System

Abstract

An object of the system according to the embodiment is to learn a new fraud technique and issue a warning.SOLUTION: A system includes a fraud determination data collection unit, an analysis unit, a learning unit, and a warning unit. The fraud determination data collection unit collects fraud determination data. The analysis unit analyzes the fraud determination data collected by the fraud determination data collection unit. The learning unit learns a new fraud technique on the basis of the result analyzed by the analysis unit. The warning unit issues a warning based on the fraud technique learned by the learning 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] Existing technologies do not adequately learn and warn to keep up with evolving fraud techniques, and there is room for improvement.

[0005] The system of the embodiment aims to learn about and warn of new fraud techniques. [Means for solving the problem]

[0006] The system according to the embodiment comprises a fraud determination data collection unit, an analysis unit, a learning unit, and a warning unit. The fraud determination data collection unit collects fraud determination data. The analysis unit analyzes the fraud determination data collected by the fraud determination data collection unit. The learning unit learns new fraud techniques based on the results of the analysis by the analysis unit. The warning unit issues a warning based on the fraud techniques learned by the learning unit. [Effects of the Invention]

[0007] An embodiment of the system can learn and alert on new fraud techniques. [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 fraud prevention system according to an embodiment of the present invention is a system that collects and analyzes fraud determination data, learns about new fraud techniques, and issues warnings. By collecting and analyzing fraud determination data, learning about new fraud techniques, and issuing warnings, the fraud prevention system can enhance fraud prevention.

[0029] The fraud prevention system according to the embodiment includes a fraud detection data collection unit, an analysis unit, a learning unit, and a warning unit. The fraud detection data collection unit collects fraud detection data. For example, the fraud detection data collection unit collects call logs. The fraud detection data collection unit can also collect text messages. The fraud detection data collection unit can also collect transaction histories. For example, the fraud detection data collection unit automatically collects call logs and stores them in a database. The text messages can be SMS or chat app messages. The transaction histories can be collected from financial institution transaction data. The analysis unit analyzes the fraud detection data collected by the fraud detection data collection unit. For example, the analysis unit identifies fraud patterns using data mining technology. The analysis unit can also analyze fraud trends using statistical analysis. The analysis unit can also build a fraud prediction model using a machine learning algorithm. For example, the analysis unit uses data mining technology to extract common patterns from past fraud cases. Statistical analysis can analyze the frequency and timing of fraud occurrences. The machine learning algorithm builds a fraud prediction model and detects new fraud methods. The learning unit learns new fraud methods based on the results of the analysis by the analysis unit. For example, the learning unit learns new fraud methods using supervised learning. The learning unit can also learn new fraud methods using unsupervised learning. The learning unit can also learn new fraud methods using reinforcement learning. For example, the learning unit uses supervised learning to learn new fraud methods based on past fraud cases. Unsupervised learning clusters data to identify new fraud methods. Reinforcement learning learns optimal fraud prevention measures through trial and error. The warning unit issues a warning based on the fraud methods learned by the learning unit. For example, the warning unit issues an alert. The warning unit can also send a notification. The warning unit can also display a warning message. For example, the warning unit issues an alert and warns the user when fraud is suspected. The notification can be sent by email or SMS. The warning message can be displayed in an application or website.As a result, the fraud prevention system according to the embodiment can strengthen fraud prevention by collecting and analyzing fraud determination data, learning about new fraud techniques, and issuing warnings.

[0030] The analysis unit can analyze the fraud determination data in chronological order to track the evolution and changes in fraud methods. The analysis unit, for example, analyzes past fraud determination data in chronological order to track changes in fraud methods. For example, it graphs changes in fraud methods over a specific period of time to identify trends. The analysis unit also uses time series analysis to identify the evolution of fraud methods. For example, it analyzes how new fraud methods have developed over time to help predict future fraud methods. The analysis unit also analyzes the fraud determination data in chronological order to identify evolutionary patterns of fraud methods. For example, it analyzes the circumstances in which specific fraud methods have come to be used. In this way, tracking the evolution and changes in fraud methods can be useful in predicting future fraud methods.

[0031] The analysis unit can integrate fraud detection data with data from different industries to identify industry-specific fraud techniques. For example, the analysis unit integrates data from different industries to identify industry-specific fraud techniques. For example, data from the financial industry and the retail industry can be compared to identify the differences in fraud techniques used in each industry. The analysis unit also analyzes fraud detection data from different industries to identify common fraud techniques and techniques that are specific to the industry. For example, it analyzes what techniques fraudsters use in which industries. The analysis unit also integrates and analyzes data from different industries to identify industry-specific fraud techniques. For example, it classifies fraud technique patterns by industry and evaluates the risk in a specific industry. In this way, by integrating data from different industries, it is possible to identify industry-specific fraud techniques.

