System

Abstract

A system is provided.SOLUTION: A system comprising: means for storing basic information acquired from a user; means for collecting information on a point service and a shopping history of the user; analyzing means using a generated AI for performing learning based on the collected information and selecting an optimum point service; proposing means for proposing a point service according to a life cycle of the user; and managing means for managing a point balance and an expiration date of the user.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] Nowadays, there are many point services, and it is very difficult for users to understand the reward rates and usage methods of each point program. As a result, users are unable to make the most of their points, resulting in missed opportunities. In addition, it is common for points to expire, which is also stressful for users. It is necessary to solve these problems and help users make efficient use of their points. [Means for solving the problem]

[0005] The present invention comprises a means for saving basic information acquired from a user and a means for collecting the user's point service information and shopping history. It also includes an analysis means using a generative AI that learns from the collected information and selects the optimal point service, and a proposal means for proposing point services according to the user's life cycle. Furthermore, by providing a means for notifying the user of information about the proposed point service to the user's terminal and a management means for managing the user's point balance and expiration date and notifying them as appropriate, the system enables users to efficiently utilize their points without missing out on opportunities to earn them. Furthermore, the proposal means uses the user's location information to propose point services in real time, enabling even more effective use.

[0006] "User" refers to an individual person or entity that uses the portal system.

[0007] "Basic Information" refers to data including a user's name, age, gender, occupation, and other identifying information.

[0008] A "point service" refers to a program that provides users with rewards or discounts that can be used later when they shop or use services.

[0009] "Shopping History" refers to a record of past purchases and usage transactions made by a User.

[0010] "Generative AI" refers to artificial intelligence technology that learns from collected data and proposes optimal solutions.

[0011] "Analysis means" refers to the processing technology used to learn and analyze the collected data.

[0012] "Proposal method" refers to a method of presenting specific ways to use the points service to users based on the results of analysis by the generation AI.

[0013] "Notification means" refers to a technology that transmits proposed information to a user terminal in real time.

[0014] "Management means" refers to a function that centrally manages the user's point balance and expiration date, and notifies the user as necessary.

[0015] "Location information" refers to data indicating the user's current location. [Brief explanation of the drawings]

[0016] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0017] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

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

[0019] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0020] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0021] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0022] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0024] [First embodiment]

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

[0026] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0027] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0029] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0033] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0034] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0035] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0036] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0037] The present invention is a system for effectively utilizing multiple point services, and is a series of functions including user registration, data collection, analysis by generation AI, proposals and notifications, and point management. Specific embodiments for implementing the present invention are described below.

[0038] User registration and saving basic information

[0039] 1. The user registers as a member using the dedicated app or website. They enter basic information (name, email address, age, gender, occupation, etc.) on their device and press the send button.

[0040] 2. The server receives the basic information sent by the user and stores it in the database. It then sends a confirmation email to the user, who must click on the link to confirm and complete the registration.

[0041] Collection of point service information and shopping history

[0042] 1. The user enters information about the point service they are using (point card name, balance, usage history, etc.) into the app. It is also possible to synchronize linked point card information with the app.

[0043] 2. The server collects the point information and shopping history provided by the user and stores them in a centralized database.

[0044] Learning by generative AI and selection of optimal point services

[0045] 1. The server trains the generation AI based on the collected user point service information and shopping history. Specifically, the generation AI learns the reward rate of each point service, how to use it, and the user's shopping patterns.

[0046] 2. Next, the server uses the generation AI to select the optimal point service. For example, it selects the optimal point card when a user purchases electronic devices.

[0047] Suggestions and Notifications

[0048] 1. The user opens the app and enters information for a new purchase (for example, the product and store information they plan to purchase).

[0049] 2. The server analyzes this new information and past data and proposes the optimal point service based on the learning results of the generating AI.

[0050] 3. The suggested content is notified to the user via the device (smartphone app). For example, a real-time notification such as "Use point card A at this store to receive 5% points" is sent.

[0051] Points Management

[0052] 1. Users can check their points balance and expiration date within the app.

[0053] 2. The server periodically checks the user's point information and sends an alert when the expiration date is approaching. For example, it may send a message saying, "Your points will expire in one month. We recommend that you use them for your next purchase."

[0054] Specific examples

[0055] For example, user B is planning to purchase a new refrigerator. User B has registered with the point utilization advisor and regularly uses a supermarket point card, but this is the first time that user B has purchased a refrigerator.

[0056] 1. User B enters an electronics store and opens the app.

[0057] 2. Enter the product information (refrigerator) you plan to purchase into the app.

[0058] 3. The server analyzes the most suitable point card based on User B's location information and shopping history.

[0059] 4. The server determines that "Using Company B's point card at this store will result in higher points" and notifies User B.

[0060] 5. Based on the suggestion, User B uses Company B's point card to purchase a refrigerator. As a result, User B can obtain the most advantageous points and enjoy a satisfying shopping experience.

[0061] As described above, the present invention helps users to make the most of point services, and provides a specific form for seamlessly performing a series of processes and improving convenience.

[0062] The processing flow will be explained below.

[0063] Step 1:

[0064] The user opens the dedicated app or website, accesses the membership registration page, enters basic information (name, email address, age, gender, occupation, etc.), and presses the registration button.

[0065] Step 2:

[0066] The server receives the basic information sent by the user and saves it in the database. After saving, the server sends a confirmation email to the user. The user completes registration by clicking the link in the email.

[0067] Step 3:

[0068] The user enters the information of the point service they use (point card name, balance, usage history, etc.) into the app, or synchronizes the information of the linked point card. When the synchronization button is pressed, the information is sent from the device to the server.

[0069] Step 4:

[0070] The server stores the point service information and shopping history received from the user in a database. Once the information collection is complete, proceed to the next step.

[0071] Step 5:

[0072] The server provides the stored data to the generation AI, which then learns from the data. The generation AI then creates a model to analyze the reward rate and usage of the point service, as well as user shopping patterns.

[0073] Step 6:

[0074] When a user makes a new purchase, they enter store information and information about the product they plan to purchase into the app. After completing the input, they press the submit button.

[0075] Step 7:

[0076] The server receives the shopping information sent by the user and provides it to the generation AI, which selects the optimal point service based on past data and new information.

[0077] Step 8:

[0078] The server creates the optimal point service proposal for the user based on the analysis results of the generation AI. The proposal is then notified to the device app. For example, it may notify the user that "If you use point card A at this store, you will receive the maximum points."

[0079] Step 9:

[0080] After the user completes their purchase, they enter the purchase information into the app, which then sends it to the server.

[0081] Step 10:

[0082] The server saves the new purchase information in the database and updates the points balance and expiration date.

[0083] Step 11:

[0084] The server periodically checks the point balance and expiration date, and notifies the user when the expiration date is approaching. For example, it may notify the user that "Your points will expire in one month. We recommend that you use them for your next purchase."

[0085] Through this series of processes, users can receive suggestions for optimal point services and use their points efficiently.

[0086] Example 1

[0087] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0088] Traditional point services required the time and effort of managing multiple point cards, making it difficult for users to make the most of their points. They also lacked the information needed to properly determine which point card would provide the most benefits. Furthermore, users often lost points without realizing their points were about to expire.

[0089] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0090] In this invention, the server includes a means for saving basic information acquired from the user, a means for collecting the user's point service information and shopping history, and an analysis means using a generation AI that learns from the collected information and selects the most suitable point service, thereby enabling the user to effectively manage and make the most of the point service.

[0091] The "means for storing basic information" is a function for safely storing basic personal information obtained from users, such as their name, email address, age, sex, and occupation.

[0092] "Means for collecting point service information and shopping history" refers to a function for collecting and centrally managing information about the point card currently in use by the user (point card name, balance, usage history, etc.) and the user's shopping history.

[0093] "Analysis method using generative AI" is a function that uses generative artificial intelligence to analyze and learn from collected point service information and shopping history to select the most suitable point service.

[0094] The "means for the user to input information about the product and store that the user plans to purchase" is a function for the user to input information about the product that the user plans to purchase and the store where the user plans to purchase the product.

[0095] "Means for notifying the user device of information about the proposed point service" is a function for notifying the user device in real time of information about the optimal point service selected by the generation AI.

[0096] The "management means for managing point balances and expiration dates and notifying them as appropriate" is a function for managing the balance on a user's point card and the expiration date of points, and sending alert notifications to the user when the expiration date is approaching.

[0097] "Means for proposing point services in real time" is a function that immediately uses new information such as the user's location information to propose optimal point services to the user in real time.

[0098] MODE FOR CARRYING OUT THE INVENTION

[0099] This invention is a system that helps users efficiently manage and use multiple point services, and provides the functions of user registration, data collection, analysis by AI generation, proposals and notifications, and point management in an integrated manner. Specific embodiments for implementing this invention are described below.

[0100] User registration and saving basic information

[0101] Users register as members using a dedicated app or website. Using a device (smartphone or PC), they enter basic information such as their name, email address, age, gender, and occupation, and then press the "Submit" button. The server receives the basic information sent from the device and securely stores it in a database using SSL communication. A confirmation email is then sent to the user, and registration is completed when the user clicks on the link in the email to confirm.

[0102] Collection of point service information and shopping history

[0103] Users enter information about the point service they are using (point card name, balance, usage history, etc.) into the app. If the app supports it, point card information can be automatically synchronized via API. The server collects the point information and shopping history provided by the user and stores it in a centralized database. This allows users to manage multiple point services on a single platform.

[0104] Learning by generative AI and selection of optimal point services

[0105] The server trains the generative AI model based on the collected user point service information and shopping history. Specifically, the reward rate for each point service, usage methods, and user shopping patterns are used as learning data. Using the trained generative AI model, the server selects the optimal point service. For example, when a user purchases a new electronic device, it can select the most advantageous point card for that product.

[0106] Suggestions and Notifications

[0107] The user opens the app and enters new shopping information (for example, the product and store information they plan to purchase). The server analyzes this new information and past data, and suggests the optimal point service based on the learning results of the generative AI. The suggested content is notified to the user in real time via their device. For example, a notification such as "When you use point card A at this store, you will receive 5% points" may be displayed within the app.

[0108] Points Management

[0109] Users can check their points balance and expiration date within the app. The server periodically checks the database and sends an alert to the user when their points are about to expire. For example, a notification might say, "Your points will expire in one month. We recommend that you use them for your next purchase."

[0110] Specific examples

[0111] For example, user B is about to purchase a new refrigerator. User B has already registered with the point service management system and regularly uses a supermarket point card, but this is the first time that user B has purchased a refrigerator.

[0112] 1. User B enters an electronics store and opens the dedicated app.

[0113] 2. Enter the product information (refrigerator) you plan to purchase into the app.

[0114] 3. The server analyzes the most suitable point card based on User B's location information and shopping history.

[0115] 4. The server determines that "using point card B at this store will result in higher points" and notifies user B.

[0116] 5. Based on the suggestion, User B purchases a refrigerator using Point Card B. As a result, User B can obtain the most advantageous points and enjoy a satisfying shopping experience.

[0117] As described above, the present invention is a system that helps users make the most of the points service. A series of processes are performed seamlessly, greatly improving convenience.

[0118] Prompt Sentence Examples

[0119] "Please explain in natural language the program process of a system that suggests the most suitable points program when a user purchases a new refrigerator. Please use either the server, terminal, or user as the subject and write in natural sentences."

[0120] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0121] Step 1: Register and save your basic information

[0122] 1. The user registers as a member using a dedicated app or website. Using a device, the user enters basic information such as name, email address, age, gender, and occupation, and presses the "Submit" button.

[0123] 2. The basic information entered is sent to the server.

[0124] 3. The server securely stores the received information in a database using SSL communication. A confirmation email is generated and sent to the user's email address. The user must click on the link in the email to confirm their account.

[0125] Step 2: Collecting loyalty program information and shopping history

[0126] 1. The user enters information about the point service they are using (point card name, balance, usage history, etc.) into the app. In some cases, the app uses a function to synchronize point card information via API.

[0127] 2. The entered point service information is sent to the server.

[0128] 3. The server stores point information and shopping history in a centralized database and manages user point statistics.

[0129] Step 3: Learning by generative AI and selection of optimal point service

[0130] 1. The server trains the generative AI model based on the user's point service information and shopping history stored in the database. The training data includes the point service's reward rate, usage method, and user shopping patterns.

[0131] 2. The generative AI analyzes this learning data and trains the model.

[0132] 3. The server uses the trained generative AI model to select the optimal point card when the user purchases a specific product, allowing the user to earn the most advantageous points.

[0133] Step 4: Propose and notify

[0134] 1. When a user makes a new purchase, they enter information about the product and store they plan to purchase within the app.

[0135] 2. The entered information is sent to the server, which analyzes it based on past data and the learning results of the generating AI.

[0136] 3. The server generates optimal point service proposals based on the analysis results.

[0137] 4. The server notifies the device of the suggested information. For example, a notification such as "Use point card A at this store to receive 5% points" is displayed within the app.

[0138] Step 5: Manage your points

[0139] 1. Users can check their points balance and expiration date within the app.

[0140] 2. The server periodically checks the point information in the database and generates an alert notification when the points are about to expire.

[0141] 3. An alert notification is sent from the server to the user's device. For example, a message such as "Your points will expire in one month. We recommend you use them for your next purchase" is displayed.

[0142] By operating the system according to the above steps, users can efficiently manage multiple point services and use the most advantageous points.

[0143] (Application example 1)

[0144] 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."

[0145] In modern society, users use multiple point services, and it is difficult to effectively manage and maximize their use of them. In particular, it is difficult to determine which point card is most advantageous for a product you plan to purchase, which often results in points being wasted. In addition, it is difficult to manage points that are approaching their expiration date, and they may expire. The purpose of this invention is to solve these problems.

[0146] 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.

[0147] In this invention, the server includes means for saving basic information acquired from the user, means for collecting the user's point service information and purchase history, analysis means using a generative AI model that learns based on the collected information and selects the optimal point service, suggestion means for proposing point services according to the user's life cycle, means for notifying the user terminal of information on the proposed point services, management means for managing the user's point balance and expiration date and notifying them as appropriate, and means for linking with an electronic payment service and proposing the optimal point card based on information on products the user plans to purchase. This allows the user to select the optimal point card and use their points efficiently.

[0148] "Basic information" refers to information obtained from a user, such as name, email address, age, sex, and occupation.

[0149] The "point service" refers to a service that includes multiple point cards used by users, their balances, usage history, etc.

[0150] "Purchase history" refers to information about products purchased by a user in the past, as well as the purchase date and time and price.

[0151] A "generative AI model" is an artificial intelligence model that learns from collected data and selects the most appropriate point service.

[0152] "Analysis means" refers to a means for analyzing basic information and purchasing history using a generative AI model.

[0153] The "proposing means" is a means for proposing the most suitable point service according to the user's life cycle and location information.

[0154] The "notification means" is a means for notifying the user terminal of information about the proposed point service in real time.

[0155] The "management means" is a means for managing the user's point balance and expiration date, and sending appropriate alert notifications.

[0156] An "electronic payment service" is a service that links with the payment method used by users when purchasing products.

[0157] A "user terminal" is an information processing device used by a user, such as a smartphone or tablet.

[0158] The present invention is a system that allows users to effectively utilize multiple point services, and provides a series of functions including user registration, data collection, analysis using a generative AI model, proposals and notifications, and point management. Specific embodiments for implementing the present invention are described below.

[0159] 1. User registration and saving basic information

[0160] A user installs the SmartPoint Manager app, creates an account, enters basic information (name, email address, age, gender, occupation, etc.), and sends it to the server.

[0161] The server receives the basic information, stores it in a database, and sends a confirmation email to the user, who clicks on the confirmation link in the email to complete the registration.

[0162] 2. Collection of point service information and purchase history

[0163] Users can enter information about their point cards through the app (e.g., point card name, balance, usage history, etc.). The app can also automatically synchronize information about linked point cards.

[0164] The server centrally manages the point information and purchase history provided by the user and stores them in a database.

[0165] 3. Learning with a generative AI model and selection of optimal point services

[0166] The server trains a generative AI model (e.g., GPT-4 (registered trademark)) based on the user's point information and purchase history collected by the server. The generative AI model analyzes the reward rate, usage method, and user shopping patterns of each point service.

[0167] The system selects the optimal point card based on the learning results, allowing users to use the most advantageous point card.

[0168] 4. Proposals and Notifications

[0169] The user inputs information about a new product they plan to purchase into the app. For example, they input information about a refrigerator they plan to purchase.

[0170] The server analyzes the input information and past data, and uses a generative AI model to propose optimal point services. The proposal results are sent to the user's smartphone in real time.

[0171] As a specific example, if a user enters an electronics store and tries to purchase a refrigerator, the app will notify them that "you will get more points if you use point card B at this store."

[0172] 5. Points Management

[0173] The app provides a feature that allows users to check their current point balance and expiration date.

[0174] The server periodically checks the user's point information and sends an alert when points are about to expire. For example, it may send a message saying, "Your points will expire in one month. We recommend that you use them for your next purchase."

[0175] Through this series of steps, users can effectively utilize the point service and enjoy maximum benefits.

[0176] Hardware and software used

[0177] Server: Performs data management, user authentication, and training and analysis of generative AI models. Use the most high-performance database possible (e.g., PostgreSQL, MySQL (registered trademark), etc.).

[0178] User device: An information processing device, such as a smartphone or tablet, on which a user installs and uses applications.

[0179] Generative AI models: Use sophisticated generative AI models such as GPT-4 to analyze collected data and make recommendations.

[0180] Prompt Sentence Examples

[0181] "Suggested item: Refrigerator. Points offer: {'name': 'Supermarket A', 'balance': 1500}, {'name': 'Electronics Store B', 'balance': 5000}. Purchase history: {'item_name': 'Freezer', 'purchase_date': '2022-01-01', 'price': 30000}"

[0182] Based on the above prompts, the generative AI model will select the optimal point card.

[0183] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0184] Step 1:

[0185] A user installs the SmartPoint Manager app and creates an account. The user enters basic information (name, email address, age, gender, occupation, etc.) and submits it. The server receives the entered basic information and stores it in a database. The server then sends a confirmation email to the user, and the user completes account registration by clicking the confirmation link in the email.

[0186] Input: Name, email address, age, gender, occupation

[0187] Output: Basic information stored in database, confirmation email sent

[0188] Step 2:

[0189] Users can input the information of multiple point cards they own into the app. They can also synchronize the information of linked point cards with the app. The server centralizes the point information and purchase history provided by the user and stores it in a database.

[0190] Input: Point card name, balance, usage history

[0191] Output: Points information and purchase history stored in the database

[0192] Step 3:

[0193] The server trains a generative AI model (e.g., GPT-4) based on the user's point information and purchase history collected. The generative AI model processes data such as the reward rate for each point service, usage methods, and user shopping patterns to generate criteria for selecting the optimal point card.

[0194] Input: Points information, purchase history

[0195] Output: Trained generative AI model

[0196] Step 4:

[0197] The user enters information about a new product they plan to purchase into the app. The server analyzes this new information and past data and uses a generative AI model to propose the optimal point service. The server generates a prompt and sends it to the generative AI model, which then recommends the optimal point card based on the analysis results.

[0198] Input: Product information to be purchased (e.g., refrigerator)

[0199] Output: Recommendation of the best loyalty card

[0200] Step 5:

[0201] The server notifies the user of the results of the suggestion in real time to their smartphone, for example, "If you use point card B at this store, you will get more points."

[0202] Input: Recommendation of the best rewards card

[0203] Output: User notification

[0204] Step 6:

[0205] Users can check their current point balance and expiration date within the app. The server periodically checks the user's point information and sends an alert when points are approaching their expiration date. For example, it may notify users that "Your points will expire in one month. We recommend that you use them for your next purchase."

[0206] Input: Points balance and expiration date

[0207] Output: Alert notification to the user

[0208] Through the above processing steps, the user can effectively utilize the point service and enjoy maximum benefits.

[0209] 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.

[0210] The present invention is a system that effectively utilizes multiple point services and recognizes user emotions to provide more personalized suggestions. This system includes means for user registration, data collection, analysis using a generative AI, suggestion adjustment using an emotion engine, and point management. Specific embodiments for implementing the present invention are described below.

[0211] User registration and saving basic information

[0212] 1. The user opens the dedicated app or website, accesses the membership registration page, enters basic information (name, email address, age, gender, occupation, etc.), and presses the registration button.

[0213] 2. The server receives the basic information sent by the user and stores it in a database, then sends a confirmation email to the user, who clicks on the link to confirm and completes the registration.

[0214] Collection of point service information and shopping history

[0215] 1. The user enters information about the point service they use (point card name, balance, usage history, etc.) into the app. It is also possible to synchronize linked point card information with the app.

[0216] 2. The server stores the point service information and shopping history received from the user in a database. Once the information collection is complete, proceed to the next step.

