system

Abstract

We provide the system. [Solution] A means of registering point card information, A means of managing usage history and expiration dates based on registered point card information, A method that uses an artificial intelligence model to analyze usage history and suggest point cards that should be used preferentially, A means of analyzing market trends using aggregated usage data and generating reports, 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 method for controlling a persona chatbot, which is 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 character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. 【Prior Art Documents】 【Patent Documents】 【0003】 【Patent Document 1】 Japanese Patent Application Laid-Open No. 2022-180282 【Summary of the Invention】 【Problems to be Solved by the Invention】 【0004】 Currently, users who manage a large number of point cards on a smartphone have difficulty grasping the usage history of each card and the expiration date of points. In addition, it is difficult to determine which card should be preferentially used, resulting in the problem that points cannot be utilized efficiently. Also, on the enterprise side, it is also a problem that the point usage trends of consumers cannot be accurately grasped and improvement measures cannot be taken. 【Means for Solving the Problems】 【0005】 This invention provides a system that centrally registers and manages point card information and analyzes the usage history of individual users using an artificial intelligence model to suggest point cards that should be used preferentially. Furthermore, by aggregating usage data and analyzing and reporting market trends, it provides useful information for companies to develop strategies based on consumer behavior. As a result, users can efficiently utilize points, and companies can implement data-driven marketing strategies. 【0006】 "Point card information" refers to data such as identification information, point balance, expiration date, and usage history related to various point cards owned by the user. 【0007】 An "artificial intelligence model" refers to a collection of algorithms and programs that learn from large amounts of data, recognize patterns, and perform predictions and classifications. 【0008】 "Usage history" refers to data that records when and how much a user has used a particular point card. 【0009】 "Expiration date" refers to the deadline by which points accumulated on a loyalty card can be used. 【0010】 "Proposal" refers to the act of showing users information that encourages them to prioritize the use of a specific loyalty card or take certain actions, based on the analysis results. 【0011】 "Aggregation" refers to the process of integrating and summarizing multiple data sets. 【0012】 "Market trends" refer to changes in consumer behavior and the market under specific conditions, and include data that shows trends and fluctuations in economic activity. 【0013】 A "report" refers to a document or data set that compiles information based on specific analysis results or survey findings. [Brief explanation of the drawing] 【0014】 [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It 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] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 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 Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined. 【Embodiments for Carrying Out the Invention】 【0015】 An example of an embodiment of the system according to the technology of the present disclosure will be described below with reference to the accompanying drawings. 【0016】 First, the terms used in the following description will be explained. 【0017】 In the following embodiments, the 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. 【0018】 In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor. 【0019】 In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like. 【0020】 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). 【0021】 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." 【0022】 [First Embodiment] 【0023】 Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment. 【0024】 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. 【0025】 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). 【0026】 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. 【0027】 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. 【0028】 The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form 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. 【0029】 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. 【0030】 Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14. 【0031】 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. 【0032】 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. 【0033】 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. 【0034】 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". 【0035】 This invention is a point card management system implemented as a smartphone application, which functions by communicating data between a server and a terminal. Users register information about their point cards through the application. This includes the card name, number, store information, and associated login information. The registered information is sent from the terminal to the server and stored in the server's database. 【0036】 The server manages the usage history and expiration dates of each point card based on the registered point card information. This allows the server to constantly track which point cards each user uses, how often, and which are about to expire. Based on an artificial intelligence model, the server analyzes user usage history and extracts specific behavioral patterns to suggest the most suitable point card usage for each user. 【0037】 As a concrete example, if a user frequently shops at a particular store, the server will prioritize suggesting a point card that offers benefits at that store. For instance, if the user has points that are about to expire, the server will suggest using those points first. This allows the user to utilize their points efficiently without waste. 【0038】 Furthermore, the server statistically aggregates data collected from all users to analyze which loyalty cards are used most frequently and under what times and conditions. This analysis is provided to companies and used as a reference for marketing strategies and promotional activities. This enables companies to effectively plan and implement campaigns tailored to specific consumer behaviors. 【0039】 In this way, users can enjoy convenience, and companies can implement data-driven strategies to provide better customer experiences and increase revenue. 【0040】 The following describes the processing flow. 【0041】 Step 1: 【0042】 The user launches the smartphone app and goes to the point card information registration screen. The user enters the card name, number, store name, and login information. 【0043】 Step 2: 【0044】 The terminal structures the point card information entered by the user into data packets and sends them to the server using a secure communication protocol. 【0045】 Step 3: 【0046】 The server analyzes the received point card information and registers it in the database. During this process, it checks for duplicate entries and registers them as new data. 【0047】 Step 4: 【0048】 The server periodically checks the usage history and expiration date of each point card in the database and updates them as needed. 【0049】 Step 5: 【0050】 The server uses an AI model to analyze the user's usage history and extract specific behavioral patterns. Based on this analysis, it generates suggestions for loyalty cards that should be used preferentially. 【0051】 Step 6: 【0052】 The terminal notifies the user of the suggested results received from the server. Based on the notified information, the user decides which point card to use. 【0053】 Step 7: 【0054】 Users present the proposed point card when making purchases or using services, and then use the points. 【0055】 Step 8: 【0056】 The server aggregates usage history data collected from all users and performs market trend analysis. This result is provided to companies and used in developing marketing strategies. 【0057】 (Example 1) 【0058】 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." 【0059】 Modern consumers possess numerous loyalty programs and points systems, but lack the means to efficiently manage and utilize them. As a result, it is difficult to effectively use points, and consumers often let many points expire before their expiration date. Furthermore, companies lack the information necessary to make appropriate decisions about how to promote products to customers, making it difficult to develop effective marketing strategies. 【0060】 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. 【0061】 In this invention, the server includes means for recording information about point programs, means for controlling usage history and end date and time based on the recorded point program information, and means for analyzing usage history using a machine learning model and suggesting point programs that should be used preferentially. This enables consumers to efficiently manage their points and companies to build effective marketing strategies based on customer behavior. 【0062】 A "points system" is a means of recording value information that customers receive for purchasing goods or services, which allows them to receive benefits and discounts. 【0063】 A "machine learning model" is a type of algorithm used for data analysis, which predicts future behavior and patterns based on past data. 【0064】 "To control" means to organize and manage information, thereby facilitating its use and analysis according to a specific purpose. 【0065】 The "end date" refers to the final date within the period during which the points program is valid, and the use of points will be restricted after that date. 【0066】 "Presenting" means clearly showing users information such as analysis results and recommended actions, and is done to support their decision-making. 【0067】 This invention is a system for efficiently managing and utilizing a user's numerous point programs. The user inputs information about their point programs through a smart device application. This information includes the program name, identification number, provider information, and login-related information. 【0068】 The terminal uses a secure communication protocol such as SSL / TLS to send the entered point medium information to the server. After receiving this, the server verifies its integrity and records the data in a database. The database is used to manage usage history and end dates and times, and systematically structures the information for each point medium. 【0069】 The server uses generative AI models such as GPT-3 (registered trademark) to analyze usage history data. This analysis extracts user behavior patterns and determines which point programs should be prioritized. Specifically, the AI ​​model considers the frequency of use and expiration date of each program to make appropriate usage suggestions. These suggestions are communicated to the user in the form of, for example, "Your points will expire soon, so we recommend using them as a priority." 【0070】 Furthermore, the server analyzes the integrated data and provides businesses with market trends. Businesses can leverage this insight to develop the most effective promotions for consumers. For example, a possible prompt might be, "Please report the most frequently used points-based payment method in the last 30 days." 【0071】 This system allows users to make effective use of their points, and enables companies to develop more precise marketing strategies. 【0072】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0073】 Step 1: 【0074】 The user enters information about the point card using an application on their smart device. This information includes the card name, identification number, provider information, and login-related information. This information is sent to the application and prepared as a data packet within the device. 【0075】 Step 2: 【0076】 The terminal sends prepared data packets to the server using a secure communication protocol such as SSL / TLS. This communication maintains the security and accuracy of the input information. After receiving the information, the server verifies its integrity and records it in the database. As a result, the information is systematically stored and prepared for future queries. 【0077】 Step 3: 【0078】 The server initiates a process to manage usage history and expiration dates based on point media information in the database. Data on the retention period and usage status of each point media is used as input, and updated management information is generated as output. Specifically, usage counts and expiration reminders are set. 【0079】 Step 4: 【0080】 The server analyzes usage history data using a generative AI model. This analysis aims to extract behavioral patterns, and the AI ​​makes predictions based on past data. Usage history and end date / time information are provided as input, and the output is a suggestion of point-earning media that should be used preferentially. 【0081】 Step 5: 【0082】 The server notifies the user of the generated suggestions. This notification includes recommended ways to use points and helps the user make decisions. The content of the notification may be displayed in the form of, for example, "Your points will expire soon, so we recommend using them as soon as possible." 【0083】 Step 6: 【0084】 The server statistically analyzes overall usage data and generates market trend reports. The input is usage data from all users within a specified period, and the output generates new insights that can be used for marketing. This information is provided to companies, contributing to the development of effective promotions. 【0085】 (Application Example 1) 【0086】 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." 【0087】 Traditional point reward services made it difficult for users to efficiently utilize the most suitable service at the optimal time. Furthermore, the manual management of point card information and usage history was cumbersome, often resulting in points expiring. Additionally, the lack of integration between electronic money transfer services and point reward services made it difficult for users to select the most appropriate service for each payment. 【0088】 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. 【0089】 In this invention, the server includes means for registering point-granting service information, means for managing usage history and expiration dates based on the registered service information, means for analyzing usage history using a machine learning algorithm and suggesting the most suitable service to use, and means for integrating with an electronic money transfer service and automatically applying the most suitable service at the time of payment. As a result, users can always automatically utilize the most suitable point-granting service, reduce point waste, and enjoy the best possible customer experience. 【0090】 "Point accrual service information" refers to point-related data for users to record and manage, including information such as the point issuer, the number of points, and the expiration date. 【0091】 A "machine learning algorithm" is a computational method used to learn from data and identify patterns. In this invention, it is a technology applied to analyze a user's usage history and provide optimal suggestions. 【0092】 An "electronic fund transfer service" is a service that enables the sending and receiving of funds via the internet or mobile devices. In this invention, it is integrated with a point accrual service and has a function to automatically adjust the optimal use of points at the time of payment. 【0093】 "Usage history" refers to records showing a user's past use of point-granting services, and is data used to analyze user behavior trends based on this history. 【0094】 "Automatic suggestion" is a function that analyzes the user's past behavior patterns and proactively presents the optimal option. In this invention, it is used to select the most suitable point reward service at the time of payment. 【0095】 The system for implementing this invention functions by registering point-granting service information on the user's terminal and transmitting it to a server via data communication. This server stores the point-granting service information received from the user's terminal in a database and manages the usage history and expiration date. Furthermore, it uses a machine learning algorithm to analyze the user's usage history and propose the optimal service. Specifically, it integrates with electronic money transfer services at the time of payment and automatically applies the optimal service according to the user's payment behavior. 【0096】 The hardware of this system will consist of mobile devices such as the user's smartphone or tablet, and the servers will utilize cloud-based servers (e.g., AWS®, Google® Cloud). For software, programming languages ​​such as Python and Node.js will be used on the server side, PostgreSQL will be used as the database, and frameworks such as Tensorflow® and PyTorch will be used for machine learning. 