[0032] The analysis department can use the fraud detection data to develop educational programs for preventing fraud and provide them to the general public. For example, the analysis department develops educational programs for preventing fraud based on the fraud detection data. For example, it creates teaching materials that introduce examples of fraudulent methods and teach the general public how to prevent them. The analysis department also uses the fraud detection data to provide online educational programs for preventing fraud. For example, it creates video content that teaches fraud techniques and countermeasures. The analysis department also holds workshops for preventing fraud based on the fraud detection data. For example, it plans an event to demonstrate fraud techniques and teach participants how to prevent them. In this way, by developing educational programs for preventing fraud and providing them to the general public, it is possible to prevent fraud victims from becoming victims.

[0033] The analysis unit can use the generative AI to analyze social media posts related to the latest fraud techniques and identify trends. For example, the analysis unit can use the generative AI to analyze social media posts and identify trends in the latest fraud techniques. For example, it can analyze hashtags and keywords related to fraud and understand trends. The analysis unit can also collect social media post data and analyze it with the generative AI to identify the latest fraud techniques. For example, it can analyze the frequency and content of posts related to fraud and discover new techniques. The analysis unit can also use the generative AI to analyze social media posts in real time to identify the latest fraud techniques. For example, it can detect a sudden increase in posts related to fraud and identify trends. In this way, it can identify trends in the latest fraud techniques by analyzing social media posts.

[0034] The analysis unit can automatically translate fraud information from different languages ​​and analyze it from a global perspective in order to incorporate the latest fraud techniques. For example, the analysis unit can automatically translate fraud information from different languages ​​and analyze it with generative AI to incorporate the latest fraud techniques. For example, it translates and analyzes fraud information in English, Chinese, Spanish, etc. The analysis unit also uses machine translation technology to collect fraud information in different languages ​​and analyze it with generative AI. For example, it translates and analyzes news articles and social media posts about fraud. The analysis unit also automatically translates fraud information from different languages ​​and analyzes it from a global perspective to identify the latest fraud techniques. For example, it analyzes the differences in fraud methods between countries and discovers new methods. This makes it possible to automatically translate fraud information from different languages ​​and analyze it from a global perspective in order to incorporate the latest fraud techniques.

[0035] When incorporating the latest fraud techniques, the analysis unit can collaborate with experts from different industries to identify industry-specific fraud techniques. The analysis unit, for example, collaborates with experts from different industries to incorporate the latest fraud techniques. For example, it collaborates with experts from the financial industry or the IT industry to identify industry-specific fraud techniques. The analysis unit also collaborates with experts from different industries to analyze the latest fraud techniques. For example, it analyzes what techniques fraudsters use in which industries based on the expert knowledge. The analysis unit also collaborates with experts from different industries to incorporate the latest fraud techniques. For example, it analyzes what techniques fraudsters use in which industries based on the expert knowledge. In this way, by collaborating with experts from different industries, it is possible to identify industry-specific fraud techniques.

[0036] The analysis unit can use the generation AI to individually customize anti-fraud messages for the elderly and provide effective warnings. The analysis unit, for example, uses the generation AI to individually customize anti-fraud messages for the elderly. For example, it generates a message that suits the elderly's name and situation, and provides an effective warning. The analysis unit also customizes anti-fraud messages for the elderly using the generation AI and provides them individually. For example, it explains fraud tactics and countermeasures in language that is easy for the elderly to understand. The analysis unit also uses the generation AI to individually customize anti-fraud messages for the elderly and provide effective warnings. For example, it suggests specific countermeasures that suit the elderly's situation. In this way, by individually customizing anti-fraud messages for the elderly, it is possible to provide effective warnings.

[0037] The analysis unit can automatically block communications suspected of being fraudulent to ensure safety in a peace of mind plan for the elderly. The analysis unit, for example, builds a system that automatically blocks communications suspected of being fraudulent in a peace of mind plan for the elderly. For example, it detects and automatically blocks phone calls and emails suspected of being fraudulent. The analysis unit also ensures the safety of the elderly by automatically blocking communications suspected of being fraudulent. For example, it filters messages suspected of being fraudulent so that they do not reach the elderly. The analysis unit also ensures safety in a peace of mind plan for the elderly by automatically blocking communications suspected of being fraudulent. For example, it automatically blocks phone calls suspected of being fraudulent. In this way, the safety of the elderly can be ensured by automatically blocking communications suspected of being fraudulent.