[0217] Learning by generative AI and selection of optimal point services

[0218] 1. The server provides the stored data to the generation AI, which then learns from the data. The generation AI then creates a model to analyze the reward rate and usage of the point service, as well as the user's shopping patterns.

[0219] 2. Next, the server uses the generation AI to select the optimal point service. For example, it selects the optimal point card when a user purchases an electrical appliance.

[0220] Suggestions and adjustments with the emotion engine

[0221] 1. When a user makes a new purchase, they enter store information and information about the product they plan to purchase into the app. After completing the entry, they press the submit button.

[0222] 2. The server receives the shopping information sent by the user and provides it to the generation AI, which selects the optimal point service based on past data and new information.

[0223] 3. Based on the analysis results of the generation AI, the server creates the optimal point service proposal for the user.

[0224] 4. The emotion engine analyzes the user's voice and facial expression data to identify the user's emotional state, for example, whether the user is in good spirits or stressed.

[0225] 5. The emotion engine adjusts the suggestions based on the user's emotional state. For example, if the user is stressed, it will provide simple suggestions, and if the user is in good spirits, it will provide detailed suggestions.

[0226] 6. The suggested content is notified to the user via the device (smartphone app). For example, the user may be notified that "If you use point card A at this store, you will receive 5% points."

[0227] Points Management

[0228] 1. After completing a purchase, the user enters the purchase information into the app, which then sends it to the server.

[0229] 2. The server saves the new purchase information in the database and updates the points balance and expiration date.

[0230] 3. The server periodically checks the point balance and expiration date, and notifies the user when the expiration date is approaching. For example, it may notify the user that "Your points will expire in one month. We recommend that you use them for your next purchase."

[0231] Specific examples

[0232] For example, user C is about to purchase a new smartphone. User C has registered with a point utilization advisor and regularly uses several point cards.

[0233] 1. User C enters an electronics store and opens the app.

[0234] 2. Enter the product information you plan to purchase (on your smartphone) into the app.

[0235] 3. The server analyzes the most suitable point card based on user C's location information and shopping history.

[0236] 4. The server determines that "Using Company C's point card at this store will give you the maximum points," and notifies User C.

[0237] 5. The emotion engine analyzes user C's voice and facial expressions and recognizes that user C is in a happy emotional state.

[0238] 6. Based on the results of the emotion engine's analysis, the display method of the proposal is adjusted and detailed instructions on how to use points are provided to User C.

[0239] 7. Based on the suggestion, User C uses Company C's point card to purchase a smartphone. As a result, they can get the most points and have a satisfying shopping experience.

[0240] In this way, the present invention helps users make the most of point services and provides a more effective user experience by recognizing the user's emotions and adjusting the content of suggestions.

[0241] The processing flow will be explained below.

[0242] Step 1:

[0243] The user opens the dedicated app or website, accesses the membership registration page, enters basic information (name, email address, age, gender, occupation, etc.), and presses the registration button.

[0244] Step 2:

[0245] The server receives the basic information sent by the user and saves it in the database. After saving, the server sends a confirmation email to the user. The user completes registration by clicking the link in the email.

[0246] Step 3:

[0247] The user enters information about the point service they are using (point card name, balance, usage history, etc.) into the app. Then, they press the point card information synchronization button to send the linked point card information from their device to the server.

[0248] Step 4:

[0249] The server stores the point service information and shopping history received from the user in a database, thereby unifying all point information held by the user.

[0250] Step 5:

[0251] The server provides the stored data to the generation AI, which then learns from it. The generation AI analyzes the reward rate, usage method, and user shopping patterns of each point service and creates a model based on that information.

[0252] Step 6:

[0253] When a user makes a new purchase, they enter the product information and store information into the app. After completing the entry, they press the send button.

[0254] Step 7:

[0255] The server receives the shopping information sent by the user and provides it to the generation AI, which analyzes the past data and new shopping information to select the optimal point service.

[0256] Step 8:

[0257] The server creates the optimal point service proposal for the user based on the analysis results of the generated AI. For example, "At this store, you will receive 5% points when you use point card A."

[0258] Step 9:

[0259] To recognize the user's emotional state, the emotion engine collects voice and facial expression data through the device's camera and microphone. The emotion engine analyzes this data to determine whether the user is excited or calm.

[0260] Step 10:

[0261] The server receives the results of the emotion engine and adjusts how the suggestions are presented based on the user's emotional state: for example, if the user is stressed, it displays brief suggestions, but if the user is relaxed, it provides detailed information.

[0262] Step 11:

[0263] The terminal receives the recommendation notification from the server and notifies the user in real time, allowing the user to use the most suitable point card when shopping.

[0264] Step 12:

[0265] After the user completes their purchase, they enter their purchase information into the app, which then sends it to the server.

[0266] Step 13:

[0267] The server saves the new purchase information in the database and updates the point balance and expiration date, thereby managing the latest status of point usage.

[0268] Step 14:

[0269] The server periodically checks the point balance and expiration date, and notifies the user when the expiration date is approaching. Example: "Your points will expire in one month. We recommend that you use them for your next purchase."

[0270] Through this series of processes, the user can receive suggestions for optimal point services and receive information according to their emotional state, allowing them to use their points effectively and comfortably.

[0271] Example 2

[0272] 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."

[0273] Conventional point service systems have difficulty in managing multiple point cards, making it difficult to propose optimal ways to use points. Furthermore, proposals do not take into account the user's emotional state, limiting the quality of the user experience. Furthermore, insufficient point expiration management and notifications have led to users wasting points.

[0274] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0275] In this invention, the server includes means for saving basic information acquired from the user, means for collecting the user's point service information and shopping history, analysis means using a generative AI model that learns based on the collected information and selects the optimal point service, adjustment means using an emotion engine that recognizes the user's emotional state, suggestion means for proposing point services according to the user's life cycle, means for notifying the user terminal of information on the proposed point services, and management means for managing the user's point balance and expiration date and notifying them as appropriate. This makes it possible to manage multiple point services in an integrated manner and suggest the optimal way to use points according to the user's emotional state.

[0276] "Means for storing basic information obtained from users" refers to a system component that has the function of collecting basic information such as a user's name, email address, age, gender, occupation, etc., and storing it in a database.

[0277] "Means for collecting user point service information and shopping history" refers to a system component that has the function of collecting information on various point cards used by users (point card name, balance, usage history, etc.) and the user's purchase history.

[0278] "Analysis means using a generative AI model" refers to a component of a system that has the function of utilizing collected point service and shopping history data to perform analysis using a generative AI model in order to select the most appropriate point service.

[0279] The "adjustment means using an emotion engine that recognizes the user's emotional state" is a component of the system that has the function of analyzing the user's voice and facial expression data, recognizing the user's emotional state (for example, whether they are in good spirits or feeling stressed), and adjusting the content of suggestions accordingly.

[0280] The "suggestion means for proposing point services according to the user's life cycle" is a component of the system that has the function of proposing the most suitable point service to the user based on the user's basic information, point service usage history, shopping patterns, and emotional state.

[0281] "Means for notifying the user device of information about the proposed point service" refers to a system component that has the function of notifying the user's device, such as a smartphone app, of information about the optimal point service selected by the generative AI model.

[0282] The "management means for managing the user's point balance and expiration date and notifying them as appropriate" is a system component that has the function of managing the balance of points held by the user and the expiration date of points, and notifying the user when the expiration date is approaching.

[0283] The present invention is a system that effectively utilizes multiple point services and recognizes user emotions to provide more personalized suggestions. The system includes means for user registration, data collection, analysis using a generative AI model, suggestion adjustment using an emotion engine, and point management. Specific embodiments are described below.

[0284] User registration and saving basic information

[0285] 1. User visits the membership registration page:

[0286] The user opens the dedicated app or website, enters basic information such as name, email address, age, gender, and occupation, and presses the registration button.

[0287] The server receives the basic information sent by the user, stores it in a database, and then sends a confirmation email to the user, who clicks on the link to confirm and completes the registration.

[0288] Collection of point service information and shopping history

[0289] 2. User enters loyalty card information:

[0290] Users enter information about the point service they use (point card name, balance, usage history, etc.) into a dedicated app. It is also possible to synchronize linked point card information with the app.

[0291] The server stores the point service information and shopping history received from the user in a database.

[0292] Learning by generative AI model and selection of optimal point service

[0293] 3. The server provides data to the generative AI model:

[0294] The server provides the stored user data to the generative AI model, which then learns from this data. The data includes point card usage history and shopping patterns.

[0295] The generative AI model analyzes the data and creates a model that understands the reward rate and usage method of the point service, as well as the user's shopping patterns.

[0296] Based on the model created by the generative AI model, the server selects the optimal point service tailored to the user's shopping behavior.

[0297] Suggestions and adjustments with the emotion engine

[0298] 4. User enters new shopping information:

[0299] When a user makes a new purchase, they enter store information and information about the item they wish to purchase into the app and press the send button.

[0300] The server receives the shopping information sent by the user and provides it to the generative AI model, which then reanalyzes the optimal point service based on the new information and past data.

[0301] 5. The server creates the proposal and the emotion engine adjusts it:

[0302] The server creates an optimal point service proposal for the user based on the analysis results of the generated AI model. The proposal might be something like, "If you use point card A at this store, you will receive 5% points."

[0303] The emotion engine analyzes the user's voice and facial expression data to determine whether the user is in good spirits or stressed.

[0304] The emotion engine tailors suggestions based on the user's emotional state, for example, providing simple, easy-to-understand suggestions if the user is stressed, and detailed suggestions if the user is in a positive mood.

[0305] The proposed content will be notified to the user via the device (smartphone app).

[0306] Points Management

[0307] 6. User enters purchase information:

[0308] After the user completes their purchase, they enter the purchase information into the app and send it from the device to the server.

[0309] The server saves the new purchase information in the database and updates the points balance and expiration date.

[0310] 7. The server notifies:

[0311] The server periodically checks the point balance and expiration date, and notifies the user when the expiration date is approaching. For example, it may notify the user that "Your points will expire in one month. We recommend that you use them for your next purchase."

[0312] Specific examples

[0313] For example, if a user is looking to buy a new smartphone:

[0314] 1. A user enters a retail store and opens the app.

[0315] 2. Enter the product information you plan to purchase (on your smartphone) into the app.

[0316] 3. The server analyzes the user's location information and shopping history to find the most suitable point card.

[0317] 4. The server notifies the customer, "At this store, you will receive the maximum points if you use point card A."

[0318] 5. The emotion engine analyzes the user's voice and facial expressions to recognize when the user is in a happy emotional state.

[0319] 6. Based on the results of the emotion engine's analysis, the display of the offer is adjusted and detailed instructions on how to use points are provided to the user.

[0320] 7. Based on the suggestion, the user uses point card A to purchase a smartphone.

[0321] Prompt Sentence Examples

[0322] Examples of prompts to input to a generative AI model include:

[0323] Select and suggest the most suitable rewards card based on the user's shopping history and rewards service information. Adjust the recommendation taking into account the user's emotional state.

[0324] This system allows users to make the most of multiple point services and receive optimal suggestions based on their emotional state.

[0325] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0326] Step 1:

[0327] User registration and saving basic information

[0328] Input: The user enters basic information such as name, email address, age, gender, and occupation into a dedicated app or website.

[0329] Specific operation: The user enters the required information into the input form and presses the registration button.

[0330] Data processing and data calculation: The server receives the basic information sent by the user, converts the input data into a database format, and stores it in the database.

[0331] Output: After saving is complete, the server will send a confirmation email to the user.

[0332] Step 2:

[0333] Collection of point service information and shopping history

[0334] Input: The user uses a dedicated app to input or synchronize information about the point card they are using (point card name, balance, usage history, etc.).

[0335] Specific operation: The user enters point card information or clicks the data synchronization button.

[0336] Data processing and data calculation: The server receives the loyalty program information and shopping history sent by the user, converts the data into a database format, and stores it in the database.

[0337] Output: Point service information and shopping history are added to the database.

[0338] Step 3:

[0339] Learning by generative AI model and selection of optimal point service

[0340] Input: The server provides stored user data (point card usage history and shopping patterns) to the generative AI model.

[0341] Specific operation: The server inputs data from the database into the generative AI model, and the AI ​​model analyzes the data.

[0342] Data processing and data calculation: The generative AI model learns the reward rate and usage method of the point service, as well as the user's shopping patterns, and creates a model to select the most suitable point service.

[0343] Output: The analytical model created by the generative AI model is output.

[0344] Step 4:

[0345] Enter new shopping information and prepare suggestions

[0346] Input: When a user makes a new purchase, they enter store information and information about the item they plan to purchase into the app and press the submit button.

[0347] Specific operation: The user enters the store name and product name into the app's input form and clicks the "Submit" button.

[0348] Data processing and data calculation: The server receives new shopping information and provides it back to the generative AI model for updated analysis.

[0349] Output: The analysis results from the generative AI model output the selection of the most suitable point service.

[0350] Step 5:

[0351] Suggestions and adjustments with the emotion engine

[0352] Input: The server creates suggestions based on the AI's analysis results and the user's new shopping information.

[0353] Specific operation: Based on the analysis results, the server creates a suggestion such as "If you use point card A at this store, you will receive 5% points."

[0354] Data processing and data calculation: The emotion engine analyzes the user's voice and facial expression data to identify the user's emotional state (e.g., cheerfulness, stress). The emotion engine adjusts the suggestions, changing their complexity and level of detail.

[0355] Output: The optimal recommendation content adjusted by the emotion engine is generated.

[0356] Step 6:

[0357] Notification of proposal details

[0358] Input: Data to inform tailored proposals.

[0359] Specific operation: The server notifies the user of the optimal proposal via a smartphone app. For example, a push notification such as "Use point card A to receive 5% points" is sent.

[0360] Output: A notification of the proposed content is sent to the user's device.

[0361] Step 7:

[0362] Points Management

[0363] Input: Purchase information entered by the user after completing a purchase.

[0364] What happens: The user enters purchase details into an in-app form and presses the "Submit" button.

[0365] Data processing and calculation: The server saves new purchase information to the database, updates point balances and expiration dates, and periodically checks the database to send out alerts when expiration dates are approaching.

[0366] Output: The updated point balance and expiration information are saved in the database, and when the expiration date approaches, the user is notified that "Your points will expire in one month."

[0367] (Application example 2)

[0368] 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."

[0369] In recent years, there has been a demand for improved consumer convenience through point services. However, the existence of multiple point services makes it difficult to determine the optimal point card usage. Furthermore, there is a lack of methods to provide customized suggestions based on the user's emotional state and real-time situation. This poses a problem that users are likely to be unable to make the most of point services.

[0370] The specification process by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for recognizing the user's emotions and adjusting the content of suggestions based on the emotional state, means for saving basic information acquired from the user, means for collecting the user's point service information and shopping history, analysis means using a generation AI, suggestion means, and management means. This makes it possible to select and suggest the optimal point service taking the user's emotions into consideration.

[0371] "Means for storing basic information obtained from the user" refers to a function that records basic information such as the user's name, email address, age, sex, and occupation, and stores it in a database.

[0372] "Means for collecting user's point service information and shopping history" is a function that collects the balance of each point card used by the user, usage history, and the user's shopping history, and stores them in a database.

[0373] "Analysis method using generative AI" is a function that uses generative AI to learn from collected information and select the most suitable point service for the user.

[0374] The "suggestion means" is a function that suggests to the user the most appropriate way to use the points service based on the user's life cycle, location information, and emotional state.

[0375] The "notification means" is a function for notifying the user's terminal of information about the proposed point service.

[0376] The "management means" is a function that manages the user's point balance and expiration date, and notifies the user when the expiration date is approaching.

[0377] The "emotion engine" is a function that analyzes the user's voice and facial expression data to recognize their emotional state and adjusts the suggestions based on that.

[0378] The invention is a system that effectively utilizes the user's point service, identifies the user's emotional state, and adjusts the content of suggestions. The main components of this system are a user terminal, a server, a generation AI, and an emotion engine.

[0379] First, the user accesses the smart glasses' application "Point Master" and registers by entering basic information. This basic information includes name, email address, age, gender, occupation, etc. The server receives this basic information and stores it in a database. Point service information and shopping history sent from the user's device are also collected and stored on the server.

[0380] Next, the server uses a generation AI based on the collected data to select the optimal point service. The generation AI analyzes the point service's reward rate, usage method, and the user's shopping patterns to select the most advantageous point card for the user. Furthermore, the suggestion content is adjusted by an emotion engine, taking into account the user's emotional state. The emotion engine analyzes the user's voice and facial expression data to determine whether the user is in good spirits or stressed. For example, if the user is in good spirits, detailed suggestions are made, and if the user is stressed, simple suggestions are provided.

[0381] The suggestions are sent to the user's device in real time. For example, when a user is browsing products in a physical store using smart glasses, they enter store information and information about the products they plan to purchase into the app and send it to the server. The server then analyzes the optimal point service based on location information and shopping history, and notifies the user's device of the results. Furthermore, after the user completes their shopping, they enter their purchase information into the app, and the server updates their point balance and expiration date and sends appropriate notifications.

[0382] The hardware used includes smart glasses (e.g., Google® Glass® or Vuzix Blade) and user devices (e.g., smartphones). The software includes an emotion recognition module, a recommendation engine, and a point service API, which work together.

[0383] As a concrete example, consider a user who is about to purchase a smartphone at an electronics retail store. The user puts on smart glasses and enters the product information they plan to purchase into the app. The server recommends the most suitable point service based on their point card information and shopping history, and notifies them that "You will receive the maximum points if you use Company A's point card at this store." At this time, the emotion engine recognizes that the user appears to be enjoying themselves, and also provides detailed instructions on how to use the points.

[0384] A specific example of a prompt is as follows:

[0385] "Please enter the following store and product information based on the user's shopping history and loyalty card information:

[0386] Store information: Electronics retailer, Shinjuku

[0387] Product information: Smartphone, Price: 50,000 yen

[0388] Please explain the recommended point cards based on the generated AI and the reasons for them.

[0389] This system allows users to make the most of the points service, and also allows them to have a more satisfying purchasing experience by adjusting the suggestions according to their emotional state.

[0390] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0391] Step 1:

[0392] A user opens the smart glasses application and enters basic information, such as name, email address, age, gender, occupation, etc. This information is sent from the user's device to the server, which then stores it in a database.

[0393] Input: User's basic information (name, email address, age, gender, occupation)

[0394] Output: User basic information stored in the database

[0395] Specific behavior:

[0396] A user accesses the application and enters the required information into the form. When the user presses the submit button, the terminal sends the information to the server, which records the information in a database.

[0397] Step 2:

[0398] The user enters information about the point service they use and their shopping history into the application. The device sends this information to the server, which then stores it in a database.

[0399] Input: Point service information (point card name, balance, usage history), shopping history

[0400] Output: Point service and shopping history information stored in the database

[0401] Specific behavior:

[0402] When a user enters their point card information or shopping history into the application and submits it, the device sends the information to the server, which then stores it in a database.

[0403] Step 3:

[0404] The server provides the collected information to the AI ​​generator, which then analyzes the reward rate and usage of the points service, as well as the user's shopping patterns, to select the most suitable points service.

[0405] Input: Point service information and shopping history stored in the database

[0406] Output: Selection result of optimal point service by generation AI

[0407] Specific behavior:

[0408] The server passes the database information to the generation AI, which processes the data and learns from it. It selects the optimal point service and returns the results to the server.

[0409] Step 4:

[0410] When a user goes shopping at a physical store, they put on the smart glasses and input the product information they plan to purchase, which is then sent to the server.

[0411] Input: Store information, product information to be purchased (product name, price)

[0412] Output: Information about the products to be purchased received by the server

[0413] Specific behavior:

[0414] The user uses the smart glasses in a physical store and enters product information into the app. When the user presses the send button, the device sends the information to the server.

[0415] Step 5:

[0416] The server uses AI generation to select the optimal point service based on the user's location and purchase information, and notifies the user of the results.

[0417] Input: Information stored in the database, current location, purchase information

[0418] Output: The optimal point service selection result notified to the user device

[0419] Specific behavior:

[0420] The server uses AI to select the optimal point service based on location information and purchase information, and the selection result is notified to the user's device.

[0421] Step 6:

[0422] The emotion engine analyzes the user's emotional state and adjusts the suggestions accordingly: for example, if the user is in good spirits, it will provide detailed suggestions, and if they are stressed, it will provide simple suggestions.

[0423] Input: User's voice data, facial expression data

[0424] Output: Adjusted proposal

[0425] Specific behavior:

[0426] The emotion engine uses the smart glasses' camera and microphone to analyze the user's voice and facial expressions to determine their emotional state, and the server then adjusts the suggestions accordingly.

[0427] Step 7:

[0428] After completing a purchase, the user enters the purchase information into the app. The device then sends the information to the server, which updates the points balance and expiration date. If necessary, the app notifies users of points approaching their expiration date.

[0429] Input: Purchase information (purchased product, price)

[0430] Output: Updated points balance, expiration notice

[0431] Specific behavior:

[0432] After a purchase, the user enters product information into the app and presses the send button. The device sends the information to the server, which updates the database with the points balance and expiration date. If a notification is required, the user is notified.