【0097】 For example, if a user shops at a specific retailer on a daily basis, the system can recognize this behavioral pattern and suggest services that allow users to use their points more efficiently at that retailer. This enables users to make better use of their points. An example of a prompt to the generating AI model would be, "Based on the most recent purchase history, please suggest the best point-earning service to use." 【0098】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0099】 Step 1: 【0100】 The user operates the terminal and registers information about the point accrual service. The user launches the application and enters information such as the service name, number of points, and expiration date. The entered data is temporarily stored on the terminal and prepared to be sent to the server. 【0101】 Step 2: 【0102】 The terminal sends the registered points information to the server. The server adds the received information to its database and compares it with existing data. As a result, the new information is recorded in the database and becomes subject to management of usage history and expiration date. 【0103】 Step 3: 【0104】 The server uses a generative AI model to analyze usage history in the database. Specifically, it applies machine learning algorithms to extract user usage patterns and detect specific consumption behaviors. The input data is the usage history of all users, and the output is the behavioral patterns of each user. 【0105】 Step 4: 【0106】 Based on the analysis results, the server generates suggestions for the optimal use of points for the user. These suggestions are sent to the user's terminal, recommending priority use of points nearing expiration and suggesting use at specific stores. This allows the server to provide users with the most efficient point management. 【0107】 Step 5: 【0108】 The terminal notifies the user of suggestions received from the server. The user can review this notification and follow the suggested optimal way to use their points. The output of this process is reduced spending and effective use of points for the user. 【0109】 Step 6: 【0110】 When a user makes a purchase using the electronic money transfer service, the terminal automatically applies the optimal points based on suggestions received from the server. As a result, the user can benefit from optimized point usage at the time of payment. 【0111】 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. 【0112】 This invention combines an emotion engine with a point card management system to provide flexible services that respond to user emotions. The server, terminal, and emotion engine work together to improve user convenience and support more appropriate point card usage. 【0113】 Users register their loyalty card information using a smartphone app. This information is sent from the device to a server and stored in a database on the server. The server uses this information to manage the usage history and expiration dates of each card. Furthermore, the server uses an artificial intelligence model to analyze this usage history and determine which loyalty cards should be prioritized. The generated recommendations are then communicated to the user via the device. 【0114】 The emotion engine utilizes various data collected from the device to analyze the user's emotions. This includes the user's facial expressions, voice tone, and input behavior. The emotion engine identifies the user's current emotional state, and the suggested content is adjusted accordingly. 【0115】 For example, if the emotion engine analyzes the user's emotion as "excited," the server can generate proactive suggestions tailored to their excitement, such as a message saying, "Get double bonus points now!" On the other hand, if the user is determined to be in a "calm" state, calm suggestions regarding important expiring points will be made. 【0116】 Furthermore, the emotion engine learns long-term behavioral patterns based on the user's emotions and feeds this back to the server. This allows the server to more accurately predict user behavior and continuously optimize loyalty card usage according to the user's emotional state. Through these functions, users can improve their quality of life, and companies can build more effective marketing strategies. 【0117】 The following describes the processing flow. 【0118】 Step 1: 【0119】 The user launches the smartphone app and opens the screen to register their loyalty card information. Here, they enter their card details. 【0120】 Step 2: 【0121】 The terminal transmits the loyalty card information entered by the user to the server. This information includes the card name, number, store name, and login information. 【0122】 Step 3: 【0123】 The server stores the received point card information in a database. It checks for duplicate registrations and adds new registrations to the database. 【0124】 Step 4: 【0125】 The server periodically checks the database and updates the usage history and expiration date of each point card, ensuring that the information is always up-to-date. 【0126】 Step 5: 【0127】 The emotion engine collects data such as the user's facial expressions, voice tone, and input behavior obtained from the device, and analyzes the user's current emotional state. 【0128】 Step 6: 【0129】 Based on the analyzed emotional data, the server works in conjunction with an AI model to generate loyalty card usage suggestions tailored to the user's emotions. For example, if active speech is detected, it will suggest limited offers. 【0130】 Step 7: 【0131】 The device notifies the user of suggestions sent from the server. These suggestions include personalized messages based on emotions. 【0132】 Step 8: 【0133】 Users make purchases and use services based on the suggested loyalty cards. In this process, emotionally-driven offers may be utilized. 【0134】 Step 9: 【0135】 The server aggregates usage data from all users and analyzes usage history based on sentiment. This information is then provided to companies to help them develop marketing strategies. 【0136】 (Example 2) 【0137】 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 will be referred to as the "terminal." 【0138】 Traditional point systems have limitations in making suggestions based on user usage history and expiration date information, making it difficult to provide flexible services that take into account users' psychological states and emotions. Furthermore, because point usage suggestions that are tailored to the user are not provided, it is difficult to effectively utilize points, and companies are unable to formulate optimal marketing strategies. 【0139】 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. 【0140】 In this invention, the server includes means for registering point information, means for managing history and expiration dates, means for analyzing history using a machine learning model and suggesting preferential point usage, and means for identifying the user's psychological state using sentiment analysis technology and adjusting the suggested content. This enables appropriate point usage suggestions according to the user's emotional state, realizing effective point utilization and strategic marketing for companies. 【0141】 "Point information" refers to data related to the user's point card, including card number, expiration date, and accumulated points. 【0142】 "History" refers to a record of points earned and used by a user in the past. 【0143】 "Expiration date" refers to information indicating the period during which points are valid, including the expiration date. 【0144】 A "machine learning model" is an artificial intelligence technology that learns patterns based on data and uses them to predict future actions and choices. 【0145】 "Emotional analysis technology" refers to technologies that analyze a user's emotions and identify their psychological state, and includes techniques such as facial recognition and voice tone analysis. 【0146】 "Suggested content" refers to specific advice or messages generated by the server to encourage users to use their points. 【0147】 This invention integrates emotion analysis technology into a point management system, enabling flexible point usage suggestions tailored to the user's emotional state. 【0148】 Users register their points information using a smartphone app. The device sends this information to a server, which stores the registered information in a database. This allows for effective management of point history and expiration dates. 【0149】 The server uses a generative AI model to analyze historical data and gain insights to suggest appropriate point usage to users. This AI model learns past usage patterns and proposes the most effective way for users to use their point cards. 【0150】 The terminal monitors user behavior and uses emotion analysis technology to determine emotional states from facial expressions, voice tone, and input actions. By utilizing human interface technology, more accurate emotional data can be obtained. The server receives this data and adjusts the suggested content according to the user's emotional state. 【0151】 Specifically, if the system analyzes that a user is in an "excited" state, thrilled about a new adventure, the server can generate proactive offers such as "Get double bonus points now!" On the other hand, if the user is in a calm state, the server will offer more subdued suggestions such as "Your points will expire soon. Take your time to prepare and use them." 【0152】 This system allows users to optimally manage their points assets, and enables companies to develop marketing strategies tailored to user needs. 【0153】 An example of a prompt message is: "Analyze the user's current emotional state and generate appropriate suggestions for using the loyalty card based on that." 【0154】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0155】 Step 1: 【0156】 The user uses the device 【0157】 The user launches the smartphone app and registers by entering their points information. The entered information includes the points card number, expiration date, and accumulated points, and the device prepares to send the data to the server accordingly. The output of this step is the registration dataset. 【0158】 Step 2: 【0159】 The terminal sends data to the server. 【0160】 The terminal sends point information to the server. This data is encrypted and transmitted securely over the network. The server receives this data and stores it in its database. The output is a record of the stored point information. 【0161】 Step 3: 【0162】 The server manages history and expiration dates. 【0163】 The server retrieves point information from the database and performs functions to manage usage history and expiration dates. It processes data regarding point acquisition, usage, and the remaining period for today, keeping the information up-to-date. The output here is the updated point history and expiration information. 【0164】 Step 4: 【0165】 The server analyzes the data using an AI model. 【0166】 The server uses a generation AI model to analyze historical data and identify points that should be prioritized. The AI ​​is prompted with questions, and the model outputs analysis results. Based on these results, the server generates suggestions. The output consists of specific suggestions. 【0167】 Step 5: 【0168】 The device collects emotional data. 【0169】 As the user interacts with the app, the device analyzes facial expressions, voice tone, and input speed to collect emotional data. Input is real-time data obtained from the user interface, and output is the collected emotional data. 【0170】 Step 6: 【0171】 The device sends data to the emotion engine. 【0172】 Emotional data is sent to the emotion engine for analysis. This data is analyzed by the engine to identify the user's emotional state. The output of this step is the evaluation result of the user's emotional state. 【0173】 Step 7: 【0174】 The server generates suggestions based on emotions. 【0175】 The server receives the analysis results from the emotion engine and adjusts the suggestions based on them. It generates promotions and point usage advice tailored to the user's emotions, such as "excitement" or "calmness," and sends the results to the terminal. The output is the adjusted suggestions. 【0176】 Step 8: 【0177】 The device notifies the user. 【0178】 The terminal notifies the user of the suggestions received from the server. Notification methods include pop-ups and real-time feedback, allowing the user to review suggestions relevant to their situation. The output of this step is the information provided to the user and their feedback. 【0179】 (Application Example 2) 【0180】 Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal". 【0181】 A challenge with point programs is the lack of appropriate approaches based on user emotions, which prevents maximizing user purchasing intent. Furthermore, the inability to effectively use points before their expiration date is a problem. To address this, it is necessary to analyze users' emotional states and provide reward information tailored to those emotions to promote the use of point programs. 【0182】 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. 【0183】 In this invention, the server includes means for storing point program information, means for analyzing usage history using an intelligent model and suggesting point programs that should be used preferentially, and means for analyzing the user's emotional state using an emotion engine and generating suggestions that correspond to those emotions. This enables flexible service provision based on the user's emotions and promotes the effective use of point programs. 【0184】 "Point program information" refers to a collection of data related to points that users can earn or use across various services, including point balances and usage conditions. 【0185】 "Usage history" refers to data that records how a user has used the points program, including details about points earned and used. 【0186】 "Expiration date" refers to information indicating the period during which earned points can be used; points become invalid after this period. 【0187】 An "intelligent model" is an analytical system based on artificial intelligence technology, which uses statistical learning to identify specific patterns and make predictions from data. 【0188】 An "emotion engine" is a system for analyzing a user's emotions, and it is a technology that identifies the user's emotional state based on facial expressions, voice tone, and other factors. 【0189】 "Means for generating proposals" refers to a method or apparatus for automatically creating and providing information and services to users based on analytical data. 【0190】 To realize this invention, the server, user terminal, and emotion engine must function in close cooperation. Specific embodiments of the system are shown below. 【0191】 First, users register their points program information in the application using a device such as a smartphone or computer. This information is sent to a server via the internet and stored in a database on the server. Based on the points program information, the server manages usage history and expiration dates, and uses an intelligent model to analyze which points programs should be prioritized for the user. 【0192】 The device is equipped with an emotion engine that collects the user's facial expressions and voice through the camera and microphone. This allows the emotion engine to analyze the user's current emotional state in real time. The analysis results are sent to a server and used as data to generate reward information and suggestions tailored to the emotional state. For example, if the analysis indicates the user is "excited" when purchasing a specific product, the server will generate and display reward information such as "Get double points now!" on the device. 【0193】 This system utilizes services such as Google Cloud Natural Language API and Amazon Rekognition as its emotion engine. Cloud platforms such as AWS and Firebase are used for server data analysis. 【0194】 As a concrete example, suppose a user selects a specific product while online shopping, and their smartphone camera analyzes their facial expression, determining that they are in an excited state. In this case, the server can quickly generate a suggestion such as, "If you purchase now while you're highly excited, you'll receive extra points," and notify the device. 【0195】 The following prompt should be entered into the generative AI model: "Analyze the user's emotions while they are online shopping and generate the most appropriate suggestion message." 【0196】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0197】 Step 1: 【0198】 Users enter and register their points program information into the application using a device such as a smartphone. The entered information is transmitted to the server via the internet. The data entered by the user includes the type of points, balance, and expiration date, and the server stores and manages this information in a database. 