the database based on the user ID.

[0355] Step 4:

[0356] The server checks the wallet balance and determines if a charge is required.

[0357] Step 5:

[0358] The server generates the wallet balance and, if necessary, a top-up proposal and returns it to the terminal.

[0359] Step 6:

[0360] The terminal displays the received wallet balance and top-up proposal on the user interface.

[0361] Step 7:

[0362] If the user accepts the charge proposal, the charge is carried out.

[0363] ---

[0364] Case 2: Notification of a nearby store's double points campaign

[0365] Step 1:

[0366] The user launches the app while out and about.

[0367] Step 2:

[0368] The device acquires the current location information and sends it to the server.

[0369] Step 3:

[0370] The server uses the user's location information, past transaction history, and data from the emotion engine to search for point doubling campaigns currently being held at nearby stores.

[0371] Step 4:

[0372] The server generates optimal campaign information and returns it to the terminal.

[0373] Step 5:

[0374] The device notifies users in real time about campaign information received, and the notifications are tailored based on the user's emotions as recognized by the emotion engine.

[0375] Step 6:

[0376] The user checks the notification and takes action based on the campaign information.

[0377] Step 7:

[0378] Users visit participating stores and make payments using the app.

[0379] ---

[0380] Case 3: Information about the next day's events before going to bed

[0381] Step 1:

[0382] The user launches the app before going to bed.

[0383] Step 2:

[0384] The terminal sends a request for next day's event information to the server.

[0385] Step 3:

[0386] The server generates optimal event and campaign information for the next day based on the user's past behavioral history and schedule for the next day, as well as data collected by the emotion engine.

[0387] Step 4:

[0388] The server returns the generated event information to the terminal.

[0389] Step 5:

[0390] The device displays the event information it receives in the user interface, which is also customized according to the user's emotions.

[0391] Step 6:

[0392] The user checks the schedule for the next day and adjusts the schedule if necessary.

[0393] These are the specific processing steps of the program, which allow users to effectively use the payment app and increase convenience in their daily lives.

[0394] Example 2

[0395] 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."

[0396] Conventional behavior recommendation systems based on a user's transaction history and location information are unable to take into account the user's emotional state, making it difficult to provide optimal recommended behaviors. Furthermore, they lacked a mechanism for adjusting notification content based on the user's emotional state and providing information at a more appropriate time and in a more appropriate manner. As a result, user convenience and satisfaction were not sufficiently improved.

[0397] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes an artificial intelligence engine means for recommending actions based on a user's transaction history and location information, a means for collecting the user's transaction history and location information, an emotion engine means for recognizing and analyzing the user's emotional data, and a means having a graphical user interface for notifying the user of recommended actions generated by the artificial intelligence engine and emotion engine. This makes it possible to comprehensively analyze the user's transaction history, location information, and emotional data and provide the user with recommended actions that are optimal for the user. In addition, the recommended actions and notification content can be customized according to the user's emotional state, significantly improving user convenience and satisfaction.

[0398] "User transaction history" refers to recorded information about purchases and financial transactions that a user has made in the past.

[0399] "Location Information" means geographic coordinate information obtained based on a user's device.

[0400] An "artificial intelligence engine" is a part of a system that uses machine learning algorithms to analyze data and generate actionable recommendations for users.

[0401] "Emotional data" is information about the psychological state of a user that can be read from facial expressions, voice, etc.

[0402] The "emotion engine" is the part of the system that recognizes and analyzes the user's emotional data.

[0403] "Recommended actions" are specific action suggestions generated by the artificial intelligence engine based on a user's transaction history, location information, and emotional data.

[0404] A "graphical user interface" is a screen display means for visually presenting information to a user via a terminal.

[0405] "Wallet Balance" means the total amount of funds currently available in a User's Digital Wallet.

[0406] A "top-up suggestion" is a recommendation to top up additional funds if your wallet balance is low.

[0407] "Benefit information" refers to information including benefits such as campaigns and discounts offered to users.

[0408] MODE FOR CARRYING OUT THE INVENTION

[0409] The system of the present invention is equipped with a complex AI engine and emotion engine that recommends optimal actions using a user's transaction history, location information, and emotion data. This system is composed of three elements: a server, a terminal, and a user, and each element functions as follows:

[0410] Initial setup and data acquisition

[0411] user:

[0412] When you first install the app, you launch it, enter your identity verification information (email address, phone number, etc.), credit card information, and agree to the terms of use, which allows us to collect accurate user data.

[0413] Device:

[0414] The information entered by the user is temporarily stored on the device and sent to the server. The device is set to periodically send location information and transaction history to the server.

[0415] server:

[0416] The server stores the user's input information in a database, periodically updates the user's transaction history and location information, and analyzes the user's facial expressions and voice data using an emotion engine, saving the data as emotion data.

[0417] Generate and notify recommended actions

[0418] server:

[0419] An artificial intelligence engine analyzes users' transaction history, location information, and real-time sentiment data to generate optimal recommendations, such as identifying points-double campaigns at specific stores or nearby special offers.

[0420] Device:

[0421] The recommended actions and campaign information received from the server are displayed to the user as pop-up or banner notifications, and the content of the notifications may be adjusted based on the user's emotional state.

[0422] Specific examples

[0423] Case 1: Morning wallet balance display and top-up suggestion

[0424] user:

[0425] Start the app in the morning.

[0426] Device:

[0427] Sends a request to the server and receives the wallet balance and top-up proposal.

[0428] server:

[0429] Checks the user's wallet balance, creates a top-up proposal if necessary, and sends the data to the terminal.

[0430] Device:

[0431] The wallet balance and top-up suggestions are displayed on the screen, and the user can top up as needed.

[0432] Case 2: Notification of a nearby store's double points campaign

[0433] user:

[0434] Reaching a specific time or location while out and about.

[0435] server:

[0436] Based on this information, the system recommends to users points doubling campaigns being held at nearby stores.

[0437] Device:

[0438] The recommended actions are notified to the user in real time, and the emotion engine ensures that notifications are displayed in a friendly tone when the user is feeling happy.

[0439] user:

[0440] Visit the notified store, pay using the app, and enjoy the double points bonus.

[0441] Case 3: Information about the next day's events before going to bed

[0442] user:

[0443] Start the app before you go to bed.

[0444] Device:

[0445] Information about events and campaigns for the next day will be displayed.

[0446] server:

[0447] Based on the user's past behavioral history and schedule for the next day, the system generates optimal event information and sends it to the device. This information is customized by the emotion engine.

[0448] Device:

[0449] This information is communicated to the user, allowing them to adjust their actions based on their schedule for the next day.

[0450] Prompt Sentence Examples

[0451] Below are some example prompts from a generative AI model:

[0452] 1. "How can I display my wallet balance in the morning and suggest topping up if needed?"

[0453] 2. "Please explain the process for notifying users on the go about a nearby store's double points promotion."

[0454] 3. "Please explain in detail how users can receive the next day's event information before going to bed."

[0455] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0456] Step 1: First-time user registration

[0457] user:

[0458] After installing the app for the first time, launch the app, enter your identity verification information (email address, phone number, etc.) and credit card information, and agree to the terms of use. Input is done through text boxes, etc.

[0459] Device:

[0460] The entered information is temporarily stored on the device. When the user presses the "Send" button, this data is sent to the server.

[0461] Input: Personal information, credit card information, agreement to terms of use

[0462] Output: Data packet sent to the server

[0463] Step 2: Send data by device

[0464] Device:

[0465] The user's input information is sent to the server using a secure communication protocol (e.g., HTTPS), and the user's location information is also obtained using GPS functionality.

[0466] server:

[0467] The received data is analyzed to check for omissions or errors. If there are no problems, it is saved in the database.

[0468] Input: User registration information, location information

[0469] Output: User information and location information stored on the server

[0470] Step 3: Data storage and analysis on the server

[0471] server:

[0472] The user's transaction history and location information are stored in a database. The user's facial expressions and voice data are analyzed by an emotion engine and stored as emotion data.

[0473] Input: Transaction history, location information, facial expression and voice data

[0474] Output: Stored transaction history, location information, and sentiment data

[0475] Step 4: Generate action recommendations using the AI ​​engine

[0476] server:

[0477] The artificial intelligence engine analyzes stored transaction history, location, and sentiment data and uses machine learning algorithms to generate optimal action recommendations.

[0478] Input: Transaction history, location information, emotional data

[0479] Output: Recommended action data

[0480] Step 5: Notifications displayed on the device

[0481] Device:

[0482] It receives recommended actions and campaign information from the server and displays them to users as pop-up or banner notifications, and adjusts the content of notifications based on data from the emotion engine.

[0483] Input: Recommended action data, campaign information, sentiment data

[0484] Output: Popup notification, banner notification

[0485] Step 6: User takes action

[0486] user:

[0487] Acting on notifications from the device, for example, topping up your wallet in response to a top-up suggestion or visiting a specific store to take advantage of a points campaign.

[0488] Input: Notification content

[0489] Output: Actual actions (charges, store visits, etc.)

[0490] At each step, the user, device, and server work together to create a system that supports optimal user behavior. This collaboration improves user convenience and provides more personalized services.

[0491] (Application example 2)

[0492] 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."

[0493] While modern electronic payment services can provide simple recommendations based on user behavior patterns and location information, they lack the ability to provide detailed recommendations and reward information that take into account the user's real-time emotional state. In particular, it is believed that more personalized services can be provided by providing notifications and suggestions based on the user's emotions. Therefore, there is a need for a system that can recommend optimal actions by integrating the use of a user's transaction history, location information, and emotional data.

[0494] 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.

[0495] In this invention, the server includes an artificial intelligence engine for recommending actions based on a user's transaction history and location information, an emotion engine for recognizing emotions, means for collecting the user's transaction history, location information, and emotion data, means having a graphical user interface for displaying recommended actions and campaign information in real time, and means for adjusting the content and display method of notifications based on the emotion engine, thereby making it possible to notify the user of bonus information and recommended actions in real time according to their emotional state.

[0496] "User transaction history" means a record of the series of purchases, payments and financial transactions that a User has made in the past.

[0497] "Location information" is geographical data about a user's current location.

[0498] An "artificial intelligence engine" is a calculation system that analyzes a user's transaction history, location information, etc., and recommends optimal actions.

[0499] The "emotion engine" is a system that recognizes the user's emotional state from facial expressions, voice, etc., and records that data.

[0500] A "graphical user interface" is a system that visually provides an interface with the user, and is a user interface consisting of icons and buttons displayed on a screen.

[0501] "Real-time notifications" are instant information or messages that users receive based on their current situation.

[0502] "Benefit information" is information about benefits that users can receive, such as discounts, double points, coupons, etc.

[0503] "Campaign Information" means information about promotions or events available to users for a specific period or under specific conditions.

[0504] "Wallet Balance" means the total amount of funds currently held in a User's electronic wallet.

[0505] A "top-up suggestion" is a suggestion that recommends a user to top up their wallet with additional funds if their wallet balance is low.

[0506] "Means for adjusting the content and display of notifications" refers to a system for adapting the message and display format of notifications received according to the user's emotional state.

[0507] The present invention provides an electronic payment service system that recommends actions based on a user's transaction history, location information, and emotional data. Specific embodiments of this system are described below.

[0508] Overall structure

[0509] The system includes the following major components:

[0510] 1. Server - Collects user data and makes recommendations using artificial intelligence and emotion engines.

[0511] 2. Terminal - The device that the user operates, such as a smartphone or tablet.

[0512] 3. User - An individual user of the system.

[0513] Initial setup and data acquisition

[0514] server

[0515] The server collects users' transaction history and location information and stores it in a database. It also records emotional data in real time using an emotion engine that recognizes users' emotional state from their facial expressions and voice data.

[0516] The server uses an artificial intelligence engine to analyze the user's transaction history, location information, and emotional data to generate optimal recommended actions.

[0517] Terminal

[0518] When a user installs and launches the application, they are prompted to enter the necessary personal information, which allows the server to retrieve accurate data.

[0519] The device displays recommended actions and campaign information received from the server to the user as a pop-up notification.

[0520] user

[0521] Users can choose actions based on recommended actions and campaign information provided through the device.

[0522] Generate and notify recommended actions

[0523] server

[0524] The server uses an artificial intelligence engine to analyze the user's transaction history, location information, and emotional data, and then runs an algorithm to recommend optimal actions, such as selecting information about a point-double campaign at a specific store or nearby special offers based on the user's current location, past purchase history, and emotional state.

[0525] Terminal

[0526] The terminal displays recommended actions, campaign information, wallet balance and top-up suggestions received from the server to the user as pop-up notifications.

[0527] Specific examples

[0528] Case 1: Morning wallet balance display and top-up suggestion

[0529] When a user launches the app in the morning, the server checks the user's wallet balance and sends a top-up suggestion if necessary.

[0530] The terminal displays the information in real time, and the user can top up as needed.

[0531] Case 2: Notification of a nearby store's double points campaign

[0532] When a user reaches the vicinity of a particular store while out and about, the server recommends a point-double campaign at a nearby store.

[0533] The device will provide real-time recommendations, tailored to the user's emotions as recognized by the emotion engine.

[0534] Case 3: Information about the next day's events before going to bed

[0535] When a user launches the app before going to bed, the server generates information about the next day's events and campaigns and sends it to the device.

[0536] The device will then notify the user of this information, allowing them to adjust their plan of action for the next day.

[0537] Examples of prompt statements

[0538] Example prompt for checking your wallet balance in the morning: "Good morning! Your current wallet balance is 500 yen. Would you like to top up?"

[0539] Example prompt for point campaign notification: "Double points campaign is currently running at XX Cafe!"

[0540] In this way, the present invention significantly improves user convenience, makes the use of electronic payment services a natural part of daily life, and provides appropriate feedback according to the user's emotional state.

[0541] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0542] Step 1:

[0543] The user installs the app and enters personal information (email address, phone number, etc.). This information is sent from the device to the server, and user-specific data is stored in a database. The input data becomes the basis for processing the user's personal authentication, transaction history, and location information. The output is a status indicating that the user has been authenticated.

[0544] Step 2:

[0545] The device periodically communicates with the server to obtain the latest recommended actions and campaign information. This communication sends the latest information on the user's transaction history, location information, and emotional data to the server. The input is a periodic status check, and the output is the latest recommended actions and campaign information.

[0546] Step 3:

[0547] The server collects the user's transaction history, location information, and emotion data and stores them in a database. In this step, a camera or microphone is used to recognize the user's emotion data, and the emotion engine processes the data in real time. The output is user data stored in a database.

[0548] Step 4:

[0549] The server uses an artificial intelligence engine to analyze the collected data (transaction history, location information, emotional data) and generate optimal recommended actions. This analysis uses a generative AI model to learn the user's behavioral patterns and generates recommended actions based on the results. The input is the user's data, and the output is recommended actions.

[0550] Step 5:

[0551] The server sends the recommended actions to the device, where the emotion engine data is used to adjust the notification content and display method. For example, if the user is feeling happy, the notification will be displayed in a friendly tone. The input is the recommended action and emotion data, and the output is the notification content and display format.

[0552] Step 6:

[0553] The device receives recommended actions and campaign information and displays it to the user as a popup notification, where wallet balance and top-up suggestions are also displayed. The input is the information received from the server, and the output is the notification displayed on the user's device.

[0554] Step 7:

[0555] The user selects an action based on the notification. For example, by visiting a recommended store and paying using the app, they can receive rewards and double points. The input is the user's choice, and the output is the transaction and its results.

[0556] Step 8:

[0557] The device resends the transaction information to the server and updates the database, which further personalizes the next recommended action or campaign information. The input is the new transaction data, and the output is the updated user data.

[0558] Through these steps, the system improves user convenience while providing appropriate feedback according to the user's emotional state.

[0559] 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.

[0560] 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.

[0561] 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.

[0562] [Second embodiment]

[0563] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0564] 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.

[0565] 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).

[0566] 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.

[0567] 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.

[0568] 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).

[0569] 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.

[0570] 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.

[0571] 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.

[0572] 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.

[0573] 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.

[0574] 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."

[0575] The present invention is a system centered around an artificial intelligence (AI) engine that recommends actions based on a user's transaction history and location information. This system collects a user's transaction history and location information, and the AI ​​engine generates optimal recommended actions based on that data and notifies the user. Specific embodiments of this system are described below.

[0576] Overall structure

[0577] The system includes the following major components:

[0578] 1. Server - Collects user data and uses an AI engine to make recommendations.

[0579] 2. Terminal - The device that the user operates, such as a smartphone or tablet.

[0580] 3. User - An individual user of the system.

[0581] Initial setup and data acquisition

[0582] user

[0583] When users first install and launch the app, they enter their personal information (email address, phone number, etc.), credit card details, and agree to the terms of use. This information is necessary to obtain accurate user data.

[0584] Terminal

[0585] When users enter their information into the app, the data is sent to the server. After the initial setup is complete, the device will periodically communicate with the server to obtain the latest recommendation behavior and campaign information.

[0586] server

[0587] The server stores the user's transaction history and location information in a database, which is then used by the AI ​​engine to analyze the user's behavioral patterns and generate optimal recommendations.