[0038] The analysis unit can provide the peace of mind plan for the elderly on different devices, thereby improving convenience. The analysis unit, for example, builds a system that provides the peace of mind plan for the elderly on smartphones, tablets, and smart speakers. For example, fraud prevention messages are provided by voice on a smart speaker. The analysis unit also provides the peace of mind plan for the elderly on different devices, thereby improving convenience. For example, fraud prevention alerts can be received on a smartphone. The analysis unit also provides the peace of mind plan for the elderly on different devices, thereby improving convenience. For example, fraud prevention messages can be displayed on a tablet. In this way, convenience can be improved by providing the peace of mind plan for the elderly on different devices.

[0039] The analysis unit can add a function for collaboration with family and caregivers to the peace of mind plan for the elderly, thereby enhancing the effectiveness of fraud prevention. The analysis unit, for example, adds a function for collaboration with family and caregivers to the peace of mind plan for the elderly, thereby enhancing the effectiveness of fraud prevention. For example, notifying family members if there is any communication that is suspected to be fraud. The analysis unit also enhances the effectiveness of the peace of mind plan for the elderly by adding a function for collaboration with family and caregivers. For example, sending fraud prevention alerts to family members and caregivers. The analysis unit also enhances the effectiveness of fraud prevention by adding a function for collaboration with family and caregivers to the peace of mind plan for the elderly, thereby enhancing the effectiveness of fraud prevention. For example, notifying family members if there is any communication that is suspected to be fraud. In this way, adding a function for collaboration with family and caregivers can enhance the effectiveness of fraud prevention.

[0040] The analysis unit can utilize the data obtained by providing the fraud prevention service to develop new business models. The analysis unit, for example, utilizes the data obtained by providing the fraud prevention service to develop new business models. For example, it provides consulting services based on fraud prevention data. The analysis unit can also utilize the data obtained by providing the fraud prevention service to develop new business models. For example, it can provide risk assessment services based on fraud prevention data. The analysis unit can also utilize the data obtained by providing the fraud prevention service to develop new business models. For example, it can provide marketing services based on fraud prevention data. In this way, new business models can be developed by utilizing the data obtained by providing the fraud prevention service.

[0041] The analysis department can diversify revenue by expanding the fraud prevention services to different markets. For example, the analysis department can diversify revenue by expanding the fraud prevention services to businesses. For example, the analysis department can provide fraud prevention services to business risk management departments. The analysis department can also diversify revenue by expanding the fraud prevention services to educational institutions. For example, the analysis department can provide fraud prevention education programs to schools and universities. The analysis department can also diversify revenue by expanding the fraud prevention services to different markets. For example, the analysis department can provide fraud prevention services to medical institutions and public institutions. In this way, the analysis department can diversify revenue by expanding the fraud prevention services to different markets.

[0042] The analysis unit can integrate the fraud prevention service with other security services to provide a comprehensive security solution. For example, the analysis unit can integrate the fraud prevention service with other security services to provide a comprehensive security solution, such as integrating it with network security or data protection services. The analysis unit can also integrate the fraud prevention service with other security services to provide a comprehensive security solution, such as integrating it with physical security services. The analysis unit can also integrate the fraud prevention service with other security services to provide a comprehensive security solution, such as integrating it with cybersecurity or information security services. In this way, the fraud prevention service can be integrated with other security services to provide a comprehensive security solution.

[0043] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0044] The fraud prevention system may further include a behavior analysis unit that analyzes a user's behavioral patterns. The behavior analysis unit may, for example, analyze a user's internet usage history to identify behaviors that pose a high risk of fraud. The behavior analysis unit may also analyze a user's purchase history to detect abnormal transactions. Furthermore, the behavior analysis unit may analyze a user's location information to identify areas with a high risk of fraud. This allows for a more accurate assessment of the risk of fraud by analyzing a user's behavioral patterns.

[0045] The fraud prevention system may further include a data integration unit that integrates information from different data sources. For example, the data integration unit may integrate data from financial institutions and social media data to assess the risk of fraud. The data integration unit may also integrate fraud data from different countries to assess the risk of fraud from a global perspective. Furthermore, the data integration unit may integrate data from different industries to identify industry-specific fraud techniques. Thus, by integrating information from different data sources, the risk of fraud can be more accurately assessed.

