system

Abstract

We provide the system. [Solution] A means for providing a suggestion request button that can be operated by the user on a terminal operated by the user, When the suggestion request button is pressed, a means of collecting user data, A means of sending collected user data to a server, A means for receiving user data on a server and performing AI analysis based on said data, A means of generating optimal suggestions for users based on AI analysis, A means of sending the generated proposal to the terminal, A means of displaying the proposal on the terminal, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In recent years, the number of users who use mobile applications for cashless payment has been increasing, but a system that provides optimal coupons and point increase methods for these users individually has not been widely spread. For this reason, there is a problem that it is difficult for users to make the most of the application and enhance its utility value. In particular, there is a need for a mechanism that automatically generates optimal proposals according to different usage patterns and needs for each user.

Means for Solving the Problems

[0005] To solve the above problems, the present invention provides the following means: a user-operable suggestion request button is provided on the user terminal, and means are provided for collecting user data (purchase history, coupon usage history, point balance, etc.) when the suggestion request button is operated. The collected user data is sent to a server, where AI analysis is performed based on the data. Based on the analysis results, the server generates the most suitable suggestion for the user (such as coupons or methods for increasing points), and the generated suggestion is sent to the terminal for display. In this way, the user can easily obtain individually customized suggestions, thereby increasing the value of using the app. The invention also provides means including the server generating multiple suggestion candidates and selecting the suggestion deemed optimal based on the user's past patterns, as well as means including strategies for increasing points based on the user's point balance.

[0006] A "Request Suggestion button" refers to a button that users operate within an application to receive the most suitable suggestions.

[0007] "User data" refers to data that includes information such as a user's past purchase history, coupon usage history, and point balance.

[0008] "Terminal" refers to devices such as mobile devices and personal computers that are operated by the user.

[0009] A "server" refers to a central processing unit that receives user data and performs AI analysis.

[0010] "AI analysis" refers to the process of using artificial intelligence to analyze user data and generate optimal suggestions.

[0011] "Suggestions" refer to information provided to users, such as coupon recommendations and methods for increasing points.

[0012] A "coupon" refers to an electronic discount voucher that allows users to purchase goods or services at a discounted price.

[0013] "Point balance" refers to the total amount of points a user possesses.

[0014] "Purchase history" refers to a record of products and services that a user has purchased in the past.

[0015] "Coupon usage history" refers to a record of coupons that a user has used in the past.

[0016] "Optimal suggestions" refer to suggestions that are considered most beneficial to the user, generated through AI analysis based on user data. [Brief explanation of the drawing]

[0017] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11]It is a sequence diagram showing the processing flow of the data processing system in Embodiment 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when combined with an emotion engine. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when combined with an emotion engine.

Modes for Carrying Out the Invention

[0018] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

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

[0020] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0021] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0022] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0023] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0024] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0025] [First Embodiment]

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

[0027] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0028] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0029] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0030] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0031] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0032] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0034] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 according to the specific processing program 56 executed on the RAM 30.

[0035] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0036] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0037] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0038] This invention relates to a system for receiving suggestions for optimal coupons and point-earning methods on a user-operated terminal. This system is implemented as follows.

[0039] The system starts operating when the user interacts with the Request Suggestion button within the application. When the Request Suggestion button is pressed, the device first collects user data. This user data includes the following information:

[0040] Purchase history: Products and services you have purchased in the past.

[0041] Coupon Usage History: Details of coupons used in the past.

[0042] Point balance: The current total amount of points.

[0043] The device collects this data and sends it to the server. The server analyzes the received data and uses AI to generate the best possible suggestions for the user. The AI ​​analysis includes the following process:

[0044] Purchase history analysis: Analyze user preferences and purchasing patterns to identify highly relevant products and services.

[0045] Analysis of coupon usage history: Identify which types of coupons are best suited to each user.

[0046] Analyzing point balances: Devise strategies to efficiently increase points.

[0047] The server generates optimal suggestions based on the results of AI analysis. These suggestions, such as coupon recommendations or methods for increasing points, are sent to the user's device.

[0048] The device displays suggestions sent from the server to the user. This display occurs as a pop-up or notification bar within the application. This allows the user to immediately use recommended coupons or check strategies for increasing points.

[0049] As a concrete example, suppose a user presses the "Optimal Suggestion" button. If that user has purchased many electronic devices in the past, the AI ​​will recommend "discount coupons for electronic devices" to this user. In addition, to help the user effectively use their current point balance, the AI ​​will suggest point strategies such as "Get double points if you purchase groceries this week."

[0050] This allows users to easily find the coupons and point-earning methods that best suit them, significantly increasing the app's value. In this way, it becomes possible to enhance user convenience and increase the frequency of application use. A specific implementation of this system includes the following processing flow.

[0051] The following describes the processing flow.

[0052] Step 1:

[0053] The user presses the "Best Suggestion" button within the application. This initiates the process of receiving suggestions.

[0054] Step 2:

[0055] The device collects user data such as purchase history, coupon usage history, and point balance. This data is collected based on the user's past usage.

[0056] Step 3:

[0057] The device sends the collected user data to the server. This transmission is typically done in the form of an HTTP POST request or similar.

[0058] Step 4:

[0059] The server analyzes the user data it receives. This analysis uses AI to understand user behavior patterns and preferences.

[0060] Step 5:

[0061] The server generates optimal suggestions based on AI analysis results. These suggestions include recommendations for coupons tailored to the user and strategies for increasing points.

[0062] Step 6:

[0063] The server sends the generated suggestion to the terminal. This suggestion is customized to enhance user convenience.

[0064] Step 7:

[0065] The device displays suggestions received from the server to the user. These suggestions are displayed using methods such as pop-ups or notification bars.

[0066] Step 8:

[0067] Users can review and utilize suggested coupons and point strategies. As a result, users can use the application more effectively.

[0068] (Example 1)

[0069] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0070] Traditional coupon and point systems require users to manually find the coupons and point accumulation methods best suited to them, which is time-consuming and requires considerable effort. Furthermore, there is a lack of methods to automatically generate optimal suggestions based on users' purchasing patterns and preferences. This situation reduces user convenience and, consequently, may lead to decreased service usage frequency and satisfaction.

[0071] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0072] In this invention, the server includes means for performing AI analysis using a generated AI model, means for generating optimal suggestions for the user, and means for transmitting the generated suggestions to the terminal. This makes it possible to automatically suggest the most suitable coupons and point-earning methods to the user based on data such as the user's purchase history, coupon usage history, and point balance.

[0073] A "device" is an electronic device that a user can operate, and includes devices such as smartphones and tablets.

[0074] The "Request Proposal Button" is an interface element that allows the system to start generating proposals when the user interacts with it.

[0075] "User data" refers to information including the user's purchase history, coupon usage history, and point balance.

[0076] A "server" is a central processing unit that receives user data, analyzes it, generates optimal suggestions, and transmits them to the terminal.

[0077] A "generative AI model" is an artificial intelligence model that analyzes user data and generates optimal suggestions.

[0078] "AI analysis" is a process that uses generative AI models to analyze user data and generate suggestions based on user preferences and patterns.

[0079] An "optimal proposal" is a proposal that includes coupons and point-earning methods that users will find most valuable, based on user data.

[0080] "Point balance" refers to the total number of points a user currently possesses.

[0081] "Displaying proposals" refers to the act of visually displaying the generated proposals on the user's device.

[0082] This invention relates to a system for receiving suggestions for optimal coupons and point-earning methods on a user-operated terminal. This system operates in cooperation with the user, terminal, and server.

[0083] 1. System startup and proposal request

[0084] The system is activated when the user operates the "Request Suggestion" button within the application. The terminal then uses this operation as a trigger to send a request for a suggestion to the system.

[0085] 2. Collection of user data

[0086] Once a suggestion request is received, the device begins collecting user data. This user data includes purchase history, coupon usage history, and points balance. This data is retrieved from the application's internal database and other related systems.

[0087] 3. Sending data

[0088] The collected user data is sent from the device to the server. This transmission typically uses HTTP requests, and the data is transferred in a standard data format such as JSON.

[0089] 4. Data analysis using AI

[0090] The server inputs the received data into a generative AI model and performs AI analysis. Examples of generative AI models used here include OpenAI's GPT-3®. The analysis process is as follows:

[0091] By analyzing purchase history, we identify user preferences and purchasing patterns.

[0092] By analyzing coupon usage history, we determine which type of coupon is most suitable.

[0093] By analyzing point balances, we will devise strategies to efficiently increase points.

[0094] 5. Generating the optimal proposal

[0095] The server generates optimal coupons and point-earning methods based on the AI ​​analysis results. The generated suggestions are saved in text format and sent to the terminal as a response.

[0096] 6. Submitting and displaying proposals

[0097] The device displays suggestions received from the server to the user. This is done using methods such as pop-ups or notification bars within the application.

[0098] 7. The user reviews the proposal.

[0099] Users can review the suggestions displayed on their devices and, if necessary, use coupons or implement point-earning strategies.

[0100] Specific example

[0101] As a concrete example, consider a case where a user presses the "Optimal Suggestion" button. If this user has purchased many electronic devices in the past, the server analyzes their purchase history and recommends discount coupons for electronic devices. It also presents a points strategy to help the user effectively use their points balance, such as "Buy groceries this week and earn double points."

[0102] Example of a prompt

[0103] The prompt text to be input to the generative AI model is as follows:

[0104] "Analyze the user's purchase history, coupon usage history, and points balance, and suggest the most suitable coupons and points-earning methods for this user."

[0105] This system allows users to easily find the coupons and point-earning methods that best suit them, significantly increasing the value of the application. Furthermore, the increased user convenience will lead to increased application usage.

[0106] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0107] Step 1:

[0108] System startup and request for proposals

[0109] The system is activated when the user operates the "Request Suggestion" button within the application.

[0110] Input: User button click operation

[0111] Output: Trigger for Request for Proposal event

[0112] Specific action: The user opens the app on their smartphone and taps the "Request Suggestion" button. The device detects this event and starts the system-wide suggestion generation process.

[0113] Step 2:

[0114] User data collection

[0115] The device collects user data.

[0116] Input: Request for Proposal Event

[0117] Output: User data (purchase history, coupon usage history, point balance)

[0118] Specific operation: The device retrieves purchase data from the past year, the type and date of use of used coupons, and the current total points from its internal database and related APIs.

[0119] Step 3:

[0120] Sending data

[0121] The device sends the collected user data to the server.

[0122] Input: User data

[0123] Output: Request to send data to the server

[0124] Specific operation: The terminal sends the user data file collected in JSON format to the server as an HTTPS request.

[0125] Step 4:

[0126] AI-powered data analysis

[0127] The server inputs the received data into a generating AI model for analysis, and then performs the AI ​​analysis.

[0128] Input: User data

[0129] Output: AI analysis results (recommended coupons, point strategies, etc.)

[0130] Specific operation: The server inputs received data into the AI ​​model and uses prompt messages to obtain analysis results. For example, "Analyze the user's purchase history, coupon usage history, and point balance, and suggest the most suitable coupons and point-earning methods for this user."

[0131] Step 5:

[0132] Generating the optimal proposal

[0133] The server generates suggestions for the most suitable coupons and point-earning methods based on the analysis results.

[0134] Input: AI analysis results

[0135] Output: Best suggestion (text format)

[0136] Specific operation: Based on the analysis results, the server generates specific suggestions tailored to the user's preferences and purchase history, such as "discount coupons for electronic devices" or "double points for grocery purchases this week."

[0137] Step 6:

[0138] Submit a proposal

[0139] The server sends the generated suggestions to the user's terminal.

[0140] Input: Best suggestion

[0141] Output: Request to submit proposal

[0142] Specific operation: The server sends JSON data containing the generated proposal to the terminal as an HTTPS response.

[0143] Step 7:

[0144] Display of proposals

[0145] The device displays suggestions it has received to the user.

[0146] Input: Suggestions from the server

[0147] Output: Suggestions displayed on the screen

[0148] Specific action: The device displays a pop-up message within the application saying, "A new discount coupon is available! Take advantage of the discount on electronic devices."

[0149] Step 8:

[0150] Review and response to the proposal

[0151] Users review the displayed suggestions and, if necessary, use coupons or implement point-earning strategies.

[0152] Input: Confirmation of proposal

[0153] Output: Use of coupons, implementation of point-increasing strategies.

[0154] Specific actions: The user clicks on a "discount coupon for electronics" and begins online shopping. They also plan to buy groceries this week.

[0155] The above outlines the specific process flow from when the system generates and displays a proposal, to when the user confirms and uses it.

[0156] (Application Example 1)

[0157] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0158] Traditional coupon and point-earning systems made it difficult for users to receive optimal suggestions in real time when selecting products in physical stores. This resulted in an inconsistent and inconvenient user purchasing experience. This, in turn, could lead to decreased user satisfaction and, consequently, a reduction in the frequency of store and service visits.

[0159] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0160] In this invention, the server includes means for providing a suggestion request button that can be operated by the user, means for collecting user data when the suggestion request button is operated, means for transmitting the collected user data to the server, means for the server to receive the user data and perform AI analysis based on the user data, means for generating optimal suggestions for the user based on the AI ​​analysis, means for transmitting the generated suggestions to a terminal, means for displaying the suggestions on the terminal, means for smart glasses to read product information in real time within a physical store, and means for displaying suggestions on the terminal based on the read product information and user data. As a result, the user can receive optimal coupons and methods for increasing points in real time during their shopping experience at a physical store.

[0161] "User data" refers to information such as a user's purchase history, coupon usage history, and point balance.

[0162] The "Request Proposal" button is a button that users press to receive the most suitable proposals.

[0163] A "server" is a computer system that analyzes user data and generates optimal suggestions.

[0164] "AI analysis" is the process of performing analysis using machine learning algorithms based on collected user data.

[0165] "Smart glasses" are devices worn by users that can read information such as product barcodes and NFC tags in real time.

[0166] "Optimal suggestions" refer to the most useful coupons and point-earning methods for the user, generated by considering the user's purchase history, coupon usage history, and point balance.

[0167] A "device" refers to an electronic device such as a smartphone or tablet that is operated by a user.

[0168] A "physical store" is a physical store where users can directly purchase products.

[0169] "Product information" refers to data obtained through barcodes, NFC tags, etc., that is useful for identifying products.

[0170] "Real-time" refers to the instantaneous process of data collection, analysis, proposal generation, and display.

[0171] The embodiments for carrying out the present invention will be described in detail below.

[0172] The system provides a user-operated suggestion request button on the user's device. When the user operates the suggestion request button, the device collects user data such as the user's purchase history, coupon usage history, and point balance. This data is transmitted to the server in real time.

[0173] The server performs AI analysis based on the received user data. This analysis uses the collected data to analyze the user's preferences and purchasing patterns, and generates optimal suggestions for the user. The server then sends the generated suggestions back to the terminal.

[0174] The suggestions are also provided in real time to users using smart glasses in physical stores. The smart glasses read product information the moment the user focuses on a specific product. Product information is obtained via barcodes or NFC tags and sent to a server. Based on this information and user data, the server generates new suggestions and displays them in real time on the smart glasses' display.

[0175] This embodiment allows users to receive the most useful coupons and point-earning methods in real time when selecting products in physical stores, improving the shopping experience. Furthermore, stores can efficiently promote users' purchasing motivations.

[0176] As a concrete example, suppose a user is wearing smart glasses and focuses on a specific product in a supermarket. The smart glasses read the product's barcode or NFC tag, and send the information to a server, associating it with past purchase history and current point balance. The server generates real-time suggestions tailored to the user, such as a "10% off coupon" or "double points if you buy groceries this week," and displays them on the smart glasses' screen.

[0177] Examples of prompt statements are as follows:

[0178] "Consider the user's purchase history, coupon usage history, and points balance, and suggest the best coupons and point-earning methods for a specific product. Example: Purchase history: Electronics, Coupon usage history: 10% off, Points balance: 1500."

[0179] In this way, the present invention provides a system that enhances user convenience and improves the in-store purchasing experience.

[0180] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0181] Step 1:

[0182] The user operates the suggestion request button on the device. At this point, the user's input is registered as the button operation begins. The device detects this operation and proceeds to the next step.

[0183] Step 2:

[0184] The device collects user data such as purchase history, coupon usage history, and point balance. The collected data is read from the device's internal storage.

[0185] Step 3:

[0186] The collected user data is sent to the server. This data includes the user's purchase history, coupon usage history, and point balance, and this is input to the server. The server prepares the received data for analysis.

[0187] Step 4:

[0188] The server performs AI analysis based on the received user data. Using the generated AI model, it analyzes user preferences and purchasing patterns. The output of this step is the selection of the most suitable suggestions for the user.

[0189] Step 5:

[0190] The server sends optimal suggestions, generated based on AI analysis, to the user's device. These suggestions include applicable coupons and methods for increasing points. The user's device receives this information and proceeds to the next step.

[0191] Step 6:

[0192] The user's device displays suggestions and notifies the user. These suggestions are displayed using in-app pop-ups or notification bars.

