System

Abstract

An object of a system according to an embodiment is to predict and prevent a new method of fraudulent payment.SOLUTION: A system according to an embodiment includes generation AI, a prediction unit, and a countermeasure formulation unit. The generated AI includes past and future predictions. The data-analyzing unit analyzes trends and patterns related to fraudulent payments based on the AI analyzed by the data-generating unit. The prediction unit predicts a new method of fraudulent payment on the basis of the trend or pattern analyzed by the data analysis unit. The countermeasure planning unit plans a countermeasure against the method of fraudulent settlement predicted by the prediction unit.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] Conventional technology has the problem that it is difficult to predict new fraudulent payment methods and prevent them before they occur.

[0005] The system according to the embodiment aims to predict new fraudulent payment methods and prevent them before they occur. [Means for solving the problem]

[0006] The system according to the embodiment comprises a generation AI, a data analysis unit, a prediction unit, and a countermeasure planning unit. The generation AI uses past data and makes future predictions. The data analysis unit analyzes trends and patterns related to fraudulent payments based on the data analyzed by the generation AI. The prediction unit predicts new fraudulent payment methods based on the trends and patterns analyzed by the data analysis unit. The countermeasure planning unit plans countermeasures against fraudulent payment methods predicted by the prediction unit. [Effects of the Invention]

[0007] The system according to the embodiment can predict new fraudulent payment methods and prevent them before they occur. [Brief explanation of the drawings]

[0008] [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. DETAILED DESCRIPTION OF THE INVENTION

[0009] 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.

[0010] First, the terms used in the following description will be explained.

[0011] 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, the 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), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] 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.

[0013] 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.

[0014] 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), and Bluetooth (registered trademark).

[0015] 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."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 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.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and 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).

[0019] 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.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. 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 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. 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.

[0022] 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.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 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.

[0025] 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. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The fraudulent payment prediction system according to an embodiment of the present invention is a system that predicts new fraudulent payment methods that are constantly being developed by fraudsters and prevents them before they actually occur. This allows the fraudulent payment prediction system to detect fraudulent payment methods in advance and plan countermeasures.

[0029] A fraudulent payment prediction system according to an embodiment includes a generation AI, a data analysis unit, a prediction unit, and a countermeasure planning unit. The generation AI uses past data and makes future predictions. For example, the generation AI analyzes past transaction history and log data to predict future fraudulent payment methods. The generation AI can also generate scenarios that take future technological advances into account and predict fraudulent methods that exploit technological vulnerabilities. The data analysis unit analyzes trends and patterns related to fraudulent payments based on the data analyzed by the generation AI. For example, the data analysis unit detects frequent patterns and anomalies to extract fraudulent payment trends. The prediction unit predicts new fraudulent payment methods based on the trends and patterns analyzed by the data analysis unit. For example, the prediction unit predicts new methods such as phishing and malware attacks. The countermeasure planning unit plans countermeasures against fraudulent payment methods predicted by the prediction unit. For example, the countermeasure planning unit proposes measures such as strengthening security and user education. This allows the fraudulent payment prediction system according to an embodiment to detect fraudulent payment methods in advance and plan countermeasures. For example, damage can be minimized by quickly taking measures against fraudulent payment methods predicted by the AI. In addition, predictions based on past data can detect future fraudulent payment methods in advance and take measures.

[0030] Generative AI can refer to security cases from different industries and predict new fraudulent payment methods that incorporate attack patterns from those industries. For example, generative AI can learn security cases from industries other than finance and predict new fraudulent payment methods that incorporate attack patterns from the healthcare or energy industries. This makes it possible to utilize knowledge from different fields to discover new threats. For example, generative AI can analyze data from the healthcare industry and predict attack patterns that exploit vulnerabilities in medical devices. Generative AI can also analyze data from the energy industry and predict attack patterns that exploit vulnerabilities in energy supply systems. This makes it possible to discover new threats by utilizing knowledge from different fields.

[0031] Generative AI can generate scenarios that take future technological evolution into account and predict fraudulent methods that will exploit future technological vulnerabilities. For example, generative AI can generate scenarios that take future technological evolution into account and predict fraudulent methods that will exploit new technological vulnerabilities that will arise as quantum computing becomes more widespread. This makes it possible to plan countermeasures that respond to future technological evolution. For example, generative AI can predict vulnerabilities in encryption technology that will arise as quantum computing becomes more widespread and propose countermeasures. Generative AI can also predict new threats that will arise as blockchain technology evolves and propose countermeasures. This makes it possible to plan countermeasures that respond to future technological evolution.

[0032] Generative AI can predict fraudulent methods that take into account the characteristics of different cultural spheres and regions, and can develop countermeasures from a global perspective. For example, generative AI can learn the characteristics of different cultural spheres and regions, and predict fraudulent methods that take into account the characteristics of Asia and Europe. This makes it possible to develop countermeasures from a global perspective. For example, generative AI can take into account the cultural background of Asia and predict fraudulent methods that are unique to Asia. Generative AI can also take into account criminal trends in Europe and predict fraudulent methods that are unique to Europe. This makes it possible to develop countermeasures from a global perspective.

