System

Abstract

A system is provided.SOLUTION: A system comprising: server means for receiving identification information of a user and initializing a model generated based on the identification information; terminal means for collecting transaction data of the user and transmitting the transaction data to a server; server means including a generative model for analyzing the received transaction data and detecting an abnormal transaction; server means for transmitting a notification to the user if an abnormal transaction is detected; and server means for receiving a response from the user and performing an approval or rejection of the transaction.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In today's smartphone payment market, while users enjoy high convenience, they are concerned about security risks such as fraudulent use and the leakage of personal information. When fraudulent transactions or abnormal access occur, they must be detected quickly and appropriate measures taken. However, current systems often lack sufficient real-time monitoring or immediate response to address these issues. Furthermore, they lack mechanisms for quickly responding to security-related questions and emergency inquiries from users. The purpose of this invention is to solve these issues. [Means for solving the problem]

[0005] The present invention provides a system including a server means for receiving a user's identification information and initializing a model generated based on the identification information, a terminal means for collecting the user's transaction data and transmitting the transaction data to the server, a server means including a generative model for analyzing the received transaction data and detecting anomalous transactions, a server means for sending a notification to the user when an anomalous transaction is detected, and a server means for receiving a response from the user and approving or rejecting the transaction. The system further includes a server means including a generative model for receiving security-related questions from the user, analyzing the questions, and generating an appropriate answer, and a means for transmitting the generated answer to the user's terminal. The system also includes a server means for receiving consultation requests from users 24 hours a day, 365 days a year and providing an appropriate response based on the request, and a means for notifying the user of the appropriate response, thereby realizing an environment in which users can use smartphone payments with peace of mind.

[0006] "User identification information" is information for identifying a user, and includes a username, password, device ID, and the like.

[0007] The "generated model" is a machine learning model that is initialized based on the user's identification information and is used to learn the user's trading patterns and detect anomalies.

[0008] The term "server means" refers to the entire server system for receiving, storing, analyzing, notifying, and processing responses from users.

[0009] "Transaction Data" refers to information relating to transactions conducted by a user, including details such as the transaction amount, transaction location, and transaction time.

[0010] "Terminal means" refers to a device used by a user, such as a smartphone or tablet, that has the function of collecting transaction data and transmitting it to a server.

[0011] A "generative model" is a model built using machine learning algorithms to detect anomalous transactions.

[0012] An "abnormal transaction" is a transaction that is determined to deviate from normal transaction patterns and may be fraudulent.

[0013] A "user terminal" is a device that a user normally uses, which provides an interface for transactions, notifications from the server, etc.

[0014] The "means for receiving a response" refers to a server-side function that receives approval or rejection information from the user and executes the next action based on that information.

[0015] "Security questions" refer to questions that users have about security and inquiries about how to deal with them.

[0016] A "generative model for question analysis" is a model that uses natural language processing to understand questions from users and generate appropriate answers to them.

[0017] "24 / 7 consultation requests" refers to requests from users seeking security consultation or help at any time.

[0018] "Means for taking appropriate action" refers to the server-side functionality that takes necessary action in response to a user request and provides appropriate advice and assistance. [Brief explanation of the drawings]

[0019] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3]FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0020] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0021] First, the terms used in the following description will be explained.

[0022] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0023] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0024] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0025] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0026] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0027] [First embodiment]

[0028] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0029] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0030] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0031] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0032] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0033] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0034] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0035] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0036] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0037] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0038] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0039] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0040] The present invention provides a system for analyzing transaction data in real time and detecting anomalous transactions by utilizing a generative model initialized using user identification information. This system is comprised of the following steps:

[0041] User registration and initial settings

[0042] A user accesses a registration form using a device such as a smartphone or tablet and enters identifying information such as personal and payment information. This information is sent to a server and stored in a database. The server then initializes a generative model based on the received identifying information and builds a user profile. This profile serves as the basis for learning the user's usual trading patterns and detecting anomalies.

[0043] Real-time monitoring and anomaly detection

[0044] When a user uses a smartphone payment system, transaction data (such as transaction amount, transaction location, and transaction time) is sent from the device to a server. The server then analyzes the received transaction data using a generative model to determine whether it deviates from normal transaction patterns. For example, a transaction involving a large amount exceeding the normal transaction amount or a transaction occurring at an unexpected time or location is considered an abnormal transaction.

[0045] If an abnormal transaction is detected, the server immediately sends a notification to the user. The notification includes details of the transaction and information for approving or rejecting the transaction. When the user views the notification, the terminal sends a response of approval or rejection to the server. Based on the user's response, the server either approves the transaction or takes measures against it as fraudulent.

[0046] Question and Answer feature

[0047] When a user has a security question, they send it to the server via their device. The server receives the question, analyzes it using a generative model, and generates an appropriate answer. The generated answer is sent to the user's device and immediately displayed to the user. For example, in response to the question, "What should I do if my account is compromised?", specific instructions are provided, such as, "Follow the steps below: 1. Lock your account. 2. Contact support."

[0048] 24 / 7 consultation service

[0049] This system has the ability to accept consultation requests from users 24 hours a day, 365 days a year. When a user requests a consultation, they send the request to the server via their terminal. The server responds appropriately based on the request and provides the necessary information and assistance. For example, if a user requests to "check their payment history," the server uses the generative model to obtain the necessary information and displays it on the user's terminal.

[0050] Specific examples

[0051] When User A tries to purchase a high-priced item online:

[0052] 1. User A selects a product on the terminal and proceeds with the purchase.

[0053] 2. Transaction data (item, amount, location, time, etc.) is sent from the terminal to the server.

[0054] 3. The server uses the generative model to analyze this transaction data and determines that it is an abnormal transaction due to the high amount.

[0055] 4. The server notifies User A's device, "A high-value transaction has been detected. Please check."

[0056] 5. User A checks the notification and presses the approval button on the terminal if he / she approves the transaction.

[0057] 6. The terminal sends the authorization information to the server, which then approves the transaction.

[0058] If User B experiences fraud:

[0059] 1. User B checks past transaction history on his / her terminal and discovers an unknown transaction.

[0060] 2. User B asks through his terminal, "What should I do if my account is fraudulently used?"

[0061] 3. The server uses the generative model to analyze User B's question and generate an appropriate answer.

[0062] 4. The server sends a message to User B's device saying, "If you experience fraudulent use, please follow the steps below: 1. Lock your account. 2. Contact support."

[0063] 5. User B follows the provided instructions to lock their account and contact the support center.

[0064] As described above, the present invention reduces the risk of fraudulent use and the leakage of personal information by monitoring user transaction data in real time and responding quickly when an abnormal transaction is detected. In addition, by providing a quick and appropriate response to user security-related questions and inquiries, users can use smartphone payments with peace of mind.

[0065] The processing flow will be explained below.

[0066] Step 1:

[0067] The user uses the terminal to input identification information such as personal information and payment information, and the terminal transmits the input information to the server.

[0068] Step 2:

[0069] The server stores the identification information received from the user in a database, and then initializes a generative model based on the received identification information to build a profile of the user.

[0070] Step 3:

[0071] When a user makes a transaction on the terminal, the terminal collects transaction data (transaction amount, transaction location, transaction time, etc.) and transmits the collected transaction data to the server.

[0072] Step 4:

[0073] The server uses the generative model to analyze the received transaction data and detects anomalous transactions that deviate from normal transaction patterns.

[0074] Step 5:

[0075] If the server detects an abnormal transaction, it immediately sends a notification to the user's device, which includes details of the transaction and the option to approve or reject the transaction.

[0076] Step 6:

[0077] The user reviews the notification at the terminal and, if they approve or reject the transaction, the terminal sends the response to the server.

[0078] Step 7:

[0079] The server receives the user's response and either approves the transaction or takes action to deter fraud. Approved transactions are processed normally, while rejected transactions are recorded as attempted transactions.

[0080] Step 8:

[0081] The user enters a security question and sends it to the server via the device, such as "What should I do if my account is compromised?"

[0082] Step 9:

[0083] The server analyzes the user's question using the generative model, generates an appropriate answer, and sends the generated answer to the user's device.

[0084] Step 10:

[0085] The server accepts consultation requests 24 hours a day, 365 days a year. Users can submit security consultations via their devices.

[0086] Step 11:

[0087] The server provides the user with an appropriate response based on the content of the consultation request, and sends the necessary information and assistance to the user's terminal.

[0088] This allows users to check the security of their transactions in real time, quickly alleviating any security concerns.

[0089] Example 1

[0090] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0091] In recent years, online payments using devices such as smartphones and tablets have become widespread. However, systems that monitor user transaction data in real time and quickly detect and respond to abnormal transactions are still inadequate. Furthermore, when users have security-related questions, there are limited ways to obtain immediate and appropriate answers. Furthermore, the lack of an environment where consultations can be made at any time poses the risk of fraudulent use and the leakage of personal information. Therefore, there is a need for a system that can effectively monitor user transaction data, detect and quickly respond to abnormal transactions, and generate appropriate answers to users' security questions.

[0092] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0093] In this invention, the server includes means for receiving a user's identification information and initializing a model generated based on the identification information; means for collecting the user's transaction data and transmitting the transaction data to the server; means for analyzing the received transaction data using the generative AI model to determine whether the transaction data deviates from normal transaction patterns; means for sending a notification to the user's terminal when an abnormal transaction is detected; and means for receiving a response from the user and approving or rejecting the transaction. This enables effective real-time monitoring of user transaction data and rapid detection and response to abnormal transactions. Furthermore, the generative AI model can provide appropriate answers to security-related questions from users, allowing users to use online payments with peace of mind. Furthermore, by responding to user consultation requests 24 hours a day, 365 days a year, the risk of further fraudulent use and personal information leaks can be reduced.

[0094] "User identification information" is information for identifying a user, such as personal information or payment information.

[0095] A "generated model" is a model constructed based on a user's identification information for learning the user's trading patterns.

[0096] The "server means" is a server that has the role of receiving, storing, analyzing, notifying, and processing responses to data.

[0097] "Terminal means" refers to a device such as a smartphone or tablet that allows a user to input and confirm transaction data and send it to the server.

[0098] "Transaction data" refers to data related to the payment made by the user, such as the transaction amount, transaction location, and transaction time.

[0099] A "generative AI model" is a model that uses artificial intelligence technology to analyze data and perform pattern recognition and anomaly detection.

[0100] An "unusual transaction" is a transaction that deviates from normal transaction patterns and is unexpected or suspected to be fraudulent.

[0101] "Notification" refers to a warning or confirmation message sent to a user when an abnormal transaction is detected.

[0102] A "response" is an operation of approval or denial that a user performs after receiving a notification.

[0103] "Security questions" are inquiries made by users to resolve doubts or concerns about fraudulent use, transaction confirmation, account management, etc.

[0104] An "appropriate answer" is the optimal response that the generative AI model analyzes and provides in response to a user's question.

[0105] A "consultation request" is a request for support regarding a problem or question a user has while using the service.

[0106] The present invention is a system that analyzes transaction data in real time and detects abnormal transactions by utilizing a generative AI model initialized using user identification information. This system is mainly composed of a server, a terminal, and a generative AI model.

[0107] User registration and initial settings

[0108] User Registration

[0109] Users access the registration form using a device such as a smartphone or tablet and enter personal information (such as name, email address, and phone number) and payment information (such as credit card information). The device then sends this identification information to the server.

[0110] Data storage and profile building

[0111] The server stores the received identification information in a database, which then initializes the generative AI model and builds a profile to learn the user's normal trading patterns. This profile serves as the basis for detecting abnormal transactions.

[0112] Real-time monitoring and anomaly detection

[0113] Transaction data transmission

[0114] When a user makes a smartphone payment, transaction data (transaction amount, transaction location, transaction time, etc.) is sent from the device to the server.

[0115] Transaction Data Analysis

[0116] The server inputs the received transaction data into a generative AI model (e.g., OpenAI's GPT-4) for analysis. The generative AI model determines whether the transaction data deviates from normal transaction patterns.

[0117] Abnormal transaction detection

[0118] The server determines the degree of anomaly in a transaction based on the analysis results of the generative AI model. For example, if the transaction amount exceeds the normal range or if the transaction is conducted in an unexpected location, it will be identified as an anomalous transaction. If an anomalous transaction is detected, the server will send a notification of the anomalous transaction to the user's device.

[0119] Notification confirmation and response

[0120] The user checks the notification on the terminal and performs an operation to approve or reject the transaction. The terminal sends response information to the server, and the server executes a process to approve or reject the transaction based on the user's response.

[0121] Question and Answer feature

[0122] Submit a question

[0123] The user enters a security question through the terminal and sends it to the server.

[0124] Question analysis and answer generation

[0125] The server inputs the received question into a generative AI model (e.g., ChatGPT) to generate an appropriate answer, which is then sent from the server to the user's device and displayed immediately.

[0126] Example: Question prompt

[0127] "If a fraudulent transaction is detected, how should I respond?"

[0128] "If I'm making a high-value transaction, do I need to set anything up beforehand?"

[0129] How can I check my past transaction history?

[0130] 24 / 7 consultation service

[0131] Submit a consultation request

[0132] A user inputs a consultation request through a terminal and transmits it to a server.

[0133] Request processing and response

[0134] The server analyzes the received request and takes appropriate action. For example, if the request is to check payment history, it retrieves the relevant history from the database and displays it on the terminal.

[0135] This system monitors user transaction data in real time, enabling rapid response if any abnormal transactions are detected. It also uses generative AI models to provide prompt and appropriate answers to security-related questions from users. Furthermore, a consultation desk is available 24 hours a day, 365 days a year, allowing users to use the system with peace of mind at any time.

[0136] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0137] Step 1: User Registration

[0138] Input: The user accesses the registration form from a device such as a smartphone or tablet and enters personal information and payment information.

[0139] Specific operation: The terminal sends the personal information (name, email address, phone number, etc.) and payment information (credit card information, etc.) entered by the user to the server.

[0140] Output: The server stores the received identification information in its database, and the user registration is complete.

[0141] Step 2: Initializing the Generative Model

[0142] Input: The server retrieves data to initialize the generative AI model based on the stored identification information.

[0143] Specific operation: The server inputs the user's identification information into the generative AI model and builds a profile to learn the user's usual trading patterns.

[0144] Output: The server saves the generated profile to a database and the generative AI model is initialized.

[0145] Step 3: Send transaction data

[0146] Input: When a user makes a smartphone payment, they enter transaction data (transaction amount, transaction location, transaction time, etc.) into the terminal.

[0147] Specific operation: The terminal sends transaction data to the server.

[0148] Output: The server takes the received transaction data and proceeds to the next analysis step.

[0149] Step 4: Transaction data analysis

[0150] Input: The server inputs the received transaction data into the generative AI model.

[0151] How it works: A generative AI model (e.g., OpenAI's GPT-4) analyzes transaction data to determine whether it deviates from normal transaction patterns.

[0152] Output: The generative AI model evaluates the degree of anomaly in the transaction and outputs the analysis results to the server.

[0153] Step 5: Identify abnormal transactions

[0154] Input: The server obtains the analysis results of the generative AI model.

[0155] Specific operation: The server determines the degree of abnormality of a transaction based on the analysis results of the generative AI model. For example, if the transaction amount exceeds the normal range or if the transaction is conducted in an unexpected location, it will be identified as an abnormal transaction.

[0156] Output: If an abnormal transaction is detected, the server sends a notification of the abnormal transaction to the user's terminal.

[0157] Step 6: Notification confirmation and response

[0158] Input: The user checks the terminal for an abnormal transaction notification.

[0159] Specific action: The user takes action to approve or reject the transaction.

[0160] Output: The terminal sends the user's response (approval or denial) to the server.

[0161] Step 7: Processing the transaction

[0162] Input: The server receives the user's response.

[0163] Specific operation: The server will execute the process to approve or reject the transaction based on the user's response. For example, if the transaction is approved, the process will proceed normally, and if it is rejected, the transaction will be canceled.

[0164] Output: The server sends the final result of the transaction to the user's terminal.

[0165] Step 8: Submit your question

[0166] Input: The user inputs the security question through the terminal and sends it to the server.

[0167] Specific operation: The terminal sends the user's question to the server.

[0168] Output: The server takes the received question and proceeds to the next parsing step.

[0169] Step 9: Question analysis and answer generation

[0170] Input: The server inputs the received question into the generative AI model.

[0171] How it works: A generative AI model (e.g., ChatGPT) analyzes the question and generates an appropriate answer.

[0172] Output: The generative AI model outputs the generated answer to the server.

[0173] Step 10: Submit your response

[0174] Input: The server gets the answer of the generative AI model.

[0175] Specific operation: The server sends the generated answer to the user's terminal.

[0176] Output: The answer is displayed on the user's terminal.

[0177] Step 11: Submit a consultation request

[0178] Input: The user inputs a consultation request through the terminal and sends it to the server.

[0179] Specific operation: The terminal sends a consultation request to the server.

[0180] Output: The server takes the received request and proceeds to the next appropriate response step.

[0181] Step 12: Request Processing and Response

[0182] Input: The server analyzes the received request.

[0183] Specific operation: The server responds appropriately based on the request content. For example, if the request is to check payment history, it retrieves the relevant history from the database and displays it on the user's device.

[0184] Output: The server sends the appropriate correspondence information to the user's terminal.

[0185] (Application example 1)

[0186] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0187] Conventional electronic payment systems have difficulty detecting fraudulent or abnormal transactions in real time, making it difficult to quickly and effectively protect user security. Furthermore, they lack the functionality to quickly provide appropriate answers to security-related questions, making it difficult to provide a 24 / 7 consultation service. This has led to problems with providing users with a sense of security.

[0188] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0189] In this invention, the server includes server means for receiving user identification information and initializing a model generated based on the identification information, terminal means for collecting user transaction data and transmitting the transaction data to the server, server means including a generative model for analyzing the received transaction data and detecting anomalous transactions, server means for transmitting a notification to the user when an anomalous transaction is detected, server means for receiving a response from the user and approving or rejecting the transaction, and application means for analyzing transaction-related data in real time and notifying the user's terminal. This makes it possible to effectively reduce the risk of fraudulent transactions by monitoring user transaction data in real time, quickly notifying the user when an anomalous transaction is detected, and receiving a response from the user.

[0190] The system also includes a server means including a generative model that receives security questions from users and analyzes the questions to generate appropriate answers, a means for transmitting the generated answers to the user's terminal, and a means for displaying transaction details and approval information so that the user can respond based on them. This makes it possible to provide quick and accurate answers when users ask questions about transactions.

[0191] The system also includes a server means for receiving consultation requests from users 24 hours a day, 365 days a year and taking appropriate action based on those requests, a means for notifying the user of the appropriate action, and a means for updating the user's profile and learning normal transaction patterns. This provides an environment where users can consult with confidence at any time, and by having the system learn the user's transaction patterns, it becomes possible to detect abnormal transactions with higher accuracy.

[0192] "User identification information" refers to information for identifying an individual user, including name, address, credit card information, and the like.

[0193] A "generative model" is a model constructed based on a user's identification information, and is a data analysis algorithm for learning a user's transaction patterns and detecting anomalous transactions.

[0194] "Transaction data" refers to information relating to a transaction conducted by a user, and includes the transaction amount, transaction location, transaction time, etc.

[0195] A "server" is a computer system for storing and processing data over the Internet.

[0196] A "terminal" is a device that is directly operated by a user, and includes smartphones, tablets, etc.

[0197] A "notification" is a message or alert sent to inform a user of specific information.

[0198] "Approval or rejection of transaction" refers to a user's response to approve or reject a transaction when the transaction is determined to be abnormal.

[0199] "Real-time" refers to data being processed and analyzed immediately at the moment it is generated.

[0200] An "application" is software with a specific function, in this case a smartphone program used to analyze and notify transaction data.

[0201] A "profile" is a collection of individual user information constructed based on the user's behavioral patterns and transaction information.

[0202] MODE FOR CARRYING OUT THE INVENTION

[0203] The present invention is a system that utilizes a generative model initialized using user identification information to analyze transaction data in real time and detect abnormal transactions. This system is composed of the following steps.

[0204] 1. User registration and initial settings

[0205] The server receives personal information and payment information entered by the user on the device and stores the corresponding identification information in a database. This identification information is used to initialize a generative model and build a user profile. The user profile serves as the basis for learning normal transaction patterns and detecting anomalous transactions.

[0206] 2. Real-time monitoring and anomaly detection

[0207] When a user makes an electronic payment, transaction data (such as transaction amount, transaction location, and transaction time) is sent from the terminal to the server. The server analyzes this transaction data and uses a generative model to determine whether it deviates from normal transaction patterns. If an abnormal transaction is determined, the server immediately sends a notification to the user and waits for a response (approval or rejection) from the user. This process makes it possible to quickly reduce the risk of abnormal transactions.

[0208] 3. Security Question and Answer Feature

[0209] When a user asks a security question, the question is sent to the server via the device. The server uses a generative model to analyze the question and generate an appropriate answer. This answer is sent to the user's device and displayed immediately. This allows the user to receive appropriate instructions and quickly respond to fraudulent use.

[0210] 4. 24 / 7 consultation service

[0211] When a user requests consultation, the request is sent to the server via their terminal. The server responds appropriately based on the request and provides the necessary information and assistance. This allows users to use the system with peace of mind at any time. The server also updates the user's profile and learns normal transaction patterns, enabling it to detect abnormal transactions with greater accuracy.

[0212] Specific examples

[0213] If a user tries to spend a large amount of money in the middle of the night, the server will determine this as an abnormal transaction and immediately send a notification to the user's terminal.

[0214] Example prompt: "You are planning to spend 120,000 yen in Osaka at 03:24."

[0215] As another example, if a user attempts a high-value transaction in an area where they do not normally make transactions, this is similarly reported as an abnormal transaction.

[0216] Example prompt: "I'm about to spend 50,000 yen at 8:30 PM in an unusual place."

[0217] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0218] Step 1:

[0219] The user accesses the registration form using a terminal and enters personal and payment information. The input data is sent to the server and stored in a database. The server then initializes a generative model based on the received identification information and builds a user profile.

[0220] (Input: User's personal information and payment information, Output: Initialized generative model and user profile)

[0221] Step 2:

[0222] When a user makes an electronic payment, transaction data (such as transaction amount, transaction location, and transaction time) is sent from the terminal to a server. The server receives this transaction data and uses a generative model to analyze whether it deviates from normal transaction patterns.