[0046] The fraud prevention system may further include a behavior prediction unit that predicts user behavior and assesses the risk of fraud based on the predicted behavior. The behavior prediction unit, for example, analyzes past behavior data of the user to predict future behavior. The behavior prediction unit may also analyze current behavior data of the user to predict behavior in real time. Furthermore, the behavior prediction unit may learn user behavior patterns and detect abnormal behavior. This allows for more accurate assessment of fraud risk by predicting user behavior.

[0047] The fraud prevention system may further include a device linking unit that enables data sharing between different devices. The device linking unit may enable data sharing between a smartphone and a PC, for example. The device linking unit may also enable data sharing between a smart watch and a tablet. The device linking unit may also enable data sharing between a smart speaker and a smart home device. This allows data sharing between different devices, thereby improving the convenience of the fraud prevention system.

[0048] The fraud prevention system may further include an expert collaboration unit that collaborates with experts from different industries to develop new methods for fraud prevention. For example, the expert collaboration unit may collaborate with experts from the financial industry to develop methods for preventing financial fraud. The expert collaboration unit may also collaborate with experts from the IT industry to develop methods for preventing cyber fraud. Furthermore, the expert collaboration unit may collaborate with legal experts to develop legal methods for fraud prevention. This makes it possible to develop new methods for fraud prevention by collaborating with experts from different industries.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The fraud detection data collection unit collects fraud detection data. For example, the fraud detection data collection unit can collect call records, text messages, and transaction history. Call records are automatically collected and stored in a database. Text messages can be collected as SMS or chat app messages, and transaction history can be collected as financial institution transaction data. Step 2: The analysis unit analyzes the fraud detection data collected by the fraud detection data collection unit. For example, the analysis unit uses data mining techniques, statistical analysis, and machine learning algorithms to identify fraud patterns, analyze fraud trends, and build fraud prediction models. This allows the analysis unit to extract common patterns from past fraud cases, analyze the frequency and timing of fraud occurrences, and detect new fraud methods. Step 3: The learning unit learns new fraud techniques based on the results of the analysis by the analysis unit. For example, the learning unit can learn new fraud techniques using supervised learning, unsupervised learning, and reinforcement learning. This allows the unit to learn new fraud techniques based on past fraud cases, cluster data, identify new fraud techniques, and learn optimal fraud prevention measures through trial and error. Step 4: The warning unit issues a warning based on the fraud techniques learned by the learning unit. For example, the warning unit issues an alert, sends a notification, or displays a warning message. This allows the unit to warn users of suspected fraud, send notifications via email or SMS, and display warning messages in applications or websites.

[0051] (Example 2) The fraud prevention system according to an embodiment of the present invention is a system that collects and analyzes fraud determination data, learns about new fraud techniques, and issues warnings. By collecting and analyzing fraud determination data, learning about new fraud techniques, and issuing warnings, the fraud prevention system can enhance fraud prevention.

[0052] The fraud prevention system according to the embodiment includes a fraud detection data collection unit, an analysis unit, a learning unit, and a warning unit. The fraud detection data collection unit collects fraud detection data. For example, the fraud detection data collection unit collects call logs. The fraud detection data collection unit can also collect text messages. The fraud detection data collection unit can also collect transaction histories. For example, the fraud detection data collection unit automatically collects call logs and stores them in a database. The text messages can be SMS or chat app messages. The transaction histories can be collected from financial institution transaction data. The analysis unit analyzes the fraud detection data collected by the fraud detection data collection unit. For example, the analysis unit identifies fraud patterns using data mining technology. The analysis unit can also analyze fraud trends using statistical analysis. The analysis unit can also build a fraud prediction model using a machine learning algorithm. For example, the analysis unit uses data mining technology to extract common patterns from past fraud cases. Statistical analysis can analyze the frequency and timing of fraud occurrences. The machine learning algorithm builds a fraud prediction model and detects new fraud methods. The learning unit learns new fraud methods based on the results of the analysis by the analysis unit. For example, the learning unit learns new fraud methods using supervised learning. The learning unit can also learn new fraud methods using unsupervised learning. The learning unit can also learn new fraud methods using reinforcement learning. For example, the learning unit uses supervised learning to learn new fraud methods based on past fraud cases. Unsupervised learning clusters data to identify new fraud methods. Reinforcement learning learns optimal fraud prevention measures through trial and error. The warning unit issues a warning based on the fraud methods learned by the learning unit. For example, the warning unit issues an alert. The warning unit can also send a notification. The warning unit can also display a warning message. For example, the warning unit issues an alert and warns the user when fraud is suspected. The notification can be sent by email or SMS. The warning message can be displayed in an application or website.As a result, the fraud prevention system according to the embodiment can strengthen fraud prevention by collecting and analyzing fraud determination data, learning about new fraud techniques, and issuing warnings.