[0193] Step 7:

[0194] When a user is wearing smart glasses in a physical store, the smart glasses read product information. This product information is obtained in real time via barcodes or NFC tags. The smart glasses then send the obtained information to the terminal and proceed to the next step.

[0195] Step 8:

[0196] The terminal receives product information transmitted from the smart glasses and compares it with past user data. This data is then sent to the server for further AI analysis. The input for this process consists of product information and past user data.

[0197] Step 9:

[0198] The server performs AI analysis in real time and generates new suggestions. These suggestions include coupons and point strategies related to the products.

[0199] Step 10:

[0200] The generated suggestions are displayed in real time on the smart glasses' screen. Users can instantly see the best suggestions to facilitate their purchasing decision.

[0201] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0202] This invention relates to a system that provides optimal coupon and point-earning methods on a user-operated terminal, and by combining it with an emotion engine, it provides customized suggestions according to the user's emotional state. This system is implemented as follows.

[0203] The system starts operating when the user presses the suggestion request button within the application. When the suggestion request button is pressed, the terminal first begins collecting user data. The collected user data includes purchase history, coupon usage history, and point balance. In addition, the user's emotional data is collected using an emotion engine. The emotion engine analyzes multiple data points such as the user's facial expressions, voice tone, and typing speed to recognize the user's emotional state.

[0204] The device sends this data to the server. The server analyzes the received user data and sentiment data and uses AI to generate the most suitable suggestions for the user. Here, sentiment data is used in particular as follows:

[0205] If a user is feeling stressed, the system will automatically generate suggestions to help them relax (for example, a relaxing coupon).

[0206] When users are satisfied, we offer suggestions to further enhance that satisfaction (for example, strategies to increase points).

[0207] The server generates suggestions such as coupon recommendations and ways to increase points, which are then sent to the user's device. The device displays the suggestions received from the server to the user. Display methods include pop-ups within the application and notification bars.

[0208] As a concrete example, suppose a user presses the "Optimal Suggestion" button. If the AI ​​determines that this user has purchased many food items in the past and is currently in a relatively relaxed emotional state, it will recommend a "food discount coupon" to this user. It will also suggest point strategies to help the user effectively use their current point balance, such as "Buy clothes this week and earn double points."

[0209] This system is highly beneficial to users because it uses an emotion engine to understand the user's real-time emotional state and provide optimal suggestions based on that understanding. Users can receive suggestions that match their emotional state, thereby significantly increasing the value of using the application. In this way, the system of the present invention, which incorporates an emotion engine, enhances user convenience and increases the frequency of application use.

[0210] The following describes the processing flow.

[0211] Step 1:

[0212] The user presses the "Best Suggestion" button within the app. This initiates the system's suggestion process.

[0213] Step 2:

[0214] The device collects user data. Specifically, it retrieves past purchase history, coupon usage history, and current point balance. This data serves as foundational information to improve user convenience.

[0215] Step 3:

[0216] The device activates an emotion engine to analyze the user's emotional state. The emotion engine collects data such as the user's facial expressions, voice tone, and typing speed. Based on the analysis results, the user's current emotional state (e.g., stress, satisfaction, fatigue) is identified.

[0217] Step 4:

[0218] The device sends collected user data and sentiment data to the server. This transmission is typically done in the form of an HTTP POST request or similar.

[0219] Step 5:

[0220] The server receives user data and sentiment data and begins AI analysis. The AI ​​analysis uses the collected data to understand the user's preferences and past behavioral patterns, preparing to generate optimal suggestions.

[0221] Step 6:

[0222] The server generates optimal suggestions based on AI analysis. For example, if it determines that the user is feeling stressed, it suggests coupons for relaxation products or services. Conversely, if the user is feeling satisfied, it provides ways to increase their points to further enhance that satisfaction.

[0223] Step 7:

[0224] The server sends the generated proposal to the terminal. This proposal includes coupon links and details of the points strategy.

[0225] Step 8:

[0226] The device receives the suggestion and displays it to the user. Display formats include in-app pop-ups and notification bars.

[0227] Step 9:

[0228] Users review the suggestions and, if necessary, use coupons or implement methods to increase their points. This not only improves user convenience but also significantly increases the value of using the application.

[0229] (Example 2)

[0230] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0231] Traditional coupon and points suggestion systems lacked the ability to provide optimal suggestions that took into account the user's emotional state, resulting in suggestions that were not always highly satisfying for users. Furthermore, the lack of customization based on the user's real-time emotional state reduced the value of the application, making it difficult to improve user convenience and satisfaction.

[0232] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0233] In this invention, the server includes means for collecting emotional data by analyzing data points such as the user's facial expressions, tone of voice, and input speed using an emotion engine that recognizes the user's emotional state; means for customizing suggestions generated based on the collected emotional data; and means for generating multiple suggestion candidates and selecting the suggestion deemed optimal based on the user's past patterns and emotional state. This enables optimal suggestions tailored to the user's emotional state, improving user convenience and satisfaction, and significantly increasing the value of the application.

[0234] "User-operated devices" refer to digital devices that users can directly operate, including smartphones, tablets, and personal computers.

[0235] A "Request a Suggestion button" is a UI element that allows a user to request a specific suggestion by clicking or tapping it.

[0236] "User data" refers to information about a user's behavior and status, such as their purchase history, coupon usage history, and point balance.

[0237] A "server" is a central processing unit that receives and analyzes user data and sentiment data, and sends the results back to the user's terminal.

[0238] "AI analysis" is the process of analyzing data using artificial intelligence technology to extract patterns and trends.

[0239] An "emotion engine" is software or hardware that analyzes data points such as a user's facial expressions, voice tone, and input speed to identify the user's emotional state.

[0240] "Emotional data" refers to information that represents the user's emotional state, collected by the emotion engine, and includes stress levels, satisfaction levels, and other factors.

[0241] "Candidate proposals" are multiple proposals generated by the server, and they serve as the basis for selecting the optimal proposal.

[0242] A "strategy to increase points" refers to a strategy for efficiently using a user's point balance, and includes methods for increasing points under specific conditions.

[0243] This invention is a system that provides optimal coupon and point-earning methods on a user-operated terminal, and by combining it with an emotion engine, it provides customized suggestions according to the user's emotional state. The following describes how this system is specifically implemented.

[0244] The system starts operating when the user interacts with the "Request Suggestion" button within the application. Upon pressing the Request Suggestion button, the device first begins collecting user data. This data includes purchase history, coupon usage history, and point balance. This data is retrieved from the device's built-in SQL database or other data storage system.

[0245] Next, the device also collects user emotion data using an emotion engine. The emotion engine analyzes multiple data points such as the user's facial expressions, voice tone, and input speed to recognize the user's emotional state. The emotion engine includes AI models for facial recognition and voice analysis, which utilize common libraries (e.g., Microsoft® Azure® Emotion Recognition API).

[0246] The collected user data and sentiment data are sent from the device to the server. This transmission uses HTTP POST requests via a REST API. The server analyzes the received data and generates optimal suggestions for the user using a generative AI model (e.g., TENSORFLOW®). Sentiment data is used particularly in suggestion generation as follows:

[0247] If a user is feeling stressed, suggestions to help them relax (such as relaxation coupons) are automatically generated.

[0248] If a user is satisfied, suggestions (such as a strategy to increase points) are provided to further enhance that satisfaction.

[0249] The suggestions generated by the server, such as coupon recommendations and methods for increasing points, are sent to the user's device. These suggestions are also sent via a REST API. The device displays the suggestions received from the server to the user. Display methods include in-app pop-ups and notification bars.

[0250] As a concrete example, suppose a user presses the "Request Suggestion" button. If the AI ​​determines that this user has purchased many food items in the past and is currently in a relatively relaxed emotional state, it will recommend a "food discount coupon" to this user. It will also suggest a points strategy to help the user effectively use their current points balance, such as "Get double points if you purchase clothing this week."

[0251] This system is highly beneficial to users because it uses an emotion engine to understand the user's real-time emotional state and provide optimal suggestions based on that understanding. Users can receive suggestions that match their emotional state, thereby significantly increasing the value of using the application. In this way, the system of the present invention, which incorporates an emotion engine, enhances user convenience and increases the frequency of application use.

[0252] Examples of prompts for generative AI models:

[0253] If a user clicks the "Request Suggestion" button and data indicates a high proportion of food purchases and a relaxed emotional state, generate the most suitable suggestions. Determine if food discount coupons or double points strategies are applicable, and provide example suggestions.

[0254] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0255] Step 1:

[0256] The user presses the "Request a Proposal" button.

[0257] Input: User actions

[0258] Output: Request for Proposal event occurs

[0259] Description: The system starts operating when the user presses the "Request Suggestion" button within the application. This action triggers the next step.

[0260] Specific action: The user clicks or taps the "Request Suggestion" button on the application screen, and the application detects this event.

[0261] Step 2:

[0262] User data collection

[0263] Input: "Request for Proposal" event

[0264] Output: Collected user data (purchase history, coupon usage history, point balance, etc.)

[0265] Description: When the device receives a "Request for Proposal" event, it begins collecting user data. This includes purchase history, coupon usage history, and points balance.

[0266] Specific operation: The application on the device retrieves the relevant user data from its built-in database (e.g., an SQL database). For example, it might execute a database query based on the user ID to retrieve past purchase history or coupon usage history.

[0267] Step 3:

[0268] Collection of emotional data

[0269] Input: "Request for Proposal" event

[0270] Output: Collected emotional data (facial expressions, voice tone, input speed, etc.)

[0271] Description: The device uses an emotion engine to collect user emotion data. The emotion engine analyzes data points such as the user's facial expressions, voice tone, and typing speed.

[0272] Specific operation: The device's camera and microphone are used to capture the user's facial expressions and voice, which are then sent to an emotion engine (e.g., an emotion recognition API) for analysis.

[0273] Step 4:

[0274] Sending data to the server

[0275] Input: User data and sentiment data

[0276] Output: Data transmission to the server is complete.

[0277] Description: Collected user data and sentiment data are sent from the device to the server. The transmission is done via an HTTP POST request.

[0278] Specific operation: The device converts the collected data into JSON format and sends it to the server via an HTTP POST request. A REST API is used for this, and the data is sent to a specified endpoint on the server.

[0279] Step 5:

[0280] Server-based data analysis and proposal generation

[0281] Input: Received user data and sentiment data

[0282] Output: Generated optimal proposals

[0283] Description: The server analyzes the received data. Using user data and sentiment data, the AI ​​model generates the most suitable suggestions for the user.

[0284] Specific operation: The server uses AI models such as TensorFlow to analyze data based on pre-trained algorithms. It then generates optimal coupons and point strategies tailored to emotional states and past behavioral patterns.

[0285] Step 6:

[0286] Transmission from the proposed server to the terminal

[0287] Input: The generated optimal proposal

[0288] Output: Completion of the proposal transmission to the terminal

[0289] Description: Convert the generated optimal proposal into JSON format and send it from the server to the terminal.

[0290] Specific operation: The server packages the proposal data in JSON format and sends it to the specified endpoint of the terminal through an HTTP POST request.

[0291] Step 7:

[0292] Proposal display on the terminal

[0293] Input: Proposal data from the server

[0294] Output: The content of the proposal displayed to the user

[0295] Description: The terminal analyzes the received proposal data and displays it to the user. Pop-ups or notification bars are used for the display method.

[0296] Specific operation: The terminal application receives the proposal data and displays it on the user interface. The user can then view the proposed coupon or point strategy on the screen.

[0297] (Application Example 2)

[0298] Next, Application Example 2 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart device 14 is referred to as the "terminal".

[0299] When users purchase products online, there is a need to improve the purchasing experience by providing optimal coupons and point-boosting strategies. However, conventional systems do not take into account the user's emotional state and only offer uniform suggestions, which can lead to decreased user satisfaction. It is necessary to solve this problem and provide optimal suggestions that respond to each user's emotional state, thereby realizing a more personalized service.

[0300] The specific processing performed by the specific 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 collecting emotional data using the camera and microphone of the terminal to analyze the user's emotional state, means for customizing suggestions based on the emotional data, means for providing a suggestion request button that can be operated by the user on the terminal operated by the user, means for collecting user data when the suggestion request button is operated, means for transmitting the collected user data to the server, means for receiving the user data on the server and performing AI analysis based on the user data, means for generating the optimal suggestion for the user based on the AI ​​analysis, means for transmitting the generated suggestion to the terminal, and means for displaying the suggestion on the terminal. This makes it possible to provide customized suggestions that take into account the user's emotional state.

[0301] The "Request Suggestion Button" is an interface that allows users to request the system to offer coupons or point increase suggestions.

[0302] "User data" refers to information about the user, such as purchase history, coupon usage history, and point balance.

[0303] "Emotional data" refers to information about a user's emotional state, analyzed from facial expressions, voice tone, input speed, and other data captured using cameras and microphones.

[0304] A "camera" is a hardware device used to capture and analyze a user's facial expressions.

[0305] The "microphone" is a hardware device for capturing and analyzing the tone of the user's voice.

[0306] "AI analysis" is data processing using artificial intelligence for the server to generate optimal proposals using user data and emotion data.

[0307] A "proposal" is a customized purchase promotion plan such as a coupon or a point increase strategy provided to the user.

[0308] [[ID=,15]]A "terminal" is a device (such as a smartphone or tablet) for the user to operate to use the system.

[0309] A "server" is a computing environment for receiving user data and emotion data, performing AI analysis to generate proposals, and transmitting them to the terminal.

[0310] A "proposal request" is an act of requesting a coupon or a point increase plan from the system, which is performed by the user operating the proposal request button.

[0311] The present invention relates to a system for providing optimal coupons and point increase methods on a terminal operated by a user. This system can make customized proposals to the user by analyzing the user's emotional state. Hereinafter, embodiments for implementing the present invention will be described.

[0312] First, a proposal request button is displayed on the terminal operated by the user. When the user operates this button, the terminal collects user data. This user data includes information such as purchase history, coupon usage history, and point balance. Also, the user's emotion data is collected using an emotion engine. The emotion data is obtained by analyzing the user's expression, voice tone, input speed, etc. using a camera and a microphone.

[0313] The collected user data and emotional data are sent to the server. The server performs AI analysis based on the received data and generates optimal suggestions. For example, if the AI ​​determines that the user is currently in a relaxed emotional state, it will recommend a relaxation-related coupon. It also selects the most suitable suggestions for the user based on past patterns.

[0314] The generated suggestions are sent to the device and displayed to the user via pop-ups, notification bars, etc. This allows the user to receive the most suitable suggestions based on their current emotional state. The suggestions also include strategies to increase the user's points based on their point balance.

[0315] The following hardware and software will be used to implement the system:

[0316] Hardware: Smartphone camera and microphone

[0317] software:

[0318] Emotion engine (uses OpenCV and a pre-trained Keras model for facial expression analysis)

[0319] AI model (suggestion generation customized with TensorFlow)

[0320] Specific example:

[0321] When a user presses the suggestion request button, the device uses its camera and microphone to capture the user's face and voice and collect emotional data. For example, if data shows the user has purchased many food items in the past and the AI ​​determines that the user is currently relaxed, it will recommend a "food discount coupon" to this user. The suggestion is notified to the user's device, and the user can use the coupon immediately.

[0322] Example of a prompt:

[0323] "Create a Python program that retrieves purchase history and recommends the most suitable coupons and point-boosting strategies based on the user's current emotional state."

[0324] This invention is highly beneficial to users because it can grasp their emotional state in real time and provide customized suggestions based on that state. Furthermore, since users can receive suggestions that match their emotional state, the value of the application is expected to increase significantly, and the frequency of use is also expected to increase.

[0325] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0326] Step 1:

[0327] The user clicks the suggestion request button.

[0328] Specific action: The user taps the suggestion request button displayed on the application screen of their device.

[0329] Input: User actions

[0330] Output: Event indicating that the Request for Suggestion button was pressed.

[0331] Step 2:

[0332] The device collects user data and sentiment data.

[0333] Specific operation: The device retrieves user data such as purchase history, coupon usage history, and point balance from its internal database. It also activates the camera and microphone and uses an emotion engine to analyze the user's facial expressions, voice tone, and input speed.

[0334] Input: Event indicating that the Request for Suggestion button was pressed.

[0335] Output: Acquired user data and sentiment data

[0336] Step 3:

[0337] The device sends the collected data to the server.

[0338] Specific operation: The device sends the acquired user data and sentiment data to the server via the network.

[0339] Input: User data and sentiment data

[0340] Output: User data and sentiment data sent to the server

[0341] Step 4:

[0342] The server receives user data and sentiment data and performs AI analysis.

[0343] Specific operation: The server inputs the received data into the AI ​​analysis engine and generates optimal suggestions based on the user's emotional state and past behavioral patterns.

[0344] Input: Received user data and sentiment data

[0345] Output: Suggestions generated by AI analysis (coupons and methods to increase points)

[0346] Step 5:

[0347] The server sends the generated proposal to the terminal.

[0348] Specific operation: The server sends the generated suggestions to the user's terminal via the network.