[0223] (Input: transaction data, Output: abnormal transaction determination results)

[0224] Step 3:

[0225] The server analyzes the transaction data using a generative model, and if an abnormal transaction is detected, it immediately sends a notification to the user's device, including details of the transaction and the option to approve or reject it.

[0226] (Input: Abnormal transaction judgment result, Output: Abnormal transaction notification to user)

[0227] Step 4:

[0228] The user checks the notification and sends a response to the server via the terminal approving or rejecting the transaction. The server receives the user's response and approves or rejects the transaction.

[0229] (Input: User response, Output: Final transaction processing result)

[0230] Step 5:

[0231] The user sends a security question from their device to the server, which uses the generative model to analyze the question and generate and send an appropriate answer.

[0232] (Input: User question, Output: Generated answer)

[0233] Step 6:

[0234] When a user sends a consultation request through the 24 / 7 consultation service, the server receives the request and responds appropriately, sending the necessary information and assistance to the user's device.

[0235] (Input: User consultation request, Output: Response result and notification to user)

[0236] Step 7:

[0237] The server constantly updates user profiles and learns normal trading patterns to improve the accuracy of the generative model, which enables more accurate detection of abnormal transactions.

[0238] (Input: new transaction data, Output: updated user profile and improved generative model)

[0239] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0240] The present invention is a system that analyzes transaction data in real time and detects abnormal transactions using a generative model and emotion engine initialized based on user identification information. The system includes a function to recognize user emotions and personalize notifications and responses based on those emotions.

[0241] User registration and initial settings

[0242] Users register by entering their personal and payment information through devices such as smartphones or tablets. This information is sent to the server and stored in a database. The server then initializes a generative model based on the received identification information to build an individual user profile. The emotion engine is also initialized, preparing to recognize emotions related to the user's transactions and responses.

[0243] Real-time monitoring and anomaly detection

[0244] When a user makes a transaction, the terminal collects transaction data (such as transaction amount, transaction location, and transaction time) and sends it to the server. The server then analyzes the transaction data using a generative model to detect anomalous transactions. For example, if a high-value transaction or an abnormal transaction pattern is detected, the server identifies it as an anomalous transaction.

[0245] When an abnormal transaction is detected, the server uses an emotion engine to evaluate the user's emotional state. The emotion engine recognizes the user's emotions from past transaction data and response data, and adjusts the notification content and countermeasures accordingly. For example, if the user is feeling anxious, the notification message will be designed to provide a more reassuring feeling.

[0246] Question and Answer feature

[0247] When a user submits a security question to the server via their device, the server uses the generative model to analyze the question and generate an appropriate answer. The emotion engine is also utilized here, providing answers that reflect the user's emotional state. For example, if the user is frustrated, the answer may include elements that will help them relax.

[0248] 24 / 7 consultation service

[0249] This system has the ability to accept consultation requests from users 24 hours a day, 365 days a year. When a user sends a consultation request via their device, the server receives it and uses an emotion engine to provide an appropriate response. For example, if the user is feeling stressed, the server will provide relaxation techniques and prompt support.

[0250] Specific examples

[0251] When User A tries to purchase a high-priced item online:

[0252] 1. User A selects a product on the terminal and proceeds with the purchase.

[0253] 2. Transaction data (item, amount, location, time) is sent from the terminal to the server.

[0254] 3. The server analyzes the transaction data using the generative model and detects abnormal transactions based on the high transaction amount.

[0255] 4. The server uses the emotion engine to evaluate User A's emotions and determines that he is feeling anxious.

[0256] 5. The server sends a reassuring notification to User A's device saying, "A high-value transaction has been detected, but it is within normal limits and safe. Please check."

[0257] 6. User A checks the notification and, if he approves the transaction, sends a response to the server via his terminal.

[0258] If User B experiences fraud:

[0259] 1. User B checks past transaction history on his / her terminal and discovers an unknown transaction.

[0260] 2. User B asks through his terminal, "What should I do if my account is fraudulently used?"

[0261] 3. The server uses the generative model to analyze the question and generate an appropriate answer.

[0262] 4. The emotion engine recognizes User B's emotional state as anxiety, so the server sends the response, "Please stay calm. Please follow the steps below to lock your account and contact support."

[0263] 5. User B follows the provided instructions to lock their account and contacts the support center.

[0264] In this way, the present invention monitors user transaction data in real time, and when an abnormal transaction is detected, provides an appropriate response taking into account the user's emotions. Furthermore, by using the emotion engine, it is possible to provide personalized responses to user security-related questions and inquiries, providing a sense of security.

[0265] The processing flow will be explained below.

[0266] Step 1:

[0267] The user uses the terminal to input identification information such as personal information and payment information, and the terminal transmits the input information to the server.

[0268] Step 2:

[0269] The server stores the identification information received from the user in a database, and then initializes the generative model and emotion engine based on the received identification information to build a user profile.

[0270] Step 3:

[0271] When a user makes a transaction on the terminal, the terminal collects transaction data (transaction amount, transaction location, transaction time, etc.) and transmits the collected transaction data to the server.

[0272] Step 4:

[0273] The server uses the generative model to analyze the received transaction data and detects anomalous transactions that deviate from normal transaction patterns.

[0274] Step 5:

[0275] If the server detects an anomalous transaction, it uses an emotion engine to evaluate the user's emotional state, for example, to determine whether the user is feeling anxious.

[0276] Step 6:

[0277] The server adjusts the notification content based on the evaluation results of the emotion engine. Notifications about abnormal transactions are sent to the user's device. For example, a message such as "A high-value transaction has been detected, but it is within the normal range and safe. Please check." is sent.

[0278] Step 7:

[0279] The user reviews the notification at the terminal and, if they approve or reject the transaction, the terminal sends the response to the server.

[0280] Step 8:

[0281] The server receives the user's response and either approves the transaction or takes action to deter fraud. Approved transactions are processed normally, while rejected transactions are recorded as attempted transactions.

[0282] Step 9:

[0283] The user enters a security question and sends it to the server via the device, such as "What should I do if my account is compromised?"

[0284] Step 10:

[0285] The server uses a generative model to analyze the user's question and generate an appropriate answer. An emotion engine is used to generate an answer that corresponds to the user's emotional state.

[0286] Step 11:

[0287] The server generates a response and sends it to the user's device, for example, "Please stay calm. Follow the steps below to lock your account and contact support."

[0288] Step 12:

[0289] The server accepts consultation requests 24 hours a day, 365 days a year. Users can submit security consultations via their devices.

[0290] Step 13:

[0291] The server receives the consultation request and provides an appropriate response using the emotion engine. Based on the request, the server sends the necessary information and assistance to the user's device.

[0292] This allows users to check the safety of their transactions in real time, quickly alleviating any security concerns. In addition, by utilizing an emotion engine, personalized responses can be made according to the user's emotional state.

[0293] Example 2

[0294] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0295] To provide a system that monitors user transaction data in real time and detects abnormal transactions, and to take appropriate action taking into account the emotional state of the user when an abnormal transaction is detected. To also provide personalized responses according to the emotional state of the user to security-related questions and consultation requests from users 24 hours a day, 365 days a year.

[0296] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0297] In this invention, the server includes means for receiving user identification information and initializing a generated model based on the identification information, means for collecting user transaction data and transmitting the transaction data to the server, means for analyzing the received transaction data and including a generative model for detecting anomalous transactions, means for evaluating the user's emotional state when an anomalous transaction is detected and adjusting notification content using an emotion engine, and means for receiving a response from the user and approving or rejecting the transaction, thereby enabling real-time transaction monitoring and response to anomalous transactions based on the user's emotional state.

[0298] The system further includes means including a generative model that receives a security question from a user and analyzes the question to generate an appropriate answer, means using an emotion engine that adjusts the generated answer according to the user's emotional state, and means for transmitting the generated answer to the user's terminal, thereby enabling the provision of a personalized answer according to the user's emotional state.

[0299] In addition, by including a means for receiving consultation requests from users 24 hours a day, 365 days a year and providing an appropriate response based on the request, a means using an emotion engine for adjusting the content of support in response to the consultation request based on the emotional state of the user, and a means for notifying the user of the appropriate response, it is possible to always provide optimized support to the user.

[0300] "Server means" refers to a computing device that receives user identification information and transaction data, analyzes the data using a generative AI model and emotion engine, detects anomalous transactions, and sends notifications.

[0301] "Terminal means" refers to electronic devices such as smartphones and tablets used by users, and is a device used to collect personal information and transaction data and send them to a server.

[0302] A "generative model" is an AI algorithm that analyzes input information such as user transaction data and is used to detect abnormal transactions and generate answers to questions.

[0303] The "emotion engine" is an algorithm that evaluates a user's emotional state based on the user's past transaction data and response data, and generates appropriate notifications and responses accordingly.

[0304] An "abnormal transaction" refers to a transaction that deviates from normal transaction patterns and may be an indication of fraud.

[0305] A "notification" is a message sent to a user when an abnormal transaction is detected, and is information that prompts the user to check the transaction or take caution.

[0306] "Terminal" refers to an electronic device (smartphone, tablet, PC, etc.) used by a user, which communicates with a server and sends and receives data.

[0307] A "user profile" is an individual set of information constructed based on a user's identification information and transaction history, and is used to initialize the generative model and operate the emotion engine.

[0308] "Personalized responses" refer to assistance and notifications that are tailored to the user's current emotional state and past behavior.

[0309] "Security questions" refer to questions that address any doubts or concerns a user may have about the system or a transaction, and the server generates appropriate answers to these questions.

[0310] "24 / 7 consultation request" refers to a request for consultation or support that a user can make to the system at any time, and is a request for an appropriate response to that request.

[0311] MODE FOR CARRYING OUT THE INVENTION

[0312] A specific embodiment of the present invention is described below: The present invention is a system that monitors user transaction data in real time, detects abnormal transactions and fraudulent activities, and, if an abnormality is detected, provides personalized notifications and responses based on the user's emotional state.

[0313] Hardware and software used

[0314] Server: A high-performance computing device used to process the generative AI model and emotion engine, including a database server where user identification information and transaction data are stored.

[0315] Terminal: An electronic device such as a smartphone, tablet, or PC used by a user to input user information and transaction data and send it to the server.

[0316] Generative AI model: An AI algorithm that analyzes user transaction data and detects anomalous transactions.

[0317] Emotion engine: An algorithm that assesses a user's emotional state based on their past transactions and responses, and tailors notifications and responses accordingly.

[0318] System Operation

[0319] First, a user registers with the system using a terminal. The user enters personal information such as name, date of birth, address, and payment information. This information is encrypted and sent to the server, which then initializes a generative model based on the received identification information and builds an individual user profile. The emotion engine is also initialized, ready to recognize emotions in the user's transactions and responses.

[0320] When a user makes a transaction, the terminal collects transaction data (such as transaction amount, transaction location, and transaction time) and sends it to the server. The server inputs the received transaction data into a generative AI model and begins analyzing it in real time. If an abnormal transaction is detected, the server uses an emotion engine to evaluate the user's emotional state. For example, if past data indicates that the user is feeling anxious, the server will send a notification to reassure the user.

[0321] The server sends the generated notification to the terminal, which displays it to the user, who can then review the notification and send approval or rejection of the transaction to the server via the terminal.

[0322] Examples:

[0323] When User A tries to purchase a high-priced item online:

[0324] 1. User A selects a product on the terminal and proceeds with the purchase.

[0325] 2. Transaction data (item, amount, location, time) is sent from the terminal to the server.

[0326] 3. The server analyzes the transaction data using the generative model and detects abnormal transactions based on the high transaction amount.

[0327] 4. The server uses the emotion engine to evaluate User A's emotions and determines that he is feeling anxious.

[0328] 5. The server sends a reassuring notification to User A's device saying, "A high-value transaction has been detected, but it is within normal limits and safe. Please check."

[0329] 6. User A checks the notification and, if he approves the transaction, sends a response to the server via his terminal.

[0330] Examples of prompts:

[0331] "Analyze the current transaction data and check for any abnormalities."

[0332] "What should I do if a user feels uneasy?"

[0333] The system also handles consultation requests 24 hours a day, 365 days a year. When a user sends a consultation request via their device, the server receives it and uses an emotion engine to provide an appropriate response. For example, if the user is feeling stressed, it can provide relaxation techniques or quick support.

[0334] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0335] Program processing flow

[0336] Step 1: User registration and initial setup

[0337] The user opens the dedicated app on their device and enters personal information (such as name, date of birth, and address) and payment information (such as credit card information) on the registration screen.

[0338] The terminal encrypts the entered personal information and payment information and sends it to the server.

[0339] The server receives the transmitted information and stores it in a database.

[0340] Input: User's personal information and payment information

[0341] Data processing: Information is encrypted and stored securely

[0342] Output: User profile stored in the server database

[0343] The server initializes a generative AI model based on the received identification information and builds an individual user profile.

[0344] The server initializes the emotion engine and prepares it for user emotion recognition.

[0345] Step 2: Collect transaction data

[0346] The user actually conducts the transaction using the terminal.

[0347] The terminal collects transaction data (transaction amount, transaction location, transaction time, etc.).

[0348] The terminal transmits the collected transaction data to the server.

[0349] Input: Transaction data (transaction amount, transaction location, transaction time, etc.)

[0350] Data processing: Collecting and formatting transaction data on the terminal

[0351] Output: Collected transaction data is encrypted and sent to the server

[0352] Step 3: Analyze transaction data and detect anomalies

[0353] The server receives the submitted transaction data and inputs it into the generative AI model.

[0354] The server analyzes transaction data using a generative AI model to detect anomalous transactions.

[0355] Input: Received transaction data

[0356] Data Computing: Data Analysis and Anomaly Detection with Generative AI Models

[0357] Output: Information about detected anomalous transactions

[0358] If the server detects an abnormal transaction, it records the details and performs emotion evaluation in the next step.

[0359] Step 4: Emotion evaluation and notification generation

[0360] The server uses an emotion engine to assess the user's emotional state in response to the anomalous transaction.

[0361] The server generates appropriate notification content based on the evaluation results.

[0362] The server sends the generated notification to the terminal.

[0363] Input: Abnormal transaction information, past transaction data, and user response data

[0364] Data Computation: Emotion engine evaluates emotional state and generates notification content

[0365] Output: A personalized notification message

[0366] The terminal displays the received notification to the user.

[0367] Step 5: Receiving and Processing User Response

[0368] The user checks the notification and responds at the terminal to approve or reject the transaction.

[0369] The terminal encrypts the user's response and sends it to the server.

[0370] The server analyzes the received user response and approves or denies the transaction.

[0371] Input: User response information (approval or rejection)

[0372] Data processing: analysis of response data

[0373] Output: Transaction status update, approved or rejected

[0374] Step 6: Generate questions and answers

[0375] The user submits a security question at the terminal.

[0376] The terminal sends a question to the server.

[0377] The server receives the question and inputs it into a generative AI model for analysis.

[0378] The server generates appropriate answers and adjusts the answers using an emotion engine.

[0379] Input: User question

[0380] Data computation: Parsing questions with generative AI models and tailoring answers with emotion engines

[0381] Output: A suitable answer for the user

[0382] The server sends the generated answer to the terminal.

[0383] The terminal displays the answer to the user.

[0384] Step 7: 24 / 7 consultation

[0385] A user sends a consultation request through a terminal.

[0386] The terminal sends a consultation request to the server.

[0387] A server receives the consultation request and uses an emotion engine to determine an appropriate response.

[0388] Input: User consultation request

[0389] Data calculation: Emotion engine evaluates emotional state and determines response

[0390] Output: personalized response

[0391] The server transmits the determined response content to the terminal.

[0392] The terminal displays the response to the user.

[0393] In this way, data processing and calculation are performed at each step, and personalized services are provided to the user.

[0394] (Application example 2)

[0395] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0396] While existing electronic payment services have systems for detecting abnormal transactions, they lack personalized responses that take into account the user's emotional state. This can cause anxiety and stress for users, resulting in a poor user experience. Another issue is that responses to security questions are standardized, and personalized answers are not provided based on the user's emotional state, resulting in a decrease in user satisfaction.

[0397] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving user identification information and initializing a model generated based on the identification information, means for collecting user transaction data and transmitting the transaction data to the server, means for analyzing the received transaction data and including a generative model for detecting anomalous transactions, means for sending a personalized notification to the user using an emotion engine when an anomalous transaction is detected, and means for receiving a response from the user and approving or rejecting the transaction. This makes it possible to detect anomalous transactions in real time and provide personalized responses that take user emotions into consideration.

[0398] The server also includes a means for receiving security questions from a user, analyzing the questions using a generative AI model, and generating an appropriate answer using an emotion engine, and a means for transmitting the generated answer to the user's device. This allows for personalized answers to be provided according to the user's emotional state, which is expected to improve the user experience.

[0399] Furthermore, the server includes a means for receiving consultation requests from users 24 hours a day, 365 days a year, and for using the emotion engine to provide an appropriate response based on the request, and a means for notifying the user of the appropriate response. This provides an environment where users can consult with confidence at any time, improving user satisfaction.

[0400] "User identification information" is data for uniquely identifying each individual user.

[0401] A "generative model" is an algorithm that is trained based on user identification information and transaction data to detect abnormal transactions and generate answers to questions.

[0402] The "server means" refers to a server device and its functions for managing user identification information and transaction data, and for performing analysis and notification.

[0403] "Terminal means" refers to equipment and communication functions for collecting data from users and transmitting it to a server.

[0404] "Transaction data" refers to information related to a user's transaction, including the transaction amount, transaction location, transaction time, and the like.

[0405] An "abnormal transaction" is a transaction that differs from normal transaction patterns and may be fraudulent.

[0406] The "means for sending a notification" is a function for sending a warning or a confirmation message to the user when an abnormal transaction is detected.

[0407] An "emotion engine" is an algorithm that analyzes a user's emotional state and adjusts the content of notifications and responses according to the user's emotions.

[0408] The "means for receiving a response" is a function that receives a response or confirmation from the user and executes processing based on that response.

[0409] "Security Questions" are questions that users can ask to answer any concerns they may have about the security of the system or fraudulent transactions.

[0410] A "consultation request" is a request from a user to the system for advice or support.

[0411] The "means for taking appropriate action using an emotion engine" is a function for providing appropriate countermeasures by taking into account the emotional state of the user.

[0412] System Configuration

[0413] A system embodying the invention comprises the following major components:

[0414] 1. Server Means

[0415] User Profile Management: A server that receives user identification and personal information and initializes the generative model and emotion engine.

[0416] Transaction data analysis: A server that receives user transaction data in real time and uses generative models to detect anomalous transactions.

[0417] Sentiment Engine: Evaluates user sentiment upon detected anomalous transactions and generates personalized notifications.

[0418] Notification and response management: Send personalized notifications to users and receive responses to approve or reject transactions.

[0419] 2. Terminal means

[0420] Data collection and transmission: A device such as a smartphone that collects user transaction data in real time and transmits it to a server.

[0421] Receive and display notifications: Receive notifications sent from the server and display them to the user.

[0422] Implementation method

[0423] 1. User registration and initial settings

[0424] Users register with the system by entering their personal and payment information using a device such as a smartphone. The registered information is sent to the server, which creates a user profile. The server then initializes the generative model and emotion engine, preparing to analyze the user's transactions and emotions.

[0425] 2. Real-time transaction monitoring

[0426] When a user makes a transaction, the terminal collects transaction data (transaction amount, transaction location, transaction time, etc.) and sends it to the server. The server analyzes the transaction data using a generative model to detect abnormal transactions.

[0427] 3. Handling Abnormal Transactions

[0428] If an abnormal transaction is detected, the server uses an emotion engine to evaluate the user's emotional state. For example, if the server determines that the user is feeling anxious, it sends a personalized notification offering reassurance. The user then sends a response to the server to approve or reject the transaction accordingly.

[0429] 4. Responses to security questions

[0430] When a user submits a security question to the server via their device, the server uses a generative AI model to analyze the question and leverages an emotion engine to generate an appropriate answer, which is also personalized according to the user's emotional state.

[0431] 5. 24 / 7 consultation service

[0432] The system has the function of accepting consultation requests from users 24 hours a day, 365 days a year. The server receives the consultation request, uses the emotion engine to respond appropriately, and notifies the user.

[0433] Hardware and software used

[0434] Servers: Server equipment, database servers (e.g., MySQL or PostgreSQL), and computing units for generative AI models (e.g., GPU servers)

[0435] Device: Smartphone, tablet, or any device with an internet connection

[0436] Software: Emotion engine (e.g., Python library EmotionAPI), trading data analysis (e.g., generative models using TensorFlow or PyTorch)

[0437] Specific examples

[0438] For example, if a user attempts to purchase a high-value item online, the transaction data is sent from the device to the server. The server detects the transaction as an abnormal transaction and uses an emotion engine to evaluate the user's emotion as "anxious." The server then sends a personalized notification saying, "A high-value transaction has been detected, but it is within normal limits and safe. Please confirm."

[0439] Prompt Sentence Examples

[0440] User ID: 1

[0441] Emotional state: Anxiety

[0442] Transaction data: {'amount': 1000, 'location': 'Online Store', 'time': 'YYYY-MM-DD HH:MM:SS'}

[0443] Question: "A high-value transaction was detected, but it is within normal limits and safe. Please confirm."

[0444] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0445] Step 1:

[0446] Users use devices such as smartphones or tablets to enter personal information and payment information and register with the system.

[0447] Input: Personal information, payment information

[0448] Data processing / calculation: The device formats this information and sends it to the server in a secure manner.

[0449] Output: Registration request data

[0450] Step 2:

[0451] The server generates a user profile based on the received personal information and payment information, stores it in a database, and initializes the generative model and emotion engine based on the user.

[0452] Input: Registration request data

[0453] Data processing / calculation: Store information in a database and perform initialization processes for the generative model and emotion engine.

[0454] Output: Initialized generative model and emotion engine

[0455] Step 3:

[0456] When a user makes a transaction, the terminal collects transaction data (transaction amount, transaction location, transaction time, etc.) in real time and transmits it to the server.

[0457] Input: Transaction data (transaction amount, transaction location, transaction time, etc.)

[0458] Data processing / calculation: The terminal formats the transaction data and sends it to the server

[0459] Output: Transaction data sent

[0460] Step 4:

[0461] The server analyzes the received transaction data using a generative model to detect abnormal transactions.