[0433] 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.

[0434] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0435] 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.

[0436] [Second embodiment]

[0437] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0438] 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.

[0439] 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).

[0440] 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.

[0441] 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.

[0442] 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).

[0443] 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. 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.

[0444] 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.

[0445] 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.

[0446] 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.

[0447] 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.

[0448] 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."

[0449] The present invention is a system for effectively utilizing multiple point services, and is a series of functions including user registration, data collection, analysis by generation AI, proposals and notifications, and point management. Specific embodiments for implementing the present invention are described below.

[0450] User registration and saving basic information

[0451] 1. The user registers as a member using the dedicated app or website. They enter basic information (name, email address, age, gender, occupation, etc.) on their device and press the send button.

[0452] 2. The server receives the basic information sent by the user and stores it in the database. It then sends a confirmation email to the user, who must click on the link to confirm and complete the registration.

[0453] Collection of point service information and shopping history

[0454] 1. The user enters information about the point service they are using (point card name, balance, usage history, etc.) into the app. It is also possible to synchronize linked point card information with the app.

[0455] 2. The server collects the point information and shopping history provided by the user and stores them in a centralized database.

[0456] Learning by generative AI and selection of optimal point services

[0457] 1. The server trains the generation AI based on the collected user point service information and shopping history. Specifically, the generation AI learns the reward rate of each point service, how to use it, and the user's shopping patterns.

[0458] 2. Next, the server uses the generation AI to select the optimal point service. For example, it selects the optimal point card when a user purchases electronic devices.

[0459] Suggestions and Notifications

[0460] 1. The user opens the app and enters information for a new purchase (for example, the product and store information they plan to purchase).

[0461] 2. The server analyzes this new information and past data and proposes the optimal point service based on the learning results of the generating AI.

[0462] 3. The suggested content is notified to the user via the device (smartphone app). For example, a real-time notification such as "Use point card A at this store to receive 5% points" is sent.

[0463] Points Management

[0464] 1. Users can check their points balance and expiration date within the app.

[0465] 2. The server periodically checks the user's point information and sends an alert when the expiration date is approaching. For example, it may send a message saying, "Your points will expire in one month. We recommend that you use them for your next purchase."

[0466] Specific examples

[0467] For example, user B is planning to purchase a new refrigerator. User B has registered with the point utilization advisor and regularly uses a supermarket point card, but this is the first time that user B has purchased a refrigerator.

[0468] 1. User B enters an electronics store and opens the app.

[0469] 2. Enter the product information (refrigerator) you plan to purchase into the app.

[0470] 3. The server analyzes the most suitable point card based on User B's location information and shopping history.

[0471] 4. The server determines that "Using Company B's point card at this store will result in higher points" and notifies User B.

[0472] 5. Based on the suggestion, User B uses Company B's point card to purchase a refrigerator. As a result, User B can obtain the most advantageous points and enjoy a satisfying shopping experience.

[0473] As described above, the present invention helps users to make the most of point services, and provides a specific form for seamlessly performing a series of processes and improving convenience.

[0474] The processing flow will be explained below.

[0475] Step 1:

[0476] The user opens the dedicated app or website, accesses the membership registration page, enters basic information (name, email address, age, gender, occupation, etc.), and presses the registration button.

[0477] Step 2:

[0478] The server receives the basic information sent by the user and saves it in the database. After saving, the server sends a confirmation email to the user. The user completes registration by clicking the link in the email.

[0479] Step 3:

[0480] The user enters the information of the point service they use (point card name, balance, usage history, etc.) into the app, or synchronizes the information of the linked point card. When the synchronization button is pressed, the information is sent from the device to the server.

[0481] Step 4:

[0482] The server stores the point service information and shopping history received from the user in a database. Once the information collection is complete, proceed to the next step.

[0483] Step 5:

[0484] The server provides the stored data to the generation AI, which then learns from the data. The generation AI then creates a model to analyze the reward rate and usage of the point service, as well as user shopping patterns.

[0485] Step 6:

[0486] When a user makes a new purchase, they enter store information and information about the product they plan to purchase into the app. After completing the input, they press the submit button.

[0487] Step 7:

[0488] The server receives the shopping information sent by the user and provides it to the generation AI, which selects the optimal point service based on past data and new information.

[0489] Step 8:

[0490] The server creates the optimal point service proposal for the user based on the analysis results of the generation AI. The proposal is then notified to the device app. For example, it may notify the user that "If you use point card A at this store, you will receive the maximum points."

[0491] Step 9:

[0492] After the user completes their purchase, they enter the purchase information into the app, which then sends it to the server.

[0493] Step 10:

[0494] The server saves the new purchase information in the database and updates the points balance and expiration date.

[0495] Step 11:

[0496] The server periodically checks the point balance and expiration date, and notifies the user when the expiration date is approaching. For example, it may notify the user that "Your points will expire in one month. We recommend that you use them for your next purchase."

[0497] Through this series of processes, users can receive suggestions for optimal point services and use their points efficiently.

[0498] Example 1

[0499] 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."

[0500] Traditional point services required the time and effort of managing multiple point cards, making it difficult for users to make the most of their points. They also lacked the information needed to properly determine which point card would provide the most benefits. Furthermore, users often lost points without realizing their points were about to expire.

[0501] 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.

[0502] In this invention, the server includes a means for saving basic information acquired from the user, a means for collecting the user's point service information and shopping history, and an analysis means using a generation AI that learns from the collected information and selects the most suitable point service, thereby enabling the user to effectively manage and make the most of the point service.

[0503] The "means for storing basic information" is a function for safely storing basic personal information obtained from users, such as their name, email address, age, sex, and occupation.

[0504] "Means for collecting point service information and shopping history" refers to a function for collecting and centrally managing information about the point card currently in use by the user (point card name, balance, usage history, etc.) and the user's shopping history.

[0505] "Analysis method using generative AI" is a function that uses generative artificial intelligence to analyze and learn from collected point service information and shopping history to select the most suitable point service.

[0506] The "means for the user to input information about the product and store that the user plans to purchase" is a function for the user to input information about the product that the user plans to purchase and the store where the user plans to purchase the product.

[0507] "Means for notifying the user device of information about the proposed point service" is a function for notifying the user device in real time of information about the optimal point service selected by the generation AI.

[0508] The "management means for managing point balances and expiration dates and notifying them as appropriate" is a function for managing the balance on a user's point card and the expiration date of points, and sending alert notifications to the user when the expiration date is approaching.

[0509] "Means for proposing point services in real time" is a function that immediately uses new information such as the user's location information to propose optimal point services to the user in real time.

[0510] MODE FOR CARRYING OUT THE INVENTION

[0511] This invention is a system that helps users efficiently manage and use multiple point services, and provides the functions of user registration, data collection, analysis by AI generation, proposals and notifications, and point management in an integrated manner. Specific embodiments for implementing this invention are described below.

[0512] User registration and saving basic information

[0513] Users register as members using a dedicated app or website. Using a device (smartphone or PC), they enter basic information such as their name, email address, age, gender, and occupation, and then press the "Submit" button. The server receives the basic information sent from the device and securely stores it in a database using SSL communication. A confirmation email is then sent to the user, and registration is completed when the user clicks on the link in the email to confirm.

[0514] Collection of point service information and shopping history

[0515] Users enter information about the point service they are using (point card name, balance, usage history, etc.) into the app. If the app supports it, point card information can be automatically synchronized via API. The server collects the point information and shopping history provided by the user and stores it in a centralized database. This allows users to manage multiple point services on a single platform.

[0516] Learning by generative AI and selection of optimal point services

[0517] The server trains the generative AI model based on the collected user point service information and shopping history. Specifically, the reward rate for each point service, usage methods, and user shopping patterns are used as learning data. Using the trained generative AI model, the server selects the optimal point service. For example, when a user purchases a new electronic device, it can select the most advantageous point card for that product.

[0518] Suggestions and Notifications

[0519] The user opens the app and enters new shopping information (for example, the product and store information they plan to purchase). The server analyzes this new information and past data, and suggests the optimal point service based on the learning results of the generative AI. The suggested content is notified to the user in real time via their device. For example, a notification such as "When you use point card A at this store, you will receive 5% points" may be displayed within the app.

[0520] Points Management

[0521] Users can check their points balance and expiration date within the app. The server periodically checks the database and sends an alert to the user when their points are about to expire. For example, a notification might say, "Your points will expire in one month. We recommend that you use them for your next purchase."

[0522] Specific examples

[0523] For example, user B is about to purchase a new refrigerator. User B has already registered with the point service management system and regularly uses a supermarket point card, but this is the first time that user B has purchased a refrigerator.

[0524] 1. User B enters an electronics store and opens the dedicated app.

[0525] 2. Enter the product information (refrigerator) you plan to purchase into the app.

[0526] 3. The server analyzes the most suitable point card based on User B's location information and shopping history.

[0527] 4. The server determines that "using point card B at this store will result in higher points" and notifies user B.

[0528] 5. Based on the suggestion, User B purchases a refrigerator using Point Card B. As a result, User B can obtain the most advantageous points and enjoy a satisfying shopping experience.

[0529] As described above, the present invention is a system that helps users make the most of the points service. A series of processes are performed seamlessly, greatly improving convenience.

[0530] Prompt Sentence Examples

[0531] "Please explain in natural language the program process of a system that suggests the most suitable points program when a user purchases a new refrigerator. Please use either the server, terminal, or user as the subject and write in natural sentences."

[0532] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0533] Step 1: Register and save your basic information

[0534] 1. The user registers as a member using a dedicated app or website. Using a device, the user enters basic information such as name, email address, age, gender, and occupation, and presses the "Submit" button.

[0535] 2. The basic information entered is sent to the server.

[0536] 3. The server securely stores the received information in a database using SSL communication. A confirmation email is generated and sent to the user's email address. The user must click on the link in the email to confirm their account.

[0537] Step 2: Collecting loyalty program information and shopping history

[0538] 1. The user enters information about the point service they are using (point card name, balance, usage history, etc.) into the app. In some cases, the app uses a function to synchronize point card information via API.

[0539] 2. The entered point service information is sent to the server.

[0540] 3. The server stores point information and shopping history in a centralized database and manages user point statistics.

[0541] Step 3: Learning by generative AI and selection of optimal point service

[0542] 1. The server trains the generative AI model based on the user's point service information and shopping history stored in the database. The training data includes the point service's reward rate, usage method, and user shopping patterns.

[0543] 2. The generative AI analyzes this learning data and trains the model.

[0544] 3. The server uses the trained generative AI model to select the optimal point card when the user purchases a specific product, allowing the user to earn the most advantageous points.

[0545] Step 4: Propose and notify

[0546] 1. When a user makes a new purchase, they enter information about the product and store they plan to purchase within the app.

[0547] 2. The entered information is sent to the server, which analyzes it based on past data and the learning results of the generating AI.

[0548] 3. The server generates optimal point service proposals based on the analysis results.

[0549] 4. The server notifies the device of the suggested information. For example, a notification such as "Use point card A at this store to receive 5% points" is displayed within the app.

[0550] Step 5: Manage your points

[0551] 1. Users can check their points balance and expiration date within the app.

[0552] 2. The server periodically checks the point information in the database and generates an alert notification when the points are about to expire.

[0553] 3. An alert notification is sent from the server to the user's device. For example, a message such as "Your points will expire in one month. We recommend you use them for your next purchase" is displayed.

[0554] By operating the system according to the above steps, users can efficiently manage multiple point services and use the most advantageous points.

[0555] (Application example 1)

[0556] 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."

[0557] In modern society, users use multiple point services, and it is difficult to effectively manage and maximize their use of them. In particular, it is difficult to determine which point card is most advantageous for a product you plan to purchase, which often results in points being wasted. In addition, it is difficult to manage points that are approaching their expiration date, and they may expire. The purpose of this invention is to solve these problems.

[0558] 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.

[0559] In this invention, the server includes means for saving basic information acquired from the user, means for collecting the user's point service information and purchase history, analysis means using a generative AI model that learns based on the collected information and selects the optimal point service, suggestion means for proposing point services according to the user's life cycle, means for notifying the user terminal of information on the proposed point services, management means for managing the user's point balance and expiration date and notifying them as appropriate, and means for linking with an electronic payment service and proposing the optimal point card based on information on products the user plans to purchase. This allows the user to select the optimal point card and use their points efficiently.

[0560] "Basic information" refers to information obtained from a user, such as name, email address, age, sex, and occupation.

[0561] The "point service" refers to a service that includes multiple point cards used by users, their balances, usage history, etc.

[0562] "Purchase history" refers to information about products purchased by a user in the past, as well as the purchase date and time and price.

[0563] A "generative AI model" is an artificial intelligence model that learns from collected data and selects the most appropriate point service.

[0564] "Analysis means" refers to a means for analyzing basic information and purchasing history using a generative AI model.

[0565] The "proposing means" is a means for proposing the most suitable point service according to the user's life cycle and location information.

[0566] The "notification means" is a means for notifying the user terminal of information about the proposed point service in real time.

[0567] The "management means" is a means for managing the user's point balance and expiration date, and sending appropriate alert notifications.

[0568] An "electronic payment service" is a service that links with the payment method used by users when purchasing products.

[0569] A "user terminal" is an information processing device used by a user, such as a smartphone or tablet.

[0570] The present invention is a system that allows users to effectively utilize multiple point services, and provides a series of functions including user registration, data collection, analysis using a generative AI model, proposals and notifications, and point management. Specific embodiments for implementing the present invention are described below.

[0571] 1. User registration and saving basic information

[0572] A user installs the SmartPoint Manager app, creates an account, enters basic information (name, email address, age, gender, occupation, etc.), and sends it to the server.

[0573] The server receives the basic information, stores it in a database, and sends a confirmation email to the user, who clicks on the confirmation link in the email to complete the registration.

[0574] 2. Collection of point service information and purchase history

[0575] Users can enter information about their point cards through the app (e.g., point card name, balance, usage history, etc.). The app can also automatically synchronize information about linked point cards.

[0576] The server centrally manages the point information and purchase history provided by the user and stores them in a database.

[0577] 3. Learning with a generative AI model and selection of optimal point services

[0578] The server trains a generative AI model (e.g., GPT-4) based on the user's point information and purchase history collected by the server. The generative AI model analyzes the reward rate, usage method, and user shopping patterns of each point service.

[0579] The system selects the optimal point card based on the learning results, allowing users to use the most advantageous point card.

[0580] 4. Proposals and Notifications

[0581] The user inputs information about a new product they plan to purchase into the app. For example, they input information about a refrigerator they plan to purchase.

[0582] The server analyzes the input information and past data, and uses a generative AI model to propose optimal point services. The proposal results are sent to the user's smartphone in real time.

[0583] As a specific example, if a user enters an electronics store and tries to purchase a refrigerator, the app will notify them that "you will get more points if you use point card B at this store."

[0584] 5. Points Management

[0585] The app provides a feature that allows users to check their current point balance and expiration date.

[0586] The server periodically checks the user's point information and sends an alert when points are about to expire. For example, it may send a message saying, "Your points will expire in one month. We recommend that you use them for your next purchase."

[0587] Through this series of steps, users can effectively utilize the point service and enjoy maximum benefits.

[0588] Hardware and software used

[0589] Server: Performs data management, user authentication, and training and analysis of generative AI models. Use the most high-performance database possible (e.g., PostgreSQL, MySQL, etc.).

[0590] User device: An information processing device, such as a smartphone or tablet, on which a user installs and uses applications.

[0591] Generative AI models: Use sophisticated generative AI models such as GPT-4 to analyze collected data and make recommendations.

[0592] Prompt Sentence Examples

[0593] "Suggested item: Refrigerator. Points offer: {'name': 'Supermarket A', 'balance': 1500}, {'name': 'Electronics Store B', 'balance': 5000}. Purchase history: {'item_name': 'Freezer', 'purchase_date': '2022-01-01', 'price': 30000}"

[0594] Based on the above prompts, the generative AI model will select the optimal point card.

[0595] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0596] Step 1:

[0597] A user installs the SmartPoint Manager app and creates an account. The user enters basic information (name, email address, age, gender, occupation, etc.) and submits it. The server receives the entered basic information and stores it in a database. The server then sends a confirmation email to the user, and the user completes account registration by clicking the confirmation link in the email.

[0598] Input: Name, email address, age, gender, occupation

[0599] Output: Basic information stored in database, confirmation email sent

[0600] Step 2:

[0601] Users can input the information of multiple point cards they own into the app. They can also synchronize the information of linked point cards with the app. The server centralizes the point information and purchase history provided by the user and stores it in a database.

[0602] Input: Point card name, balance, usage history

[0603] Output: Points information and purchase history stored in the database

[0604] Step 3:

[0605] The server trains a generative AI model (e.g., GPT-4) based on the user's point information and purchase history collected. The generative AI model processes data such as the reward rate for each point service, usage methods, and user shopping patterns to generate criteria for selecting the optimal point card.

[0606] Input: Points information, purchase history

[0607] Output: Trained generative AI model

[0608] Step 4:

[0609] The user enters information about a new product they plan to purchase into the app. The server analyzes this new information and past data and uses a generative AI model to propose the optimal point service. The server generates a prompt and sends it to the generative AI model, which then recommends the optimal point card based on the analysis results.

[0610] Input: Product information to be purchased (e.g., refrigerator)

[0611] Output: Recommendation of the best loyalty card

[0612] Step 5:

[0613] The server notifies the user of the results of the suggestion in real time to their smartphone, for example, "If you use point card B at this store, you will get more points."

[0614] Input: Recommendation of the best rewards card

[0615] Output: User notification

[0616] Step 6:

[0617] Users can check their current point balance and expiration date within the app. The server periodically checks the user's point information and sends an alert when points are approaching their expiration date. For example, it may notify users that "Your points will expire in one month. We recommend that you use them for your next purchase."

[0618] Input: Points balance and expiration date

[0619] Output: Alert notification to the user

[0620] Through the above processing steps, the user can effectively utilize the point service and enjoy maximum benefits.

[0621] 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.

[0622] The present invention is a system that effectively utilizes multiple point services and recognizes user emotions to provide more personalized suggestions. This system includes means for user registration, data collection, analysis using a generative AI, suggestion adjustment using an emotion engine, and point management. Specific embodiments for implementing the present invention are described below.

[0623] User registration and saving basic information

[0624] 1. The user opens the dedicated app or website, accesses the membership registration page, enters basic information (name, email address, age, gender, occupation, etc.), and presses the registration button.

[0625] 2. The server receives the basic information sent by the user and stores it in a database, then sends a confirmation email to the user, who clicks on the link to confirm and completes the registration.

[0626] Collection of point service information and shopping history

[0627] 1. The user enters information about the point service they use (point card name, balance, usage history, etc.) into the app. It is also possible to synchronize linked point card information with the app.

[0628] 2. The server stores the point service information and shopping history received from the user in a database. Once the information collection is complete, proceed to the next step.

[0629] Learning by generative AI and selection of optimal point services

[0630] 1. The server provides the stored data to the generation AI, which then learns from the data. The generation AI then creates a model to analyze the reward rate and usage of the point service, as well as the user's shopping patterns.

[0631] 2. Next, the server uses the generation AI to select the optimal point service. For example, it selects the optimal point card when a user purchases an electrical appliance.

[0632] Suggestions and adjustments with the emotion engine

[0633] 1. When a user makes a new purchase, they enter store information and information about the product they plan to purchase into the app. After completing the entry, they press the submit button.

[0634] 2. The server receives the shopping information sent by the user and provides it to the generation AI, which selects the optimal point service based on past data and new information.

[0635] 3. Based on the analysis results of the generation AI, the server creates the optimal point service proposal for the user.

[0636] 4. The emotion engine analyzes the user's voice and facial expression data to identify the user's emotional state, for example, whether the user is in good spirits or stressed.

[0637] 5. The emotion engine adjusts the suggestions based on the user's emotional state. For example, if the user is stressed, it will provide simple suggestions, and if the user is in good spirits, it will provide detailed suggestions.

[0638] 6. The suggested content is notified to the user via the device (smartphone app). For example, the user may be notified that "If you use point card A at this store, you will receive 5% points."

[0639] Points Management

[0640] 1. After completing a purchase, the user enters the purchase information into the app, which then sends it to the server.

[0641] 2. The server saves the new purchase information in the database and updates the points balance and expiration date.

[0642] 3. The server periodically checks the point balance and expiration date, and notifies the user when the expiration date is approaching. For example, it may notify the user that "Your points will expire in one month. We recommend that you use them for your next purchase."

[0643] Specific examples

[0644] For example, user C is about to purchase a new smartphone. User C has registered with a point utilization advisor and regularly uses several point cards.

[0645] 1. User C enters an electronics store and opens the app.

[0646] 2. Enter the product information you plan to purchase (on your smartphone) into the app.