[0349] Input: Proposals generated by AI analysis

[0350] Output: Suggestions sent to the terminal

[0351] Step 6:

[0352] The device notifies and displays suggestions to the user.

[0353] Specific action: The device displays the received suggestion to the user via a pop-up notification or notification bar.

[0354] Input: Submitted proposal

[0355] Output: Suggestions displayed to the user (coupons, methods for increasing points, etc.)

[0356] Step 7:

[0357] The user reviews and uses the suggestions they receive.

[0358] Specific actions: The user reviews the displayed suggestions and either uses a coupon or implements a strategy to increase points.

[0359] Input: Suggestions displayed to the user

[0360] Output: User actions based on the suggestion (e.g., purchase, point usage)

[0361] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0362] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0363] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0364] [Second Embodiment]

[0365] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0366] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0367] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0368] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0369] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0370] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0371] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0372] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0373] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0374] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0375] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0376] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. 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".

[0377] This invention relates to a system for receiving suggestions for optimal coupons and point-earning methods on a user-operated terminal. This system is implemented as follows.

[0378] The system starts operating when the user interacts with the Request Suggestion button within the application. When the Request Suggestion button is pressed, the device first collects user data. This user data includes the following information:

[0379] Purchase history: Products and services you have purchased in the past.

[0380] Coupon Usage History: Details of coupons used in the past.

[0381] Point balance: The current total amount of points.

[0382] The device collects this data and sends it to the server. The server analyzes the received data and uses AI to generate the best possible suggestions for the user. The AI ​​analysis includes the following process:

[0383] Purchase history analysis: Analyze user preferences and purchasing patterns to identify highly relevant products and services.

[0384] Analysis of coupon usage history: Identify which types of coupons are best suited to each user.

[0385] Analyzing point balances: Devise strategies to efficiently increase points.

[0386] The server generates optimal suggestions based on the results of AI analysis. These suggestions, such as coupon recommendations or methods for increasing points, are sent to the user's device.

[0387] The device displays suggestions sent from the server to the user. This display occurs as a pop-up or notification bar within the application. This allows the user to immediately use recommended coupons or check strategies for increasing points.

[0388] As a concrete example, suppose a user presses the "Optimal Suggestion" button. If that user has purchased many electronic devices in the past, the AI ​​will recommend "discount coupons for electronic devices" to this user. In addition, to help the user effectively use their current point balance, the AI ​​will suggest point strategies such as "Get double points if you purchase groceries this week."

[0389] This allows users to easily find the coupons and point-earning methods that best suit them, significantly increasing the app's value. In this way, it becomes possible to enhance user convenience and increase the frequency of application use. A specific implementation of this system includes the following processing flow.

[0390] The following describes the processing flow.

[0391] Step 1:

[0392] The user presses the "Best Suggestion" button within the application. This initiates the process of receiving suggestions.

[0393] Step 2:

[0394] The device collects user data such as purchase history, coupon usage history, and point balance. This data is collected based on the user's past usage.

[0395] Step 3:

[0396] The device sends the collected user data to the server. This transmission is typically done in the form of an HTTP POST request or similar.

[0397] Step 4:

[0398] The server analyzes the user data it receives. This analysis uses AI to understand user behavior patterns and preferences.

[0399] Step 5:

[0400] The server generates optimal suggestions based on AI analysis results. These suggestions include recommendations for coupons tailored to the user and strategies for increasing points.

[0401] Step 6:

[0402] The server sends the generated suggestion to the terminal. This suggestion is customized to enhance user convenience.

[0403] Step 7:

[0404] The device displays suggestions received from the server to the user. These suggestions are displayed using methods such as pop-ups or notification bars.

[0405] Step 8:

[0406] Users can review and utilize suggested coupons and point strategies. As a result, users can use the application more effectively.

[0407] (Example 1)

[0408] Next, we will describe Example 1. 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."

[0409] Traditional coupon and point systems require users to manually find the coupons and point accumulation methods best suited to them, which is time-consuming and requires considerable effort. Furthermore, there is a lack of methods to automatically generate optimal suggestions based on users' purchasing patterns and preferences. This situation reduces user convenience and, consequently, may lead to decreased service usage frequency and satisfaction.

[0410] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0411] In this invention, the server includes means for performing AI analysis using a generated AI model, means for generating optimal suggestions for the user, and means for transmitting the generated suggestions to the terminal. This makes it possible to automatically suggest the most suitable coupons and point-earning methods to the user based on data such as the user's purchase history, coupon usage history, and point balance.

[0412] A "device" is an electronic device that a user can operate, and includes devices such as smartphones and tablets.

[0413] The "Request Proposal Button" is an interface element that allows the system to start generating proposals when the user interacts with it.

[0414] "User data" refers to information including the user's purchase history, coupon usage history, and point balance.

[0415] A "server" is a central processing unit that receives user data, analyzes it, generates optimal suggestions, and transmits them to the terminal.

[0416] A "generative AI model" is an artificial intelligence model that analyzes user data and generates optimal suggestions.

[0417] "AI analysis" is a process that uses generative AI models to analyze user data and generate suggestions based on user preferences and patterns.

[0418] An "optimal proposal" is a proposal that includes coupons and point-earning methods that users will find most valuable, based on user data.

[0419] "Point balance" refers to the total number of points a user currently possesses.

[0420] "Displaying proposals" refers to the act of visually displaying the generated proposals on the user's device.

[0421] This invention relates to a system for receiving suggestions for optimal coupons and point-earning methods on a user-operated terminal. This system operates in cooperation with the user, terminal, and server.

[0422] 1. System startup and proposal request

[0423] The system is activated when the user operates the "Request Suggestion" button within the application. The terminal then uses this operation as a trigger to send a request for a suggestion to the system.

[0424] 2. Collection of user data

[0425] Once a suggestion request is received, the device begins collecting user data. This user data includes purchase history, coupon usage history, and points balance. This data is retrieved from the application's internal database and other related systems.

[0426] 3. Sending data

[0427] The collected user data is sent from the device to the server. This transmission typically uses HTTP requests, and the data is transferred in a standard data format such as JSON.

[0428] 4. Data analysis using AI

[0429] The server inputs the received data into a generative AI model and performs AI analysis. A possible generative AI model used here could be OpenAI's GPT-3, for example. The analysis process is as follows:

[0430] By analyzing purchase history, we identify user preferences and purchasing patterns.

[0431] By analyzing coupon usage history, we determine which type of coupon is most suitable.

[0432] By analyzing point balances, we will devise strategies to efficiently increase points.

[0433] 5. Generating the optimal proposal

[0434] The server generates optimal coupons and point-earning methods based on the AI ​​analysis results. The generated suggestions are saved in text format and sent to the terminal as a response.

[0435] 6. Submitting and displaying proposals

[0436] The device displays suggestions received from the server to the user. This is done using methods such as pop-ups or notification bars within the application.

[0437] 7. The user reviews the proposal.

[0438] Users can review the suggestions displayed on their devices and, if necessary, use coupons or implement point-earning strategies.

[0439] Specific example

[0440] As a concrete example, consider a case where a user presses the "Optimal Suggestion" button. If this user has purchased many electronic devices in the past, the server analyzes their purchase history and recommends discount coupons for electronic devices. It also presents a points strategy to help the user effectively use their points balance, such as "Buy groceries this week and earn double points."

[0441] Example of a prompt

[0442] The prompt text to be input to the generative AI model is as follows:

[0443] "Analyze the user's purchase history, coupon usage history, and points balance, and suggest the most suitable coupons and points-earning methods for this user."

[0444] This system allows users to easily find the coupons and point-earning methods that best suit them, significantly increasing the value of the application. Furthermore, the increased user convenience will lead to increased application usage.

[0445] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0446] Step 1:

[0447] System startup and request for proposals

[0448] The system is activated when the user operates the "Request Suggestion" button within the application.

[0449] Input: User button click operation

[0450] Output: Trigger for Request for Proposal event

[0451] Specific action: The user opens the app on their smartphone and taps the "Request Suggestion" button. The device detects this event and starts the system-wide suggestion generation process.

[0452] Step 2:

[0453] User data collection

[0454] The device collects user data.

[0455] Input: Request for Proposal Event

[0456] Output: User data (purchase history, coupon usage history, point balance)

[0457] Specific operation: The device retrieves purchase data from the past year, the type and date of use of used coupons, and the current total points from its internal database and related APIs.

[0458] Step 3:

[0459] Sending data

[0460] The device sends the collected user data to the server.

[0461] Input: User data

[0462] Output: Request to send data to the server

[0463] Specific operation: The terminal sends the user data file collected in JSON format to the server as an HTTPS request.

[0464] Step 4:

[0465] AI-powered data analysis

[0466] The server inputs the received data into a generating AI model for analysis, and then performs the AI ​​analysis.

[0467] Input: User data

[0468] Output: AI analysis results (recommended coupons, point strategies, etc.)

[0469] Specific operation: The server inputs received data into the AI ​​model and uses prompt messages to obtain analysis results. For example, "Analyze the user's purchase history, coupon usage history, and point balance, and suggest the most suitable coupons and point-earning methods for this user."

[0470] Step 5:

[0471] Generating the optimal proposal

[0472] The server generates suggestions for the most suitable coupons and point-earning methods based on the analysis results.

[0473] Input: AI analysis results

[0474] Output: Best suggestion (text format)

[0475] Specific operation: Based on the analysis results, the server generates specific suggestions tailored to the user's preferences and purchase history, such as "discount coupons for electronic devices" or "double points for grocery purchases this week."

[0476] Step 6:

[0477] Submit a proposal

[0478] The server sends the generated suggestions to the user's terminal.

[0479] Input: Best suggestion

[0480] Output: Request to submit proposal

[0481] Specific operation: The server sends JSON data containing the generated proposal to the terminal as an HTTPS response.

[0482] Step 7:

[0483] Display of proposals

[0484] The device displays suggestions it has received to the user.

[0485] Input: Suggestions from the server

[0486] Output: Suggestions displayed on the screen

[0487] Specific action: The device displays a pop-up message within the application saying, "A new discount coupon is available! Take advantage of the discount on electronic devices."

[0488] Step 8:

[0489] Review and response to the proposal

[0490] Users review the displayed suggestions and, if necessary, use coupons or implement point-earning strategies.

[0491] Input: Confirmation of proposal

[0492] Output: Use of coupons, implementation of point-increasing strategies.

[0493] Specific actions: The user clicks on a "discount coupon for electronics" and begins online shopping. They also plan to buy groceries this week.

[0494] The above outlines the specific process flow from when the system generates and displays a proposal, to when the user confirms and uses it.

[0495] (Application Example 1)

[0496] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0497] Traditional coupon and point-earning systems made it difficult for users to receive optimal suggestions in real time when selecting products in physical stores. This resulted in an inconsistent and inconvenient user purchasing experience. This, in turn, could lead to decreased user satisfaction and, consequently, a reduction in the frequency of store and service visits.

[0498] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0499] In this invention, the server includes means for providing a suggestion request button that can be operated by the user, means for collecting user data when the suggestion request button is operated, means for transmitting the collected user data to the server, means for the server to receive the user data and perform AI analysis based on the user data, means for generating optimal suggestions for the user based on the AI ​​analysis, means for transmitting the generated suggestions to a terminal, means for displaying the suggestions on the terminal, means for smart glasses to read product information in real time within a physical store, and means for displaying suggestions on the terminal based on the read product information and user data. As a result, the user can receive optimal coupons and methods for increasing points in real time during their shopping experience at a physical store.

[0500] "User data" refers to information such as a user's purchase history, coupon usage history, and point balance.

[0501] The "Request Proposal" button is a button that users press to receive the most suitable proposals.

[0502] A "server" is a computer system that analyzes user data and generates optimal suggestions.

[0503] "AI analysis" is the process of performing analysis using machine learning algorithms based on collected user data.

[0504] "Smart glasses" are devices worn by users that can read information such as product barcodes and NFC tags in real time.

[0505] "Optimal suggestions" refer to the most useful coupons and point-earning methods for the user, generated by considering the user's purchase history, coupon usage history, and point balance.

[0506] A "device" refers to an electronic device such as a smartphone or tablet that is operated by a user.

[0507] A "physical store" is a physical store where users can directly purchase products.

[0508] "Product information" refers to data obtained through barcodes, NFC tags, etc., that is useful for identifying products.

[0509] "Real-time" refers to the instantaneous process of data collection, analysis, proposal generation, and display.

[0510] The embodiments for carrying out the present invention will be described in detail below.

[0511] The system provides a user-operated suggestion request button on the user's device. When the user operates the suggestion request button, the device collects user data such as the user's purchase history, coupon usage history, and point balance. This data is transmitted to the server in real time.

[0512] The server performs AI analysis based on the received user data. This analysis uses the collected data to analyze the user's preferences and purchasing patterns, and generates optimal suggestions for the user. The server then sends the generated suggestions back to the terminal.

[0513] The suggestions are also provided in real time to users using smart glasses in physical stores. The smart glasses read product information the moment the user focuses on a specific product. Product information is obtained via barcodes or NFC tags and sent to a server. Based on this information and user data, the server generates new suggestions and displays them in real time on the smart glasses' display.

[0514] This embodiment allows users to receive the most useful coupons and point-earning methods in real time when selecting products in physical stores, improving the shopping experience. Furthermore, stores can efficiently promote users' purchasing motivations.

[0515] As a concrete example, suppose a user is wearing smart glasses and focuses on a specific product in a supermarket. The smart glasses read the product's barcode or NFC tag, and send the information to a server, associating it with past purchase history and current point balance. The server generates real-time suggestions tailored to the user, such as a "10% off coupon" or "double points if you buy groceries this week," and displays them on the smart glasses' screen.

[0516] Examples of prompt statements are as follows:

[0517] "Consider the user's purchase history, coupon usage history, and points balance, and suggest the best coupons and point-earning methods for a specific product. Example: Purchase history: Electronics, Coupon usage history: 10% off, Points balance: 1500."

[0518] In this way, the present invention provides a system that enhances user convenience and improves the in-store purchasing experience.

[0519] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0520] Step 1:

[0521] The user operates the suggestion request button on the device. At this point, the user's input is registered as the button operation begins. The device detects this operation and proceeds to the next step.

[0522] Step 2:

[0523] The device collects user data such as purchase history, coupon usage history, and point balance. The collected data is read from the device's internal storage.

[0524] Step 3:

[0525] The collected user data is sent to the server. This data includes the user's purchase history, coupon usage history, and point balance, and this is input to the server. The server prepares the received data for analysis.

[0526] Step 4:

[0527] The server performs AI analysis based on the received user data. Using the generated AI model, it analyzes user preferences and purchasing patterns. The output of this step is the selection of the most suitable suggestions for the user.

[0528] Step 5:

[0529] The server sends optimal suggestions, generated based on AI analysis, to the user's device. These suggestions include applicable coupons and methods for increasing points. The user's device receives this information and proceeds to the next step.

[0530] Step 6:

[0531] The user's device displays suggestions and notifies the user. These suggestions are displayed using in-app pop-ups or notification bars.

[0532] Step 7:

[0533] When a user is wearing smart glasses in a physical store, the smart glasses read product information. This product information is obtained in real time via barcodes or NFC tags. The smart glasses then send the obtained information to the terminal and proceed to the next step.

[0534] Step 8:

[0535] The terminal receives product information transmitted from the smart glasses and compares it with past user data. This data is then sent to the server for further AI analysis. The input for this process consists of product information and past user data.

[0536] Step 9:

[0537] The server performs AI analysis in real time and generates new suggestions. These suggestions include coupons and point strategies related to the products.

[0538] Step 10:

[0539] The generated suggestions are displayed in real time on the smart glasses' screen. Users can instantly see the best suggestions to facilitate their purchasing decision.

[0540] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0541] This invention relates to a system that provides optimal coupon and point-earning methods on a user-operated terminal, and by combining it with an emotion engine, it provides customized suggestions according to the user's emotional state. This system is implemented as follows.

[0542] The system starts operating when the user presses the suggestion request button within the application. When the suggestion request button is pressed, the terminal first begins collecting user data. The collected user data includes purchase history, coupon usage history, and point balance. In addition, the user's emotional data is collected using an emotion engine. The emotion engine analyzes multiple data points such as the user's facial expressions, voice tone, and typing speed to recognize the user's emotional state.

[0543] The device sends this data to the server. The server analyzes the received user data and sentiment data and uses AI to generate the most suitable suggestions for the user. Here, sentiment data is used in particular as follows:

[0544] If a user is feeling stressed, the system will automatically generate suggestions to help them relax (for example, a relaxing coupon).

[0545] When users are satisfied, we offer suggestions to further enhance that satisfaction (for example, strategies to increase points).

[0546] The server generates suggestions such as coupon recommendations and ways to increase points, which are then sent to the user's device. The device displays the suggestions received from the server to the user. Display methods include pop-ups within the application and notification bars.

[0547] As a concrete example, suppose a user presses the "Optimal Suggestion" button. If the AI ​​determines that this user has purchased many food items in the past and is currently in a relatively relaxed emotional state, it will recommend a "food discount coupon" to this user. It will also suggest point strategies to help the user effectively use their current point balance, such as "Buy clothes this week and earn double points."