[0588] Generate and notify recommended actions

[0589] server

[0590] By analyzing the user's transaction history and location information, the AI ​​engine runs an algorithm that recommends the best course of action for that day. For example, it selects information about a point-double campaign at a specific store or nearby special offers based on the user's current location and past purchase history.

[0591] Terminal

[0592] The device receives recommended actions and campaign information from the server and displays them to the user as pop-up notifications. For example, when the device is turned on in the morning, it displays the wallet balance and top-up suggestions, and when the device is out and about, it notifies the user of special offers at nearby stores.

[0593] Specific examples

[0594] Case 1: Morning wallet balance display and top-up suggestion

[0595] A user launches the app in the morning.

[0596] The terminal sends a request to the server and receives the wallet balance and a top-up offer.

[0597] The server checks the user's wallet balance and sends data to the device, including a suggestion if a charge is needed.

[0598] The terminal displays the wallet balance and top-up suggestions on the screen, and the user can top up as needed.

[0599] Case 2: Notification of a nearby store's double points campaign

[0600] When a user reaches a specific time or location while out and about, the server uses that information to recommend point-double campaigns being held at nearby stores.

[0601] The device will notify the user of the recommended actions in real time.

[0602] Users can visit the notified store and make payments using the app to enjoy the double points benefit.

[0603] Case 3: Information about the next day's events before going to bed

[0604] When a user launches the app before going to bed, information about the next day's events and campaigns will be displayed on the device.

[0605] The server generates optimal event information based on the user's past behavioral history and the next day's schedule and sends it to the device.

[0606] The device will then notify the user of this information, allowing them to adjust their actions based on their schedule for the next day.

[0607] In this way, the present invention significantly improves user convenience, allowing the use of payment applications to become a natural part of everyday life.

[0608] The processing flow will be explained below.

[0609] Specific processing steps of the program

[0610] Case 1: Morning wallet balance display and top-up suggestion

[0611] Step 1:

[0612] A user launches the app in the morning.

[0613] Step 2:

[0614] The terminal sends a request to the server for the wallet balance and a top-up proposal.

[0615] Step 3:

[0616] The server retrieves the wallet balance from the database based on the user ID.

[0617] Step 4:

[0618] The server checks the wallet balance and determines if a charge is required.

[0619] Step 5:

[0620] The server generates the wallet balance and, if necessary, a top-up proposal and returns it to the terminal.

[0621] Step 6:

[0622] The terminal displays the received wallet balance and top-up proposal on the user interface.

[0623] Step 7:

[0624] If the user accepts the charge proposal, the charge is carried out.

[0625] ---

[0626] Case 2: Notification of a nearby store's double points campaign

[0627] Step 1:

[0628] The user launches the app while out and about.

[0629] Step 2:

[0630] The device acquires the current location information and sends it to the server.

[0631] Step 3:

[0632] The server uses the user's location information and past transaction history to search for point doubling campaigns currently being held at nearby stores.

[0633] Step 4:

[0634] The server generates optimal campaign information and returns it to the terminal.

[0635] Step 5:

[0636] The device notifies the user of campaign information received in real time.

[0637] Step 6:

[0638] The user checks the notification and takes action based on the campaign information.

[0639] Step 7:

[0640] Users visit participating stores and make payments using the app.

[0641] ---

[0642] Case 3: Information about the next day's events before going to bed

[0643] Step 1:

[0644] The user launches the app before going to bed.

[0645] Step 2:

[0646] The terminal sends a request for next day's event information to the server.

[0647] Step 3:

[0648] The server retrieves the user's past behavior history and the next day's schedule from the database.

[0649] Step 4:

[0650] The server generates optimal event and campaign information for the next day.

[0651] Step 5:

[0652] The server returns the generated event information to the terminal.

[0653] Step 6:

[0654] The event information received by the terminal is displayed on the user interface.

[0655] Step 7:

[0656] The user checks the schedule for the next day and adjusts the schedule if necessary.

[0657] These are the specific processing steps of the program, which allow users to effectively use the payment app and increase convenience in their daily lives.

[0658] Example 1

[0659] 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."

[0660] Users need to be recommended the most appropriate actions at the right time in their daily lives, but current systems have difficulty effectively utilizing transaction history and location information to provide users with the most useful information in real time. Another challenge is providing accurate and useful information while ensuring user privacy and data security.

[0661] 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.

[0662] In this invention, the server includes an artificial intelligence engine for recommending actions based on the user's transaction history and location information, means for collecting the user's transaction history and location information, display means for notifying the user of the recommended actions generated by the artificial intelligence engine, means for the terminal to encrypt the user's input information and transmit it to the server, means for the server to store the user information in a database and generate a user ID, means for the terminal to periodically acquire the user's location information and transaction history and transmit them to the server, means for the server to analyze the data using the AI ​​engine, and means for the server to generate optimal recommended actions based on the analysis results and transmit them to the terminal. This allows users to receive optimal recommended actions in real time in their daily lives, improving their purchasing experience and enabling efficient action planning. Data security and privacy protection are also ensured.

[0663] "User" refers to an individual entity that uses this system.

[0664] "Transaction History" means historical information about purchases and payments made by a User.

[0665] "Location information" refers to data that indicates a user's current location and movement history.

[0666] "Artificial Intelligence Engine" refers to software or a system that contains algorithms for recommending optimal actions based on a user's transaction history and location information.

[0667] "Means for collection" means a method or device for incorporating a user's transaction history and location information into the system.

[0668] "Means for notifying" refers to means for informing the user of the generated recommendation action.

[0669] "Display Means" means a device or software feature for visually presenting information to a user.

[0670] "Device" refers to an electronic device such as a smartphone or tablet operated by a user.

[0671] "Encryption" means a technique or process that transforms data so that it cannot be read by third parties.

[0672] "Server" refers to the remote computer system that stores user information and performs data analysis using an AI engine.

[0673] "Database" means a system for systematically storing data such as user information, transaction history, and location information.

[0674] "User ID" refers to an identifier that uniquely identifies a user.

[0675] "Periodic" means repeated at regular intervals of time.

[0676] "Means of analysis" refers to the methods and technologies used to analyze collected data using an AI engine.

[0677] "Optimal recommended action" means the action or option that is most beneficial to the user and chosen based on specific criteria.

[0678] The present invention is a system centered around an artificial intelligence engine that recommends actions based on a user's transaction history and location information. The system includes the following main components:

[0679] Overall structure

[0680] The system includes the following major components:

[0681] 1. Server - Collects user data and uses an AI engine to make recommendations.

[0682] 2. Device - An electronic device that a user operates, such as a smartphone or tablet.

[0683] 3. User - An individual entity that uses the System.

[0684] Initial setup and data acquisition

[0685] user

[0686] When a user installs and launches the app for the first time, they enter their email address, phone number, credit card information, etc., and agree to the terms of use. This series of operations ensures that the necessary data is accurately collected.

[0687] Terminal

[0688] The information entered by the user is sent from the device to the server. The information is encrypted before being sent. After the initial setup is complete, the device will periodically communicate with the server to obtain the latest recommendation behavior and campaign information.

[0689] server

[0690] The server stores the received user information in a database, and also generates a user ID and creates an account for the user.

[0691] Generate recommended actions

[0692] Terminal

[0693] The device periodically collects the user's location information and records the history of transactions as they occur.

[0694] user

[0695] When a user makes a transaction using the app, details of the transaction (such as date, time, location, and amount) are recorded.

[0696] server

[0697] Transaction history and location information sent from the terminal are continuously received and stored in a database.

[0698] The server runs an AI engine that analyzes users' transaction history and location information, for example, using machine learning models to analyze their purchasing and travel patterns.

[0699] server

[0700] Based on the analysis results, the AI ​​engine generates optimal recommendations, such as double points campaigns at specific stores or events to visit on that day.

[0701] Recommended Action Notifications

[0702] server

[0703] The server transmits the generated recommended actions and campaign information to the terminal.

[0704] Terminal

[0705] The device processes the recommended actions received from the server and presents them to the user as a pop-up notification.

[0706] Specific examples

[0707] Case 1: Morning wallet balance display and top-up suggestion

[0708] user

[0709] Start the app in the morning.

[0710] Terminal

[0711] The terminal sends a request to the server and receives the wallet balance and a top-up offer.

[0712] server

[0713] The server checks the user's wallet balance and, if a charge is required, sends the suggested charge data.

[0714] Terminal

[0715] The terminal will display the wallet balance and top-up suggestions as a pop-up on the screen.

[0716] user

[0717] If necessary, tap the "Charge" button to perform the charging operation.

[0718] Case 2: Notification of a nearby store's double points campaign

[0719] user

[0720] Reach a specific location while out and about.

[0721] Terminal

[0722] The current location is obtained using the GPS function and the location information is sent to the server.

[0723] server

[0724] Analyze and identify point doubling campaigns at nearby stores based on the user's location information and past transaction history.

[0725] server

[0726] Send recommended actions (points double campaign information) to the device.

[0727] Terminal

[0728] The device will notify the user of this information in real time as a pop-up notification.

[0729] user

[0730] Check the notification and head to the store to redeem the offer.

[0731] Case 3: Information about the next day's events before going to bed

[0732] user

[0733] Start the app before you go to bed.

[0734] Terminal

[0735] A request is sent to the server to receive event information and campaign information for the next day.

[0736] server

[0737] Generates optimal event information based on the user's past behavioral history and the next day's schedule.

[0738] server

[0739] The generated event information is sent to the terminal.

[0740] Terminal

[0741] The device notifies the user of the event information.

[0742] user

[0743] Schedule the event for the next day.

[0744] Prompt Sentence Examples

[0745] Describe how when a user launches the app in the morning, the device receives the wallet balance and top-up suggestions from the server.

[0746]

[0747] Please explain the procedure for when a user reaches a specific point, the server sends information about a point double campaign at a nearby store, and the terminal notifies the user of this.

[0748] Such prompts can be used to input specific cases into the generative AI model.

[0749] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0750] Step 1:

[0751] A user launches the app for the first time, enters their email address, phone number, and credit card information, and agrees to the terms of use.

[0752] The terminal encrypts the entered information and sends it to the server.

[0753] Input: User's email address, phone number, credit card information, and agreement to the terms of use.

[0754] Output: Encrypted user information is sent to the server.

[0755] Specific operations: Receives information input through a user interface, encrypts it, and sends an HTTP request to the server.

[0756] Step 2:

[0757] The server stores the received user information in a database and generates a user ID.

[0758] Input: Encrypted user information.

[0759] Output: User information stored in the database and the generated user ID.

[0760] What it does: Stores the information in a database and generates and records a unique user ID.

[0761] Step 3:

[0762] The device periodically collects the user's location information and transaction history and sends them to the server.

[0763] Input: GPS location, details of when the transaction occurred.

[0764] Output: Location information and transaction history are sent to the server.

[0765] Specific operation: The device's GPS function is used to obtain location information, and history information is sent to the server each time a transaction is completed.

[0766] Step 4:

[0767] The server stores the received location information and transaction history in a database.

[0768] Input: Location information and transaction history received from the device.

[0769] Output: Location information and transaction history stored in a database.

[0770] Specific operation: Accurately record the received data in a database and organize it by user.

[0771] Step 5:

[0772] The server runs an AI engine that analyzes the user's transaction history and location information.

[0773] Input: Transaction history and location information stored in a database.

[0774] Output: Action recommendations generated as a result of the analysis.

[0775] What it does: It uses machine learning algorithms to analyze data and identify patterns in user behavior.

[0776] Step 6:

[0777] Based on the analysis results, the server generates optimal recommended actions and sends them to the device.

[0778] Input: Analysis results of the AI ​​engine.

[0779] Output: The recommended action data is sent to the device.

[0780] Specific operation: Based on the analysis results, an optimal action plan is created and sent to the terminal in JSON format.

[0781] Step 7:

[0782] The device processes the recommended actions received from the server and displays them to the user as a pop-up notification.

[0783] Input: Recommended action data sent from the server.

[0784] Output: Recommended action displayed as a popup notification.

[0785] Specific behavior: Analyzes received data and presents it visually through a user interface.

[0786] Step 8:

[0787] The user views the notification and acts on the suggested action displayed.

[0788] Input: The suggested action displayed on the device.

[0789] Output: User actions.

[0790] Specific action: The user checks the notification and performs a specific action, such as visiting a store or topping up.

[0791] (Application example 1)

[0792] 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."

[0793] In today's brick-and-mortar stores, users have limited access to useful information on special offers and campaigns in real time, creating a need for improved user purchasing experiences. There is also a need for systems that effectively utilize users' location information and transaction history to provide optimal recommendations for each individual user. Systems that can solve these issues and improve user convenience are anticipated.

[0794] 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.

[0795] In this invention, the server includes an artificial intelligence engine for recommending actions based on a user's transaction history and location information, means for collecting the user's transaction history and location information, means having a graphical user interface for notifying the user of the recommended actions generated by the artificial intelligence engine, and means for recommending information on benefits and campaigns at physical stores in real time based on the user's current location. This allows the user to obtain optimal information on benefits and campaigns in real time, which is expected to improve the purchasing experience.

[0796] "User" means an individual consumer who uses this system.

[0797] "Transaction history" refers to a record of a user's past purchases and transactions.

[0798] "Location information" refers to data about a user's current location and past travel routes.

[0799] The "artificial intelligence engine for recommending actions" is a system that uses algorithms to analyze transaction history and location information and suggest optimal actions.

[0800] A "graphical user interface" is an application's screen display that allows a user to visually receive information.

[0801] "Benefits and campaign information" refers to information such as discounts, points, and limited-edition products for the purpose of sales promotion.

[0802] A "physical store" is a physical store where users actually visit and purchase products.

[0803] "Real-time recommendation methods" are technologies and mechanisms that instantly provide users with information on special offers and campaigns that are tailored to their current situation.

[0804] Overall structure

[0805] This invention is a system that recommends actions based on a user's transaction history and location information. The main components include a server, a terminal, and a user.

[0806] 1. Server:

[0807] Collection of transaction history and location information: Transaction history and location information are periodically received from users' smartphones and tablets and stored in a database.

[0808] Artificial Intelligence Engine: Analyzes collected data and learns user behavior patterns. This engine uses AI algorithms to generate optimal recommended actions.

[0809] Generate recommended actions: Based on the user's current location, the system processes information on special offers and campaigns in physical stores in real time.

[0810] 2. Terminal:

[0811] Location information acquisition: The user's current location is measured using the GPS sensor built into smartphones and tablets.

[0812] Communication with the server: Sends the user's transaction history and location information to the server, and receives notifications of recommended actions from the server.

[0813] Graphical User Interface: Visually displays recommended actions, rewards information, wallet balance and top-up suggestions to the user.

[0814] 3. User:

[0815] Initial Setup: After installing the application, enter your identity and credit card information.

[0816] Receive and act on notifications: Receive special offers and campaign information and use it to make in-store purchases.

[0817] Explanation of program processing

[0818] 1. Hardware and Software:

[0819] Hardware: Smartphone (with GPS sensor), server

[0820] software:

[0821] geopy: A Python library for obtaining user location information.

[0822] requests: An HTTP request library for communicating with servers.

[0823] 2. Data processing and calculation:

[0824] Server: Based on the user's transaction history and location information, the AI ​​engine generates optimal recommendations, especially for determining special offers and campaign information near the user's current location in real time.

[0825] Device: Receives recommended actions and campaign information sent from the server and notifies the user. Location information is also periodically acquired and sent to the server.

[0826] Specific examples

[0827] For example, if a user is shopping in Tokyo, the smartphone's GPS sensor will acquire their current location and send it to the server. Based on that location information and past transaction history, the server will determine that the user is at a nearby convenience store and send a real-time notification to the device recommending a double points campaign. By receiving this notification, the user can enjoy shopping at a physical store and get great deals.

[0828] Example prompt sentence:

[0829] "You are currently developing an AI assistant that recommends optimal actions based on a user's transaction history and location information. This AI assistant recommends products and campaign information to users in real time while they are shopping in a physical store. Build a scenario in which, while the user is shopping in Tokyo, they are notified of a points double campaign currently being held at a nearby convenience store."

[0830] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0831] Step 1:

[0832] The device receives the user's initial setup information (identity verification information, credit card information). By inputting this initial setup information and sending it to the server, a user account is created and the application can be used. After the initial setup information is sent, an account ID is output.

[0833] Step 2:

[0834] The device acquires the user's location information. In this step, the smartphone's GPS sensor is used to measure the current location and send that location information to the server. The acquired location information is used as input, and location data is output.

[0835] Step 3:

[0836] The server receives the user's transaction history and location information and stores them in a database. The stored transaction history and location information are used as inputs, and behavioral pattern data for each user is output.

[0837] Step 4:

[0838] The AI ​​engine on the server analyzes the stored data and learns each user's behavioral patterns. At this stage, transaction history and location information are used as input, and behavioral recommendations are output as the analysis result.

[0839] Step 5:

[0840] The server's artificial intelligence engine generates information about special offers and campaigns based on the user's current location. In this step, the server inputs the user's current location information and past behavioral pattern data, and outputs the most suitable special offer information.

[0841] Step 6:

[0842] The server sends the generated bonus information to the terminal in real time. This notification information (bonus information) is input and sent to the terminal, which outputs a bonus notification that is displayed to the user.