[0462] Input: Transaction data sent

[0463] Data processing / calculation: Analysis of abnormal transactions using generative models

[0464] Output: Detected abnormal transactions

[0465] Step 5:

[0466] If an abnormal transaction is detected, the server uses an emotion engine to assess the user's emotional state, and based on that, generates a reassuring personalized notification and sends it to the user's device.

[0467] Input: Detected abnormal transactions, past user behavior data

[0468] Data processing / calculation: Emotional state evaluation by emotion engine, generation of notification messages

[0469] Output: A personalized notification message

[0470] Step 6:

[0471] The user checks the notification on the terminal and sends a response to the server approving or rejecting the transaction.

[0472] Input: Notification message

[0473] Data processing / calculation: User confirmation and response input, response transmission from terminal to server

[0474] Output: Approval or rejection response data

[0475] Step 7:

[0476] Based on the response received, the server will either approve or reject the transaction.

[0477] Input: Approval or rejection response data

[0478] Data processing / calculation: Approval or rejection execution process

[0479] Output: Transaction approval or rejection result

[0480] Step 8:

[0481] The user sends a security question to the server via the terminal.

[0482] Input: Question

[0483] Data processing / calculation: Sending query data

[0484] Output: Received query data

[0485] Step 9:

[0486] The server uses a generative AI model to analyze the question and generates an appropriate answer using an emotion engine.

[0487] Input: Received query data

[0488] Data processing / calculation: Question analysis using a generative AI model, emotion evaluation and answer generation using an emotion engine

[0489] Output: personalized answer

[0490] Step 10:

[0491] The server sends the generated answer to the user's terminal.

[0492] Input: Personalized Answer

[0493] Data processing / calculation: Sending response data

[0494] Output: The answer displayed on the user's terminal

[0495] Step 11:

[0496] The server accepts consultation requests from users 24 hours a day, 365 days a year, and uses an emotion engine to provide an appropriate response based on the request.

[0497] Input: Consultation request

[0498] Data processing / calculation: Analysis of consultation content and generation of countermeasures using an emotion engine

[0499] Output: personalized response

[0500] Step 12:

[0501] The server will notify the user of the appropriate action.

[0502] Input: Personalized Response

[0503] Data processing / calculation: Sending notification data

[0504] Output: The solution displayed on the user's device

[0505] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0506] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0507] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0508] [Second embodiment]

[0509] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0510] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0511] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0512] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0513] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0514] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0515] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0516] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0517] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0518] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0519] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0520] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0521] The present invention provides a system for analyzing transaction data in real time and detecting anomalous transactions by utilizing a generative model initialized using user identification information. This system is comprised of the following steps:

[0522] User registration and initial settings

[0523] A user accesses a registration form using a device such as a smartphone or tablet and enters identifying information such as personal and payment information. This information is sent to a server and stored in a database. The server then initializes a generative model based on the received identifying information and builds a user profile. This profile serves as the basis for learning the user's usual trading patterns and detecting anomalies.

[0524] Real-time monitoring and anomaly detection

[0525] When a user uses a smartphone payment system, transaction data (such as transaction amount, transaction location, and transaction time) is sent from the device to a server. The server then analyzes the received transaction data using a generative model to determine whether it deviates from normal transaction patterns. For example, a transaction involving a large amount exceeding the normal transaction amount or a transaction occurring at an unexpected time or location is considered an abnormal transaction.

[0526] If an abnormal transaction is detected, the server immediately sends a notification to the user. The notification includes details of the transaction and information for approving or rejecting the transaction. When the user views the notification, the terminal sends a response of approval or rejection to the server. Based on the user's response, the server either approves the transaction or takes measures against it as fraudulent.

[0527] Question and Answer feature

[0528] When a user has a security question, they send it to the server via their device. The server receives the question, analyzes it using a generative model, and generates an appropriate answer. The generated answer is sent to the user's device and immediately displayed to the user. For example, in response to the question, "What should I do if my account is compromised?", specific instructions are provided, such as, "Follow the steps below: 1. Lock your account. 2. Contact support."

[0529] 24 / 7 consultation service

[0530] This system has the ability to accept consultation requests from users 24 hours a day, 365 days a year. When a user requests a consultation, they send the request to the server via their terminal. The server responds appropriately based on the request and provides the necessary information and assistance. For example, if a user requests to "check their payment history," the server uses the generative model to obtain the necessary information and displays it on the user's terminal.

[0531] Specific examples

[0532] When User A tries to purchase a high-priced item online:

[0533] 1. User A selects a product on the terminal and proceeds with the purchase.

[0534] 2. Transaction data (item, amount, location, time, etc.) is sent from the terminal to the server.

[0535] 3. The server uses the generative model to analyze this transaction data and determines that it is an abnormal transaction due to the high amount.

[0536] 4. The server notifies User A's device, "A high-value transaction has been detected. Please check."

[0537] 5. User A checks the notification and presses the approval button on the terminal if he / she approves the transaction.

[0538] 6. The terminal sends the authorization information to the server, which then approves the transaction.

[0539] If User B experiences fraud:

[0540] 1. User B checks past transaction history on his / her terminal and discovers an unknown transaction.

[0541] 2. User B asks through his terminal, "What should I do if my account is fraudulently used?"

[0542] 3. The server uses the generative model to analyze User B's question and generate an appropriate answer.

[0543] 4. The server sends a message to User B's device saying, "If you experience fraudulent use, please follow the steps below: 1. Lock your account. 2. Contact support."

[0544] 5. User B follows the provided instructions to lock their account and contact the support center.

[0545] As described above, the present invention reduces the risk of fraudulent use and the leakage of personal information by monitoring user transaction data in real time and responding quickly when an abnormal transaction is detected. In addition, by providing a quick and appropriate response to user security-related questions and inquiries, users can use smartphone payments with peace of mind.

[0546] The processing flow will be explained below.

[0547] Step 1:

[0548] The user uses the terminal to input identification information such as personal information and payment information, and the terminal transmits the input information to the server.

[0549] Step 2:

[0550] The server stores the identification information received from the user in a database, and then initializes a generative model based on the received identification information to build a profile of the user.

[0551] Step 3:

[0552] When a user makes a transaction on the terminal, the terminal collects transaction data (transaction amount, transaction location, transaction time, etc.) and transmits the collected transaction data to the server.

[0553] Step 4:

[0554] The server uses the generative model to analyze the received transaction data and detects anomalous transactions that deviate from normal transaction patterns.

[0555] Step 5:

[0556] If the server detects an abnormal transaction, it immediately sends a notification to the user's device, including details of the transaction and the option to approve or reject the transaction.

[0557] Step 6:

[0558] The user reviews the notification at the terminal and, if they approve or reject the transaction, the terminal sends the response to the server.

[0559] Step 7:

[0560] The server receives the user's response and either approves the transaction or takes action to deter fraud. Approved transactions are processed normally, while rejected transactions are recorded as attempted transactions.

[0561] Step 8:

[0562] The user enters a security question and sends it to the server via the device, such as "What should I do if my account is compromised?"

[0563] Step 9:

[0564] The server analyzes the user's question using the generative model, generates an appropriate answer, and sends the generated answer to the user's device.

[0565] Step 10:

[0566] The server accepts consultation requests 24 hours a day, 365 days a year. Users can submit security consultations via their devices.

[0567] Step 11:

[0568] The server provides the user with an appropriate response based on the content of the consultation request, and sends the necessary information and assistance to the user's terminal.

[0569] This allows users to check the security of their transactions in real time, quickly alleviating any security concerns.

[0570] Example 1

[0571] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0572] In recent years, online payments using devices such as smartphones and tablets have become widespread. However, systems that monitor user transaction data in real time and quickly detect and respond to abnormal transactions are still inadequate. Furthermore, when users have security-related questions, there are limited ways to obtain immediate and appropriate answers. Furthermore, the lack of an environment where consultations can be made at any time poses the risk of fraudulent use and the leakage of personal information. Therefore, there is a need for a system that can effectively monitor user transaction data, detect and quickly respond to abnormal transactions, and generate appropriate answers to users' security questions.

[0573] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0574] In this invention, the server includes means for receiving a user's identification information and initializing a model generated based on the identification information; means for collecting the user's transaction data and transmitting the transaction data to the server; means for analyzing the received transaction data using the generative AI model to determine whether the transaction data deviates from normal transaction patterns; means for sending a notification to the user's terminal when an abnormal transaction is detected; and means for receiving a response from the user and approving or rejecting the transaction. This enables effective real-time monitoring of user transaction data and rapid detection and response to abnormal transactions. Furthermore, the generative AI model can provide appropriate answers to security-related questions from users, allowing users to use online payments with peace of mind. Furthermore, by responding to user consultation requests 24 hours a day, 365 days a year, the risk of further fraudulent use and personal information leaks can be reduced.

[0575] "User identification information" is information for identifying a user, such as personal information or payment information.

[0576] A "generated model" is a model constructed based on a user's identification information for learning the user's trading patterns.

[0577] The "server means" is a server that has the role of receiving, storing, analyzing, notifying, and processing responses to data.

[0578] "Terminal means" refers to a device such as a smartphone or tablet that allows a user to input and confirm transaction data and send it to the server.

[0579] "Transaction data" refers to data related to the payment made by the user, such as the transaction amount, transaction location, and transaction time.

[0580] A "generative AI model" is a model that uses artificial intelligence technology to analyze data and perform pattern recognition and anomaly detection.

[0581] An "unusual transaction" is a transaction that deviates from normal transaction patterns and is unexpected or suspected to be fraudulent.

[0582] "Notification" refers to a warning or confirmation message sent to a user when an abnormal transaction is detected.

[0583] A "response" is an operation of approval or denial that a user performs after receiving a notification.

[0584] "Security questions" are inquiries made by users to resolve doubts or concerns about fraudulent use, transaction confirmation, account management, etc.

[0585] An "appropriate answer" is the optimal response that the generative AI model analyzes and provides in response to a user's question.

[0586] A "consultation request" is a request for support regarding a problem or question a user has while using the service.

[0587] The present invention is a system that analyzes transaction data in real time and detects abnormal transactions by utilizing a generative AI model initialized using user identification information. This system is mainly composed of a server, a terminal, and a generative AI model.

[0588] User registration and initial settings

[0589] User Registration

[0590] Users access the registration form using a device such as a smartphone or tablet and enter personal information (such as name, email address, and phone number) and payment information (such as credit card information). The device then sends this identification information to the server.

[0591] Data storage and profile building

[0592] The server stores the received identification information in a database, which then initializes the generative AI model and builds a profile to learn the user's normal trading patterns. This profile serves as the basis for detecting abnormal transactions.

[0593] Real-time monitoring and anomaly detection

[0594] Transaction data transmission

[0595] When a user makes a smartphone payment, transaction data (transaction amount, transaction location, transaction time, etc.) is sent from the device to the server.

[0596] Transaction Data Analysis

[0597] The server inputs the received transaction data into a generative AI model (e.g., OpenAI's GPT-4) for analysis. The generative AI model determines whether the transaction data deviates from normal transaction patterns.

[0598] Abnormal transaction detection

[0599] The server determines the degree of anomaly in a transaction based on the analysis results of the generative AI model. For example, if the transaction amount exceeds the normal range or if the transaction is conducted in an unexpected location, it will be identified as an anomalous transaction. If an anomalous transaction is detected, the server will send a notification of the anomalous transaction to the user's device.

[0600] Notification confirmation and response

[0601] The user checks the notification on the terminal and performs an operation to approve or reject the transaction. The terminal sends response information to the server, and the server executes a process to approve or reject the transaction based on the user's response.

[0602] Question and Answer feature

[0603] Submit a question

[0604] The user enters a security question through the terminal and sends it to the server.

[0605] Question analysis and answer generation

[0606] The server inputs the received question into a generative AI model (e.g., ChatGPT) to generate an appropriate answer, which is then sent from the server to the user's device and displayed immediately.

[0607] Example: Question prompt

[0608] "If a fraudulent transaction is detected, how should I respond?"

[0609] "If I'm making a high-value transaction, do I need to set anything up beforehand?"

[0610] How can I check my past transaction history?

[0611] 24 / 7 consultation service

[0612] Submit a consultation request

[0613] A user inputs a consultation request through a terminal and transmits it to a server.

[0614] Request processing and response

[0615] The server analyzes the received request and takes appropriate action. For example, if the request is to check payment history, it retrieves the relevant history from the database and displays it on the terminal.

[0616] This system monitors user transaction data in real time, enabling rapid response if any abnormal transactions are detected. It also uses generative AI models to provide prompt and appropriate answers to security-related questions from users. Furthermore, a consultation desk is available 24 hours a day, 365 days a year, allowing users to use the system with peace of mind at any time.

[0617] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0618] Step 1: User Registration

[0619] Input: The user accesses the registration form from a device such as a smartphone or tablet and enters personal information and payment information.

[0620] Specific operation: The terminal sends the personal information (name, email address, phone number, etc.) and payment information (credit card information, etc.) entered by the user to the server.

[0621] Output: The server stores the received identification information in its database and the user registration is complete.

[0622] Step 2: Initializing the generative model

[0623] Input: The server retrieves data to initialize the generative AI model based on the stored identification information.

[0624] Specific operation: The server inputs the user's identification information into the generative AI model and builds a profile to learn the user's usual trading patterns.

[0625] Output: The server saves the generated profile to a database and the generative AI model is initialized.

[0626] Step 3: Send transaction data

[0627] Input: When a user makes a smartphone payment, they enter transaction data (transaction amount, transaction location, transaction time, etc.) into the terminal.

[0628] Specific operation: The terminal sends transaction data to the server.

[0629] Output: The server takes the received transaction data and proceeds to the next analysis step.

[0630] Step 4: Transaction data analysis

[0631] Input: The server inputs the received transaction data into the generative AI model.

[0632] How it works: A generative AI model (e.g., OpenAI's GPT-4) analyzes transaction data to determine whether it deviates from normal transaction patterns.

[0633] Output: The generative AI model evaluates the degree of anomaly in the transaction and outputs the analysis results to the server.

[0634] Step 5: Identify abnormal transactions

[0635] Input: The server obtains the analysis results of the generative AI model.

[0636] Specific operation: The server determines the degree of abnormality of a transaction based on the analysis results of the generative AI model. For example, if the transaction amount exceeds the normal range or if the transaction is conducted in an unexpected location, it will be identified as an abnormal transaction.

[0637] Output: If an abnormal transaction is detected, the server sends a notification of the abnormal transaction to the user's terminal.

[0638] Step 6: Notification confirmation and response

[0639] Input: The user checks the terminal for an abnormal transaction notification.

[0640] Specific action: The user takes action to approve or reject the transaction.

[0641] Output: The terminal sends the user's response (approval or denial) to the server.

[0642] Step 7: Processing the transaction

[0643] Input: The server receives the user's response.

[0644] Specific operation: The server will execute the process to approve or reject the transaction based on the user's response. For example, if the transaction is approved, the process will proceed normally, and if it is rejected, the transaction will be canceled.

[0645] Output: The server sends the final result of the transaction to the user's terminal.

[0646] Step 8: Submit your question

[0647] Input: The user inputs the security question through the terminal and sends it to the server.

[0648] Specific operation: The terminal sends the user's question to the server.

[0649] Output: The server takes the received question and proceeds to the next parsing step.

[0650] Step 9: Question analysis and answer generation

[0651] Input: The server inputs the received question into the generative AI model.

[0652] How it works: A generative AI model (e.g., ChatGPT) analyzes the question and generates an appropriate answer.

[0653] Output: The generative AI model outputs the generated answer to the server.

[0654] Step 10: Submit your response

[0655] Input: The server gets the answer of the generative AI model.

[0656] Specific operation: The server sends the generated answer to the user's terminal.

[0657] Output: The answer is displayed on the user's terminal.

[0658] Step 11: Submit a consultation request

[0659] Input: The user inputs a consultation request through the terminal and sends it to the server.

[0660] Specific operation: The terminal sends a consultation request to the server.

[0661] Output: The server takes the received request and proceeds to the next appropriate response step.

[0662] Step 12: Request Processing and Response

[0663] Input: The server analyzes the received request.

[0664] Specific operation: The server responds appropriately based on the request content. For example, if the request is to check payment history, it retrieves the relevant history from the database and displays it on the user's device.

[0665] Output: The server sends the appropriate correspondence information to the user's terminal.

[0666] (Application example 1)

[0667] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0668] Conventional electronic payment systems have difficulty detecting fraudulent or abnormal transactions in real time, making it difficult to quickly and effectively protect user security. Furthermore, they lack the functionality to quickly provide appropriate answers to security-related questions, making it difficult to provide a 24 / 7 consultation service. This has led to problems with providing users with a sense of security.

[0669] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0670] In this invention, the server includes server means for receiving user identification information and initializing a model generated based on the identification information, terminal means for collecting user transaction data and transmitting the transaction data to the server, server means including a generative model for analyzing the received transaction data and detecting anomalous transactions, server means for transmitting a notification to the user when an anomalous transaction is detected, server means for receiving a response from the user and approving or rejecting the transaction, and application means for analyzing transaction-related data in real time and notifying the user's terminal. This makes it possible to effectively reduce the risk of fraudulent transactions by monitoring user transaction data in real time, quickly notifying the user when an anomalous transaction is detected, and receiving a response from the user.

[0671] The system also includes a server means including a generative model that receives security questions from users and analyzes the questions to generate appropriate answers, a means for transmitting the generated answers to the user's terminal, and a means for displaying transaction details and approval information so that the user can respond based on them. This makes it possible to provide quick and accurate answers when users ask questions about transactions.

[0672] The system also includes a server means for receiving consultation requests from users 24 hours a day, 365 days a year and taking appropriate action based on those requests, a means for notifying the user of the appropriate action, and a means for updating the user's profile and learning normal transaction patterns. This provides an environment where users can consult with confidence at any time, and by having the system learn the user's transaction patterns, it becomes possible to detect abnormal transactions with higher accuracy.

[0673] "User identification information" refers to information for identifying an individual user, including name, address, credit card information, and the like.

[0674] A "generative model" is a model constructed based on a user's identification information, and is a data analysis algorithm for learning a user's transaction patterns and detecting anomalous transactions.

[0675] "Transaction data" refers to information relating to a transaction conducted by a user, and includes the transaction amount, transaction location, transaction time, etc.

[0676] A "server" is a computer system for storing and processing data over the Internet.

[0677] A "terminal" is a device that is directly operated by a user, and includes smartphones, tablets, etc.

[0678] A "notification" is a message or alert sent to inform a user of specific information.

[0679] "Approval or rejection of transaction" refers to a user's response to approve or reject a transaction when the transaction is determined to be abnormal.

[0680] "Real-time" refers to data being processed and analyzed immediately at the moment it is generated.

[0681] An "application" is software with a specific function, in this case a smartphone program used to analyze and notify transaction data.

[0682] A "profile" is a collection of individual user information constructed based on the user's behavioral patterns and transaction information.

[0683] MODE FOR CARRYING OUT THE INVENTION

[0684] The present invention is a system that utilizes a generative model initialized using user identification information to analyze transaction data in real time and detect abnormal transactions. This system is composed of the following steps.

[0685] 1. User registration and initial settings

[0686] The server receives personal information and payment information entered by the user on the device and stores the corresponding identification information in a database. This identification information is used to initialize a generative model and build a user profile. The user profile serves as the basis for learning normal transaction patterns and detecting anomalous transactions.

[0687] 2. Real-time monitoring and anomaly detection

[0688] When a user makes an electronic payment, transaction data (such as transaction amount, transaction location, and transaction time) is sent from the terminal to the server. The server analyzes this transaction data and uses a generative model to determine whether it deviates from normal transaction patterns. If an abnormal transaction is determined, the server immediately sends a notification to the user and waits for a response (approval or rejection) from the user. This process makes it possible to quickly reduce the risk of abnormal transactions.

[0689] 3. Security Question and Answer Feature

[0690] When a user asks a security question, the question is sent to the server via the device. The server uses a generative model to analyze the question and generate an appropriate answer. This answer is sent to the user's device and displayed immediately. This allows the user to receive appropriate instructions and quickly respond to fraudulent use.

[0691] 4. 24 / 7 consultation service

[0692] When a user requests consultation, the request is sent to the server via their terminal. The server responds appropriately based on the request and provides the necessary information and assistance. This allows users to use the system with peace of mind at any time. The server also updates the user's profile and learns normal transaction patterns, enabling it to detect abnormal transactions with greater accuracy.

[0693] Specific examples

[0694] If a user tries to spend a large amount of money in the middle of the night, the server will determine this as an abnormal transaction and immediately send a notification to the user's terminal.

[0695] Example prompt: "You are planning to spend 120,000 yen in Osaka at 03:24."

[0696] As another example, if a user attempts a high-value transaction in an area where they do not normally make transactions, this is similarly reported as an abnormal transaction.

[0697] Example prompt: "I'm about to spend 50,000 yen at 8:30 PM in an unusual place."

[0698] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0699] Step 1:

[0700] The user accesses the registration form using a terminal and enters personal and payment information. The input data is sent to the server and stored in a database. The server then initializes a generative model based on the received identification information and builds a user profile.

[0701] (Input: User's personal information and payment information, Output: Initialized generative model and user profile)

[0702] Step 2:

[0703] When a user makes an electronic payment, transaction data (such as transaction amount, transaction location, and transaction time) is sent from the terminal to a server. The server receives this transaction data and uses a generative model to analyze whether it deviates from normal transaction patterns.

[0704] (Input: transaction data, Output: abnormal transaction determination results)

[0705] Step 3:

[0706] The server analyzes the transaction data using a generative model, and if an abnormal transaction is detected, it immediately sends a notification to the user's device, including details of the transaction and the option to approve or reject it.

[0707] (Input: Abnormal transaction judgment result, Output: Abnormal transaction notification to user)

[0708] Step 4:

[0709] The user checks the notification and sends a response to the server via the terminal approving or rejecting the transaction. The server receives the user's response and approves or rejects the transaction.

[0710] (Input: User response, Output: Final transaction processing result)

[0711] Step 5:

[0712] The user sends a security question from their device to the server, which uses the generative model to analyze the question and generate and send an appropriate answer.

[0713] (Input: User question, Output: Generated answer)

[0714] Step 6:

[0715] When a user sends a consultation request through the 24 / 7 consultation service, the server receives the request and responds appropriately, sending the necessary information and assistance to the user's device.