[0647] 3. The server analyzes the most suitable point card based on user C's location information and shopping history.

[0648] 4. The server determines that "Using Company C's point card at this store will give you the maximum points," and notifies User C.

[0649] 5. The emotion engine analyzes user C's voice and facial expressions and recognizes that user C is in a happy emotional state.

[0650] 6. Based on the results of the emotion engine's analysis, the display method of the proposal is adjusted and detailed instructions on how to use points are provided to User C.

[0651] 7. Based on the suggestion, User C uses Company C's point card to purchase a smartphone. As a result, they can get the most points and have a satisfying shopping experience.

[0652] In this way, the present invention helps users make the most of point services and provides a more effective user experience by recognizing the user's emotions and adjusting the content of suggestions.

[0653] The processing flow will be explained below.

[0654] Step 1:

[0655] The user opens the dedicated app or website, accesses the membership registration page, enters basic information (name, email address, age, gender, occupation, etc.), and presses the registration button.

[0656] Step 2:

[0657] The server receives the basic information sent by the user and saves it in the database. After saving, the server sends a confirmation email to the user. The user completes registration by clicking the link in the email.

[0658] Step 3:

[0659] The user enters information about the point service they are using (point card name, balance, usage history, etc.) into the app. Then, they press the point card information synchronization button to send the linked point card information from their device to the server.

[0660] Step 4:

[0661] The server stores the point service information and shopping history received from the user in a database, thereby unifying all point information held by the user.

[0662] Step 5:

[0663] The server provides the stored data to the generation AI, which then learns from it. The generation AI analyzes the reward rate, usage method, and user shopping patterns of each point service and creates a model based on that information.

[0664] Step 6:

[0665] When a user makes a new purchase, they enter the product information and store information into the app. After completing the entry, they press the send button.

[0666] Step 7:

[0667] The server receives the shopping information sent by the user and provides it to the generation AI, which analyzes the past data and new shopping information to select the optimal point service.

[0668] Step 8:

[0669] The server creates the optimal point service proposal for the user based on the analysis results of the generated AI. For example, "At this store, you will receive 5% points when you use point card A."

[0670] Step 9:

[0671] To recognize the user's emotional state, the emotion engine collects voice and facial expression data through the device's camera and microphone. The emotion engine analyzes this data to determine whether the user is excited or calm.

[0672] Step 10:

[0673] The server receives the results of the emotion engine and adjusts how the suggestions are presented based on the user's emotional state: for example, if the user is stressed, it displays brief suggestions, but if the user is relaxed, it provides detailed information.

[0674] Step 11:

[0675] The terminal receives the recommendation notification from the server and notifies the user in real time, allowing the user to use the most suitable point card when shopping.

[0676] Step 12:

[0677] After the user completes their purchase, they enter their purchase information into the app, which then sends it to the server.

[0678] Step 13:

[0679] The server saves the new purchase information in the database and updates the point balance and expiration date, thereby managing the latest status of point usage.

[0680] Step 14:

[0681] The server periodically checks the point balance and expiration date, and notifies the user when the expiration date is approaching. Example: "Your points will expire in one month. We recommend that you use them for your next purchase."

[0682] Through this series of processes, the user can receive suggestions for optimal point services and receive information according to their emotional state, allowing them to use their points effectively and comfortably.

[0683] Example 2

[0684] 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."

[0685] Conventional point service systems have difficulty in managing multiple point cards, making it difficult to propose optimal ways to use points. Furthermore, proposals do not take into account the user's emotional state, limiting the quality of the user experience. Furthermore, insufficient point expiration management and notifications have led to users wasting points.

[0686] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0687] In this invention, the server includes means for saving basic information acquired from the user, means for collecting the user's point service information and shopping history, analysis means using a generative AI model that learns based on the collected information and selects the optimal point service, adjustment means using an emotion engine that recognizes the user's emotional state, suggestion means for proposing point services according to the user's life cycle, means for notifying the user terminal of information on the proposed point services, and management means for managing the user's point balance and expiration date and notifying them as appropriate. This makes it possible to manage multiple point services in an integrated manner and suggest the optimal way to use points according to the user's emotional state.

[0688] "Means for storing basic information obtained from users" refers to a system component that has the function of collecting basic information such as a user's name, email address, age, gender, occupation, etc., and storing it in a database.

[0689] "Means for collecting user point service information and shopping history" refers to a system component that has the function of collecting information on various point cards used by users (point card name, balance, usage history, etc.) and the user's purchase history.

[0690] "Analysis means using a generative AI model" refers to a component of a system that has the function of utilizing collected point service and shopping history data to perform analysis using a generative AI model in order to select the most appropriate point service.

[0691] The "adjustment means using an emotion engine that recognizes the user's emotional state" is a component of the system that has the function of analyzing the user's voice and facial expression data, recognizing the user's emotional state (for example, whether they are in good spirits or feeling stressed), and adjusting the content of suggestions accordingly.

[0692] The "suggestion means for proposing point services according to the user's life cycle" is a component of the system that has the function of proposing the most suitable point service to the user based on the user's basic information, point service usage history, shopping patterns, and emotional state.

[0693] "Means for notifying the user device of information about the proposed point service" refers to a system component that has the function of notifying the user's device, such as a smartphone app, of information about the optimal point service selected by the generative AI model.

[0694] The "management means for managing the user's point balance and expiration date and notifying them as appropriate" is a system component that has the function of managing the balance of points held by the user and the expiration date of points, and notifying the user when the expiration date is approaching.

[0695] The present invention is a system that effectively utilizes multiple point services and recognizes user emotions to provide more personalized suggestions. The system includes means for user registration, data collection, analysis using a generative AI model, suggestion adjustment using an emotion engine, and point management. Specific embodiments are described below.

[0696] User registration and saving basic information

[0697] 1. User visits the membership registration page:

[0698] The user opens the dedicated app or website, enters basic information such as name, email address, age, gender, and occupation, and presses the registration button.

[0699] The server receives the basic information sent by the user, stores it in a database, and then sends a confirmation email to the user, who clicks on the link to confirm and completes the registration.

[0700] Collection of point service information and shopping history

[0701] 2. User enters loyalty card information:

[0702] Users enter information about the point service they use (point card name, balance, usage history, etc.) into a dedicated app. It is also possible to synchronize linked point card information with the app.

[0703] The server stores the point service information and shopping history received from the user in a database.

[0704] Learning by generative AI model and selection of optimal point service

[0705] 3. The server provides data to the generative AI model:

[0706] The server provides the stored user data to the generative AI model, which then learns from this data. The data includes point card usage history and shopping patterns.

[0707] The generative AI model analyzes the data and creates a model that understands the reward rate and usage method of the point service, as well as the user's shopping patterns.

[0708] Based on the model created by the generative AI model, the server selects the optimal point service tailored to the user's shopping behavior.

[0709] Suggestions and adjustments with the emotion engine

[0710] 4. User enters new shopping information:

[0711] When a user makes a new purchase, they enter store information and information about the item they wish to purchase into the app and press the send button.

[0712] The server receives the shopping information sent by the user and provides it to the generative AI model, which then reanalyzes the optimal point service based on the new information and past data.

[0713] 5. The server creates the proposal and the emotion engine adjusts it:

[0714] The server creates an optimal point service proposal for the user based on the analysis results of the generated AI model. The proposal might be something like, "If you use point card A at this store, you will receive 5% points."

[0715] The emotion engine analyzes the user's voice and facial expression data to determine whether the user is in good spirits or stressed.

[0716] The emotion engine tailors suggestions based on the user's emotional state, for example, providing simple, easy-to-understand suggestions if the user is stressed, and detailed suggestions if the user is in a positive mood.

[0717] The proposed content will be notified to the user via the device (smartphone app).

[0718] Points Management

[0719] 6. User enters purchase information:

[0720] After the user completes their purchase, they enter the purchase information into the app and send it from the device to the server.

[0721] The server saves the new purchase information in the database and updates the points balance and expiration date.

[0722] 7. The server notifies:

[0723] The server periodically checks the point balance and expiration date, and notifies the user when the expiration date is approaching. For example, it may notify the user that "Your points will expire in one month. We recommend that you use them for your next purchase."

[0724] Specific examples

[0725] For example, if a user is looking to buy a new smartphone:

[0726] 1. A user enters a retail store and opens the app.

[0727] 2. Enter the product information you plan to purchase (on your smartphone) into the app.

[0728] 3. The server analyzes the user's location information and shopping history to find the most suitable point card.

[0729] 4. The server notifies the customer, "At this store, you will receive the maximum points if you use point card A."

[0730] 5. The emotion engine analyzes the user's voice and facial expressions to recognize when the user is in a happy emotional state.

[0731] 6. Based on the results of the emotion engine's analysis, the display of the offer is adjusted and detailed instructions on how to use points are provided to the user.

[0732] 7. Based on the suggestion, the user uses point card A to purchase a smartphone.

[0733] Prompt Sentence Examples

[0734] Examples of prompts to input to a generative AI model include:

[0735] Select and suggest the most suitable rewards card based on the user's shopping history and rewards service information. Adjust the recommendation taking into account the user's emotional state.

[0736] This system allows users to make the most of multiple point services and receive optimal suggestions based on their emotional state.

[0737] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0738] Step 1:

[0739] User registration and saving basic information

[0740] Input: The user enters basic information such as name, email address, age, gender, and occupation into a dedicated app or website.

[0741] Specific operation: The user enters the required information into the input form and presses the registration button.

[0742] Data processing and data calculation: The server receives the basic information sent by the user, converts the input data into a database format, and stores it in the database.

[0743] Output: After saving is complete, the server will send a confirmation email to the user.

[0744] Step 2:

[0745] Collection of point service information and shopping history

[0746] Input: The user uses a dedicated app to input or synchronize information about the point card they are using (point card name, balance, usage history, etc.).

[0747] Specific operation: The user enters point card information or clicks the data synchronization button.

[0748] Data processing and data calculation: The server receives the loyalty program information and shopping history sent by the user, converts the data into a database format, and stores it in the database.

[0749] Output: Point service information and shopping history are added to the database.

[0750] Step 3:

[0751] Learning by generative AI model and selection of optimal point service

[0752] Input: The server provides stored user data (point card usage history and shopping patterns) to the generative AI model.

[0753] Specific operation: The server inputs data from the database into the generative AI model, and the AI ​​model analyzes the data.

[0754] Data processing and data calculation: The generative AI model learns the reward rate and usage method of the point service, as well as the user's shopping patterns, and creates a model to select the most suitable point service.

[0755] Output: The analytical model created by the generative AI model is output.

[0756] Step 4:

[0757] Enter new shopping information and prepare suggestions

[0758] Input: When a user makes a new purchase, they enter store information and information about the item they plan to purchase into the app and press the submit button.

[0759] Specific operation: The user enters the store name and product name into the app's input form and clicks the "Submit" button.

[0760] Data processing and data calculation: The server receives new shopping information and provides it back to the generative AI model for updated analysis.

[0761] Output: The analysis results from the generative AI model output the selection of the most suitable point service.

[0762] Step 5:

[0763] Suggestions and adjustments with the emotion engine

[0764] Input: The server creates suggestions based on the AI's analysis results and the user's new shopping information.

[0765] Specific operation: Based on the analysis results, the server creates a suggestion such as "If you use point card A at this store, you will receive 5% points."

[0766] Data processing and data calculation: The emotion engine analyzes the user's voice and facial expression data to identify the user's emotional state (e.g., cheerfulness, stress). The emotion engine adjusts the suggestions, changing their complexity and level of detail.

[0767] Output: The optimal recommendation content adjusted by the emotion engine is generated.

[0768] Step 6:

[0769] Notification of proposal details

[0770] Input: Data to inform tailored proposals.

[0771] Specific operation: The server notifies the user of the optimal proposal via a smartphone app. For example, a push notification such as "Use point card A to receive 5% points" is sent.

[0772] Output: A notification of the proposed content is sent to the user's device.

[0773] Step 7:

[0774] Points Management

[0775] Input: Purchase information entered by the user after completing a purchase.

[0776] What happens: The user enters purchase details into an in-app form and presses the "Submit" button.

[0777] Data processing and calculation: The server saves new purchase information to the database, updates point balances and expiration dates, and periodically checks the database to send out alerts when expiration dates are approaching.

[0778] Output: The updated point balance and expiration information are saved in the database, and when the expiration date approaches, the user is notified that "Your points will expire in one month."

[0779] (Application example 2)

[0780] 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."

[0781] In recent years, there has been a demand for improved consumer convenience through point services. However, the existence of multiple point services makes it difficult to determine the optimal point card usage. Furthermore, there is a lack of methods to provide customized suggestions based on the user's emotional state and real-time situation. This poses a problem that users are likely to be unable to make the most of point services.

[0782] The specification process by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for recognizing the user's emotions and adjusting the content of suggestions based on the emotional state, means for saving basic information acquired from the user, means for collecting the user's point service information and shopping history, analysis means using a generation AI, suggestion means, and management means. This makes it possible to select and suggest the optimal point service taking the user's emotions into consideration.

[0783] "Means for storing basic information obtained from the user" refers to a function that records basic information such as the user's name, email address, age, sex, and occupation, and stores it in a database.

[0784] "Means for collecting user's point service information and shopping history" is a function that collects the balance of each point card used by the user, usage history, and the user's shopping history, and stores them in a database.

[0785] "Analysis method using generative AI" is a function that uses generative AI to learn from collected information and select the most suitable point service for the user.

[0786] The "suggestion means" is a function that suggests to the user the most appropriate way to use the points service based on the user's life cycle, location information, and emotional state.

[0787] The "notification means" is a function for notifying the user's terminal of information about the proposed point service.

[0788] The "management means" is a function that manages the user's point balance and expiration date, and notifies the user when the expiration date is approaching.

[0789] The "emotion engine" is a function that analyzes the user's voice and facial expression data to recognize their emotional state and adjusts the suggestions based on that.

[0790] The invention is a system that effectively utilizes the user's point service, identifies the user's emotional state, and adjusts the content of suggestions. The main components of this system are a user terminal, a server, a generation AI, and an emotion engine.

[0791] First, the user accesses the smart glasses' application "Point Master" and registers by entering basic information. This basic information includes name, email address, age, gender, occupation, etc. The server receives this basic information and stores it in a database. Point service information and shopping history sent from the user's device are also collected and stored on the server.

[0792] Next, the server uses a generation AI based on the collected data to select the optimal point service. The generation AI analyzes the point service's reward rate, usage method, and the user's shopping patterns to select the most advantageous point card for the user. Furthermore, the suggestion content is adjusted by an emotion engine, taking into account the user's emotional state. The emotion engine analyzes the user's voice and facial expression data to determine whether the user is in good spirits or stressed. For example, if the user is in good spirits, detailed suggestions are made, and if the user is stressed, simple suggestions are provided.

[0793] The suggestions are sent to the user's device in real time. For example, when a user is browsing products in a physical store using smart glasses, they enter store information and information about the products they plan to purchase into the app and send it to the server. The server then analyzes the optimal point service based on location information and shopping history, and notifies the user's device of the results. Furthermore, after the user completes their shopping, they enter their purchase information into the app, and the server updates their point balance and expiration date and sends appropriate notifications.

[0794] The hardware used includes smart glasses (e.g., Google Glass or Vuzix Blade) and user devices (e.g., smartphones).The software includes an emotion recognition module, a recommendation engine, and a point service API, which work together.

[0795] As a concrete example, consider a user who is about to purchase a smartphone at an electronics retail store. The user puts on smart glasses and enters the product information they plan to purchase into the app. The server recommends the most suitable point service based on their point card information and shopping history, and notifies them that "You will receive the maximum points if you use Company A's point card at this store." At this time, the emotion engine recognizes that the user appears to be enjoying themselves, and also provides detailed instructions on how to use the points.

[0796] A specific example of a prompt is as follows:

[0797] "Please enter the following store and product information based on the user's shopping history and loyalty card information:

[0798] Store information: Electronics retailer, Shinjuku

[0799] Product information: Smartphone, Price: 50,000 yen

[0800] Please explain the recommended point cards based on the generated AI and the reasons for them.

[0801] This system allows users to make the most of the points service, and also allows them to have a more satisfying purchasing experience by adjusting the suggestions according to their emotional state.

[0802] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0803] Step 1:

[0804] A user opens the smart glasses application and enters basic information, such as name, email address, age, gender, occupation, etc. This information is sent from the user's device to the server, which then stores it in a database.

[0805] Input: User's basic information (name, email address, age, gender, occupation)

[0806] Output: User basic information stored in the database

[0807] Specific behavior:

[0808] A user accesses the application and enters the required information into the form. When the user presses the submit button, the terminal sends the information to the server, which records the information in a database.

[0809] Step 2:

[0810] The user enters information about the point service they use and their shopping history into the application. The device sends this information to the server, which then stores it in a database.

[0811] Input: Point service information (point card name, balance, usage history), shopping history

[0812] Output: Point service and shopping history information stored in the database

[0813] Specific behavior:

[0814] When a user enters their point card information or shopping history into the application and submits it, the device sends the information to the server, which then stores it in a database.

[0815] Step 3:

[0816] The server provides the collected information to the AI ​​generator, which then analyzes the reward rate and usage of the points service, as well as the user's shopping patterns, to select the most suitable points service.

[0817] Input: Point service information and shopping history stored in the database

[0818] Output: Selection result of optimal point service by generation AI

[0819] Specific behavior:

[0820] The server passes the database information to the generation AI, which processes the data and learns from it. It selects the optimal point service and returns the results to the server.

[0821] Step 4:

[0822] When a user goes shopping at a physical store, they put on the smart glasses and input the product information they plan to purchase, which is then sent to the server.

[0823] Input: Store information, product information to be purchased (product name, price)

[0824] Output: Information about the products to be purchased received by the server

[0825] Specific behavior:

[0826] The user uses the smart glasses in a physical store and enters product information into the app. When the user presses the send button, the device sends the information to the server.

[0827] Step 5:

[0828] The server uses AI generation to select the optimal point service based on the user's location and purchase information, and notifies the user of the results.

[0829] Input: Information stored in the database, current location, purchase information

[0830] Output: The optimal point service selection result notified to the user device

[0831] Specific behavior:

[0832] The server uses AI to select the optimal point service based on location information and purchase information, and the selection result is notified to the user's device.

[0833] Step 6:

[0834] The emotion engine analyzes the user's emotional state and adjusts the suggestions accordingly: for example, if the user is in good spirits, it will provide detailed suggestions, and if they are stressed, it will provide simple suggestions.

[0835] Input: User's voice data, facial expression data

[0836] Output: Adjusted proposal

[0837] Specific behavior:

[0838] The emotion engine uses the smart glasses' camera and microphone to analyze the user's voice and facial expressions to determine their emotional state, and the server then adjusts the suggestions accordingly.

[0839] Step 7:

[0840] After completing a purchase, the user enters the purchase information into the app. The device then sends the information to the server, which updates the points balance and expiration date. If necessary, the app notifies users of points approaching their expiration date.

[0841] Input: Purchase information (purchased product, price)

[0842] Output: Updated points balance, expiration notice

[0843] Specific behavior:

[0844] After a purchase, the user enters product information into the app and presses the send button. The device sends the information to the server, which updates the database with the points balance and expiration date. If a notification is required, the user is notified.

[0845] 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.

[0846] 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.

[0847] 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.

[0848] [Third embodiment]

[0849] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0850] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0851] 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).

[0852] 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.

[0853] 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.

[0854] 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).

[0855] 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. 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.

[0856] 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.

[0857] 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.

[0858] 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.

[0859] 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.

[0860] 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."

[0861] The present invention is a system for effectively utilizing multiple point services, and is a series of functions including user registration, data collection, analysis by generation AI, proposals and notifications, and point management. Specific embodiments for implementing the present invention are described below.

[0862] User registration and saving basic information

[0863] 1. The user registers as a member using the dedicated app or website. They enter basic information (name, email address, age, gender, occupation, etc.) on their device and press the send button.

[0864] 2. The server receives the basic information sent by the user and stores it in the database. It then sends a confirmation email to the user, who must click on the link to confirm and complete the registration.

[0865] Collection of point service information and shopping history

[0866] 1. The user enters information about the point service they are using (point card name, balance, usage history, etc.) into the app. It is also possible to synchronize linked point card information with the app.

[0867] 2. The server collects the point information and shopping history provided by the user and stores them in a centralized database.

[0868] Learning by generative AI and selection of optimal point services

[0869] 1. The server trains the generation AI based on the collected user point service information and shopping history. Specifically, the generation AI learns the reward rate of each point service, how to use it, and the user's shopping patterns.

[0870] 2. Next, the server uses the generation AI to select the optimal point service. For example, it selects the optimal point card when a user purchases electronic devices.

[0871] Suggestions and Notifications

[0872] 1. The user opens the app and enters information for a new purchase (for example, the product and store information they plan to purchase).

[0873] 2. The server analyzes this new information and past data and proposes the optimal point service based on the learning results of the generating AI.