[0548] This system is highly beneficial to users because it uses an emotion engine to understand the user's real-time emotional state and provide optimal suggestions based on that understanding. Users can receive suggestions that match their emotional state, thereby significantly increasing the value of using the application. In this way, the system of the present invention, which incorporates an emotion engine, enhances user convenience and increases the frequency of application use.

[0549] The following describes the processing flow.

[0550] Step 1:

[0551] The user presses the "Best Suggestion" button within the app. This initiates the system's suggestion process.

[0552] Step 2:

[0553] The device collects user data. Specifically, it retrieves past purchase history, coupon usage history, and current point balance. This data serves as foundational information to improve user convenience.

[0554] Step 3:

[0555] The device activates an emotion engine to analyze the user's emotional state. The emotion engine collects data such as the user's facial expressions, voice tone, and typing speed. Based on the analysis results, the user's current emotional state (e.g., stress, satisfaction, fatigue) is identified.

[0556] Step 4:

[0557] The device sends collected user data and sentiment data to the server. This transmission is typically done in the form of an HTTP POST request or similar.

[0558] Step 5:

[0559] The server receives user data and sentiment data and begins AI analysis. The AI ​​analysis uses the collected data to understand the user's preferences and past behavioral patterns, preparing to generate optimal suggestions.

[0560] Step 6:

[0561] The server generates optimal suggestions based on AI analysis. For example, if it determines that the user is feeling stressed, it suggests coupons for relaxation products or services. Conversely, if the user is feeling satisfied, it provides ways to increase their points to further enhance that satisfaction.

[0562] Step 7:

[0563] The server sends the generated proposal to the terminal. This proposal includes coupon links and details of the points strategy.

[0564] Step 8:

[0565] The device receives the suggestion and displays it to the user. Display formats include in-app pop-ups and notification bars.

[0566] Step 9:

[0567] Users review the suggestions and, if necessary, use coupons or implement methods to increase their points. This not only improves user convenience but also significantly increases the value of using the application.

[0568] (Example 2)

[0569] Next, we will describe Example 2. 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".

[0570] Traditional coupon and points suggestion systems lacked the ability to provide optimal suggestions that took into account the user's emotional state, resulting in suggestions that were not always highly satisfying for users. Furthermore, the lack of customization based on the user's real-time emotional state reduced the value of the application, making it difficult to improve user convenience and satisfaction.

[0571] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0572] In this invention, the server includes means for collecting emotional data by analyzing data points such as the user's facial expressions, tone of voice, and input speed using an emotion engine that recognizes the user's emotional state; means for customizing suggestions generated based on the collected emotional data; and means for generating multiple suggestion candidates and selecting the suggestion deemed optimal based on the user's past patterns and emotional state. This enables optimal suggestions tailored to the user's emotional state, improving user convenience and satisfaction, and significantly increasing the value of the application.

[0573] "User-operated devices" refer to digital devices that users can directly operate, including smartphones, tablets, and personal computers.

[0574] A "Request a Suggestion button" is a UI element that allows a user to request a specific suggestion by clicking or tapping it.

[0575] "User data" refers to information about a user's behavior and status, such as their purchase history, coupon usage history, and point balance.

[0576] A "server" is a central processing unit that receives and analyzes user data and sentiment data, and sends the results back to the user's terminal.

[0577] "AI analysis" is the process of analyzing data using artificial intelligence technology to extract patterns and trends.

[0578] An "emotion engine" is software or hardware that analyzes data points such as a user's facial expressions, voice tone, and input speed to identify the user's emotional state.

[0579] "Emotional data" refers to information that represents the user's emotional state, collected by the emotion engine, and includes stress levels, satisfaction levels, and other factors.

[0580] "Candidate proposals" are multiple proposals generated by the server, and they serve as the basis for selecting the optimal proposal.

[0581] A "strategy to increase points" refers to a strategy for efficiently using a user's point balance, and includes methods for increasing points under specific conditions.

[0582] This invention is a system that provides optimal coupon and point-earning methods on a user-operated terminal, and by combining it with an emotion engine, it provides customized suggestions according to the user's emotional state. The following describes how this system is specifically implemented.

[0583] The system starts operating when the user interacts with the "Request Suggestion" button within the application. Upon pressing the Request Suggestion button, the device first begins collecting user data. This data includes purchase history, coupon usage history, and point balance. This data is retrieved from the device's built-in SQL database or other data storage system.

[0584] Next, the device also collects user emotion data using an emotion engine. The emotion engine analyzes multiple data points such as the user's facial expressions, voice tone, and input speed to recognize the user's emotional state. The emotion engine includes AI models for facial recognition and voice analysis, which utilize common libraries (e.g., Microsoft Azure's emotion recognition API).

[0585] The collected user data and sentiment data are sent from the device to the server. This transmission uses HTTP POST requests via a REST API. The server analyzes the received data and generates optimal suggestions for the user using a generative AI model (e.g., TensorFlow). Sentiment data, in particular, is used in suggestion generation as follows:

[0586] If a user is feeling stressed, suggestions to help them relax (such as relaxation coupons) are automatically generated.

[0587] If a user is satisfied, suggestions (such as a strategy to increase points) are provided to further enhance that satisfaction.

[0588] The suggestions generated by the server, such as coupon recommendations and methods for increasing points, are sent to the user's device. These suggestions are also sent via a REST API. The device displays the suggestions received from the server to the user. Display methods include in-app pop-ups and notification bars.

[0589] As a concrete example, suppose a user presses the "Request Suggestion" button. If the AI ​​determines that this user has purchased many food items in the past and is currently in a relatively relaxed emotional state, it will recommend a "food discount coupon" to this user. It will also suggest a points strategy to help the user effectively use their current points balance, such as "Get double points if you purchase clothing this week."

[0590] This system is highly beneficial to users because it uses an emotion engine to understand the user's real-time emotional state and provide optimal suggestions based on that understanding. Users can receive suggestions that match their emotional state, thereby significantly increasing the value of using the application. In this way, the system of the present invention, which incorporates an emotion engine, enhances user convenience and increases the frequency of application use.

[0591] Examples of prompts for generative AI models:

[0592] If a user clicks the "Request Suggestion" button and data indicates a high proportion of food purchases and a relaxed emotional state, generate the most suitable suggestions. Determine if food discount coupons or double points strategies are applicable, and provide example suggestions.

[0593] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0594] Step 1:

[0595] The user presses the "Request a Proposal" button.

[0596] Input: User actions

[0597] Output: Request for Proposal event occurs

[0598] Description: The system starts operating when the user presses the "Request Suggestion" button within the application. This action triggers the next step.

[0599] Specific action: The user clicks or taps the "Request Suggestion" button on the application screen, and the application detects this event.

[0600] Step 2:

[0601] User data collection

[0602] Input: "Request for Proposal" event

[0603] Output: Collected user data (purchase history, coupon usage history, point balance, etc.)

[0604] Description: When the device receives a "Request for Proposal" event, it begins collecting user data. This includes purchase history, coupon usage history, and points balance.

[0605] Specific operation: The application on the device retrieves the relevant user data from its built-in database (e.g., an SQL database). For example, it might execute a database query based on the user ID to retrieve past purchase history or coupon usage history.

[0606] Step 3:

[0607] Collection of emotional data

[0608] Input: "Request for Proposal" event

[0609] Output: Collected emotional data (facial expressions, voice tone, input speed, etc.)

[0610] Description: The device uses an emotion engine to collect user emotion data. The emotion engine analyzes data points such as the user's facial expressions, voice tone, and typing speed.

[0611] Specific operation: The device's camera and microphone are used to capture the user's facial expressions and voice, which are then sent to an emotion engine (e.g., an emotion recognition API) for analysis.

[0612] Step 4:

[0613] Sending data to the server

[0614] Input: User data and sentiment data

[0615] Output: Data transmission to the server is complete.

[0616] Description: Collected user data and sentiment data are sent from the device to the server. The transmission is done via an HTTP POST request.

[0617] Specific operation: The device converts the collected data into JSON format and sends it to the server via an HTTP POST request. A REST API is used for this, and the data is sent to a specified endpoint on the server.

[0618] Step 5:

[0619] Server-based data analysis and proposal generation

[0620] Input: Received user data and sentiment data

[0621] Output: Generated optimal proposals

[0622] Description: The server analyzes the received data. Using user data and sentiment data, the AI ​​model generates the most suitable suggestions for the user.

[0623] Specific operation: The server uses AI models such as TensorFlow to analyze data based on pre-trained algorithms. It then generates optimal coupons and point strategies tailored to emotional states and past behavioral patterns.

[0624] Step 6:

[0625] Sending proposals from the server to the terminal

[0626] Input: Generated optimal proposal

[0627] Output: Suggestion sent to terminal complete.

[0628] Description: Converts the generated optimal suggestions into JSON format and sends them from the server to the terminal.

[0629] Specific operation: The server packages the proposed data in JSON format and sends it to the specified endpoint on the terminal via an HTTP POST request.

[0630] Step 7:

[0631] Suggestion display on the device

[0632] Input: Proposal data from the server

[0633] Output: Suggestions displayed to the user

[0634] Description: The device analyzes received suggestion data and displays it to the user. Pop-ups and notification bars are used for display.

[0635] Specific operation: The application on the device receives the suggestion data and displays it in the user interface. The user can then view the suggested coupons and point strategies on the screen.

[0636] (Application Example 2)

[0637] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0638] When users purchase products online, there is a need to improve the purchasing experience by providing optimal coupons and point-boosting strategies. However, conventional systems do not take into account the user's emotional state and only offer uniform suggestions, which can lead to decreased user satisfaction. It is necessary to solve this problem and provide optimal suggestions that respond to each user's emotional state, thereby realizing a more personalized service.

[0639] The specific processing performed by the specific 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 collecting emotional data using the camera and microphone of the terminal to analyze the user's emotional state, means for customizing suggestions based on the emotional data, means for providing a suggestion request button that can be operated by the user on the terminal operated by the user, means for collecting user data when the suggestion request button is operated, means for transmitting the collected user data to the server, means for receiving the user data on the server and performing AI analysis based on the user data, means for generating the optimal suggestion for the user based on the AI ​​analysis, means for transmitting the generated suggestion to the terminal, and means for displaying the suggestion on the terminal. This makes it possible to provide customized suggestions that take into account the user's emotional state.

[0640] The "Request Suggestion Button" is an interface that allows users to request the system to offer coupons or point increase suggestions.

[0641] "User data" refers to information about the user, such as purchase history, coupon usage history, and point balance.

[0642] "Emotional data" refers to information about a user's emotional state, analyzed from facial expressions, voice tone, input speed, and other data captured using cameras and microphones.

[0643] A "camera" is a hardware device used to capture and analyze a user's facial expressions.

[0644] A "microphone" is a hardware device used to capture and analyze the tone of a user's voice.

[0645] "AI analysis" refers to data processing using artificial intelligence, where a server uses user data and sentiment data to generate optimal suggestions.

[0646] A "proposal" is a customized purchase promotion plan offered to users, such as coupons or point increase strategies.

[0647] A "terminal" is a device (such as a smartphone or tablet) that a user operates to access and use a system.

[0648] A "server" is a computing environment that receives user data and sentiment data, performs AI analysis to generate suggestions, and sends them to the terminal.

[0649] A "Request for Suggestions" is an action taken by a user to request suggestions from the system, such as coupons or point increases, by clicking a suggestion request button.

[0650] This invention relates to a system that provides optimal coupon and point-earning methods on a user-operated terminal. This system can provide customized suggestions to the user by analyzing the user's emotional state. The following describes embodiments for carrying out this invention.

[0651] First, a suggestion request button is displayed on the user's device. When the user presses this button, the device collects user data. This user data includes information such as purchase history, coupon usage history, and point balance. In addition, the user's emotional data is collected using an emotion engine. Emotional data is obtained by analyzing the user's facial expressions, voice tone, input speed, etc., using the camera and microphone.

[0652] The collected user data and emotional data are sent to the server. The server performs AI analysis based on the received data and generates optimal suggestions. For example, if the AI ​​determines that the user is currently in a relaxed emotional state, it will recommend a relaxation-related coupon. It also selects the most suitable suggestions for the user based on past patterns.

[0653] The generated suggestions are sent to the device and displayed to the user via pop-ups, notification bars, etc. This allows the user to receive the most suitable suggestions based on their current emotional state. The suggestions also include strategies to increase the user's points based on their point balance.

[0654] The following hardware and software will be used to implement the system:

[0655] Hardware: Smartphone camera and microphone

[0656] software:

[0657] Emotion engine (uses OpenCV and a pre-trained Keras model for facial expression analysis)

[0658] AI model (suggestion generation customized with TensorFlow)

[0659] Specific example:

[0660] When a user presses the suggestion request button, the device uses its camera and microphone to capture the user's face and voice and collect emotional data. For example, if data shows the user has purchased many food items in the past and the AI ​​determines that the user is currently relaxed, it will recommend a "food discount coupon" to this user. The suggestion is notified to the user's device, and the user can use the coupon immediately.

[0661] Example of a prompt:

[0662] "Create a Python program that retrieves purchase history and recommends the most suitable coupons and point-boosting strategies based on the user's current emotional state."

[0663] This invention is highly beneficial to users because it can grasp their emotional state in real time and provide customized suggestions based on that state. Furthermore, since users can receive suggestions that match their emotional state, the value of the application is expected to increase significantly, and the frequency of use is also expected to increase.

[0664] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0665] Step 1:

[0666] The user clicks the suggestion request button.

[0667] Specific action: The user taps the suggestion request button displayed on the application screen of their device.

[0668] Input: User actions

[0669] Output: Event indicating that the Request for Suggestion button was pressed.

[0670] Step 2:

[0671] The device collects user data and sentiment data.

[0672] Specific operation: The device retrieves user data such as purchase history, coupon usage history, and point balance from its internal database. It also activates the camera and microphone and uses an emotion engine to analyze the user's facial expressions, voice tone, and input speed.

[0673] Input: Event indicating that the Request for Suggestion button was pressed.

[0674] Output: Acquired user data and sentiment data

[0675] Step 3:

[0676] The device sends the collected data to the server.

[0677] Specific operation: The device sends the acquired user data and sentiment data to the server via the network.

[0678] Input: User data and sentiment data

[0679] Output: User data and sentiment data sent to the server

[0680] Step 4:

[0681] The server receives user data and sentiment data and performs AI analysis.

[0682] Specific operation: The server inputs the received data into the AI ​​analysis engine and generates optimal suggestions based on the user's emotional state and past behavioral patterns.

[0683] Input: Received user data and sentiment data

[0684] Output: Suggestions generated by AI analysis (coupons and methods to increase points)

[0685] Step 5:

[0686] The server sends the generated proposal to the terminal.

[0687] Specific operation: The server sends the generated suggestions to the user's terminal via the network.

[0688] Input: Proposals generated by AI analysis

[0689] Output: Suggestions sent to the terminal

[0690] Step 6:

[0691] The device notifies and displays suggestions to the user.

[0692] Specific action: The device displays the received suggestion to the user via a pop-up notification or notification bar.

[0693] Input: Submitted proposal

[0694] Output: Suggestions displayed to the user (coupons, methods for increasing points, etc.)

[0695] Step 7:

[0696] The user reviews and uses the suggestions they receive.

[0697] Specific actions: The user reviews the displayed suggestions and either uses a coupon or implements a strategy to increase points.

[0698] Input: Suggestions displayed to the user

[0699] Output: User actions based on the suggestion (e.g., purchase, point usage)

[0700] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0701] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0702] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0703] [Third Embodiment]

[0704] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0705] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0706] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0707] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0708] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0709] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0710] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0711] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0712] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0713] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0714] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0715] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0716] This invention relates to a system for receiving suggestions for optimal coupons and point-earning methods on a user-operated terminal. This system is implemented as follows.

[0717] The system starts operating when the user interacts with the Request Suggestion button within the application. When the Request Suggestion button is pressed, the device first collects user data. This user data includes the following information:

[0718] Purchase history: Products and services you have purchased in the past.

[0719] Coupon Usage History: Details of coupons used in the past.

[0720] Point balance: The current total amount of points.

[0721] The device collects this data and sends it to the server. The server analyzes the received data and uses AI to generate the best possible suggestions for the user. The AI ​​analysis includes the following process:

[0722] Purchase history analysis: Analyze user preferences and purchasing patterns to identify highly relevant products and services.

[0723] Analysis of coupon usage history: Identify which types of coupons are best suited to each user.

[0724] Analyzing point balances: Devise strategies to efficiently increase points.

[0725] The server generates optimal suggestions based on the results of AI analysis. These suggestions, such as coupon recommendations or methods for increasing points, are sent to the user's device.

[0726] The device displays suggestions sent from the server to the user. This display occurs as a pop-up or notification bar within the application. This allows the user to immediately use recommended coupons or check strategies for increasing points.