[0716] (Input: User consultation request, Output: Response result and notification to user)

[0717] Step 7:

[0718] The server constantly updates user profiles and learns normal trading patterns to improve the accuracy of the generative model, which enables more accurate detection of abnormal transactions.

[0719] (Input: new transaction data, Output: updated user profile and improved generative model)

[0720] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0721] The present invention is a system that analyzes transaction data in real time and detects abnormal transactions using a generative model and emotion engine initialized based on user identification information. The system includes a function to recognize user emotions and personalize notifications and responses based on those emotions.

[0722] User registration and initial settings

[0723] Users register by entering their personal and payment information through devices such as smartphones or tablets. This information is sent to the server and stored in a database. The server then initializes a generative model based on the received identification information to build an individual user profile. The emotion engine is also initialized, preparing to recognize emotions related to the user's transactions and responses.

[0724] Real-time monitoring and anomaly detection

[0725] When a user makes a transaction, the terminal collects transaction data (such as transaction amount, transaction location, and transaction time) and sends it to the server. The server then analyzes the transaction data using a generative model to detect anomalous transactions. For example, if a high-value transaction or an abnormal transaction pattern is detected, the server identifies it as an anomalous transaction.

[0726] When an abnormal transaction is detected, the server uses an emotion engine to evaluate the user's emotional state. The emotion engine recognizes the user's emotions from past transaction data and response data, and adjusts the notification content and countermeasures accordingly. For example, if the user is feeling anxious, the notification message will be designed to provide a more reassuring feeling.

[0727] Question and Answer feature

[0728] When a user submits a security question to the server via their device, the server uses the generative model to analyze the question and generate an appropriate answer. The emotion engine is also utilized here, providing answers that reflect the user's emotional state. For example, if the user is frustrated, the answer may include elements that will help them relax.

[0729] 24 / 7 consultation service

[0730] This system has the ability to accept consultation requests from users 24 hours a day, 365 days a year. When a user sends a consultation request via their device, the server receives it and uses an emotion engine to provide an appropriate response. For example, if the user is feeling stressed, the server will provide relaxation techniques and prompt support.

[0731] Specific examples

[0732] When User A tries to purchase a high-priced item online:

[0733] 1. User A selects a product on the terminal and proceeds with the purchase.

[0734] 2. Transaction data (item, amount, location, time) is sent from the terminal to the server.

[0735] 3. The server analyzes the transaction data using the generative model and detects abnormal transactions based on the high transaction amount.

[0736] 4. The server uses the emotion engine to evaluate User A's emotions and determines that he is feeling anxious.

[0737] 5. The server sends a reassuring notification to User A's device saying, "A high-value transaction has been detected, but it is within normal limits and safe. Please check."

[0738] 6. User A checks the notification and, if he approves the transaction, sends a response to the server via his terminal.

[0739] If User B experiences fraud:

[0740] 1. User B checks past transaction history on his / her terminal and discovers an unknown transaction.

[0741] 2. User B asks through his terminal, "What should I do if my account is fraudulently used?"

[0742] 3. The server uses the generative model to analyze the question and generate an appropriate answer.

[0743] 4. The emotion engine recognizes User B's emotional state as anxiety, so the server sends the response, "Please stay calm. Please follow the steps below to lock your account and contact support."

[0744] 5. User B follows the provided instructions to lock their account and contacts the support center.

[0745] In this way, the present invention monitors user transaction data in real time, and when an abnormal transaction is detected, provides an appropriate response taking into account the user's emotions. Furthermore, by using the emotion engine, it is possible to provide personalized responses to user security-related questions and inquiries, providing a sense of security.

[0746] The processing flow will be explained below.

[0747] Step 1:

[0748] The user uses the terminal to input identification information such as personal information and payment information, and the terminal transmits the input information to the server.

[0749] Step 2:

[0750] The server stores the identification information received from the user in a database, and then initializes the generative model and emotion engine based on the received identification information to build a user profile.

[0751] Step 3:

[0752] When a user makes a transaction on the terminal, the terminal collects transaction data (transaction amount, transaction location, transaction time, etc.) and transmits the collected transaction data to the server.

[0753] Step 4:

[0754] The server uses the generative model to analyze the received transaction data and detects anomalous transactions that deviate from normal transaction patterns.

[0755] Step 5:

[0756] If the server detects an anomalous transaction, it uses an emotion engine to evaluate the user's emotional state, for example, to determine whether the user is feeling anxious.

[0757] Step 6:

[0758] The server adjusts the notification content based on the evaluation results of the emotion engine. Notifications about abnormal transactions are sent to the user's device. For example, a message such as "A high-value transaction has been detected, but it is within the normal range and safe. Please check." is sent.

[0759] Step 7:

[0760] The user reviews the notification at the terminal and, if they approve or reject the transaction, the terminal sends the response to the server.

[0761] Step 8:

[0762] The server receives the user's response and either approves the transaction or takes action to deter fraud. Approved transactions are processed normally, while rejected transactions are recorded as attempted transactions.

[0763] Step 9:

[0764] The user enters a security question and sends it to the server via the device, such as "What should I do if my account is compromised?"

[0765] Step 10:

[0766] The server uses a generative model to analyze the user's question and generate an appropriate answer. An emotion engine is used to generate an answer that corresponds to the user's emotional state.

[0767] Step 11:

[0768] The server generates a response and sends it to the user's device, for example, "Please stay calm. Follow the steps below to lock your account and contact support."

[0769] Step 12:

[0770] The server accepts consultation requests 24 hours a day, 365 days a year. Users can submit security consultations via their devices.

[0771] Step 13:

[0772] The server receives the consultation request and provides an appropriate response using the emotion engine. Based on the request, the server sends the necessary information and assistance to the user's device.

[0773] This allows users to check the safety of their transactions in real time, quickly alleviating any security concerns. In addition, by utilizing an emotion engine, personalized responses can be made according to the user's emotional state.

[0774] Example 2

[0775] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0776] To provide a system that monitors user transaction data in real time and detects abnormal transactions, and to take appropriate action taking into account the emotional state of the user when an abnormal transaction is detected. To also provide personalized responses according to the emotional state of the user to security-related questions and consultation requests from users 24 hours a day, 365 days a year.

[0777] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0778] In this invention, the server includes means for receiving user identification information and initializing a generated model based on the identification information, means for collecting user transaction data and transmitting the transaction data to the server, means for analyzing the received transaction data and including a generative model for detecting anomalous transactions, means for evaluating the user's emotional state when an anomalous transaction is detected and adjusting notification content using an emotion engine, and means for receiving a response from the user and approving or rejecting the transaction, thereby enabling real-time transaction monitoring and response to anomalous transactions based on the user's emotional state.

[0779] The system further includes means including a generative model that receives a security question from a user and analyzes the question to generate an appropriate answer, means using an emotion engine that adjusts the generated answer according to the user's emotional state, and means for transmitting the generated answer to the user's terminal, thereby enabling the provision of a personalized answer according to the user's emotional state.

[0780] In addition, by including a means for receiving consultation requests from users 24 hours a day, 365 days a year and providing an appropriate response based on the request, a means using an emotion engine for adjusting the content of support in response to the consultation request based on the emotional state of the user, and a means for notifying the user of the appropriate response, it is possible to always provide optimized support to the user.

[0781] "Server means" refers to a computing device that receives user identification information and transaction data, analyzes the data using a generative AI model and emotion engine, detects anomalous transactions, and sends notifications.

[0782] "Terminal means" refers to electronic devices such as smartphones and tablets used by users, and is a device used to collect personal information and transaction data and send them to a server.

[0783] A "generative model" is an AI algorithm that analyzes input information such as user transaction data and is used to detect abnormal transactions and generate answers to questions.

[0784] The "emotion engine" is an algorithm that evaluates a user's emotional state based on the user's past transaction data and response data, and generates appropriate notifications and responses accordingly.

[0785] An "abnormal transaction" refers to a transaction that deviates from normal transaction patterns and may be an indication of fraud.

[0786] A "notification" is a message sent to a user when an abnormal transaction is detected, and is information that prompts the user to check the transaction or take caution.

[0787] "Terminal" refers to an electronic device (smartphone, tablet, PC, etc.) used by a user, which communicates with a server and sends and receives data.

[0788] A "user profile" is an individual set of information constructed based on a user's identification information and transaction history, and is used to initialize the generative model and operate the emotion engine.

[0789] "Personalized responses" refer to assistance and notifications that are tailored to the user's current emotional state and past behavior.

[0790] "Security questions" refer to questions that address any doubts or concerns a user may have about the system or a transaction, and the server generates appropriate answers to these questions.

[0791] "24 / 7 consultation request" refers to a request for consultation or support that a user can make to the system at any time, and is a request for an appropriate response to that request.

[0792] MODE FOR CARRYING OUT THE INVENTION

[0793] A specific embodiment of the present invention is described below: The present invention is a system that monitors user transaction data in real time, detects abnormal transactions and fraudulent activities, and, if an abnormality is detected, provides personalized notifications and responses based on the user's emotional state.

[0794] Hardware and software used

[0795] Server: A high-performance computing device used to process the generative AI model and emotion engine, including a database server where user identification information and transaction data are stored.

[0796] Terminal: An electronic device such as a smartphone, tablet, or PC used by a user to input user information and transaction data and send it to the server.

[0797] Generative AI model: An AI algorithm that analyzes user transaction data and detects anomalous transactions.

[0798] Emotion engine: An algorithm that assesses a user's emotional state based on their past transactions and responses, and tailors notifications and responses accordingly.

[0799] System Operation

[0800] First, a user registers with the system using a terminal. The user enters personal information such as name, date of birth, address, and payment information. This information is encrypted and sent to the server, which then initializes a generative model based on the received identification information and builds an individual user profile. The emotion engine is also initialized, ready to recognize emotions in the user's transactions and responses.

[0801] When a user makes a transaction, the terminal collects transaction data (such as transaction amount, transaction location, and transaction time) and sends it to the server. The server inputs the received transaction data into a generative AI model and begins analyzing it in real time. If an abnormal transaction is detected, the server uses an emotion engine to evaluate the user's emotional state. For example, if past data indicates that the user is feeling anxious, the server will send a notification to reassure the user.

[0802] The server sends the generated notification to the terminal, which displays it to the user, who can then review the notification and send approval or rejection of the transaction to the server via the terminal.

[0803] Examples:

[0804] When User A tries to purchase a high-priced item online:

[0805] 1. User A selects a product on the terminal and proceeds with the purchase.

[0806] 2. Transaction data (item, amount, location, time) is sent from the terminal to the server.

[0807] 3. The server analyzes the transaction data using the generative model and detects abnormal transactions based on the high transaction amount.

[0808] 4. The server uses the emotion engine to evaluate User A's emotions and determines that he is feeling anxious.

[0809] 5. The server sends a reassuring notification to User A's device saying, "A high-value transaction has been detected, but it is within normal limits and safe. Please check."

[0810] 6. User A checks the notification and, if he approves the transaction, sends a response to the server via his terminal.

[0811] Examples of prompts:

[0812] "Analyze the current transaction data and check for any abnormalities."

[0813] "What should I do if a user feels uneasy?"

[0814] The system also handles consultation requests 24 hours a day, 365 days a year. When a user sends a consultation request via their device, the server receives it and uses an emotion engine to provide an appropriate response. For example, if the user is feeling stressed, it can provide relaxation techniques or quick support.

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

[0816] Program processing flow

[0817] Step 1: User registration and initial setup

[0818] The user opens the dedicated app on their device and enters personal information (such as name, date of birth, and address) and payment information (such as credit card information) on the registration screen.

[0819] The terminal encrypts the entered personal information and payment information and sends it to the server.

[0820] The server receives the transmitted information and stores it in a database.

[0821] Input: User's personal information and payment information

[0822] Data processing: Information is encrypted and stored securely

[0823] Output: User profile stored in the server database

[0824] The server initializes a generative AI model based on the received identification information and builds an individual user profile.

[0825] The server initializes the emotion engine and prepares it for user emotion recognition.

[0826] Step 2: Collect transaction data

[0827] The user actually conducts the transaction using the terminal.

[0828] The terminal collects transaction data (transaction amount, transaction location, transaction time, etc.).

[0829] The terminal transmits the collected transaction data to the server.

[0830] Input: Transaction data (transaction amount, transaction location, transaction time, etc.)

[0831] Data processing: Collecting and formatting transaction data on the terminal

[0832] Output: Collected transaction data is encrypted and sent to the server

[0833] Step 3: Analyze transaction data and detect anomalies

[0834] The server receives the submitted transaction data and inputs it into the generative AI model.

[0835] The server analyzes transaction data using a generative AI model to detect anomalous transactions.

[0836] Input: Received transaction data

[0837] Data Computing: Data Analysis and Anomaly Detection with Generative AI Models

[0838] Output: Information about detected anomalous transactions

[0839] If the server detects an abnormal transaction, it records the details and performs emotion evaluation in the next step.

[0840] Step 4: Emotion evaluation and notification generation

[0841] The server uses an emotion engine to assess the user's emotional state in response to the anomalous transaction.

[0842] The server generates appropriate notification content based on the evaluation results.

[0843] The server sends the generated notification to the terminal.

[0844] Input: Abnormal transaction information, past transaction data, and user response data

[0845] Data Computation: Emotion engine evaluates emotional state and generates notification content

[0846] Output: A personalized notification message

[0847] The terminal displays the received notification to the user.

[0848] Step 5: Receiving and Processing User Response

[0849] The user checks the notification and responds at the terminal to approve or reject the transaction.

[0850] The terminal encrypts the user's response and sends it to the server.

[0851] The server analyzes the received user response and approves or denies the transaction.

[0852] Input: User response information (approval or rejection)

[0853] Data processing: analysis of response data

[0854] Output: Transaction status update, approved or rejected

[0855] Step 6: Generate questions and answers

[0856] The user submits a security question at the terminal.

[0857] The terminal sends a question to the server.

[0858] The server receives the question and inputs it into a generative AI model for analysis.

[0859] The server generates appropriate answers and adjusts the answers using an emotion engine.

[0860] Input: User question

[0861] Data computation: Parsing questions with generative AI models and tailoring answers with emotion engines

[0862] Output: A suitable answer for the user

[0863] The server sends the generated answer to the terminal.

[0864] The terminal displays the answer to the user.

[0865] Step 7: 24 / 7 consultation

[0866] A user sends a consultation request through a terminal.

[0867] The terminal sends a consultation request to the server.

[0868] A server receives the consultation request and uses an emotion engine to determine an appropriate response.

[0869] Input: User consultation request

[0870] Data calculation: Emotion engine evaluates emotional state and determines response

[0871] Output: personalized response

[0872] The server transmits the determined response content to the terminal.

[0873] The terminal displays the response to the user.

[0874] In this way, data processing and calculation are performed at each step, and personalized services are provided to the user.

[0875] (Application example 2)

[0876] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0877] While existing electronic payment services have systems for detecting abnormal transactions, they lack personalized responses that take into account the user's emotional state. This can cause anxiety and stress for users, resulting in a poor user experience. Another issue is that responses to security questions are standardized, and personalized answers are not provided based on the user's emotional state, resulting in a decrease in user satisfaction.

[0878] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving user identification information and initializing a model generated based on the identification information, means for collecting user transaction data and transmitting the transaction data to the server, means for analyzing the received transaction data and including a generative model for detecting anomalous transactions, means for sending a personalized notification to the user using an emotion engine when an anomalous transaction is detected, and means for receiving a response from the user and approving or rejecting the transaction. This makes it possible to detect anomalous transactions in real time and provide personalized responses that take user emotions into consideration.

[0879] The server also includes a means for receiving security questions from a user, analyzing the questions using a generative AI model, and generating an appropriate answer using an emotion engine, and a means for transmitting the generated answer to the user's device. This allows for personalized answers to be provided according to the user's emotional state, which is expected to improve the user experience.

[0880] Furthermore, the server includes a means for receiving consultation requests from users 24 hours a day, 365 days a year, and for using the emotion engine to provide an appropriate response based on the request, and a means for notifying the user of the appropriate response. This provides an environment where users can consult with confidence at any time, improving user satisfaction.

[0881] "User identification information" is data for uniquely identifying each individual user.

[0882] A "generative model" is an algorithm that is trained based on user identification information and transaction data to detect abnormal transactions and generate answers to questions.

[0883] The "server means" refers to a server device and its functions for managing user identification information and transaction data, and for performing analysis and notification.

[0884] "Terminal means" refers to equipment and communication functions for collecting data from users and transmitting it to a server.

[0885] "Transaction data" refers to information related to a user's transaction, including the transaction amount, transaction location, transaction time, and the like.

[0886] An "abnormal transaction" is a transaction that differs from normal transaction patterns and may be fraudulent.

[0887] The "means for sending a notification" is a function for sending a warning or a confirmation message to the user when an abnormal transaction is detected.

[0888] An "emotion engine" is an algorithm that analyzes a user's emotional state and adjusts the content of notifications and responses according to the user's emotions.

[0889] The "means for receiving a response" is a function that receives a response or confirmation from the user and executes processing based on that response.

[0890] "Security Questions" are questions that users can ask to answer any concerns they may have about the security of the system or fraudulent transactions.

[0891] A "consultation request" is a request from a user to the system for advice or support.

[0892] The "means for taking appropriate action using an emotion engine" is a function for providing appropriate countermeasures by taking into account the emotional state of the user.

[0893] System Configuration

[0894] A system embodying the invention comprises the following major components:

[0895] 1. Server Means

[0896] User Profile Management: A server that receives user identification and personal information and initializes the generative model and emotion engine.

[0897] Transaction data analysis: A server that receives user transaction data in real time and uses generative models to detect anomalous transactions.

[0898] Sentiment Engine: Evaluates user sentiment upon detected anomalous transactions and generates personalized notifications.

[0899] Notification and response management: Send personalized notifications to users and receive responses to approve or reject transactions.

[0900] 2. Terminal means

[0901] Data collection and transmission: A device such as a smartphone that collects user transaction data in real time and transmits it to a server.

[0902] Receive and display notifications: Receive notifications sent from the server and display them to the user.

[0903] Implementation method

[0904] 1. User registration and initial settings

[0905] Users register with the system by entering their personal and payment information using a device such as a smartphone. The registered information is sent to the server, which creates a user profile. The server then initializes the generative model and emotion engine, preparing to analyze the user's transactions and emotions.

[0906] 2. Real-time transaction monitoring

[0907] When a user makes a transaction, the terminal collects transaction data (transaction amount, transaction location, transaction time, etc.) and sends it to the server. The server analyzes the transaction data using a generative model to detect abnormal transactions.

[0908] 3. Handling Abnormal Transactions

[0909] If an abnormal transaction is detected, the server uses an emotion engine to evaluate the user's emotional state. For example, if the server determines that the user is feeling anxious, it sends a personalized notification offering reassurance. The user then sends a response to the server to approve or reject the transaction accordingly.

[0910] 4. Responses to security questions

[0911] When a user submits a security question to the server via their device, the server uses a generative AI model to analyze the question and leverages an emotion engine to generate an appropriate answer, which is also personalized according to the user's emotional state.

[0912] 5. 24 / 7 consultation service

[0913] The system has the function of accepting consultation requests from users 24 hours a day, 365 days a year. The server receives the consultation request, uses the emotion engine to respond appropriately, and notifies the user.

[0914] Hardware and software used

[0915] Servers: Server equipment, database servers (e.g., MySQL or PostgreSQL), and computing units for generative AI models (e.g., GPU servers)

[0916] Device: Smartphone, tablet, or any device with an internet connection

[0917] Software: Emotion engine (e.g., Python library EmotionAPI), trading data analysis (e.g., generative models using TensorFlow or PyTorch)

[0918] Specific examples

[0919] For example, if a user attempts to purchase a high-value item online, the transaction data is sent from the device to the server. The server detects the transaction as an abnormal transaction and uses an emotion engine to evaluate the user's emotion as "anxious." The server then sends a personalized notification saying, "A high-value transaction has been detected, but it is within normal limits and safe. Please confirm."

[0920] Prompt Sentence Examples

[0921] User ID: 1

[0922] Emotional state: Anxiety

[0923] Transaction data: {'amount': 1000, 'location': 'Online Store', 'time': 'YYYY-MM-DD HH:MM:SS'}

[0924] Question: "A high-value transaction was detected, but it is within normal limits and safe. Please confirm."

[0925] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0926] Step 1:

[0927] Users use devices such as smartphones or tablets to enter personal information and payment information and register with the system.

[0928] Input: Personal information, payment information

[0929] Data processing / calculation: The device formats this information and sends it to the server in a secure manner.

[0930] Output: Registration request data

[0931] Step 2:

[0932] The server generates a user profile based on the received personal information and payment information, stores it in a database, and initializes the generative model and emotion engine based on the user.

[0933] Input: Registration request data

[0934] Data processing / calculation: Store information in a database and perform initialization processes for the generative model and emotion engine.

[0935] Output: Initialized generative model and emotion engine

[0936] Step 3:

[0937] When a user makes a transaction, the terminal collects transaction data (transaction amount, transaction location, transaction time, etc.) in real time and transmits it to the server.

[0938] Input: Transaction data (transaction amount, transaction location, transaction time, etc.)

[0939] Data processing / calculation: The terminal formats the transaction data and sends it to the server

[0940] Output: Transaction data sent

[0941] Step 4:

[0942] The server analyzes the received transaction data using a generative model to detect abnormal transactions.

[0943] Input: Transaction data sent

[0944] Data processing / calculation: Analysis of abnormal transactions using generative models

[0945] Output: Detected abnormal transactions

[0946] Step 5:

[0947] If an abnormal transaction is detected, the server uses an emotion engine to assess the user's emotional state, and based on that, generates a reassuring personalized notification and sends it to the user's device.

[0948] Input: Detected abnormal transactions, past user behavior data

[0949] Data processing / calculation: Emotional state evaluation by emotion engine, generation of notification messages

[0950] Output: A personalized notification message

[0951] Step 6:

[0952] The user checks the notification on the terminal and sends a response to the server approving or rejecting the transaction.

[0953] Input: Notification message

[0954] Data processing / calculation: User confirmation and response input, response transmission from terminal to server

[0955] Output: Approval or rejection response data

[0956] Step 7:

[0957] Based on the response received, the server will either approve or reject the transaction.

[0958] Input: Approval or rejection response data

[0959] Data processing / calculation: Approval or rejection execution process

[0960] Output: Transaction approval or rejection result

[0961] Step 8:

[0962] The user sends a security question to the server via the terminal.