[0033] The generation AI can refer to fraudulent methods used during past natural disasters and social unrest and predict new fraudulent methods that may arise in emergencies. For example, the generation AI can learn fraudulent methods used during past natural disasters and refer to fraudulent methods used during earthquakes and floods to predict new fraudulent methods. This makes it possible to strengthen emergency countermeasures. For example, the generation AI can learn fraudulent methods used during earthquakes and predict new fraudulent methods that are expected to occur when an earthquake occurs. The generation AI can also learn fraudulent methods used during floods and predict new fraudulent methods that are expected to occur when a flood occurs. This makes it possible to strengthen emergency countermeasures.

[0034] Generative AI can predict new methods by taking into account not only past data but also future market and technological trends. For example, generative AI can learn from past data and future market trends, and predict new methods that take into account the growth of emerging markets and technological innovations. This makes it possible to devise countermeasures that respond to future threats. For example, generative AI can predict new fraud methods by taking into account the growth of emerging markets. Generative AI can also predict new fraud methods by taking into account technological innovations. This makes it possible to devise countermeasures that respond to future threats.

[0035] Generative AI can incorporate data from different industries and predict new fraudulent methods from a cross-industry perspective. For example, generative AI can learn data from different industries and predict new fraudulent methods that incorporate data from the medical and energy industries. This makes it possible to discover new threats by utilizing knowledge from different industries. For example, generative AI can analyze data from the medical industry and predict attack patterns that exploit vulnerabilities in medical devices. Generative AI can also analyze data from the energy industry and predict attack patterns that exploit vulnerabilities in energy supply systems. This makes it possible to discover new threats by utilizing knowledge from different industries.

[0036] Generative AI can analyze data from different languages ​​and cultural spheres and predict new methods from a global perspective. For example, generative AI can learn data from different languages ​​and cultural spheres, analyze data in English, French, and Chinese, and predict new fraud methods. This makes it possible to devise countermeasures from a global perspective. For example, generative AI can analyze English data and predict fraud methods specific to English-speaking countries. Generative AI can also analyze French data and predict fraud methods specific to French-speaking countries. This makes it possible to devise countermeasures from a global perspective.

[0037] Generative AI can analyze not only historical data but also real-time social media data to predict new methods that reflect the latest trends. For example, generative AI can learn from historical data and real-time social media data, analyze Twitter and Facebook data, and predict new methods that reflect the latest trends. This makes it possible to develop countermeasures that respond to the latest threats. For example, generative AI can analyze Twitter data and predict new methods that reflect the latest trends. Generative AI can also analyze Facebook data and predict new methods that reflect the latest trends. This makes it possible to develop countermeasures that respond to the latest threats.

[0038] The generation AI can analyze data from different time periods and seasons, and predict fraudulent methods taking into account temporal trends. For example, the generation AI can learn payment data from different time periods, analyze late-night and early-morning data, and predict fraudulent methods taking into account temporal trends. This makes it possible to devise countermeasures that respond to threats for each time period. For example, the generation AI can analyze late-night data and predict fraudulent methods that are specific to late-night transactions. The generation AI can also analyze early-morning data and predict fraudulent methods that are specific to early morning transactions. This makes it possible to devise countermeasures that respond to threats for each time period.

[0039] Generative AI can analyze payment data from different industries and predict new fraud methods from a cross-industry perspective. For example, generative AI can learn payment data from different industries, analyze data from the medical and energy industries, and predict new fraud methods from a cross-industry perspective. This makes it possible to discover new threats by leveraging knowledge from different industries. For example, generative AI can analyze data from the medical industry and predict attack patterns that exploit vulnerabilities in medical devices. Generative AI can also analyze data from the energy industry and predict attack patterns that exploit vulnerabilities in energy supply systems. This makes it possible to discover new threats by leveraging knowledge from different industries.

[0040] Generative AI can analyze real-time payment data and predict fraudulent methods that reflect the latest trends. Generative AI, for example, learns real-time payment data, analyzes ongoing transaction data, and predicts fraudulent methods that reflect the latest trends. This makes it possible to devise countermeasures that respond to the latest threats. For example, generative AI can analyze ongoing transaction data and predict fraudulent methods that reflect the latest trends. Generative AI can also analyze real-time transaction data and predict fraudulent methods that reflect the latest trends. This makes it possible to devise countermeasures that respond to the latest threats.

[0041] Generative AI can analyze not only incident data from competitors in the same industry, but also incident data from other industries, and predict new fraud methods from a cross-sector perspective. For example, generative AI can learn from incident data from competitors in the same industry and incident data from other industries, analyze data from the medical and energy industries, and predict new fraud methods from a cross-sector perspective. This makes it possible to discover new threats by leveraging knowledge from different industries. For example, generative AI can analyze data from the medical industry and predict attack patterns that exploit vulnerabilities in medical devices. Generative AI can also analyze data from the energy industry and predict attack patterns that exploit vulnerabilities in energy supply systems. This makes it possible to discover new threats by leveraging knowledge from different industries.

[0042] Generative AI can analyze incident data from different industries and regions and predict new fraud methods from a global perspective. For example, generative AI can learn incident data from different industries and regions, analyze data from the medical and energy industries, and predict new fraud methods from a global perspective. This makes it possible to discover new threats by utilizing knowledge from different industries. For example, generative AI can analyze data from the medical industry and predict attack patterns that exploit vulnerabilities in medical devices. Generative AI can also analyze data from the energy industry and predict attack patterns that exploit vulnerabilities in energy supply systems. This makes it possible to discover new threats by utilizing knowledge from different industries.