【0199】 Step 2: 【0200】 The device uses its built-in camera and microphone to collect the user's facial expressions and voice data in real time and transmits it to the emotion engine. The emotion engine analyzes this data to identify the user's emotional state. The analysis results are generated as emotion tags such as "excited" and "calm," and sent to the server. 【0201】 Step 3: 【0202】 The server uses an intelligent model to analyze the received emotional state information. In this process, it combines points program information with the user's emotional state to generate response messages that include appropriate suggestions and rewards. The generated information influences the points program's expiration date and recommended point usage. 【0203】 Step 4: 【0204】 Response messages and reward information generated by the server are presented to the user via their device. This presentation is done via push notifications or in-app pop-ups, allowing users to consider using the points program based on this information. 【0205】 Step 5: 【0206】 Based on the information presented, the user decides whether to purchase a product or use points. The user's decision is then returned to Step 1 and entered into the system as new points program information, which serves as the basis for subsequent analysis and suggestions. 【0207】 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. 【0208】 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. 【0209】 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. 【0210】 [Second Embodiment] 【0211】 Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment. 【0212】 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. 【0213】 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). 【0214】 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. 【0215】 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. 【0216】 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). 【0217】 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. 【0218】 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. 【0219】 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. 【0220】 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. 【0221】 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. 【0222】 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". 【0223】 This invention is a point card management system implemented as a smartphone application, which functions by communicating data between a server and a terminal. Users register information about their point cards through the application. This includes the card name, number, store information, and associated login information. The registered information is sent from the terminal to the server and stored in the server's database. 【0224】 The server manages the usage history and expiration dates of each point card based on the registered point card information. This allows the server to constantly track which point cards each user uses, how often, and which are about to expire. Based on an artificial intelligence model, the server analyzes user usage history and extracts specific behavioral patterns to suggest the most suitable point card usage for each user. 【0225】 As a concrete example, if a user frequently shops at a particular store, the server will prioritize suggesting a point card that offers benefits at that store. For instance, if the user has points that are about to expire, the server will suggest using those points first. This allows the user to utilize their points efficiently without waste. 【0226】 Furthermore, the server statistically aggregates data collected from all users to analyze which loyalty cards are used most frequently and under what times and conditions. This analysis is provided to companies and used as a reference for marketing strategies and promotional activities. This enables companies to effectively plan and implement campaigns tailored to specific consumer behaviors. 【0227】 In this way, users can enjoy convenience, and companies can implement data-driven strategies to provide better customer experiences and increase revenue. 【0228】 The following describes the processing flow. 【0229】 Step 1: 【0230】 The user launches the smartphone app and goes to the point card information registration screen. The user enters the card name, number, store name, and login information. 【0231】 Step 2: 【0232】 The terminal structures the point card information entered by the user into data packets and sends them to the server using a secure communication protocol. 【0233】 Step 3: 【0234】 The server analyzes the received point card information and registers it in the database. During this process, it checks for duplicate entries and registers them as new data. 【0235】 Step 4: 【0236】 The server periodically checks the usage history and expiration date of each point card in the database and updates them as needed. 【0237】 Step 5: 【0238】 The server uses an AI model to analyze the user's usage history and extract specific behavioral patterns. Based on this analysis, it generates suggestions for loyalty cards that should be used preferentially. 【0239】 Step 6: 【0240】 The terminal notifies the user of the suggested results received from the server. Based on the notified information, the user decides which point card to use. 【0241】 Step 7: 【0242】 Users present the proposed point card when making purchases or using services, and then use the points. 【0243】 Step 8: 【0244】 The server aggregates usage history data collected from all users and performs market trend analysis. This result is provided to companies and used in developing marketing strategies. 【0245】 (Example 1) 【0246】 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." 【0247】 Modern consumers possess numerous loyalty programs and points systems, but lack the means to efficiently manage and utilize them. As a result, it is difficult to effectively use points, and consumers often let many points expire before their expiration date. Furthermore, companies lack the information necessary to make appropriate decisions about how to promote products to customers, making it difficult to develop effective marketing strategies. 【0248】 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. 【0249】 In this invention, the server includes means for recording information about point programs, means for controlling usage history and end date and time based on the recorded point program information, and means for analyzing usage history using a machine learning model and suggesting point programs that should be used preferentially. This enables consumers to efficiently manage their points and companies to build effective marketing strategies based on customer behavior. 【0250】 A "points system" is a means of recording value information that customers receive for purchasing goods or services, which allows them to receive benefits and discounts. 【0251】 A "machine learning model" is a type of algorithm used for data analysis, which predicts future behavior and patterns based on past data. 【0252】 "To control" means to organize and manage information, thereby facilitating its use and analysis according to a specific purpose. 【0253】 The "end date" refers to the final date within the period during which the points program is valid, and the use of points will be restricted after that date. 【0254】 "Presenting" means clearly showing users information such as analysis results and recommended actions, and is done to support their decision-making. 【0255】 This invention is a system for efficiently managing and utilizing a user's numerous point programs. The user inputs information about their point programs through a smart device application. This information includes the program name, identification number, provider information, and login-related information. 【0256】 The terminal uses a secure communication protocol such as SSL / TLS to send the entered point medium information to the server. After receiving this, the server verifies its integrity and records the data in a database. The database is used to manage usage history and end dates and times, and systematically structures the information for each point medium. 【0257】 The server uses generative AI models such as GPT-3 to analyze usage history data. This analysis extracts user behavior patterns and determines which point programs should be prioritized. Specifically, the AI ​​model considers the frequency of use and expiration dates of each program to make appropriate usage suggestions. These suggestions are communicated to the user in the form of a notification such as, "Your points will expire soon, so we recommend using them as a priority." 【0258】 Furthermore, the server analyzes the integrated data and provides businesses with market trends. Businesses can leverage this insight to develop the most effective promotions for consumers. For example, a possible prompt might be, "Please report the most frequently used points-based payment method in the last 30 days." 【0259】 This system allows users to make effective use of their points, and enables companies to develop more precise marketing strategies. 【0260】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0261】 Step 1: 【0262】 The user enters information about the point card using an application on their smart device. This information includes the card name, identification number, provider information, and login-related information. This information is sent to the application and prepared as a data packet within the device. 【0263】 Step 2: 【0264】 The terminal sends prepared data packets to the server using a secure communication protocol such as SSL / TLS. This communication maintains the security and accuracy of the input information. After receiving the information, the server verifies its integrity and records it in the database. As a result, the information is systematically stored and prepared for future queries. 【0265】 Step 3: 【0266】 The server initiates a process to manage usage history and expiration dates based on point media information in the database. Data on the retention period and usage status of each point media is used as input, and updated management information is generated as output. Specifically, usage counts and expiration reminders are set. 【0267】 Step 4: 【0268】 The server analyzes usage history data using a generative AI model. This analysis aims to extract behavioral patterns, and the AI ​​makes predictions based on past data. Usage history and end date / time information are provided as input, and the output is a suggestion of point-earning media that should be used preferentially. 【0269】 Step 5: 【0270】 The server notifies the user of the generated suggestions. This notification includes recommended ways to use points and helps the user make decisions. The content of the notification may be displayed in the form of, for example, "Your points will expire soon, so we recommend using them as soon as possible." 【0271】 Step 6: 【0272】 The server statistically analyzes overall usage data and generates market trend reports. The input is usage data from all users within a specified period, and the output generates new insights that can be used for marketing. This information is provided to companies, contributing to the development of effective promotions. 【0273】 (Application Example 1) 【0274】 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." 【0275】 Traditional point reward services made it difficult for users to efficiently utilize the most suitable service at the optimal time. Furthermore, the manual management of point card information and usage history was cumbersome, often resulting in points expiring. Additionally, the lack of integration between electronic money transfer services and point reward services made it difficult for users to select the most appropriate service for each payment. 【0276】 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. 【0277】 In this invention, the server includes means for registering point-granting service information, means for managing usage history and expiration dates based on the registered service information, means for analyzing usage history using a machine learning algorithm and suggesting the most suitable service to use, and means for integrating with an electronic money transfer service and automatically applying the most suitable service at the time of payment. As a result, users can always automatically utilize the most suitable point-granting service, reduce point waste, and enjoy the best possible customer experience. 【0278】 "Point accrual service information" refers to point-related data for users to record and manage, including information such as the point issuer, the number of points, and the expiration date. 【0279】 A "machine learning algorithm" is a computational method used to learn from data and identify patterns. In this invention, it is a technology applied to analyze a user's usage history and provide optimal suggestions. 【0280】 An "electronic fund transfer service" is a service that enables the sending and receiving of funds via the internet or mobile devices. In this invention, it is integrated with a point accrual service and has a function to automatically adjust the optimal use of points at the time of payment. 【0281】 "Usage history" refers to records showing a user's past use of point-granting services, and is data used to analyze user behavior trends based on this history. 【0282】 "Automatic suggestion" is a function that analyzes the user's past behavior patterns and proactively presents the optimal option. In this invention, it is used to select the most suitable point reward service at the time of payment. 【0283】 The system for implementing this invention functions by registering point awarding service information on the user's terminal and transmitting it to the server through data communication. This server stores the point awarding service information received from the user terminal in a database and manages the usage history and expiration date. Furthermore, it uses a machine learning algorithm to analyze the user's usage history and propose an optimal service. Specifically, it is integrated with an electronic funds transfer service at the time of payment, and an optimal service is automatically applied according to the user's payment behavior. 【0284】 As the hardware of this system, mobile devices such as the user's smartphone or tablet are used, and for the server, cloud-based servers (e.g., AWS, Google Cloud) are utilized. For software, on the server side, programming languages such as Python and Node.js are used, PostgreSQL etc. are used as the database, and frameworks such as TensorFlow and PyTorch are used for machine learning. 【0285】 For example, when a user shops at a specific retail store daily, the system can recognize this behavior pattern and present a service that allows points to be used more efficiently at that retail store. As a result, the user can effectively utilize the points. An example of a prompt sentence for the generative AI model is "Please propose the optimal point awarding service to be used based on the latest purchase history." 【0286】 The flow of the specific process in Application Example 1 will be described using FIG. 12. 【0287】 Step 1: 【0288】 The user operates the terminal to register point awarding service information. The user launches the application and enters information such as the service name, number of points, and expiration date. The input data is temporarily stored in the terminal and is ready to be transmitted to the server. 【0289】 Step 2: 【0290】 The terminal sends the registered points information to the server. The server adds the received information to its database and compares it with existing data. As a result, the new information is recorded in the database and becomes subject to management of usage history and expiration date. 【0291】 Step 3: 【0292】 The server uses a generative AI model to analyze usage history in the database. Specifically, it applies machine learning algorithms to extract user usage patterns and detect specific consumption behaviors. The input data is the usage history of all users, and the output is the behavioral patterns of each user. 【0293】 Step 4: 【0294】 Based on the analysis results, the server generates suggestions for the optimal use of points for the user. These suggestions are sent to the user's terminal, recommending priority use of points nearing expiration and suggesting use at specific stores. This allows the server to provide users with the most efficient point management. 【0295】 Step 5: 【0296】 The terminal notifies the user of suggestions received from the server. The user can review this notification and follow the suggested optimal way to use their points. The output of this process is reduced spending and effective use of points for the user. 【0297】 Step 6: 【0298】 When a user makes a purchase using the electronic money transfer service, the terminal automatically applies the optimal points based on suggestions received from the server. As a result, the user can benefit from optimized point usage at the time of payment. 【0299】 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. 