[0874] 3. The suggested content is notified to the user via the device (smartphone app). For example, a real-time notification such as "Use point card A at this store to receive 5% points" is sent.

[0875] Points Management

[0876] 1. Users can check their points balance and expiration date within the app.

[0877] 2. The server periodically checks the user's point information and sends an alert when the expiration date is approaching. For example, it may send a message saying, "Your points will expire in one month. We recommend that you use them for your next purchase."

[0878] Specific examples

[0879] For example, user B is planning to purchase a new refrigerator. User B has registered with the point utilization advisor and regularly uses a supermarket point card, but this is the first time that user B has purchased a refrigerator.

[0880] 1. User B enters an electronics store and opens the app.

[0881] 2. Enter the product information (refrigerator) you plan to purchase into the app.

[0882] 3. The server analyzes the most suitable point card based on User B's location information and shopping history.

[0883] 4. The server determines that "Using Company B's point card at this store will result in higher points" and notifies User B.

[0884] 5. Based on the suggestion, User B uses Company B's point card to purchase a refrigerator. As a result, User B can obtain the most advantageous points and enjoy a satisfying shopping experience.

[0885] As described above, the present invention helps users to make the most of point services, and provides a specific form for seamlessly performing a series of processes and improving convenience.

[0886] The processing flow will be explained below.

[0887] Step 1:

[0888] The user opens the dedicated app or website, accesses the membership registration page, enters basic information (name, email address, age, gender, occupation, etc.), and presses the registration button.

[0889] Step 2:

[0890] The server receives the basic information sent by the user and saves it in the database. After saving, the server sends a confirmation email to the user. The user completes registration by clicking the link in the email.

[0891] Step 3:

[0892] The user enters the information of the point service they use (point card name, balance, usage history, etc.) into the app, or synchronizes the information of the linked point card. When the synchronization button is pressed, the information is sent from the device to the server.

[0893] Step 4:

[0894] The server stores the point service information and shopping history received from the user in a database. Once the information collection is complete, proceed to the next step.

[0895] Step 5:

[0896] The server provides the stored data to the generation AI, which then learns from the data. The generation AI then creates a model to analyze the reward rate and usage of the point service, as well as user shopping patterns.

[0897] Step 6:

[0898] When a user makes a new purchase, they enter store information and information about the product they plan to purchase into the app. After completing the input, they press the submit button.

[0899] Step 7:

[0900] The server receives the shopping information sent by the user and provides it to the generation AI, which selects the optimal point service based on past data and new information.

[0901] Step 8:

[0902] The server creates the optimal point service proposal for the user based on the analysis results of the generation AI. The proposal is then notified to the device app. For example, it may notify the user that "If you use point card A at this store, you will receive the maximum points."

[0903] Step 9:

[0904] After the user completes their purchase, they enter the purchase information into the app, which then sends it to the server.

[0905] Step 10:

[0906] The server saves the new purchase information in the database and updates the points balance and expiration date.

[0907] Step 11:

[0908] The server periodically checks the point balance and expiration date, and notifies the user when the expiration date is approaching. For example, it may notify the user that "Your points will expire in one month. We recommend that you use them for your next purchase."

[0909] Through this series of processes, users can receive suggestions for optimal point services and use their points efficiently.

[0910] Example 1

[0911] 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."

[0912] Traditional point services required the time and effort of managing multiple point cards, making it difficult for users to make the most of their points. They also lacked the information needed to properly determine which point card would provide the most benefits. Furthermore, users often lost points without realizing their points were about to expire.

[0913] 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.

[0914] In this invention, the server includes a means for saving basic information acquired from the user, a means for collecting the user's point service information and shopping history, and an analysis means using a generation AI that learns from the collected information and selects the most suitable point service, thereby enabling the user to effectively manage and make the most of the point service.

[0915] The "means for storing basic information" is a function for safely storing basic personal information obtained from users, such as their name, email address, age, sex, and occupation.

[0916] "Means for collecting point service information and shopping history" refers to a function for collecting and centrally managing information about the point card currently in use by the user (point card name, balance, usage history, etc.) and the user's shopping history.

[0917] "Analysis method using generative AI" is a function that uses generative artificial intelligence to analyze and learn from collected point service information and shopping history to select the most suitable point service.

[0918] The "means for the user to input information about the product and store that the user plans to purchase" is a function for the user to input information about the product that the user plans to purchase and the store where the user plans to purchase the product.

[0919] "Means for notifying the user device of information about the proposed point service" is a function for notifying the user device in real time of information about the optimal point service selected by the generation AI.

[0920] The "management means for managing point balances and expiration dates and notifying them as appropriate" is a function for managing the balance on a user's point card and the expiration date of points, and sending alert notifications to the user when the expiration date is approaching.

[0921] "Means for proposing point services in real time" is a function that immediately uses new information such as the user's location information to propose optimal point services to the user in real time.

[0922] MODE FOR CARRYING OUT THE INVENTION

[0923] This invention is a system that helps users efficiently manage and use multiple point services, and provides the functions of user registration, data collection, analysis by AI generation, proposals and notifications, and point management in an integrated manner. Specific embodiments for implementing this invention are described below.

[0924] User registration and saving basic information

[0925] Users register as members using a dedicated app or website. Using a device (smartphone or PC), they enter basic information such as their name, email address, age, gender, and occupation, and then press the "Submit" button. The server receives the basic information sent from the device and securely stores it in a database using SSL communication. A confirmation email is then sent to the user, and registration is completed when the user clicks on the link in the email to confirm.

[0926] Collection of point service information and shopping history

[0927] Users enter information about the point service they are using (point card name, balance, usage history, etc.) into the app. If the app supports it, point card information can be automatically synchronized via API. The server collects the point information and shopping history provided by the user and stores it in a centralized database. This allows users to manage multiple point services on a single platform.

[0928] Learning by generative AI and selection of optimal point services

[0929] The server trains the generative AI model based on the collected user point service information and shopping history. Specifically, the reward rate for each point service, usage methods, and user shopping patterns are used as learning data. Using the trained generative AI model, the server selects the optimal point service. For example, when a user purchases a new electronic device, it can select the most advantageous point card for that product.

[0930] Suggestions and Notifications

[0931] The user opens the app and enters new shopping information (for example, the product and store information they plan to purchase). The server analyzes this new information and past data, and suggests the optimal point service based on the learning results of the generative AI. The suggested content is notified to the user in real time via their device. For example, a notification such as "When you use point card A at this store, you will receive 5% points" may be displayed within the app.

[0932] Points Management

[0933] Users can check their points balance and expiration date within the app. The server periodically checks the database and sends an alert to the user when their points are about to expire. For example, a notification might say, "Your points will expire in one month. We recommend that you use them for your next purchase."

[0934] Specific examples

[0935] For example, user B is about to purchase a new refrigerator. User B has already registered with the point service management system and regularly uses a supermarket point card, but this is the first time that user B has purchased a refrigerator.

[0936] 1. User B enters an electronics store and opens the dedicated app.

[0937] 2. Enter the product information (refrigerator) you plan to purchase into the app.

[0938] 3. The server analyzes the most suitable point card based on User B's location information and shopping history.

[0939] 4. The server determines that "using point card B at this store will result in higher points" and notifies user B.

[0940] 5. Based on the suggestion, User B purchases a refrigerator using Point Card B. As a result, User B can obtain the most advantageous points and enjoy a satisfying shopping experience.

[0941] As described above, the present invention is a system that helps users make the most of the points service. A series of processes are performed seamlessly, greatly improving convenience.

[0942] Prompt Sentence Examples

[0943] "Please explain in natural language the program process of a system that suggests the most suitable points program when a user purchases a new refrigerator. Please use either the server, terminal, or user as the subject and write in natural sentences."

[0944] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0945] Step 1: Register and save your basic information

[0946] 1. The user registers as a member using a dedicated app or website. Using a device, the user enters basic information such as name, email address, age, gender, and occupation, and presses the "Submit" button.

[0947] 2. The basic information entered is sent to the server.

[0948] 3. The server securely stores the received information in a database using SSL communication. A confirmation email is generated and sent to the user's email address. The user must click on the link in the email to confirm their account.

[0949] Step 2: Collecting loyalty program information and shopping history

[0950] 1. The user enters information about the point service they are using (point card name, balance, usage history, etc.) into the app. In some cases, the app uses a function to synchronize point card information via API.

[0951] 2. The entered point service information is sent to the server.

[0952] 3. The server stores point information and shopping history in a centralized database and manages user point statistics.

[0953] Step 3: Learning by generative AI and selection of optimal point service

[0954] 1. The server trains the generative AI model based on the user's point service information and shopping history stored in the database. The training data includes the point service's reward rate, usage method, and user shopping patterns.

[0955] 2. The generative AI analyzes this learning data and trains the model.

[0956] 3. The server uses the trained generative AI model to select the optimal point card when the user purchases a specific product, allowing the user to earn the most advantageous points.

[0957] Step 4: Propose and notify

[0958] 1. When a user makes a new purchase, they enter information about the product and store they plan to purchase within the app.

[0959] 2. The entered information is sent to the server, which analyzes it based on past data and the learning results of the generating AI.

[0960] 3. The server generates optimal point service proposals based on the analysis results.

[0961] 4. The server notifies the device of the suggested information. For example, a notification such as "Use point card A at this store to receive 5% points" is displayed within the app.

[0962] Step 5: Manage your points

[0963] 1. Users can check their points balance and expiration date within the app.

[0964] 2. The server periodically checks the point information in the database and generates an alert notification when the points are about to expire.

[0965] 3. An alert notification is sent from the server to the user's device. For example, a message such as "Your points will expire in one month. We recommend you use them for your next purchase" is displayed.

[0966] By operating the system according to the above steps, users can efficiently manage multiple point services and use the most advantageous points.

[0967] (Application example 1)

[0968] 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."

[0969] In modern society, users use multiple point services, and it is difficult to effectively manage and maximize their use of them. In particular, it is difficult to determine which point card is most advantageous for a product you plan to purchase, which often results in points being wasted. In addition, it is difficult to manage points that are approaching their expiration date, and they may expire. The purpose of this invention is to solve these problems.

[0970] 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.

[0971] In this invention, the server includes means for saving basic information acquired from the user, means for collecting the user's point service information and purchase history, analysis means using a generative AI model that learns based on the collected information and selects the optimal point service, suggestion means for proposing point services according to the user's life cycle, means for notifying the user terminal of information on the proposed point services, management means for managing the user's point balance and expiration date and notifying them as appropriate, and means for linking with an electronic payment service and proposing the optimal point card based on information on products the user plans to purchase. This allows the user to select the optimal point card and use their points efficiently.

[0972] "Basic information" refers to information obtained from a user, such as name, email address, age, sex, and occupation.

[0973] The "point service" refers to a service that includes multiple point cards used by users, their balances, usage history, etc.

[0974] "Purchase history" refers to information about products purchased by a user in the past, as well as the purchase date and time and price.

[0975] A "generative AI model" is an artificial intelligence model that learns from collected data and selects the most appropriate point service.

[0976] "Analysis means" refers to a means for analyzing basic information and purchasing history using a generative AI model.

[0977] The "proposing means" is a means for proposing the most suitable point service according to the user's life cycle and location information.

[0978] The "notification means" is a means for notifying the user terminal of information about the proposed point service in real time.

[0979] The "management means" is a means for managing the user's point balance and expiration date, and sending appropriate alert notifications.

[0980] An "electronic payment service" is a service that links with the payment method used by users when purchasing products.

[0981] A "user terminal" is an information processing device used by a user, such as a smartphone or tablet.

[0982] The present invention is a system that allows users to effectively utilize multiple point services, and provides a series of functions including user registration, data collection, analysis using a generative AI model, proposals and notifications, and point management. Specific embodiments for implementing the present invention are described below.

[0983] 1. User registration and saving basic information

[0984] A user installs the SmartPoint Manager app, creates an account, enters basic information (name, email address, age, gender, occupation, etc.), and sends it to the server.

[0985] The server receives the basic information, stores it in a database, and sends a confirmation email to the user, who clicks on the confirmation link in the email to complete the registration.

[0986] 2. Collection of point service information and purchase history

[0987] Users can enter information about their point cards through the app (e.g., point card name, balance, usage history, etc.). The app can also automatically synchronize information about linked point cards.

[0988] The server centrally manages the point information and purchase history provided by the user and stores them in a database.

[0989] 3. Learning with a generative AI model and selection of optimal point services

[0990] The server trains a generative AI model (e.g., GPT-4) based on the user's point information and purchase history collected by the server. The generative AI model analyzes the reward rate, usage method, and user shopping patterns of each point service.

[0991] The system selects the optimal point card based on the learning results, allowing users to use the most advantageous point card.

[0992] 4. Proposals and Notifications

[0993] The user inputs information about a new product they plan to purchase into the app. For example, they input information about a refrigerator they plan to purchase.

[0994] The server analyzes the input information and past data, and uses a generative AI model to propose optimal point services. The proposal results are sent to the user's smartphone in real time.

[0995] As a specific example, if a user enters an electronics store and tries to purchase a refrigerator, the app will notify them that "you will get more points if you use point card B at this store."

[0996] 5. Points Management

[0997] The app provides a feature that allows users to check their current point balance and expiration date.

[0998] The server periodically checks the user's point information and sends an alert when points are about to expire. For example, it may send a message saying, "Your points will expire in one month. We recommend that you use them for your next purchase."

[0999] Through this series of steps, users can effectively utilize the point service and enjoy maximum benefits.

[1000] Hardware and software used

[1001] Server: Performs data management, user authentication, and training and analysis of generative AI models. Use the most high-performance database possible (e.g., PostgreSQL, MySQL, etc.).

[1002] User device: An information processing device, such as a smartphone or tablet, on which a user installs and uses applications.

[1003] Generative AI models: Use sophisticated generative AI models such as GPT-4 to analyze collected data and make recommendations.

[1004] Prompt Sentence Examples

[1005] "Suggested item: Refrigerator. Points offer: {'name': 'Supermarket A', 'balance': 1500}, {'name': 'Electronics Store B', 'balance': 5000}. Purchase history: {'item_name': 'Freezer', 'purchase_date': '2022-01-01', 'price': 30000}"

[1006] Based on the above prompts, the generative AI model will select the optimal point card.

[1007] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1008] Step 1:

[1009] A user installs the SmartPoint Manager app and creates an account. The user enters basic information (name, email address, age, gender, occupation, etc.) and submits it. The server receives the entered basic information and stores it in a database. The server then sends a confirmation email to the user, and the user completes account registration by clicking the confirmation link in the email.

[1010] Input: Name, email address, age, gender, occupation

[1011] Output: Basic information stored in database, confirmation email sent

[1012] Step 2:

[1013] Users can input the information of multiple point cards they own into the app. They can also synchronize the information of linked point cards with the app. The server centralizes the point information and purchase history provided by the user and stores it in a database.

[1014] Input: Point card name, balance, usage history

[1015] Output: Points information and purchase history stored in the database

[1016] Step 3:

[1017] The server trains a generative AI model (e.g., GPT-4) based on the user's point information and purchase history collected. The generative AI model processes data such as the reward rate for each point service, usage methods, and user shopping patterns to generate criteria for selecting the optimal point card.

[1018] Input: Points information, purchase history

[1019] Output: Trained generative AI model

[1020] Step 4:

[1021] The user enters information about a new product they plan to purchase into the app. The server analyzes this new information and past data and uses a generative AI model to propose the optimal point service. The server generates a prompt and sends it to the generative AI model, which then recommends the optimal point card based on the analysis results.

[1022] Input: Product information to be purchased (e.g., refrigerator)

[1023] Output: Recommendation of the best loyalty card

[1024] Step 5:

[1025] The server notifies the user of the results of the suggestion in real time to their smartphone, for example, "If you use point card B at this store, you will get more points."

[1026] Input: Recommendation of the best rewards card

[1027] Output: User notification

[1028] Step 6:

[1029] Users can check their current point balance and expiration date within the app. The server periodically checks the user's point information and sends an alert when points are approaching their expiration date. For example, it may notify users that "Your points will expire in one month. We recommend that you use them for your next purchase."

[1030] Input: Points balance and expiration date

[1031] Output: Alert notification to the user

[1032] Through the above processing steps, the user can effectively utilize the point service and enjoy maximum benefits.

[1033] 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.

[1034] The present invention is a system that effectively utilizes multiple point services and recognizes user emotions to provide more personalized suggestions. This system includes means for user registration, data collection, analysis using a generative AI, suggestion adjustment using an emotion engine, and point management. Specific embodiments for implementing the present invention are described below.

[1035] User registration and saving basic information

[1036] 1. The user opens the dedicated app or website, accesses the membership registration page, enters basic information (name, email address, age, gender, occupation, etc.), and presses the registration button.

[1037] 2. The server receives the basic information sent by the user and stores it in a database, then sends a confirmation email to the user, who clicks on the link to confirm and completes the registration.

[1038] Collection of point service information and shopping history

[1039] 1. The user enters information about the point service they use (point card name, balance, usage history, etc.) into the app. It is also possible to synchronize linked point card information with the app.

[1040] 2. The server stores the point service information and shopping history received from the user in a database. Once the information collection is complete, proceed to the next step.

[1041] Learning by generative AI and selection of optimal point services

[1042] 1. The server provides the stored data to the generation AI, which then learns from the data. The generation AI then creates a model to analyze the reward rate and usage of the point service, as well as the user's shopping patterns.

[1043] 2. Next, the server uses the generation AI to select the optimal point service. For example, it selects the optimal point card when a user purchases an electrical appliance.

[1044] Suggestions and adjustments with the emotion engine

[1045] 1. When a user makes a new purchase, they enter store information and information about the product they plan to purchase into the app. After completing the entry, they press the submit button.

[1046] 2. The server receives the shopping information sent by the user and provides it to the generation AI, which selects the optimal point service based on past data and new information.

[1047] 3. Based on the analysis results of the generation AI, the server creates the optimal point service proposal for the user.

[1048] 4. The emotion engine analyzes the user's voice and facial expression data to identify the user's emotional state, for example, whether the user is in good spirits or stressed.

[1049] 5. The emotion engine adjusts the suggestions based on the user's emotional state. For example, if the user is stressed, it will provide simple suggestions, and if the user is in good spirits, it will provide detailed suggestions.

[1050] 6. The suggested content is notified to the user via the device (smartphone app). For example, the user may be notified that "If you use point card A at this store, you will receive 5% points."

[1051] Points Management

[1052] 1. After completing a purchase, the user enters the purchase information into the app, which then sends it to the server.

[1053] 2. The server saves the new purchase information in the database and updates the points balance and expiration date.

[1054] 3. The server periodically checks the point balance and expiration date, and notifies the user when the expiration date is approaching. For example, it may notify the user that "Your points will expire in one month. We recommend that you use them for your next purchase."

[1055] Specific examples

[1056] For example, user C is about to purchase a new smartphone. User C has registered with a point utilization advisor and regularly uses several point cards.

[1057] 1. User C enters an electronics store and opens the app.

[1058] 2. Enter the product information you plan to purchase (on your smartphone) into the app.

[1059] 3. The server analyzes the most suitable point card based on user C's location information and shopping history.

[1060] 4. The server determines that "Using Company C's point card at this store will give you the maximum points," and notifies User C.

[1061] 5. The emotion engine analyzes user C's voice and facial expressions and recognizes that user C is in a happy emotional state.

[1062] 6. Based on the results of the emotion engine's analysis, the display method of the proposal is adjusted and detailed instructions on how to use points are provided to User C.

[1063] 7. Based on the suggestion, User C uses Company C's point card to purchase a smartphone. As a result, they can get the most points and have a satisfying shopping experience.

[1064] In this way, the present invention helps users make the most of point services and provides a more effective user experience by recognizing the user's emotions and adjusting the content of suggestions.

[1065] The processing flow will be explained below.

[1066] Step 1:

[1067] The user opens the dedicated app or website, accesses the membership registration page, enters basic information (name, email address, age, gender, occupation, etc.), and presses the registration button.

[1068] Step 2:

[1069] The server receives the basic information sent by the user and saves it in the database. After saving, the server sends a confirmation email to the user. The user completes registration by clicking the link in the email.

[1070] Step 3:

[1071] The user enters information about the point service they are using (point card name, balance, usage history, etc.) into the app. Then, they press the point card information synchronization button to send the linked point card information from their device to the server.

[1072] Step 4:

[1073] The server stores the point service information and shopping history received from the user in a database, thereby unifying all point information held by the user.

[1074] Step 5:

[1075] The server provides the stored data to the generation AI, which then learns from it. The generation AI analyzes the reward rate, usage method, and user shopping patterns of each point service and creates a model based on that information.

[1076] Step 6:

[1077] When a user makes a new purchase, they enter the product information and store information into the app. After completing the entry, they press the send button.

[1078] Step 7:

[1079] The server receives the shopping information sent by the user and provides it to the generation AI, which analyzes the past data and new shopping information to select the optimal point service.