[0727] As a concrete example, suppose a user presses the "Optimal Suggestion" button. If that user has purchased many electronic devices in the past, the AI ​​will recommend "discount coupons for electronic devices" to this user. In addition, to help the user effectively use their current point balance, the AI ​​will suggest point strategies such as "Get double points if you purchase groceries this week."

[0728] This allows users to easily find the coupons and point-earning methods that best suit them, significantly increasing the app's value. In this way, it becomes possible to enhance user convenience and increase the frequency of application use. A specific implementation of this system includes the following processing flow.

[0729] The following describes the processing flow.

[0730] Step 1:

[0731] The user presses the "Best Suggestion" button within the application. This initiates the process of receiving suggestions.

[0732] Step 2:

[0733] The device collects user data such as purchase history, coupon usage history, and point balance. This data is collected based on the user's past usage.

[0734] Step 3:

[0735] The device sends the collected user data to the server. This transmission is typically done in the form of an HTTP POST request or similar.

[0736] Step 4:

[0737] The server analyzes the user data it receives. This analysis uses AI to understand user behavior patterns and preferences.

[0738] Step 5:

[0739] The server generates optimal suggestions based on AI analysis results. These suggestions include recommendations for coupons tailored to the user and strategies for increasing points.

[0740] Step 6:

[0741] The server sends the generated suggestion to the terminal. This suggestion is customized to enhance user convenience.

[0742] Step 7:

[0743] The device displays suggestions received from the server to the user. These suggestions are displayed using methods such as pop-ups or notification bars.

[0744] Step 8:

[0745] Users can review and utilize suggested coupons and point strategies. As a result, users can use the application more effectively.

[0746] (Example 1)

[0747] Next, we will describe Example 1. 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."

[0748] Traditional coupon and point systems require users to manually find the coupons and point accumulation methods best suited to them, which is time-consuming and requires considerable effort. Furthermore, there is a lack of methods to automatically generate optimal suggestions based on users' purchasing patterns and preferences. This situation reduces user convenience and, consequently, may lead to decreased service usage frequency and satisfaction.

[0749] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0750] In this invention, the server includes means for performing AI analysis using a generated AI model, means for generating optimal suggestions for the user, and means for transmitting the generated suggestions to the terminal. This makes it possible to automatically suggest the most suitable coupons and point-earning methods to the user based on data such as the user's purchase history, coupon usage history, and point balance.

[0751] A "device" is an electronic device that a user can operate, and includes devices such as smartphones and tablets.

[0752] The "Request Proposal Button" is an interface element that allows the system to start generating proposals when the user interacts with it.

[0753] "User data" refers to information including the user's purchase history, coupon usage history, and point balance.

[0754] A "server" is a central processing unit that receives user data, analyzes it, generates optimal suggestions, and transmits them to the terminal.

[0755] A "generative AI model" is an artificial intelligence model that analyzes user data and generates optimal suggestions.

[0756] "AI analysis" is a process that uses generative AI models to analyze user data and generate suggestions based on user preferences and patterns.

[0757] An "optimal proposal" is a proposal that includes coupons and point-earning methods that users will find most valuable, based on user data.

[0758] "Point balance" refers to the total number of points a user currently possesses.

[0759] "Displaying proposals" refers to the act of visually displaying the generated proposals on the user's device.

[0760] This invention relates to a system for receiving suggestions for optimal coupons and point-earning methods on a user-operated terminal. This system operates in cooperation with the user, terminal, and server.

[0761] 1. System startup and proposal request

[0762] The system is activated when the user operates the "Request Suggestion" button within the application. The terminal then uses this operation as a trigger to send a request for a suggestion to the system.

[0763] 2. Collection of user data

[0764] Once a suggestion request is received, the device begins collecting user data. This user data includes purchase history, coupon usage history, and points balance. This data is retrieved from the application's internal database and other related systems.

[0765] 3. Sending data

[0766] The collected user data is sent from the device to the server. This transmission typically uses HTTP requests, and the data is transferred in a standard data format such as JSON.

[0767] 4. Data analysis using AI

[0768] The server inputs the received data into a generative AI model and performs AI analysis. A possible generative AI model used here could be OpenAI's GPT-3, for example. The analysis process is as follows:

[0769] By analyzing purchase history, we identify user preferences and purchasing patterns.

[0770] By analyzing coupon usage history, we determine which type of coupon is most suitable.

[0771] By analyzing point balances, we will devise strategies to efficiently increase points.

[0772] 5. Generating the optimal proposal

[0773] The server generates optimal coupons and point-earning methods based on the AI ​​analysis results. The generated suggestions are saved in text format and sent to the terminal as a response.

[0774] 6. Submitting and displaying proposals

[0775] The device displays suggestions received from the server to the user. This is done using methods such as pop-ups or notification bars within the application.

[0776] 7. The user reviews the proposal.

[0777] Users can review the suggestions displayed on their devices and, if necessary, use coupons or implement point-earning strategies.

[0778] Specific example

[0779] As a concrete example, consider a case where a user presses the "Optimal Suggestion" button. If this user has purchased many electronic devices in the past, the server analyzes their purchase history and recommends discount coupons for electronic devices. It also presents a points strategy to help the user effectively use their points balance, such as "Buy groceries this week and earn double points."

[0780] Example of a prompt

[0781] The prompt text to be input to the generative AI model is as follows:

[0782] "Analyze the user's purchase history, coupon usage history, and points balance, and suggest the most suitable coupons and points-earning methods for this user."

[0783] This system allows users to easily find the coupons and point-earning methods that best suit them, significantly increasing the value of the application. Furthermore, the increased user convenience will lead to increased application usage.

[0784] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0785] Step 1:

[0786] System startup and request for proposals

[0787] The system is activated when the user operates the "Request Suggestion" button within the application.

[0788] Input: User button click operation

[0789] Output: Trigger for Request for Proposal event

[0790] Specific action: The user opens the app on their smartphone and taps the "Request Suggestion" button. The device detects this event and starts the system-wide suggestion generation process.

[0791] Step 2:

[0792] User data collection

[0793] The device collects user data.

[0794] Input: Request for Proposal Event

[0795] Output: User data (purchase history, coupon usage history, point balance)

[0796] Specific operation: The device retrieves purchase data from the past year, the type and date of use of used coupons, and the current total points from its internal database and related APIs.

[0797] Step 3:

[0798] Sending data

[0799] The device sends the collected user data to the server.

[0800] Input: User data

[0801] Output: Request to send data to the server

[0802] Specific operation: The terminal sends the user data file collected in JSON format to the server as an HTTPS request.

[0803] Step 4:

[0804] AI-powered data analysis

[0805] The server inputs the received data into a generating AI model for analysis, and then performs the AI ​​analysis.

[0806] Input: User data

[0807] Output: AI analysis results (recommended coupons, point strategies, etc.)

[0808] Specific operation: The server inputs received data into the AI ​​model and uses prompt messages to obtain analysis results. For example, "Analyze the user's purchase history, coupon usage history, and point balance, and suggest the most suitable coupons and point-earning methods for this user."

[0809] Step 5:

[0810] Generating the optimal proposal

[0811] The server generates suggestions for the most suitable coupons and point-earning methods based on the analysis results.

[0812] Input: AI analysis results

[0813] Output: Best suggestion (text format)

[0814] Specific operation: Based on the analysis results, the server generates specific suggestions tailored to the user's preferences and purchase history, such as "discount coupons for electronic devices" or "double points for grocery purchases this week."

[0815] Step 6:

[0816] Submit a proposal

[0817] The server sends the generated suggestions to the user's terminal.

[0818] Input: Best suggestion

[0819] Output: Request to submit proposal

[0820] Specific operation: The server sends JSON data containing the generated proposal to the terminal as an HTTPS response.

[0821] Step 7:

[0822] Display of proposals

[0823] The device displays suggestions it has received to the user.

[0824] Input: Suggestions from the server

[0825] Output: Suggestions displayed on the screen

[0826] Specific action: The device displays a pop-up message within the application saying, "A new discount coupon is available! Take advantage of the discount on electronic devices."

[0827] Step 8:

[0828] Review and response to the proposal

[0829] Users review the displayed suggestions and, if necessary, use coupons or implement point-earning strategies.

[0830] Input: Confirmation of proposal

[0831] Output: Use of coupons, implementation of point-increasing strategies.

[0832] Specific actions: The user clicks on a "discount coupon for electronics" and begins online shopping. They also plan to buy groceries this week.

[0833] The above outlines the specific process flow from when the system generates and displays a proposal, to when the user confirms and uses it.

[0834] (Application Example 1)

[0835] Next, we will explain Application Example 1. In the following explanation, 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."

[0836] Traditional coupon and point-earning systems made it difficult for users to receive optimal suggestions in real time when selecting products in physical stores. This resulted in an inconsistent and inconvenient user purchasing experience. This, in turn, could lead to decreased user satisfaction and, consequently, a reduction in the frequency of store and service visits.

[0837] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0838] In this invention, the server includes means for providing a suggestion request button that can be operated by the user, means for collecting user data when the suggestion request button is operated, means for transmitting the collected user data to the server, means for the server to receive the user data and perform AI analysis based on the user data, means for generating optimal suggestions for the user based on the AI ​​analysis, means for transmitting the generated suggestions to a terminal, means for displaying the suggestions on the terminal, means for smart glasses to read product information in real time within a physical store, and means for displaying suggestions on the terminal based on the read product information and user data. As a result, the user can receive optimal coupons and methods for increasing points in real time during their shopping experience at a physical store.

[0839] "User data" refers to information such as a user's purchase history, coupon usage history, and point balance.

[0840] The "Request Proposal" button is a button that users press to receive the most suitable proposals.

[0841] A "server" is a computer system that analyzes user data and generates optimal suggestions.

[0842] "AI analysis" is the process of performing analysis using machine learning algorithms based on collected user data.

[0843] "Smart glasses" are devices worn by users that can read information such as product barcodes and NFC tags in real time.

[0844] "Optimal suggestions" refer to the most useful coupons and point-earning methods for the user, generated by considering the user's purchase history, coupon usage history, and point balance.

[0845] A "device" refers to an electronic device such as a smartphone or tablet that is operated by a user.

[0846] A "physical store" is a physical store where users can directly purchase products.

[0847] "Product information" refers to data obtained through barcodes, NFC tags, etc., that is useful for identifying products.

[0848] "Real-time" refers to the instantaneous process of data collection, analysis, proposal generation, and display.

[0849] The embodiments for carrying out the present invention will be described in detail below.

[0850] The system provides a user-operated suggestion request button on the user's device. When the user operates the suggestion request button, the device collects user data such as the user's purchase history, coupon usage history, and point balance. This data is transmitted to the server in real time.

[0851] The server performs AI analysis based on the received user data. This analysis uses the collected data to analyze the user's preferences and purchasing patterns, and generates optimal suggestions for the user. The server then sends the generated suggestions back to the terminal.

[0852] The suggestions are also provided in real time to users using smart glasses in physical stores. The smart glasses read product information the moment the user focuses on a specific product. Product information is obtained via barcodes or NFC tags and sent to a server. Based on this information and user data, the server generates new suggestions and displays them in real time on the smart glasses' display.

[0853] This embodiment allows users to receive the most useful coupons and point-earning methods in real time when selecting products in physical stores, improving the shopping experience. Furthermore, stores can efficiently promote users' purchasing motivations.

[0854] As a concrete example, suppose a user is wearing smart glasses and focuses on a specific product in a supermarket. The smart glasses read the product's barcode or NFC tag, and send the information to a server, associating it with past purchase history and current point balance. The server generates real-time suggestions tailored to the user, such as a "10% off coupon" or "double points if you buy groceries this week," and displays them on the smart glasses' screen.

[0855] Examples of prompt statements are as follows:

[0856] "Consider the user's purchase history, coupon usage history, and points balance, and suggest the best coupons and point-earning methods for a specific product. Example: Purchase history: Electronics, Coupon usage history: 10% off, Points balance: 1500."

[0857] In this way, the present invention provides a system that enhances user convenience and improves the in-store purchasing experience.

[0858] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0859] Step 1:

[0860] The user operates the suggestion request button on the device. At this point, the user's input is registered as the button operation begins. The device detects this operation and proceeds to the next step.

[0861] Step 2:

[0862] The device collects user data such as purchase history, coupon usage history, and point balance. The collected data is read from the device's internal storage.

[0863] Step 3:

[0864] The collected user data is sent to the server. This data includes the user's purchase history, coupon usage history, and point balance, and this is input to the server. The server prepares the received data for analysis.

[0865] Step 4:

[0866] The server performs AI analysis based on the received user data. Using the generated AI model, it analyzes user preferences and purchasing patterns. The output of this step is the selection of the most suitable suggestions for the user.

[0867] Step 5:

[0868] The server sends optimal suggestions, generated based on AI analysis, to the user's device. These suggestions include applicable coupons and methods for increasing points. The user's device receives this information and proceeds to the next step.

[0869] Step 6:

[0870] The user's device displays suggestions and notifies the user. These suggestions are displayed using in-app pop-ups or notification bars.

[0871] Step 7:

[0872] When a user is wearing smart glasses in a physical store, the smart glasses read product information. This product information is obtained in real time via barcodes or NFC tags. The smart glasses then send the obtained information to the terminal and proceed to the next step.

[0873] Step 8:

[0874] The terminal receives product information transmitted from the smart glasses and compares it with past user data. This data is then sent to the server for further AI analysis. The input for this process consists of product information and past user data.

[0875] Step 9:

[0876] The server performs AI analysis in real time and generates new suggestions. These suggestions include coupons and point strategies related to the products.

[0877] Step 10:

[0878] The generated suggestions are displayed in real time on the smart glasses' screen. Users can instantly see the best suggestions to facilitate their purchasing decision.

[0879] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0880] This invention relates to a system that provides optimal coupon and point-earning methods on a user-operated terminal, and by combining it with an emotion engine, it provides customized suggestions according to the user's emotional state. This system is implemented as follows.

[0881] The system starts operating when the user presses the suggestion request button within the application. When the suggestion request button is pressed, the terminal first begins collecting user data. The collected user data includes purchase history, coupon usage history, and point balance. In addition, the user's emotional data is collected using an emotion engine. The emotion engine analyzes multiple data points such as the user's facial expressions, voice tone, and typing speed to recognize the user's emotional state.

[0882] The device sends this data to the server. The server analyzes the received user data and sentiment data and uses AI to generate the most suitable suggestions for the user. Here, sentiment data is used in particular as follows:

[0883] If a user is feeling stressed, the system will automatically generate suggestions to help them relax (for example, a relaxing coupon).

[0884] When users are satisfied, we offer suggestions to further enhance that satisfaction (for example, strategies to increase points).

[0885] The server generates suggestions such as coupon recommendations and ways to increase points, which are then sent to the user's device. The device displays the suggestions received from the server to the user. Display methods include pop-ups within the application and notification bars.

[0886] As a concrete example, suppose a user presses the "Optimal Suggestion" button. If the AI ​​determines that this user has purchased many food items in the past and is currently in a relatively relaxed emotional state, it will recommend a "food discount coupon" to this user. It will also suggest point strategies to help the user effectively use their current point balance, such as "Buy clothes this week and earn double points."

[0887] This system is highly beneficial to users because it uses an emotion engine to understand the user's real-time emotional state and provide optimal suggestions based on that understanding. Users can receive suggestions that match their emotional state, thereby significantly increasing the value of using the application. In this way, the system of the present invention, which incorporates an emotion engine, enhances user convenience and increases the frequency of application use.

[0888] The following describes the processing flow.

[0889] Step 1:

[0890] The user presses the "Best Suggestion" button within the app. This initiates the system's suggestion process.

[0891] Step 2:

[0892] The device collects user data. Specifically, it retrieves past purchase history, coupon usage history, and current point balance. This data serves as foundational information to improve user convenience.

[0893] Step 3:

[0894] The device activates an emotion engine to analyze the user's emotional state. The emotion engine collects data such as the user's facial expressions, voice tone, and typing speed. Based on the analysis results, the user's current emotional state (e.g., stress, satisfaction, fatigue) is identified.

[0895] Step 4:

[0896] The device sends collected user data and sentiment data to the server. This transmission is typically done in the form of an HTTP POST request or similar.

[0897] Step 5:

[0898] The server receives user data and sentiment data and begins AI analysis. The AI ​​analysis uses the collected data to understand the user's preferences and past behavioral patterns, preparing to generate optimal suggestions.

[0899] Step 6:

[0900] The server generates optimal suggestions based on AI analysis. For example, if it determines that the user is feeling stressed, it suggests coupons for relaxation products or services. Conversely, if the user is feeling satisfied, it provides ways to increase their points to further enhance that satisfaction.

[0901] Step 7:

[0902] The server sends the generated proposal to the terminal. This proposal includes coupon links and details of the points strategy.

[0903] Step 8:

[0904] The device receives the suggestion and displays it to the user. Display formats include in-app pop-ups and notification bars.

[0905] Step 9:

[0906] Users review the suggestions and, if necessary, use coupons or implement methods to increase their points. This not only improves user convenience but also significantly increases the value of using the application.