【0300】 This invention combines an emotion engine with a point card management system to provide flexible services that respond to user emotions. The server, terminal, and emotion engine work together to improve user convenience and support more appropriate point card usage. 【0301】 Users register their loyalty card information using a smartphone app. This information is sent from the device to a server and stored in a database on the server. The server uses this information to manage the usage history and expiration dates of each card. Furthermore, the server uses an artificial intelligence model to analyze this usage history and determine which loyalty cards should be prioritized. The generated recommendations are then communicated to the user via the device. 【0302】 The emotion engine utilizes various data collected from the device to analyze the user's emotions. This includes the user's facial expressions, voice tone, and input behavior. The emotion engine identifies the user's current emotional state, and the suggested content is adjusted accordingly. 【0303】 For example, if the emotion engine analyzes the user's emotion as "excited," the server can generate proactive suggestions tailored to their excitement, such as a message saying, "Get double bonus points now!" On the other hand, if the user is determined to be in a "calm" state, calm suggestions regarding important expiring points will be made. 【0304】 In addition, the emotion engine learns long-term behavioral patterns based on the user's emotions and provides feedback on this to the server. As a result, the server can make more accurate predictions of user behavior and continuously optimize the use of the point cards according to the emotional state. Through these functions, it is possible for the user to improve the quality of life and for the company to build more effective marketing strategies. 【0305】 The processing flow will be described below. 【0306】 Step 1: 【0307】 The user launches the smartphone app and opens the screen for registering point card information. Here, the detailed information of the card is entered. 【0308】 Step 2: 【0309】 The terminal sends the point card information input by the user to the server. This information includes the card name, number, store name, and login information. 【0310】 Step 3: 【0311】 The server stores the received point card information in the database. It checks for duplicate registrations and adds it to the database if it is a new registration. 【0312】 Step 4: 【0313】 The server periodically checks the database and updates the usage history and expiration date of each point card. This is to maintain the latest information. 【0314】 Step 5: 【0315】 The emotion engine collects data such as the user's facial expressions, voice tones, and input actions obtained from the terminal and analyzes the user's current emotional state. 【0316】 Step 6: 【0317】 Based on the analyzed emotional data, the server works in conjunction with an AI model to generate loyalty card usage suggestions tailored to the user's emotions. For example, if active speech is detected, it will suggest limited offers. 【0318】 Step 7: 【0319】 The device notifies the user of suggestions sent from the server. These suggestions include personalized messages based on emotions. 【0320】 Step 8: 【0321】 Users make purchases and use services based on the suggested loyalty cards. In this process, emotionally-driven offers may be utilized. 【0322】 Step 9: 【0323】 The server aggregates usage data from all users and analyzes usage history based on sentiment. This information is then provided to companies to help them develop marketing strategies. 【0324】 (Example 2) 【0325】 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". 【0326】 Traditional point systems have limitations in making suggestions based on user usage history and expiration date information, making it difficult to provide flexible services that take into account users' psychological states and emotions. Furthermore, because point usage suggestions that are tailored to the user are not provided, it is difficult to effectively utilize points, and companies are unable to formulate optimal marketing strategies. 【0327】 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. 【0328】 In this invention, the server includes means for registering point information, means for managing history and expiration dates, means for analyzing history using a machine learning model and suggesting preferential point usage, and means for identifying the user's psychological state using sentiment analysis technology and adjusting the suggested content. This enables appropriate point usage suggestions according to the user's emotional state, realizing effective point utilization and strategic marketing for companies. 【0329】 "Point information" refers to data related to the user's point card, including card number, expiration date, and accumulated points. 【0330】 "History" refers to a record of points earned and used by a user in the past. 【0331】 "Expiration date" refers to information indicating the period during which points are valid, including the expiration date. 【0332】 A "machine learning model" is an artificial intelligence technology that learns patterns based on data and uses them to predict future actions and choices. 【0333】 "Emotional analysis technology" refers to technologies that analyze a user's emotions and identify their psychological state, and includes techniques such as facial recognition and voice tone analysis. 【0334】 "Suggested content" refers to specific advice or messages generated by the server to encourage users to use their points. 【0335】 This invention integrates emotion analysis technology into a point management system, enabling flexible point usage suggestions tailored to the user's emotional state. 【0336】 Users register their points information using a smartphone app. The device sends this information to a server, which stores the registered information in a database. This allows for effective management of point history and expiration dates. 【0337】 The server uses a generative AI model to analyze historical data and gain insights to suggest appropriate point usage to users. This AI model learns past usage patterns and proposes the most effective way for users to use their point cards. 【0338】 The terminal monitors user behavior and uses emotion analysis technology to determine emotional states from facial expressions, voice tone, and input actions. By utilizing human interface technology, more accurate emotional data can be obtained. The server receives this data and adjusts the suggested content according to the user's emotional state. 【0339】 Specifically, if the system analyzes that a user is in an "excited" state, thrilled about a new adventure, the server can generate proactive offers such as "Get double bonus points now!" On the other hand, if the user is in a calm state, the server will offer more subdued suggestions such as "Your points will expire soon. Take your time to prepare and use them." 【0340】 This system allows users to optimally manage their points assets, and enables companies to develop marketing strategies tailored to user needs. 【0341】 An example of a prompt message is: "Analyze the user's current emotional state and generate appropriate suggestions for using the loyalty card based on that." 【0342】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0343】 Step 1: 【0344】 The user uses the device 【0345】 The user launches the smartphone app and registers by entering their points information. The entered information includes the points card number, expiration date, and accumulated points, and the device prepares to send the data to the server accordingly. The output of this step is the registration dataset. 【0346】 Step 2: 【0347】 The terminal sends data to the server. 【0348】 The terminal sends point information to the server. This data is encrypted and transmitted securely over the network. The server receives this data and stores it in its database. The output is a record of the stored point information. 【0349】 Step 3: 【0350】 The server manages history and expiration dates. 【0351】 The server retrieves point information from the database and performs functions to manage usage history and expiration dates. It processes data regarding point acquisition, usage, and the remaining period for today, keeping the information up-to-date. The output here is the updated point history and expiration information. 【0352】 Step 4: 【0353】 The server analyzes the data using an AI model. 【0354】 The server uses a generation AI model to analyze historical data and identify points that should be prioritized. The AI ​​is prompted with questions, and the model outputs analysis results. Based on these results, the server generates suggestions. The output consists of specific suggestions. 【0355】 Step 5: 【0356】 The device collects emotional data. 【0357】 As the user interacts with the app, the device analyzes facial expressions, voice tone, and input speed to collect emotional data. Input is real-time data obtained from the user interface, and output is the collected emotional data. 【0358】 Step 6: 【0359】 The device sends data to the emotion engine. 【0360】 Emotional data is sent to the emotion engine for analysis. This data is analyzed by the engine to identify the user's emotional state. The output of this step is the evaluation result of the user's emotional state. 【0361】 Step 7: 【0362】 The server generates suggestions based on emotions. 【0363】 The server receives the analysis results from the emotion engine and adjusts the suggestions based on them. It generates promotions and point usage advice tailored to the user's emotions, such as "excitement" or "calmness," and sends the results to the terminal. The output is the adjusted suggestions. 【0364】 Step 8: 【0365】 The device notifies the user. 【0366】 The terminal notifies the user of the suggestions received from the server. Notification methods include pop-ups and real-time feedback, allowing the user to review suggestions relevant to their situation. The output of this step is the information provided to the user and their feedback. 【0367】 (Application Example 2) 【0368】 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." 【0369】 A challenge with point programs is the lack of appropriate approaches based on user emotions, which prevents maximizing user purchasing intent. Furthermore, the inability to effectively use points before their expiration date is a problem. To address this, it is necessary to analyze users' emotional states and provide reward information tailored to those emotions to promote the use of point programs. 【0370】 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. 【0371】 In this invention, the server includes means for storing point program information, means for analyzing usage history using an intelligent model and suggesting point programs that should be used preferentially, and means for analyzing the user's emotional state using an emotion engine and generating suggestions that correspond to those emotions. This enables flexible service provision based on the user's emotions and promotes the effective use of point programs. 【0372】 "Point program information" refers to a collection of data related to points that users can earn or use across various services, including point balances and usage conditions. 【0373】 "Usage history" refers to data that records how a user has used the points program, including details about points earned and used. 【0374】 "Expiration date" refers to information indicating the period during which earned points can be used; points become invalid after this period. 【0375】 An "intelligent model" is an analytical system based on artificial intelligence technology, which uses statistical learning to identify specific patterns and make predictions from data. 【0376】 An "emotion engine" is a system for analyzing a user's emotions, and it is a technology that identifies the user's emotional state based on facial expressions, voice tone, and other factors. 【0377】 "Means for generating proposals" refers to a method or apparatus for automatically creating and providing information and services to users based on analytical data. 【0378】 To realize this invention, the server, user terminal, and emotion engine must function in close cooperation. Specific embodiments of the system are shown below. 【0379】 First, users register their points program information in the application using a device such as a smartphone or computer. This information is sent to a server via the internet and stored in a database on the server. Based on the points program information, the server manages usage history and expiration dates, and uses an intelligent model to analyze which points programs should be prioritized for the user. 【0380】 The device is equipped with an emotion engine that collects the user's facial expressions and voice through the camera and microphone. This allows the emotion engine to analyze the user's current emotional state in real time. The analysis results are sent to a server and used as data to generate reward information and suggestions tailored to the emotional state. For example, if the analysis indicates the user is "excited" when purchasing a specific product, the server will generate and display reward information such as "Get double points now!" on the device. 【0381】 This system utilizes services such as Google Cloud Natural Language API and Amazon Rekognition as its emotion engine. Cloud platforms such as AWS and Firebase are used for server data analysis. 【0382】 As a concrete example, suppose a user selects a specific product while online shopping, and their smartphone camera analyzes their facial expression, determining that they are in an excited state. In this case, the server can quickly generate a suggestion such as, "If you purchase now while you're highly excited, you'll receive extra points," and notify the device. 【0383】 The following prompt should be entered into the generative AI model: "Analyze the user's emotions while they are online shopping and generate the most appropriate suggestion message." 【0384】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0385】 Step 1: 【0386】 Users enter and register their points program information into the application using a device such as a smartphone. The entered information is transmitted to the server via the internet. The data entered by the user includes the type of points, balance, and expiration date, and the server stores and manages this information in a database. 【0387】 Step 2: 【0388】 The device uses its built-in camera and microphone to collect the user's facial expressions and voice data in real time and transmits it to the emotion engine. The emotion engine analyzes this data to identify the user's emotional state. The analysis results are generated as emotion tags such as "excited" and "calm," and sent to the server. 【0389】 Step 3: 【0390】 The server uses an intelligent model to analyze the received emotional state information. In this process, it combines points program information with the user's emotional state to generate response messages that include appropriate suggestions and rewards. The generated information influences the points program's expiration date and recommended point usage. 【0391】 Step 4: 【0392】 Response messages and reward information generated by the server are presented to the user via their device. This presentation is done via push notifications or in-app pop-ups, allowing users to consider using the points program based on this information. 【0393】 Step 5: 【0394】 Based on the information presented, the user decides whether to purchase a product or use points. The user's decision is then returned to Step 1 and entered into the system as new points program information, which serves as the basis for subsequent analysis and suggestions. 【0395】 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. 【0396】 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. 【0397】 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. 【0398】 [Third Embodiment] 【0399】 Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment. 【0400】 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. 【0401】 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). 【0402】 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. 【0403】 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. 【0404】 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). 【0405】 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. 【0406】 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. 【0407】 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. 【0408】 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. 【0409】 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. 【0410】 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". 【0411】 This invention is a point card management system implemented as a smartphone application, which functions by communicating data between a server and a terminal. Users register information about their point cards through the application. This includes the card name, number, store information, and associated login information. The registered information is sent from the terminal to the server and stored in the server's database. 