[0843] Step 7:

[0844] The device notifies the user of the received reward information through a graphical user interface. In this step, the reward information is input and a notification is output to be displayed on the user's smartphone screen.

[0845] Step 8:

[0846] The user receives the notified benefit information and makes a purchase at the physical store based on it. In this step, the notified benefit information is used as input and the purchasing behavior at the physical store is output.

[0847] Step 9:

[0848] After the user finishes shopping, the terminal sends a new transaction history to the server. In this step, the post-purchase transaction data is used as input and the new transaction history to be sent to the server is used as output.

[0849] Step 10:

[0850] The server again saves the new transaction history in the database, and the AI ​​engine updates the data. In this step, the new transaction history is used as input and updated behavioral pattern data is output.

[0851] By repeating this process, the system can improve user convenience.

[0852] 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.

[0853] The present invention is a system that includes an artificial intelligence (AI) engine that recommends actions based on a user's transaction history and location information, and also incorporates an emotion engine that recognizes the user's emotions. This system uses the AI ​​engine to generate optimal recommended actions based on the collected user transaction history, location information, and emotion data, and notifies the user. Specific embodiments of this system are described below.

[0854] Overall structure

[0855] The system includes the following major components:

[0856] 1. Server - Collects user data and makes recommendations using AI and emotion engines.

[0857] 2. Terminal - The device that the user operates, such as a smartphone or tablet.

[0858] 3. User - An individual user of the system.

[0859] Initial setup and data acquisition

[0860] user

[0861] When users first install and launch the app, they enter their personal information (email address, phone number, etc.), credit card details, and agree to the terms of use. This information is necessary to obtain accurate user data.

[0862] Terminal

[0863] When users enter their information into the app, the data is sent to the server. After the initial setup is complete, the device will periodically communicate with the server to obtain the latest recommendation behavior and campaign information.

[0864] server

[0865] The server stores the user's transaction history and location information in a database. It also records the user's real-time emotional state using an emotion engine that recognizes emotions from the user's facial expressions and voice data. Based on this, the AI ​​engine analyzes the user's behavioral patterns and generates optimal recommendations.

[0866] Generate and notify recommended actions

[0867] server

[0868] By analyzing a user's transaction history, location information, and emotional data, the AI ​​engine runs an algorithm that recommends the best course of action for that day. For example, it selects information about a point-double campaign at a specific store or nearby special offers based on the user's current location, past purchase history, and emotional state.

[0869] Terminal

[0870] The device receives recommended actions and campaign information from the server and displays them to the user as pop-up notifications. For example, when the device is turned on in the morning, it displays the wallet balance and top-up suggestions, and when the device is out and about, it notifies the user of special offers at nearby stores. The content and display method of notifications may also be adjusted depending on the user's emotional state.

[0871] Specific examples

[0872] Case 1: Morning wallet balance display and top-up suggestion

[0873] A user launches the app in the morning.

[0874] The terminal sends a request to the server and receives the wallet balance and a top-up offer.

[0875] The server checks the user's wallet balance and sends data to the device, including a suggestion if a charge is needed.

[0876] The terminal displays the wallet balance and top-up suggestions on the screen, and the user can top up as needed.

[0877] Case 2: Notification of a nearby store's double points campaign

[0878] When a user reaches a specific time or location while out and about, the server uses that information to recommend point-double campaigns being held at nearby stores.

[0879] The device will notify the user of the recommended actions in real time, and the notification will be adjusted based on the user's emotions as recognized by the emotion engine. For example, if the user is feeling happy, the notification will be displayed in a friendly tone.

[0880] Users can visit the notified store and make payments using the app to enjoy the double points benefit.

[0881] Case 3: Information about the next day's events before going to bed

[0882] When a user launches the app before going to bed, information about the next day's events and campaigns will be displayed on the device.

[0883] The server generates optimal event information based on the user's past behavior history, the next day's schedule, and data collected by the emotion engine, and sends it to the device. This information is also customized according to the user's emotions.

[0884] The device will then notify the user of this information, allowing them to adjust their actions based on their schedule for the next day.

[0885] In this way, the present invention significantly improves user convenience, integrates the use of payment applications into everyday life, and provides appropriate feedback based on the user's emotional state.

[0886] The processing flow will be explained below.

[0887] Specific processing steps of the program

[0888] Case 1: Morning wallet balance display and top-up suggestion

[0889] Step 1:

[0890] A user launches the app in the morning.

[0891] Step 2:

[0892] The terminal sends a request to the server for the wallet balance and a top-up proposal.

[0893] Step 3:

[0894] The server retrieves the wallet balance from the database based on the user ID.

[0895] Step 4:

[0896] The server checks the wallet balance and determines if a charge is required.

[0897] Step 5:

[0898] The server generates the wallet balance and, if necessary, a top-up proposal and returns it to the terminal.

[0899] Step 6:

[0900] The terminal displays the received wallet balance and top-up proposal on the user interface.

[0901] Step 7:

[0902] If the user accepts the charge proposal, the charge is carried out.

[0903] ---

[0904] Case 2: Notification of a nearby store's double points campaign

[0905] Step 1:

[0906] The user launches the app while out and about.

[0907] Step 2:

[0908] The device acquires the current location information and sends it to the server.

[0909] Step 3:

[0910] The server uses the user's location information, past transaction history, and data from the emotion engine to search for point doubling campaigns currently being held at nearby stores.

[0911] Step 4:

[0912] The server generates optimal campaign information and returns it to the terminal.

[0913] Step 5:

[0914] The device notifies users in real time about campaign information received, and the notifications are tailored based on the user's emotions as recognized by the emotion engine.

[0915] Step 6:

[0916] The user checks the notification and takes action based on the campaign information.

[0917] Step 7:

[0918] Users visit participating stores and make payments using the app.

[0919] ---

[0920] Case 3: Information about the next day's events before going to bed

[0921] Step 1:

[0922] The user launches the app before going to bed.

[0923] Step 2:

[0924] The terminal sends a request for next day's event information to the server.

[0925] Step 3:

[0926] The server generates optimal event and campaign information for the next day based on the user's past behavioral history and schedule for the next day, as well as data collected by the emotion engine.

[0927] Step 4:

[0928] The server returns the generated event information to the terminal.

[0929] Step 5:

[0930] The device displays the event information it receives in the user interface, which is also customized according to the user's emotions.

[0931] Step 6:

[0932] The user checks the schedule for the next day and adjusts the schedule if necessary.

[0933] These are the specific processing steps of the program, which allow users to effectively use the payment app and increase convenience in their daily lives.

[0934] Example 2

[0935] 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."

[0936] Conventional behavior recommendation systems based on a user's transaction history and location information are unable to take into account the user's emotional state, making it difficult to provide optimal recommended behaviors. Furthermore, they lacked a mechanism for adjusting notification content based on the user's emotional state and providing information at a more appropriate time and in a more appropriate manner. As a result, user convenience and satisfaction were not sufficiently improved.

[0937] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes an artificial intelligence engine means for recommending actions based on a user's transaction history and location information, a means for collecting the user's transaction history and location information, an emotion engine means for recognizing and analyzing the user's emotional data, and a means having a graphical user interface for notifying the user of recommended actions generated by the artificial intelligence engine and emotion engine. This makes it possible to comprehensively analyze the user's transaction history, location information, and emotional data and provide the user with recommended actions that are optimal for the user. In addition, the recommended actions and notification content can be customized according to the user's emotional state, significantly improving user convenience and satisfaction.

[0938] "User transaction history" refers to recorded information about purchases and financial transactions that a user has made in the past.

[0939] "Location Information" means geographic coordinate information obtained based on a user's device.

[0940] An "artificial intelligence engine" is a part of a system that uses machine learning algorithms to analyze data and generate actionable recommendations for users.

[0941] "Emotional data" is information about the psychological state of a user that can be read from facial expressions, voice, etc.

[0942] The "emotion engine" is the part of the system that recognizes and analyzes the user's emotional data.

[0943] "Recommended actions" are specific action suggestions generated by the artificial intelligence engine based on a user's transaction history, location information, and emotional data.

[0944] A "graphical user interface" is a screen display means for visually presenting information to a user via a terminal.

[0945] "Wallet Balance" means the total amount of funds currently available in a User's Digital Wallet.

[0946] A "top-up suggestion" is a recommendation to top up additional funds if your wallet balance is low.

[0947] "Benefit information" refers to information including benefits such as campaigns and discounts offered to users.

[0948] MODE FOR CARRYING OUT THE INVENTION

[0949] The system of the present invention is equipped with a complex AI engine and emotion engine that recommends optimal actions using a user's transaction history, location information, and emotion data. This system is composed of three elements: a server, a terminal, and a user, and each element functions as follows:

[0950] Initial setup and data acquisition

[0951] user:

[0952] When you first install the app, you launch it, enter your identity verification information (email address, phone number, etc.), credit card information, and agree to the terms of use, which allows us to collect accurate user data.

[0953] Device:

[0954] The information entered by the user is temporarily stored on the device and sent to the server. The device is set to periodically send location information and transaction history to the server.

[0955] server:

[0956] The server stores the user's input information in a database, periodically updates the user's transaction history and location information, and analyzes the user's facial expressions and voice data using an emotion engine, saving the data as emotion data.

[0957] Generate and notify recommended actions

[0958] server:

[0959] An artificial intelligence engine analyzes users' transaction history, location information, and real-time sentiment data to generate optimal recommendations, such as identifying points-double campaigns at specific stores or nearby special offers.

[0960] Device:

[0961] The recommended actions and campaign information received from the server are displayed to the user as pop-up or banner notifications, and the content of the notifications may be adjusted based on the user's emotional state.

[0962] Specific examples

[0963] Case 1: Morning wallet balance display and top-up suggestion

[0964] user:

[0965] Start the app in the morning.

[0966] Device:

[0967] Sends a request to the server and receives the wallet balance and top-up proposal.

[0968] server:

[0969] Checks the user's wallet balance, creates a top-up proposal if necessary, and sends the data to the terminal.

[0970] Device:

[0971] The wallet balance and top-up suggestions are displayed on the screen, and the user can top up as needed.

[0972] Case 2: Notification of a nearby store's double points campaign

[0973] user:

[0974] Reaching a specific time or location while out and about.

[0975] server:

[0976] Based on this information, the system recommends to users points doubling campaigns being held at nearby stores.

[0977] Device:

[0978] The recommended actions are notified to the user in real time, and the emotion engine ensures that notifications are displayed in a friendly tone when the user is feeling happy.

[0979] user:

[0980] Visit the notified store, pay using the app, and enjoy the double points bonus.

[0981] Case 3: Information about the next day's events before going to bed

[0982] user:

[0983] Start the app before you go to bed.

[0984] Device:

[0985] Information about events and campaigns for the next day will be displayed.

[0986] server:

[0987] Based on the user's past behavioral history and schedule for the next day, the system generates optimal event information and sends it to the device. This information is customized by the emotion engine.

[0988] Device:

[0989] This information is communicated to the user, allowing them to adjust their actions based on their schedule for the next day.

[0990] Prompt Sentence Examples

[0991] Below are some example prompts from a generative AI model:

[0992] 1. "How can I display my wallet balance in the morning and suggest topping up if needed?"

[0993] 2. "Please explain the process for notifying users on the go about a nearby store's double points promotion."

[0994] 3. "Please explain in detail how users can receive the next day's event information before going to bed."

[0995] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0996] Step 1: First-time user registration

[0997] user:

[0998] After installing the app for the first time, launch the app, enter your identity verification information (email address, phone number, etc.) and credit card information, and agree to the terms of use. Input is done through text boxes, etc.

[0999] Device:

[1000] The entered information is temporarily stored on the device. When the user presses the "Send" button, this data is sent to the server.

[1001] Input: Personal information, credit card information, agreement to terms of use

[1002] Output: Data packet sent to the server

[1003] Step 2: Send data by device

[1004] Device:

[1005] The user's input information is sent to the server using a secure communication protocol (e.g., HTTPS), and the user's location information is also obtained using GPS functionality.

[1006] server:

[1007] The received data is analyzed to check for omissions or errors. If there are no problems, it is saved in the database.

[1008] Input: User registration information, location information

[1009] Output: User information and location information stored on the server

[1010] Step 3: Data storage and analysis on the server

[1011] server:

[1012] The user's transaction history and location information are stored in a database. The user's facial expressions and voice data are analyzed by an emotion engine and stored as emotion data.

[1013] Input: Transaction history, location information, facial expression and voice data

[1014] Output: Stored transaction history, location information, and sentiment data

[1015] Step 4: Generate action recommendations using the AI ​​engine

[1016] server:

[1017] The artificial intelligence engine analyzes stored transaction history, location, and sentiment data and uses machine learning algorithms to generate optimal action recommendations.

[1018] Input: Transaction history, location information, emotional data

[1019] Output: Recommended action data

[1020] Step 5: Notifications displayed on the device

[1021] Device:

[1022] It receives recommended actions and campaign information from the server and displays them to users as pop-up or banner notifications, and adjusts the content of notifications based on data from the emotion engine.

[1023] Input: Recommended action data, campaign information, sentiment data

[1024] Output: Popup notification, banner notification

[1025] Step 6: User takes action

[1026] user:

[1027] Acting on notifications from the device, for example, topping up your wallet in response to a top-up suggestion or visiting a specific store to take advantage of a points campaign.

[1028] Input: Notification content

[1029] Output: Actual actions (charges, store visits, etc.)

[1030] At each step, the user, device, and server work together to create a system that supports optimal user behavior. This collaboration improves user convenience and provides more personalized services.

[1031] (Application example 2)

[1032] 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."

[1033] While modern electronic payment services can provide simple recommendations based on user behavior patterns and location information, they lack the ability to provide detailed recommendations and reward information that take into account the user's real-time emotional state. In particular, it is believed that more personalized services can be provided by providing notifications and suggestions based on the user's emotions. Therefore, there is a need for a system that can recommend optimal actions by integrating the use of a user's transaction history, location information, and emotional data.

[1034] 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.

[1035] In this invention, the server includes an artificial intelligence engine for recommending actions based on a user's transaction history and location information, an emotion engine for recognizing emotions, means for collecting the user's transaction history, location information, and emotion data, means having a graphical user interface for displaying recommended actions and campaign information in real time, and means for adjusting the content and display method of notifications based on the emotion engine, thereby making it possible to notify the user of bonus information and recommended actions in real time according to their emotional state.

[1036] "User transaction history" means a record of the series of purchases, payments and financial transactions that a User has made in the past.

[1037] "Location information" is geographical data about a user's current location.

[1038] An "artificial intelligence engine" is a calculation system that analyzes a user's transaction history, location information, etc., and recommends optimal actions.

[1039] The "emotion engine" is a system that recognizes the user's emotional state from facial expressions, voice, etc., and records that data.

[1040] A "graphical user interface" is a system that visually provides an interface with the user, and is a user interface consisting of icons and buttons displayed on a screen.

[1041] "Real-time notifications" are instant information or messages that users receive based on their current situation.

[1042] "Benefit information" is information about benefits that users can receive, such as discounts, double points, coupons, etc.

[1043] "Campaign Information" means information about promotions or events available to users for a specific period or under specific conditions.

[1044] "Wallet Balance" means the total amount of funds currently held in a User's electronic wallet.

[1045] A "top-up suggestion" is a suggestion that recommends a user to top up their wallet with additional funds if their wallet balance is low.

[1046] "Means for adjusting the content and display of notifications" refers to a system for adapting the message and display format of notifications received according to the user's emotional state.

[1047] The present invention provides an electronic payment service system that recommends actions based on a user's transaction history, location information, and emotional data. Specific embodiments of this system are described below.

[1048] Overall structure

[1049] The system includes the following major components:

[1050] 1. Server - Collects user data and makes recommendations using artificial intelligence and emotion engines.

[1051] 2. Terminal - The device that the user operates, such as a smartphone or tablet.

[1052] 3. User - An individual user of the system.

[1053] Initial setup and data acquisition

[1054] server

[1055] The server collects users' transaction history and location information and stores it in a database. It also records emotional data in real time using an emotion engine that recognizes users' emotional state from their facial expressions and voice data.

[1056] The server uses an artificial intelligence engine to analyze the user's transaction history, location information, and emotional data to generate optimal recommended actions.

[1057] Terminal

[1058] When a user installs and launches the application, they are prompted to enter the necessary personal information, which allows the server to retrieve accurate data.

[1059] The device displays recommended actions and campaign information received from the server to the user as a pop-up notification.

[1060] user

[1061] Users can choose actions based on recommended actions and campaign information provided through the device.

[1062] Generate and notify recommended actions

[1063] server

[1064] The server uses an artificial intelligence engine to analyze the user's transaction history, location information, and emotional data, and then runs an algorithm to recommend optimal actions, such as selecting information about a point-double campaign at a specific store or nearby special offers based on the user's current location, past purchase history, and emotional state.

[1065] Terminal

[1066] The terminal displays recommended actions, campaign information, wallet balance and top-up suggestions received from the server to the user as pop-up notifications.