[0907] (Example 2)

[0908] Next, we will describe Example 2. 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."

[0909] Traditional coupon and points suggestion systems lacked the ability to provide optimal suggestions that took into account the user's emotional state, resulting in suggestions that were not always highly satisfying for users. Furthermore, the lack of customization based on the user's real-time emotional state reduced the value of the application, making it difficult to improve user convenience and satisfaction.

[0910] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0911] In this invention, the server includes means for collecting emotional data by analyzing data points such as the user's facial expressions, tone of voice, and input speed using an emotion engine that recognizes the user's emotional state; means for customizing suggestions generated based on the collected emotional data; and means for generating multiple suggestion candidates and selecting the suggestion deemed optimal based on the user's past patterns and emotional state. This enables optimal suggestions tailored to the user's emotional state, improving user convenience and satisfaction, and significantly increasing the value of the application.

[0912] "User-operated devices" refer to digital devices that users can directly operate, including smartphones, tablets, and personal computers.

[0913] A "Request a Suggestion button" is a UI element that allows a user to request a specific suggestion by clicking or tapping it.

[0914] "User data" refers to information about a user's behavior and status, such as their purchase history, coupon usage history, and point balance.

[0915] A "server" is a central processing unit that receives and analyzes user data and sentiment data, and sends the results back to the user's terminal.

[0916] "AI analysis" is the process of analyzing data using artificial intelligence technology to extract patterns and trends.

[0917] An "emotion engine" is software or hardware that analyzes data points such as a user's facial expressions, voice tone, and input speed to identify the user's emotional state.

[0918] "Emotional data" refers to information that represents the user's emotional state, collected by the emotion engine, and includes stress levels, satisfaction levels, and other factors.

[0919] "Candidate proposals" are multiple proposals generated by the server, and they serve as the basis for selecting the optimal proposal.

[0920] A "strategy to increase points" refers to a strategy for efficiently using a user's point balance, and includes methods for increasing points under specific conditions.

[0921] This invention is a system that provides optimal coupon and point-earning methods on a user-operated terminal, and by combining it with an emotion engine, it provides customized suggestions according to the user's emotional state. The following describes how this system is specifically implemented.

[0922] The system starts operating when the user interacts with the "Request Suggestion" button within the application. Upon pressing the Request Suggestion button, the device first begins collecting user data. This data includes purchase history, coupon usage history, and point balance. This data is retrieved from the device's built-in SQL database or other data storage system.

[0923] Next, the device also collects user emotion data using an emotion engine. The emotion engine analyzes multiple data points such as the user's facial expressions, voice tone, and input speed to recognize the user's emotional state. The emotion engine includes AI models for facial recognition and voice analysis, which utilize common libraries (e.g., Microsoft Azure's emotion recognition API).

[0924] The collected user data and sentiment data are sent from the device to the server. This transmission uses HTTP POST requests via a REST API. The server analyzes the received data and generates optimal suggestions for the user using a generative AI model (e.g., TensorFlow). Sentiment data, in particular, is used in suggestion generation as follows:

[0925] If a user is feeling stressed, suggestions to help them relax (such as relaxation coupons) are automatically generated.

[0926] If a user is satisfied, suggestions (such as a strategy to increase points) are provided to further enhance that satisfaction.

[0927] The suggestions generated by the server, such as coupon recommendations and methods for increasing points, are sent to the user's device. These suggestions are also sent via a REST API. The device displays the suggestions received from the server to the user. Display methods include in-app pop-ups and notification bars.

[0928] As a concrete example, suppose a user presses the "Request Suggestion" button. If the AI ​​determines that this user has purchased many food items in the past and is currently in a relatively relaxed emotional state, it will recommend a "food discount coupon" to this user. It will also suggest a points strategy to help the user effectively use their current points balance, such as "Get double points if you purchase clothing this week."

[0929] This system is highly beneficial to users because it uses an emotion engine to understand the user's real-time emotional state and provide optimal suggestions based on that understanding. Users can receive suggestions that match their emotional state, thereby significantly increasing the value of using the application. In this way, the system of the present invention, which incorporates an emotion engine, enhances user convenience and increases the frequency of application use.

[0930] Examples of prompts for generative AI models:

[0931] If a user clicks the "Request Suggestion" button and data indicates a high proportion of food purchases and a relaxed emotional state, generate the most suitable suggestions. Determine if food discount coupons or double points strategies are applicable, and provide example suggestions.

[0932] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0933] Step 1:

[0934] The user presses the "Request a Proposal" button.

[0935] Input: User actions

[0936] Output: Request for Proposal event occurs

[0937] Description: The system starts operating when the user presses the "Request Suggestion" button within the application. This action triggers the next step.

[0938] Specific action: The user clicks or taps the "Request Suggestion" button on the application screen, and the application detects this event.

[0939] Step 2:

[0940] User data collection

[0941] Input: "Request for Proposal" event

[0942] Output: Collected user data (purchase history, coupon usage history, point balance, etc.)

[0943] Description: When the device receives a "Request for Proposal" event, it begins collecting user data. This includes purchase history, coupon usage history, and points balance.

[0944] Specific operation: The application on the device retrieves the relevant user data from its built-in database (e.g., an SQL database). For example, it might execute a database query based on the user ID to retrieve past purchase history or coupon usage history.

[0945] Step 3:

[0946] Collection of emotional data

[0947] Input: "Request for Proposal" event

[0948] Output: Collected emotional data (facial expressions, voice tone, input speed, etc.)

[0949] Description: The device uses an emotion engine to collect user emotion data. The emotion engine analyzes data points such as the user's facial expressions, voice tone, and typing speed.

[0950] Specific operation: The device's camera and microphone are used to capture the user's facial expressions and voice, which are then sent to an emotion engine (e.g., an emotion recognition API) for analysis.

[0951] Step 4:

[0952] Sending data to the server

[0953] Input: User data and sentiment data

[0954] Output: Data transmission to the server is complete.

[0955] Description: Collected user data and sentiment data are sent from the device to the server. The transmission is done via an HTTP POST request.

[0956] Specific operation: The device converts the collected data into JSON format and sends it to the server via an HTTP POST request. A REST API is used for this, and the data is sent to a specified endpoint on the server.

[0957] Step 5:

[0958] Server-based data analysis and proposal generation

[0959] Input: Received user data and sentiment data

[0960] Output: Generated optimal proposals

[0961] Description: The server analyzes the received data. Using user data and sentiment data, the AI ​​model generates the most suitable suggestions for the user.

[0962] Specific operation: The server uses AI models such as TensorFlow to analyze data based on pre-trained algorithms. It then generates optimal coupons and point strategies tailored to emotional states and past behavioral patterns.

[0963] Step 6:

[0964] Sending proposals from the server to the terminal

[0965] Input: Generated optimal proposal

[0966] Output: Suggestion sent to terminal complete.

[0967] Description: Converts the generated optimal suggestions into JSON format and sends them from the server to the terminal.

[0968] Specific operation: The server packages the proposed data in JSON format and sends it to the specified endpoint on the terminal via an HTTP POST request.

[0969] Step 7:

[0970] Suggestion display on the device

[0971] Input: Proposal data from the server

[0972] Output: Suggestions displayed to the user

[0973] Description: The device analyzes received suggestion data and displays it to the user. Pop-ups and notification bars are used for display.

[0974] Specific operation: The application on the device receives the suggestion data and displays it in the user interface. The user can then view the suggested coupons and point strategies on the screen.

[0975] (Application Example 2)

[0976] Next, we will explain application example 2. In the following explanation, 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."

[0977] When users purchase products online, there is a need to improve the purchasing experience by providing optimal coupons and point-boosting strategies. However, conventional systems do not take into account the user's emotional state and only offer uniform suggestions, which can lead to decreased user satisfaction. It is necessary to solve this problem and provide optimal suggestions that respond to each user's emotional state, thereby realizing a more personalized service.

[0978] The specific processing performed by the specific 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 collecting emotional data using the camera and microphone of the terminal to analyze the user's emotional state, means for customizing suggestions based on the emotional data, means for providing a suggestion request button that can be operated by the user on the terminal operated by the user, means for collecting user data when the suggestion request button is operated, means for transmitting the collected user data to the server, means for receiving the user data on the server and performing AI analysis based on the user data, means for generating the optimal suggestion for the user based on the AI ​​analysis, means for transmitting the generated suggestion to the terminal, and means for displaying the suggestion on the terminal. This makes it possible to provide customized suggestions that take into account the user's emotional state.

[0979] The "Request Suggestion Button" is an interface that allows users to request the system to offer coupons or point increase suggestions.

[0980] "User data" refers to information about the user, such as purchase history, coupon usage history, and point balance.

[0981] "Emotional data" refers to information about a user's emotional state, analyzed from facial expressions, voice tone, input speed, and other data captured using cameras and microphones.

[0982] A "camera" is a hardware device used to capture and analyze a user's facial expressions.

[0983] A "microphone" is a hardware device used to capture and analyze the tone of a user's voice.

[0984] "AI analysis" refers to data processing using artificial intelligence, where a server uses user data and sentiment data to generate optimal suggestions.

[0985] A "proposal" is a customized purchase promotion plan offered to users, such as coupons or point increase strategies.

[0986] A "terminal" is a device (such as a smartphone or tablet) that a user operates to access and use a system.

[0987] A "server" is a computing environment that receives user data and sentiment data, performs AI analysis to generate suggestions, and sends them to the terminal.

[0988] A "Request for Suggestions" is an action taken by a user to request suggestions from the system, such as coupons or point increases, by clicking a suggestion request button.

[0989] This invention relates to a system that provides optimal coupon and point-earning methods on a user-operated terminal. This system can provide customized suggestions to the user by analyzing the user's emotional state. The following describes embodiments for carrying out this invention.

[0990] First, a suggestion request button is displayed on the user's device. When the user presses this button, the device collects user data. This user data includes information such as purchase history, coupon usage history, and point balance. In addition, the user's emotional data is collected using an emotion engine. Emotional data is obtained by analyzing the user's facial expressions, voice tone, input speed, etc., using the camera and microphone.

[0991] The collected user data and emotional data are sent to the server. The server performs AI analysis based on the received data and generates optimal suggestions. For example, if the AI ​​determines that the user is currently in a relaxed emotional state, it will recommend a relaxation-related coupon. It also selects the most suitable suggestions for the user based on past patterns.

[0992] The generated suggestions are sent to the device and displayed to the user via pop-ups, notification bars, etc. This allows the user to receive the most suitable suggestions based on their current emotional state. The suggestions also include strategies to increase the user's points based on their point balance.

[0993] The following hardware and software will be used to implement the system:

[0994] Hardware: Smartphone camera and microphone

[0995] software:

[0996] Emotion engine (uses OpenCV and a pre-trained Keras model for facial expression analysis)

[0997] AI model (suggestion generation customized with TensorFlow)

[0998] Specific example:

[0999] When a user presses the suggestion request button, the device uses its camera and microphone to capture the user's face and voice and collect emotional data. For example, if data shows the user has purchased many food items in the past and the AI ​​determines that the user is currently relaxed, it will recommend a "food discount coupon" to this user. The suggestion is notified to the user's device, and the user can use the coupon immediately.

[1000] Example of a prompt:

[1001] "Create a Python program that retrieves purchase history and recommends the most suitable coupons and point-boosting strategies based on the user's current emotional state."

[1002] This invention is highly beneficial to users because it can grasp their emotional state in real time and provide customized suggestions based on that state. Furthermore, since users can receive suggestions that match their emotional state, the value of the application is expected to increase significantly, and the frequency of use is also expected to increase.

[1003] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1004] Step 1:

[1005] The user clicks the suggestion request button.

[1006] Specific action: The user taps the suggestion request button displayed on the application screen of their device.

[1007] Input: User actions

[1008] Output: Event indicating that the Request for Suggestion button was pressed.

[1009] Step 2:

[1010] The device collects user data and sentiment data.

[1011] Specific operation: The device retrieves user data such as purchase history, coupon usage history, and point balance from its internal database. It also activates the camera and microphone and uses an emotion engine to analyze the user's facial expressions, voice tone, and input speed.

[1012] Input: Event indicating that the Request for Suggestion button was pressed.

[1013] Output: Acquired user data and sentiment data

[1014] Step 3:

[1015] The device sends the collected data to the server.

[1016] Specific operation: The device sends the acquired user data and sentiment data to the server via the network.

[1017] Input: User data and sentiment data

[1018] Output: User data and sentiment data sent to the server

[1019] Step 4:

[1020] The server receives user data and sentiment data and performs AI analysis.

[1021] Specific operation: The server inputs the received data into the AI ​​analysis engine and generates optimal suggestions based on the user's emotional state and past behavioral patterns.

[1022] Input: Received user data and sentiment data

[1023] Output: Suggestions generated by AI analysis (coupons and methods to increase points)

[1024] Step 5:

[1025] The server sends the generated proposal to the terminal.

[1026] Specific operation: The server sends the generated suggestions to the user's terminal via the network.

[1027] Input: Proposals generated by AI analysis

[1028] Output: Suggestions sent to the terminal

[1029] Step 6:

[1030] The device notifies and displays suggestions to the user.

[1031] Specific action: The device displays the received suggestion to the user via a pop-up notification or notification bar.

[1032] Input: Submitted proposal

[1033] Output: Suggestions displayed to the user (coupons, methods for increasing points, etc.)

[1034] Step 7:

[1035] The user reviews and uses the suggestions they receive.

[1036] Specific actions: The user reviews the displayed suggestions and either uses a coupon or implements a strategy to increase points.

[1037] Input: Suggestions displayed to the user

[1038] Output: User actions based on the suggestion (e.g., purchase, point usage)

[1039] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1040] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1041] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[1042] [Fourth Embodiment]

[1043] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1044] As shown in Figure 7, the 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.

[1045] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1046] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[1047] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[1048] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[1049] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[1050] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive 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 robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[1051] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[1052] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[1053] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1054] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[1055] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1056] This invention relates to a system for receiving suggestions for optimal coupons and point-earning methods on a user-operated terminal. This system is implemented as follows.

[1057] The system starts operating when the user interacts with the Request Suggestion button within the application. When the Request Suggestion button is pressed, the device first collects user data. This user data includes the following information:

[1058] Purchase history: Products and services you have purchased in the past.

[1059] Coupon Usage History: Details of coupons used in the past.

[1060] Point balance: The current total amount of points.

[1061] The device collects this data and sends it to the server. The server analyzes the received data and uses AI to generate the best possible suggestions for the user. The AI ​​analysis includes the following process:

[1062] Purchase history analysis: Analyze user preferences and purchasing patterns to identify highly relevant products and services.

[1063] Analysis of coupon usage history: Identify which types of coupons are best suited to each user.

[1064] Analyzing point balances: Devise strategies to efficiently increase points.

[1065] The server generates optimal suggestions based on the results of AI analysis. These suggestions, such as coupon recommendations or methods for increasing points, are sent to the user's device.

[1066] The device displays suggestions sent from the server to the user. This display occurs as a pop-up or notification bar within the application. This allows the user to immediately use recommended coupons or check strategies for increasing points.

[1067] As a concrete example, suppose a user presses the "Optimal Suggestion" button. If that user has purchased many electronic devices in the past, the AI ​​will recommend "discount coupons for electronic devices" to this user. In addition, to help the user effectively use their current point balance, the AI ​​will suggest point strategies such as "Get double points if you purchase groceries this week."

[1068] This allows users to easily find the coupons and point-earning methods that best suit them, significantly increasing the app's value. In this way, it becomes possible to enhance user convenience and increase the frequency of application use. A specific implementation of this system includes the following processing flow.

[1069] The following describes the processing flow.

[1070] Step 1:

[1071] The user presses the "Best Suggestion" button within the application. This initiates the process of receiving suggestions.

[1072] Step 2:

[1073] The device collects user data such as purchase history, coupon usage history, and point balance. This data is collected based on the user's past usage.

[1074] Step 3:

[1075] The device sends the collected user data to the server. This transmission is typically done in the form of an HTTP POST request or similar.

[1076] Step 4:

[1077] The server analyzes the user data it receives. This analysis uses AI to understand user behavior patterns and preferences.

[1078] Step 5:

[1079] The server generates optimal suggestions based on AI analysis results. These suggestions include recommendations for coupons tailored to the user and strategies for increasing points.

[1080] Step 6:

[1081] The server sends the generated suggestion to the terminal. This suggestion is customized to enhance user convenience.

[1082] Step 7:

[1083] The device displays suggestions received from the server to the user. These suggestions are displayed using methods such as pop-ups or notification bars.

[1084] Step 8:

[1085] Users can review and utilize suggested coupons and point strategies. As a result, users can use the application more effectively.

[1086] (Example 1)

[1087] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1088] Traditional coupon and point systems require users to manually find the coupons and point accumulation methods best suited to them, which is time-consuming and requires considerable effort. Furthermore, there is a lack of methods to automatically generate optimal suggestions based on users' purchasing patterns and preferences. This situation reduces user convenience and, consequently, may lead to decreased service usage frequency and satisfaction.