[0043] Generative AI can analyze not only incident data but also real-time news data, and predict fraudulent methods that reflect the latest trends. For example, generative AI can learn incident data and real-time news data, analyze data on ongoing incidents and accidents, and predict fraudulent methods that reflect the latest trends. This makes it possible to devise countermeasures that respond to the latest threats. For example, generative AI can analyze data on ongoing incidents and predict fraudulent methods that reflect the latest trends. Generative AI can also analyze real-time news data and predict fraudulent methods that reflect the latest trends. This makes it possible to devise countermeasures that respond to the latest threats.

[0044] Generative AI can propose not only countermeasures against predicted fraudulent methods, but also preventive measures and educational programs. Generative AI, for example, can propose not only countermeasures against predicted fraudulent methods, but also preventive measures and educational programs. For example, generative AI can generate security education curricula and guidelines for preventive measures. This makes it possible to strengthen proactive measures. For example, generative AI can propose security education curricula and strengthen education for users. Generative AI can also propose guidelines for preventive measures and strengthen proactive measures. This makes it possible to strengthen proactive measures.

[0045] Generative AI can refer to security measures in different industries and propose new measures from a cross-industry perspective. For example, generative AI can learn security measures in different industries, refer to measures in the medical and energy industries, and propose new measures from a cross-industry perspective. This makes it possible to utilize knowledge from different industries to plan new measures. For example, generative AI can refer to security measures in the medical industry and propose security enhancement measures for medical devices. Generative AI can also refer to security measures in the energy industry and propose security enhancement measures for energy supply systems. This makes it possible to plan new measures by utilizing knowledge from different industries.

[0046] Generative AI can refer to security measures in different regions and cultural spheres and propose new measures from a global perspective. For example, generative AI can learn security measures in different regions and cultural spheres, refer to measures in Asia and Europe, and propose new measures from a global perspective. This makes it possible to plan measures that address threats specific to each region. For example, generative AI can refer to security measures in Asia and propose measures that address threats specific to Asia. Generative AI can also refer to security measures in Europe and propose measures that address threats specific to Europe. This makes it possible to plan measures that address threats specific to each region.

[0047] Generative AI can propose countermeasures not only for predicted fraudulent methods, but also from the perspectives of risk management and compliance. Generative AI can propose countermeasures not only for predicted fraudulent methods, but also from the perspectives of risk management and compliance. For example, generative AI can propose risk assessments and risk mitigation measures. This makes it possible to formulate comprehensive countermeasures. For example, generative AI can conduct risk assessments and propose risk mitigation measures. Generative AI can also propose countermeasures from a compliance perspective, proposing measures to strengthen legal compliance and internal audits. This makes it possible to formulate comprehensive countermeasures.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] The fraudulent payment prediction system can also include a behavioral analysis unit that analyzes user behavioral patterns. The behavioral analysis unit analyzes a user's past transaction history and website browsing history to detect behavior that deviates from normal behavioral patterns. For example, the behavioral analysis unit can detect anomalies when a user makes a high-value transaction during an unusual time or when the user accesses the system from an unusual area. This enables early detection of fraudulent payments based on user behavioral patterns. The behavioral analysis unit can also analyze a user's device information and IP address and issue a warning if the access is not from a device or IP address that is normally used. This enables multi-layered security measures based on user behavioral patterns.

[0050] The fraudulent payment prediction system can further include a social network analysis unit that analyzes a user's social network. The social network analysis unit analyzes a user's social media accounts and friendships to assess the risk of fraudulent payments. For example, if a user's friend has fallen victim to a fraudulent payment, the user's risk can be assessed based on that information. This makes it possible to assess risk based on the user's social network. The social network analysis unit can also analyze the user's comments and posts on social media to predict the risk of fraudulent payments. This allows for multi-layered security measures based on the user's social network.

[0051] The fraudulent payment prediction system can further include a purchase history analysis unit that analyzes a user's purchase history. The purchase history analysis unit analyzes the user's past purchase history and detects transactions that deviate from normal purchasing patterns. For example, the purchase history analysis unit can detect anomalies when a user suddenly purchases a high-priced item that they do not normally purchase, or when a transaction occurs at a store that the user does not normally visit. This enables early detection of fraudulent payments based on the user's purchasing patterns. The purchase history analysis unit can also build a predictive model based on the user's purchase history and evaluate the risk of future fraudulent payments. This enables multi-layered security measures based on the user's purchasing patterns.

[0052] The fraudulent payment prediction system can further include a device analysis unit that analyzes user device information. The device analysis unit analyzes information about the device used by the user and issues a warning if access is not from a device normally used. For example, the device analysis unit can record information about the smartphone or PC normally used by the user and detect anomalies if access is from a different device. This enables early detection of fraudulent payments based on the user's device information. The device analysis unit can also build a predictive model based on the user's device information and evaluate the risk of future fraudulent payments. This enables multi-layered security measures based on the user's device information.