[0053] The analysis unit can analyze the text and audio data included in the fraud determination data to identify the fraudster's emotional patterns. The analysis unit, for example, analyzes the text and audio data included in the fraud determination data to identify the fraudster's emotional patterns. For example, it extracts specific words and phrases used by the fraudster through emotional analysis and estimates their psychological state. The analysis unit also uses emotional analysis to clarify the fraudster's behavioral patterns based on the fraud determination data. For example, it analyzes what emotions the fraudster expresses in what situations, and uses this information to help predict fraudulent acts. The analysis unit also uses emotional analysis to track the fraudster's psychological changes and identify new fraud techniques. For example, it analyzes the difference in emotional patterns when the fraudster is successful and when he or she fails. In this way, identifying the fraudster's emotional patterns can be used to help predict fraudulent acts.

[0054] The analysis unit can analyze the fraud determination data in chronological order to track the evolution and changes in fraud methods. The analysis unit, for example, analyzes past fraud determination data in chronological order to track changes in fraud methods. For example, it graphs changes in fraud methods over a specific period of time to identify trends. The analysis unit also uses time series analysis to identify the evolution of fraud methods. For example, it analyzes how new fraud methods have developed over time to help predict future fraud methods. The analysis unit also analyzes the fraud determination data in chronological order to identify evolutionary patterns of fraud methods. For example, it analyzes the circumstances in which specific fraud methods have come to be used. In this way, tracking the evolution and changes in fraud methods can be useful in predicting future fraud methods.

[0055] The analysis unit can use the emotion estimation function to analyze the emotions of fraud victims and propose measures to reduce the psychological burden on the victims. For example, the analysis unit can analyze the emotion data of fraud victims and propose measures to reduce the psychological burden. For example, it can propose counseling methods to reduce the fear and anxiety felt by victims. The analysis unit can also use the emotion estimation function to monitor the emotional state of fraud victims in real time and propose appropriate measures. For example, it can provide ways for victims to relax when they feel stressed. The analysis unit can also propose specific measures to reduce the psychological burden based on the emotion data of fraud victims. For example, it can build a support system that allows victims to feel at ease. This can be useful in supporting fraud victims by proposing measures to reduce the psychological burden on them.

[0056] The analysis unit can integrate fraud detection data with data from different industries to identify industry-specific fraud techniques. For example, the analysis unit integrates data from different industries to identify industry-specific fraud techniques. For example, data from the financial industry and the retail industry can be compared to identify the differences in fraud techniques used in each industry. The analysis unit also analyzes fraud detection data from different industries to identify common fraud techniques and techniques that are specific to the industry. For example, it analyzes what techniques fraudsters use in which industries. The analysis unit also integrates and analyzes data from different industries to identify industry-specific fraud techniques. For example, it classifies fraud technique patterns by industry and evaluates the risk in a specific industry. In this way, by integrating data from different industries, it is possible to identify industry-specific fraud techniques.

[0057] The analysis department can use the fraud detection data to develop educational programs for preventing fraud and provide them to the general public. For example, the analysis department develops educational programs for preventing fraud based on the fraud detection data. For example, it creates teaching materials that introduce examples of fraudulent methods and teach the general public how to prevent them. The analysis department also uses the fraud detection data to provide online educational programs for preventing fraud. For example, it creates video content that teaches fraud techniques and countermeasures. The analysis department also holds workshops for preventing fraud based on the fraud detection data. For example, it plans an event to demonstrate fraud techniques and teach participants how to prevent them. In this way, by developing educational programs for preventing fraud and providing them to the general public, it is possible to prevent fraud victims from becoming victims.