[1067] Specific examples

[1068] Case 1: Morning wallet balance display and top-up suggestion

[1069] When a user launches the app in the morning, the server checks the user's wallet balance and sends a top-up suggestion if necessary.

[1070] The terminal displays the information in real time, and the user can top up as needed.

[1071] Case 2: Notification of a nearby store's double points campaign

[1072] When a user reaches the vicinity of a particular store while out and about, the server recommends a point-double campaign at a nearby store.

[1073] The device will provide real-time recommendations, tailored to the user's emotions as recognized by the emotion engine.

[1074] Case 3: Information about the next day's events before going to bed

[1075] When a user launches the app before going to bed, the server generates information about the next day's events and campaigns and sends it to the device.

[1076] The device will then notify the user of this information, allowing them to adjust their plan of action for the next day.

[1077] Examples of prompt statements

[1078] Example prompt for checking your wallet balance in the morning: "Good morning! Your current wallet balance is 500 yen. Would you like to top up?"

[1079] Example prompt for point campaign notification: "Double points campaign is currently running at XX Cafe!"

[1080] In this way, the present invention significantly improves user convenience, makes the use of electronic payment services a natural part of daily life, and provides appropriate feedback according to the user's emotional state.

[1081] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1082] Step 1:

[1083] The user installs the app and enters personal information (email address, phone number, etc.). This information is sent from the device to the server, and user-specific data is stored in a database. The input data becomes the basis for processing the user's personal authentication, transaction history, and location information. The output is a status indicating that the user has been authenticated.

[1084] Step 2:

[1085] The device periodically communicates with the server to obtain the latest recommended actions and campaign information. This communication sends the latest information on the user's transaction history, location information, and emotional data to the server. The input is a periodic status check, and the output is the latest recommended actions and campaign information.

[1086] Step 3:

[1087] The server collects the user's transaction history, location information, and emotion data and stores them in a database. In this step, a camera or microphone is used to recognize the user's emotion data, and the emotion engine processes the data in real time. The output is user data stored in a database.

[1088] Step 4:

[1089] The server uses an artificial intelligence engine to analyze the collected data (transaction history, location information, emotional data) and generate optimal recommended actions. This analysis uses a generative AI model to learn the user's behavioral patterns and generates recommended actions based on the results. The input is the user's data, and the output is recommended actions.

[1090] Step 5:

[1091] The server sends the recommended actions to the device, where the emotion engine data is used to adjust the notification content and display method. For example, if the user is feeling happy, the notification will be displayed in a friendly tone. The input is the recommended action and emotion data, and the output is the notification content and display format.

[1092] Step 6:

[1093] The device receives recommended actions and campaign information and displays it to the user as a popup notification, where wallet balance and top-up suggestions are also displayed. The input is the information received from the server, and the output is the notification displayed on the user's device.

[1094] Step 7:

[1095] The user selects an action based on the notification. For example, by visiting a recommended store and paying using the app, they can receive rewards and double points. The input is the user's choice, and the output is the transaction and its results.

[1096] Step 8:

[1097] The device resends the transaction information to the server and updates the database, which further personalizes the next recommended action or campaign information. The input is the new transaction data, and the output is the updated user data.

[1098] Through these steps, the system improves user convenience while providing appropriate feedback according to the user's emotional state.

[1099] 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.

[1100] 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.

[1101] 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.

[1102] [Third embodiment]

[1103] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1104] 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.

[1105] 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).

[1106] 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.

[1107] 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.

[1108] 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).

[1109] 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.

[1110] 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.

[1111] 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.

[1112] 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.

[1113] 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.

[1114] 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."

[1115] The present invention is a system centered around an artificial intelligence (AI) engine that recommends actions based on a user's transaction history and location information. This system collects a user's transaction history and location information, and the AI ​​engine generates optimal recommended actions based on that data and notifies the user. Specific embodiments of this system are described below.

[1116] Overall structure

[1117] The system includes the following major components:

[1118] 1. Server - Collects user data and uses an AI engine to make recommendations.

[1119] 2. Terminal - The device that the user operates, such as a smartphone or tablet.

[1120] 3. User - An individual user of the system.

[1121] Initial setup and data acquisition

[1122] user

[1123] When users first install and launch the app, they enter their personal information (email address, phone number, etc.), credit card details, and agree to the terms of use. This information is necessary to obtain accurate user data.

[1124] Terminal

[1125] When users enter their information into the app, the data is sent to the server. After the initial setup is complete, the device will periodically communicate with the server to obtain the latest recommendation behavior and campaign information.

[1126] server

[1127] The server stores the user's transaction history and location information in a database, which is then used by the AI ​​engine to analyze the user's behavioral patterns and generate optimal recommendations.

[1128] Generate and notify recommended actions

[1129] server

[1130] By analyzing the user's transaction history and location information, the AI ​​engine runs an algorithm that recommends the best course of action for that day. For example, it selects information about a point-double campaign at a specific store or nearby special offers based on the user's current location and past purchase history.

[1131] Terminal

[1132] The device receives recommended actions and campaign information from the server and displays them to the user as pop-up notifications. For example, when the device is turned on in the morning, it displays the wallet balance and top-up suggestions, and when the device is out and about, it notifies the user of special offers at nearby stores.

[1133] Specific examples

[1134] Case 1: Morning wallet balance display and top-up suggestion

[1135] A user launches the app in the morning.

[1136] The terminal sends a request to the server and receives the wallet balance and a top-up offer.

[1137] The server checks the user's wallet balance and sends data to the device, including a suggestion if a charge is needed.

[1138] The terminal displays the wallet balance and top-up suggestions on the screen, and the user can top up as needed.

[1139] Case 2: Notification of a nearby store's double points campaign

[1140] When a user reaches a specific time or location while out and about, the server uses that information to recommend point-double campaigns being held at nearby stores.

[1141] The device will notify the user of the recommended actions in real time.

[1142] Users can visit the notified store and make payments using the app to enjoy the double points benefit.

[1143] Case 3: Information about the next day's events before going to bed

[1144] When a user launches the app before going to bed, information about the next day's events and campaigns will be displayed on the device.

[1145] The server generates optimal event information based on the user's past behavioral history and the next day's schedule and sends it to the device.

[1146] The device will then notify the user of this information, allowing them to adjust their actions based on their schedule for the next day.

[1147] In this way, the present invention significantly improves user convenience, allowing the use of payment applications to become a natural part of everyday life.

[1148] The processing flow will be explained below.

[1149] Specific processing steps of the program

[1150] Case 1: Morning wallet balance display and top-up suggestion

[1151] Step 1:

[1152] A user launches the app in the morning.

[1153] Step 2:

[1154] The terminal sends a request to the server for the wallet balance and a top-up proposal.

[1155] Step 3:

[1156] The server retrieves the wallet balance from the database based on the user ID.

[1157] Step 4:

[1158] The server checks the wallet balance and determines if a charge is required.

[1159] Step 5:

[1160] The server generates the wallet balance and, if necessary, a top-up proposal and returns it to the terminal.

[1161] Step 6:

[1162] The terminal displays the received wallet balance and top-up proposal on the user interface.

[1163] Step 7:

[1164] If the user accepts the charge proposal, the charge is carried out.

[1165] ---

[1166] Case 2: Notification of a nearby store's double points campaign

[1167] Step 1:

[1168] The user launches the app while out and about.

[1169] Step 2:

[1170] The device acquires the current location information and sends it to the server.

[1171] Step 3:

[1172] The server uses the user's location information and past transaction history to search for point doubling campaigns currently being held at nearby stores.

[1173] Step 4:

[1174] The server generates optimal campaign information and returns it to the terminal.

[1175] Step 5:

[1176] The device notifies the user of campaign information received in real time.

[1177] Step 6:

[1178] The user checks the notification and takes action based on the campaign information.

[1179] Step 7:

[1180] Users visit participating stores and make payments using the app.

[1181] ---

[1182] Case 3: Information about the next day's events before going to bed

[1183] Step 1:

[1184] The user launches the app before going to bed.

[1185] Step 2:

[1186] The terminal sends a request for next day's event information to the server.

[1187] Step 3:

[1188] The server retrieves the user's past behavior history and the next day's schedule from the database.

[1189] Step 4:

[1190] The server generates optimal event and campaign information for the next day.

[1191] Step 5:

[1192] The server returns the generated event information to the terminal.

[1193] Step 6:

[1194] The event information received by the terminal is displayed on the user interface.

[1195] Step 7:

[1196] The user checks the schedule for the next day and adjusts the schedule if necessary.

[1197] These are the specific processing steps of the program, which allow users to effectively use the payment app and increase convenience in their daily lives.

[1198] Example 1

[1199] 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."

[1200] Users need to be recommended the most appropriate actions at the right time in their daily lives, but current systems have difficulty effectively utilizing transaction history and location information to provide users with the most useful information in real time. Another challenge is providing accurate and useful information while ensuring user privacy and data security.

[1201] 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.

[1202] In this invention, the server includes an artificial intelligence engine for recommending actions based on the user's transaction history and location information, means for collecting the user's transaction history and location information, display means for notifying the user of the recommended actions generated by the artificial intelligence engine, means for the terminal to encrypt the user's input information and transmit it to the server, means for the server to store the user information in a database and generate a user ID, means for the terminal to periodically acquire the user's location information and transaction history and transmit them to the server, means for the server to analyze the data using the AI ​​engine, and means for the server to generate optimal recommended actions based on the analysis results and transmit them to the terminal. This allows users to receive optimal recommended actions in real time in their daily lives, improving their purchasing experience and enabling efficient action planning. Data security and privacy protection are also ensured.

[1203] "User" refers to an individual entity that uses this system.

[1204] "Transaction History" means historical information about purchases and payments made by a User.

[1205] "Location information" refers to data that indicates a user's current location and movement history.

[1206] "Artificial Intelligence Engine" refers to software or a system that contains algorithms for recommending optimal actions based on a user's transaction history and location information.

[1207] "Means for collection" means a method or device for incorporating a user's transaction history and location information into the system.

[1208] "Means for notifying" refers to means for informing the user of the generated recommendation action.

[1209] "Display Means" means a device or software feature for visually presenting information to a user.

[1210] "Device" refers to an electronic device such as a smartphone or tablet operated by a user.

[1211] "Encryption" means a technique or process that transforms data so that it cannot be read by third parties.

[1212] "Server" refers to the remote computer system that stores user information and performs data analysis using an AI engine.

[1213] "Database" means a system for systematically storing data such as user information, transaction history, and location information.

[1214] "User ID" refers to an identifier that uniquely identifies a user.

[1215] "Periodic" means repeated at regular intervals of time.

[1216] "Means of analysis" refers to the methods and technologies used to analyze collected data using an AI engine.

[1217] "Optimal recommended action" means the action or option that is most beneficial to the user and chosen based on specific criteria.

[1218] The present invention is a system centered around an artificial intelligence engine that recommends actions based on a user's transaction history and location information. The system includes the following main components:

[1219] Overall structure

[1220] The system includes the following major components:

[1221] 1. Server - Collects user data and uses an AI engine to make recommendations.

[1222] 2. Device - An electronic device that a user operates, such as a smartphone or tablet.

[1223] 3. User - An individual entity that uses the System.

[1224] Initial setup and data acquisition

[1225] user

[1226] When a user installs and launches the app for the first time, they enter their email address, phone number, credit card information, etc., and agree to the terms of use. This series of operations ensures that the necessary data is accurately collected.

[1227] Terminal

[1228] The information entered by the user is sent from the device to the server. The information is encrypted before being sent. After the initial setup is complete, the device will periodically communicate with the server to obtain the latest recommendation behavior and campaign information.

[1229] server

[1230] The server stores the received user information in a database, and also generates a user ID and creates an account for the user.

[1231] Generate recommended actions

[1232] Terminal

[1233] The device periodically collects the user's location information and records the history of transactions as they occur.

[1234] user

[1235] When a user makes a transaction using the app, details of the transaction (such as date, time, location, and amount) are recorded.

[1236] server

[1237] Transaction history and location information sent from the terminal are continuously received and stored in a database.

[1238] The server runs an AI engine that analyzes users' transaction history and location information, for example, using machine learning models to analyze their purchasing and travel patterns.

[1239] server

[1240] Based on the analysis results, the AI ​​engine generates optimal recommendations, such as double points campaigns at specific stores or events to visit on that day.

[1241] Recommended Action Notifications

[1242] server

[1243] The server transmits the generated recommended actions and campaign information to the terminal.

[1244] Terminal

[1245] The device processes the recommended actions received from the server and presents them to the user as a pop-up notification.

[1246] Specific examples

[1247] Case 1: Morning wallet balance display and top-up suggestion

[1248] user

[1249] Start the app in the morning.

[1250] Terminal

[1251] The terminal sends a request to the server and receives the wallet balance and a top-up offer.

[1252] server

[1253] The server checks the user's wallet balance and, if a charge is required, sends the suggested charge data.

[1254] Terminal

[1255] The terminal will display the wallet balance and top-up suggestions as a pop-up on the screen.

[1256] user

[1257] If necessary, tap the "Charge" button to perform the charging operation.

[1258] Case 2: Notification of a nearby store's double points campaign

[1259] user

[1260] Reach a specific location while out and about.

[1261] Terminal

[1262] The current location is obtained using the GPS function and the location information is sent to the server.

[1263] server

[1264] Analyze and identify point doubling campaigns at nearby stores based on the user's location information and past transaction history.

[1265] server

[1266] Send recommended actions (points double campaign information) to the device.

[1267] Terminal

[1268] The device will notify the user of this information in real time as a pop-up notification.

[1269] user

[1270] Check the notification and head to the store to redeem the offer.

[1271] Case 3: Information about the next day's events before going to bed

[1272] user

[1273] Start the app before you go to bed.

[1274] Terminal

[1275] A request is sent to the server to receive event information and campaign information for the next day.

[1276] server

[1277] Generates optimal event information based on the user's past behavioral history and the next day's schedule.

[1278] server

[1279] The generated event information is sent to the terminal.

[1280] Terminal

[1281] The device notifies the user of the event information.

[1282] user

[1283] Schedule the event for the next day.

[1284] Prompt Sentence Examples

[1285] Describe how when a user launches the app in the morning, the device receives the wallet balance and top-up suggestions from the server.

[1286]

[1287] Please explain the procedure for when a user reaches a specific point, the server sends information about a point double campaign at a nearby store, and the terminal notifies the user of this.

[1288] Such prompts can be used to input specific cases into the generative AI model.

[1289] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1290] Step 1:

[1291] A user launches the app for the first time, enters their email address, phone number, and credit card information, and agrees to the terms of use.

[1292] The terminal encrypts the entered information and sends it to the server.

[1293] Input: User's email address, phone number, credit card information, and agreement to the terms of use.

[1294] Output: Encrypted user information is sent to the server.

[1295] Specific operations: Receives information input through a user interface, encrypts it, and sends an HTTP request to the server.

[1296] Step 2:

[1297] The server stores the received user information in a database and generates a user ID.

[1298] Input: Encrypted user information.

[1299] Output: User information stored in the database and the generated user ID.

[1300] What it does: Stores the information in a database and generates and records a unique user ID.

[1301] Step 3:

[1302] The device periodically collects the user's location information and transaction history and sends them to the server.

[1303] Input: GPS location, details of when the transaction occurred.

[1304] Output: Location information and transaction history are sent to the server.

[1305] Specific operation: The device's GPS function is used to obtain location information, and history information is sent to the server each time a transaction is completed.

[1306] Step 4:

[1307] The server stores the received location information and transaction history in a database.

[1308] Input: Location information and transaction history received from the device.

[1309] Output: Location information and transaction history stored in a database.

[1310] Specific operation: Accurately record the received data in a database and organize it by user.

[1311] Step 5:

[1312] The server runs an AI engine that analyzes the user's transaction history and location information.

[1313] Input: Transaction history and location information stored in a database.

[1314] Output: Action recommendations generated as a result of the analysis.

[1315] What it does: It uses machine learning algorithms to analyze data and identify patterns in user behavior.

[1316] Step 6:

[1317] Based on the analysis results, the server generates optimal recommended actions and sends them to the device.

[1318] Input: Analysis results of the AI ​​engine.

[1319] Output: The recommended action data is sent to the device.

[1320] Specific operation: Based on the analysis results, an optimal action plan is created and sent to the terminal in JSON format.

[1321] Step 7:

[1322] The device processes the recommended actions received from the server and displays them to the user as a pop-up notification.

[1323] Input: Recommended action data sent from the server.

[1324] Output: Recommended action displayed as a popup notification.

[1325] Specific behavior: Analyzes received data and presents it visually through a user interface.

[1326] Step 8:

[1327] The user views the notification and acts on the suggested action displayed.

[1328] Input: The suggested action displayed on the device.

[1329] Output: User actions.

[1330] Specific action: The user checks the notification and performs a specific action, such as visiting a store or topping up.

[1331] (Application example 1)

[1332] 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."

[1333] In today's brick-and-mortar stores, users have limited access to useful information on special offers and campaigns in real time, creating a need for improved user purchasing experiences. There is also a need for systems that effectively utilize users' location information and transaction history to provide optimal recommendations for each individual user. Systems that can solve these issues and improve user convenience are anticipated.

[1334] 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.