[0053] The fraudulent payment prediction system can further include a location information analysis unit that analyzes the user's location information. The location information analysis unit analyzes the user's current location and past movement history to detect location information that deviates from the user's normal range of activity. For example, the location information analysis unit can detect anomalies when there is access from an area the user does not normally visit. This enables early detection of fraudulent payments based on the user's location information. The location information analysis unit can also build a predictive model based on the user's location information and evaluate the risk of future fraudulent payments. This enables multi-layered security measures based on the user's location information.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: Generative AI uses past data and makes future predictions. For example, generative AI analyzes past transaction history and log data to predict future fraudulent payment methods. Generative AI can also generate scenarios that take into account future technological evolution and predict fraudulent methods that exploit technological vulnerabilities. Step 2: The data analysis unit analyzes trends and patterns related to fraudulent payments based on the data analyzed by the generation AI. For example, the data analysis unit detects frequent patterns and anomalies to extract trends in fraudulent payments. Step 3: The prediction unit predicts new fraudulent payment methods based on the trends and patterns analyzed by the data analysis unit. For example, the prediction unit predicts new methods such as phishing and malware attacks. Step 4: The countermeasure planning unit plans countermeasures against the fraudulent payment methods predicted by the prediction unit. For example, the countermeasure planning unit proposes measures such as strengthening security and user education.

[0056] (Example 2) The fraudulent payment prediction system according to an embodiment of the present invention is a system that predicts new fraudulent payment methods that are constantly being developed by fraudsters and prevents them before they actually occur. This allows the fraudulent payment prediction system to detect fraudulent payment methods in advance and plan countermeasures.

[0057] A fraudulent payment prediction system according to an embodiment includes a generation AI, a data analysis unit, a prediction unit, and a countermeasure planning unit. The generation AI uses past data and makes future predictions. For example, the generation AI analyzes past transaction history and log data to predict future fraudulent payment methods. The generation AI can also generate scenarios that take future technological advances into account and predict fraudulent methods that exploit technological vulnerabilities. The data analysis unit analyzes trends and patterns related to fraudulent payments based on the data analyzed by the generation AI. For example, the data analysis unit detects frequent patterns and anomalies to extract fraudulent payment trends. The prediction unit predicts new fraudulent payment methods based on the trends and patterns analyzed by the data analysis unit. For example, the prediction unit predicts new methods such as phishing and malware attacks. The countermeasure planning unit plans countermeasures against fraudulent payment methods predicted by the prediction unit. For example, the countermeasure planning unit proposes measures such as strengthening security and user education. This allows the fraudulent payment prediction system according to an embodiment to detect fraudulent payment methods in advance and plan countermeasures. For example, damage can be minimized by quickly taking measures against fraudulent payment methods predicted by the AI. In addition, predictions based on past data can detect future fraudulent payment methods in advance and take measures.

[0058] Generative AI can refer to security cases from different industries and predict new fraudulent payment methods that incorporate attack patterns from those industries. For example, generative AI can learn security cases from industries other than finance and predict new fraudulent payment methods that incorporate attack patterns from the healthcare or energy industries. This makes it possible to utilize knowledge from different fields to discover new threats. For example, generative AI can analyze data from the healthcare industry and predict attack patterns that exploit vulnerabilities in medical devices. Generative AI can also analyze data from the energy industry and predict attack patterns that exploit vulnerabilities in energy supply systems. This makes it possible to discover new threats by utilizing knowledge from different fields.

[0059] Generative AI can generate scenarios that take future technological evolution into account and predict fraudulent methods that will exploit future technological vulnerabilities. For example, generative AI can generate scenarios that take future technological evolution into account and predict fraudulent methods that will exploit new technological vulnerabilities that will arise as quantum computing becomes more widespread. This makes it possible to plan countermeasures that respond to future technological evolution. For example, generative AI can predict vulnerabilities in encryption technology that will arise as quantum computing becomes more widespread and propose countermeasures. Generative AI can also predict new threats that will arise as blockchain technology evolves and propose countermeasures. This makes it possible to plan countermeasures that respond to future technological evolution.

[0060] The generation AI is equipped with an emotion estimation function that collects users' emotional reactions to predicted fraudulent methods in real time and identifies methods that cause emotional anxiety, allowing it to prioritize countermeasures against methods identified by the emotion estimation function. The generation AI, for example, collects users' emotional reactions to predicted fraudulent methods in real time and identifies methods that cause emotional anxiety. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. This allows it to prioritize countermeasures against methods that cause emotional anxiety. For example, the generation AI analyzes the user's facial expression data and identifies methods that cause emotional anxiety. The generation AI can also analyze the user's voice data and identify methods that cause emotional anxiety. This allows it to prioritize countermeasures against methods that cause emotional anxiety.

[0061] Generative AI can predict fraudulent methods that take into account the characteristics of different cultural spheres and regions, and can develop countermeasures from a global perspective. For example, generative AI can learn the characteristics of different cultural spheres and regions, and predict fraudulent methods that take into account the characteristics of Asia and Europe. This makes it possible to develop countermeasures from a global perspective. For example, generative AI can take into account the cultural background of Asia and predict fraudulent methods that are unique to Asia. Generative AI can also take into account criminal trends in Europe and predict fraudulent methods that are unique to Europe. This makes it possible to develop countermeasures from a global perspective.

[0062] The generation AI can refer to fraudulent methods used during past natural disasters and social unrest and predict new fraudulent methods that may arise in emergencies. For example, the generation AI can learn fraudulent methods used during past natural disasters and refer to fraudulent methods used during earthquakes and floods to predict new fraudulent methods. This makes it possible to strengthen emergency countermeasures. For example, the generation AI can learn fraudulent methods used during earthquakes and predict new fraudulent methods that are expected to occur when an earthquake occurs. The generation AI can also learn fraudulent methods used during floods and predict new fraudulent methods that are expected to occur when a flood occurs. This makes it possible to strengthen emergency countermeasures.