[1089] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[1090] In this invention, the server includes means for performing AI analysis using a generated AI model, means for generating optimal suggestions for the user, and means for transmitting the generated suggestions to the terminal. This makes it possible to automatically suggest the most suitable coupons and point-earning methods to the user based on data such as the user's purchase history, coupon usage history, and point balance.

[1091] A "device" is an electronic device that a user can operate, and includes devices such as smartphones and tablets.

[1092] The "Request Proposal Button" is an interface element that allows the system to begin generating proposals when the user interacts with it.

[1093] "User data" refers to information including the user's purchase history, coupon usage history, and point balance.

[1094] A "server" is a central processing unit that receives user data, analyzes it, generates optimal suggestions, and transmits them to the terminal.

[1095] A "generative AI model" is an artificial intelligence model that analyzes user data and generates optimal suggestions.

[1096] "AI analysis" is the process of analyzing user data using generative AI models to generate suggestions based on user preferences and patterns.

[1097] An "optimal proposal" is a proposal that includes coupons and point-earning methods that users will find most valuable, based on user data.

[1098] "Point balance" refers to the total number of points a user currently possesses.

[1099] "Displaying proposals" refers to the act of visually displaying the generated proposals on the user's device.

[1100] This invention relates to a system for receiving suggestions for optimal coupons and point-earning methods on a user-operated terminal. This system operates in cooperation with the user, terminal, and server.

[1101] 1. System startup and proposal request

[1102] The system is activated when the user operates the "Request Suggestion" button within the application. The terminal then uses this operation as a trigger to send a request for a suggestion to the system.

[1103] 2. Collection of user data

[1104] Once a suggestion request is received, the device begins collecting user data. This user data includes purchase history, coupon usage history, and points balance. This data is retrieved from the application's internal database and other related systems.

[1105] 3. Sending data

[1106] The collected user data is sent from the device to the server. This transmission typically uses HTTP requests, and the data is transferred in a standard data format such as JSON.

[1107] 4. Data analysis using AI

[1108] The server inputs the received data into a generative AI model and performs AI analysis. A possible generative AI model used here could be OpenAI's GPT-3, for example. The analysis process is as follows:

[1109] By analyzing purchase history, we identify user preferences and purchasing patterns.

[1110] By analyzing coupon usage history, we determine which type of coupon is most suitable.

[1111] By analyzing point balances, we will devise strategies to efficiently increase points.

[1112] 5. Generating the optimal proposal

[1113] The server generates optimal coupons and point-earning methods based on the AI ​​analysis results. The generated suggestions are saved in text format and sent to the terminal as a response.

[1114] 6. Submitting and displaying proposals

[1115] The device displays suggestions received from the server to the user. This is done using methods such as pop-ups or notification bars within the application.

[1116] 7. The user reviews the proposal.

[1117] Users can review the suggestions displayed on their devices and, if necessary, use coupons or implement point-earning strategies.

[1118] Specific example

[1119] As a concrete example, consider a case where a user presses the "Optimal Suggestion" button. If this user has purchased many electronic devices in the past, the server analyzes their purchase history and recommends discount coupons for electronic devices. It also presents a points strategy to help the user effectively use their points balance, such as "Buy groceries this week and earn double points."

[1120] Example of a prompt

[1121] The prompt text to be input to the generative AI model is as follows:

[1122] "Analyze the user's purchase history, coupon usage history, and points balance, and suggest the most suitable coupons and points-earning methods for this user."

[1123] This system allows users to easily find the coupons and point-earning methods that best suit them, significantly increasing the value of the application. Furthermore, increased user convenience will lead to increased application usage.

[1124] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1125] Step 1:

[1126] System startup and request for proposals

[1127] The system is activated when the user operates the "Request Suggestion" button within the application.

[1128] Input: User button click operation

[1129] Output: Trigger for Request for Proposal event

[1130] Specific action: The user opens the app on their smartphone and taps the "Request Suggestion" button. The device detects this event and starts the system-wide suggestion generation process.

[1131] Step 2:

[1132] User data collection

[1133] The device collects user data.

[1134] Input: Request for Proposal Event

[1135] Output: User data (purchase history, coupon usage history, point balance)

[1136] Specific operation: The device retrieves purchase data from the past year, the type and date of use of used coupons, and the current total points from its internal database and related APIs.

[1137] Step 3:

[1138] Sending data

[1139] The device sends the collected user data to the server.

[1140] Input: User data

[1141] Output: Request to send data to the server

[1142] Specific operation: The terminal sends the user data file collected in JSON format to the server as an HTTPS request.

[1143] Step 4:

[1144] AI-powered data analysis

[1145] The server inputs the received data into a generating AI model for analysis, and then performs the AI ​​analysis.

[1146] Input: User data

[1147] Output: AI analysis results (recommended coupons, point strategies, etc.)

[1148] Specific operation: The server inputs received data into the AI ​​model and uses prompt messages to obtain analysis results. For example, "Analyze the user's purchase history, coupon usage history, and point balance, and suggest the most suitable coupons and point-earning methods for this user."

[1149] Step 5:

[1150] Generating the optimal proposal

[1151] The server generates suggestions for the most suitable coupons and point-earning methods based on the analysis results.

[1152] Input: AI analysis results

[1153] Output: Best suggestion (text format)

[1154] Specific operation: Based on the analysis results, the server generates specific suggestions tailored to the user's preferences and purchase history, such as "discount coupons for electronic devices" or "double points for grocery purchases this week."

[1155] Step 6:

[1156] Submit a proposal

[1157] The server sends the generated suggestions to the user's terminal.

[1158] Input: Best suggestion

[1159] Output: Request to submit proposal

[1160] Specific operation: The server sends JSON data containing the generated proposal to the terminal as an HTTPS response.

[1161] Step 7:

[1162] Display of proposals

[1163] The device displays suggestions it has received to the user.

[1164] Input: Suggestions from the server

[1165] Output: Suggestions displayed on the screen

[1166] Specific action: The device displays a pop-up message within the application saying, "A new discount coupon is available! Take advantage of the discount on electronic devices."

[1167] Step 8:

[1168] Review and response to the proposal

[1169] Users review the displayed suggestions and, if necessary, use coupons or implement point-earning strategies.

[1170] Input: Confirmation of proposal

[1171] Output: Use of coupons, implementation of point-increasing strategies.

[1172] Specific actions: The user clicks on a "discount coupon for electronics" and begins online shopping. They also plan to buy groceries this week.

[1173] The above outlines the specific process flow from when the system generates and displays a proposal, to when the user confirms and uses it.

[1174] (Application Example 1)

[1175] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1176] Traditional coupon and point-earning systems made it difficult for users to receive optimal suggestions in real time when selecting products in physical stores. This resulted in an inconsistent and inconvenient user purchasing experience. This, in turn, could lead to decreased user satisfaction and, consequently, a reduction in the frequency of store and service visits.

[1177] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1178] In this invention, the server includes means for providing a suggestion request button that can be operated by the user, means for collecting user data when the suggestion request button is operated, means for transmitting the collected user data to the server, means for the server to receive the user data and perform AI analysis based on the user data, means for generating optimal suggestions for the user based on the AI ​​analysis, means for transmitting the generated suggestions to a terminal, means for displaying the suggestions on the terminal, means for smart glasses to read product information in real time within a physical store, and means for displaying suggestions on the terminal based on the read product information and user data. As a result, the user can receive optimal coupons and methods for increasing points in real time during their shopping experience at a physical store.

[1179] "User data" refers to information such as a user's purchase history, coupon usage history, and point balance.

[1180] The "Request Proposal" button is a button that users press to receive the most suitable proposals.

[1181] A "server" is a computer system that analyzes user data and generates optimal suggestions.

[1182] "AI analysis" is the process of performing analysis using machine learning algorithms based on collected user data.

[1183] "Smart glasses" are devices worn by users that can read information such as product barcodes and NFC tags in real time.

[1184] "Optimal suggestions" refer to the most useful coupons and point-earning methods for the user, generated by considering the user's purchase history, coupon usage history, and point balance.

[1185] A "device" refers to an electronic device such as a smartphone or tablet that is operated by a user.

[1186] A "physical store" is a physical store where users can directly purchase products.

[1187] "Product information" refers to data obtained through barcodes, NFC tags, etc., that is useful for identifying products.

[1188] "Real-time" refers to the instantaneous process of data collection, analysis, proposal generation, and display.

[1189] The embodiments for carrying out the present invention will be described in detail below.

[1190] The system provides a user-operated suggestion request button on the user's device. When the user operates the suggestion request button, the device collects user data such as the user's purchase history, coupon usage history, and point balance. This data is transmitted to the server in real time.

[1191] The server performs AI analysis based on the received user data. This analysis uses the collected data to analyze the user's preferences and purchasing patterns, and generates optimal suggestions for the user. The server then sends the generated suggestions back to the terminal.

[1192] The suggestions are also provided in real time to users using smart glasses in physical stores. The smart glasses read product information the moment the user focuses on a specific product. Product information is obtained via barcodes or NFC tags and sent to a server. Based on this information and user data, the server generates new suggestions and displays them in real time on the smart glasses' display.

[1193] This embodiment allows users to receive the most useful coupons and point-earning methods in real time when selecting products in physical stores, improving the shopping experience. Furthermore, stores can efficiently promote users' purchasing motivations.

[1194] As a concrete example, imagine a user wearing smart glasses and focusing on a specific product in a supermarket. The smart glasses read the product's barcode or NFC tag, link it to past purchase history and current point balance, and send the information to a server. The server generates real-time suggestions tailored to the user, such as a "10% off coupon" or "double points if you buy groceries this week," and displays them on the smart glasses' screen.

[1195] Examples of prompt statements are as follows:

[1196] "Consider the user's purchase history, coupon usage history, and points balance, and suggest the best coupons and point-earning methods for a specific product. Example: Purchase history: Electronics, Coupon usage history: 10% off, Points balance: 1500."

[1197] In this way, the present invention provides a system that enhances user convenience and improves the in-store purchasing experience.

[1198] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1199] Step 1:

[1200] The user operates the suggestion request button on the device. At this point, the user's input is registered as the button operation begins. The device detects this operation and proceeds to the next step.

[1201] Step 2:

[1202] The device collects user data such as purchase history, coupon usage history, and point balance. The collected data is read from the device's internal storage.

[1203] Step 3:

[1204] The collected user data is sent to the server. This data includes the user's purchase history, coupon usage history, and point balance, and this is input to the server. The server prepares the received data for analysis.

[1205] Step 4:

[1206] The server performs AI analysis based on the received user data. Using the generated AI model, it analyzes user preferences and purchasing patterns. The output of this step is the selection of the most suitable suggestions for the user.

[1207] Step 5:

[1208] The server sends optimal suggestions, generated based on AI analysis, to the user's device. These suggestions include applicable coupons and methods for increasing points. The user's device receives this information and proceeds to the next step.

[1209] Step 6:

[1210] The user's device displays suggestions and notifies the user. These suggestions are displayed using in-app pop-ups or notification bars.

[1211] Step 7:

[1212] When a user is wearing smart glasses in a physical store, the smart glasses read product information. This product information is obtained in real time via barcodes or NFC tags. The smart glasses then send the obtained information to the terminal and proceed to the next step.

[1213] Step 8:

[1214] The terminal receives product information transmitted from the smart glasses and compares it with past user data. This data is then sent to the server for further AI analysis. The input for this process consists of product information and past user data.

[1215] Step 9:

[1216] The server performs AI analysis in real time and generates new suggestions. These suggestions include coupons and point strategies related to the products.

[1217] Step 10:

[1218] The generated suggestions are displayed in real time on the smart glasses' screen. Users can instantly see the best suggestions to facilitate their purchasing decision.

[1219] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1220] This invention relates to a system that provides optimal coupon and point-earning methods on a user-operated terminal, and by combining it with an emotion engine, it provides customized suggestions according to the user's emotional state. This system is implemented as follows.

[1221] The system starts operating when the user presses the suggestion request button within the application. When the suggestion request button is pressed, the terminal first begins collecting user data. The collected user data includes purchase history, coupon usage history, and point balance. In addition, the user's emotional data is collected using an emotion engine. The emotion engine analyzes multiple data points such as the user's facial expressions, voice tone, and typing speed to recognize the user's emotional state.

[1222] The device sends this data to the server. The server analyzes the received user data and sentiment data and uses AI to generate the most suitable suggestions for the user. Here, sentiment data is used in particular as follows:

[1223] If a user is feeling stressed, the system will automatically generate suggestions to help them relax (for example, a relaxing coupon).

[1224] When users are satisfied, we offer suggestions to further enhance that satisfaction (for example, strategies to increase points).

[1225] The server generates suggestions such as coupon recommendations and ways to increase points, which are then sent to the user's device. The device displays the suggestions received from the server to the user. Display methods include pop-ups within the application and notification bars.

[1226] As a concrete example, suppose a user presses the "Optimal Suggestion" button. If the AI ​​determines that this user has purchased many food items in the past and is currently in a relatively relaxed emotional state, it will recommend a "food discount coupon" to this user. It will also suggest point strategies to help the user effectively use their current point balance, such as "Buy clothes this week and earn double points."

[1227] This system is highly beneficial to users because it uses an emotion engine to understand the user's real-time emotional state and provide optimal suggestions based on that understanding. Users can receive suggestions that match their emotional state, thereby significantly increasing the value of using the application. In this way, the system of the present invention, which incorporates an emotion engine, enhances user convenience and increases the frequency of application use.

[1228] The following describes the processing flow.

[1229] Step 1:

[1230] The user presses the "Best Suggestion" button within the app. This initiates the system's suggestion process.

[1231] Step 2:

[1232] The device collects user data. Specifically, it retrieves past purchase history, coupon usage history, and current point balance. This data serves as foundational information to improve user convenience.

[1233] Step 3:

[1234] The device activates an emotion engine to analyze the user's emotional state. The emotion engine collects data such as the user's facial expressions, voice tone, and typing speed. Based on the analysis results, the user's current emotional state (e.g., stress, satisfaction, fatigue) is identified.

[1235] Step 4:

[1236] The device sends collected user data and sentiment data to the server. This transmission is typically done in the form of an HTTP POST request or similar.

[1237] Step 5:

[1238] The server receives user data and sentiment data and begins AI analysis. The AI ​​analysis uses the collected data to understand the user's preferences and past behavioral patterns, preparing to generate optimal suggestions.

[1239] Step 6:

[1240] The server generates optimal suggestions based on AI analysis. For example, if it determines that the user is feeling stressed, it suggests coupons for relaxation products or services. Conversely, if the user is feeling satisfied, it provides ways to increase their points to further enhance that satisfaction.

[1241] Step 7:

[1242] The server sends the generated proposal to the terminal. This proposal includes coupon links and details of the points strategy.

[1243] Step 8:

[1244] The device receives the suggestion and displays it to the user. Display formats include in-app pop-ups and notification bars.

[1245] Step 9:

[1246] Users review the suggestions and, if necessary, use coupons or implement methods to increase their points. This not only improves user convenience but also significantly increases the value of using the application.

[1247] (Example 2)

[1248] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1249] Traditional coupon and points suggestion systems lacked the ability to provide optimal suggestions that took into account the user's emotional state, resulting in suggestions that were not always highly satisfying for users. Furthermore, the lack of customization based on the user's real-time emotional state reduced the value of the application, making it difficult to improve user convenience and satisfaction.

[1250] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[1251] In this invention, the server includes means for collecting emotional data by analyzing data points such as the user's facial expressions, tone of voice, and input speed using an emotion engine that recognizes the user's emotional state; means for customizing suggestions generated based on the collected emotional data; and means for generating multiple suggestion candidates and selecting the suggestion deemed optimal based on the user's past patterns and emotional state. This enables optimal suggestions tailored to the user's emotional state, improving user convenience and satisfaction, and significantly increasing the value of the application.

[1252] "User-operated devices" refer to digital devices that users can directly operate, including smartphones, tablets, and personal computers.

[1253] A "Request a Suggestion button" is a UI element that allows a user to request a specific suggestion by clicking or tapping it.

[1254] "User data" refers to information about a user's behavior and status, such as their purchase history, coupon usage history, and point balance.

[1255] A "server" is a central processing unit that receives and analyzes user data and sentiment data, and sends the results back to the user's terminal.

[1256] "AI analysis" is the process of analyzing data using artificial intelligence technology to extract patterns and trends.

[1257] An "emotion engine" is software or hardware that analyzes data points such as a user's facial expressions, voice tone, and input speed to identify the user's emotional state.

[1258] "Emotional data" refers to information that represents the user's emotional state, collected by the emotion engine, and includes stress levels, satisfaction levels, and other factors.

[1259] "Candidate proposals" are multiple proposals generated by the server, and they serve as the basis for selecting the optimal proposal.

[1260] A "strategy to increase points" refers to a strategy for efficiently using a user's point balance, and includes methods for increasing points under specific conditions.

[1261] This invention is a system that provides optimal coupon and point-earning methods on a user-operated terminal, and by combining it with an emotion engine, it provides customized suggestions according to the user's emotional state. The following describes how this system is specifically implemented.