【0412】 The server manages the usage history and expiration dates of each point card based on the registered point card information. This allows the server to constantly track which point cards each user uses, how often, and which are about to expire. Based on an artificial intelligence model, the server analyzes user usage history and extracts specific behavioral patterns to suggest the most suitable point card usage for each user. 【0413】 As a concrete example, if a user frequently shops at a particular store, the server will prioritize suggesting a point card that offers benefits at that store. For instance, if the user has points that are about to expire, the server will suggest using those points first. This allows the user to utilize their points efficiently without waste. 【0414】 Furthermore, the server statistically aggregates data collected from all users to analyze which loyalty cards are used most frequently and under what times and conditions. This analysis is provided to companies and used as a reference for marketing strategies and promotional activities. This enables companies to effectively plan and implement campaigns tailored to specific consumer behaviors. 【0415】 In this way, users can enjoy convenience, and companies can implement data-driven strategies to provide better customer experiences and increase revenue. 【0416】 The following describes the processing flow. 【0417】 Step 1: 【0418】 The user launches the smartphone app and goes to the point card information registration screen. The user enters the card name, number, store name, and login information. 【0419】 Step 2: 【0420】 The terminal structures the point card information entered by the user into data packets and sends them to the server using a secure communication protocol. 【0421】 Step 3: 【0422】 The server analyzes the received point card information and registers it in the database. During this process, it checks for duplicate entries and registers them as new data. 【0423】 Step 4: 【0424】 The server periodically checks the usage history and expiration date of each point card in the database and updates them as needed. 【0425】 Step 5: 【0426】 The server uses an AI model to analyze the user's usage history and extract specific behavioral patterns. Based on this analysis, it generates suggestions for loyalty cards that should be used preferentially. 【0427】 Step 6: 【0428】 The terminal notifies the user of the suggested results received from the server. Based on the notified information, the user decides which point card to use. 【0429】 Step 7: 【0430】 Users present the proposed point card when making purchases or using services, and then use the points. 【0431】 Step 8: 【0432】 The server aggregates usage history data collected from all users and performs market trend analysis. This result is provided to companies and used in developing marketing strategies. 【0433】 (Example 1) 【0434】 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." 【0435】 Modern consumers possess numerous loyalty programs and points systems, but lack the means to efficiently manage and utilize them. As a result, it is difficult to effectively use points, and consumers often let many points expire before their expiration date. Furthermore, companies lack the information necessary to make appropriate decisions about how to promote products to customers, making it difficult to develop effective marketing strategies. 【0436】 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. 【0437】 In this invention, the server includes means for recording information about point programs, means for controlling usage history and end date and time based on the recorded point program information, and means for analyzing usage history using a machine learning model and suggesting point programs that should be used preferentially. This enables consumers to efficiently manage their points and companies to build effective marketing strategies based on customer behavior. 【0438】 A "points system" is a means of recording value information that customers receive for purchasing goods or services, which allows them to receive benefits and discounts. 【0439】 A "machine learning model" is a type of algorithm used for data analysis, which predicts future behavior and patterns based on past data. 【0440】 "To control" means to organize and manage information, thereby facilitating its use and analysis according to a specific purpose. 【0441】 The "end date" refers to the final date within the period during which the points program is valid, and the use of points will be restricted after that date. 【0442】 "Presenting" means clearly showing users information such as analysis results and recommended actions, and is done to support their decision-making. 【0443】 This invention is a system for efficiently managing and utilizing a user's numerous point programs. The user inputs information about their point programs through a smart device application. This information includes the program name, identification number, provider information, and login-related information. 【0444】 The terminal uses a secure communication protocol such as SSL / TLS to send the entered point medium information to the server. After receiving this, the server verifies its integrity and records the data in a database. The database is used to manage usage history and end dates and times, and systematically structures the information for each point medium. 【0445】 The server uses generative AI models such as GPT-3 to analyze usage history data. This analysis extracts user behavior patterns and determines which point programs should be prioritized. Specifically, the AI ​​model considers the frequency of use and expiration dates of each program to make appropriate usage suggestions. These suggestions are communicated to the user in the form of a notification such as, "Your points will expire soon, so we recommend using them as a priority." 【0446】 Furthermore, the server analyzes the integrated data and provides businesses with market trends. Businesses can leverage this insight to develop the most effective promotions for consumers. For example, a possible prompt might be, "Please report the most frequently used points-based payment method in the last 30 days." 【0447】 This system allows users to make effective use of their points, and enables companies to develop more precise marketing strategies. 【0448】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0449】 Step 1: 【0450】 The user enters information about the point card using an application on their smart device. This information includes the card name, identification number, provider information, and login-related information. This information is sent to the application and prepared as a data packet within the device. 【0451】 Step 2: 【0452】 The terminal sends prepared data packets to the server using a secure communication protocol such as SSL / TLS. This communication maintains the security and accuracy of the input information. After receiving the information, the server verifies its integrity and records it in the database. As a result, the information is systematically stored and prepared for future queries. 【0453】 Step 3: 【0454】 The server initiates a process to manage usage history and expiration dates based on point media information in the database. Data on the retention period and usage status of each point media is used as input, and updated management information is generated as output. Specifically, usage counts and expiration reminders are set. 【0455】 Step 4: 【0456】 The server analyzes usage history data using a generative AI model. This analysis aims to extract behavioral patterns, and the AI ​​makes predictions based on past data. Usage history and end date / time information are provided as input, and the output is a suggestion of point-earning media that should be used preferentially. 【0457】 Step 5: 【0458】 The server notifies the user of the generated suggestions. This notification includes recommended ways to use points and helps the user make decisions. The content of the notification may be displayed in the form of, for example, "Your points will expire soon, so we recommend using them as soon as possible." 【0459】 Step 6: 【0460】 The server statistically analyzes overall usage data and generates market trend reports. The input is usage data from all users within a specified period, and the output generates new insights that can be used for marketing. This information is provided to companies, contributing to the development of effective promotions. 【0461】 (Application Example 1) 【0462】 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." 【0463】 Traditional point reward services made it difficult for users to efficiently utilize the most suitable service at the optimal time. Furthermore, the manual management of point card information and usage history was cumbersome, often resulting in points expiring. Additionally, the lack of integration between electronic money transfer services and point reward services made it difficult for users to select the most appropriate service for each payment. 【0464】 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. 【0465】 In this invention, the server includes means for registering point-granting service information, means for managing usage history and expiration dates based on the registered service information, means for analyzing usage history using a machine learning algorithm and suggesting the most suitable service to use, and means for integrating with an electronic money transfer service and automatically applying the most suitable service at the time of payment. As a result, users can always automatically utilize the most suitable point-granting service, reduce point waste, and enjoy the best possible customer experience. 【0466】 "Point accrual service information" refers to point-related data for users to record and manage, including information such as the point issuer, the number of points, and the expiration date. 【0467】 A "machine learning algorithm" is a computational method used to learn from data and identify patterns. In this invention, it is a technology applied to analyze a user's usage history and provide optimal suggestions. 【0468】 An "electronic fund transfer service" is a service that enables the sending and receiving of funds via the internet or mobile devices. In this invention, it is integrated with a point accrual service and has a function to automatically adjust the optimal use of points at the time of payment. 【0469】 "Usage history" refers to records showing a user's past use of point-granting services, and is data used to analyze user behavior trends based on this history. 【0470】 "Automatic suggestion" is a function that analyzes the user's past behavior patterns and proactively presents the optimal option. In this invention, it is used to select the most suitable point reward service at the time of payment. 【0471】 The system for implementing this invention functions by registering point-granting service information on the user's terminal and transmitting it to a server via data communication. This server stores the point-granting service information received from the user's terminal in a database and manages the usage history and expiration date. Furthermore, it uses a machine learning algorithm to analyze the user's usage history and propose the optimal service. Specifically, it integrates with electronic money transfer services at the time of payment and automatically applies the optimal service according to the user's payment behavior. 【0472】 The hardware of this system will consist of mobile devices such as users' smartphones and tablets, and the servers will utilize cloud-based servers (e.g., AWS, Google Cloud). For software, programming languages ​​such as Python and Node.js will be used on the server side, PostgreSQL will be used for the database, and frameworks such as TensorFlow and PyTorch will be used for machine learning. 【0473】 For example, if a user shops at a specific retailer on a daily basis, the system can recognize this behavioral pattern and suggest services that allow users to use their points more efficiently at that retailer. This enables users to make better use of their points. An example of a prompt to the generating AI model would be, "Based on the most recent purchase history, please suggest the best point-earning service to use." 【0474】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0475】 Step 1: 【0476】 The user operates the terminal and registers information about the point accrual service. The user launches the application and enters information such as the service name, number of points, and expiration date. The entered data is temporarily stored on the terminal and prepared to be sent to the server. 【0477】 Step 2: 【0478】 The terminal sends the registered points information to the server. The server adds the received information to its database and compares it with existing data. As a result, the new information is recorded in the database and becomes subject to management of usage history and expiration date. 【0479】 Step 3: 【0480】 The server uses a generative AI model to analyze usage history in the database. Specifically, it applies machine learning algorithms to extract user usage patterns and detect specific consumption behaviors. The input data is the usage history of all users, and the output is the behavioral patterns of each user. 【0481】 Step 4: 【0482】 Based on the analysis results, the server generates suggestions for the optimal use of points for the user. These suggestions are sent to the user's terminal, recommending priority use of points nearing expiration and suggesting use at specific stores. This allows the server to provide users with the most efficient point management. 【0483】 Step 5: 【0484】 The terminal notifies the user of suggestions received from the server. The user can review this notification and follow the suggested optimal way to use their points. The output of this process is reduced spending and effective use of points for the user. 【0485】 Step 6: 【0486】 When a user makes a purchase using the electronic money transfer service, the terminal automatically applies the optimal points based on suggestions received from the server. As a result, the user can benefit from optimized point usage at the time of payment. 【0487】 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. 【0488】 This invention combines an emotion engine with a point card management system to provide flexible services that respond to user emotions. The server, terminal, and emotion engine work together to improve user convenience and support more appropriate point card usage. 【0489】 Users register their loyalty card information using a smartphone app. This information is sent from the device to a server and stored in a database on the server. The server uses this information to manage the usage history and expiration dates of each card. Furthermore, the server uses an artificial intelligence model to analyze this usage history and determine which loyalty cards should be prioritized. The generated recommendations are then communicated to the user via the device. 【0490】 The emotion engine utilizes various data collected from the device to analyze the user's emotions. This includes the user's facial expressions, voice tone, and input behavior. The emotion engine identifies the user's current emotional state, and the suggested content is adjusted accordingly. 【0491】 For example, if the emotion engine analyzes the user's emotion as "excited," the server can generate proactive suggestions tailored to their excitement, such as a message saying, "Get double bonus points now!" On the other hand, if the user is determined to be in a "calm" state, calm suggestions regarding important expiring points will be made. 【0492】 Furthermore, the emotion engine learns long-term behavioral patterns based on the user's emotions and feeds this back to the server. This allows the server to more accurately predict user behavior and continuously optimize loyalty card usage according to the user's emotional state. Through these functions, users can improve their quality of life, and companies can build more effective marketing strategies. 【0493】 The following describes the processing flow. 【0494】 Step 1: 【0495】 The user launches the smartphone app and opens the screen to register their loyalty card information. Here, they enter their card details. 【0496】 Step 2: 【0497】 The terminal transmits the loyalty card information entered by the user to the server. This information includes the card name, number, store name, and login information. 【0498】 Step 3: 【0499】 The server stores the received point card information in a database. It checks for duplicate registrations and adds new registrations to the database. 【0500】 Step 4: 【0501】 The server periodically checks the database and updates the usage history and expiration date of each point card, ensuring that the information is always up-to-date. 【0502】 Step 5: 【0503】 The emotion engine collects data such as the user's facial expressions, voice tone, and input behavior obtained from the device, and analyzes the user's current emotional state. 