[0063] Generative AI can predict new methods by taking into account not only past data but also future market and technological trends. For example, generative AI can learn from past data and future market trends, and predict new methods that take into account the growth of emerging markets and technological innovations. This makes it possible to devise countermeasures that respond to future threats. For example, generative AI can predict new fraud methods by taking into account the growth of emerging markets. Generative AI can also predict new fraud methods by taking into account technological innovations. This makes it possible to devise countermeasures that respond to future threats.

[0064] Generative AI can incorporate data from different industries and predict new fraudulent methods from a cross-industry perspective. For example, generative AI can learn data from different industries and predict new fraudulent methods that incorporate data from the medical and energy industries. This makes it possible to discover new threats by utilizing knowledge from different industries. For example, generative AI can analyze data from the medical industry and predict attack patterns that exploit vulnerabilities in medical devices. Generative AI can also analyze data from the energy industry and predict attack patterns that exploit vulnerabilities in energy supply systems. This makes it possible to discover new threats by utilizing knowledge from different industries.

[0065] The generation AI is equipped with an emotion estimation function that analyzes users' emotional reactions to past fraudulent methods and predicts new methods based on methods that elicited strong emotional reactions. The generation AI can predict new methods based on methods identified by the emotion estimation function. For example, the generation AI analyzes users' emotional reactions to past fraudulent methods and identifies methods that elicited strong emotional reactions. For example, the generation AI analyzes users' facial expressions and voice to calculate an emotion score. This makes it possible to predict new methods based on methods that elicited strong emotional reactions. For example, the generation AI analyzes users' facial expression data to identify methods that elicited strong emotional reactions. The generation AI can also analyze users' voice data to identify methods that elicited strong emotional reactions. This makes it possible to predict new methods based on methods that elicited strong emotional reactions.

[0066] Generative AI can analyze data from different languages ​​and cultural spheres and predict new methods from a global perspective. For example, generative AI can learn data from different languages ​​and cultural spheres, analyze data in English, French, and Chinese, and predict new fraud methods. This makes it possible to devise countermeasures from a global perspective. For example, generative AI can analyze English data and predict fraud methods specific to English-speaking countries. Generative AI can also analyze French data and predict fraud methods specific to French-speaking countries. This makes it possible to devise countermeasures from a global perspective.

[0067] Generative AI can analyze not only historical data but also real-time social media data to predict new methods that reflect the latest trends. For example, generative AI can learn from historical data and real-time social media data, analyze Twitter and Facebook data, and predict new methods that reflect the latest trends. This makes it possible to develop countermeasures that respond to the latest threats. For example, generative AI can analyze Twitter data and predict new methods that reflect the latest trends. Generative AI can also analyze Facebook data and predict new methods that reflect the latest trends. This makes it possible to develop countermeasures that respond to the latest threats.

[0068] The generation AI is equipped with an emotion estimation function that monitors users' emotional reactions to past fraudulent methods in real time and predicts new methods based on the methods that cause the most anxiety. It can predict new methods based on the methods identified by the emotion estimation function. For example, the generation AI monitors users' emotional reactions to past fraudulent methods in real time and identifies the methods that cause the most anxiety. For example, it analyzes the user's facial expressions and voice to calculate an emotion score. This makes it possible to predict new methods based on methods that cause emotional anxiety. For example, the generation AI analyzes user facial expression data to identify methods that cause emotional anxiety. The generation AI can also analyze user voice data to identify methods that cause emotional anxiety. This makes it possible to predict new methods based on methods that cause emotional anxiety.

[0069] The generation AI can analyze data from different time periods and seasons, and predict fraudulent methods taking into account temporal trends. For example, the generation AI can learn payment data from different time periods, analyze late-night and early-morning data, and predict fraudulent methods taking into account temporal trends. This makes it possible to devise countermeasures that respond to threats for each time period. For example, the generation AI can analyze late-night data and predict fraudulent methods that are specific to late-night transactions. The generation AI can also analyze early-morning data and predict fraudulent methods that are specific to early morning transactions. This makes it possible to devise countermeasures that respond to threats for each time period.

[0070] The generation AI is equipped with an emotion estimation function that collects users' emotional reactions to fraudulent methods based on payment data in real time and identifies methods that provoke strong emotional reactions, allowing it to prioritize countermeasures against methods identified by the emotion estimation function. For example, the generation AI collects users' emotional reactions to fraudulent methods based on payment data in real time and identifies methods that provoke strong emotional reactions. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. This allows it to prioritize countermeasures against methods that provoke strong emotional reactions. For example, the generation AI analyzes the user's facial expression data and identifies methods that provoke strong emotional reactions. The generation AI can also analyze the user's voice data and identify methods that provoke strong emotional reactions. This allows it to prioritize countermeasures against methods that provoke strong emotional reactions.

[0071] Generative AI can analyze payment data from different industries and predict new fraud methods from a cross-industry perspective. For example, generative AI can learn payment data from different industries, analyze data from the medical and energy industries, and predict new fraud methods from a cross-industry perspective. This makes it possible to discover new threats by leveraging knowledge from different industries. For example, generative AI can analyze data from the medical industry and predict attack patterns that exploit vulnerabilities in medical devices. Generative AI can also analyze data from the energy industry and predict attack patterns that exploit vulnerabilities in energy supply systems. This makes it possible to discover new threats by leveraging knowledge from different industries.