[1262] The system starts operating when the user interacts with the "Request Suggestion" button within the application. Upon pressing the Request Suggestion button, the device first begins collecting user data. This data includes purchase history, coupon usage history, and point balance. This data is retrieved from the device's built-in SQL database or other data storage system.

[1263] Next, the device also collects user emotion data using an emotion engine. The emotion engine analyzes multiple data points such as the user's facial expressions, voice tone, and input speed to recognize the user's emotional state. The emotion engine includes AI models for facial recognition and voice analysis, which utilize common libraries (e.g., Microsoft Azure's emotion recognition API).

[1264] The collected user data and sentiment data are sent from the device to the server. This transmission uses HTTP POST requests via a REST API. The server analyzes the received data and generates optimal suggestions for the user using a generative AI model (e.g., TensorFlow). Sentiment data, in particular, is used in suggestion generation as follows:

[1265] If a user is feeling stressed, suggestions to help them relax (such as relaxation coupons) are automatically generated.

[1266] If a user is satisfied, suggestions (such as a strategy to increase points) are provided to further enhance that satisfaction.

[1267] The suggestions generated by the server, such as coupon recommendations and methods for increasing points, are sent to the user's device. These suggestions are also sent via a REST API. The device displays the suggestions received from the server to the user. Display methods include in-app pop-ups and notification bars.

[1268] As a concrete example, suppose a user presses the "Request Suggestion" button. If the AI ​​determines that this user has purchased many food items in the past and is currently in a relatively relaxed emotional state, it will recommend a "food discount coupon" to this user. It will also suggest a points strategy to help the user effectively use their current points balance, such as "Get double points if you purchase clothing this week."

[1269] This system is highly beneficial to users because it uses an emotion engine to understand the user's real-time emotional state and provide optimal suggestions based on that understanding. Users can receive suggestions that match their emotional state, thereby significantly increasing the value of using the application. In this way, the system of the present invention, which incorporates an emotion engine, enhances user convenience and increases the frequency of application use.

[1270] Examples of prompts for generative AI models:

[1271] If a user clicks the "Request Suggestion" button and data indicates a high proportion of food purchases and a relaxed emotional state, generate the most suitable suggestions. Determine if food discount coupons or double points strategies are applicable, and provide example suggestions.

[1272] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1273] Step 1:

[1274] The user presses the "Request a Proposal" button.

[1275] Input: User actions

[1276] Output: Request for Proposal event occurs

[1277] Description: The system starts operating when the user presses the "Request Suggestion" button within the application. This action triggers the next step.

[1278] Specific action: The user clicks or taps the "Request Suggestion" button on the application screen, and the application detects this event.

[1279] Step 2:

[1280] User data collection

[1281] Input: "Request for Proposal" event

[1282] Output: Collected user data (purchase history, coupon usage history, point balance, etc.)

[1283] Description: When the device receives a "Request for Proposal" event, it begins collecting user data. This includes purchase history, coupon usage history, and points balance.

[1284] Specific operation: The application on the device retrieves the relevant user data from its built-in database (e.g., an SQL database). For example, it might execute a database query based on the user ID to retrieve past purchase history or coupon usage history.

[1285] Step 3:

[1286] Collection of emotional data

[1287] Input: "Request for Proposal" event

[1288] Output: Collected emotional data (facial expressions, voice tone, input speed, etc.)

[1289] Description: The device uses an emotion engine to collect user emotion data. The emotion engine analyzes data points such as the user's facial expressions, voice tone, and typing speed.

[1290] Specific operation: The device's camera and microphone are used to capture the user's facial expressions and voice, which are then sent to an emotion engine (e.g., an emotion recognition API) for analysis.

[1291] Step 4:

[1292] Sending data to the server

[1293] Input: User data and sentiment data

[1294] Output: Data transmission to the server is complete.

[1295] Description: Collected user data and sentiment data are sent from the device to the server. The transmission is done via an HTTP POST request.

[1296] Specific operation: The device converts the collected data into JSON format and sends it to the server via an HTTP POST request. A REST API is used for this, and the data is sent to a specified endpoint on the server.

[1297] Step 5:

[1298] Server-based data analysis and proposal generation

[1299] Input: Received user data and sentiment data

[1300] Output: Generated optimal proposals

[1301] Description: The server analyzes the received data. Using user data and sentiment data, the AI ​​model generates the most suitable suggestions for the user.

[1302] Specific operation: The server uses AI models such as TensorFlow to analyze data based on pre-trained algorithms. It then generates optimal coupons and point strategies tailored to emotional states and past behavioral patterns.

[1303] Step 6:

[1304] Sending proposals from the server to the terminal

[1305] Input: Generated optimal proposal

[1306] Output: Suggestion sent to terminal complete.

[1307] Description: Converts the generated optimal suggestions into JSON format and sends them from the server to the terminal.

[1308] Specific operation: The server packages the proposed data in JSON format and sends it to the specified endpoint on the terminal via an HTTP POST request.

[1309] Step 7:

[1310] Suggestion display on the device

[1311] Input: Proposal data from the server

[1312] Output: Suggestions displayed to the user

[1313] Description: The device analyzes received suggestion data and displays it to the user. Pop-ups and notification bars are used for display.

[1314] Specific operation: The application on the device receives the suggestion data and displays it in the user interface. The user can then view the suggested coupons and point strategies on the screen.

[1315] (Application Example 2)

[1316] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1317] When users purchase products online, there is a need to improve the purchasing experience by providing optimal coupons and point-boosting strategies. However, conventional systems do not take into account the user's emotional state and only offer uniform suggestions, which can lead to decreased user satisfaction. It is necessary to solve this problem and provide optimal suggestions that respond to each user's emotional state, thereby realizing a more personalized service.

[1318] The specific processing performed by the specific 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 collecting emotional data using the camera and microphone of the terminal to analyze the user's emotional state, means for customizing suggestions based on the emotional data, means for providing a suggestion request button that can be operated by the user on the terminal operated by the user, means for collecting user data when the suggestion request button is operated, means for transmitting the collected user data to the server, means for receiving the user data on the server and performing AI analysis based on the user data, means for generating the optimal suggestion for the user based on the AI ​​analysis, means for transmitting the generated suggestion to the terminal, and means for displaying the suggestion on the terminal. This makes it possible to provide customized suggestions that take into account the user's emotional state.

[1319] The "Request Suggestion Button" is an interface that allows users to request the system to offer coupons or point increase suggestions.

[1320] "User data" refers to information about the user, such as purchase history, coupon usage history, and point balance.

[1321] "Emotional data" refers to information about a user's emotional state, analyzed from facial expressions, voice tone, input speed, and other data captured using cameras and microphones.

[1322] A "camera" is a hardware device used to capture and analyze a user's facial expressions.

[1323] A "microphone" is a hardware device used to capture and analyze the tone of a user's voice.

[1324] "AI analysis" refers to data processing using artificial intelligence, where a server uses user data and sentiment data to generate optimal suggestions.

[1325] A "proposal" is a customized purchase promotion plan offered to users, such as coupons or point increase strategies.

[1326] A "terminal" is a device (such as a smartphone or tablet) that a user operates to access and use a system.

[1327] A "server" is a computing environment that receives user data and sentiment data, performs AI analysis to generate suggestions, and sends them to the terminal.

[1328] A "Request for Suggestions" is an action taken by a user to request suggestions from the system, such as coupons or point increases, by clicking a suggestion request button.

[1329] This invention relates to a system that provides optimal coupon and point-earning methods on a user-operated terminal. This system can provide customized suggestions to the user by analyzing the user's emotional state. The following describes embodiments for carrying out this invention.

[1330] First, a suggestion request button is displayed on the user's device. When the user presses this button, the device collects user data. This user data includes information such as purchase history, coupon usage history, and point balance. In addition, the user's emotional data is collected using an emotion engine. Emotional data is obtained by analyzing the user's facial expressions, voice tone, input speed, etc., using the camera and microphone.

[1331] The collected user data and emotional data are sent to the server. The server performs AI analysis based on the received data and generates optimal suggestions. For example, if the AI ​​determines that the user is currently in a relaxed emotional state, it will recommend a relaxation-related coupon. It also selects the most suitable suggestions for the user based on past patterns.

[1332] The generated suggestions are sent to the device and displayed to the user via pop-ups, notification bars, etc. This allows the user to receive the most suitable suggestions based on their current emotional state. The suggestions also include strategies to increase the user's points based on their point balance.

[1333] The following hardware and software will be used to implement the system:

[1334] Hardware: Smartphone camera and microphone

[1335] software:

[1336] Emotion engine (uses OpenCV and a pre-trained Keras model for facial expression analysis)

[1337] AI model (suggestion generation customized with TensorFlow)

[1338] Specific example:

[1339] When a user presses the suggestion request button, the device uses its camera and microphone to capture the user's face and voice and collect emotional data. For example, if data shows the user has purchased many food items in the past and the AI ​​determines that the user is currently relaxed, it will recommend a "food discount coupon" to this user. The suggestion is notified to the user's device, and the user can use the coupon immediately.

[1340] Example of a prompt:

[1341] "Create a Python program that retrieves purchase history and recommends the most suitable coupons and point-boosting strategies based on the user's current emotional state."

[1342] This invention is highly beneficial to users because it can grasp their emotional state in real time and provide customized suggestions based on that state. Furthermore, since users can receive suggestions that match their emotional state, the value of the application is expected to increase significantly, and the frequency of use is also expected to increase.

[1343] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1344] Step 1:

[1345] The user clicks the suggestion request button.

[1346] Specific action: The user taps the suggestion request button displayed on the application screen of their device.

[1347] Input: User actions

[1348] Output: Event indicating that the Request for Suggestion button was pressed.

[1349] Step 2:

[1350] The device collects user data and sentiment data.

[1351] Specific operation: The device retrieves user data such as purchase history, coupon usage history, and point balance from its internal database. It also activates the camera and microphone and uses an emotion engine to analyze the user's facial expressions, voice tone, and input speed.

[1352] Input: Event indicating that the Request for Suggestion button was pressed.

[1353] Output: Acquired user data and sentiment data

[1354] Step 3:

[1355] The device sends the collected data to the server.

[1356] Specific operation: The device sends the acquired user data and sentiment data to the server via the network.

[1357] Input: User data and sentiment data

[1358] Output: User data and sentiment data sent to the server

[1359] Step 4:

[1360] The server receives user data and sentiment data and performs AI analysis.

[1361] Specific operation: The server inputs the received data into the AI ​​analysis engine and generates optimal suggestions based on the user's emotional state and past behavioral patterns.

[1362] Input: Received user data and sentiment data

[1363] Output: Suggestions generated by AI analysis (coupons and methods to increase points)

[1364] Step 5:

[1365] The server sends the generated proposal to the terminal.

[1366] Specific operation: The server sends the generated suggestions to the user's terminal via the network.

[1367] Input: Proposals generated by AI analysis

[1368] Output: Suggestions sent to the terminal

[1369] Step 6:

[1370] The device notifies and displays suggestions to the user.

[1371] Specific action: The device displays the received suggestion to the user via a pop-up notification or notification bar.

[1372] Input: Submitted proposal

[1373] Output: Suggestions displayed to the user (coupons, methods for increasing points, etc.)

[1374] Step 7:

[1375] The user reviews and uses the suggestions they receive.

[1376] Specific actions: The user reviews the displayed suggestions and either uses a coupon or implements a strategy to increase points.

[1377] Input: Suggestions displayed to the user

[1378] Output: User actions based on the suggestion (e.g., purchase, point usage)

[1379] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1380] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1381] 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 this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[1382] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1383] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[1384] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[1385] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[1386] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[1387] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[1388] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[1389] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[1390] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[1391] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[1392] 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.

[1393] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[1394] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[1395] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[1396] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[1397] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[1398] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[1399] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[1400] The following is further disclosed regarding the embodiments described above.

[1401] (Claim 1)

[1402] A means for providing a user-operable suggestion request button on a user-operated terminal,

[1403] When the suggestion request button is pressed, a means of collecting user data,

[1404] A means of sending collected user data to a server,

[1405] A means for receiving user data on a server and performing AI analysis based on said data,

[1406] A means of generating optimal suggestions for users based on AI analysis,

[1407] A means of sending the generated proposal to the terminal,

[1408] A means of displaying the proposal on the terminal,

[1409] A system that includes this.

[1410] (Claim 2)

[1411] The system according to claim 1, comprising means for a server to generate multiple candidate suggestions and select the suggestion that is deemed optimal based on the user's past patterns.

[1412] (Claim 3)

[1413] The system according to claim 1, comprising means for a strategy to increase points based on the user's point balance.

[1414] "Example 1"

[1415] (Claim 1)

[1416] A means for providing a user-operable suggestion request button on a user-operated terminal,

[1417] When the suggestion request button is pressed, a means of collecting user data,

[1418] A means of sending collected user data to a server,

[1419] A means for receiving user data on a server and performing AI analysis using a generated AI model based on said data,

[1420] A means of generating optimal suggestions for the user based on AI analysis,

[1421] A means of sending the generated proposal to the terminal,

[1422] A means of displaying the proposal on the terminal,

[1423] ...

[1424] A system that includes this.

[1425] (Claim 2)

[1426] The system according to claim 1, comprising means for a server to generate multiple candidate proposals and select the proposal that is determined to be optimal based on past patterns.

[1427] (Claim 3)

[1428] The system according to claim 1, comprising means for a strategy to increase points based on the user's point balance.

[1429] "Application Example 1"

[1430] (Claim 1)

[1431] A means for providing a user-operable suggestion request button on a user-operated terminal,

[1432] When the suggestion request button is pressed, a means of collecting user data,

[1433] A means of sending collected user data to a server,

[1434] A means for receiving user data on a server and performing AI analysis based on said data,

[1435] A means of generating optimal suggestions for users based on AI analysis,

[1436] A means of sending the generated proposal to the terminal,

[1437] A means of displaying the proposal on the terminal,

[1438] A method for smart glasses to read product information in real time within a physical store,

[1439] A means of displaying suggestions on the terminal based on the scanned product information and user data,

[1440] A system that includes this.

[1441] (Claim 2)

[1442] The system according to claim 1, comprising means for a server to generate multiple candidate suggestions and select the suggestion that is deemed optimal based on the user's past patterns.

[1443] (Claim 3)

[1444] The system according to claim 1, comprising means for a strategy to increase points based on the user's point balance.

[1445] "Example 2 of combining an emotion engine"

[1446] (Claim 1)

[1447] A means for providing a user-operable suggestion request button on a user-operated terminal,

[1448] When the suggestion request button is pressed, a means of collecting user data,

[1449] A means of sending collected user data to a server,

[1450] A means for receiving user data on a server and performing AI analysis based on said data,

[1451] A means of generating optimal suggestions for users based on AI analysis,

[1452] A means of sending the generated proposal to the terminal,

[1453] A means of displaying the proposal on the terminal,

[1454] A means of collecting emotional data by using an emotion engine that recognizes the user's emotional state and analyzing data points such as the user's facial expressions, voice tone, and input speed,

[1455] A means to customize suggestions generated based on sentiment data,

[1456] A system that includes this.

[1457] (Claim 2)

[1458] The system according to claim 1, comprising means for a server to generate multiple candidate suggestions and select the suggestion that is deemed optimal based on the user's past patterns and emotional state.

[1459] (Claim 3)

[1460] The system according to claim 1, comprising means for a strategy to increase points based on the user's point balance.

[1461] "Application example 2 of combining emotional engines"

[1462] (Claim 1)

[1463] A means for providing a user-operable suggestion request button on a user-operated terminal,

[1464] When the suggestion request button is pressed, a means of collecting user data,

[1465] A means of sending collected user data to a server,

[1466] A means for receiving user data on a server and performing AI analysis based on said data,

[1467] A means of generating optimal suggestions for users based on AI analysis,

[1468] A means of sending the generated proposal to the terminal,

[1469] A means of displaying the proposal on the terminal,

[1470] A means of collecting emotional data using the device's camera and microphone to analyze the user's emotional state,

[1471] A means of customizing suggestions based on emotional data,

[1472] A system that includes this.

[1473] (Claim 2)

[1474] The system according to claim 1, comprising means for a server to generate multiple candidate suggestions and select the suggestion that is deemed optimal based on the user's past patterns.

[1475] (Claim 3)

[1476] The system according to claim 1, comprising means for a strategy to increase points based on the user's point balance. [Explanation of symbols]

[1477] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for providing a user-operable suggestion request button on a user-operated terminal, When the suggestion request button is pressed, a means of collecting user data, A means of sending collected user data to a server, A means for receiving user data on a server and performing AI analysis based on said data, A means of generating optimal suggestions for users based on AI analysis, A means of sending the generated proposal to the terminal, A means of displaying the proposal on the terminal, A system that includes this.

2. The system according to claim 1, comprising means for a server to generate multiple candidate suggestions and select the suggestion that is deemed optimal based on the user's past patterns.

3. The system according to claim 1, comprising means for a strategy to increase points based on the user's point balance.

Images