[0963] Input: Question

[0964] Data processing / calculation: Sending query data

[0965] Output: Received query data

[0966] Step 9:

[0967] The server uses a generative AI model to analyze the question and generates an appropriate answer using an emotion engine.

[0968] Input: Received query data

[0969] Data processing / calculation: Question analysis using a generative AI model, emotion evaluation and answer generation using an emotion engine

[0970] Output: personalized answer

[0971] Step 10:

[0972] The server sends the generated answer to the user's terminal.

[0973] Input: Personalized Answer

[0974] Data processing / calculation: Sending response data

[0975] Output: The answer displayed on the user's terminal

[0976] Step 11:

[0977] The server accepts consultation requests from users 24 hours a day, 365 days a year, and uses an emotion engine to provide an appropriate response based on the request.

[0978] Input: Consultation request

[0979] Data processing / calculation: Analysis of consultation content and generation of countermeasures using an emotion engine

[0980] Output: personalized response

[0981] Step 12:

[0982] The server will notify the user of the appropriate action.

[0983] Input: Personalized Response

[0984] Data processing / calculation: Sending notification data

[0985] Output: The solution displayed on the user's device

[0986] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0987] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0988] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0989] [Third embodiment]

[0990] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0991] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0992] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0993] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0994] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0995] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0996] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0997] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0998] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0999] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1000] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1001] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[1002] The present invention provides a system for analyzing transaction data in real time and detecting anomalous transactions by utilizing a generative model initialized using user identification information. This system is comprised of the following steps:

[1003] User registration and initial settings

[1004] A user accesses a registration form using a device such as a smartphone or tablet and enters identifying information such as personal and payment information. This information is sent to a server and stored in a database. The server then initializes a generative model based on the received identifying information and builds a user profile. This profile serves as the basis for learning the user's usual trading patterns and detecting anomalies.

[1005] Real-time monitoring and anomaly detection

[1006] When a user uses a smartphone payment system, transaction data (such as transaction amount, transaction location, and transaction time) is sent from the device to a server. The server then analyzes the received transaction data using a generative model to determine whether it deviates from normal transaction patterns. For example, a transaction involving a large amount exceeding the normal transaction amount or a transaction occurring at an unexpected time or location is considered an abnormal transaction.

[1007] If an abnormal transaction is detected, the server immediately sends a notification to the user. The notification includes details of the transaction and information for approving or rejecting the transaction. When the user views the notification, the terminal sends a response of approval or rejection to the server. Based on the user's response, the server either approves the transaction or takes measures against it as fraudulent.

[1008] Question and Answer feature

[1009] When a user has a security question, they send it to the server via their device. The server receives the question, analyzes it using a generative model, and generates an appropriate answer. The generated answer is sent to the user's device and immediately displayed to the user. For example, in response to the question, "What should I do if my account is compromised?", specific instructions are provided, such as, "Follow the steps below: 1. Lock your account. 2. Contact support."

[1010] 24 / 7 consultation service

[1011] This system has the ability to accept consultation requests from users 24 hours a day, 365 days a year. When a user requests a consultation, they send the request to the server via their terminal. The server responds appropriately based on the request and provides the necessary information and assistance. For example, if a user requests to "check their payment history," the server uses the generative model to obtain the necessary information and displays it on the user's terminal.

[1012] Specific examples

[1013] When User A tries to purchase a high-priced item online:

[1014] 1. User A selects a product on the terminal and proceeds with the purchase.

[1015] 2. Transaction data (item, amount, location, time, etc.) is sent from the terminal to the server.

[1016] 3. The server uses the generative model to analyze this transaction data and determines that it is an abnormal transaction due to the high amount.

[1017] 4. The server notifies User A's device, "A high-value transaction has been detected. Please check."

[1018] 5. User A checks the notification and presses the approval button on the terminal if he / she approves the transaction.

[1019] 6. The terminal sends the authorization information to the server, which then approves the transaction.

[1020] If User B experiences fraud:

[1021] 1. User B checks past transaction history on his / her terminal and discovers an unknown transaction.

[1022] 2. User B asks through his terminal, "What should I do if my account is fraudulently used?"

[1023] 3. The server uses the generative model to analyze User B's question and generate an appropriate answer.

[1024] 4. The server sends a message to User B's device saying, "If you experience fraudulent use, please follow the steps below: 1. Lock your account. 2. Contact support."

[1025] 5. User B follows the provided instructions to lock their account and contact the support center.

[1026] As described above, the present invention reduces the risk of fraudulent use and the leakage of personal information by monitoring user transaction data in real time and responding quickly when an abnormal transaction is detected. In addition, by providing a quick and appropriate response to user security-related questions and inquiries, users can use smartphone payments with peace of mind.

[1027] The processing flow will be explained below.

[1028] Step 1:

[1029] The user uses the terminal to input identification information such as personal information and payment information, and the terminal transmits the input information to the server.

[1030] Step 2:

[1031] The server stores the identification information received from the user in a database, and then initializes a generative model based on the received identification information to build a profile of the user.

[1032] Step 3:

[1033] When a user makes a transaction on the terminal, the terminal collects transaction data (transaction amount, transaction location, transaction time, etc.) and transmits the collected transaction data to the server.

[1034] Step 4:

[1035] The server uses the generative model to analyze the received transaction data and detects anomalous transactions that deviate from normal transaction patterns.

[1036] Step 5:

[1037] If the server detects an abnormal transaction, it immediately sends a notification to the user's device, including details of the transaction and the option to approve or reject the transaction.

[1038] Step 6:

[1039] The user reviews the notification at the terminal and, if they approve or reject the transaction, the terminal sends the response to the server.

[1040] Step 7:

[1041] The server receives the user's response and either approves the transaction or takes action to deter fraud. Approved transactions are processed normally, while rejected transactions are recorded as attempted transactions.

[1042] Step 8:

[1043] The user enters a security question and sends it to the server via the device, such as "What should I do if my account is compromised?"

[1044] Step 9:

[1045] The server analyzes the user's question using the generative model, generates an appropriate answer, and sends the generated answer to the user's device.

[1046] Step 10:

[1047] The server accepts consultation requests 24 hours a day, 365 days a year. Users can submit security consultations via their devices.

[1048] Step 11:

[1049] The server provides the user with an appropriate response based on the content of the consultation request, and sends the necessary information and assistance to the user's terminal.

[1050] This allows users to check the security of their transactions in real time, quickly alleviating any security concerns.

[1051] Example 1

[1052] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1053] In recent years, online payments using devices such as smartphones and tablets have become widespread. However, systems that monitor user transaction data in real time and quickly detect and respond to abnormal transactions are still inadequate. Furthermore, when users have security-related questions, there are limited ways to obtain immediate and appropriate answers. Furthermore, the lack of an environment where consultations can be made at any time poses the risk of fraudulent use and the leakage of personal information. Therefore, there is a need for a system that can effectively monitor user transaction data, detect and quickly respond to abnormal transactions, and generate appropriate answers to users' security questions.

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

[1055] In this invention, the server includes means for receiving a user's identification information and initializing a model generated based on the identification information; means for collecting the user's transaction data and transmitting the transaction data to the server; means for analyzing the received transaction data using the generative AI model to determine whether the transaction data deviates from normal transaction patterns; means for sending a notification to the user's terminal when an abnormal transaction is detected; and means for receiving a response from the user and approving or rejecting the transaction. This enables effective real-time monitoring of user transaction data and rapid detection and response to abnormal transactions. Furthermore, the generative AI model can provide appropriate answers to security-related questions from users, allowing users to use online payments with peace of mind. Furthermore, by responding to user consultation requests 24 hours a day, 365 days a year, the risk of further fraudulent use and personal information leaks can be reduced.

[1056] "User identification information" is information for identifying a user, such as personal information or payment information.

[1057] A "generated model" is a model constructed based on a user's identification information for learning the user's trading patterns.

[1058] The "server means" is a server that has the role of receiving, storing, analyzing, notifying, and processing responses to data.

[1059] "Terminal means" refers to a device such as a smartphone or tablet that allows a user to input and confirm transaction data and send it to the server.

[1060] "Transaction data" refers to data related to the payment made by the user, such as the transaction amount, transaction location, and transaction time.

[1061] A "generative AI model" is a model that uses artificial intelligence technology to analyze data and perform pattern recognition and anomaly detection.

[1062] An "unusual transaction" is a transaction that deviates from normal transaction patterns and is unexpected or suspected to be fraudulent.

[1063] "Notification" refers to a warning or confirmation message sent to a user when an abnormal transaction is detected.

[1064] A "response" is an operation of approval or denial that a user performs after receiving a notification.

[1065] "Security questions" are inquiries made by users to resolve doubts or concerns about fraudulent use, transaction confirmation, account management, etc.

[1066] An "appropriate answer" is the optimal response that the generative AI model analyzes and provides in response to a user's question.

[1067] A "consultation request" is a request for support regarding a problem or question a user has while using the service.

[1068] The present invention is a system that analyzes transaction data in real time and detects abnormal transactions by utilizing a generative AI model initialized using user identification information. This system is mainly composed of a server, a terminal, and a generative AI model.

[1069] User registration and initial settings

[1070] User Registration

[1071] Users access the registration form using a device such as a smartphone or tablet and enter personal information (such as name, email address, and phone number) and payment information (such as credit card information). The device then sends this identification information to the server.

[1072] Data storage and profile building

[1073] The server stores the received identification information in a database, which then initializes the generative AI model and builds a profile to learn the user's normal trading patterns. This profile serves as the basis for detecting abnormal transactions.

[1074] Real-time monitoring and anomaly detection

[1075] Transaction data transmission

[1076] When a user makes a smartphone payment, transaction data (transaction amount, transaction location, transaction time, etc.) is sent from the device to the server.

[1077] Transaction Data Analysis

[1078] The server inputs the received transaction data into a generative AI model (e.g., OpenAI's GPT-4) for analysis. The generative AI model determines whether the transaction data deviates from normal transaction patterns.

[1079] Abnormal transaction detection

[1080] The server determines the degree of anomaly in a transaction based on the analysis results of the generative AI model. For example, if the transaction amount exceeds the normal range or if the transaction is conducted in an unexpected location, it will be identified as an anomalous transaction. If an anomalous transaction is detected, the server will send a notification of the anomalous transaction to the user's device.

[1081] Notification confirmation and response

[1082] The user checks the notification on the terminal and performs an operation to approve or reject the transaction. The terminal sends response information to the server, and the server executes a process to approve or reject the transaction based on the user's response.

[1083] Question and Answer feature

[1084] Submit a question

[1085] The user enters a security question through the terminal and sends it to the server.

[1086] Question analysis and answer generation

[1087] The server inputs the received question into a generative AI model (e.g., ChatGPT) to generate an appropriate answer, which is then sent from the server to the user's device and displayed immediately.

[1088] Example: Question prompt

[1089] "If a fraudulent transaction is detected, how should I respond?"

[1090] "If I'm making a high-value transaction, do I need to set anything up beforehand?"

[1091] How can I check my past transaction history?

[1092] 24 / 7 consultation service

[1093] Submit a consultation request

[1094] A user inputs a consultation request through a terminal and transmits it to a server.

[1095] Request processing and response

[1096] The server analyzes the received request and takes appropriate action. For example, if the request is to check payment history, it retrieves the relevant history from the database and displays it on the terminal.

[1097] This system monitors user transaction data in real time, enabling rapid response if any abnormal transactions are detected. It also uses generative AI models to provide prompt and appropriate answers to security-related questions from users. Furthermore, a consultation desk is available 24 hours a day, 365 days a year, allowing users to use the system with peace of mind at any time.

[1098] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1099] Step 1: User Registration

[1100] Input: The user accesses the registration form from a device such as a smartphone or tablet and enters personal information and payment information.

[1101] Specific operation: The terminal sends the personal information (name, email address, phone number, etc.) and payment information (credit card information, etc.) entered by the user to the server.

[1102] Output: The server stores the received identification information in its database and the user registration is complete.

[1103] Step 2: Initializing the generative model

[1104] Input: The server retrieves data to initialize the generative AI model based on the stored identification information.

[1105] Specific operation: The server inputs the user's identification information into the generative AI model and builds a profile to learn the user's usual trading patterns.

[1106] Output: The server saves the generated profile to a database and the generative AI model is initialized.

[1107] Step 3: Send transaction data

[1108] Input: When a user makes a smartphone payment, they enter transaction data (transaction amount, transaction location, transaction time, etc.) into the terminal.

[1109] Specific operation: The terminal sends transaction data to the server.

[1110] Output: The server takes the received transaction data and proceeds to the next analysis step.

[1111] Step 4: Transaction data analysis

[1112] Input: The server inputs the received transaction data into the generative AI model.

[1113] How it works: A generative AI model (e.g., OpenAI's GPT-4) analyzes transaction data to determine whether it deviates from normal transaction patterns.

[1114] Output: The generative AI model evaluates the degree of anomaly in the transaction and outputs the analysis results to the server.

[1115] Step 5: Identify abnormal transactions

[1116] Input: The server obtains the analysis results of the generative AI model.

[1117] Specific operation: The server determines the degree of abnormality of a transaction based on the analysis results of the generative AI model. For example, if the transaction amount exceeds the normal range or if the transaction is conducted in an unexpected location, it will be identified as an abnormal transaction.

[1118] Output: If an abnormal transaction is detected, the server sends a notification of the abnormal transaction to the user's terminal.

[1119] Step 6: Notification confirmation and response

[1120] Input: The user checks the terminal for an abnormal transaction notification.

[1121] Specific action: The user takes action to approve or reject the transaction.

[1122] Output: The terminal sends the user's response (approval or denial) to the server.

[1123] Step 7: Processing the transaction

[1124] Input: The server receives the user's response.

[1125] Specific operation: The server will execute the process to approve or reject the transaction based on the user's response. For example, if the transaction is approved, the process will proceed normally, and if it is rejected, the transaction will be canceled.

[1126] Output: The server sends the final result of the transaction to the user's terminal.

[1127] Step 8: Submit your question

[1128] Input: The user inputs the security question through the terminal and sends it to the server.

[1129] Specific operation: The terminal sends the user's question to the server.

[1130] Output: The server takes the received question and proceeds to the next parsing step.

[1131] Step 9: Question analysis and answer generation

[1132] Input: The server inputs the received question into the generative AI model.

[1133] How it works: A generative AI model (e.g., ChatGPT) analyzes the question and generates an appropriate answer.

[1134] Output: The generative AI model outputs the generated answer to the server.

[1135] Step 10: Submit your response

[1136] Input: The server gets the answer of the generative AI model.

[1137] Specific operation: The server sends the generated answer to the user's terminal.

[1138] Output: The answer is displayed on the user's terminal.

[1139] Step 11: Submit a consultation request

[1140] Input: The user inputs a consultation request through the terminal and sends it to the server.

[1141] Specific operation: The terminal sends a consultation request to the server.

[1142] Output: The server takes the received request and proceeds to the next appropriate response step.

[1143] Step 12: Request Processing and Response

[1144] Input: The server analyzes the received request.

[1145] Specific operation: The server responds appropriately based on the request content. For example, if the request is to check payment history, it retrieves the relevant history from the database and displays it on the user's device.

[1146] Output: The server sends the appropriate correspondence information to the user's terminal.

[1147] (Application example 1)

[1148] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1149] Conventional electronic payment systems have difficulty detecting fraudulent or abnormal transactions in real time, making it difficult to quickly and effectively protect user security. Furthermore, they lack the functionality to quickly provide appropriate answers to security-related questions, making it difficult to provide a 24 / 7 consultation service. This has led to problems with providing users with a sense of security.

[1150] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1151] In this invention, the server includes server means for receiving user identification information and initializing a model generated based on the identification information, terminal means for collecting user transaction data and transmitting the transaction data to the server, server means including a generative model for analyzing the received transaction data and detecting anomalous transactions, server means for transmitting a notification to the user when an anomalous transaction is detected, server means for receiving a response from the user and approving or rejecting the transaction, and application means for analyzing transaction-related data in real time and notifying the user's terminal. This makes it possible to effectively reduce the risk of fraudulent transactions by monitoring user transaction data in real time, quickly notifying the user when an anomalous transaction is detected, and receiving a response from the user.

[1152] The system also includes a server means including a generative model that receives security questions from users and analyzes the questions to generate appropriate answers, a means for transmitting the generated answers to the user's terminal, and a means for displaying transaction details and approval information so that the user can respond based on them. This makes it possible to provide quick and accurate answers when users ask questions about transactions.

[1153] The system also includes a server means for receiving consultation requests from users 24 hours a day, 365 days a year and taking appropriate action based on those requests, a means for notifying the user of the appropriate action, and a means for updating the user's profile and learning normal transaction patterns. This provides an environment where users can consult with confidence at any time, and by having the system learn the user's transaction patterns, it becomes possible to detect abnormal transactions with higher accuracy.

[1154] "User identification information" refers to information for identifying an individual user, including name, address, credit card information, and the like.

[1155] A "generative model" is a model constructed based on a user's identification information, and is a data analysis algorithm for learning a user's transaction patterns and detecting anomalous transactions.

[1156] "Transaction data" refers to information relating to a transaction conducted by a user, and includes the transaction amount, transaction location, transaction time, etc.

[1157] A "server" is a computer system for storing and processing data over the Internet.

[1158] A "terminal" is a device that is directly operated by a user, and includes smartphones, tablets, etc.

[1159] A "notification" is a message or alert sent to inform a user of specific information.

[1160] "Approval or rejection of transaction" refers to a user's response to approve or reject a transaction when the transaction is determined to be abnormal.

[1161] "Real-time" refers to data being processed and analyzed immediately at the moment it is generated.

[1162] An "application" is software with a specific function, in this case a smartphone program used to analyze and notify transaction data.

[1163] A "profile" is a collection of individual user information constructed based on the user's behavioral patterns and transaction information.

[1164] MODE FOR CARRYING OUT THE INVENTION

[1165] The present invention is a system that utilizes a generative model initialized using user identification information to analyze transaction data in real time and detect anomalous transactions. This system is composed of the following steps.

[1166] 1. User registration and initial settings

[1167] The server receives personal information and payment information entered by the user on the device and stores the corresponding identification information in a database. This identification information is used to initialize a generative model and build a user profile. The user profile serves as the basis for learning normal transaction patterns and detecting anomalous transactions.

[1168] 2. Real-time monitoring and anomaly detection

[1169] When a user makes an electronic payment, transaction data (such as transaction amount, transaction location, and transaction time) is sent from the terminal to the server. The server analyzes this transaction data and uses a generative model to determine whether it deviates from normal transaction patterns. If an abnormal transaction is determined, the server immediately sends a notification to the user and waits for a response (approval or rejection) from the user. This process makes it possible to quickly reduce the risk of abnormal transactions.

[1170] 3. Security Question and Answer Feature

[1171] When a user asks a security question, the question is sent to the server via the device. The server uses a generative model to analyze the question and generate an appropriate answer. This answer is sent to the user's device and displayed immediately. This allows the user to receive appropriate instructions and quickly respond to fraudulent use.

[1172] 4. 24 / 7 consultation service

[1173] When a user requests consultation, the request is sent to the server via their terminal. The server responds appropriately based on the request and provides the necessary information and assistance. This allows users to use the system with peace of mind at any time. The server also updates the user's profile and learns normal transaction patterns, enabling it to detect abnormal transactions with greater accuracy.

[1174] Specific examples

[1175] If a user tries to spend a large amount of money in the middle of the night, the server will determine this as an abnormal transaction and immediately send a notification to the user's terminal.

[1176] Example prompt: "You are planning to spend 120,000 yen in Osaka at 03:24."

[1177] As another example, if a user attempts a high-value transaction in an area where they do not normally make transactions, this is similarly reported as an abnormal transaction.

[1178] Example prompt: "I'm about to spend 50,000 yen at 8:30 PM in an unusual place."

[1179] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1180] Step 1:

[1181] The user accesses the registration form using a terminal and enters personal and payment information. The input data is sent to the server and stored in a database. The server then initializes a generative model based on the received identification information and builds a user profile.

[1182] (Input: User's personal information and payment information, Output: Initialized generative model and user profile)

[1183] Step 2:

[1184] When a user makes an electronic payment, transaction data (such as transaction amount, transaction location, and transaction time) is sent from the terminal to a server. The server receives this transaction data and uses a generative model to analyze whether it deviates from normal transaction patterns.

[1185] (Input: transaction data, Output: abnormal transaction determination results)

[1186] Step 3:

[1187] The server analyzes the transaction data using a generative model, and if an abnormal transaction is detected, it immediately sends a notification to the user's device, including details of the transaction and the option to approve or reject it.

[1188] (Input: Abnormal transaction judgment result, Output: Abnormal transaction notification to user)

[1189] Step 4:

[1190] The user checks the notification and sends a response to the server via the terminal approving or rejecting the transaction. The server receives the user's response and approves or rejects the transaction.

[1191] (Input: User response, Output: Final transaction processing result)

[1192] Step 5:

[1193] The user sends a security question from their device to the server, which uses the generative model to analyze the question and generate and send an appropriate answer.

[1194] (Input: User question, Output: Generated answer)

[1195] Step 6:

[1196] When a user sends a consultation request through the 24 / 7 consultation service, the server receives the request and responds appropriately, sending the necessary information and assistance to the user's device.

[1197] (Input: User consultation request, Output: Response result and notification to user)

[1198] Step 7:

[1199] The server constantly updates user profiles and learns normal trading patterns to improve the accuracy of the generative model, which enables more accurate detection of abnormal transactions.

[1200] (Input: new transaction data, Output: updated user profile and improved generative model)

[1201] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1202] The present invention is a system that analyzes transaction data in real time and detects abnormal transactions using a generative model and emotion engine initialized based on user identification information. The system includes a function to recognize user emotions and personalize notifications and responses based on those emotions.

[1203] User registration and initial settings

[1204] Users register by entering their personal and payment information through devices such as smartphones or tablets. This information is sent to the server and stored in a database. The server then initializes a generative model based on the received identification information to build an individual user profile. The emotion engine is also initialized, preparing to recognize emotions related to the user's transactions and responses.

[1205] Real-time monitoring and anomaly detection

[1206] When a user makes a transaction, the terminal collects transaction data (such as transaction amount, transaction location, and transaction time) and sends it to the server. The server then analyzes the transaction data using a generative model to detect anomalous transactions. For example, if a high-value transaction or an abnormal transaction pattern is detected, the server identifies it as an anomalous transaction.