[1080] Step 8:

[1081] The server creates the optimal point service proposal for the user based on the analysis results of the generated AI. For example, "At this store, you will receive 5% points when you use point card A."

[1082] Step 9:

[1083] To recognize the user's emotional state, the emotion engine collects voice and facial expression data through the device's camera and microphone. The emotion engine analyzes this data to determine whether the user is excited or calm.

[1084] Step 10:

[1085] The server receives the results of the emotion engine and adjusts how the suggestions are presented based on the user's emotional state: for example, if the user is stressed, it displays brief suggestions, but if the user is relaxed, it provides detailed information.

[1086] Step 11:

[1087] The terminal receives the recommendation notification from the server and notifies the user in real time, allowing the user to use the most suitable point card when shopping.

[1088] Step 12:

[1089] After the user completes their purchase, they enter their purchase information into the app, which then sends it to the server.

[1090] Step 13:

[1091] The server saves the new purchase information in the database and updates the point balance and expiration date, thereby managing the latest status of point usage.

[1092] Step 14:

[1093] The server periodically checks the point balance and expiration date, and notifies the user when the expiration date is approaching. Example: "Your points will expire in one month. We recommend that you use them for your next purchase."

[1094] Through this series of processes, the user can receive suggestions for optimal point services and receive information according to their emotional state, allowing them to use their points effectively and comfortably.

[1095] Example 2

[1096] 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."

[1097] Conventional point service systems have difficulty in managing multiple point cards, making it difficult to propose optimal ways to use points. Furthermore, proposals do not take into account the user's emotional state, limiting the quality of the user experience. Furthermore, insufficient point expiration management and notifications have led to users wasting points.

[1098] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1099] In this invention, the server includes means for saving basic information acquired from the user, means for collecting the user's point service information and shopping history, analysis means using a generative AI model that learns based on the collected information and selects the optimal point service, adjustment means using an emotion engine that recognizes the user's emotional state, suggestion means for proposing point services according to the user's life cycle, means for notifying the user terminal of information on the proposed point services, and management means for managing the user's point balance and expiration date and notifying them as appropriate. This makes it possible to manage multiple point services in an integrated manner and suggest the optimal way to use points according to the user's emotional state.

[1100] "Means for storing basic information obtained from users" refers to a system component that has the function of collecting basic information such as a user's name, email address, age, gender, occupation, etc., and storing it in a database.

[1101] "Means for collecting user point service information and shopping history" refers to a system component that has the function of collecting information on various point cards used by users (point card name, balance, usage history, etc.) and the user's purchase history.

[1102] "Analysis means using a generative AI model" refers to a component of a system that has the function of utilizing collected point service and shopping history data to perform analysis using a generative AI model in order to select the most appropriate point service.

[1103] The "adjustment means using an emotion engine that recognizes the user's emotional state" is a component of the system that has the function of analyzing the user's voice and facial expression data, recognizing the user's emotional state (for example, whether they are in good spirits or feeling stressed), and adjusting the content of suggestions accordingly.

[1104] The "suggestion means for proposing point services according to the user's life cycle" is a component of the system that has the function of proposing the most suitable point service to the user based on the user's basic information, point service usage history, shopping patterns, and emotional state.

[1105] "Means for notifying the user device of information about the proposed point service" refers to a system component that has the function of notifying the user's device, such as a smartphone app, of information about the optimal point service selected by the generative AI model.

[1106] The "management means for managing the user's point balance and expiration date and notifying them as appropriate" is a system component that has the function of managing the balance of points held by the user and the expiration date of points, and notifying the user when the expiration date is approaching.

[1107] The present invention is a system that effectively utilizes multiple point services and recognizes user emotions to provide more personalized suggestions. The system includes means for user registration, data collection, analysis using a generative AI model, suggestion adjustment using an emotion engine, and point management. Specific embodiments are described below.

[1108] User registration and saving basic information

[1109] 1. User visits the membership registration page:

[1110] The user opens the dedicated app or website, enters basic information such as name, email address, age, gender, and occupation, and presses the registration button.

[1111] The server receives the basic information sent by the user, stores it in a database, and then sends a confirmation email to the user, who clicks on the link to confirm and completes the registration.

[1112] Collection of point service information and shopping history

[1113] 2. User enters loyalty card information:

[1114] Users enter information about the point service they use (point card name, balance, usage history, etc.) into a dedicated app. It is also possible to synchronize linked point card information with the app.

[1115] The server stores the point service information and shopping history received from the user in a database.

[1116] Learning by generative AI model and selection of optimal point service

[1117] 3. The server provides data to the generative AI model:

[1118] The server provides the stored user data to the generative AI model, which then learns from this data. The data includes point card usage history and shopping patterns.

[1119] The generative AI model analyzes the data and creates a model that understands the reward rate and usage method of the point service, as well as the user's shopping patterns.

[1120] Based on the model created by the generative AI model, the server selects the optimal point service tailored to the user's shopping behavior.

[1121] Suggestions and adjustments with the emotion engine

[1122] 4. User enters new shopping information:

[1123] When a user makes a new purchase, they enter store information and information about the item they wish to purchase into the app and press the send button.

[1124] The server receives the shopping information sent by the user and provides it to the generative AI model, which then reanalyzes the optimal point service based on the new information and past data.

[1125] 5. The server creates the proposal and the emotion engine adjusts it:

[1126] The server creates an optimal point service proposal for the user based on the analysis results of the generated AI model. The proposal might be something like, "If you use point card A at this store, you will receive 5% points."

[1127] The emotion engine analyzes the user's voice and facial expression data to determine whether the user is in good spirits or stressed.

[1128] The emotion engine tailors suggestions based on the user's emotional state, for example, providing simple, easy-to-understand suggestions if the user is stressed, and detailed suggestions if the user is in a positive mood.

[1129] The proposed content will be notified to the user via the device (smartphone app).

[1130] Points Management

[1131] 6. User enters purchase information:

[1132] After the user completes their purchase, they enter the purchase information into the app and send it from the device to the server.

[1133] The server saves the new purchase information in the database and updates the points balance and expiration date.

[1134] 7. The server notifies:

[1135] The server periodically checks the point balance and expiration date, and notifies the user when the expiration date is approaching. For example, it may notify the user that "Your points will expire in one month. We recommend that you use them for your next purchase."

[1136] Specific examples

[1137] For example, if a user is looking to buy a new smartphone:

[1138] 1. A user enters a retail store and opens the app.

[1139] 2. Enter the product information you plan to purchase (on your smartphone) into the app.

[1140] 3. The server analyzes the user's location information and shopping history to find the most suitable point card.

[1141] 4. The server notifies the customer, "At this store, you will receive the maximum points if you use point card A."

[1142] 5. The emotion engine analyzes the user's voice and facial expressions to recognize when the user is in a happy emotional state.

[1143] 6. Based on the results of the emotion engine's analysis, the display of the offer is adjusted and detailed instructions on how to use points are provided to the user.

[1144] 7. Based on the suggestion, the user uses point card A to purchase a smartphone.

[1145] Prompt Sentence Examples

[1146] Examples of prompts to input to a generative AI model include:

[1147] Select and suggest the most suitable rewards card based on the user's shopping history and rewards service information. Adjust the recommendation taking into account the user's emotional state.

[1148] This system allows users to make the most of multiple point services and receive optimal suggestions based on their emotional state.

[1149] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1150] Step 1:

[1151] User registration and saving basic information

[1152] Input: The user enters basic information such as name, email address, age, gender, and occupation into a dedicated app or website.

[1153] Specific operation: The user enters the required information into the input form and presses the registration button.

[1154] Data processing and data calculation: The server receives the basic information sent by the user, converts the input data into a database format, and stores it in the database.

[1155] Output: After saving is complete, the server will send a confirmation email to the user.

[1156] Step 2:

[1157] Collection of point service information and shopping history

[1158] Input: The user uses a dedicated app to input or synchronize information about the point card they are using (point card name, balance, usage history, etc.).

[1159] Specific operation: The user enters point card information or clicks the data synchronization button.

[1160] Data processing and data calculation: The server receives the loyalty program information and shopping history sent by the user, converts the data into a database format, and stores it in the database.

[1161] Output: Point service information and shopping history are added to the database.

[1162] Step 3:

[1163] Learning by generative AI model and selection of optimal point service

[1164] Input: The server provides stored user data (point card usage history and shopping patterns) to the generative AI model.

[1165] Specific operation: The server inputs data from the database into the generative AI model, and the AI ​​model analyzes the data.

[1166] Data processing and data calculation: The generative AI model learns the reward rate and usage method of the point service, as well as the user's shopping patterns, and creates a model to select the most suitable point service.

[1167] Output: The analytical model created by the generative AI model is output.

[1168] Step 4:

[1169] Enter new shopping information and prepare suggestions

[1170] Input: When a user makes a new purchase, they enter store information and information about the item they plan to purchase into the app and press the submit button.

[1171] Specific operation: The user enters the store name and product name into the app's input form and clicks the "Submit" button.

[1172] Data processing and data calculation: The server receives new shopping information and provides it back to the generative AI model for updated analysis.

[1173] Output: The analysis results from the generative AI model output the selection of the most suitable point service.

[1174] Step 5:

[1175] Suggestions and adjustments with the emotion engine

[1176] Input: The server creates suggestions based on the AI's analysis results and the user's new shopping information.

[1177] Specific operation: Based on the analysis results, the server creates a suggestion such as "If you use point card A at this store, you will receive 5% points."

[1178] Data processing and data calculation: The emotion engine analyzes the user's voice and facial expression data to identify the user's emotional state (e.g., cheerfulness, stress). The emotion engine adjusts the suggestions, changing their complexity and level of detail.

[1179] Output: The optimal recommendation content adjusted by the emotion engine is generated.

[1180] Step 6:

[1181] Notification of proposal details

[1182] Input: Data to inform tailored proposals.

[1183] Specific operation: The server notifies the user of the optimal proposal via a smartphone app. For example, a push notification such as "Use point card A to receive 5% points" is sent.

[1184] Output: A notification of the proposed content is sent to the user's device.

[1185] Step 7:

[1186] Points Management

[1187] Input: Purchase information entered by the user after completing a purchase.

[1188] What happens: The user enters purchase details into an in-app form and presses the "Submit" button.

[1189] Data processing and calculation: The server saves new purchase information to the database, updates point balances and expiration dates, and periodically checks the database to send out alerts when expiration dates are approaching.

[1190] Output: The updated point balance and expiration information are saved in the database, and when the expiration date approaches, the user is notified that "Your points will expire in one month."

[1191] (Application example 2)

[1192] 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."

[1193] In recent years, there has been a demand for improved consumer convenience through point services. However, the existence of multiple point services makes it difficult to determine the optimal point card usage. Furthermore, there is a lack of methods to provide customized suggestions based on the user's emotional state and real-time situation. This poses a problem that users are likely to be unable to make the most of point services.

[1194] The specification process by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for recognizing the user's emotions and adjusting the content of suggestions based on the emotional state, means for saving basic information acquired from the user, means for collecting the user's point service information and shopping history, analysis means using a generation AI, suggestion means, and management means. This makes it possible to select and suggest the optimal point service taking the user's emotions into consideration.

[1195] "Means for storing basic information obtained from the user" refers to a function that records basic information such as the user's name, email address, age, sex, and occupation, and stores it in a database.

[1196] "Means for collecting user's point service information and shopping history" is a function that collects the balance of each point card used by the user, usage history, and the user's shopping history, and stores them in a database.

[1197] "Analysis method using generative AI" is a function that uses generative AI to learn from collected information and select the most suitable point service for the user.

[1198] The "suggestion means" is a function that suggests to the user the most appropriate way to use the points service based on the user's life cycle, location information, and emotional state.

[1199] The "notification means" is a function for notifying the user's terminal of information about the proposed point service.

[1200] The "management means" is a function that manages the user's point balance and expiration date, and notifies the user when the expiration date is approaching.

[1201] The "emotion engine" is a function that analyzes the user's voice and facial expression data to recognize their emotional state and adjusts the suggestions based on that.

[1202] The invention is a system that effectively utilizes the user's point service, identifies the user's emotional state, and adjusts the content of suggestions. The main components of this system are a user terminal, a server, a generation AI, and an emotion engine.

[1203] First, the user accesses the smart glasses' application "Point Master" and registers by entering basic information. This basic information includes name, email address, age, gender, occupation, etc. The server receives this basic information and stores it in a database. Point service information and shopping history sent from the user's device are also collected and stored on the server.

[1204] Next, the server uses a generation AI based on the collected data to select the optimal point service. The generation AI analyzes the point service's reward rate, usage method, and the user's shopping patterns to select the most advantageous point card for the user. Furthermore, the suggestion content is adjusted by an emotion engine, taking into account the user's emotional state. The emotion engine analyzes the user's voice and facial expression data to determine whether the user is in good spirits or stressed. For example, if the user is in good spirits, detailed suggestions are made, and if the user is stressed, simple suggestions are provided.

[1205] The suggestions are sent to the user's device in real time. For example, when a user is browsing products in a physical store using smart glasses, they enter store information and information about the products they plan to purchase into the app and send it to the server. The server then analyzes the optimal point service based on location information and shopping history, and notifies the user's device of the results. Furthermore, after the user completes their shopping, they enter their purchase information into the app, and the server updates their point balance and expiration date and sends appropriate notifications.

[1206] The hardware used includes smart glasses (e.g., Google Glass or Vuzix Blade) and user devices (e.g., smartphones).The software includes an emotion recognition module, a recommendation engine, and a point service API, which work together.

[1207] As a concrete example, consider a user who is about to purchase a smartphone at an electronics retail store. The user puts on smart glasses and enters the product information they plan to purchase into the app. The server recommends the most suitable point service based on their point card information and shopping history, and notifies them that "You will receive the maximum points if you use Company A's point card at this store." At this time, the emotion engine recognizes that the user appears to be enjoying themselves, and also provides detailed instructions on how to use the points.

[1208] A specific example of a prompt is as follows:

[1209] "Please enter the following store and product information based on the user's shopping history and loyalty card information:

[1210] Store information: Electronics retailer, Shinjuku

[1211] Product information: Smartphone, Price: 50,000 yen

[1212] Please explain the recommended point cards based on the generated AI and the reasons for them.

[1213] This system allows users to make the most of the points service, and also allows them to have a more satisfying purchasing experience by adjusting the suggestions according to their emotional state.

[1214] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1215] Step 1:

[1216] A user opens the smart glasses application and enters basic information, such as name, email address, age, gender, occupation, etc. This information is sent from the user's device to the server, which then stores it in a database.

[1217] Input: User's basic information (name, email address, age, gender, occupation)

[1218] Output: User basic information stored in the database

[1219] Specific behavior:

[1220] A user accesses the application and enters the required information into the form. When the user presses the submit button, the terminal sends the information to the server, which records the information in a database.

[1221] Step 2:

[1222] The user enters information about the point service they use and their shopping history into the application. The device sends this information to the server, which then stores it in a database.

[1223] Input: Point service information (point card name, balance, usage history), shopping history

[1224] Output: Point service and shopping history information stored in the database

[1225] Specific behavior:

[1226] When a user enters their point card information or shopping history into the application and submits it, the device sends the information to the server, which then stores it in a database.

[1227] Step 3:

[1228] The server provides the collected information to the AI ​​generator, which then analyzes the reward rate and usage of the points service, as well as the user's shopping patterns, to select the most suitable points service.

[1229] Input: Point service information and shopping history stored in the database

[1230] Output: Selection result of optimal point service by generation AI

[1231] Specific behavior:

[1232] The server passes the database information to the generation AI, which processes the data and learns from it. It selects the optimal point service and returns the results to the server.

[1233] Step 4:

[1234] When a user goes shopping at a physical store, they put on the smart glasses and input the product information they plan to purchase, which is then sent to the server.

[1235] Input: Store information, product information to be purchased (product name, price)

[1236] Output: Information about the products to be purchased received by the server

[1237] Specific behavior:

[1238] The user uses the smart glasses in a physical store and enters product information into the app. When the user presses the send button, the device sends the information to the server.

[1239] Step 5:

[1240] The server uses AI generation to select the optimal point service based on the user's location and purchase information, and notifies the user of the results.

[1241] Input: Information stored in the database, current location, purchase information

[1242] Output: The optimal point service selection result notified to the user device

[1243] Specific behavior:

[1244] The server uses AI to select the optimal point service based on location information and purchase information, and the selection result is notified to the user's device.

[1245] Step 6:

[1246] The emotion engine analyzes the user's emotional state and adjusts the suggestions accordingly: for example, if the user is in good spirits, it will provide detailed suggestions, and if they are stressed, it will provide simple suggestions.

[1247] Input: User's voice data, facial expression data

[1248] Output: Adjusted proposal

[1249] Specific behavior:

[1250] The emotion engine uses the smart glasses' camera and microphone to analyze the user's voice and facial expressions to determine their emotional state, and the server then adjusts the suggestions accordingly.

[1251] Step 7:

[1252] After completing a purchase, the user enters the purchase information into the app. The device then sends the information to the server, which updates the points balance and expiration date. If necessary, the app notifies users of points approaching their expiration date.

[1253] Input: Purchase information (purchased product, price)

[1254] Output: Updated points balance, expiration notice

[1255] Specific behavior:

[1256] After a purchase, the user enters product information into the app and presses the send button. The device sends the information to the server, which updates the database with the points balance and expiration date. If a notification is required, the user is notified.

[1257] 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.

[1258] 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.

[1259] 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.

[1260] [Fourth embodiment]

[1261] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1262] 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.

[1263] 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).

[1264] 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.

[1265] 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.

[1266] 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).

[1267] 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. 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.

[1268] 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.

[1269] 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.

[1270] 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.

[1271] 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.

[1272] 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.

[1273] 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."

[1274] The present invention is a system for effectively utilizing multiple point services, and is a series of functions including user registration, data collection, analysis by generation AI, proposals and notifications, and point management. Specific embodiments for implementing the present invention are described below.

[1275] User registration and saving basic information

[1276] 1. The user registers as a member using the dedicated app or website. They enter basic information (name, email address, age, gender, occupation, etc.) on their device and press the send button.

[1277] 2. The server receives the basic information sent by the user and stores it in the database. It then sends a confirmation email to the user, who must click on the link to confirm and complete the registration.

[1278] Collection of point service information and shopping history

[1279] 1. The user enters information about the point service they are using (point card name, balance, usage history, etc.) into the app. It is also possible to synchronize linked point card information with the app.

[1280] 2. The server collects the point information and shopping history provided by the user and stores them in a centralized database.

[1281] Learning by generative AI and selection of optimal point services

[1282] 1. The server trains the generation AI based on the collected user point service information and shopping history. Specifically, the generation AI learns the reward rate of each point service, how to use it, and the user's shopping patterns.

[1283] 2. Next, the server uses the generation AI to select the optimal point service. For example, it selects the optimal point card when a user purchases electronic devices.

[1284] Suggestions and Notifications

[1285] 1. The user opens the app and enters information for a new purchase (for example, the product and store information they plan to purchase).

[1286] 2. The server analyzes this new information and past data and proposes the optimal point service based on the learning results of the generating AI.

[1287] 3. The suggested content is notified to the user via the device (smartphone app). For example, a real-time notification such as "Use point card A at this store to receive 5% points" is sent.

[1288] Points Management

[1289] 1. Users can check their points balance and expiration date within the app.

[1290] 2. The server periodically checks the user's point information and sends an alert when the expiration date is approaching. For example, it may send a message saying, "Your points will expire in one month. We recommend that you use them for your next purchase."

[1291] Specific examples

[1292] For example, user B is planning to purchase a new refrigerator. User B has registered with the point utilization advisor and regularly uses a supermarket point card, but this is the first time that user B has purchased a refrigerator.

[1293] 1. User B enters an electronics store and opens the app.

[1294] 2. Enter the product information (refrigerator) you plan to purchase into the app.

[1295] 3. The server analyzes the most suitable point card based on User B's location information and shopping history.

[1296] 4. The server determines that "Using Company B's point card at this store will result in higher points" and notifies User B.

[1297] 5. Based on the suggestion, User B uses Company B's point card to purchase a refrigerator. As a result, User B can obtain the most advantageous points and enjoy a satisfying shopping experience.

[1298] As described above, the present invention helps users to make the most of point services, and provides a specific form for seamlessly performing a series of processes and improving convenience.

[1299] The processing flow will be explained below.

[1300] Step 1:

[1301] The user opens the dedicated app or website, accesses the membership registration page, enters basic information (name, email address, age, gender, occupation, etc.), and presses the registration button.

[1302] Step 2:

[1303] The server receives the basic information sent by the user and saves it in the database. After saving, the server sends a confirmation email to the user. The user completes registration by clicking the link in the email.

[1304] Step 3:

[1305] The user enters the information of the point service they use (point card name, balance, usage history, etc.) into the app, or synchronizes the information of the linked point card. When the synchronization button is pressed, the information is sent from the device to the server.