[0058] The analysis unit can use the emotion estimation function to provide fraud prevention alerts based on the fraud determination data in real time. The analysis unit, for example, uses the emotion estimation function to build a system that provides fraud prevention alerts based on the fraud determination data in real time. For example, it detects communications that are suspected of being fraudulent and immediately issues an alert. The analysis unit also analyzes the fraud determination data and provides fraud prevention alerts using the emotion estimation function. For example, it detects situations that are high risk of fraud and sends an alert to the user. The analysis unit also uses the emotion estimation function to develop a system that provides real-time alerts based on the fraud determination data. For example, it detects transactions that are suspected of being fraudulent and immediately issues an alert. By providing fraud prevention alerts in real time, it is possible to prevent fraud damage before it occurs.

[0059] The analysis unit can use the generative AI to analyze social media posts related to the latest fraud techniques and identify trends. For example, the analysis unit can use the generative AI to analyze social media posts and identify trends in the latest fraud techniques. For example, it can analyze hashtags and keywords related to fraud and understand trends. The analysis unit can also collect social media post data and analyze it with the generative AI to identify the latest fraud techniques. For example, it can analyze the frequency and content of posts related to fraud and discover new techniques. The analysis unit can also use the generative AI to analyze social media posts in real time to identify the latest fraud techniques. For example, it can detect a sudden increase in posts related to fraud and identify trends. In this way, it can identify trends in the latest fraud techniques by analyzing social media posts.

[0060] The analysis unit can use the emotion estimation function to analyze the emotional reactions of victims to fraudulent techniques and develop effective prevention measures. For example, the analysis unit can use the emotion estimation function to analyze the emotional reactions of victims to fraudulent techniques and develop effective prevention measures. For example, measures can be proposed to reduce the fear and anxiety felt by the victim. The analysis unit can also analyze the emotional reactions of victims to fraudulent techniques and develop effective prevention measures. For example, measures can be proposed to reduce the fear and anxiety felt by the victim. The analysis unit can also use the emotion estimation function to monitor the emotional reactions of victims to fraudulent techniques in real time and develop effective prevention measures. For example, measures can be proposed to reduce the fear and anxiety felt by the victim. In this way, effective prevention measures can be developed by analyzing the emotional reactions of victims.

[0061] The analysis unit can automatically translate fraud information from different languages ​​and analyze it from a global perspective in order to incorporate the latest fraud techniques. For example, the analysis unit can automatically translate fraud information from different languages ​​and analyze it with generative AI to incorporate the latest fraud techniques. For example, it translates and analyzes fraud information in English, Chinese, Spanish, etc. The analysis unit also uses machine translation technology to collect fraud information in different languages ​​and analyze it with generative AI. For example, it translates and analyzes news articles and social media posts about fraud. The analysis unit also automatically translates fraud information from different languages ​​and analyzes it from a global perspective to identify the latest fraud techniques. For example, it analyzes the differences in fraud methods between countries and discovers new methods. This makes it possible to automatically translate fraud information from different languages ​​and analyze it from a global perspective in order to incorporate the latest fraud techniques.

[0062] When incorporating the latest fraud techniques, the analysis unit can collaborate with experts from different industries to identify industry-specific fraud techniques. The analysis unit, for example, collaborates with experts from different industries to incorporate the latest fraud techniques. For example, it collaborates with experts from the financial industry or the IT industry to identify industry-specific fraud techniques. The analysis unit also collaborates with experts from different industries to analyze the latest fraud techniques. For example, it analyzes what techniques fraudsters use in which industries based on the expert knowledge. The analysis unit also collaborates with experts from different industries to incorporate the latest fraud techniques. For example, it analyzes what techniques fraudsters use in which industries based on the expert knowledge. In this way, by collaborating with experts from different industries, it is possible to identify industry-specific fraud techniques.

[0063] The analysis unit can use the emotion estimation function to monitor the user's emotional reactions to the latest fraud techniques in real time and provide optimal prevention measures. The analysis unit, for example, uses the emotion estimation function to monitor the user's emotional reactions to the latest fraud techniques in real time and provide optimal prevention measures. For example, measures to reduce the fear and anxiety felt by the user are proposed. The analysis unit also analyzes the user's emotional reactions to the latest fraud techniques and provides optimal prevention measures. For example, measures to reduce the fear and anxiety felt by the user are proposed. The analysis unit also uses the emotion estimation function to monitor the user's emotional reactions to the latest fraud techniques in real time and provide optimal prevention measures. For example, measures to reduce the fear and anxiety felt by the user are proposed. In this way, by monitoring the user's emotional reactions in real time, optimal prevention measures can be provided.