[1207] When an abnormal transaction is detected, the server uses an emotion engine to evaluate the user's emotional state. The emotion engine recognizes the user's emotions from past transaction data and response data, and adjusts the notification content and countermeasures accordingly. For example, if the user is feeling anxious, the notification message will be designed to provide a more reassuring feeling.

[1208] Question and Answer feature

[1209] When a user submits a security question to the server via their device, the server uses the generative model to analyze the question and generate an appropriate answer. The emotion engine is also utilized here, providing answers that reflect the user's emotional state. For example, if the user is frustrated, the answer may include elements that will help them relax.

[1210] 24 / 7 consultation service

[1211] This system has the ability to accept consultation requests from users 24 hours a day, 365 days a year. When a user sends a consultation request via their device, the server receives it and uses an emotion engine to provide an appropriate response. For example, if the user is feeling stressed, the server will provide relaxation techniques and prompt support.

[1212] Specific examples

[1213] When User A tries to purchase a high-priced item online:

[1214] 1. User A selects a product on the terminal and proceeds with the purchase.

[1215] 2. Transaction data (item, amount, location, time) is sent from the terminal to the server.

[1216] 3. The server analyzes the transaction data using the generative model and detects abnormal transactions based on the high transaction amount.

[1217] 4. The server uses the emotion engine to evaluate User A's emotions and determines that he is feeling anxious.

[1218] 5. The server sends a reassuring notification to User A's device saying, "A high-value transaction has been detected, but it is within normal limits and safe. Please check."

[1219] 6. User A checks the notification and, if he approves the transaction, sends a response to the server via his terminal.

[1220] If User B experiences fraud:

[1221] 1. User B checks past transaction history on his / her terminal and discovers an unknown transaction.

[1222] 2. User B asks through his terminal, "What should I do if my account is fraudulently used?"

[1223] 3. The server uses the generative model to analyze the question and generate an appropriate answer.

[1224] 4. The emotion engine recognizes User B's emotional state as anxiety, so the server sends the response, "Please stay calm. Please follow the steps below to lock your account and contact support."

[1225] 5. User B follows the provided instructions to lock their account and contacts the support center.

[1226] In this way, the present invention monitors user transaction data in real time, and when an abnormal transaction is detected, provides an appropriate response taking into account the user's emotions. Furthermore, by using the emotion engine, it is possible to provide personalized responses to user security-related questions and inquiries, providing a sense of security.

[1227] The processing flow will be explained below.

[1228] Step 1:

[1229] The user uses the terminal to input identification information such as personal information and payment information, and the terminal transmits the input information to the server.

[1230] Step 2:

[1231] The server stores the identification information received from the user in a database, and then initializes the generative model and emotion engine based on the received identification information to build a user profile.

[1232] Step 3:

[1233] When a user makes a transaction on the terminal, the terminal collects transaction data (transaction amount, transaction location, transaction time, etc.) and transmits the collected transaction data to the server.

[1234] Step 4:

[1235] The server uses the generative model to analyze the received transaction data and detects anomalous transactions that deviate from normal transaction patterns.

[1236] Step 5:

[1237] If the server detects an anomalous transaction, it uses an emotion engine to evaluate the user's emotional state, for example, to determine whether the user is feeling anxious.

[1238] Step 6:

[1239] The server adjusts the notification content based on the evaluation results of the emotion engine. Notifications about abnormal transactions are sent to the user's device. For example, a message such as "A high-value transaction has been detected, but it is within the normal range and safe. Please check." is sent.

[1240] Step 7:

[1241] The user reviews the notification at the terminal and, if they approve or reject the transaction, the terminal sends the response to the server.

[1242] Step 8:

[1243] The server receives the user's response and either approves the transaction or takes action to deter fraud. Approved transactions are processed normally, while rejected transactions are recorded as attempted transactions.

[1244] Step 9:

[1245] The user enters a security question and sends it to the server via the device, such as "What should I do if my account is compromised?"

[1246] Step 10:

[1247] The server uses a generative model to analyze the user's question and generate an appropriate answer. An emotion engine is used to generate an answer that corresponds to the user's emotional state.

[1248] Step 11:

[1249] The server generates a response and sends it to the user's device, for example, "Please stay calm. Follow the steps below to lock your account and contact support."

[1250] Step 12:

[1251] The server accepts consultation requests 24 hours a day, 365 days a year. Users can submit security consultations via their devices.

[1252] Step 13:

[1253] The server receives the consultation request and provides an appropriate response using the emotion engine. Based on the request, the server sends the necessary information and assistance to the user's device.

[1254] This allows users to check the safety of their transactions in real time, quickly alleviating security concerns, and by utilizing an emotion engine, personalized responses can be provided according to the user's emotional state.

[1255] Example 2

[1256] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1257] To provide a system that monitors user transaction data in real time and detects abnormal transactions, and to take appropriate action taking into account the emotional state of the user when an abnormal transaction is detected. To also provide personalized responses according to the emotional state of the user to security-related questions and consultation requests from users 24 hours a day, 365 days a year.

[1258] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1259] In this invention, the server includes means for receiving user identification information and initializing a generated model based on the identification information, means for collecting user transaction data and transmitting the transaction data to the server, means for analyzing the received transaction data and including a generative model for detecting anomalous transactions, means for evaluating the user's emotional state when an anomalous transaction is detected and adjusting notification content using an emotion engine, and means for receiving a response from the user and approving or rejecting the transaction, thereby enabling real-time transaction monitoring and response to anomalous transactions based on the user's emotional state.

[1260] The system further includes means including a generative model that receives a security question from a user and analyzes the question to generate an appropriate answer, means using an emotion engine that adjusts the generated answer according to the user's emotional state, and means for transmitting the generated answer to the user's terminal, thereby enabling the provision of a personalized answer according to the user's emotional state.

[1261] In addition, by including a means for receiving consultation requests from users 24 hours a day, 365 days a year and providing an appropriate response based on the request, a means using an emotion engine for adjusting the content of support in response to the consultation request based on the emotional state of the user, and a means for notifying the user of the appropriate response, it is possible to always provide optimized support to the user.

[1262] "Server means" refers to a computing device that receives user identification information and transaction data, analyzes the data using a generative AI model and emotion engine, detects anomalous transactions, and sends notifications.

[1263] "Terminal means" refers to electronic devices such as smartphones and tablets used by users, and is a device used to collect personal information and transaction data and send them to a server.

[1264] A "generative model" is an AI algorithm that analyzes input information such as user transaction data and is used to detect abnormal transactions and generate answers to questions.

[1265] The "emotion engine" is an algorithm that evaluates a user's emotional state based on the user's past transaction data and response data, and generates appropriate notifications and responses accordingly.

[1266] An "abnormal transaction" refers to a transaction that deviates from normal transaction patterns and may be an indication of fraud.

[1267] A "notification" is a message sent to a user when an abnormal transaction is detected, and is information that prompts the user to check the transaction or take caution.

[1268] "Terminal" refers to an electronic device (smartphone, tablet, PC, etc.) used by a user, which communicates with a server and sends and receives data.

[1269] A "user profile" is an individual set of information constructed based on a user's identification information and transaction history, and is used to initialize the generative model and operate the emotion engine.

[1270] "Personalized responses" refer to assistance and notifications that are tailored to the user's current emotional state and past behavior.

[1271] "Security questions" refer to questions that address any doubts or concerns a user may have about the system or a transaction, and the server generates appropriate answers to these questions.

[1272] "24 / 7 consultation request" refers to a request for consultation or support that a user can make to the system at any time, and is a request for an appropriate response to that request.

[1273] MODE FOR CARRYING OUT THE INVENTION

[1274] A specific embodiment of the present invention is described below: The present invention is a system that monitors user transaction data in real time, detects abnormal transactions and fraudulent activities, and, if an abnormality is detected, provides personalized notifications and responses based on the user's emotional state.

[1275] Hardware and software used

[1276] Server: A high-performance computing device used to process the generative AI model and emotion engine, including a database server where user identification information and transaction data are stored.

[1277] Terminal: An electronic device such as a smartphone, tablet, or PC used by a user to input user information and transaction data and send it to the server.

[1278] Generative AI model: An AI algorithm that analyzes user transaction data and detects anomalous transactions.

[1279] Emotion engine: An algorithm that assesses a user's emotional state based on their past transactions and responses, and tailors notifications and responses accordingly.

[1280] System Operation

[1281] First, a user registers with the system using a terminal. The user enters personal information such as name, date of birth, address, and payment information. This information is encrypted and sent to the server, which then initializes a generative model based on the received identification information and builds an individual user profile. The emotion engine is also initialized, ready to recognize emotions in the user's transactions and responses.

[1282] When a user makes a transaction, the terminal collects transaction data (such as transaction amount, transaction location, and transaction time) and sends it to the server. The server inputs the received transaction data into a generative AI model and begins analyzing it in real time. If an abnormal transaction is detected, the server uses an emotion engine to evaluate the user's emotional state. For example, if past data indicates that the user is feeling anxious, the server will send a notification to reassure the user.

[1283] The server sends the generated notification to the terminal, which displays it to the user, who can then review the notification and send approval or rejection of the transaction to the server via the terminal.

[1284] Examples:

[1285] When User A tries to purchase a high-priced item online:

[1286] 1. User A selects a product on the terminal and proceeds with the purchase.

[1287] 2. Transaction data (item, amount, location, time) is sent from the terminal to the server.

[1288] 3. The server analyzes the transaction data using the generative model and detects abnormal transactions based on the high transaction amount.

[1289] 4. The server uses the emotion engine to evaluate User A's emotions and determines that he is feeling anxious.

[1290] 5. The server sends a reassuring notification to User A's device saying, "A high-value transaction has been detected, but it is within normal limits and safe. Please check."

[1291] 6. User A checks the notification and, if he approves the transaction, sends a response to the server via his terminal.

[1292] Examples of prompts:

[1293] "Analyze the current transaction data and check for any abnormalities."

[1294] "What should I do if a user feels uneasy?"

[1295] The system also handles consultation requests 24 hours a day, 365 days a year. When a user sends a consultation request via their device, the server receives it and uses an emotion engine to provide an appropriate response. For example, if the user is feeling stressed, it can provide relaxation techniques or quick support.

[1296] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1297] Program processing flow

[1298] Step 1: User registration and initial setup

[1299] The user opens the dedicated app on their device and enters personal information (such as name, date of birth, and address) and payment information (such as credit card information) on the registration screen.

[1300] The terminal encrypts the entered personal information and payment information and sends it to the server.

[1301] The server receives the transmitted information and stores it in a database.

[1302] Input: User's personal information and payment information

[1303] Data processing: Information is encrypted and stored securely

[1304] Output: User profile stored in the server database

[1305] The server initializes a generative AI model based on the received identification information and builds an individual user profile.

[1306] The server initializes the emotion engine and prepares it for user emotion recognition.

[1307] Step 2: Collect transaction data

[1308] The user actually conducts the transaction using the terminal.

[1309] The terminal collects transaction data (transaction amount, transaction location, transaction time, etc.).

[1310] The terminal transmits the collected transaction data to the server.

[1311] Input: Transaction data (transaction amount, transaction location, transaction time, etc.)

[1312] Data processing: Collecting and formatting transaction data on the terminal

[1313] Output: Collected transaction data is encrypted and sent to the server

[1314] Step 3: Analyze transaction data and detect anomalies

[1315] The server receives the submitted transaction data and inputs it into the generative AI model.

[1316] The server analyzes transaction data using a generative AI model to detect anomalous transactions.

[1317] Input: Received transaction data

[1318] Data Computing: Data Analysis and Anomaly Detection with Generative AI Models

[1319] Output: Information about detected anomalous transactions

[1320] If the server detects an abnormal transaction, it records the details and performs emotion evaluation in the next step.

[1321] Step 4: Sentiment evaluation and notification generation

[1322] The server uses an emotion engine to assess the user's emotional state in response to the anomalous transaction.

[1323] The server generates appropriate notification content based on the evaluation results.

[1324] The server sends the generated notification to the terminal.

[1325] Input: Abnormal transaction information, past transaction data, and user response data

[1326] Data Computation: Emotion engine evaluates emotional state and generates notification content

[1327] Output: A personalized notification message

[1328] The terminal displays the received notification to the user.

[1329] Step 5: Receiving and Processing User Response

[1330] The user checks the notification and responds at the terminal to approve or reject the transaction.

[1331] The terminal encrypts the user's response and sends it to the server.

[1332] The server analyzes the received user response and approves or denies the transaction.

[1333] Input: User response information (approval or rejection)

[1334] Data processing: analysis of response data

[1335] Output: Transaction status update, approved or rejected

[1336] Step 6: Generate questions and answers

[1337] The user submits a security question at the terminal.

[1338] The terminal sends a query to the server.

[1339] The server receives the question and inputs it into a generative AI model for analysis.

[1340] The server generates appropriate answers and adjusts the answers using an emotion engine.

[1341] Input: User question

[1342] Data computation: Parsing questions with generative AI models and tailoring answers with emotion engines

[1343] Output: A suitable answer for the user

[1344] The server sends the generated answer to the terminal.

[1345] The terminal displays the answer to the user.

[1346] Step 7: 24 / 7 consultation

[1347] A user sends a consultation request through a terminal.

[1348] The terminal sends a consultation request to the server.

[1349] A server receives the consultation request and uses an emotion engine to determine an appropriate response.

[1350] Input: User's consultation request

[1351] Data calculation: Emotion engine evaluates emotional state and determines response

[1352] Output: personalized response

[1353] The server transmits the determined response content to the terminal.

[1354] The terminal displays the response to the user.

[1355] In this way, data processing and calculation are performed at each step, and personalized services are provided to the user.

[1356] (Application example 2)

[1357] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1358] While existing electronic payment services have systems for detecting abnormal transactions, they lack personalized responses that take into account the user's emotional state. This can cause anxiety and stress for users, resulting in a poor user experience. Another issue is that responses to security questions are standardized, and personalized answers are not provided based on the user's emotional state, resulting in a decrease in user satisfaction.

[1359] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving user identification information and initializing a model generated based on the identification information, means for collecting user transaction data and transmitting the transaction data to the server, means for analyzing the received transaction data and including a generative model for detecting anomalous transactions, means for sending a personalized notification to the user using an emotion engine when an anomalous transaction is detected, and means for receiving a response from the user and approving or rejecting the transaction. This makes it possible to detect anomalous transactions in real time and provide personalized responses that take user emotions into consideration.

[1360] The server also includes a means for receiving security questions from a user, analyzing the questions using a generative AI model, and generating an appropriate answer using an emotion engine, and a means for transmitting the generated answer to the user's device. This allows for personalized answers to be provided according to the user's emotional state, which is expected to improve the user experience.

[1361] Furthermore, the server includes a means for receiving consultation requests from users 24 hours a day, 365 days a year, and for using the emotion engine to provide an appropriate response based on the request, and a means for notifying the user of the appropriate response. This provides an environment where users can consult with confidence at any time, improving user satisfaction.

[1362] "User identification information" is data for uniquely identifying each individual user.

[1363] A "generative model" is an algorithm that is trained based on user identification information and transaction data to detect abnormal transactions and generate answers to questions.

[1364] The "server means" refers to a server device and its functions for managing user identification information and transaction data, and for performing analysis and notification.

[1365] "Terminal means" refers to equipment and communication functions for collecting data from users and transmitting it to a server.

[1366] "Transaction data" refers to information related to a user's transaction, including the transaction amount, transaction location, transaction time, and the like.

[1367] An "abnormal transaction" is a transaction that differs from normal transaction patterns and may be fraudulent.

[1368] The "means for sending a notification" is a function for sending a warning or a confirmation message to the user when an abnormal transaction is detected.

[1369] An "emotion engine" is an algorithm that analyzes a user's emotional state and adjusts the content of notifications and responses according to the user's emotions.

[1370] The "means for receiving a response" is a function that receives a response or confirmation from a user and executes processing based on that response.

[1371] "Security Questions" are questions that users can ask to answer any concerns they may have about the security of the system or fraudulent transactions.

[1372] A "consultation request" is a request from a user to the system for advice or support.

[1373] The "means for taking appropriate action using an emotion engine" is a function for providing appropriate countermeasures by taking into account the emotional state of the user.

[1374] System Configuration

[1375] A system embodying the invention comprises the following major components:

[1376] 1. Server Means

[1377] User Profile Management: A server that receives user identification and personal information and initializes the generative model and emotion engine.

[1378] Transaction data analysis: A server that receives user transaction data in real time and uses generative models to detect anomalous transactions.

[1379] Sentiment Engine: Evaluates user sentiment upon detected anomalous transactions and generates personalized notifications.

[1380] Notification and response management: Send personalized notifications to users and receive responses to approve or reject transactions.

[1381] 2. Terminal means

[1382] Data collection and transmission: A device such as a smartphone that collects user transaction data in real time and transmits it to a server.

[1383] Receive and display notifications: Receive notifications sent from the server and display them to the user.

[1384] Implementation method

[1385] 1. User registration and initial settings

[1386] Users register with the system by entering their personal and payment information using a device such as a smartphone. The registered information is sent to the server, which creates a user profile. The server then initializes the generative model and emotion engine, preparing to analyze the user's transactions and emotions.

[1387] 2. Real-time transaction monitoring

[1388] When a user makes a transaction, the terminal collects transaction data (transaction amount, transaction location, transaction time, etc.) and sends it to the server. The server analyzes the transaction data using a generative model to detect abnormal transactions.

[1389] 3. Handling Abnormal Transactions

[1390] If an abnormal transaction is detected, the server uses an emotion engine to evaluate the user's emotional state. For example, if the server determines that the user is feeling anxious, it sends a personalized notification offering reassurance. The user then sends a response to the server to approve or reject the transaction accordingly.

[1391] 4. Responses to security questions

[1392] When a user submits a security question to the server via their device, the server uses a generative AI model to analyze the question and leverages an emotion engine to generate an appropriate answer, which is also personalized according to the user's emotional state.

[1393] 5. 24 / 7 consultation service

[1394] The system has the function of accepting consultation requests from users 24 hours a day, 365 days a year. The server receives the consultation request, uses the emotion engine to respond appropriately, and notifies the user.

[1395] Hardware and software used

[1396] Servers: Server equipment, database servers (e.g., MySQL or PostgreSQL), and computing units for generative AI models (e.g., GPU servers)

[1397] Device: Smartphone, tablet, or any device with an internet connection

[1398] Software: Emotion engine (e.g., Python library EmotionAPI), trading data analysis (e.g., generative models using TensorFlow or PyTorch)

[1399] Specific examples

[1400] For example, if a user attempts to purchase a high-value item online, the transaction data is sent from the device to the server. The server detects the transaction as an abnormal transaction and uses an emotion engine to evaluate the user's emotion as "anxious." The server then sends a personalized notification saying, "A high-value transaction has been detected, but it is within normal limits and safe. Please confirm."

[1401] Prompt Sentence Examples

[1402] User ID: 1

[1403] Emotional state: Anxiety

[1404] Transaction data: {'amount': 1000, 'location': 'Online Store', 'time': 'YYYY-MM-DD HH:MM:SS'}

[1405] Question: "A high-value transaction was detected, but it is within normal limits and safe. Please confirm."

[1406] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1407] Step 1:

[1408] Users use devices such as smartphones or tablets to enter personal information and payment information and register with the system.

[1409] Input: Personal information, payment information

[1410] Data processing / calculation: The device formats this information and sends it to the server in a secure manner.

[1411] Output: Registration request data

[1412] Step 2:

[1413] The server generates a user profile based on the received personal information and payment information, stores it in a database, and initializes the generative model and emotion engine based on the user.

[1414] Input: Registration request data

[1415] Data processing / calculation: Store information in a database and perform initialization processes for the generative model and emotion engine.

[1416] Output: Initialized generative model and emotion engine

[1417] Step 3:

[1418] When a user makes a transaction, the terminal collects transaction data (transaction amount, transaction location, transaction time, etc.) in real time and transmits it to the server.

[1419] Input: Transaction data (transaction amount, transaction location, transaction time, etc.)

[1420] Data processing / calculation: The terminal formats the transaction data and sends it to the server

[1421] Output: Transaction data sent

[1422] Step 4:

[1423] The server analyzes the received transaction data using a generative model to detect abnormal transactions.

[1424] Input: Transaction data sent

[1425] Data processing / calculation: Analysis of abnormal transactions using generative models

[1426] Output: Detected abnormal transactions

[1427] Step 5:

[1428] If an abnormal transaction is detected, the server uses an emotion engine to assess the user's emotional state, and based on that, generates a reassuring personalized notification and sends it to the user's device.

[1429] Input: Detected abnormal transactions, past user behavior data

[1430] Data processing / calculation: Emotional state evaluation by emotion engine, generation of notification messages

[1431] Output: A personalized notification message

[1432] Step 6:

[1433] The user checks the notification on the terminal and sends a response to the server approving or rejecting the transaction.

[1434] Input: Notification message

[1435] Data processing / calculation: User confirmation and response input, response transmission from terminal to server

[1436] Output: Approval or rejection response data

[1437] Step 7:

[1438] Based on the response received, the server will either approve or reject the transaction.

[1439] Input: Approval or rejection response data

[1440] Data processing / calculation: Approval or rejection execution process

[1441] Output: Transaction approval or rejection result

[1442] Step 8:

[1443] The user sends a security question to the server via the terminal.

[1444] Input: Question

[1445] Data processing / calculation: Sending query data

[1446] Output: Received query data

[1447] Step 9:

[1448] The server uses a generative AI model to analyze the question and generates an appropriate answer using an emotion engine.

[1449] Input: Received query data

[1450] Data processing / calculation: Question analysis using a generative AI model, emotion evaluation and answer generation using an emotion engine

[1451] Output: personalized answer

[1452] Step 10:

[1453] The server sends the generated answer to the user's terminal.

[1454] Input: Personalized Answer

[1455] Data processing / calculation: Sending response data

[1456] Output: The answer displayed on the user's terminal

[1457] Step 11:

[1458] The server accepts consultation requests from users 24 hours a day, 365 days a year, and uses an emotion engine to provide an appropriate response based on the request.

[1459] Input: Consultation request

[1460] Data processing / calculation: Analysis of consultation content and generation of countermeasures using an emotion engine

[1461] Output: personalized response

[1462] Step 12:

[1463] The server will notify the user of the appropriate action.