[0072] Generative AI can analyze real-time payment data and predict fraudulent methods that reflect the latest trends. Generative AI, for example, learns real-time payment data, analyzes ongoing transaction data, and predicts fraudulent methods that reflect the latest trends. This makes it possible to devise countermeasures that respond to the latest threats. For example, generative AI can analyze ongoing transaction data and predict fraudulent methods that reflect the latest trends. Generative AI can also analyze real-time transaction data and predict fraudulent methods that reflect the latest trends. This makes it possible to devise countermeasures that respond to the latest threats.

[0073] Generative AI can analyze not only incident data from competitors in the same industry, but also incident data from other industries, and predict new fraud methods from a cross-sector perspective. For example, generative AI can learn from incident data from competitors in the same industry and incident data from other industries, analyze data from the medical and energy industries, and predict new fraud methods from a cross-sector perspective. This makes it possible to discover new threats by leveraging knowledge from different industries. For example, generative AI can analyze data from the medical industry and predict attack patterns that exploit vulnerabilities in medical devices. Generative AI can also analyze data from the energy industry and predict attack patterns that exploit vulnerabilities in energy supply systems. This makes it possible to discover new threats by leveraging knowledge from different industries.

[0074] The generation AI is equipped with an emotion estimation function that collects users' emotional reactions to fraudulent methods based on incident data from competitors in the same industry in real time and identifies methods that provoke strong emotional reactions, allowing it to prioritize countermeasures against methods identified by the emotion estimation function. For example, the generation AI collects users' emotional reactions to fraudulent methods based on incident data from competitors in the same industry in real time and identifies methods that provoke strong emotional reactions. For example, it analyzes users' facial expressions and voices and calculates an emotion score. This allows it to prioritize countermeasures against methods that provoke strong emotional reactions. For example, the generation AI analyzes users' facial expression data and identifies methods that provoke strong emotional reactions. The generation AI can also analyze users' voice data and identify methods that provoke strong emotional reactions. This allows it to prioritize countermeasures against methods that provoke strong emotional reactions.

[0075] Generative AI can analyze incident data from different industries and regions and predict new fraud methods from a global perspective. For example, generative AI can learn incident data from different industries and regions, analyze data from the medical and energy industries, and predict new fraud methods from a global perspective. This makes it possible to discover new threats by utilizing knowledge from different industries. For example, generative AI can analyze data from the medical industry and predict attack patterns that exploit vulnerabilities in medical devices. Generative AI can also analyze data from the energy industry and predict attack patterns that exploit vulnerabilities in energy supply systems. This makes it possible to discover new threats by utilizing knowledge from different industries.

[0076] Generative AI can analyze not only incident data but also real-time news data, and predict fraudulent methods that reflect the latest trends. For example, generative AI can learn incident data and real-time news data, analyze data on ongoing incidents and accidents, and predict fraudulent methods that reflect the latest trends. This makes it possible to devise countermeasures that respond to the latest threats. For example, generative AI can analyze data on ongoing incidents and predict fraudulent methods that reflect the latest trends. Generative AI can also analyze real-time news data and predict fraudulent methods that reflect the latest trends. This makes it possible to devise countermeasures that respond to the latest threats.

[0077] The generation AI is equipped with an emotion estimation function that monitors users' emotional reactions to fraudulent methods based on incident data from competitors in the same industry in real time and identifies the methods that cause the most anxiety, allowing it to prioritize countermeasures against the methods identified by the emotion estimation function. For example, the generation AI monitors users' emotional reactions to fraudulent methods based on incident data from competitors in the same industry in real time and identifies the methods that cause the most anxiety. For example, it analyzes users' facial expressions and voice and calculates an emotion score. This allows it to prioritize countermeasures against methods that cause emotional anxiety. For example, the generation AI analyzes users' facial expression data and identifies methods that cause emotional anxiety. The generation AI can also analyze users' voice data and identify methods that cause emotional anxiety. This allows it to prioritize countermeasures against methods that cause emotional anxiety.

[0078] Generative AI can propose not only countermeasures against predicted fraudulent methods, but also preventive measures and educational programs. Generative AI, for example, can propose not only countermeasures against predicted fraudulent methods, but also preventive measures and educational programs. For example, generative AI can generate security education curricula and guidelines for preventive measures. This makes it possible to strengthen proactive measures. For example, generative AI can propose security education curricula and strengthen education for users. Generative AI can also propose guidelines for preventive measures and strengthen proactive measures. This makes it possible to strengthen proactive measures.

[0079] Generative AI can refer to security measures in different industries and propose new measures from a cross-industry perspective. For example, generative AI can learn security measures in different industries, refer to measures in the medical and energy industries, and propose new measures from a cross-industry perspective. This makes it possible to utilize knowledge from different industries to plan new measures. For example, generative AI can refer to security measures in the medical industry and propose security enhancement measures for medical devices. Generative AI can also refer to security measures in the energy industry and propose security enhancement measures for energy supply systems. This makes it possible to plan new measures by utilizing knowledge from different industries.

[0080] Generative AI can refer to security measures in different regions and cultural spheres and propose new measures from a global perspective. For example, generative AI can learn security measures in different regions and cultural spheres, refer to measures in Asia and Europe, and propose new measures from a global perspective. This makes it possible to plan measures that address threats specific to each region. For example, generative AI can refer to security measures in Asia and propose measures that address threats specific to Asia. Generative AI can also refer to security measures in Europe and propose measures that address threats specific to Europe. This makes it possible to plan measures that address threats specific to each region.