[0064] The analysis unit can use the generation AI to individually customize anti-fraud messages for the elderly and provide effective warnings. The analysis unit, for example, uses the generation AI to individually customize anti-fraud messages for the elderly. For example, it generates a message that suits the elderly's name and situation, and provides an effective warning. The analysis unit also customizes anti-fraud messages for the elderly using the generation AI and provides them individually. For example, it explains fraud tactics and countermeasures in language that is easy for the elderly to understand. The analysis unit also uses the generation AI to individually customize anti-fraud messages for the elderly and provide effective warnings. For example, it suggests specific countermeasures that suit the elderly's situation. In this way, by individually customizing anti-fraud messages for the elderly, it is possible to provide effective warnings.

[0065] The analysis unit can automatically block communications suspected of being fraudulent to ensure safety in a peace of mind plan for the elderly. The analysis unit, for example, builds a system that automatically blocks communications suspected of being fraudulent in a peace of mind plan for the elderly. For example, it detects and automatically blocks phone calls and emails suspected of being fraudulent. The analysis unit also ensures the safety of the elderly by automatically blocking communications suspected of being fraudulent. For example, it filters messages suspected of being fraudulent so that they do not reach the elderly. The analysis unit also ensures safety in a peace of mind plan for the elderly by automatically blocking communications suspected of being fraudulent. For example, it automatically blocks phone calls suspected of being fraudulent. In this way, the safety of the elderly can be ensured by automatically blocking communications suspected of being fraudulent.

[0066] The analysis unit can use the emotion estimation function to monitor the emotional state of the elderly person and issue a warning when there is a high risk of fraud. The analysis unit, for example, uses the emotion estimation function to monitor the emotional state of the elderly person in real time and issue a warning when there is a high risk of fraud. For example, it issues a warning when the elderly person feels anxious or scared. The analysis unit also builds a system that monitors the emotional state of the elderly person and issues a warning when there is a high risk of fraud. For example, it issues a warning when the elderly person feels stressed. The analysis unit also uses the emotion estimation function to monitor the emotional state of the elderly person and issue a warning when there is a high risk of fraud. For example, it issues a warning when the elderly person feels anxious or scared. In this way, by monitoring the emotional state of the elderly person and issuing a warning when there is a high risk of fraud, it is possible to prevent fraud from occurring.

[0067] The analysis unit can provide the peace of mind plan for the elderly on different devices, thereby improving convenience. The analysis unit, for example, builds a system that provides the peace of mind plan for the elderly on smartphones, tablets, and smart speakers. For example, fraud prevention messages are provided by voice on a smart speaker. The analysis unit also provides the peace of mind plan for the elderly on different devices, thereby improving convenience. For example, fraud prevention alerts can be received on a smartphone. The analysis unit also provides the peace of mind plan for the elderly on different devices, thereby improving convenience. For example, fraud prevention messages can be displayed on a tablet. In this way, convenience can be improved by providing the peace of mind plan for the elderly on different devices.

[0068] The analysis unit can add a function for collaboration with family and caregivers to the peace of mind plan for the elderly, thereby enhancing the effectiveness of fraud prevention. The analysis unit, for example, adds a function for collaboration with family and caregivers to the peace of mind plan for the elderly, thereby enhancing the effectiveness of fraud prevention. For example, notifying family members if there is any communication that is suspected to be fraud. The analysis unit also enhances the effectiveness of the peace of mind plan for the elderly by adding a function for collaboration with family and caregivers. For example, sending fraud prevention alerts to family members and caregivers. The analysis unit also enhances the effectiveness of fraud prevention by adding a function for collaboration with family and caregivers to the peace of mind plan for the elderly, thereby enhancing the effectiveness of fraud prevention. For example, notifying family members if there is any communication that is suspected to be fraud. In this way, adding a function for collaboration with family and caregivers can enhance the effectiveness of fraud prevention.

[0069] The analysis unit uses the emotion estimation function to provide a message with a relaxing effect that corresponds to the emotional state of the elderly person, thereby increasing a sense of security. The analysis unit, for example, uses the emotion estimation function to build a system that provides a message with a relaxing effect that corresponds to the emotional state of the elderly person. For example, when the elderly person feels anxious, a message to help them relax is sent. The analysis unit also monitors the emotional state of the elderly person and provides a message with a relaxing effect. For example, when the elderly person feels stressed, music is played that helps them relax. The analysis unit also uses the emotion estimation function to provide a message with a relaxing effect that corresponds to the emotional state of the elderly person, thereby increasing a sense of security. For example, when the elderly person feels anxious, a message to help them relax is sent. In this way, a message with a relaxing effect that corresponds to the emotional state of the elderly person is sent, thereby increasing a sense of security.