[1306] Step 4:

[1307] The server stores the point service information and shopping history received from the user in a database. Once the information collection is complete, proceed to the next step.

[1308] Step 5:

[1309] The server provides the stored data to the generation AI, which then learns from the data. The generation AI then creates a model to analyze the reward rate and usage of the point service, as well as user shopping patterns.

[1310] Step 6:

[1311] When a user makes a new purchase, they enter store information and information about the product they plan to purchase into the app. After completing the input, they press the submit button.

[1312] Step 7:

[1313] The server receives the shopping information sent by the user and provides it to the generation AI, which selects the optimal point service based on past data and new information.

[1314] Step 8:

[1315] The server creates the optimal point service proposal for the user based on the analysis results of the generation AI. The proposal is then notified to the device app. For example, it may notify the user that "If you use point card A at this store, you will receive the maximum points."

[1316] Step 9:

[1317] After the user completes their purchase, they enter the purchase information into the app, which then sends it to the server.

[1318] Step 10:

[1319] The server saves the new purchase information in the database and updates the points balance and expiration date.

[1320] Step 11:

[1321] The server periodically checks the point balance and expiration date, and notifies the user when the expiration date is approaching. For example, it may notify the user that "Your points will expire in one month. We recommend that you use them for your next purchase."

[1322] Through this series of processes, users can receive suggestions for optimal point services and use their points efficiently.

[1323] Example 1

[1324] 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."

[1325] Traditional point services required the time and effort of managing multiple point cards, making it difficult for users to make the most of their points. They also lacked the information needed to properly determine which point card would provide the most benefits. Furthermore, users often lost points without realizing their points were about to expire.

[1326] 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.

[1327] In this invention, the server includes a means for saving basic information acquired from the user, a means for collecting the user's point service information and shopping history, and an analysis means using a generation AI that learns from the collected information and selects the most suitable point service, thereby enabling the user to effectively manage and make the most of the point service.

[1328] The "means for storing basic information" is a function for safely storing basic personal information obtained from users, such as their name, email address, age, sex, and occupation.

[1329] "Means for collecting point service information and shopping history" refers to a function for collecting and centrally managing information about the point card currently in use by the user (point card name, balance, usage history, etc.) and the user's shopping history.

[1330] "Analysis method using generative AI" is a function that uses generative artificial intelligence to analyze and learn from collected point service information and shopping history to select the most suitable point service.

[1331] The "means for the user to input information about the product and store that the user plans to purchase" is a function for the user to input information about the product that the user plans to purchase and the store where the user plans to purchase the product.

[1332] "Means for notifying the user device of information about the proposed point service" is a function for notifying the user device in real time of information about the optimal point service selected by the generation AI.

[1333] The "management means for managing point balances and expiration dates and notifying them as appropriate" is a function for managing the balance on a user's point card and the expiration date of points, and sending alert notifications to the user when the expiration date is approaching.

[1334] "Means for proposing point services in real time" is a function that immediately uses new information such as the user's location information to propose optimal point services to the user in real time.

[1335] MODE FOR CARRYING OUT THE INVENTION

[1336] This invention is a system that helps users efficiently manage and use multiple point services, and provides the functions of user registration, data collection, analysis by AI generation, proposals and notifications, and point management in an integrated manner. Specific embodiments for implementing this invention are described below.

[1337] User registration and saving basic information

[1338] Users register as members using a dedicated app or website. Using a device (smartphone or PC), they enter basic information such as their name, email address, age, gender, and occupation, and then press the "Submit" button. The server receives the basic information sent from the device and securely stores it in a database using SSL communication. A confirmation email is then sent to the user, and registration is completed when the user clicks on the link in the email to confirm.

[1339] Collection of point service information and shopping history

[1340] Users enter information about the point service they are using (point card name, balance, usage history, etc.) into the app. If the app supports it, point card information can be automatically synchronized via API. The server collects the point information and shopping history provided by the user and stores it in a centralized database. This allows users to manage multiple point services on a single platform.

[1341] Learning by generative AI and selection of optimal point services

[1342] The server trains the generative AI model based on the collected user point service information and shopping history. Specifically, the reward rate for each point service, usage methods, and user shopping patterns are used as learning data. Using the trained generative AI model, the server selects the optimal point service. For example, when a user purchases a new electronic device, it can select the most advantageous point card for that product.

[1343] Suggestions and Notifications

[1344] The user opens the app and enters new shopping information (for example, the product and store information they plan to purchase). The server analyzes this new information and past data, and suggests the optimal point service based on the learning results of the generative AI. The suggested content is notified to the user in real time via their device. For example, a notification such as "When you use point card A at this store, you will receive 5% points" may be displayed within the app.

[1345] Points Management

[1346] Users can check their points balance and expiration date within the app. The server periodically checks the database and sends an alert to the user when their points are about to expire. For example, a notification might say, "Your points will expire in one month. We recommend that you use them for your next purchase."

[1347] Specific examples

[1348] For example, user B is about to purchase a new refrigerator. User B has already registered with the point service management system and regularly uses a supermarket point card, but this is the first time that user B has purchased a refrigerator.

[1349] 1. User B enters an electronics store and opens the dedicated app.

[1350] 2. Enter the product information (refrigerator) you plan to purchase into the app.

[1351] 3. The server analyzes the most suitable point card based on User B's location information and shopping history.

[1352] 4. The server determines that "using point card B at this store will result in higher points" and notifies user B.

[1353] 5. Based on the suggestion, User B purchases a refrigerator using Point Card B. As a result, User B can obtain the most advantageous points and enjoy a satisfying shopping experience.

[1354] As described above, the present invention is a system that helps users make the most of the points service. A series of processes are performed seamlessly, greatly improving convenience.

[1355] Prompt Sentence Examples

[1356] "Please explain in natural language the program process of a system that suggests the most suitable points program when a user purchases a new refrigerator. Please use either the server, terminal, or user as the subject and write in natural sentences."

[1357] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1358] Step 1: Register and save your basic information

[1359] 1. The user registers as a member using a dedicated app or website. Using a device, the user enters basic information such as name, email address, age, gender, and occupation, and presses the "Submit" button.

[1360] 2. The basic information entered is sent to the server.

[1361] 3. The server securely stores the received information in a database using SSL communication. A confirmation email is generated and sent to the user's email address. The user must click on the link in the email to confirm their account.

[1362] Step 2: Collecting loyalty program information and shopping history

[1363] 1. The user enters information about the point service they are using (point card name, balance, usage history, etc.) into the app. In some cases, the app uses a function to synchronize point card information via API.

[1364] 2. The entered point service information is sent to the server.

[1365] 3. The server stores point information and shopping history in a centralized database and manages user point statistics.

[1366] Step 3: Learning by generative AI and selection of optimal point service

[1367] 1. The server trains the generative AI model based on the user's point service information and shopping history stored in the database. The training data includes the point service's reward rate, usage method, and user shopping patterns.

[1368] 2. The generative AI analyzes this learning data and trains the model.

[1369] 3. The server uses the trained generative AI model to select the optimal point card when the user purchases a specific product, allowing the user to earn the most advantageous points.

[1370] Step 4: Propose and notify

[1371] 1. When a user makes a new purchase, they enter information about the product and store they plan to purchase within the app.

[1372] 2. The entered information is sent to the server, which analyzes it based on past data and the learning results of the generating AI.

[1373] 3. The server generates optimal point service proposals based on the analysis results.

[1374] 4. The server notifies the device of the suggested information. For example, a notification such as "Use point card A at this store to receive 5% points" is displayed within the app.

[1375] Step 5: Manage your points

[1376] 1. Users can check their points balance and expiration date within the app.

[1377] 2. The server periodically checks the point information in the database and generates an alert notification when the points are about to expire.

[1378] 3. An alert notification is sent from the server to the user's device. For example, a message such as "Your points will expire in one month. We recommend you use them for your next purchase" is displayed.

[1379] By operating the system according to the above steps, users can efficiently manage multiple point services and use the most advantageous points.

[1380] (Application example 1)

[1381] 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."

[1382] In modern society, users use multiple point services, and it is difficult to effectively manage and maximize their use of them. In particular, it is difficult to determine which point card is most advantageous for a product you plan to purchase, which often results in points being wasted. In addition, it is difficult to manage points that are approaching their expiration date, and they may expire. The purpose of this invention is to solve these problems.

[1383] 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.

[1384] In this invention, the server includes means for saving basic information acquired from the user, means for collecting the user's point service information and purchase history, analysis means using a generative AI model that learns based on the collected information and selects the optimal point service, suggestion means for proposing point services according to the user's life cycle, means for notifying the user terminal of information on the proposed point services, management means for managing the user's point balance and expiration date and notifying them as appropriate, and means for linking with an electronic payment service and proposing the optimal point card based on information on products the user plans to purchase. This allows the user to select the optimal point card and use their points efficiently.

[1385] "Basic information" refers to information obtained from a user, such as name, email address, age, sex, and occupation.

[1386] The "point service" refers to a service that includes multiple point cards used by users, their balances, usage history, etc.

[1387] "Purchase history" refers to information about products purchased by a user in the past, as well as the purchase date and time and price.

[1388] A "generative AI model" is an artificial intelligence model that learns from collected data and selects the most appropriate point service.

[1389] "Analysis means" refers to a means for analyzing basic information and purchasing history using a generative AI model.

[1390] The "proposing means" is a means for proposing the most suitable point service according to the user's life cycle and location information.

[1391] The "notification means" is a means for notifying the user terminal of information about the proposed point service in real time.

[1392] The "management means" is a means for managing the user's point balance and expiration date, and sending appropriate alert notifications.

[1393] An "electronic payment service" is a service that links with the payment method used by users when purchasing products.

[1394] A "user terminal" is an information processing device used by a user, such as a smartphone or tablet.

[1395] The present invention is a system that allows users to effectively utilize multiple point services, and provides a series of functions including user registration, data collection, analysis using a generative AI model, proposals and notifications, and point management. Specific embodiments for implementing the present invention are described below.

[1396] 1. User registration and saving basic information

[1397] A user installs the SmartPoint Manager app, creates an account, enters basic information (name, email address, age, gender, occupation, etc.), and sends it to the server.

[1398] The server receives the basic information, stores it in a database, and sends a confirmation email to the user, who clicks on the confirmation link in the email to complete the registration.

[1399] 2. Collection of point service information and purchase history

[1400] Users can enter information about their point cards through the app (e.g., point card name, balance, usage history, etc.). The app can also automatically synchronize information about linked point cards.

[1401] The server centrally manages the point information and purchase history provided by the user and stores them in a database.

[1402] 3. Learning with a generative AI model and selection of optimal point services

[1403] The server trains a generative AI model (e.g., GPT-4) based on the user's point information and purchase history collected by the server. The generative AI model analyzes the reward rate, usage method, and user shopping patterns of each point service.

[1404] The system selects the optimal point card based on the learning results, allowing users to use the most advantageous point card.

[1405] 4. Proposals and Notifications

[1406] The user inputs information about a new product they plan to purchase into the app. For example, they input information about a refrigerator they plan to purchase.

[1407] The server analyzes the input information and past data, and uses a generative AI model to propose optimal point services. The proposal results are sent to the user's smartphone in real time.

[1408] As a specific example, if a user enters an electronics store and tries to purchase a refrigerator, the app will notify them that "you will get more points if you use point card B at this store."

[1409] 5. Points Management

[1410] The app provides a feature that allows users to check their current point balance and expiration date.

[1411] The server periodically checks the user's point information and sends an alert when points are about to expire. For example, it may send a message saying, "Your points will expire in one month. We recommend that you use them for your next purchase."

[1412] Through this series of steps, users can effectively utilize the point service and enjoy maximum benefits.

[1413] Hardware and software used

[1414] Server: Performs data management, user authentication, and training and analysis of generative AI models. Use the most high-performance database possible (e.g., PostgreSQL, MySQL, etc.).

[1415] User device: An information processing device, such as a smartphone or tablet, on which a user installs and uses applications.

[1416] Generative AI models: Use sophisticated generative AI models such as GPT-4 to analyze collected data and make recommendations.

[1417] Prompt Sentence Examples

[1418] "Suggested item: Refrigerator. Points offer: {'name': 'Supermarket A', 'balance': 1500}, {'name': 'Electronics Store B', 'balance': 5000}. Purchase history: {'item_name': 'Freezer', 'purchase_date': '2022-01-01', 'price': 30000}"

[1419] Based on the above prompts, the generative AI model will select the optimal point card.

[1420] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1421] Step 1:

[1422] A user installs the SmartPoint Manager app and creates an account. The user enters basic information (name, email address, age, gender, occupation, etc.) and submits it. The server receives the entered basic information and stores it in a database. The server then sends a confirmation email to the user, and the user completes account registration by clicking the confirmation link in the email.

[1423] Input: Name, email address, age, gender, occupation

[1424] Output: Basic information stored in database, confirmation email sent

[1425] Step 2:

[1426] Users can input the information of multiple point cards they own into the app. They can also synchronize the information of linked point cards with the app. The server centralizes the point information and purchase history provided by the user and stores it in a database.

[1427] Input: Point card name, balance, usage history

[1428] Output: Points information and purchase history stored in the database

[1429] Step 3:

[1430] The server trains a generative AI model (e.g., GPT-4) based on the user's point information and purchase history collected. The generative AI model processes data such as the reward rate for each point service, usage methods, and user shopping patterns to generate criteria for selecting the optimal point card.

[1431] Input: Points information, purchase history

[1432] Output: Trained generative AI model

[1433] Step 4:

[1434] The user enters information about a new product they plan to purchase into the app. The server analyzes this new information and past data and uses a generative AI model to propose the optimal point service. The server generates a prompt and sends it to the generative AI model, which then recommends the optimal point card based on the analysis results.

[1435] Input: Product information to be purchased (e.g., refrigerator)

[1436] Output: Recommendation of the best loyalty card

[1437] Step 5:

[1438] The server notifies the user of the results of the suggestion in real time to their smartphone, for example, "If you use point card B at this store, you will get more points."

[1439] Input: Recommendation of the best rewards card

[1440] Output: User notification

[1441] Step 6:

[1442] Users can check their current point balance and expiration date within the app. The server periodically checks the user's point information and sends an alert when points are approaching their expiration date. For example, it may notify users that "Your points will expire in one month. We recommend that you use them for your next purchase."

[1443] Input: Points balance and expiration date

[1444] Output: Alert notification to the user

[1445] Through the above processing steps, the user can effectively utilize the point service and enjoy maximum benefits.

[1446] 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.

[1447] The present invention is a system that effectively utilizes multiple point services and recognizes user emotions to provide more personalized suggestions. This system includes means for user registration, data collection, analysis using a generative AI, suggestion adjustment using an emotion engine, and point management. Specific embodiments for implementing the present invention are described below.

[1448] User registration and saving basic information

[1449] 1. The user opens the dedicated app or website, accesses the membership registration page, enters basic information (name, email address, age, gender, occupation, etc.), and presses the registration button.

[1450] 2. The server receives the basic information sent by the user and stores it in a database, then sends a confirmation email to the user, who clicks on the link to confirm and completes the registration.

[1451] Collection of point service information and shopping history

[1452] 1. The user enters information about the point service they use (point card name, balance, usage history, etc.) into the app. It is also possible to synchronize linked point card information with the app.

[1453] 2. The server stores the point service information and shopping history received from the user in a database. Once the information collection is complete, proceed to the next step.

[1454] Learning by generative AI and selection of optimal point services

[1455] 1. The server provides the stored data to the generation AI, which then learns from the data. The generation AI then creates a model to analyze the reward rate and usage of the point service, as well as the user's shopping patterns.

[1456] 2. Next, the server uses the generation AI to select the optimal point service. For example, it selects the optimal point card when a user purchases an electrical appliance.

[1457] Suggestions and adjustments with the emotion engine

[1458] 1. When a user makes a new purchase, they enter store information and information about the product they plan to purchase into the app. After completing the entry, they press the submit button.

[1459] 2. The server receives the shopping information sent by the user and provides it to the generation AI, which selects the optimal point service based on past data and new information.

[1460] 3. Based on the analysis results of the generation AI, the server creates the optimal point service proposal for the user.

[1461] 4. The emotion engine analyzes the user's voice and facial expression data to identify the user's emotional state, for example, whether the user is in good spirits or stressed.

[1462] 5. The emotion engine adjusts the suggestions based on the user's emotional state. For example, if the user is stressed, it will provide simple suggestions, and if the user is in good spirits, it will provide detailed suggestions.

[1463] 6. The suggested content is notified to the user via the device (smartphone app). For example, the user may be notified that "If you use point card A at this store, you will receive 5% points."

[1464] Points Management

[1465] 1. After completing a purchase, the user enters the purchase information into the app, which then sends it to the server.

[1466] 2. The server saves the new purchase information in the database and updates the points balance and expiration date.

[1467] 3. The server periodically checks the point balance and expiration date, and notifies the user when the expiration date is approaching. For example, it may notify the user that "Your points will expire in one month. We recommend that you use them for your next purchase."

[1468] Specific examples

[1469] For example, user C is about to purchase a new smartphone. User C has registered with a point utilization advisor and regularly uses several point cards.

[1470] 1. User C enters an electronics store and opens the app.

[1471] 2. Enter the product information you plan to purchase (on your smartphone) into the app.

[1472] 3. The server analyzes the most suitable point card based on user C's location information and shopping history.

[1473] 4. The server determines that "Using Company C's point card at this store will give you the maximum points," and notifies User C.

[1474] 5. The emotion engine analyzes user C's voice and facial expressions and recognizes that user C is in a happy emotional state.

[1475] 6. Based on the results of the emotion engine's analysis, the display method of the proposal is adjusted and detailed instructions on how to use points are provided to User C.

[1476] 7. Based on the suggestion, User C uses Company C's point card to purchase a smartphone. As a result, they can get the most points and have a satisfying shopping experience.

[1477] In this way, the present invention helps users make the most of point services and provides a more effective user experience by recognizing the user's emotions and adjusting the content of suggestions.

[1478] The processing flow will be explained below.

[1479] Step 1:

[1480] The user opens the dedicated app or website, accesses the membership registration page, enters basic information (name, email address, age, gender, occupation, etc.), and presses the registration button.

[1481] Step 2:

[1482] The server receives the basic information sent by the user and saves it in the database. After saving, the server sends a confirmation email to the user. The user completes registration by clicking the link in the email.

[1483] Step 3:

[1484] The user enters information about the point service they are using (point card name, balance, usage history, etc.) into the app. Then, they press the point card information synchronization button to send the linked point card information from their device to the server.

[1485] Step 4:

[1486] The server stores the point service information and shopping history received from the user in a database, thereby unifying all point information held by the user.

[1487] Step 5:

[1488] The server provides the stored data to the generation AI, which then learns from it. The generation AI analyzes the reward rate, usage method, and user shopping patterns of each point service and creates a model based on that information.

[1489] Step 6:

[1490] When a user makes a new purchase, they enter the product information and store information into the app. After completing the entry, they press the send button.

[1491] Step 7:

[1492] The server receives the shopping information sent by the user and provides it to the generation AI, which analyzes the past data and new shopping information to select the optimal point service.

[1493] Step 8:

[1494] The server creates the optimal point service proposal for the user based on the analysis results of the generated AI. For example, "At this store, you will receive 5% points when you use point card A."

[1495] Step 9:

[1496] To recognize the user's emotional state, the emotion engine collects voice and facial expression data through the device's camera and microphone. The emotion engine analyzes this data to determine whether the user is excited or calm.

[1497] Step 10:

[1498] The server receives the results of the emotion engine and adjusts how the suggestions are presented based on the user's emotional state: for example, if the user is stressed, it displays brief suggestions, but if the user is relaxed, it provides detailed information.

[1499] Step 11:

[1500] The terminal receives the recommendation notification from the server and notifies the user in real time, allowing the user to use the most suitable point card when shopping.

[1501] Step 12:

[1502] After the user completes their purchase, they enter their purchase information into the app, which then sends it to the server.

[1503] Step 13:

[1504] The server saves the new purchase information in the database and updates the point balance and expiration date, thereby managing the latest status of point usage.

[1505] Step 14:

[1506] The server periodically checks the point balance and expiration date, and notifies the user when the expiration date is approaching. Example: "Your points will expire in one month. We recommend that you use them for your next purchase."

[1507] Through this series of processes, the user can receive suggestions for optimal point services and receive information according to their emotional state, allowing them to use their points effectively and comfortably.

[1508] Example 2

[1509] 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."

[1510] Conventional point service systems have difficulty in managing multiple point cards, making it difficult to propose optimal ways to use points. Furthermore, proposals do not take into account the user's emotional state, limiting the quality of the user experience. Furthermore, insufficient point expiration management and notifications have led to users wasting points.