[0081] Generative AI can propose countermeasures not only for predicted fraudulent methods, but also from the perspectives of risk management and compliance. Generative AI can propose countermeasures not only for predicted fraudulent methods, but also from the perspectives of risk management and compliance. For example, generative AI can propose risk assessments and risk mitigation measures. This makes it possible to formulate comprehensive countermeasures. For example, generative AI can conduct risk assessments and propose risk mitigation measures. Generative AI can also propose countermeasures from a compliance perspective, proposing measures to strengthen legal compliance and internal audits. This makes it possible to formulate comprehensive countermeasures.

[0082] The generation AI is equipped with an emotion estimation function that monitors users' emotional reactions to countermeasures against predicted fraudulent methods in real time and identifies the countermeasures that will give them the most sense of security, and can prioritize the implementation of countermeasures identified by the emotion estimation function. The generation AI, for example, monitors users' emotional reactions to countermeasures against predicted fraudulent methods in real time and identifies the countermeasures that will give them the most sense of security. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. This allows the implementation of countermeasures that will give them an emotional sense of security to be prioritized. For example, the generation AI analyzes the user's facial expression data and identifies countermeasures that will give them an emotional sense of security. The generation AI can also analyze the user's voice data and identify countermeasures that will give them an emotional sense of security to be prioritized.

[0083] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0084] The fraudulent payment prediction system can also include a behavioral analysis unit that analyzes user behavioral patterns. The behavioral analysis unit analyzes a user's past transaction history and website browsing history to detect behavior that deviates from normal behavioral patterns. For example, the behavioral analysis unit can detect anomalies when a user makes a high-value transaction during an unusual time or when the user accesses the system from an unusual area. This enables early detection of fraudulent payments based on user behavioral patterns. The behavioral analysis unit can also analyze a user's device information and IP address and issue a warning if the access is not from a device or IP address that is normally used. This enables multi-layered security measures based on user behavioral patterns.

[0085] The fraudulent payment prediction system can further include an emotion evaluation unit that estimates a user's emotions and evaluates the risk of fraudulent payments based on the estimated user emotions. The emotion evaluation unit analyzes the user's facial expressions and voice to calculate an emotion score. For example, if a user is feeling anxious or stressed, the emotion score can be used to determine that the risk of fraudulent payments is increased. This allows additional security checks to be performed on users who are emotionally anxious. The emotion evaluation unit can also analyze behavioral data such as the user's input speed and mouse movements to detect emotional changes. This allows for flexible security measures to be implemented according to the user's emotional state.

[0086] The fraudulent payment prediction system can further include a social network analysis unit that analyzes a user's social network. The social network analysis unit analyzes a user's social media accounts and friendships to assess the risk of fraudulent payments. For example, if a user's friend has fallen victim to a fraudulent payment, the user's risk can be assessed based on that information. This makes it possible to assess risk based on the user's social network. The social network analysis unit can also analyze the user's comments and posts on social media to predict the risk of fraudulent payments. This allows for multi-layered security measures based on the user's social network.

[0087] The fraudulent payment prediction system can further include an emotion prediction unit that estimates a user's emotions and predicts fraudulent payment methods based on the estimated user emotions. The emotion prediction unit analyzes the user's facial expressions and voice to calculate an emotion score. For example, if a user is feeling anxious or fearful, a new fraudulent payment method can be predicted based on the emotion score. This makes it possible to issue a warning about specific fraud methods to users who are emotionally anxious. The emotion prediction unit can also analyze behavioral data such as the user's typing speed and mouse movements to detect emotional changes. This allows for flexible security measures to be implemented according to the user's emotional state.

[0088] The fraudulent payment prediction system can further include a purchase history analysis unit that analyzes a user's purchase history. The purchase history analysis unit analyzes the user's past purchase history and detects transactions that deviate from normal purchasing patterns. For example, the purchase history analysis unit can detect anomalies when a user suddenly purchases a high-priced item that they do not normally purchase, or when a transaction occurs at a store that the user does not normally visit. This enables early detection of fraudulent payments based on the user's purchasing patterns. The purchase history analysis unit can also build a predictive model based on the user's purchase history and evaluate the risk of future fraudulent payments. This enables multi-layered security measures based on the user's purchasing patterns.

[0089] The fraudulent payment prediction system can further include an emotional risk assessment unit that estimates a user's emotions and assesses the risk of fraudulent payments based on the estimated user emotions. The emotional risk assessment unit analyzes the user's facial expressions and voice to calculate an emotional score. For example, if a user is feeling stressed or anxious, the emotional score can be used to determine that the risk of fraudulent payments is increased. This allows additional security checks to be performed on users who are emotionally anxious. The emotional risk assessment unit can also analyze behavioral data such as the user's typing speed and mouse movements to detect emotional changes. This allows for flexible security measures to be implemented according to the user's emotional state.

[0090] The fraudulent payment prediction system can further include a device analysis unit that analyzes user device information. The device analysis unit analyzes information about the device used by the user and issues a warning if access is not from a device normally used. For example, the device analysis unit can record information about the smartphone or PC normally used by the user and detect anomalies if access is from a different device. This enables early detection of fraudulent payments based on the user's device information. The device analysis unit can also build a predictive model based on the user's device information and evaluate the risk of future fraudulent payments. This enables multi-layered security measures based on the user's device information.