[1335] In this invention, the server includes an artificial intelligence engine for recommending actions based on a user's transaction history and location information, means for collecting the user's transaction history and location information, means having a graphical user interface for notifying the user of the recommended actions generated by the artificial intelligence engine, and means for recommending information on benefits and campaigns at physical stores in real time based on the user's current location. This allows the user to obtain optimal information on benefits and campaigns in real time, which is expected to improve the purchasing experience.

[1336] "User" means an individual consumer who uses this system.

[1337] "Transaction history" refers to a record of a user's past purchases and transactions.

[1338] "Location information" refers to data about a user's current location and past travel routes.

[1339] The "artificial intelligence engine for recommending actions" is a system that uses algorithms to analyze transaction history and location information and suggest optimal actions.

[1340] A "graphical user interface" is an application's screen display that allows a user to visually receive information.

[1341] "Benefits and campaign information" refers to information such as discounts, points, and limited-edition products for the purpose of sales promotion.

[1342] A "physical store" is a physical store where users actually visit and purchase products.

[1343] "Real-time recommendation methods" are technologies and mechanisms that instantly provide users with information on special offers and campaigns that are tailored to their current situation.

[1344] Overall structure

[1345] This invention is a system that recommends actions based on a user's transaction history and location information. The main components include a server, a terminal, and a user.

[1346] 1. Server:

[1347] Collection of transaction history and location information: Transaction history and location information are periodically received from users' smartphones and tablets and stored in a database.

[1348] Artificial Intelligence Engine: Analyzes collected data and learns user behavior patterns. This engine uses AI algorithms to generate optimal recommended actions.

[1349] Generate recommended actions: Based on the user's current location, the system processes information on special offers and campaigns in physical stores in real time.

[1350] 2. Terminal:

[1351] Location information acquisition: The user's current location is measured using the GPS sensor built into smartphones and tablets.

[1352] Communication with the server: Sends the user's transaction history and location information to the server, and receives notifications of recommended actions from the server.

[1353] Graphical User Interface: Visually displays recommended actions, rewards information, wallet balance and top-up suggestions to the user.

[1354] 3. User:

[1355] Initial Setup: After installing the application, enter your identity and credit card information.

[1356] Receive and act on notifications: Receive special offers and campaign information and use it to make in-store purchases.

[1357] Explanation of program processing

[1358] 1. Hardware and Software:

[1359] Hardware: Smartphone (with GPS sensor), server

[1360] software:

[1361] geopy: A Python library for obtaining user location information.

[1362] requests: An HTTP request library for communicating with servers.

[1363] 2. Data processing and calculation:

[1364] Server: Based on the user's transaction history and location information, the AI ​​engine generates optimal recommendations, especially for determining special offers and campaign information near the user's current location in real time.

[1365] Device: Receives recommended actions and campaign information sent from the server and notifies the user. Location information is also periodically acquired and sent to the server.

[1366] Specific examples

[1367] For example, if a user is shopping in Tokyo, the smartphone's GPS sensor will acquire their current location and send it to the server. Based on that location information and past transaction history, the server will determine that the user is at a nearby convenience store and send a real-time notification to the device recommending a double points campaign. By receiving this notification, the user can enjoy shopping at a physical store and get great deals.

[1368] Example prompt sentence:

[1369] "You are currently developing an AI assistant that recommends optimal actions based on a user's transaction history and location information. This AI assistant recommends products and campaign information to users in real time while they are shopping in a physical store. Build a scenario in which, while the user is shopping in Tokyo, they are notified of a points double campaign currently being held at a nearby convenience store."

[1370] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1371] Step 1:

[1372] The device receives the user's initial setup information (identity verification information, credit card information). By inputting this initial setup information and sending it to the server, a user account is created and the application can be used. After the initial setup information is sent, an account ID is output.

[1373] Step 2:

[1374] The device acquires the user's location information. In this step, the smartphone's GPS sensor is used to measure the current location and send that location information to the server. The acquired location information is used as input, and location data is output.

[1375] Step 3:

[1376] The server receives the user's transaction history and location information and stores them in a database. The stored transaction history and location information are used as inputs, and behavioral pattern data for each user is output.

[1377] Step 4:

[1378] The AI ​​engine on the server analyzes the stored data and learns each user's behavioral patterns. At this stage, transaction history and location information are used as input, and behavioral recommendations are output as the analysis result.

[1379] Step 5:

[1380] The server's artificial intelligence engine generates information about special offers and campaigns based on the user's current location. In this step, the server inputs the user's current location information and past behavioral pattern data, and outputs the most suitable special offer information.

[1381] Step 6:

[1382] The server sends the generated bonus information to the terminal in real time. This notification information (bonus information) is input and sent to the terminal, which outputs a bonus notification that is displayed to the user.

[1383] Step 7:

[1384] The device notifies the user of the received reward information through a graphical user interface. In this step, the reward information is input and a notification is output to be displayed on the user's smartphone screen.

[1385] Step 8:

[1386] The user receives the notified benefit information and makes a purchase at the physical store based on it. In this step, the notified benefit information is used as input and the purchasing behavior at the physical store is output.

[1387] Step 9:

[1388] After the user finishes shopping, the terminal sends a new transaction history to the server. In this step, the post-purchase transaction data is used as input and the new transaction history to be sent to the server is used as output.

[1389] Step 10:

[1390] The server again saves the new transaction history in the database, and the AI ​​engine updates the data. In this step, the new transaction history is used as input and updated behavioral pattern data is output.

[1391] By repeating this process, the system can improve user convenience.

[1392] 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.

[1393] The present invention is a system that includes an artificial intelligence (AI) engine that recommends actions based on a user's transaction history and location information, and also incorporates an emotion engine that recognizes the user's emotions. This system uses the AI ​​engine to generate optimal recommended actions based on the collected user transaction history, location information, and emotion data, and notifies the user. Specific embodiments of this system are described below.

[1394] Overall structure

[1395] The system includes the following major components:

[1396] 1. Server - Collects user data and makes recommendations using AI and emotion engines.

[1397] 2. Terminal - The device that the user operates, such as a smartphone or tablet.

[1398] 3. User - An individual user of the system.

[1399] Initial setup and data acquisition

[1400] user

[1401] When users first install and launch the app, they enter their personal information (email address, phone number, etc.), credit card details, and agree to the terms of use. This information is necessary to obtain accurate user data.

[1402] Terminal

[1403] When users enter their information into the app, the data is sent to the server. After the initial setup is complete, the device will periodically communicate with the server to obtain the latest recommendation behavior and campaign information.

[1404] server

[1405] The server stores the user's transaction history and location information in a database. It also records the user's real-time emotional state using an emotion engine that recognizes emotions from the user's facial expressions and voice data. Based on this, the AI ​​engine analyzes the user's behavioral patterns and generates optimal recommendations.

[1406] Generate and notify recommended actions

[1407] server

[1408] By analyzing a user's transaction history, location information, and emotional data, the AI ​​engine runs an algorithm that recommends the best course of action for that day. For example, it selects information about a point-double campaign at a specific store or nearby special offers based on the user's current location, past purchase history, and emotional state.

[1409] Terminal

[1410] The device receives recommended actions and campaign information from the server and displays them to the user as pop-up notifications. For example, when the device is turned on in the morning, it displays the wallet balance and top-up suggestions, and when the device is out and about, it notifies the user of special offers at nearby stores. The content and display method of notifications may also be adjusted depending on the user's emotional state.

[1411] Specific examples

[1412] Case 1: Morning wallet balance display and top-up suggestion

[1413] A user launches the app in the morning.

[1414] The terminal sends a request to the server and receives the wallet balance and a top-up offer.

[1415] The server checks the user's wallet balance and sends data to the device, including a suggestion if a charge is needed.

[1416] The terminal displays the wallet balance and top-up suggestions on the screen, and the user can top up as needed.

[1417] Case 2: Notification of a nearby store's double points campaign

[1418] When a user reaches a specific time or location while out and about, the server uses that information to recommend point-double campaigns being held at nearby stores.

[1419] The device will notify the user of the recommended actions in real time, and the notification will be adjusted based on the user's emotions as recognized by the emotion engine. For example, if the user is feeling happy, the notification will be displayed in a friendly tone.

[1420] Users can visit the notified store and make payments using the app to enjoy the double points benefit.

[1421] Case 3: Information about the next day's events before going to bed

[1422] When a user launches the app before going to bed, information about the next day's events and campaigns will be displayed on the device.

[1423] The server generates optimal event information based on the user's past behavior history, the next day's schedule, and data collected by the emotion engine, and sends it to the device. This information is also customized according to the user's emotions.

[1424] The device will then notify the user of this information, allowing them to adjust their actions based on their schedule for the next day.

[1425] In this way, the present invention significantly improves user convenience, integrates the use of payment applications into everyday life, and provides appropriate feedback based on the user's emotional state.

[1426] The processing flow will be explained below.

[1427] Specific processing steps of the program

[1428] Case 1: Morning wallet balance display and top-up suggestion

[1429] Step 1:

[1430] A user launches the app in the morning.

[1431] Step 2:

[1432] The terminal sends a request to the server for the wallet balance and a top-up proposal.

[1433] Step 3:

[1434] The server retrieves the wallet balance from the database based on the user ID.

[1435] Step 4:

[1436] The server checks the wallet balance and determines if a charge is required.

[1437] Step 5:

[1438] The server generates the wallet balance and, if necessary, a top-up proposal and returns it to the terminal.

[1439] Step 6:

[1440] The terminal displays the received wallet balance and top-up proposal on the user interface.

[1441] Step 7:

[1442] If the user accepts the charge proposal, the charge is carried out.

[1443] ---

[1444] Case 2: Notification of a nearby store's double points campaign

[1445] Step 1:

[1446] The user launches the app while out and about.

[1447] Step 2:

[1448] The device acquires the current location information and sends it to the server.

[1449] Step 3:

[1450] The server uses the user's location information, past transaction history, and data from the emotion engine to search for point doubling campaigns currently being held at nearby stores.

[1451] Step 4:

[1452] The server generates optimal campaign information and returns it to the terminal.

[1453] Step 5:

[1454] The device notifies users in real time about campaign information received, and the notifications are tailored based on the user's emotions as recognized by the emotion engine.

[1455] Step 6:

[1456] The user checks the notification and takes action based on the campaign information.

[1457] Step 7:

[1458] Users visit participating stores and make payments using the app.

[1459] ---

[1460] Case 3: Information about the next day's events before going to bed

[1461] Step 1:

[1462] The user launches the app before going to bed.

[1463] Step 2:

[1464] The terminal sends a request for next day's event information to the server.

[1465] Step 3:

[1466] The server generates optimal event and campaign information for the next day based on the user's past behavioral history and schedule for the next day, as well as data collected by the emotion engine.

[1467] Step 4:

[1468] The server returns the generated event information to the terminal.

[1469] Step 5:

[1470] The device displays the event information it receives in the user interface, which is also customized according to the user's emotions.

[1471] Step 6:

[1472] The user checks the schedule for the next day and adjusts the schedule if necessary.

[1473] These are the specific processing steps of the program, which allow users to effectively use the payment app and increase convenience in their daily lives.

[1474] Example 2

[1475] 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."

[1476] Conventional behavior recommendation systems based on a user's transaction history and location information are unable to take into account the user's emotional state, making it difficult to provide optimal recommended behaviors. Furthermore, they lacked a mechanism for adjusting notification content based on the user's emotional state and providing information at a more appropriate time and in a more appropriate manner. As a result, user convenience and satisfaction were not sufficiently improved.

[1477] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes an artificial intelligence engine means for recommending actions based on a user's transaction history and location information, a means for collecting the user's transaction history and location information, an emotion engine means for recognizing and analyzing the user's emotional data, and a means having a graphical user interface for notifying the user of recommended actions generated by the artificial intelligence engine and emotion engine. This makes it possible to comprehensively analyze the user's transaction history, location information, and emotional data and provide the user with recommended actions that are optimal for the user. In addition, the recommended actions and notification content can be customized according to the user's emotional state, significantly improving user convenience and satisfaction.

[1478] "User transaction history" refers to recorded information about purchases and financial transactions that a user has made in the past.

[1479] "Location Information" means geographic coordinate information obtained based on a user's device.

[1480] An "artificial intelligence engine" is a part of a system that uses machine learning algorithms to analyze data and generate actionable recommendations for users.

[1481] "Emotional data" is information about the psychological state of a user that can be read from facial expressions, voice, etc.

[1482] The "emotion engine" is the part of the system that recognizes and analyzes the user's emotional data.

[1483] "Recommended actions" are specific action suggestions generated by the artificial intelligence engine based on a user's transaction history, location information, and emotional data.

[1484] A "graphical user interface" is a screen display means for visually presenting information to a user via a terminal.

[1485] "Wallet Balance" means the total amount of funds currently available in a User's Digital Wallet.

[1486] A "top-up suggestion" is a recommendation to top up additional funds if your wallet balance is low.

[1487] "Benefit information" refers to information including benefits such as campaigns and discounts offered to users.

[1488] MODE FOR CARRYING OUT THE INVENTION

[1489] The system of the present invention is equipped with a complex AI engine and emotion engine that recommends optimal actions using a user's transaction history, location information, and emotion data. This system is composed of three elements: a server, a terminal, and a user, and each element functions as follows:

[1490] Initial setup and data acquisition

[1491] user:

[1492] When you first install the app, you launch it, enter your identity verification information (email address, phone number, etc.), credit card information, and agree to the terms of use, which allows us to collect accurate user data.

[1493] Device:

[1494] The information entered by the user is temporarily stored on the device and sent to the server. The device is set to periodically send location information and transaction history to the server.

[1495] server:

[1496] The server stores the user's input information in a database, periodically updates the user's transaction history and location information, and analyzes the user's facial expressions and voice data using an emotion engine, saving the data as emotion data.

[1497] Generate and notify recommended actions

[1498] server:

[1499] An artificial intelligence engine analyzes users' transaction history, location information, and real-time sentiment data to generate optimal recommendations, such as identifying points-double campaigns at specific stores or nearby special offers.

[1500] Device:

[1501] The recommended actions and campaign information received from the server are displayed to the user as pop-up or banner notifications, and the content of the notifications may be adjusted based on the user's emotional state.

[1502] Specific examples

[1503] Case 1: Morning wallet balance display and top-up suggestion

[1504] user:

[1505] Start the app in the morning.

[1506] Device:

[1507] Sends a request to the server and receives the wallet balance and top-up proposal.

[1508] server:

[1509] Checks the user's wallet balance, creates a top-up proposal if necessary, and sends the data to the terminal.

[1510] Device:

[1511] The wallet balance and top-up suggestions are displayed on the screen, and the user can top up as needed.

[1512] Case 2: Notification of a nearby store's double points campaign

[1513] user:

[1514] Reaching a specific time or location while out and about.

[1515] server:

[1516] Based on this information, the system recommends to users points doubling campaigns being held at nearby stores.

[1517] Device:

[1518] The recommended actions are notified to the user in real time, and the emotion engine ensures that notifications are displayed in a friendly tone when the user is feeling happy.

[1519] user:

[1520] Visit the notified store, pay using the app, and enjoy the double points bonus.

[1521] Case 3: Information about the next day's events before going to bed

[1522] user:

[1523] Start the app before you go to bed.

[1524] Device:

[1525] Information about events and campaigns for the next day will be displayed.

[1526] server:

[1527] Based on the user's past behavioral history and schedule for the next day, the system generates optimal event information and sends it to the device. This information is customized by the emotion engine.

[1528] Device:

[1529] This information is communicated to the user, allowing them to adjust their actions based on their schedule for the next day.

[1530] Prompt Sentence Examples

[1531] Below are some example prompts from a generative AI model:

[1532] 1. "How can I display my wallet balance in the morning and suggest topping up if needed?"

[1533] 2. "Please explain the process for notifying users on the go about a nearby store's double points promotion."

[1534] 3. "Please explain in detail how users can receive the next day's event information before going to bed."

[1535] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1536] Step 1: First-time user registration

[1537] user:

[1538] After installing the app for the first time, launch the app, enter your identity verification information (email address, phone number, etc.) and credit card information, and agree to the terms of use. Input is done through text boxes, etc.

[1539] Device:

[1540] The entered information is temporarily stored on the device. When the user presses the "Send" button, this data is sent to the server.

[1541] Input: Personal information, credit card information, agreement to terms of use

[1542] Output: Data packet sent to the server

[1543] Step 2: Send data by device

[1544] Device:

[1545] The user's input information is sent to the server using a secure communication protocol (e.g., HTTPS), and the user's location information is also obtained using GPS functionality.

[1546] server:

[1547] The received data is analyzed to check for omissions or errors. If there are no problems, it is saved in the database.

[1548] Input: User registration information, location information

[1549] Output: User information and location information stored on the server

[1550] Step 3: Data storage and analysis on the server

[1551] server:

[1552] The user's transaction history and location information are stored in a database. The user's facial expressions and voice data are analyzed by an emotion engine and stored as emotion data.

[1553] Input: Transaction history, location information, facial expression and voice data

[1554] Output: Stored transaction history, location information, and sentiment data

[1555] Step 4: Generate action recommendations using the AI ​​engine

[1556] server:

[1557] The artificial intelligence engine analyzes stored transaction history, location, and sentiment data and uses machine learning algorithms to generate optimal action recommendations.