[1464] Input: Personalized Response

[1465] Data processing / calculation: Sending notification data

[1466] Output: The solution displayed on the user's device

[1467] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1468] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1469] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1470] [Fourth embodiment]

[1471] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1472] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1473] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1474] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1475] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1476] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1477] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1478] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1479] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1480] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1481] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1482] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1483] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1484] The present invention provides a system for analyzing transaction data in real time and detecting anomalous transactions by utilizing a generative model initialized using user identification information. This system is comprised of the following steps:

[1485] User registration and initial settings

[1486] A user accesses a registration form using a device such as a smartphone or tablet and enters identifying information such as personal and payment information. This information is sent to a server and stored in a database. The server then initializes a generative model based on the received identifying information and builds a user profile. This profile serves as the basis for learning the user's usual trading patterns and detecting anomalies.

[1487] Real-time monitoring and anomaly detection

[1488] When a user uses a smartphone payment system, transaction data (such as transaction amount, transaction location, and transaction time) is sent from the device to a server. The server then analyzes the received transaction data using a generative model to determine whether it deviates from normal transaction patterns. For example, a transaction involving a large amount exceeding the normal transaction amount or a transaction occurring at an unexpected time or location is considered an abnormal transaction.

[1489] If an abnormal transaction is detected, the server immediately sends a notification to the user. The notification includes details of the transaction and information for approving or rejecting the transaction. When the user views the notification, the terminal sends a response of approval or rejection to the server. Based on the user's response, the server either approves the transaction or takes measures against it as fraudulent.

[1490] Question and Answer feature

[1491] When a user has a security question, they send it to the server via their device. The server receives the question, analyzes it using a generative model, and generates an appropriate answer. The generated answer is sent to the user's device and immediately displayed to the user. For example, in response to the question, "What should I do if my account is compromised?", specific instructions are provided, such as, "Follow the steps below: 1. Lock your account. 2. Contact support."

[1492] 24 / 7 consultation service

[1493] This system has the ability to accept consultation requests from users 24 hours a day, 365 days a year. When a user requests a consultation, they send the request to the server via their terminal. The server responds appropriately based on the request and provides the necessary information and assistance. For example, if a user requests to "check their payment history," the server uses the generative model to obtain the necessary information and displays it on the user's terminal.

[1494] Specific examples

[1495] When User A tries to purchase a high-priced item online:

[1496] 1. User A selects a product on the terminal and proceeds with the purchase.

[1497] 2. Transaction data (item, amount, location, time, etc.) is sent from the terminal to the server.

[1498] 3. The server uses the generative model to analyze this transaction data and determines that it is an abnormal transaction due to the high amount.

[1499] 4. The server notifies User A's device, "A high-value transaction has been detected. Please check."

[1500] 5. User A checks the notification and presses the approval button on the terminal if he / she approves the transaction.

[1501] 6. The terminal sends the authorization information to the server, which then approves the transaction.

[1502] If User B experiences fraud:

[1503] 1. User B checks past transaction history on his / her terminal and discovers an unknown transaction.

[1504] 2. User B asks through his terminal, "What should I do if my account is fraudulently used?"

[1505] 3. The server uses the generative model to analyze User B's question and generate an appropriate answer.

[1506] 4. The server sends a message to User B's device saying, "If you experience fraudulent use, please follow the steps below: 1. Lock your account. 2. Contact support."

[1507] 5. User B follows the provided instructions to lock their account and contact the support center.

[1508] As described above, the present invention reduces the risk of fraudulent use and the leakage of personal information by monitoring user transaction data in real time and responding quickly when an abnormal transaction is detected. In addition, by providing a quick and appropriate response to user security-related questions and inquiries, users can use smartphone payments with peace of mind.

[1509] The processing flow will be explained below.

[1510] Step 1:

[1511] The user uses the terminal to input identification information such as personal information and payment information, and the terminal transmits the input information to the server.

[1512] Step 2:

[1513] The server stores the identification information received from the user in a database, and then initializes a generative model based on the received identification information to build a profile of the user.

[1514] Step 3:

[1515] When a user makes a transaction on the terminal, the terminal collects transaction data (transaction amount, transaction location, transaction time, etc.) and transmits the collected transaction data to the server.

[1516] Step 4:

[1517] The server uses the generative model to analyze the received transaction data and detects anomalous transactions that deviate from normal transaction patterns.

[1518] Step 5:

[1519] If the server detects an abnormal transaction, it immediately sends a notification to the user's device, which includes details of the transaction and the option to approve or reject the transaction.

[1520] Step 6:

[1521] The user reviews the notification at the terminal and, if they approve or reject the transaction, the terminal sends the response to the server.

[1522] Step 7:

[1523] The server receives the user's response and either approves the transaction or takes action to deter fraud. Approved transactions are processed normally, while rejected transactions are recorded as attempted transactions.

[1524] Step 8:

[1525] The user enters a security question and sends it to the server via the device, such as "What should I do if my account is compromised?"

[1526] Step 9:

[1527] The server analyzes the user's question using the generative model, generates an appropriate answer, and sends the generated answer to the user's device.

[1528] Step 10:

[1529] The server accepts consultation requests 24 hours a day, 365 days a year. Users can submit security consultations via their devices.

[1530] Step 11:

[1531] The server provides the user with an appropriate response based on the content of the consultation request, and sends the necessary information and assistance to the user's terminal.

[1532] This allows users to check the security of their transactions in real time, quickly alleviating any security concerns.

[1533] Example 1

[1534] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1535] In recent years, online payments using devices such as smartphones and tablets have become widespread. However, systems that monitor user transaction data in real time and quickly detect and respond to abnormal transactions are still inadequate. Furthermore, when users have security-related questions, there are limited ways to obtain immediate and appropriate answers. Furthermore, the lack of an environment where consultations can be made at any time poses the risk of fraudulent use and the leakage of personal information. Therefore, there is a need for a system that can effectively monitor user transaction data, detect and quickly respond to abnormal transactions, and generate appropriate answers to users' security questions.

[1536] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1537] In this invention, the server includes means for receiving a user's identification information and initializing a model generated based on the identification information; means for collecting the user's transaction data and transmitting the transaction data to the server; means for analyzing the received transaction data using the generative AI model to determine whether the transaction data deviates from normal transaction patterns; means for sending a notification to the user's terminal when an abnormal transaction is detected; and means for receiving a response from the user and approving or rejecting the transaction. This enables effective real-time monitoring of user transaction data and rapid detection and response to abnormal transactions. Furthermore, the generative AI model can provide appropriate answers to security-related questions from users, allowing users to use online payments with peace of mind. Furthermore, by responding to user consultation requests 24 hours a day, 365 days a year, the risk of further fraudulent use and personal information leaks can be reduced.

[1538] "User identification information" is information for identifying a user, such as personal information or payment information.

[1539] A "generated model" is a model constructed based on a user's identification information for learning the user's trading patterns.

[1540] The "server means" is a server that has the role of receiving, storing, analyzing, notifying, and processing responses to data.

[1541] "Terminal means" refers to a device such as a smartphone or tablet that allows a user to input and confirm transaction data and send it to the server.

[1542] "Transaction data" refers to data related to the payment made by the user, such as the transaction amount, transaction location, and transaction time.

[1543] A "generative AI model" is a model that uses artificial intelligence technology to analyze data and perform pattern recognition and anomaly detection.

[1544] An "unusual transaction" is a transaction that deviates from normal transaction patterns and is unexpected or suspected to be fraudulent.

[1545] "Notification" refers to a warning or confirmation message sent to a user when an abnormal transaction is detected.

[1546] A "response" is an operation of approval or denial that a user performs after receiving a notification.

[1547] "Security questions" are inquiries made by users to resolve doubts or concerns about fraudulent use, transaction confirmation, account management, etc.

[1548] An "appropriate answer" is the optimal response that the generative AI model analyzes and provides in response to a user's question.

[1549] A "consultation request" is a request for support regarding a problem or question a user has while using the service.

[1550] The present invention is a system that analyzes transaction data in real time and detects abnormal transactions by utilizing a generative AI model initialized using user identification information. This system is mainly composed of a server, a terminal, and a generative AI model.

[1551] User registration and initial settings

[1552] User Registration

[1553] Users access the registration form using a device such as a smartphone or tablet and enter personal information (such as name, email address, and phone number) and payment information (such as credit card information). The device then sends this identification information to the server.

[1554] Data storage and profile building

[1555] The server stores the received identification information in a database, which then initializes the generative AI model and builds a profile to learn the user's normal trading patterns. This profile serves as the basis for detecting abnormal transactions.

[1556] Real-time monitoring and anomaly detection

[1557] Transaction data transmission

[1558] When a user makes a smartphone payment, transaction data (transaction amount, transaction location, transaction time, etc.) is sent from the device to the server.

[1559] Transaction Data Analysis

[1560] The server inputs the received transaction data into a generative AI model (e.g., OpenAI's GPT-4) for analysis. The generative AI model determines whether the transaction data deviates from normal transaction patterns.

[1561] Abnormal transaction detection

[1562] The server determines the degree of anomaly in a transaction based on the analysis results of the generative AI model. For example, if the transaction amount exceeds the normal range or if the transaction is conducted in an unexpected location, it will be identified as an anomalous transaction. If an anomalous transaction is detected, the server will send a notification of the anomalous transaction to the user's device.

[1563] Notification confirmation and response

[1564] The user checks the notification on the terminal and performs an operation to approve or reject the transaction. The terminal sends response information to the server, and the server executes a process to approve or reject the transaction based on the user's response.

[1565] Question and Answer feature

[1566] Submit a question

[1567] The user enters a security question through the terminal and sends it to the server.

[1568] Question analysis and answer generation

[1569] The server inputs the received question into a generative AI model (e.g., ChatGPT) to generate an appropriate answer, which is then sent from the server to the user's device and displayed immediately.

[1570] Example: Question prompt

[1571] "If a fraudulent transaction is detected, how should I respond?"

[1572] "If I'm making a high-value transaction, do I need to set anything up beforehand?"

[1573] How can I check my past transaction history?

[1574] 24 / 7 consultation service

[1575] Submit a consultation request

[1576] A user inputs a consultation request through a terminal and transmits it to a server.

[1577] Request processing and response

[1578] The server analyzes the received request and takes appropriate action. For example, if the request is to check payment history, it retrieves the relevant history from the database and displays it on the terminal.

[1579] This system monitors user transaction data in real time, enabling rapid response if any abnormal transactions are detected. It also uses generative AI models to provide prompt and appropriate answers to security-related questions from users. Furthermore, a consultation desk is available 24 hours a day, 365 days a year, allowing users to use the system with peace of mind at any time.

[1580] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1581] Step 1: User Registration

[1582] Input: The user accesses the registration form from a device such as a smartphone or tablet and enters personal information and payment information.

[1583] Specific operation: The terminal sends the personal information (name, email address, phone number, etc.) and payment information (credit card information, etc.) entered by the user to the server.

[1584] Output: The server stores the received identification information in its database and the user registration is complete.

[1585] Step 2: Initializing the generative model

[1586] Input: The server retrieves data to initialize the generative AI model based on the stored identification information.

[1587] Specific operation: The server inputs the user's identification information into the generative AI model and builds a profile to learn the user's usual trading patterns.

[1588] Output: The server saves the generated profile to a database and the generative AI model is initialized.

[1589] Step 3: Send transaction data

[1590] Input: When a user makes a smartphone payment, they enter transaction data (transaction amount, transaction location, transaction time, etc.) into the terminal.

[1591] Specific operation: The terminal sends transaction data to the server.

[1592] Output: The server takes the received transaction data and proceeds to the next analysis step.

[1593] Step 4: Transaction data analysis

[1594] Input: The server inputs the received transaction data into the generative AI model.

[1595] How it works: A generative AI model (e.g., OpenAI's GPT-4) analyzes transaction data to determine whether it deviates from normal transaction patterns.

[1596] Output: The generative AI model evaluates the degree of anomaly in the transaction and outputs the analysis results to the server.

[1597] Step 5: Identify abnormal transactions

[1598] Input: The server obtains the analysis results of the generative AI model.

[1599] Specific operation: The server determines the degree of abnormality of a transaction based on the analysis results of the generative AI model. For example, if the transaction amount exceeds the normal range or if the transaction is conducted in an unexpected location, it will be identified as an abnormal transaction.

[1600] Output: If an abnormal transaction is detected, the server sends a notification of the abnormal transaction to the user's terminal.

[1601] Step 6: Notification confirmation and response

[1602] Input: The user checks the terminal for an abnormal transaction notification.

[1603] Specific action: The user takes action to approve or reject the transaction.

[1604] Output: The terminal sends the user's response (approval or denial) to the server.

[1605] Step 7: Processing the transaction

[1606] Input: The server receives the user's response.

[1607] Specific operation: The server will execute the process to approve or reject the transaction based on the user's response. For example, if the transaction is approved, the process will proceed normally, and if it is rejected, the transaction will be canceled.

[1608] Output: The server sends the final result of the transaction to the user's terminal.

[1609] Step 8: Submit your question

[1610] Input: The user inputs the security question through the terminal and sends it to the server.

[1611] Specific operation: The terminal sends the user's question to the server.

[1612] Output: The server takes the received question and proceeds to the next parsing step.

[1613] Step 9: Question analysis and answer generation

[1614] Input: The server inputs the received question into the generative AI model.

[1615] How it works: A generative AI model (e.g., ChatGPT) analyzes the question and generates an appropriate answer.

[1616] Output: The generative AI model outputs the generated answer to the server.

[1617] Step 10: Submit your response

[1618] Input: The server gets the answer of the generative AI model.

[1619] Specific operation: The server sends the generated answer to the user's terminal.

[1620] Output: The answer is displayed on the user's terminal.

[1621] Step 11: Submit a consultation request

[1622] Input: The user inputs a consultation request through the terminal and sends it to the server.

[1623] Specific operation: The terminal sends a consultation request to the server.

[1624] Output: The server takes the received request and proceeds to the next appropriate response step.

[1625] Step 12: Request Processing and Response

[1626] Input: The server analyzes the received request.

[1627] Specific operation: The server responds appropriately based on the request content. For example, if the request is to check payment history, it retrieves the relevant history from the database and displays it on the user's device.

[1628] Output: The server sends the appropriate correspondence information to the user's terminal.

[1629] (Application example 1)

[1630] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1631] Conventional electronic payment systems have difficulty detecting fraudulent or abnormal transactions in real time, making it difficult to quickly and effectively protect user security. Furthermore, they lack the functionality to quickly provide appropriate answers to security-related questions, making it difficult to provide a 24 / 7 consultation service. This has led to problems with providing users with a sense of security.

[1632] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1633] In this invention, the server includes server means for receiving user identification information and initializing a model generated based on the identification information, terminal means for collecting user transaction data and transmitting the transaction data to the server, server means including a generative model for analyzing the received transaction data and detecting anomalous transactions, server means for transmitting a notification to the user when an anomalous transaction is detected, server means for receiving a response from the user and approving or rejecting the transaction, and application means for analyzing transaction-related data in real time and notifying the user's terminal. This makes it possible to effectively reduce the risk of fraudulent transactions by monitoring user transaction data in real time, quickly notifying the user when an anomalous transaction is detected, and receiving a response from the user.

[1634] The system also includes a server means including a generative model that receives security questions from users and analyzes the questions to generate appropriate answers, a means for transmitting the generated answers to the user's terminal, and a means for displaying transaction details and approval information so that the user can respond based on them. This makes it possible to provide quick and accurate answers when users ask questions about transactions.

[1635] The system also includes a server means for receiving consultation requests from users 24 hours a day, 365 days a year and taking appropriate action based on those requests, a means for notifying the user of the appropriate action, and a means for updating the user's profile and learning normal transaction patterns. This provides an environment where users can consult with confidence at any time, and by having the system learn the user's transaction patterns, it becomes possible to detect abnormal transactions with higher accuracy.

[1636] "User identification information" refers to information for identifying an individual user, including name, address, credit card information, and the like.

[1637] A "generative model" is a model constructed based on a user's identification information, and is a data analysis algorithm for learning a user's transaction patterns and detecting anomalous transactions.

[1638] "Transaction data" refers to information relating to a transaction conducted by a user, and includes the transaction amount, transaction location, transaction time, etc.

[1639] A "server" is a computer system for storing and processing data over the Internet.

[1640] A "terminal" is a device that is directly operated by a user, and includes smartphones, tablets, etc.

[1641] A "notification" is a message or alert sent to inform a user of specific information.

[1642] "Approval or rejection of transaction" refers to a user's response to approve or reject a transaction when the transaction is determined to be abnormal.

[1643] "Real-time" refers to data being processed and analyzed immediately at the moment it is generated.

[1644] An "application" is software with a specific function, in this case a smartphone program used to analyze and notify transaction data.

[1645] A "profile" is a collection of individual user information constructed based on the user's behavioral patterns and transaction information.

[1646] MODE FOR CARRYING OUT THE INVENTION

[1647] The present invention is a system that utilizes a generative model initialized using user identification information to analyze transaction data in real time and detect anomalous transactions. This system is composed of the following steps.

[1648] 1. User registration and initial settings

[1649] The server receives personal information and payment information entered by the user on the device and stores the corresponding identification information in a database. This identification information is used to initialize a generative model and build a user profile. The user profile serves as the basis for learning normal transaction patterns and detecting anomalous transactions.

[1650] 2. Real-time monitoring and anomaly detection

[1651] When a user makes an electronic payment, transaction data (such as transaction amount, transaction location, and transaction time) is sent from the terminal to the server. The server analyzes this transaction data and uses a generative model to determine whether it deviates from normal transaction patterns. If an abnormal transaction is determined, the server immediately sends a notification to the user and waits for a response (approval or rejection) from the user. This process makes it possible to quickly reduce the risk of abnormal transactions.

[1652] 3. Security Question and Answer Feature

[1653] When a user asks a security question, the question is sent to the server via the device. The server uses a generative model to analyze the question and generate an appropriate answer. This answer is sent to the user's device and displayed immediately. This allows the user to receive appropriate instructions and quickly respond to fraudulent use.

[1654] 4. 24 / 7 consultation service

[1655] When a user requests consultation, the request is sent to the server via their terminal. The server responds appropriately based on the request and provides the necessary information and assistance. This allows users to use the system with peace of mind at any time. The server also updates the user's profile and learns normal transaction patterns, enabling it to detect abnormal transactions with greater accuracy.

[1656] Specific examples

[1657] If a user tries to spend a large amount of money in the middle of the night, the server will determine this as an abnormal transaction and immediately send a notification to the user's terminal.

[1658] Example prompt: "You are planning to spend 120,000 yen in Osaka at 03:24."

[1659] As another example, if a user attempts a high-value transaction in an area where they do not normally make transactions, this is similarly reported as an abnormal transaction.

[1660] Example prompt: "I'm about to spend 50,000 yen at 8:30 PM in an unusual place."

[1661] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1662] Step 1:

[1663] The user accesses the registration form using a terminal and enters personal and payment information. The input data is sent to the server and stored in a database. The server then initializes a generative model based on the received identification information and builds a user profile.

[1664] (Input: User's personal information and payment information, Output: Initialized generative model and user profile)

[1665] Step 2:

[1666] When a user makes an electronic payment, transaction data (such as transaction amount, transaction location, and transaction time) is sent from the terminal to a server. The server receives this transaction data and uses a generative model to analyze whether it deviates from normal transaction patterns.

[1667] (Input: transaction data, Output: abnormal transaction determination results)

[1668] Step 3:

[1669] The server analyzes the transaction data using a generative model, and if an abnormal transaction is detected, it immediately sends a notification to the user's device, including details of the transaction and the option to approve or reject it.

[1670] (Input: Abnormal transaction judgment result, Output: Abnormal transaction notification to user)

[1671] Step 4:

[1672] The user checks the notification and sends a response to the server via the terminal approving or rejecting the transaction. The server receives the user's response and approves or rejects the transaction.

[1673] (Input: User response, Output: Final transaction processing result)

[1674] Step 5:

[1675] The user sends a security question from their device to the server, which uses the generative model to analyze the question and generate and send an appropriate answer.

[1676] (Input: User question, Output: Generated answer)

[1677] Step 6:

[1678] When a user sends a consultation request through the 24 / 7 consultation service, the server receives the request and responds appropriately, sending the necessary information and assistance to the user's device.

[1679] (Input: User consultation request, Output: Response result and notification to user)

[1680] Step 7:

[1681] The server constantly updates user profiles and learns normal trading patterns to improve the accuracy of the generative model, which enables more accurate detection of abnormal transactions.

[1682] (Input: new transaction data, Output: updated user profile and improved generative model)

[1683] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1684] The present invention is a system that analyzes transaction data in real time and detects abnormal transactions using a generative model and emotion engine initialized based on user identification information. The system includes a function to recognize user emotions and personalize notifications and responses based on those emotions.

[1685] User registration and initial settings

[1686] Users register by entering their personal and payment information through devices such as smartphones or tablets. This information is sent to the server and stored in a database. The server then initializes a generative model based on the received identification information to build an individual user profile. The emotion engine is also initialized, preparing to recognize emotions related to the user's transactions and responses.

[1687] Real-time monitoring and anomaly detection

[1688] When a user makes a transaction, the terminal collects transaction data (such as transaction amount, transaction location, and transaction time) and sends it to the server. The server then analyzes the transaction data using a generative model to detect anomalous transactions. For example, if a high-value transaction or an abnormal transaction pattern is detected, the server identifies it as an anomalous transaction.

[1689] When an abnormal transaction is detected, the server uses an emotion engine to evaluate the user's emotional state. The emotion engine recognizes the user's emotions from past transaction data and response data, and adjusts the notification content and countermeasures accordingly. For example, if the user is feeling anxious, the notification message will be designed to provide a more reassuring feeling.

[1690] Question and Answer feature

[1691] When a user submits a security question to the server via their device, the server uses the generative model to analyze the question and generate an appropriate answer. The emotion engine is also utilized here, providing answers that reflect the user's emotional state. For example, if the user is frustrated, the answer may include elements that will help them relax.

[1692] 24 / 7 consultation service

[1693] This system has the ability to accept consultation requests from users 24 hours a day, 365 days a year. When a user sends a consultation request via their device, the server receives it and uses an emotion engine to provide an appropriate response. For example, if the user is feeling stressed, the server will provide relaxation techniques and prompt support.