[1511] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1512] In this invention, the server includes means for saving basic information acquired from the user, means for collecting the user's point service information and shopping history, analysis means using a generative AI model that learns based on the collected information and selects the optimal point service, adjustment means using an emotion engine that recognizes the user's emotional state, suggestion means for proposing point services according to the user's life cycle, means for notifying the user terminal of information on the proposed point services, and management means for managing the user's point balance and expiration date and notifying them as appropriate. This makes it possible to manage multiple point services in an integrated manner and suggest the optimal way to use points according to the user's emotional state.

[1513] "Means for storing basic information obtained from users" refers to a system component that has the function of collecting basic information such as a user's name, email address, age, gender, occupation, etc., and storing it in a database.

[1514] "Means for collecting user point service information and shopping history" refers to a system component that has the function of collecting information on various point cards used by users (point card name, balance, usage history, etc.) and the user's purchase history.

[1515] "Analysis means using a generative AI model" refers to a component of a system that has the function of utilizing collected point service and shopping history data to perform analysis using a generative AI model in order to select the most appropriate point service.

[1516] The "adjustment means using an emotion engine that recognizes the user's emotional state" is a component of the system that has the function of analyzing the user's voice and facial expression data, recognizing the user's emotional state (for example, whether they are in good spirits or feeling stressed), and adjusting the content of suggestions accordingly.

[1517] The "suggestion means for proposing point services according to the user's life cycle" is a component of the system that has the function of proposing the most suitable point service to the user based on the user's basic information, point service usage history, shopping patterns, and emotional state.

[1518] "Means for notifying the user device of information about the proposed point service" refers to a system component that has the function of notifying the user's device, such as a smartphone app, of information about the optimal point service selected by the generative AI model.

[1519] The "management means for managing the user's point balance and expiration date and notifying them as appropriate" is a system component that has the function of managing the balance of points held by the user and the expiration date of points, and notifying the user when the expiration date is approaching.

[1520] The present invention is a system that effectively utilizes multiple point services and recognizes user emotions to provide more personalized suggestions. The system includes means for user registration, data collection, analysis using a generative AI model, suggestion adjustment using an emotion engine, and point management. Specific embodiments are described below.

[1521] User registration and saving basic information

[1522] 1. User visits the membership registration page:

[1523] The user opens the dedicated app or website, enters basic information such as name, email address, age, gender, and occupation, and presses the registration button.

[1524] The server receives the basic information sent by the user, stores it in a database, and then sends a confirmation email to the user, who clicks on the link to confirm and completes the registration.

[1525] Collection of point service information and shopping history

[1526] 2. User enters loyalty card information:

[1527] Users enter information about the point service they use (point card name, balance, usage history, etc.) into a dedicated app. It is also possible to synchronize linked point card information with the app.

[1528] The server stores the point service information and shopping history received from the user in a database.

[1529] Learning by generative AI model and selection of optimal point service

[1530] 3. The server provides data to the generative AI model:

[1531] The server provides the stored user data to the generative AI model, which then learns from this data. The data includes point card usage history and shopping patterns.

[1532] The generative AI model analyzes the data and creates a model that understands the reward rate and usage method of the point service, as well as the user's shopping patterns.

[1533] Based on the model created by the generative AI model, the server selects the optimal point service tailored to the user's shopping behavior.

[1534] Suggestions and adjustments with the emotion engine

[1535] 4. User enters new shopping information:

[1536] When a user makes a new purchase, they enter store information and information about the item they wish to purchase into the app and press the send button.

[1537] The server receives the shopping information sent by the user and provides it to the generative AI model, which then reanalyzes the optimal point service based on the new information and past data.

[1538] 5. The server creates the proposal and the emotion engine adjusts it:

[1539] The server creates an optimal point service proposal for the user based on the analysis results of the generated AI model. The proposal might be something like, "If you use point card A at this store, you will receive 5% points."

[1540] The emotion engine analyzes the user's voice and facial expression data to determine whether the user is in good spirits or stressed.

[1541] The emotion engine tailors suggestions based on the user's emotional state, for example, providing simple, easy-to-understand suggestions if the user is stressed, and detailed suggestions if the user is in a positive mood.

[1542] The proposed content will be notified to the user via the device (smartphone app).

[1543] Points Management

[1544] 6. User enters purchase information:

[1545] After the user completes their purchase, they enter the purchase information into the app and send it from the device to the server.

[1546] The server saves the new purchase information in the database and updates the points balance and expiration date.

[1547] 7. The server notifies:

[1548] The server periodically checks the point balance and expiration date, and notifies the user when the expiration date is approaching. For example, it may notify the user that "Your points will expire in one month. We recommend that you use them for your next purchase."

[1549] Specific examples

[1550] For example, if a user is looking to buy a new smartphone:

[1551] 1. A user enters a retail store and opens the app.

[1552] 2. Enter the product information you plan to purchase (on your smartphone) into the app.

[1553] 3. The server analyzes the user's location information and shopping history to find the most suitable point card.

[1554] 4. The server notifies the customer, "At this store, you will receive the maximum points if you use point card A."

[1555] 5. The emotion engine analyzes the user's voice and facial expressions to recognize when the user is in a happy emotional state.

[1556] 6. Based on the results of the emotion engine's analysis, the display of the offer is adjusted and detailed instructions on how to use points are provided to the user.

[1557] 7. Based on the suggestion, the user uses point card A to purchase a smartphone.

[1558] Prompt Sentence Examples

[1559] Examples of prompts to input to a generative AI model include:

[1560] Select and suggest the most suitable rewards card based on the user's shopping history and rewards service information. Adjust the recommendation taking into account the user's emotional state.

[1561] This system allows users to make the most of multiple point services and receive optimal suggestions based on their emotional state.

[1562] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1563] Step 1:

[1564] User registration and saving basic information

[1565] Input: The user enters basic information such as name, email address, age, gender, and occupation into a dedicated app or website.

[1566] Specific operation: The user enters the required information into the input form and presses the registration button.

[1567] Data processing and data calculation: The server receives the basic information sent by the user, converts the input data into a database format, and stores it in the database.

[1568] Output: After saving is complete, the server will send a confirmation email to the user.

[1569] Step 2:

[1570] Collection of point service information and shopping history

[1571] Input: The user uses a dedicated app to input or synchronize information about the point card they are using (point card name, balance, usage history, etc.).

[1572] Specific operation: The user enters point card information or clicks the data synchronization button.

[1573] Data processing and data calculation: The server receives the loyalty program information and shopping history sent by the user, converts the data into a database format, and stores it in the database.

[1574] Output: Point service information and shopping history are added to the database.

[1575] Step 3:

[1576] Learning by generative AI model and selection of optimal point service

[1577] Input: The server provides stored user data (point card usage history and shopping patterns) to the generative AI model.

[1578] Specific operation: The server inputs data from the database into the generative AI model, and the AI ​​model analyzes the data.

[1579] Data processing and data calculation: The generative AI model learns the reward rate and usage method of the point service, as well as the user's shopping patterns, and creates a model to select the most suitable point service.

[1580] Output: The analytical model created by the generative AI model is output.

[1581] Step 4:

[1582] Enter new shopping information and prepare suggestions

[1583] Input: When a user makes a new purchase, they enter store information and information about the item they plan to purchase into the app and press the submit button.

[1584] Specific operation: The user enters the store name and product name into the app's input form and clicks the "Submit" button.

[1585] Data processing and data calculation: The server receives new shopping information and provides it back to the generative AI model for updated analysis.

[1586] Output: The analysis results from the generative AI model output the selection of the most suitable point service.

[1587] Step 5:

[1588] Suggestions and adjustments with the emotion engine

[1589] Input: The server creates suggestions based on the AI's analysis results and the user's new shopping information.

[1590] Specific operation: Based on the analysis results, the server creates a suggestion such as "If you use point card A at this store, you will receive 5% points."

[1591] Data processing and data calculation: The emotion engine analyzes the user's voice and facial expression data to identify the user's emotional state (e.g., cheerfulness, stress). The emotion engine adjusts the suggestions, changing their complexity and level of detail.

[1592] Output: The optimal recommendation content adjusted by the emotion engine is generated.

[1593] Step 6:

[1594] Notification of proposal details

[1595] Input: Data to inform tailored proposals.

[1596] Specific operation: The server notifies the user of the optimal proposal via a smartphone app. For example, a push notification such as "Use point card A to receive 5% points" is sent.

[1597] Output: A notification of the proposed content is sent to the user's device.

[1598] Step 7:

[1599] Points Management

[1600] Input: Purchase information entered by the user after completing a purchase.

[1601] What happens: The user enters purchase details into an in-app form and presses the "Submit" button.

[1602] Data processing and calculation: The server saves new purchase information to the database, updates point balances and expiration dates, and periodically checks the database to send out alerts when expiration dates are approaching.

[1603] Output: The updated point balance and expiration information are saved in the database, and when the expiration date approaches, the user is notified that "Your points will expire in one month."

[1604] (Application example 2)

[1605] 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."

[1606] In recent years, there has been a demand for improved consumer convenience through point services. However, the existence of multiple point services makes it difficult to determine the optimal point card usage. Furthermore, there is a lack of methods to provide customized suggestions based on the user's emotional state and real-time situation. This poses a problem that users are likely to be unable to make the most of point services.

[1607] The specification process by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for recognizing the user's emotions and adjusting the content of suggestions based on the emotional state, means for saving basic information acquired from the user, means for collecting the user's point service information and shopping history, analysis means using a generation AI, suggestion means, and management means. This makes it possible to select and suggest the optimal point service taking the user's emotions into consideration.

[1608] "Means for storing basic information obtained from the user" refers to a function that records basic information such as the user's name, email address, age, sex, and occupation, and stores it in a database.

[1609] "Means for collecting user's point service information and shopping history" is a function that collects the balance of each point card used by the user, usage history, and the user's shopping history, and stores them in a database.

[1610] "Analysis method using generative AI" is a function that uses generative AI to learn from collected information and select the most suitable point service for the user.

[1611] The "suggestion means" is a function that suggests to the user the most appropriate way to use the points service based on the user's life cycle, location information, and emotional state.

[1612] The "notification means" is a function for notifying the user's terminal of information about the proposed point service.

[1613] The "management means" is a function that manages the user's point balance and expiration date, and notifies the user when the expiration date is approaching.

[1614] The "emotion engine" is a function that analyzes the user's voice and facial expression data to recognize their emotional state and adjusts the suggestions based on that.

[1615] The invention is a system that effectively utilizes the user's point service, identifies the user's emotional state, and adjusts the content of suggestions. The main components of this system are a user terminal, a server, a generation AI, and an emotion engine.

[1616] First, the user accesses the smart glasses' application "Point Master" and registers by entering basic information. This basic information includes name, email address, age, gender, occupation, etc. The server receives this basic information and stores it in a database. Point service information and shopping history sent from the user's device are also collected and stored on the server.

[1617] Next, the server uses a generation AI based on the collected data to select the optimal point service. The generation AI analyzes the point service's reward rate, usage method, and the user's shopping patterns to select the most advantageous point card for the user. Furthermore, the suggestion content is adjusted by an emotion engine, taking into account the user's emotional state. The emotion engine analyzes the user's voice and facial expression data to determine whether the user is in good spirits or stressed. For example, if the user is in good spirits, detailed suggestions are made, and if the user is stressed, simple suggestions are provided.

[1618] The suggestions are sent to the user's device in real time. For example, when a user is browsing products in a physical store using smart glasses, they enter store information and information about the products they plan to purchase into the app and send it to the server. The server then analyzes the optimal point service based on location information and shopping history, and notifies the user's device of the results. Furthermore, after the user completes their shopping, they enter their purchase information into the app, and the server updates their point balance and expiration date and sends appropriate notifications.

[1619] The hardware used includes smart glasses (e.g., Google Glass or Vuzix Blade) and user devices (e.g., smartphones).The software includes an emotion recognition module, a recommendation engine, and a point service API, which work together.

[1620] As a concrete example, consider a user who is about to purchase a smartphone at an electronics retail store. The user puts on smart glasses and enters the product information they plan to purchase into the app. The server recommends the most suitable point service based on their point card information and shopping history, and notifies them that "You will receive the maximum points if you use Company A's point card at this store." At this time, the emotion engine recognizes that the user appears to be enjoying themselves, and also provides detailed instructions on how to use the points.

[1621] A specific example of a prompt is as follows:

[1622] "Please enter the following store and product information based on the user's shopping history and loyalty card information:

[1623] Store information: Electronics retailer, Shinjuku

[1624] Product information: Smartphone, Price: 50,000 yen

[1625] Please explain the recommended point cards based on the generated AI and the reasons for them.

[1626] This system allows users to make the most of the points service, and also allows them to have a more satisfying purchasing experience by adjusting the suggestions according to their emotional state.

[1627] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1628] Step 1:

[1629] A user opens the smart glasses application and enters basic information, such as name, email address, age, gender, occupation, etc. This information is sent from the user's device to the server, which then stores it in a database.

[1630] Input: User's basic information (name, email address, age, gender, occupation)

[1631] Output: User basic information stored in the database

[1632] Specific behavior:

[1633] A user accesses the application and enters the required information into the form. When the user presses the submit button, the terminal sends the information to the server, which records the information in a database.

[1634] Step 2:

[1635] The user enters information about the point service they use and their shopping history into the application. The device sends this information to the server, which then stores it in a database.

[1636] Input: Point service information (point card name, balance, usage history), shopping history

[1637] Output: Point service and shopping history information stored in the database

[1638] Specific behavior:

[1639] When a user enters their point card information or shopping history into the application and submits it, the device sends the information to the server, which then stores it in a database.

[1640] Step 3:

[1641] The server provides the collected information to the AI ​​generator, which then analyzes the reward rate and usage of the points service, as well as the user's shopping patterns, to select the most suitable points service.

[1642] Input: Point service information and shopping history stored in the database

[1643] Output: Selection result of optimal point service by generation AI

[1644] Specific behavior:

[1645] The server passes the database information to the generation AI, which processes the data and learns from it. It selects the optimal point service and returns the results to the server.

[1646] Step 4:

[1647] When a user goes shopping at a physical store, they put on the smart glasses and input the product information they plan to purchase, which is then sent to the server.

[1648] Input: Store information, product information to be purchased (product name, price)

[1649] Output: Information about the products to be purchased received by the server

[1650] Specific behavior:

[1651] The user uses the smart glasses in a physical store and enters product information into the app. When the user presses the send button, the device sends the information to the server.

[1652] Step 5:

[1653] The server uses AI generation to select the optimal point service based on the user's location and purchase information, and notifies the user of the results.

[1654] Input: Information stored in the database, current location, purchase information

[1655] Output: The optimal point service selection result notified to the user device

[1656] Specific behavior:

[1657] The server uses AI to select the optimal point service based on location information and purchase information, and the selection result is notified to the user's device.

[1658] Step 6:

[1659] The emotion engine analyzes the user's emotional state and adjusts the suggestions accordingly: for example, if the user is in good spirits, it will provide detailed suggestions, and if they are stressed, it will provide simple suggestions.

[1660] Input: User's voice data, facial expression data

[1661] Output: Adjusted proposal

[1662] Specific behavior:

[1663] The emotion engine uses the smart glasses' camera and microphone to analyze the user's voice and facial expressions to determine their emotional state, and the server then adjusts the suggestions accordingly.

[1664] Step 7:

[1665] After completing a purchase, the user enters the purchase information into the app. The device then sends the information to the server, which updates the points balance and expiration date. If necessary, the app notifies users of points approaching their expiration date.

[1666] Input: Purchase information (purchased product, price)

[1667] Output: Updated points balance, expiration notice

[1668] Specific behavior:

[1669] After a purchase, the user enters product information into the app and presses the send button. The device sends the information to the server, which updates the database with the points balance and expiration date. If a notification is required, the user is notified.

[1670] 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.

[1671] 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.

[1672] 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.

[1673] 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.

[1674] 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.

[1675] 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.

[1676] 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).

[1677] 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.

[1678] 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."

[1679] 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.

[1680] 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).

[1681] 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.

[1682] 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.

[1683] 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.

[1684] 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.

[1685] 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.

[1686] 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.

[1687] 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.

[1688] 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.

[1689] 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.

[1690] 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.

[1691] The following is further disclosed regarding the above embodiment.

[1692] (Claim 1)

[1693] a means for storing basic information obtained from the user;

[1694] A means for collecting information on a user's point service and shopping history;

[1695] An analytical method using generative AI that learns from collected information and selects the optimal point service,

[1696] Proposal means for proposing point services according to the user's life cycle;

[1697] means for notifying a user terminal of information on the proposed point service;

[1698] A management means for managing the user's point balance and expiration date and notifying them as appropriate;

[1699] A system including:

[1700] (Claim 2)

[1701] 2. The system according to claim 1, wherein the suggestion means uses the user's location information to suggest point services in real time.

[1702] (Claim 3)

[1703] 2. The system according to claim 1, wherein the management means issues an alert to the user when the expiration date of the points is approaching.

[1704] (Claim 4)

[1705] 2. The system according to claim 1, wherein the analysis means integrates data from multiple point services used by the user and suggests optimal ways to use points.

[1706] "Example 1"

[1707] (Claim 1)

[1708] a means for storing basic information obtained from the user;

[1709] A means for collecting information on a user's point service and shopping history;

[1710] An analytical method using generative AI that learns from collected information and selects the optimal point service,

[1711] A means for the user to input information about the product and store they plan to purchase;

[1712] means for notifying a user terminal of information on the proposed point service;

[1713] A management means for managing the user's point balance and expiration date and notifying them as appropriate;

[1714] A system including:

[1715] (Claim 2)

[1716] 2. The system according to claim 1, wherein the suggestion means uses the user's location information to suggest point services in real time.

[1717] (Claim 3)

[1718] 2. The system according to claim 1, wherein the management means issues an alert to the user when the expiration date of the points is approaching.

[1719] "Application Example 1"

[1720] (Claim 1)

[1721] a means for storing basic information obtained from the user;

[1722] A means for collecting information on point services and purchase history of users;

[1723] An analytical method using a generative AI model that learns from collected information and selects the optimal point service;

[1724] Proposal means for proposing point services according to the user's life cycle;

[1725] means for notifying a user terminal of information on the proposed point service;

[1726] A management means for managing the user's point balance and expiration date and notifying them as appropriate;

[1727] In cooperation with electronic payment services, we will propose the most suitable point card based on the product information of the user's planned purchase.

[1728] A system including:

[1729] (Claim 2)

[1730] 2. The system according to claim 1, wherein the suggestion means uses the user's location information to suggest point services in real time.

[1731] (Claim 3)

[1732] 2. The system according to claim 1, wherein the management means issues an alert to the user when the expiration date of the points is approaching.

[1733] "Example 2: Combining Emotion Engines"

[1734] (Claim 1)

[1735] a means for storing basic information obtained from the user;

[1736] A means for collecting information on a user's point service and shopping history;

[1737] An analytical method using a generative AI model that learns from collected information and selects the optimal point service;

[1738] an adjustment means using an emotion engine that recognizes the user's emotional state;

[1739] Proposal means for proposing point services according to the user's life cycle;

[1740] means for notifying a user terminal of information on the proposed point service;

[1741] A management means for managing the user's point balance and expiration date and notifying them as appropriate;

[1742] A system including:

[1743] (Claim 2)

[1744] 2. The system according to claim 1, wherein the suggestion means uses the user's location information to suggest point services in real time.

[1745] (Claim 3)

[1746] 2. The system according to claim 1, wherein the management means issues an alert to the user when the expiration date of the points is approaching.

[1747] "Application example 2 when combining emotion engines"

[1748] (Claim 1)

[1749] a means for storing basic information obtained from the user;

[1750] A means for collecting information on a user's point service and shopping history;

[1751] An analytical method using generative AI that learns from collected information and selects the optimal point service,

[1752] Proposal means for proposing point services according to the user's life cycle;

[1753] means for notifying a user terminal of information on the proposed point service;

[1754] A management means for managing the user's point balance and expiration date and notifying them as appropriate;

[1755] A system that includes an emotion engine that recognizes a user's emotions and adjusts suggestions based on their emotional state.

[1756] (Claim 2)

[1757] 2. The system according to claim 1, wherein the suggestion means uses the user's location information and emotional state to suggest point services in real time.

[1758] (Claim 3)

[1759] 2. The system according to claim 1, wherein the management means issues an alert to the user when the expiration date of the points is approaching, and adjusts the content of the notification according to the user's emotional state. [Explanation of symbols]

[1760] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a means for storing basic information obtained from the user; A means for collecting information on a user's point service and shopping history; An analytical method using generative AI that learns from collected information and selects the optimal point service, Proposal means for proposing point services according to the user's life cycle; means for notifying a user terminal of information on the proposed point service; A management means for managing the user's point balance and expiration date and notifying them as appropriate; A system including:

2. The system according to claim 1 , wherein the suggestion means uses the user's location information to suggest point services in real time.

3. The system according to claim 1, wherein the management means issues an alert to the user when the expiration date of the points is approaching.

4. 2. The system according to claim 1, wherein the analysis means integrates data from a plurality of point services used by the user and proposes an optimal way to use the points.

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