[0091] The fraudulent payment prediction system can further include an emotion / modality prediction unit that estimates a user's emotions and predicts fraudulent payment methods based on the estimated user emotions. The emotion / modality prediction unit analyzes the user's facial expressions and voice to calculate an emotion score. For example, if a user is feeling fear or anxiety, a new fraudulent payment method can be predicted based on the emotion score. This makes it possible to issue a warning about specific fraud methods to users who are emotionally anxious. The emotion / modality prediction unit can also analyze behavioral data such as the user's typing speed and mouse movements to detect emotional changes. This allows for flexible security measures to be implemented according to the user's emotional state.

[0092] The fraudulent payment prediction system can further include a location information analysis unit that analyzes the user's location information. The location information analysis unit analyzes the user's current location and past movement history to detect location information that deviates from the user's normal range of activity. For example, the location information analysis unit can detect anomalies when there is access from an area the user does not normally visit. This enables early detection of fraudulent payments based on the user's location information. The location information analysis unit can also build a predictive model based on the user's location information and evaluate the risk of future fraudulent payments. This enables multi-layered security measures based on the user's location information.

[0093] The fraudulent payment prediction system can further include an emotional risk assessment unit that estimates a user's emotions and assesses the risk of fraudulent payments based on the estimated user emotions. The emotional risk assessment unit analyzes the user's facial expressions and voice to calculate an emotional score. For example, if a user is feeling stressed or anxious, the emotional score can be used to determine that the risk of fraudulent payments is increased. This allows additional security checks to be performed on users who are emotionally anxious. The emotional risk assessment unit can also analyze behavioral data such as the user's typing speed and mouse movements to detect emotional changes. This allows for flexible security measures to be implemented according to the user's emotional state.

[0094] The processing flow of the second embodiment will be briefly explained below.

[0095] Step 1: Generative AI uses past data and makes future predictions. For example, generative AI analyzes past transaction history and log data to predict future fraudulent payment methods. Generative AI can also generate scenarios that take into account future technological evolution and predict fraudulent methods that exploit technological vulnerabilities. Step 2: The data analysis unit analyzes trends and patterns related to fraudulent payments based on the data analyzed by the generation AI. For example, the data analysis unit detects frequent patterns and anomalies to extract trends in fraudulent payments. Step 3: The prediction unit predicts new fraudulent payment methods based on the trends and patterns analyzed by the data analysis unit. For example, the prediction unit predicts new methods such as phishing and malware attacks. Step 4: The countermeasure planning unit plans countermeasures against the fraudulent payment methods predicted by the prediction unit. For example, the countermeasure planning unit proposes measures such as strengthening security and user education.

[0096] 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.

[0097] 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> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). 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 speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. 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. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0098] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0099] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0100] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0101] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and 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 and / or a LAN.

[0102] 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.

[0103] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0104] 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 user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0109] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0110] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0111] 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.

[0112] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0113] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0114] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0115] 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.

[0116] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and 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 and / or a LAN.

[0117] 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.

[0118] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0119] 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 user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0120] 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.

[0121] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0122] 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.

[0123] 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. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0124] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0125] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0126] 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.

[0127] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0128] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0129] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0130] 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.

[0131] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and 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 and / or a LAN.

[0132] 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.

[0133] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0134] 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 image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0135] 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.

[0136] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the 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.

[0137] 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.

[0138] 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.

[0139] 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. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0140] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0141] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0142] 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.

[0143] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0144] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0145] 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.

[0146] FIG. 9 illustrates 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 behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions 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.

[0147] 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.

[0148] 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).

[0149] 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 expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, 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 expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, 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.

[0150] 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."

[0151] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0152] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0153] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0154] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0155] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0156] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0157] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0158] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0159] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0160] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0161] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0162] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0163] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. Generative AI uses generative AI to analyze past data and predict the future. a data analysis unit that analyzes trends and patterns related to fraudulent payments based on the data analyzed by the generation AI; a prediction unit that predicts new fraudulent payment methods based on the trends and patterns analyzed by the data analysis unit; a countermeasure planning unit that plans countermeasures against the fraudulent payment methods predicted by the prediction unit. A system characterized by:

2. The generated AI is Refer to security cases from different industries to predict new fraudulent payment methods that incorporate attack patterns from other fields.

2. The system of claim 1.

3. The generated AI is Predict fraudulent methods that take into account the characteristics of different cultures and regions, and develop countermeasures from a global perspective 2. The system of claim 1.

4. The generated AI is Incorporating data from different industries to predict new fraud methods from a cross-industry perspective 2. The system of claim 1.

5. The generated AI is Analyze incident data from not only competitors but also other industries to predict new fraud methods from a cross-disciplinary perspective 2. The system of claim 1.

6. The generated AI is It has an emotion estimation function that collects users' emotional reactions to predicted fraudulent methods in real time and identifies methods that cause emotional anxiety. Prioritize countermeasures against techniques identified by the emotion estimation function 2. The system of claim 1.

7. The generated AI is It has an emotion estimation function that analyzes users' emotional reactions to past fraudulent methods and predicts new methods based on methods that have elicited strong emotional reactions. Predicting new tactics based on the tactics identified by the emotion estimation function 2. The system of claim 1.

8. The generated AI is It has an emotion estimation function that collects users' emotional reactions to fraudulent methods based on payment data in real time and identifies methods that provoke strong emotional reactions. Prioritize countermeasures against techniques identified by the emotion estimation function 2. The system of claim 1.

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