[1694] Specific examples

[1695] When User A tries to purchase a high-priced item online:

[1696] 1. User A selects a product on the terminal and proceeds with the purchase.

[1697] 2. Transaction data (item, amount, location, time) is sent from the terminal to the server.

[1698] 3. The server analyzes the transaction data using the generative model and detects abnormal transactions based on the high transaction amount.

[1699] 4. The server uses the emotion engine to evaluate User A's emotions and determines that he is feeling anxious.

[1700] 5. The server sends a reassuring notification to User A's device saying, "A high-value transaction has been detected, but it is within normal limits and safe. Please check."

[1701] 6. User A checks the notification and, if he approves the transaction, sends a response to the server via his terminal.

[1702] If User B experiences fraud:

[1703] 1. User B checks past transaction history on his / her terminal and discovers an unknown transaction.

[1704] 2. User B asks through his terminal, "What should I do if my account is fraudulently used?"

[1705] 3. The server uses the generative model to analyze the question and generate an appropriate answer.

[1706] 4. The emotion engine recognizes User B's emotional state as anxiety, so the server sends the response, "Please stay calm. Please follow the steps below to lock your account and contact support."

[1707] 5. User B follows the provided instructions to lock their account and contacts the support center.

[1708] In this way, the present invention monitors user transaction data in real time, and when an abnormal transaction is detected, provides an appropriate response taking into account the user's emotions. Furthermore, by using the emotion engine, it is possible to provide personalized responses to user security-related questions and inquiries, providing a sense of security.

[1709] The processing flow will be explained below.

[1710] Step 1:

[1711] The user uses the terminal to input identification information such as personal information and payment information, and the terminal transmits the input information to the server.

[1712] Step 2:

[1713] The server stores the identification information received from the user in a database, and then initializes the generative model and emotion engine based on the received identification information to build a user profile.

[1714] Step 3:

[1715] When a user makes a transaction on the terminal, the terminal collects transaction data (transaction amount, transaction location, transaction time, etc.) and transmits the collected transaction data to the server.

[1716] Step 4:

[1717] The server uses the generative model to analyze the received transaction data and detects anomalous transactions that deviate from normal transaction patterns.

[1718] Step 5:

[1719] If the server detects an anomalous transaction, it uses an emotion engine to evaluate the user's emotional state, for example, to determine whether the user is feeling anxious.

[1720] Step 6:

[1721] The server adjusts the notification content based on the evaluation results of the emotion engine. Notifications about abnormal transactions are sent to the user's device. For example, a message such as "A high-value transaction has been detected, but it is within the normal range and safe. Please check." is sent.

[1722] Step 7:

[1723] The user reviews the notification at the terminal and, if they approve or reject the transaction, the terminal sends the response to the server.

[1724] Step 8:

[1725] The server receives the user's response and either approves the transaction or takes action to deter fraud. Approved transactions are processed normally, while rejected transactions are recorded as attempted transactions.

[1726] Step 9:

[1727] The user enters a security question and sends it to the server via the device, such as "What should I do if my account is compromised?"

[1728] Step 10:

[1729] The server uses a generative model to analyze the user's question and generate an appropriate answer. An emotion engine is used to generate an answer that corresponds to the user's emotional state.

[1730] Step 11:

[1731] The server generates a response and sends it to the user's device, for example, "Please stay calm. Follow the steps below to lock your account and contact support."

[1732] Step 12:

[1733] The server accepts consultation requests 24 hours a day, 365 days a year. Users can submit security consultations via their devices.

[1734] Step 13:

[1735] The server receives the consultation request and provides an appropriate response using the emotion engine. Based on the request, the server sends the necessary information and assistance to the user's device.

[1736] This allows users to check the safety of their transactions in real time, quickly alleviating security concerns, and by utilizing an emotion engine, personalized responses can be provided according to the user's emotional state.

[1737] Example 2

[1738] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1739] To provide a system that monitors user transaction data in real time and detects abnormal transactions, and to take appropriate action taking into account the emotional state of the user when an abnormal transaction is detected. To also provide personalized responses according to the emotional state of the user to security-related questions and consultation requests from users 24 hours a day, 365 days a year.

[1740] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1741] In this invention, the server includes means for receiving user identification information and initializing a generated model based on the identification information, means for collecting user transaction data and transmitting the transaction data to the server, means for analyzing the received transaction data and including a generative model for detecting anomalous transactions, means for evaluating the user's emotional state when an anomalous transaction is detected and adjusting notification content using an emotion engine, and means for receiving a response from the user and approving or rejecting the transaction, thereby enabling real-time transaction monitoring and response to anomalous transactions based on the user's emotional state.

[1742] The system further includes means including a generative model that receives a security question from a user and analyzes the question to generate an appropriate answer, means using an emotion engine that adjusts the generated answer according to the user's emotional state, and means for transmitting the generated answer to the user's terminal, thereby enabling the provision of a personalized answer according to the user's emotional state.

[1743] In addition, by including a means for receiving consultation requests from users 24 hours a day, 365 days a year and providing an appropriate response based on the request, a means using an emotion engine for adjusting the content of support in response to the consultation request based on the emotional state of the user, and a means for notifying the user of the appropriate response, it is possible to always provide optimized support to the user.

[1744] "Server means" refers to a computing device that receives user identification information and transaction data, analyzes the data using a generative AI model and emotion engine, detects anomalous transactions, and sends notifications.

[1745] "Terminal means" refers to electronic devices such as smartphones and tablets used by users, and is a device used to collect personal information and transaction data and send them to a server.

[1746] A "generative model" is an AI algorithm that analyzes input information such as user transaction data and is used to detect abnormal transactions and generate answers to questions.

[1747] The "emotion engine" is an algorithm that evaluates a user's emotional state based on the user's past transaction data and response data, and generates appropriate notifications and responses accordingly.

[1748] An "abnormal transaction" refers to a transaction that deviates from normal transaction patterns and may be an indication of fraud.

[1749] A "notification" is a message sent to a user when an abnormal transaction is detected, and is information that prompts the user to check the transaction or take caution.

[1750] "Terminal" refers to an electronic device (smartphone, tablet, PC, etc.) used by a user, which communicates with a server and sends and receives data.

[1751] A "user profile" is an individual set of information constructed based on a user's identification information and transaction history, and is used to initialize the generative model and operate the emotion engine.

[1752] "Personalized responses" refer to assistance and notifications that are tailored to the user's current emotional state and past behavior.

[1753] "Security questions" refer to questions that address any doubts or concerns a user may have about the system or a transaction, and the server generates appropriate answers to these questions.

[1754] "24 / 7 consultation request" refers to a request for consultation or support that a user can make to the system at any time, and is a request for an appropriate response to that request.

[1755] MODE FOR CARRYING OUT THE INVENTION

[1756] A specific embodiment of the present invention is described below: The present invention is a system that monitors user transaction data in real time, detects abnormal transactions and fraudulent activities, and, if an abnormality is detected, provides personalized notifications and responses based on the user's emotional state.

[1757] Hardware and software used

[1758] Server: A high-performance computing device used to process the generative AI model and emotion engine, including a database server where user identification information and transaction data are stored.

[1759] Terminal: An electronic device such as a smartphone, tablet, or PC used by a user to input user information and transaction data and send it to the server.

[1760] Generative AI model: An AI algorithm that analyzes user transaction data and detects anomalous transactions.

[1761] Emotion engine: An algorithm that assesses a user's emotional state based on their past transactions and responses, and tailors notifications and responses accordingly.

[1762] System Operation

[1763] First, a user registers with the system using a terminal. The user enters personal information such as name, date of birth, address, and payment information. This information is encrypted and sent to the server, which then initializes a generative model based on the received identification information and builds an individual user profile. The emotion engine is also initialized, ready to recognize emotions in the user's transactions and responses.

[1764] When a user makes a transaction, the terminal collects transaction data (such as transaction amount, transaction location, and transaction time) and sends it to the server. The server inputs the received transaction data into a generative AI model and begins analyzing it in real time. If an abnormal transaction is detected, the server uses an emotion engine to evaluate the user's emotional state. For example, if past data indicates that the user is feeling anxious, the server will send a notification to reassure the user.

[1765] The server sends the generated notification to the terminal, which displays it to the user, who can then review the notification and send approval or rejection of the transaction to the server via the terminal.

[1766] Examples:

[1767] When User A tries to purchase a high-priced item online:

[1768] 1. User A selects a product on the terminal and proceeds with the purchase.

[1769] 2. Transaction data (item, amount, location, time) is sent from the terminal to the server.

[1770] 3. The server analyzes the transaction data using the generative model and detects abnormal transactions based on the high transaction amount.

[1771] 4. The server uses the emotion engine to evaluate User A's emotions and determines that he is feeling anxious.

[1772] 5. The server sends a reassuring notification to User A's device saying, "A high-value transaction has been detected, but it is within normal limits and safe. Please check."

[1773] 6. User A checks the notification and, if he approves the transaction, sends a response to the server via his terminal.

[1774] Examples of prompts:

[1775] "Analyze the current transaction data and check for any abnormalities."

[1776] "What should I do if a user feels uneasy?"

[1777] The system also handles consultation requests 24 hours a day, 365 days a year. When a user sends a consultation request via their device, the server receives it and uses an emotion engine to provide an appropriate response. For example, if the user is feeling stressed, it can provide relaxation techniques or quick support.

[1778] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1779] Program processing flow

[1780] Step 1: User registration and initial setup

[1781] The user opens the dedicated app on their device and enters personal information (such as name, date of birth, and address) and payment information (such as credit card information) on the registration screen.

[1782] The terminal encrypts the entered personal information and payment information and sends it to the server.

[1783] The server receives the transmitted information and stores it in a database.

[1784] Input: User's personal information and payment information

[1785] Data processing: Information is encrypted and stored securely

[1786] Output: User profile stored in the server database

[1787] The server initializes a generative AI model based on the received identification information and builds an individual user profile.

[1788] The server initializes the emotion engine and prepares it for user emotion recognition.

[1789] Step 2: Collect transaction data

[1790] The user actually conducts the transaction using the terminal.

[1791] The terminal collects transaction data (transaction amount, transaction location, transaction time, etc.).

[1792] The terminal transmits the collected transaction data to the server.

[1793] Input: Transaction data (transaction amount, transaction location, transaction time, etc.)

[1794] Data processing: Collecting and formatting transaction data on the terminal

[1795] Output: Collected transaction data is encrypted and sent to the server

[1796] Step 3: Analyze transaction data and detect anomalies

[1797] The server receives the submitted transaction data and inputs it into the generative AI model.

[1798] The server analyzes transaction data using a generative AI model to detect anomalous transactions.

[1799] Input: Received transaction data

[1800] Data Computing: Data Analysis and Anomaly Detection with Generative AI Models

[1801] Output: Information about detected anomalous transactions

[1802] If the server detects an abnormal transaction, it records the details and performs emotion evaluation in the next step.

[1803] Step 4: Sentiment evaluation and notification generation

[1804] The server uses an emotion engine to assess the user's emotional state in response to the anomalous transaction.

[1805] The server generates appropriate notification content based on the evaluation results.

[1806] The server sends the generated notification to the terminal.

[1807] Input: Abnormal transaction information, past transaction data, and user response data

[1808] Data Computation: Emotion engine evaluates emotional state and generates notification content

[1809] Output: A personalized notification message

[1810] The terminal displays the received notification to the user.

[1811] Step 5: Receiving and Processing User Response

[1812] The user checks the notification and responds at the terminal to approve or reject the transaction.

[1813] The terminal encrypts the user's response and sends it to the server.

[1814] The server analyzes the received user response and approves or denies the transaction.

[1815] Input: User response information (approval or rejection)

[1816] Data processing: analysis of response data

[1817] Output: Transaction status update, approved or rejected

[1818] Step 6: Generate questions and answers

[1819] The user submits a security question at the terminal.

[1820] The terminal sends a query to the server.

[1821] The server receives the question and inputs it into a generative AI model for analysis.

[1822] The server generates appropriate answers and adjusts the answers using an emotion engine.

[1823] Input: User question

[1824] Data computation: Parsing questions with generative AI models and tailoring answers with emotion engines

[1825] Output: A suitable answer for the user

[1826] The server sends the generated answer to the terminal.

[1827] The terminal displays the answer to the user.

[1828] Step 7: 24 / 7 consultation

[1829] A user sends a consultation request through a terminal.

[1830] The terminal sends a consultation request to the server.

[1831] A server receives the consultation request and uses an emotion engine to determine an appropriate response.

[1832] Input: User's consultation request

[1833] Data calculation: Emotion engine evaluates emotional state and determines response

[1834] Output: personalized response

[1835] The server transmits the determined response content to the terminal.

[1836] The terminal displays the response to the user.

[1837] In this way, data processing and calculation are performed at each step, and personalized services are provided to the user.

[1838] (Application example 2)

[1839] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1840] While existing electronic payment services have systems for detecting abnormal transactions, they lack personalized responses that take into account the user's emotional state. This can cause anxiety and stress for users, resulting in a poor user experience. Another issue is that responses to security questions are standardized, and personalized answers are not provided based on the user's emotional state, resulting in a decrease in user satisfaction.

[1841] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving user identification information and initializing a model generated based on the identification information, means for collecting user transaction data and transmitting the transaction data to the server, means for analyzing the received transaction data and including a generative model for detecting anomalous transactions, means for sending a personalized notification to the user using an emotion engine when an anomalous transaction is detected, and means for receiving a response from the user and approving or rejecting the transaction. This makes it possible to detect anomalous transactions in real time and provide personalized responses that take user emotions into consideration.

[1842] The server also includes a means for receiving security questions from a user, analyzing the questions using a generative AI model, and generating an appropriate answer using an emotion engine, and a means for transmitting the generated answer to the user's device. This allows for personalized answers to be provided according to the user's emotional state, which is expected to improve the user experience.

[1843] Furthermore, the server includes a means for receiving consultation requests from users 24 hours a day, 365 days a year, and for using the emotion engine to provide an appropriate response based on the request, and a means for notifying the user of the appropriate response. This provides an environment where users can consult with confidence at any time, improving user satisfaction.

[1844] "User identification information" is data for uniquely identifying each individual user.

[1845] A "generative model" is an algorithm that is trained based on user identification information and transaction data to detect abnormal transactions and generate answers to questions.

[1846] The "server means" refers to a server device and its functions for managing user identification information and transaction data, and for performing analysis and notification.

[1847] "Terminal means" refers to equipment and communication functions for collecting data from users and transmitting it to a server.

[1848] "Transaction data" refers to information related to a user's transaction, including the transaction amount, transaction location, transaction time, and the like.

[1849] An "abnormal transaction" is a transaction that differs from normal transaction patterns and may be fraudulent.

[1850] The "means for sending a notification" is a function for sending a warning or a confirmation message to the user when an abnormal transaction is detected.

[1851] An "emotion engine" is an algorithm that analyzes a user's emotional state and adjusts the content of notifications and responses according to the user's emotions.

[1852] The "means for receiving a response" is a function that receives a response or confirmation from a user and executes processing based on that response.

[1853] "Security Questions" are questions that users can ask to answer any concerns they may have about the security of the system or fraudulent transactions.

[1854] A "consultation request" is a request from a user to the system for advice or support.

[1855] The "means for taking appropriate action using an emotion engine" is a function for providing appropriate countermeasures by taking into account the emotional state of the user.

[1856] System Configuration

[1857] A system embodying the invention comprises the following major components:

[1858] 1. Server Means

[1859] User Profile Management: A server that receives user identification and personal information and initializes the generative model and emotion engine.

[1860] Transaction data analysis: A server that receives user transaction data in real time and uses generative models to detect anomalous transactions.

[1861] Sentiment Engine: Evaluates user sentiment upon detected anomalous transactions and generates personalized notifications.

[1862] Notification and response management: Send personalized notifications to users and receive responses to approve or reject transactions.

[1863] 2. Terminal means

[1864] Data collection and transmission: A device such as a smartphone that collects user transaction data in real time and transmits it to a server.

[1865] Receive and display notifications: Receive notifications sent from the server and display them to the user.

[1866] Implementation method

[1867] 1. User registration and initial settings

[1868] Users register with the system by entering their personal and payment information using a device such as a smartphone. The registered information is sent to the server, which creates a user profile. The server then initializes the generative model and emotion engine, preparing to analyze the user's transactions and emotions.

[1869] 2. Real-time transaction monitoring

[1870] When a user makes a transaction, the terminal collects transaction data (transaction amount, transaction location, transaction time, etc.) and sends it to the server. The server analyzes the transaction data using a generative model to detect abnormal transactions.

[1871] 3. Handling Abnormal Transactions

[1872] If an abnormal transaction is detected, the server uses an emotion engine to evaluate the user's emotional state. For example, if the server determines that the user is feeling anxious, it sends a personalized notification offering reassurance. The user then sends a response to the server to approve or reject the transaction accordingly.

[1873] 4. Responses to security questions

[1874] When a user submits a security question to the server via their device, the server uses a generative AI model to analyze the question and leverages an emotion engine to generate an appropriate answer, which is also personalized according to the user's emotional state.

[1875] 5. 24 / 7 consultation service

[1876] The system has the function of accepting consultation requests from users 24 hours a day, 365 days a year. The server receives the consultation request, uses the emotion engine to respond appropriately, and notifies the user.

[1877] Hardware and software used

[1878] Servers: Server equipment, database servers (e.g., MySQL or PostgreSQL), and computing units for generative AI models (e.g., GPU servers)

[1879] Device: Smartphone, tablet, or any device with an internet connection

[1880] Software: Emotion engine (e.g., Python library EmotionAPI), trading data analysis (e.g., generative models using TensorFlow or PyTorch)

[1881] Specific examples

[1882] For example, if a user attempts to purchase a high-value item online, the transaction data is sent from the device to the server. The server detects the transaction as an abnormal transaction and uses an emotion engine to evaluate the user's emotion as "anxious." The server then sends a personalized notification saying, "A high-value transaction has been detected, but it is within normal limits and safe. Please confirm."

[1883] Prompt Sentence Examples

[1884] User ID: 1

[1885] Emotional state: Anxiety

[1886] Transaction data: {'amount': 1000, 'location': 'Online Store', 'time': 'YYYY-MM-DD HH:MM:SS'}

[1887] Question: "A high-value transaction was detected, but it is within normal limits and safe. Please confirm."

[1888] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1889] Step 1:

[1890] Users use devices such as smartphones or tablets to enter personal information and payment information and register with the system.

[1891] Input: Personal information, payment information

[1892] Data processing / calculation: The device formats this information and sends it to the server in a secure manner.

[1893] Output: Registration request data

[1894] Step 2:

[1895] The server generates a user profile based on the received personal information and payment information, stores it in a database, and initializes the generative model and emotion engine based on the user.

[1896] Input: Registration request data

[1897] Data processing / calculation: Store information in a database and perform initialization processes for the generative model and emotion engine.

[1898] Output: Initialized generative model and emotion engine

[1899] Step 3:

[1900] When a user makes a transaction, the terminal collects transaction data (transaction amount, transaction location, transaction time, etc.) in real time and transmits it to the server.

[1901] Input: Transaction data (transaction amount, transaction location, transaction time, etc.)

[1902] Data processing / calculation: The terminal formats the transaction data and sends it to the server

[1903] Output: Transaction data sent

[1904] Step 4:

[1905] The server analyzes the received transaction data using a generative model to detect abnormal transactions.

[1906] Input: Transaction data sent

[1907] Data processing / calculation: Analysis of abnormal transactions using generative models

[1908] Output: Detected abnormal transactions

[1909] Step 5:

[1910] If an abnormal transaction is detected, the server uses an emotion engine to assess the user's emotional state, and based on that, generates a reassuring personalized notification and sends it to the user's device.

[1911] Input: Detected abnormal transactions, past user behavior data

[1912] Data processing / calculation: Emotional state evaluation by emotion engine, generation of notification messages

[1913] Output: A personalized notification message

[1914] Step 6:

[1915] The user checks the notification on the terminal and sends a response to the server approving or rejecting the transaction.

[1916] Input: Notification message

[1917] Data processing / calculation: User confirmation and response input, response transmission from terminal to server

[1918] Output: Approval or rejection response data

[1919] Step 7:

[1920] Based on the response received, the server will either approve or reject the transaction.

[1921] Input: Approval or rejection response data

[1922] Data processing / calculation: Approval or rejection execution process

[1923] Output: Transaction approval or rejection result

[1924] Step 8:

[1925] The user sends a security question to the server via the terminal.

[1926] Input: Question

[1927] Data processing / calculation: Sending query data

[1928] Output: Received query data

[1929] Step 9:

[1930] The server uses a generative AI model to analyze the question and generates an appropriate answer using an emotion engine.

[1931] Input: Received query data

[1932] Data processing / calculation: Question analysis using a generative AI model, emotion evaluation and answer generation using an emotion engine

[1933] Output: personalized answer

[1934] Step 10:

[1935] The server sends the generated answer to the user's terminal.

[1936] Input: Personalized Answer

[1937] Data processing / calculation: Sending response data

[1938] Output: The answer displayed on the user's terminal

[1939] Step 11:

[1940] The server accepts consultation requests from users 24 hours a day, 365 days a year, and uses an emotion engine to provide an appropriate response based on the request.

[1941] Input: Consultation request

[1942] Data processing / calculation: Analysis of consultation content and generation of countermeasures using an emotion engine

[1943] Output: personalized response

[1944] Step 12:

[1945] The server will notify the user of the appropriate action.

[1946] Input: Personalized Response

[1947] Data processing / calculation: Sending notification data

[1948] Output: The solution displayed on the user's device

[1949] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1950] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1951] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1952] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1953] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1954] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1955] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1956] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1957] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1958] The emotion identification model 59 inputs user input into a pre-tra...

Claims

1. server means for receiving a user's identification information and initializing the generated model based on the identification information; a terminal means for collecting user transaction data and transmitting the transaction data to a server; a server means including a generative model for analyzing received transaction data and detecting anomalous transactions; a server means for sending a notification to a user when an abnormal transaction is detected; server means for receiving a response from the user and for approving or denying the transaction; A system including:

2. a server means including a generative model for receiving security questions from a user and analyzing the questions to generate appropriate answers; means for transmitting the generated answer to the user's terminal; The system of claim 1 further comprising:

3. A server means for receiving consultation requests from users 24 hours a day, 365 days a year and providing appropriate responses based on the requests; a means for informing the user of the appropriate action; The system of claim 1 further comprising:

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