[1558] Input: Transaction history, location information, emotional data

[1559] Output: Recommended action data

[1560] Step 5: Notifications displayed on the device

[1561] Device:

[1562] It receives recommended actions and campaign information from the server and displays them to users as pop-up or banner notifications, and adjusts the content of notifications based on data from the emotion engine.

[1563] Input: Recommended action data, campaign information, sentiment data

[1564] Output: Popup notification, banner notification

[1565] Step 6: User takes action

[1566] user:

[1567] Acting on notifications from the device, for example, topping up your wallet in response to a top-up suggestion or visiting a specific store to take advantage of a points campaign.

[1568] Input: Notification content

[1569] Output: Actual actions (charges, store visits, etc.)

[1570] At each step, the user, device, and server work together to create a system that supports optimal user behavior. This collaboration improves user convenience and provides more personalized services.

[1571] (Application example 2)

[1572] 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."

[1573] While modern electronic payment services can provide simple recommendations based on user behavior patterns and location information, they lack the ability to provide detailed recommendations and reward information that take into account the user's real-time emotional state. In particular, it is believed that more personalized services can be provided by providing notifications and suggestions based on the user's emotions. Therefore, there is a need for a system that can recommend optimal actions by integrating the use of a user's transaction history, location information, and emotional data.

[1574] 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.

[1575] In this invention, the server includes an artificial intelligence engine for recommending actions based on a user's transaction history and location information, an emotion engine for recognizing emotions, means for collecting the user's transaction history, location information, and emotion data, means having a graphical user interface for displaying recommended actions and campaign information in real time, and means for adjusting the content and display method of notifications based on the emotion engine, thereby making it possible to notify the user of bonus information and recommended actions in real time according to their emotional state.

[1576] "User transaction history" means a record of the series of purchases, payments and financial transactions that a User has made in the past.

[1577] "Location information" is geographical data about a user's current location.

[1578] An "artificial intelligence engine" is a calculation system that analyzes a user's transaction history, location information, etc., and recommends optimal actions.

[1579] The "emotion engine" is a system that recognizes the user's emotional state from facial expressions, voice, etc., and records that data.

[1580] A "graphical user interface" is a system that visually provides an interface with the user, and is a user interface consisting of icons and buttons displayed on a screen.

[1581] "Real-time notifications" are instant information or messages that users receive based on their current situation.

[1582] "Benefit information" is information about benefits that users can receive, such as discounts, double points, coupons, etc.

[1583] "Campaign Information" means information about promotions or events available to users for a specific period or under specific conditions.

[1584] "Wallet Balance" means the total amount of funds currently held in a User's electronic wallet.

[1585] A "top-up suggestion" is a suggestion that recommends a user to top up their wallet with additional funds if their wallet balance is low.

[1586] "Means for adjusting the content and display of notifications" refers to a system for adapting the message and display format of notifications received according to the user's emotional state.

[1587] The present invention provides an electronic payment service system that recommends actions based on a user's transaction history, location information, and emotional data. Specific embodiments of this system are described below.

[1588] Overall structure

[1589] The system includes the following major components:

[1590] 1. Server - Collects user data and makes recommendations using artificial intelligence and emotion engines.

[1591] 2. Terminal - The device that the user operates, such as a smartphone or tablet.

[1592] 3. User - An individual user of the system.

[1593] Initial setup and data acquisition

[1594] server

[1595] The server collects users' transaction history and location information and stores it in a database. It also records emotional data in real time using an emotion engine that recognizes users' emotional state from their facial expressions and voice data.

[1596] The server uses an artificial intelligence engine to analyze the user's transaction history, location information, and emotional data to generate optimal recommended actions.

[1597] Terminal

[1598] When a user installs and launches the application, they are prompted to enter the necessary personal information, which allows the server to retrieve accurate data.

[1599] The device displays recommended actions and campaign information received from the server to the user as a pop-up notification.

[1600] user

[1601] Users can choose actions based on recommended actions and campaign information provided through the device.

[1602] Generate and notify recommended actions

[1603] server

[1604] The server uses an artificial intelligence engine to analyze the user's transaction history, location information, and emotional data, and then runs an algorithm to recommend optimal actions, such as selecting information about a point-double campaign at a specific store or nearby special offers based on the user's current location, past purchase history, and emotional state.

[1605] Terminal

[1606] The terminal displays recommended actions, campaign information, wallet balance and top-up suggestions received from the server to the user as pop-up notifications.

[1607] Specific examples

[1608] Case 1: Morning wallet balance display and top-up suggestion

[1609] When a user launches the app in the morning, the server checks the user's wallet balance and sends a top-up suggestion if necessary.

[1610] The terminal displays the information in real time, and the user can top up as needed.

[1611] Case 2: Notification of a nearby store's double points campaign

[1612] When a user reaches the vicinity of a particular store while out and about, the server recommends a point-double campaign at a nearby store.

[1613] The device will provide real-time recommendations, tailored to the user's emotions as recognized by the emotion engine.

[1614] Case 3: Information about the next day's events before going to bed

[1615] When a user launches the app before going to bed, the server generates information about the next day's events and campaigns and sends it to the device.

[1616] The device will then notify the user of this information, allowing them to adjust their plan of action for the next day.

[1617] Examples of prompt statements

[1618] Example prompt for checking your wallet balance in the morning: "Good morning! Your current wallet balance is 500 yen. Would you like to top up?"

[1619] Example prompt for point campaign notification: "Double points campaign is currently running at XX Cafe!"

[1620] In this way, the present invention significantly improves user convenience, makes the use of electronic payment services a natural part of daily life, and provides appropriate feedback according to the user's emotional state.

[1621] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1622] Step 1:

[1623] The user installs the app and enters personal information (email address, phone number, etc.). This information is sent from the device to the server, and user-specific data is stored in a database. The input data becomes the basis for processing the user's personal authentication, transaction history, and location information. The output is a status indicating that the user has been authenticated.

[1624] Step 2:

[1625] The device periodically communicates with the server to obtain the latest recommended actions and campaign information. This communication sends the latest information on the user's transaction history, location information, and emotional data to the server. The input is a periodic status check, and the output is the latest recommended actions and campaign information.

[1626] Step 3:

[1627] The server collects the user's transaction history, location information, and emotion data and stores them in a database. In this step, a camera or microphone is used to recognize the user's emotion data, and the emotion engine processes the data in real time. The output is user data stored in a database.

[1628] Step 4:

[1629] The server uses an artificial intelligence engine to analyze the collected data (transaction history, location information, emotional data) and generate optimal recommended actions. This analysis uses a generative AI model to learn the user's behavioral patterns and generates recommended actions based on the results. The input is the user's data, and the output is recommended actions.

[1630] Step 5:

[1631] The server sends the recommended actions to the device, where the emotion engine data is used to adjust the notification content and display method. For example, if the user is feeling happy, the notification will be displayed in a friendly tone. The input is the recommended action and emotion data, and the output is the notification content and display format.

[1632] Step 6:

[1633] The device receives recommended actions and campaign information and displays it to the user as a popup notification, where wallet balance and top-up suggestions are also displayed. The input is the information received from the server, and the output is the notification displayed on the user's device.

[1634] Step 7:

[1635] The user selects an action based on the notification. For example, by visiting a recommended store and paying using the app, they can receive rewards and double points. The input is the user's choice, and the output is the transaction and its results.

[1636] Step 8:

[1637] The device resends the transaction information to the server and updates the database, which further personalizes the next recommended action or campaign information. The input is the new transaction data, and the output is the updated user data.

[1638] Through these steps, the system improves user convenience while providing appropriate feedback according to the user's emotional state.

[1639] 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.

[1640] 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.

[1641] 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.

[1642] [Fourth embodiment]

[1643] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1644] 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.

[1645] 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).

[1646] 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.

[1647] 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.

[1648] 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).

[1649] 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.

[1650] 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.

[1651] 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.

[1652] 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.

[1653] 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.

[1654] 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.

[1655] 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."

[1656] The present invention is a system centered around an artificial intelligence (AI) engine that recommends actions based on a user's transaction history and location information. This system collects a user's transaction history and location information, and the AI ​​engine generates optimal recommended actions based on that data and notifies the user. Specific embodiments of this system are described below.

[1657] Overall structure

[1658] The system includes the following major components:

[1659] 1. Server - Collects user data and uses an AI engine to make recommendations.

[1660] 2. Terminal - The device that the user operates, such as a smartphone or tablet.

[1661] 3. User - An individual user of the system.

[1662] Initial setup and data acquisition

[1663] user

[1664] When users first install and launch the app, they enter their personal information (email address, phone number, etc.), credit card details, and agree to the terms of use. This information is necessary to obtain accurate user data.

[1665] Terminal

[1666] When users enter their information into the app, the data is sent to the server. After the initial setup is complete, the device will periodically communicate with the server to obtain the latest recommendation behavior and campaign information.

[1667] server

[1668] The server stores the user's transaction history and location information in a database, which is then used by the AI ​​engine to analyze the user's behavioral patterns and generate optimal recommendations.

[1669] Generate and notify recommended actions

[1670] server

[1671] By analyzing the user's transaction history and location information, the AI ​​engine runs an algorithm that recommends the best course of action for that day. For example, it selects information about a point-double campaign at a specific store or nearby special offers based on the user's current location and past purchase history.

[1672] Terminal

[1673] The device receives recommended actions and campaign information from the server and displays them to the user as pop-up notifications. For example, when the device is turned on in the morning, it displays the wallet balance and top-up suggestions, and when the device is out and about, it notifies the user of special offers at nearby stores.

[1674] Specific examples

[1675] Case 1: Morning wallet balance display and top-up suggestion

[1676] A user launches the app in the morning.

[1677] The terminal sends a request to the server and receives the wallet balance and a top-up offer.

[1678] The server checks the user's wallet balance and sends data to the device, including a suggestion if a charge is needed.

[1679] The terminal displays the wallet balance and top-up suggestions on the screen, and the user can top up as needed.

[1680] Case 2: Notification of a nearby store's double points campaign

[1681] When a user reaches a specific time or location while out and about, the server uses that information to recommend point-double campaigns being held at nearby stores.

[1682] The device will notify the user of the recommended actions in real time.

[1683] Users can visit the notified store and make payments using the app to enjoy the double points benefit.

[1684] Case 3: Information about the next day's events before going to bed

[1685] When a user launches the app before going to bed, information about the next day's events and campaigns will be displayed on the device.

[1686] The server generates optimal event information based on the user's past behavioral history and the next day's schedule and sends it to the device.

[1687] The device will then notify the user of this information, allowing them to adjust their actions based on their schedule for the next day.

[1688] In this way, the present invention significantly improves user convenience, allowing the use of payment applications to become a natural part of everyday life.

[1689] The processing flow will be explained below.

[1690] Specific processing steps of the program

[1691] Case 1: Morning wallet balance display and top-up suggestion

[1692] Step 1:

[1693] A user launches the app in the morning.

[1694] Step 2:

[1695] The terminal sends a request to the server for the wallet balance and a top-up proposal.

[1696] Step 3:

[1697] The server retrieves the wallet balance from the database based on the user ID.

[1698] Step 4:

[1699] The server checks the wallet balance and determines if a charge is required.

[1700] Step 5:

[1701] The server generates the wallet balance and, if necessary, a top-up proposal and returns it to the terminal.

[1702] Step 6:

[1703] The terminal displays the received wallet balance and top-up proposal on the user interface.

[1704] Step 7:

[1705] If the user accepts the charge proposal, the charge is carried out.

[1706] ---

[1707] Case 2: Notification of a nearby store's double points campaign

[1708] Step 1:

[1709] The user launches the app while out and about.

[1710] Step 2:

[1711] The device acquires the current location information and sends it to the server.

[1712] Step 3:

[1713] The server uses the user's location information and past transaction history to search for point doubling campaigns currently being held at nearby stores.

[1714] Step 4:

[1715] The server generates optimal campaign information and returns it to the terminal.

[1716] Step 5:

[1717] The device notifies the user of campaign information received in real time.

[1718] Step 6:

[1719] The user checks the notification and takes action based on the campaign information.

[1720] Step 7:

[1721] Users visit participating stores and make payments using the app.

[1722] ---

[1723] Case 3: Information about the next day's events before going to bed

[1724] Step 1:

[1725] The user launches the app before going to bed.

[1726] Step 2:

[1727] The terminal sends a request for next day's event information to the server.

[1728] Step 3:

[1729] The server retrieves the user's past behavior history and the next day's schedule from the database.

[1730] Step 4:

[1731] The server generates optimal event and campaign information for the next day.

[1732] Step 5:

[1733] The server returns the generated event information to the terminal.

[1734] Step 6:

[1735] The event information received by the terminal is displayed on the user interface.

[1736] Step 7:

[1737] The user checks the schedule for the next day and adjusts the schedule if necessary.

[1738] These are the specific processing steps of the program, which allow users to effectively use the payment app and increase convenience in their daily lives.

[1739] Example 1

[1740] 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."

[1741] Users need to be recommended the most appropriate actions at the right time in their daily lives, but current systems have difficulty effectively utilizing transaction history and location information to provide users with the most useful information in real time. Another challenge is providing accurate and useful information while ensuring user privacy and data security.

[1742] 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.

[1743] In this invention, the server includes an artificial intelligence engine for recommending actions based on the user's transaction history and location information, means for collecting the user's transaction history and location information, display means for notifying the user of the recommended actions generated by the artificial intelligence engine, means for the terminal to encrypt the user's input information and transmit it to the server, means for the server to store the user information in a database and generate a user ID, means for the terminal to periodically acquire the user's location information and transaction history and transmit them to the server, means for the server to analyze the data using the AI ​​engine, and means for the server to generate optimal recommended actions based on the analysis results and transmit them to the terminal. This allows users to receive optimal recommended actions in real time in their daily lives, improving their purchasing experience and enabling efficient action planning. Data security and privacy protection are also ensured.

[1744] "User" refers to an individual entity that uses this system.

[1745] "Transaction History" means historical information about purchases and payments made by a User.

[1746] "Location information" refers to data that indicates a user's current location and movement history.

[1747] "Artificial Intelligence Engine" refers to software or a system that contains algorithms for recommending optimal actions based on a user's transaction history and location information.

[1748] "Means for collection" means a method or device for incorporating a user's transaction history and location information into the system.

[1749] "Means for notifying" refers to means for informing the user of the generated recommendation action.

[1750] "Display Means" means a device or software feature for visually presenting information to a user.

[1751] "Device" refers to an electronic device such as a smartphone or tablet operated by a user.

[1752] "Encryption" means a technique or process that transforms data so that it cannot be read by third parties.

[1753] "Server" refers to the remote computer system that stores user information and performs data analysis using an AI engine.

[1754] "Database" means a system for systematically storing data such as user information, transaction history, and location information.

[1755] "User ID" refers to an identifier that uniquely identifies a user.

[1756] "Periodic" means repeated at regular intervals of time.

[1757] "Means of analysis" refers to the methods and technologies used to analyze collected data using an AI engine.

[1758] "Optimal recommended action" means the action or option that is most beneficial to the user and chosen based on specific criteria.

[1759] The present invention is a system centered around an artificial intelligence engine that recommends actions based on a user's transaction history and location information. The system includes the following main components:

[1760] Overall structure

[1761] The system includes the following major components:

[1762] 1. Server - Collects user data and uses an AI engine to make recommendations.

[1763] 2. Device - An electronic device that a user operates, such as a smartphone or tablet.

[1764] 3. User - An individual entity that uses the System.

[1765] Initial setup and data acquisition

[1766] user

[1767] When a user installs and launches the app for the first time, they enter their email address, phone number, credit card information, etc., and agree to the terms of use. This series of operations ensures that the necessary data is accurately collected.

[1768] Terminal

[1769] The information entered by the user is sent from the device to the server. The information is encrypted before being sent. After the initial setup is complete, the device will periodically communicate with the server to obtain the latest recommendation behavior and campaign information.

[1770] server

[1771] The server stores the received user information in a database, and also generates a user ID and creates an account for the user.

[1772] Generate recommended actions

[1773] Terminal

[1774] The device periodically collects the user's location information and records the history of transactions as they occur.

[1775] user

[1776] When a user makes a transaction using the app, details of the transaction (such as date, time, location, and amount) are recorded.

[1777] server

[1778] Transaction history and location information sent from the terminal are continuously received and stored in a database.

[1779] The server runs an AI engine that analyzes users' transaction history and location information, for example, using machine learning models to analyze their purchasing and travel patterns.

[1780] server

[1781] Based on the analysis results, the AI ​​engine generates optimal recommendations, such as double points campaigns at specific stores or events to visit on that day.

[1782] Recommended Action Notifications

[1783] server

[1784] The server transmits the generated recommended actions and campaign information to the terminal.

[1785] Terminal

[1786] The device processes the recommended actions received from the server and presents them to the user as a pop-up notification.

[1787] Specific examples

[1788] Case 1: Morning wallet balance display and top-up suggestion

[1789] user

[1790] Start the app in the morning.