【0504】 Step 6: 【0505】 Based on the analyzed emotional data, the server works in conjunction with an AI model to generate loyalty card usage suggestions tailored to the user's emotions. For example, if active speech is detected, it will suggest limited offers. 【0506】 Step 7: 【0507】 The device notifies the user of suggestions sent from the server. These suggestions include personalized messages based on emotions. 【0508】 Step 8: 【0509】 Users make purchases and use services based on the suggested loyalty cards. In this process, emotionally-driven offers may be utilized. 【0510】 Step 9: 【0511】 The server aggregates usage data from all users and analyzes usage history based on sentiment. This information is then provided to companies to help them develop marketing strategies. 【0512】 (Example 2) 【0513】 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." 【0514】 Traditional point systems have limitations in making suggestions based on user usage history and expiration date information, making it difficult to provide flexible services that take into account users' psychological states and emotions. Furthermore, because point usage suggestions that are tailored to the user are not provided, it is difficult to effectively utilize points, and companies are unable to formulate optimal marketing strategies. 【0515】 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. 【0516】 In this invention, the server includes means for registering point information, means for managing history and expiration dates, means for analyzing history using a machine learning model and suggesting preferential point usage, and means for identifying the user's psychological state using sentiment analysis technology and adjusting the suggested content. This enables appropriate point usage suggestions according to the user's emotional state, realizing effective point utilization and strategic marketing for companies. 【0517】 "Point information" refers to data related to the user's point card, including card number, expiration date, and accumulated points. 【0518】 "History" refers to a record of points earned and used by a user in the past. 【0519】 "Expiration date" refers to information indicating the period during which points are valid, including the expiration date. 【0520】 A "machine learning model" is an artificial intelligence technology that learns patterns based on data and uses them to predict future actions and choices. 【0521】 "Emotional analysis technology" refers to technologies that analyze a user's emotions and identify their psychological state, and includes techniques such as facial recognition and voice tone analysis. 【0522】 "Suggested content" refers to specific advice or messages generated by the server to encourage users to use their points. 【0523】 This invention integrates emotion analysis technology into a point management system, enabling flexible point usage suggestions tailored to the user's emotional state. 【0524】 Users register their points information using a smartphone app. The device sends this information to a server, which stores the registered information in a database. This allows for effective management of point history and expiration dates. 【0525】 The server uses a generative AI model to analyze historical data and gain insights to suggest appropriate point usage to users. This AI model learns past usage patterns and proposes the most effective way for users to use their point cards. 【0526】 The terminal monitors user behavior and uses emotion analysis technology to determine emotional states from facial expressions, voice tone, and input actions. By utilizing human interface technology, more accurate emotional data can be obtained. The server receives this data and adjusts the suggested content according to the user's emotional state. 【0527】 Specifically, if the system analyzes that a user is in an "excited" state, thrilled about a new adventure, the server can generate proactive offers such as "Get double bonus points now!" On the other hand, if the user is in a calm state, the server will offer more subdued suggestions such as "Your points will expire soon. Take your time to prepare and use them." 【0528】 This system allows users to optimally manage their points assets, and enables companies to develop marketing strategies tailored to user needs. 【0529】 An example of a prompt message is: "Analyze the user's current emotional state and generate appropriate suggestions for using the loyalty card based on that." 【0530】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0531】 Step 1: 【0532】 The user uses the device 【0533】 The user launches the smartphone app and registers by entering their points information. The entered information includes the points card number, expiration date, and accumulated points, and the device prepares to send the data to the server accordingly. The output of this step is the registration dataset. 【0534】 Step 2: 【0535】 The terminal sends data to the server. 【0536】 The terminal sends point information to the server. This data is encrypted and transmitted securely over the network. The server receives this data and stores it in its database. The output is a record of the stored point information. 【0537】 Step 3: 【0538】 The server manages history and expiration dates. 【0539】 The server retrieves point information from the database and performs functions to manage usage history and expiration dates. It processes data regarding point acquisition, usage, and the remaining period for today, keeping the information up-to-date. The output here is the updated point history and expiration information. 【0540】 Step 4: 【0541】 The server analyzes the data using an AI model. 【0542】 The server uses a generation AI model to analyze historical data and identify points that should be prioritized. The AI ​​is prompted with questions, and the model outputs analysis results. Based on these results, the server generates suggestions. The output consists of specific suggestions. 【0543】 Step 5: 【0544】 The device collects emotional data. 【0545】 As the user interacts with the app, the device analyzes facial expressions, voice tone, and input speed to collect emotional data. Input is real-time data obtained from the user interface, and output is the collected emotional data. 【0546】 Step 6: 【0547】 The device sends data to the emotion engine. 【0548】 Emotional data is sent to the emotion engine for analysis. This data is analyzed by the engine to identify the user's emotional state. The output of this step is the evaluation result of the user's emotional state. 【0549】 Step 7: 【0550】 The server generates suggestions based on emotions. 【0551】 The server receives the analysis results from the emotion engine and adjusts the suggestions based on them. It generates promotions and point usage advice tailored to the user's emotions, such as "excitement" or "calmness," and sends the results to the terminal. The output is the adjusted suggestions. 【0552】 Step 8: 【0553】 The device notifies the user. 【0554】 The terminal notifies the user of the suggestions received from the server. Notification methods include pop-ups and real-time feedback, allowing the user to review suggestions relevant to their situation. The output of this step is the information provided to the user and their feedback. 【0555】 (Application Example 2) 【0556】 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." 【0557】 A challenge with point programs is the lack of appropriate approaches based on user emotions, which prevents maximizing user purchasing intent. Furthermore, the inability to effectively use points before their expiration date is a problem. To address this, it is necessary to analyze users' emotional states and provide reward information tailored to those emotions to promote the use of point programs. 【0558】 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. 【0559】 In this invention, the server includes means for storing point program information, means for analyzing usage history using an intelligent model and suggesting point programs that should be used preferentially, and means for analyzing the user's emotional state using an emotion engine and generating suggestions that correspond to those emotions. This enables flexible service provision based on the user's emotions and promotes the effective use of point programs. 【0560】 "Point program information" refers to a collection of data related to points that users can earn or use across various services, including point balances and usage conditions. 【0561】 "Usage history" refers to data that records how a user has used the points program, including details about points earned and used. 【0562】 "Expiration date" refers to information indicating the period during which earned points can be used; points become invalid after this period. 【0563】 An "intelligent model" is an analytical system based on artificial intelligence technology, which uses statistical learning to identify specific patterns and make predictions from data. 【0564】 An "emotion engine" is a system for analyzing a user's emotions, and it is a technology that identifies the user's emotional state based on facial expressions, voice tone, and other factors. 【0565】 "Means for generating proposals" refers to a method or apparatus for automatically creating and providing information and services to users based on analytical data. 【0566】 To realize this invention, the server, user terminal, and emotion engine must function in close cooperation. Specific embodiments of the system are shown below. 【0567】 First, users register their points program information in the application using a device such as a smartphone or computer. This information is sent to a server via the internet and stored in a database on the server. Based on the points program information, the server manages usage history and expiration dates, and uses an intelligent model to analyze which points programs should be prioritized for the user. 【0568】 The device is equipped with an emotion engine that collects the user's facial expressions and voice through the camera and microphone. This allows the emotion engine to analyze the user's current emotional state in real time. The analysis results are sent to a server and used as data to generate reward information and suggestions tailored to the emotional state. For example, if the analysis indicates the user is "excited" when purchasing a specific product, the server will generate and display reward information such as "Get double points now!" on the device. 【0569】 This system utilizes services such as Google Cloud Natural Language API and Amazon Rekognition as its emotion engine. Cloud platforms such as AWS and Firebase are used for server data analysis. 【0570】 As a concrete example, suppose a user selects a specific product while online shopping, and their smartphone camera analyzes their facial expression, determining that they are in an excited state. In this case, the server can quickly generate a suggestion such as, "If you purchase now while you're highly excited, you'll receive extra points," and notify the device. 【0571】 The following prompt should be entered into the generative AI model: "Analyze the user's emotions while they are online shopping and generate the most appropriate suggestion message." 【0572】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0573】 Step 1: 【0574】 Users enter and register their points program information into the application using a device such as a smartphone. The entered information is transmitted to the server via the internet. The data entered by the user includes the type of points, balance, and expiration date, and the server stores and manages this information in a database. 【0575】 Step 2: 【0576】 The device uses its built-in camera and microphone to collect the user's facial expressions and voice data in real time and transmits it to the emotion engine. The emotion engine analyzes this data to identify the user's emotional state. The analysis results are generated as emotion tags such as "excited" and "calm," and sent to the server. 【0577】 Step 3: 【0578】 The server uses an intelligent model to analyze the received emotional state information. In this process, it combines points program information with the user's emotional state to generate response messages that include appropriate suggestions and rewards. The generated information influences the points program's expiration date and recommended point usage. 【0579】 Step 4: 【0580】 Response messages and reward information generated by the server are presented to the user via their device. This presentation is done via push notifications or in-app pop-ups, allowing users to consider using the points program based on this information. 【0581】 Step 5: 【0582】 Based on the information presented, the user decides whether to purchase a product or use points. The user's decision is then returned to Step 1 and entered into the system as new points program information, which serves as the basis for subsequent analysis and suggestions. 【0583】 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. 【0584】 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. 【0585】 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. 【0586】 [Fourth Embodiment] 【0587】 Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment. 【0588】 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. 【0589】 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). 【0590】 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. 【0591】 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. 【0592】 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). 【0593】 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. 【0594】 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. 【0595】 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. 【0596】 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. 【0597】 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. 【0598】 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. 【0599】 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". 【0600】 This invention is a point card management system implemented as a smartphone application, which functions by communicating data between a server and a terminal. Users register information about their point cards through the application. This includes the card name, number, store information, and associated login information. The registered information is sent from the terminal to the server and stored in the server's database. 【0601】 The server manages the usage history and expiration dates of each point card based on the registered point card information. This allows the server to constantly track which point cards each user uses, how often, and which are about to expire. Based on an artificial intelligence model, the server analyzes user usage history and extracts specific behavioral patterns to suggest the most suitable point card usage for each user. 【0602】 As a concrete example, if a user frequently shops at a particular store, the server will prioritize suggesting a point card that offers benefits at that store. For instance, if the user has points that are about to expire, the server will suggest using those points first. This allows the user to utilize their points efficiently without waste. 【0603】 Furthermore, the server statistically aggregates data collected from all users to analyze which loyalty cards are used most frequently and under what times and conditions. This analysis is provided to companies and used as a reference for marketing strategies and promotional activities. This enables companies to effectively plan and implement campaigns tailored to specific consumer behaviors. 【0604】 In this way, users can enjoy convenience, and companies can implement data-driven strategies to provide better customer experiences and increase revenue. 【0605】 The following describes the processing flow. 【0606】 Step 1: 【0607】 The user launches the smartphone app and goes to the point card information registration screen. The user enters the card name, number, store name, and login information. 【0608】 Step 2: 【0609】 The terminal structures the point card information entered by the user into data packets and sends them to the server using a secure communication protocol. 【0610】 Step 3: 【0611】 The server analyzes the received point card information and registers it in the database. During this process, it checks for duplicate entries and registers them as new data. 【0612】 Step 4: 【0613】 The server periodically checks the usage history and expiration date of each point card in the database and updates them as needed. 【0614】 Step 5: 【0615】 The server uses an AI model to analyze the user's usage history and extract specific behavioral patterns. Based on this analysis, it generates suggestions for loyalty cards that should be used preferentially. 【0616】 Step 6: 【0617】 The terminal notifies the user of the suggested results received from the server. Based on the notified information, the user decides which point card to use. 【0618】 Step 7: 【0619】 Users present the proposed point card when making purchases or using services, and then use the points. 【0620】 Step 8: 【0621】 The server aggregates usage history data collected from all users and performs market trend analysis. This result is provided to companies and used in developing marketing strategies. 【0622】 (Example 1) 【0623】 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". 【0624】 Modern consumers possess numerous loyalty programs and points systems, but lack the means to efficiently manage and utilize them. As a result, it is difficult to effectively use points, and consumers often let many points expire before their expiration date. Furthermore, companies lack the information necessary to make appropriate decisions about how to promote products to customers, making it difficult to develop effective marketing strategies. 【0625】 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. 【0626】 In this invention, the server includes means for recording information about point programs, means for controlling usage history and end date and time based on the recorded point program information, and means for analyzing usage history using a machine learning model and suggesting point programs that should be used preferentially. This enables consumers to efficiently manage their points and companies to build effective marketing strategies based on customer behavior. 【0627】 A "points system" is a means of recording value information that customers receive for purchasing goods or services, which allows them to receive benefits and discounts. 【0628】 A "machine learning model" is a type of algorithm used for data analysis, which predicts future behavior and patterns based on past data. 【0629】 "To control" means to organize and manage information, thereby facilitating its use and analysis according to a specific purpose. 【0630】 The "end date" refers to the final date within the period during which the points program is valid, and the use of points will be restricted after that date. 【0631】 "Presenting" means clearly showing users information such as analysis results and recommended actions, and is done to support their decision-making. 【0632】 This invention is a system for efficiently managing and utilizing a user's numerous point programs. The user inputs information about their point programs through a smart device application. This information includes the program name, identification number, provider information, and login-related information. 【0633】 The terminal uses a secure communication protocol such as SSL / TLS to send the entered point medium information to the server. After receiving this, the server verifies its integrity and records the data in a database. The database is used to manage usage history and end dates and times, and systematically structures the information for each point medium. 【0634】 The server uses generative AI models such as GPT-3 to analyze usage history data. This analysis extracts user behavior patterns and determines which point programs should be prioritized. Specifically, the AI ​​model considers the frequency of use and expiration dates of each program to make appropriate usage suggestions. These suggestions are communicated to the user in the form of a notification such as, "Your points will expire soon, so we recommend using them as a priority." 【0635】 Furthermore, the server analyzes the integrated data and provides businesses with market trends. Businesses can leverage this insight to develop the most effective promotions for consumers. For example, a possible prompt might be, "Please report the most frequently used points-based payment method in the last 30 days." 【0636】 This system allows users to make effective use of their points, and enables companies to develop more precise marketing strategies. 【0637】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0638】 Step 1: 【0639】 The user enters information about the point card using an application on their smart device. This information includes the card name, identification number, provider information, and login-related information. This information is sent to the application and prepared as a data packet within the device. 【0640】 Step 2: 【0641】 The terminal sends prepared data packets to the server using a secure communication protocol such as SSL / TLS. This communication maintains the security and accuracy of the input information. After receiving the information, the server verifies its integrity and records it in the database. As a result, the information is systematically stored and prepared for future queries. 【0642】 Step 3: 【0643】 The server initiates a process to manage usage history and expiration dates based on point media information in the database. Data on the retention period and usage status of each point media is used as input, and updated management information is generated as output. Specifically, usage counts and expiration reminders are set. 【0644】 Step 4: 【0645】 The server analyzes usage history data using a generative AI model. This analysis aims to extract behavioral patterns, and the AI ​​makes predictions based on past data. Usage history and end date / time information are provided as input, and the output is a suggestion of point-earning media that should be used preferentially. 【0646】 Step 5: 【0647】 The server notifies the user of the generated suggestions. This notification includes recommended ways to use points and helps the user make decisions. The content of the notification may be displayed in the form of, for example, "Your points will expire soon, so we recommend using them as soon as possible." 【0648】 Step 6: 【0649】 The server statistically analyzes overall usage data and generates market trend reports. The input is usage data from all users within a specified period, and the output generates new insights that can be used for marketing. This information is provided to companies, contributing to the development of effective promotions. 【0650】 (Application Example 1) 【0651】 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". 【0652】 Traditional point reward services made it difficult for users to efficiently utilize the most suitable service at the optimal time. Furthermore, the manual management of point card information and usage history was cumbersome, often resulting in points expiring. Additionally, the lack of integration between electronic money transfer services and point reward services made it difficult for users to select the most appropriate service for each payment. 【0653】 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. 【0654】 In this invention, the server includes means for registering point-granting service information, means for managing usage history and expiration dates based on the registered service information, means for analyzing usage history using a machine learning algorithm and suggesting the most suitable service to use, and means for integrating with an electronic money transfer service and automatically applying the most suitable service at the time of payment. As a result, users can always automatically utilize the most suitable point-granting service, reduce point waste, and enjoy the best possible customer experience. 【0655】 "Point accrual service information" refers to point-related data for users to record and manage, including information such as the point issuer, the number of points, and the expiration date. 【0656】 A "machine learning algorithm" is a computational method used to learn from data and identify patterns. In this invention, it is a technology applied to analyze a user's usage history and provide optimal suggestions. 【0657】 An "electronic fund transfer service" is a service that enables the sending and receiving of funds via the internet or mobile devices. In this invention, it is integrated with a point accrual service and has a function to automatically adjust the optimal use of points at the time of payment. 【0658】 "Usage history" refers to records showing a user's past use of point-granting services, and is data used to analyze user behavior trends based on this history. 【0659】 "Automatic suggestion" is a function that analyzes the user's past behavior patterns and proactively presents the optimal option. In this invention, it is used to select the most suitable point reward service at the time of payment. 【0660】 The system for implementing this invention functions by registering point-granting service information on the user's terminal and transmitting it to a server via data communication. This server stores the point-granting service information received from the user's terminal in a database and manages the usage history and expiration date. Furthermore, it uses a machine learning algorithm to analyze the user's usage history and propose the optimal service. Specifically, it integrates with electronic money transfer services at the time of payment and automatically applies the optimal service according to the user's payment behavior. 【0661】 The hardware of this system will consist of mobile devices such as users' smartphones and tablets, and the servers will utilize cloud-based servers (e.g., AWS, Google Cloud). For software, programming languages ​​such as Python and Node.js will be used on the server side, PostgreSQL will be used for the database, and frameworks such as TensorFlow and PyTorch will be used for machine learning. 【0662】 For example, if a user shops at a specific retailer on a daily basis, the system can recognize this behavioral pattern and suggest services that allow users to use their points more efficiently at that retailer. This enables users to make better use of their points. An example of a prompt to the generating AI model would be, "Based on the most recent purchase history, please suggest the best point-earning service to use." 【0663】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0664】 Step 1: 【0665】 The user operates the terminal and registers information about the point accrual service. The user launches the application and enters information such as the service name, number of points, and expiration date. The entered data is temporarily stored on the terminal and prepared to be sent to the server. 【0666】 Step 2: 【0667】 The terminal sends the registered points information to the server. The server adds the received information to its database and compares it with existing data. As a result, the new information is recorded in the database and becomes subject to management of usage history and expiration date. 【0668】 Step 3: 【0669】 The server uses a generative AI model to analyze usage history in the database. Specifically, it applies machine learning algorithms to extract user usage patterns and detect specific consumption behaviors. The input data is the usage history of all users, and the output is the behavioral patterns of each user. 【0670】 Step 4: 【0671】 Based on the analysis results, the server generates suggestions for the optimal use of points for the user. These suggestions are sent to the user's terminal, recommending priority use of points nearing expiration and suggesting use at specific stores. This allows the server to provide users with the most efficient point management. 【0672】 Step 5: 【0673】 The terminal notifies the user of suggestions received from the server. The user can review this notification and follow the suggested optimal way to use their points. The output of this process is reduced spending and effective use of points for the user. 【0674】 Step 6: 【0675】 When a user makes a purchase using the electronic money transfer service, the terminal automatically applies the optimal points based on suggestions received from the server. As a result, the user can benefit from optimized point usage at the time of payment. 【0676】 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. 【0677】 This invention combines an emotion engine with a point card management system to provide flexible services that respond to user emotions. The server, terminal, and emotion engine work together to improve user convenience and support more appropriate point card usage. 【0678】 Users register their loyalty card information using a smartphone app. This information is sent from the device to a server and stored in a database on the server. The server uses this information to manage the usage history and expiration dates of each card. Furthermore, the server uses an artificial intelligence model to analyze this usage history and determine which loyalty cards should be prioritized. The generated recommendations are then communicated to the user via the device. 【0679】 The emotion engine utilizes various data collected from the device to analyze the user's emotions. This includes the user's facial expressions, voice tone, and input behavior. The emotion engine identifies the user's current emotional state, and the suggested content is adjusted accordingly. 【0680】 For example, if the emotion engine analyzes the user's emotion as "excited," the server can generate proactive suggestions tailored to their excitement, such as a message saying, "Get double bonus points now!" On the other hand, if the user is determined to be in a "calm" state, calm suggestions regarding important expiring points will be made. 【0681】 Furthermore, the emotion engine learns long-term behavioral patterns based on the user's emotions and feeds this back to the server. This allows the server to more accurately predict user behavior and continuously optimize loyalty card usage according to the user's emotional state. Through these functions, users can improve their quality of life, and companies can build more effective marketing strategies. 【0682】 The following describes the processing flow. 【0683】 Step 1: 【0684】 The user launches the smartphone app and opens the screen to register their loyalty card information. Here, they enter their card details. 【0685】 Step 2: 【0686】 The terminal transmits the loyalty card information entered by the user to the server. This information includes the card name, number, store name, and login information. 【0687】 Step 3: 【0688】 The server stores the received point card information in a database. It checks for duplicate registrations and adds new registrations to the database. 【0689】 Step 4: 【0690】 The server periodically checks the database and updates the usage history and expiration date of each point card, ensuring that the information is always up-to-date. 【0691】 Step 5: 【0692】 The emotion engine collects data such as the user's facial expressions, voice tone, and input behavior obtained from the device, and analyzes the user's current emotional state. 【0693】 Step 6: 【0694】 Based on the analyzed emotional data, the server works in conjunction with an AI model to generate loyalty card usage suggestions tailored to the user's emotions. For example, if active speech is detected, it will suggest limited offers. 【0695】 Step 7: 【0696】 The device notifies the user of suggestions sent from the server. These suggestions include personalized messages based on emotions. 【0697】 Step 8: 【0698】 Users make purchases and use services based on the suggested loyalty cards. In this process, emotionally-driven offers may be utilized. 【0699】 Step 9: 【0700】 The server aggregates usage data from all users and analyzes usage history based on sentiment. This information is then provided to companies to help them develop marketing strategies. 【0701】 (Example 2) 【0702】 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". 【0703】 Traditional point systems have limitations in making suggestions based on user usage history and expiration date information, making it difficult to provide flexible services that take into account users' psychological states and emotions. Furthermore, because point usage suggestions that are tailored to the user are not provided, it is difficult to effectively utilize points, and companies are unable to formulate optimal marketing strategies. 【0704】 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. 【0705】 In this invention, the server includes means for registering point information, means for managing history and expiration dates, means for analyzing history using a machine learning model and suggesting preferential point usage, and means for identifying the user's psychological state using sentiment analysis technology and adjusting the suggested content. This enables appropriate point usage suggestions according to the user's emotional state, realizing effective point utilization and strategic marketing for companies. 【0706】 "Point information" refers to data related to the user's point card, including card number, expiration date, and accumulated points. 【0707】 "History" refers to a record of points earned and used by a user in the past. 【0708】 "Expiration date" refers to information indicating the period during which points are valid, including the expiration date. 【0709】 A "machine learning model" is an artificial intelligence technology that learns patterns based on data and uses them to predict future actions and choices. 【0710】 "Emotional analysis technology" refers to technologies that analyze a user's emotions and identify their psychological state, and includes techniques such as facial recognition and voice tone analysis. 