[1791] Terminal

[1792] The terminal sends a request to the server and receives the wallet balance and a top-up offer.

[1793] server

[1794] The server checks the user's wallet balance and, if a charge is required, sends the suggested charge data.

[1795] Terminal

[1796] The terminal will display the wallet balance and top-up suggestions as a pop-up on the screen.

[1797] user

[1798] If necessary, tap the "Charge" button to perform the charging operation.

[1799] Case 2: Notification of a nearby store's double points campaign

[1800] user

[1801] Reach a specific location while out and about.

[1802] Terminal

[1803] The current location is obtained using the GPS function and the location information is sent to the server.

[1804] server

[1805] Analyze and identify point doubling campaigns at nearby stores based on the user's location information and past transaction history.

[1806] server

[1807] Send recommended actions (points double campaign information) to the device.

[1808] Terminal

[1809] The device will notify the user of this information in real time as a pop-up notification.

[1810] user

[1811] Check the notification and head to the store to redeem the offer.

[1812] Case 3: Information about the next day's events before going to bed

[1813] user

[1814] Start the app before you go to bed.

[1815] Terminal

[1816] A request is sent to the server to receive event information and campaign information for the next day.

[1817] server

[1818] Generates optimal event information based on the user's past behavioral history and the next day's schedule.

[1819] server

[1820] The generated event information is sent to the terminal.

[1821] Terminal

[1822] The device notifies the user of the event information.

[1823] user

[1824] Schedule the event for the next day.

[1825] Prompt Sentence Examples

[1826] Describe how when a user launches the app in the morning, the device receives the wallet balance and top-up suggestions from the server.

[1827]

[1828] Please explain the procedure for when a user reaches a specific point, the server sends information about a point double campaign at a nearby store, and the terminal notifies the user of this.

[1829] Such prompts can be used to input specific cases into the generative AI model.

[1830] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1831] Step 1:

[1832] A user launches the app for the first time, enters their email address, phone number, and credit card information, and agrees to the terms of use.

[1833] The terminal encrypts the entered information and sends it to the server.

[1834] Input: User's email address, phone number, credit card information, and agreement to the terms of use.

[1835] Output: Encrypted user information is sent to the server.

[1836] Specific operations: Receives information input through a user interface, encrypts it, and sends an HTTP request to the server.

[1837] Step 2:

[1838] The server stores the received user information in a database and generates a user ID.

[1839] Input: Encrypted user information.

[1840] Output: User information stored in the database and the generated user ID.

[1841] What it does: Stores the information in a database and generates and records a unique user ID.

[1842] Step 3:

[1843] The device periodically collects the user's location information and transaction history and sends them to the server.

[1844] Input: GPS location, details of when the transaction occurred.

[1845] Output: Location information and transaction history are sent to the server.

[1846] Specific operation: The device's GPS function is used to obtain location information, and history information is sent to the server each time a transaction is completed.

[1847] Step 4:

[1848] The server stores the received location information and transaction history in a database.

[1849] Input: Location information and transaction history received from the device.

[1850] Output: Location information and transaction history stored in a database.

[1851] Specific operation: Accurately record the received data in a database and organize it by user.

[1852] Step 5:

[1853] The server runs an AI engine that analyzes the user's transaction history and location information.

[1854] Input: Transaction history and location information stored in a database.

[1855] Output: Action recommendations generated as a result of the analysis.

[1856] What it does: It uses machine learning algorithms to analyze data and identify patterns in user behavior.

[1857] Step 6:

[1858] Based on the analysis results, the server generates optimal recommended actions and sends them to the device.

[1859] Input: Analysis results of the AI ​​engine.

[1860] Output: The recommended action data is sent to the device.

[1861] Specific operation: Based on the analysis results, an optimal action plan is created and sent to the terminal in JSON format.

[1862] Step 7:

[1863] The device processes the recommended actions received from the server and displays them to the user as a pop-up notification.

[1864] Input: Recommended action data sent from the server.

[1865] Output: Recommended action displayed as a popup notification.

[1866] Specific behavior: Analyzes received data and presents it visually through a user interface.

[1867] Step 8:

[1868] The user views the notification and acts on the suggested action displayed.

[1869] Input: The suggested action displayed on the device.

[1870] Output: User actions.

[1871] Specific action: The user checks the notification and performs a specific action, such as visiting a store or topping up.

[1872] (Application example 1)

[1873] 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."

[1874] In today's brick-and-mortar stores, users have limited access to useful information on special offers and campaigns in real time, creating a need for improved user purchasing experiences. There is also a need for systems that effectively utilize users' location information and transaction history to provide optimal recommendations for each individual user. Systems that can solve these issues and improve user convenience are anticipated.

[1875] 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.

[1876] In this invention, the server includes an artificial intelligence engine for recommending actions based on a user's transaction history and location information, means for collecting the user's transaction history and location information, means having a graphical user interface for notifying the user of the recommended actions generated by the artificial intelligence engine, and means for recommending information on benefits and campaigns at physical stores in real time based on the user's current location. This allows the user to obtain optimal information on benefits and campaigns in real time, which is expected to improve the purchasing experience.

[1877] "User" means an individual consumer who uses this system.

[1878] "Transaction history" refers to a record of a user's past purchases and transactions.

[1879] "Location information" refers to data about a user's current location and past travel routes.

[1880] The "artificial intelligence engine for recommending actions" is a system that uses algorithms to analyze transaction history and location information and suggest optimal actions.

[1881] A "graphical user interface" is an application's screen display that allows a user to visually receive information.

[1882] "Benefits and campaign information" refers to information such as discounts, points, and limited-edition products for the purpose of sales promotion.

[1883] A "physical store" is a physical store where users actually visit and purchase products.

[1884] "Real-time recommendation methods" are technologies and mechanisms that instantly provide users with information on special offers and campaigns that are tailored to their current situation.

[1885] Overall structure

[1886] This invention is a system that recommends actions based on a user's transaction history and location information. The main components include a server, a terminal, and a user.

[1887] 1. Server:

[1888] Collection of transaction history and location information: Transaction history and location information are periodically received from users' smartphones and tablets and stored in a database.

[1889] Artificial Intelligence Engine: Analyzes collected data and learns user behavior patterns. This engine uses AI algorithms to generate optimal recommended actions.

[1890] Generate recommended actions: Based on the user's current location, the system processes information on special offers and campaigns in physical stores in real time.

[1891] 2. Terminal:

[1892] Location information acquisition: The user's current location is measured using the GPS sensor built into smartphones and tablets.

[1893] Communication with the server: Sends the user's transaction history and location information to the server, and receives notifications of recommended actions from the server.

[1894] Graphical User Interface: Visually displays recommended actions, rewards information, wallet balance and top-up suggestions to the user.

[1895] 3. User:

[1896] Initial Setup: After installing the application, enter your identity and credit card information.

[1897] Receive and act on notifications: Receive special offers and campaign information and use it to make in-store purchases.

[1898] Explanation of program processing

[1899] 1. Hardware and Software:

[1900] Hardware: Smartphone (with GPS sensor), server

[1901] software:

[1902] geopy: A Python library for obtaining user location information.

[1903] requests: An HTTP request library for communicating with servers.

[1904] 2. Data processing and calculation:

[1905] Server: Based on the user's transaction history and location information, the AI ​​engine generates optimal recommendations, especially for determining special offers and campaign information near the user's current location in real time.

[1906] Device: Receives recommended actions and campaign information sent from the server and notifies the user. Location information is also periodically acquired and sent to the server.

[1907] Specific examples

[1908] For example, if a user is shopping in Tokyo, the smartphone's GPS sensor will acquire their current location and send it to the server. Based on that location information and past transaction history, the server will determine that the user is at a nearby convenience store and send a real-time notification to the device recommending a double points campaign. By receiving this notification, the user can enjoy shopping at a physical store and get great deals.

[1909] Example prompt sentence:

[1910] "You are currently developing an AI assistant that recommends optimal actions based on a user's transaction history and location information. This AI assistant recommends products and campaign information to users in real time while they are shopping in a physical store. Build a scenario in which, while the user is shopping in Tokyo, they are notified of a points double campaign currently being held at a nearby convenience store."

[1911] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1912] Step 1:

[1913] The device receives the user's initial setup information (identity verification information, credit card information). By inputting this initial setup information and sending it to the server, a user account is created and the application can be used. After the initial setup information is sent, an account ID is output.

[1914] Step 2:

[1915] The device acquires the user's location information. In this step, the smartphone's GPS sensor is used to measure the current location and send that location information to the server. The acquired location information is used as input, and location data is output.

[1916] Step 3:

[1917] The server receives the user's transaction history and location information and stores them in a database. The stored transaction history and location information are used as inputs, and behavioral pattern data for each user is output.

[1918] Step 4:

[1919] The AI ​​engine on the server analyzes the stored data and learns each user's behavioral patterns. At this stage, transaction history and location information are used as input, and behavioral recommendations are output as the analysis result.

[1920] Step 5:

[1921] The server's artificial intelligence engine generates information about special offers and campaigns based on the user's current location. In this step, the server inputs the user's current location information and past behavioral pattern data, and outputs the most suitable special offer information.

[1922] Step 6:

[1923] The server sends the generated bonus information to the terminal in real time. This notification information (bonus information) is input and sent to the terminal, which outputs a bonus notification that is displayed to the user.

[1924] Step 7:

[1925] The device notifies the user of the received reward information through a graphical user interface. In this step, the reward information is input and a notification is output to be displayed on the user's smartphone screen.

[1926] Step 8:

[1927] The user receives the notified benefit information and makes a purchase at the physical store based on it. In this step, the notified benefit information is used as input and the purchasing behavior at the physical store is output.

[1928] Step 9:

[1929] After the user finishes shopping, the terminal sends a new transaction history to the server. In this step, the post-purchase transaction data is used as input and the new transaction history to be sent to the server is used as output.

[1930] Step 10:

[1931] The server again saves the new transaction history in the database, and the AI ​​engine updates the data. In this step, the new transaction history is used as input and updated behavioral pattern data is output.

[1932] By repeating this process, the system can improve user convenience.

[1933] 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.

[1934] The present invention is a system that includes an artificial intelligence (AI) engine that recommends actions based on a user's transaction history and location information, and also incorporates an emotion engine that recognizes the user's emotions. This system uses the AI ​​engine to generate optimal recommended actions based on the collected user transaction history, location information, and emotion data, and notifies the user. Specific embodiments of this system are described below.

[1935] Overall structure

[1936] The system includes the following major components:

[1937] 1. Server - Collects user data and makes recommendations using AI and emotion engines.

[1938] 2. Terminal - The device that the user operates, such as a smartphone or tablet.

[1939] 3. User - An individual user of the system.

[1940] Initial setup and data acquisition

[1941] user

[1942] When users first install and launch the app, they enter their personal information (email address, phone number, etc.), credit card details, and agree to the terms of use. This information is necessary to obtain accurate user data.

[1943] Terminal

[1944] When users enter their information into the app, the data is sent to the server. After the initial setup is complete, the device will periodically communicate with the server to obtain the latest recommendation behavior and campaign information.

[1945] server

[1946] The server stores the user's transaction history and location information in a database. It also records the user's real-time emotional state using an emotion engine that recognizes emotions from the user's facial expressions and voice data. Based on this, the AI ​​engine analyzes the user's behavioral patterns and generates optimal recommendations.

[1947] Generate and notify recommended actions

[1948] server

[1949] By analyzing a user's transaction history, location information, and emotional data, the AI ​​engine runs an algorithm that recommends the best course of action for that day. For example, it selects information about a point-double campaign at a specific store or nearby special offers based on the user's current location, past purchase history, and emotional state.

[1950] Terminal

[1951] The device receives recommended actions and campaign information from the server and displays them to the user as pop-up notifications. For example, when the device is turned on in the morning, it displays the wallet balance and top-up suggestions, and when the device is out and about, it notifies the user of special offers at nearby stores. The content and display method of notifications may also be adjusted depending on the user's emotional state.

[1952] Specific examples

[1953] Case 1: Morning wallet balance display and top-up suggestion

[1954] A user launches the app in the morning.

[1955] The terminal sends a request to the server and receives the wallet balance and a top-up offer.

[1956] The server checks the user's wallet balance and sends data to the device, including a suggestion if a charge is needed.

[1957] The terminal displays the wallet balance and top-up suggestions on the screen, and the user can top up as needed.

[1958] Case 2: Notification of a nearby store's double points campaign

[1959] When a user reaches a specific time or location while out and about, the server uses that information to recommend point-double campaigns being held at nearby stores.

[1960] The device will notify the user of the recommended actions in real time, and the notification will be adjusted based on the user's emotions as recognized by the emotion engine. For example, if the user is feeling happy, the notification will be displayed in a friendly tone.

[1961] Users can visit the notified store and make payments using the app to enjoy the double points benefit.

[1962] Case 3: Information about the next day's events before going to bed

[1963] When a user launches the app before going to bed, information about the next day's events and campaigns will be displayed on the device.

[1964] The server generates optimal event information based on the user's past behavior history, the next day's schedule, and data collected by the emotion engine, and sends it to the device. This information is also customized according to the user's emotions.

[1965] The device will then notify the user of this information, allowing them to adjust their actions based on their schedule for the next day.

[1966] In this way, the present invention significantly improves user convenience, integrates the use of payment applications into everyday life, and provides appropriate feedback based on the user's emotional state.

[1967] The processing flow will be explained below.

[1968] Specific processing steps of the program

[1969] Case 1: Morning wallet balance display and top-up suggestion

[1970] Step 1:

[1971] A user launches the app in the morning.

[1972] Step 2:

[1973] The terminal sends a request to the server for the wallet balance and a top-up proposal.

[1974] Step 3:

[1975] The server retrieves the wallet balance from the database based on the user ID.

[1976] Step 4:

[1977] The server checks the wallet balance and determines if a charge is required.

[1978] Step 5:

[1979] The server generates the wallet balance and, if necessary, a top-up proposal and returns it to the terminal.

[1980] Step 6:

[1981] The terminal displays the received wallet balance and top-up proposal on the user interface.

[1982] Step 7:

[1983] If the user accepts the charge proposal, the charge is carried out.

[1984] ---

[1985] Case 2: Notification of a nearby store's double points campaign

[1986] Step 1:

[1987] The user launches the app while out and about.

[1988] Step 2:

[1989] The device acquires the current location information and sends it to the server.

[1990] Step 3:

[1991] The server uses the user's location information, past transaction history, and data from the emotion engine to search for point doubling campaigns currently being held at nearby stores.

[1992] Step 4:

[1993] The server generates optimal campaign information and returns it to the terminal.

[1994] Step 5:

[1995] The device notifies users in real time about campaign information received, and the notifications are tailored based on the user's emotions as recognized by the emotion engine.

[1996] Step 6:

[1997] The user checks the notification and takes action based on the campaign information.

[1998] Step 7:

[1999] Users visit participating stores and make payments using the app.

[2000] ---

[2001] Case 3: Information about the next day's events before going to bed

[2002] Step 1:

[2003] The user launches the app before going to bed.

[2004] Step 2:

[2005] The terminal sends a request for next day's event information to the server.

[2006] Step 3:

[2007] The server generates optimal event and campaign information for the next day based on the user's past behavioral history and schedule for the next day, as well as data collected by the emotion engine.

[2008] Step 4:

[2009] The server returns the generated event information to the terminal.

[2010] Step 5:

[2011] The device displays the event information it receives in the user interface, which is also customized according to the user's emotions.

[2012] Step 6:

[2013] The user checks the schedule for the next day and adjusts the schedule if necessary.

[2014] These are the specific processing steps of the program, which allow users to effectively use the payment app and increase convenience in their daily lives.

[2015] Example 2

[2016] 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."

[2017] Conventional behavior recommendation systems based on a user's transaction history and location information are unable to take into account the user's emotional state, making it difficult to provide optimal recommended behaviors. Furthermore, they lacked a mechanism for adjusting notification content based on the user's emotional state and providing information at a more appropriate time and in a more appropriate manner. As a result, user convenience and satisfaction were not sufficiently improved.

[2018] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes an artificial intelligence engine means for recommending actions based on a user's transaction history and location information, a means for collecting the user's transaction history and location information, an emotion engine means for recognizing and analyzing the user's emotional data, and a means having a graphical user interface for notifying the user of recommended actions generated by the artificial intelligence engine and emotion engine. This makes it possible to comprehensively analyze the user's transaction history, location information, and emotional data and provide the user with recommended actions that are optimal for the user. In addition, the recommended actions and notification content can be customized according to the user's emotional state, significantly improving user convenience and satisfaction.

[2019] "User transaction history" refers to recorded information about purchases and financial transactions that a user has made in the past.

[2020] "Location Information" means geographic coordinate information obtained based on a user's device.

[2021] An "artificial intelligence engine" is a part of a system that uses machine learning algorithms to analyze data and generate actionable recommendations for users.

[2022] "Emotional data" is information about the psychological state of a user that can be read from facial expressions, voice, etc.

[2023] The "emotion engine" is the part of the system that recognizes and analyzes the user's emotional data.

[2024] "Recommended actions" are specific action suggestions generated by the artificial intelligence engine based on a user's transaction history, location information, and emotional data.