【0711】 "Suggested content" refers to specific advice or messages generated by the server to encourage users to use their points. 【0712】 This invention integrates emotion analysis technology into a point management system, enabling flexible point usage suggestions tailored to the user's emotional state. 【0713】 Users register their points information using a smartphone app. The device sends this information to a server, which stores the registered information in a database. This allows for effective management of point history and expiration dates. 【0714】 The server uses a generative AI model to analyze historical data and gain insights to suggest appropriate point usage to users. This AI model learns past usage patterns and proposes the most effective way for users to use their point cards. 【0715】 The terminal monitors user behavior and uses emotion analysis technology to determine emotional states from facial expressions, voice tone, and input actions. By utilizing human interface technology, more accurate emotional data can be obtained. The server receives this data and adjusts the suggested content according to the user's emotional state. 【0716】 Specifically, if the system analyzes that a user is in an "excited" state, thrilled about a new adventure, the server can generate proactive offers such as "Get double bonus points now!" On the other hand, if the user is in a calm state, the server will offer more subdued suggestions such as "Your points will expire soon. Take your time to prepare and use them." 【0717】 This system allows users to optimally manage their points assets, and enables companies to develop marketing strategies tailored to user needs. 【0718】 An example of a prompt message is: "Analyze the user's current emotional state and generate appropriate suggestions for using the loyalty card based on that." 【0719】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0720】 Step 1: 【0721】 The user uses the device 【0722】 The user launches the smartphone app and registers by entering their points information. The entered information includes the points card number, expiration date, and accumulated points, and the device prepares to send the data to the server accordingly. The output of this step is the registration dataset. 【0723】 Step 2: 【0724】 The terminal sends data to the server. 【0725】 The terminal sends point information to the server. This data is encrypted and transmitted securely over the network. The server receives this data and stores it in its database. The output is a record of the stored point information. 【0726】 Step 3: 【0727】 The server manages history and expiration dates. 【0728】 The server retrieves point information from the database and performs functions to manage usage history and expiration dates. It processes data regarding point acquisition, usage, and the remaining period for today, keeping the information up-to-date. The output here is the updated point history and expiration information. 【0729】 Step 4: 【0730】 The server analyzes the data using an AI model. 【0731】 The server uses a generation AI model to analyze historical data and identify points that should be prioritized. The AI ​​is prompted with questions, and the model outputs analysis results. Based on these results, the server generates suggestions. The output consists of specific suggestions. 【0732】 Step 5: 【0733】 The device collects emotional data. 【0734】 As the user interacts with the app, the device analyzes facial expressions, voice tone, and input speed to collect emotional data. Input is real-time data obtained from the user interface, and output is the collected emotional data. 【0735】 Step 6: 【0736】 The device sends data to the emotion engine. 【0737】 Emotional data is sent to the emotion engine for analysis. This data is analyzed by the engine to identify the user's emotional state. The output of this step is the evaluation result of the user's emotional state. 【0738】 Step 7: 【0739】 The server generates suggestions based on emotions. 【0740】 The server receives the analysis results from the emotion engine and adjusts the suggestions based on them. It generates promotions and point usage advice tailored to the user's emotions, such as "excitement" or "calmness," and sends the results to the terminal. The output is the adjusted suggestions. 【0741】 Step 8: 【0742】 The device notifies the user. 【0743】 The terminal notifies the user of the suggestions received from the server. Notification methods include pop-ups and real-time feedback, allowing the user to review suggestions relevant to their situation. The output of this step is the information provided to the user and their feedback. 【0744】 (Application Example 2) 【0745】 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". 【0746】 A challenge with point programs is the lack of appropriate approaches based on user emotions, which prevents maximizing user purchasing intent. Furthermore, the inability to effectively use points before their expiration date is a problem. To address this, it is necessary to analyze users' emotional states and provide reward information tailored to those emotions to promote the use of point programs. 【0747】 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. 【0748】 In this invention, the server includes means for storing point program information, means for analyzing usage history using an intelligent model and suggesting point programs that should be used preferentially, and means for analyzing the user's emotional state using an emotion engine and generating suggestions that correspond to those emotions. This enables flexible service provision based on the user's emotions and promotes the effective use of point programs. 【0749】 "Point program information" refers to a collection of data related to points that users can earn or use across various services, including point balances and usage conditions. 【0750】 "Usage history" refers to data that records how a user has used the points program, including details about points earned and used. 【0751】 "Expiration date" refers to information indicating the period during which earned points can be used; points become invalid after this period. 【0752】 An "intelligent model" is an analytical system based on artificial intelligence technology, which uses statistical learning to identify specific patterns and make predictions from data. 【0753】 An "emotion engine" is a system for analyzing a user's emotions, and it is a technology that identifies the user's emotional state based on facial expressions, voice tone, and other factors. 【0754】 "Means for generating proposals" refers to a method or apparatus for automatically creating and providing information and services to users based on analytical data. 【0755】 To realize this invention, the server, user terminal, and emotion engine must function in close cooperation. Specific embodiments of the system are shown below. 【0756】 First, users register their points program information in the application using a device such as a smartphone or computer. This information is sent to a server via the internet and stored in a database on the server. Based on the points program information, the server manages usage history and expiration dates, and uses an intelligent model to analyze which points programs should be prioritized for the user. 【0757】 The device is equipped with an emotion engine that collects the user's facial expressions and voice through the camera and microphone. This allows the emotion engine to analyze the user's current emotional state in real time. The analysis results are sent to a server and used as data to generate reward information and suggestions tailored to the emotional state. For example, if the analysis indicates the user is "excited" when purchasing a specific product, the server will generate and display reward information such as "Get double points now!" on the device. 【0758】 This system utilizes services such as Google Cloud Natural Language API and Amazon Rekognition as its emotion engine. Cloud platforms such as AWS and Firebase are used for server data analysis. 【0759】 As a concrete example, suppose a user selects a specific product while online shopping, and their smartphone camera analyzes their facial expression, determining that they are in an excited state. In this case, the server can quickly generate a suggestion such as, "If you purchase now while you're highly excited, you'll receive extra points," and notify the device. 【0760】 The following prompt should be entered into the generative AI model: "Analyze the user's emotions while they are online shopping and generate the most appropriate suggestion message." 【0761】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0762】 Step 1: 【0763】 Users enter and register their points program information into the application using a device such as a smartphone. The entered information is transmitted to the server via the internet. The data entered by the user includes the type of points, balance, and expiration date, and the server stores and manages this information in a database. 【0764】 Step 2: 【0765】 The device uses its built-in camera and microphone to collect the user's facial expressions and voice data in real time and transmits it to the emotion engine. The emotion engine analyzes this data to identify the user's emotional state. The analysis results are generated as emotion tags such as "excited" and "calm," and sent to the server. 【0766】 Step 3: 【0767】 The server uses an intelligent model to analyze the received emotional state information. In this process, it combines points program information with the user's emotional state to generate response messages that include appropriate suggestions and rewards. The generated information influences the points program's expiration date and recommended point usage. 【0768】 Step 4: 【0769】 Response messages and reward information generated by the server are presented to the user via their device. This presentation is done via push notifications or in-app pop-ups, allowing users to consider using the points program based on this information. 【0770】 Step 5: 【0771】 Based on the information presented, the user decides whether to purchase a product or use points. The user's decision is then returned to Step 1 and entered into the system as new points program information, which serves as the basis for subsequent analysis and suggestions. 【0772】 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. 【0773】 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. 【0774】 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. 【0775】 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. 【0776】 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. 【0777】 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. 【0778】 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. 【0779】 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. 【0780】 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." 【0781】 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. 【0782】 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. 【0783】 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. 【0784】 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. 【0785】 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. 【0786】 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. 【0787】 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. 【0788】 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. 【0789】 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. 【0790】 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. 【0791】 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. 【0792】 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 as being incorporated by reference. 【0793】 The following is further disclosed regarding the embodiments described above. 【0794】 (Claim 1) 【0795】 A means of registering point card information, 【0796】 A means of managing usage history and expiration dates based on registered point card information, 【0797】 A method that uses an artificial intelligence model to analyze usage history and suggest point cards that should be used preferentially, 【0798】 A means of analyzing market trends using aggregated usage data and generating reports, 【0799】 A system that includes this. 【0800】 (Claim 2) 【0801】 The system according to claim 1, which makes priority offers based on the expiration date of a point card. 【0802】 (Claim 3) 【0803】 The system according to claim 1, which displays a notification to encourage use to the user. 【0804】 "Example 1" 【0805】 (Claim 1) 【0806】 A means of recording information related to point systems, 【0807】 A means of controlling usage history and end date and time based on recorded point media information, 【0808】 A method for analyzing usage history using a machine learning model and suggesting point-based services that should be prioritized for use, 【0809】 A means of analyzing market trends using statistically compiled usage information and generating reports, 【0810】 A means for transmitting point media information to a central management device via a network through a user's device, 【0811】 A means by which a central management device notifies users of recommended actions according to their usage status, 【0812】 A system that includes this. 【0813】 (Claim 2) 【0814】 The system according to claim 1 that makes priority proposals based on the end date and time of the point system. 【0815】 (Claim 3) 【0816】 The system according to claim 1, which displays a notification to encourage use to the user. 【0817】 "Application Example 1" 【0818】 (Claim 1) 【0819】 A means of registering information about point reward services, 【0820】 A means of managing usage history and expiration dates based on registered service information, 【0821】 A method for analyzing usage history using machine learning algorithms and suggesting the most suitable services to use, 【0822】 A means of analyzing market trends using aggregated usage data and generating reports, 【0823】 A means of integrating with electronic money transfer services, where the most suitable service is automatically applied at the time of payment, 【0824】 A system that includes this. 【0825】 (Claim 2) 【0826】 The system according to claim 1, which automatically proposes a point reward service when electronic funds are transferred. 【0827】 (Claim 3) 【0828】 The system according to claim 1, which displays a usage promotion notice to consumers. 【0829】 "Example 2 of combining an emotion engine" 【0830】 (Claim 1) 【0831】 A means of registering point information, 【0832】 A means of managing the history and expiration date based on registered point information, 【0833】 A method for analyzing history using machine learning models and suggesting points that should be prioritized for use, 【0834】 A means of identifying the user's psychological state using emotion analysis technology and adjusting the suggested content accordingly, 【0835】 A means of analyzing market trends using aggregated usage data and generating reports, 【0836】 A system that includes this. 【0837】 (Claim 2) 【0838】 The system according to claim 1, which makes priority proposals based on the expiration date of points. 【0839】 (Claim 3) 【0840】 The system according to claim 1, which displays a notification to encourage use to the user and provides suggestions tailored to their psychological state. 【0841】 "Application example 2 when combining with an emotional engine" 【0842】 (Claim 1) 【0843】 Means for storing point program information, 【0844】 A means of managing usage history and expiration date based on stored point program information, 【0845】 A means of analyzing usage history using an intelligent model and proposing point programs that should be used preferentially, 【0846】 A means of analyzing market conditions using aggregated usage data and generating reports, 【0847】 A means of analyzing the user's emotional state using an emotion engine and generating suggestions that correspond to those emotions, 【0848】 A means of promoting the use of the points program by providing reward information based on the current emotional state, 【0849】 A system that includes this. 【0850】 (Claim 2) 【0851】 The system according to claim 1, which makes preferential suggestions based on the expiration date of a points program and the emotional state of the user. 【0852】 (Claim 3) 【0853】 The system according to claim 1, which displays a notification to encourage use to a user that corresponds to their emotional state. [Explanation of symbols] 【0854】 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

[Claim 1] A means of registering point card information, A means of managing usage history and expiration dates based on registered point card information, A method that uses an artificial intelligence model to analyze usage history and suggest point cards that should be used preferentially, A means of analyzing market trends using aggregated usage data and generating reports, A system that includes this. [Claim 2] The system according to claim 1, which makes priority offers based on the expiration date of a point card. [Claim 3] The system according to claim 1, which displays a notification to encourage use to the user.

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