[2025] A "graphical user interface" is a screen display means for visually presenting information to a user via a terminal.

[2026] "Wallet Balance" means the total amount of funds currently available in a User's Digital Wallet.

[2027] A "top-up suggestion" is a recommendation to top up additional funds if your wallet balance is low.

[2028] "Benefit information" refers to information including benefits such as campaigns and discounts offered to users.

[2029] MODE FOR CARRYING OUT THE INVENTION

[2030] The system of the present invention is equipped with a complex AI engine and emotion engine that recommends optimal actions using a user's transaction history, location information, and emotion data. This system is composed of three elements: a server, a terminal, and a user, and each element functions as follows:

[2031] Initial setup and data acquisition

[2032] user:

[2033] When you first install the app, you launch it, enter your identity verification information (email address, phone number, etc.), credit card information, and agree to the terms of use, which allows us to collect accurate user data.

[2034] Device:

[2035] The information entered by the user is temporarily stored on the device and sent to the server. The device is set to periodically send location information and transaction history to the server.

[2036] server:

[2037] The server stores the user's input information in a database, periodically updates the user's transaction history and location information, and analyzes the user's facial expressions and voice data using an emotion engine, saving the data as emotion data.

[2038] Generate and notify recommended actions

[2039] server:

[2040] An artificial intelligence engine analyzes users' transaction history, location information, and real-time sentiment data to generate optimal recommendations, such as identifying points-double campaigns at specific stores or nearby special offers.

[2041] Device:

[2042] The recommended actions and campaign information received from the server are displayed to the user as pop-up or banner notifications, and the content of the notifications may be adjusted based on the user's emotional state.

[2043] Specific examples

[2044] Case 1: Morning wallet balance display and top-up suggestion

[2045] user:

[2046] Start the app in the morning.

[2047] Device:

[2048] Sends a request to the server and receives the wallet balance and top-up proposal.

[2049] server:

[2050] Checks the user's wallet balance, creates a top-up proposal if necessary, and sends the data to the terminal.

[2051] Device:

[2052] The wallet balance and top-up suggestions are displayed on the screen, and the user can top up as needed.

[2053] Case 2: Notification of a nearby store's double points campaign

[2054] user:

[2055] Reaching a specific time or location while out and about.

[2056] server:

[2057] Based on this information, the system recommends to users points doubling campaigns being held at nearby stores.

[2058] Device:

[2059] The recommended actions are notified to the user in real time, and the emotion engine ensures that notifications are displayed in a friendly tone when the user is feeling happy.

[2060] user:

[2061] Visit the notified store, pay using the app, and enjoy the double points bonus.

[2062] Case 3: Information about the next day's events before going to bed

[2063] user:

[2064] Start the app before you go to bed.

[2065] Device:

[2066] Information about events and campaigns for the next day will be displayed.

[2067] server:

[2068] Based on the user's past behavioral history and schedule for the next day, the system generates optimal event information and sends it to the device. This information is customized by the emotion engine.

[2069] Device:

[2070] This information is communicated to the user, allowing them to adjust their actions based on their schedule for the next day.

[2071] Prompt Sentence Examples

[2072] Below are some example prompts from a generative AI model:

[2073] 1. "How can I display my wallet balance in the morning and suggest topping up if needed?"

[2074] 2. "Please explain the process for notifying users on the go about a nearby store's double points promotion."

[2075] 3. "Please explain in detail how users can receive the next day's event information before going to bed."

[2076] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2077] Step 1: First-time user registration

[2078] user:

[2079] After installing the app for the first time, launch the app, enter your identity verification information (email address, phone number, etc.) and credit card information, and agree to the terms of use. Input is done through text boxes, etc.

[2080] Device:

[2081] The entered information is temporarily stored on the device. When the user presses the "Send" button, this data is sent to the server.

[2082] Input: Personal information, credit card information, agreement to terms of use

[2083] Output: Data packet sent to the server

[2084] Step 2: Send data by device

[2085] Device:

[2086] The user's input information is sent to the server using a secure communication protocol (e.g., HTTPS), and the user's location information is also obtained using GPS functionality.

[2087] server:

[2088] The received data is analyzed to check for omissions or errors. If there are no problems, it is saved in the database.

[2089] Input: User registration information, location information

[2090] Output: User information and location information stored on the server

[2091] Step 3: Data storage and analysis on the server

[2092] server:

[2093] The user's transaction history and location information are stored in a database. The user's facial expressions and voice data are analyzed by an emotion engine and stored as emotion data.

[2094] Input: Transaction history, location information, facial expression and voice data

[2095] Output: Stored transaction history, location information, and sentiment data

[2096] Step 4: Generate action recommendations using the AI ​​engine

[2097] server:

[2098] The artificial intelligence engine analyzes stored transaction history, location, and sentiment data and uses machine learning algorithms to generate optimal action recommendations.

[2099] Input: Transaction history, location information, emotional data

[2100] Output: Recommended action data

[2101] Step 5: Notifications displayed on the device

[2102] Device:

[2103] It receives recommended actions and campaign information from the server and displays them to users as pop-up or banner notifications, and adjusts the content of notifications based on data from the emotion engine.

[2104] Input: Recommended action data, campaign information, sentiment data

[2105] Output: Popup notification, banner notification

[2106] Step 6: User takes action

[2107] user:

[2108] Acting on notifications from the device, for example, topping up your wallet in response to a top-up suggestion or visiting a specific store to take advantage of a points campaign.

[2109] Input: Notification content

[2110] Output: Actual actions (charges, store visits, etc.)

[2111] At each step, the user, device, and server work together to create a system that supports optimal user behavior. This collaboration improves user convenience and provides more personalized services.

[2112] (Application example 2)

[2113] 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."

[2114] While modern electronic payment services can provide simple recommendations based on user behavior patterns and location information, they lack the ability to provide detailed recommendations and reward information that take into account the user's real-time emotional state. In particular, it is believed that more personalized services can be provided by providing notifications and suggestions based on the user's emotions. Therefore, there is a need for a system that can recommend optimal actions by integrating the use of a user's transaction history, location information, and emotional data.

[2115] 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.

[2116] In this invention, the server includes an artificial intelligence engine for recommending actions based on a user's transaction history and location information, an emotion engine for recognizing emotions, means for collecting the user's transaction history, location information, and emotion data, means having a graphical user interface for displaying recommended actions and campaign information in real time, and means for adjusting the content and display method of notifications based on the emotion engine, thereby making it possible to notify the user of bonus information and recommended actions in real time according to their emotional state.

[2117] "User transaction history" means a record of the series of purchases, payments and financial transactions that a User has made in the past.

[2118] "Location information" is geographical data about a user's current location.

[2119] An "artificial intelligence engine" is a calculation system that analyzes a user's transaction history, location information, etc., and recommends optimal actions.

[2120] The "emotion engine" is a system that recognizes the user's emotional state from facial expressions, voice, etc., and records that data.

[2121] A "graphical user interface" is a system that visually provides an interface with the user, and is a user interface consisting of icons and buttons displayed on a screen.

[2122] "Real-time notifications" are instant information or messages that users receive based on their current situation.

[2123] "Benefit information" is information about benefits that users can receive, such as discounts, double points, coupons, etc.

[2124] "Campaign Information" means information about promotions or events available to users for a specific period or under specific conditions.

[2125] "Wallet Balance" means the total amount of funds currently held in a User's electronic wallet.

[2126] A "top-up suggestion" is a suggestion that recommends a user to top up their wallet with additional funds if their wallet balance is low.

[2127] "Means for adjusting the content and display of notifications" refers to a system for adapting the message and display format of notifications received according to the user's emotional state.

[2128] The present invention provides an electronic payment service system that recommends actions based on a user's transaction history, location information, and emotional data. Specific embodiments of this system are described below.

[2129] Overall structure

[2130] The system includes the following major components:

[2131] 1. Server - Collects user data and makes recommendations using artificial intelligence and emotion engines.

[2132] 2. Terminal - The device that the user operates, such as a smartphone or tablet.

[2133] 3. User - An individual user of the system.

[2134] Initial setup and data acquisition

[2135] server

[2136] The server collects users' transaction history and location information and stores it in a database. It also records emotional data in real time using an emotion engine that recognizes users' emotional state from their facial expressions and voice data.

[2137] The server uses an artificial intelligence engine to analyze the user's transaction history, location information, and emotional data to generate optimal recommended actions.

[2138] Terminal

[2139] When a user installs and launches the application, they are prompted to enter the necessary personal information, which allows the server to retrieve accurate data.

[2140] The device displays recommended actions and campaign information received from the server to the user as a pop-up notification.

[2141] user

[2142] Users can choose actions based on recommended actions and campaign information provided through the device.

[2143] Generate and notify recommended actions

[2144] server

[2145] The server uses an artificial intelligence engine to analyze the user's transaction history, location information, and emotional data, and then runs an algorithm to recommend optimal actions, such as selecting information about a point-double campaign at a specific store or nearby special offers based on the user's current location, past purchase history, and emotional state.

[2146] Terminal

[2147] The terminal displays recommended actions, campaign information, wallet balance and top-up suggestions received from the server to the user as pop-up notifications.

[2148] Specific examples

[2149] Case 1: Morning wallet balance display and top-up suggestion

[2150] When a user launches the app in the morning, the server checks the user's wallet balance and sends a top-up suggestion if necessary.

[2151] The terminal displays the information in real time, and the user can top up as needed.

[2152] Case 2: Notification of a nearby store's double points campaign

[2153] When a user reaches the vicinity of a particular store while out and about, the server recommends a point-double campaign at a nearby store.

[2154] The device will provide real-time recommendations, tailored to the user's emotions as recognized by the emotion engine.

[2155] Case 3: Information about the next day's events before going to bed

[2156] When a user launches the app before going to bed, the server generates information about the next day's events and campaigns and sends it to the device.

[2157] The device will then notify the user of this information, allowing them to adjust their plan of action for the next day.

[2158] Examples of prompt statements

[2159] Example prompt for checking your wallet balance in the morning: "Good morning! Your current wallet balance is 500 yen. Would you like to top up?"

[2160] Example prompt for point campaign notification: "Double points campaign is currently running at XX Cafe!"

[2161] In this way, the present invention significantly improves user convenience, makes the use of electronic payment services a natural part of daily life, and provides appropriate feedback according to the user's emotional state.

[2162] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2163] Step 1:

[2164] The user installs the app and enters personal information (email address, phone number, etc.). This information is sent from the device to the server, and user-specific data is stored in a database. The input data becomes the basis for processing the user's personal authentication, transaction history, and location information. The output is a status indicating that the user has been authenticated.

[2165] Step 2:

[2166] The device periodically communicates with the server to obtain the latest recommended actions and campaign information. This communication sends the latest information on the user's transaction history, location information, and emotional data to the server. The input is a periodic status check, and the output is the latest recommended actions and campaign information.

[2167] Step 3:

[2168] The server collects the user's transaction history, location information, and emotion data and stores them in a database. In this step, a camera or microphone is used to recognize the user's emotion data, and the emotion engine processes the data in real time. The output is user data stored in a database.

[2169] Step 4:

[2170] The server uses an artificial intelligence engine to analyze the collected data (transaction history, location information, emotional data) and generate optimal recommended actions. This analysis uses a generative AI model to learn the user's behavioral patterns and generates recommended actions based on the results. The input is the user's data, and the output is recommended actions.

[2171] Step 5:

[2172] The server sends the recommended actions to the device, where the emotion engine data is used to adjust the notification content and display method. For example, if the user is feeling happy, the notification will be displayed in a friendly tone. The input is the recommended action and emotion data, and the output is the notification content and display format.

[2173] Step 6:

[2174] The device receives recommended actions and campaign information and displays it to the user as a popup notification, where wallet balance and top-up suggestions are also displayed. The input is the information received from the server, and the output is the notification displayed on the user's device.

[2175] Step 7:

[2176] The user selects an action based on the notification. For example, by visiting a recommended store and paying using the app, they can receive rewards and double points. The input is the user's choice, and the output is the transaction and its results.

[2177] Step 8:

[2178] The device resends the transaction information to the server and updates the database, which further personalizes the next recommended action or campaign information. The input is the new transaction data, and the output is the updated user data.

[2179] Through these steps, the system improves user convenience while providing appropriate feedback according to the user's emotional state.

[2180] 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.

[2181] 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.

[2182] 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.

[2183] 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.

[2184] 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.

[2185] 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.

[2186] 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).

[2187] 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.

[2188] 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."

[2189] 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.

[2190] 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).

[2191] 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.

[2192] 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.

[2193] 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.

[2194] 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.

[2195] 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.

[2196] 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.

[2197] 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.

[2198] 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.

[2199] 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.

[2200] 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.

[2201] The following is further disclosed regarding the above embodiment.

[2202] (Claim 1)

[2203] An artificial intelligence engine to recommend actions based on users' transaction history and location information;

[2204] means for collecting transaction history and location information of said users;

[2205] means having a graphical user interface for notifying a user of the recommended actions generated by the artificial intelligence engine;

[2206] A system including:

[2207] (Claim 2)

[2208] 10. The system of claim 1, wherein the graphical user interface further comprises means for displaying wallet balances and recharge suggestions.

[2209] (Claim 3)

[2210] The system of claim 1, wherein the artificial intelligence engine further comprises means for generating nearby special offer information based on the user's location information, and means for notifying the user's terminal of the special offer information.

[2211] "Example 1"

[2212] (Claim 1)

[2213] An artificial intelligence engine to recommend actions based on users' transaction history and location information;

[2214] means for collecting transaction history and location information of said users;

[2215] a display means for notifying a user of the recommended actions generated by the artificial intelligence engine;

[2216] A means for the terminal to encrypt the user's input information and send it to the server;

[2217] A means for the server to store user information in a database and generate user IDs;

[2218] A means for the terminal to periodically acquire the user's location information and transaction history and transmit them to a server;

[2219] A means for the server to analyze the data using an AI engine;

[2220] A means for the server to generate optimal recommended actions based on the analysis results and transmit the actions to the terminal;

[2221] A system including:

[2222] (Claim 2)

[2223] 10. The system of claim 1, wherein said display means further comprises means for displaying a wallet balance and a recharge suggestion.

[2224] (Claim 3)

[2225] The system of claim 1, wherein the artificial intelligence engine further comprises means for generating nearby special offer information based on the user's location information, and means for notifying the user's terminal of the special offer information.

[2226] "Application Example 1"

[2227] (Claim 1)

[2228] An artificial intelligence engine to recommend actions based on users' transaction history and location information;

[2229] means for collecting transaction history and location information of said users;

[2230] means having a graphical user interface for notifying a user of the recommended actions generated by the artificial intelligence engine;

[2231] A means to recommend in-store special offers and campaign information in real time based on the user's current location,

[2232] A system including:

[2233] (Claim 2)

[2234] 10. The system of claim 1, wherein the graphical user interface further comprises means for displaying wallet balances and recharge suggestions.

[2235] (Claim 3)

[2236] The system of claim 1, wherein the artificial intelligence engine further comprises means for generating nearby special offer information based on the user's location information, and means for notifying the user's terminal of the special offer information.

[2237] "Example 2: Combining Emotion Engines"

[2238] (Claim 1)

[2239] An artificial intelligence engine to recommend actions based on users' transaction history and location information;

[2240] means for collecting transaction history and location information of said users;

[2241] an emotion engine that recognizes and analyzes users' emotion data;

[2242] a means having a graphical user interface for notifying a user of the recommended actions generated by the artificial intelligence engine and the emotion engine;

[2243] A system including:

[2244] (Claim 2)

[2245] 10. The system of claim 1, wherein the graphical user interface further comprises means for displaying wallet balances and recharge suggestions.

[2246] (Claim 3)

[2247] The system of claim 1, wherein the artificial intelligence engine further comprises means for generating nearby special offer information based on the user's location information, and means for notifying the user's terminal of the special offer information.

[2248] "Application example 2 when combining emotion engines"

[2249] (Claim 1)

[2250] An artificial intelligence engine to recommend actions based on users' transaction history and location information;

[2251] an emotion engine for recognizing emotions;

[2252] means for collecting transaction history, location information, and emotion data of said users;

[2253] means having a graphical user interface for displaying recommended actions and campaign information in real time;

[2254] means for adjusting the content and display method of a notification based on the emotion engine;

[2255] A system including:

[2256] (Claim 2)

[2257] 10. The system of claim 1, wherein the graphical user interface displays wallet balances and top-up suggestions and further comprises means for customizing based on emotions.

[2258] (Claim 3)

[2259] 2. The system of claim 1, wherein the artificial intelligence engine further comprises means for generating nearby special offers based on the user's location information and emotion data and notifying the user in real time. [Explanation of symbols]

[2260] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. An artificial intelligence engine to recommend actions based on users' transaction history and location information; means for collecting transaction history and location information of said users; means having a graphical user interface for notifying a user of the recommended actions generated by the artificial intelligence engine; A system including:

2. 10. The system of claim 1, wherein the graphical user interface further comprises means for displaying wallet balances and recharge suggestions.

3. The system according to claim 1, wherein the artificial intelligence engine further comprises means for generating nearby special offer information based on the user's location information, and means for notifying the user's terminal of the special offer information.

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