[0070] The analysis unit can utilize the data obtained by providing the fraud prevention service to develop new business models. The analysis unit, for example, utilizes the data obtained by providing the fraud prevention service to develop new business models. For example, it provides consulting services based on fraud prevention data. The analysis unit can also utilize the data obtained by providing the fraud prevention service to develop new business models. For example, it can provide risk assessment services based on fraud prevention data. The analysis unit can also utilize the data obtained by providing the fraud prevention service to develop new business models. For example, it can provide marketing services based on fraud prevention data. In this way, new business models can be developed by utilizing the data obtained by providing the fraud prevention service.

[0071] The analysis unit uses the emotion estimation function to monitor user satisfaction in real time and use the results to improve the service. The analysis unit, for example, uses the emotion estimation function to monitor user satisfaction in real time and use the results to improve the service. For example, it identifies areas for improvement of the service based on the user's emotion score. The analysis unit also monitors user satisfaction in real time and uses the emotion estimation function to use the results to improve the service. For example, it analyzes user feedback and improves the service. The analysis unit also uses the emotion estimation function to monitor user satisfaction in real time and use the results to improve the service. For example, it identifies areas for improvement of the service based on the user's emotion score. In this way, monitoring user satisfaction in real time can be used to improve the service.

[0072] The analysis department can diversify revenue by expanding the fraud prevention services to different markets. For example, the analysis department can diversify revenue by expanding the fraud prevention services to businesses. For example, the analysis department can provide fraud prevention services to business risk management departments. The analysis department can also diversify revenue by expanding the fraud prevention services to educational institutions. For example, the analysis department can provide fraud prevention education programs to schools and universities. The analysis department can also diversify revenue by expanding the fraud prevention services to different markets. For example, the analysis department can provide fraud prevention services to medical institutions and public institutions. In this way, the analysis department can diversify revenue by expanding the fraud prevention services to different markets.

[0073] The analysis unit can integrate the fraud prevention service with other security services to provide a comprehensive security solution. For example, the analysis unit can integrate the fraud prevention service with other security services to provide a comprehensive security solution, such as integrating it with network security or data protection services. The analysis unit can also integrate the fraud prevention service with other security services to provide a comprehensive security solution, such as integrating it with physical security services. The analysis unit can also integrate the fraud prevention service with other security services to provide a comprehensive security solution, such as integrating it with cybersecurity or information security services. In this way, the fraud prevention service can be integrated with other security services to provide a comprehensive security solution.

[0074] The analysis unit uses the emotion estimation function to analyze the user's emotional response and maximize the effectiveness of the social contribution activities. The analysis unit, for example, uses the emotion estimation function to analyze the user's emotional response and maximize the effectiveness of the social contribution activities. For example, it analyzes what kind of social contribution activities the user will identify with. The analysis unit also monitors the user's emotional response in real time and uses the emotion estimation function to maximize the effectiveness of the social contribution activities. For example, it adjusts the activity content based on the user's emotion score. The analysis unit also uses the emotion estimation function to analyze the user's emotional response and maximize the effectiveness of the social contribution activities. For example, it analyzes what kind of social contribution activities the user will identify with. In this way, the effectiveness of the social contribution activities can be maximized by analyzing the user's emotional response.

[0075] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0076] The fraud prevention system may further include a behavior analysis unit that analyzes a user's behavioral patterns. The behavior analysis unit may, for example, analyze a user's internet usage history to identify behaviors that pose a high risk of fraud. The behavior analysis unit may also analyze a user's purchase history to detect abnormal transactions. Furthermore, the behavior analysis unit may analyze a user's location information to identify areas with a high risk of fraud. This allows for a more accurate assessment of the risk of fraud by analyzing a user's behavioral patterns.

[0077] The fraud prevention system may further include an emotion evaluation unit that estimates a user's emotion and evaluates the risk of fraud based on the estimated emotion. The emotion evaluation unit may, for example, analyze the user's emotion when receiving a message that is suspected to be fraudulent and evaluate the risk. The emotion evaluation unit may also analyze the user's emotion when receiving a phone call that is suspected to be fraudulent and evaluate the risk. The emotion evaluation unit may also analyze the user's emotion when making a transaction that is suspected to be fraudulent and evaluate the risk. In this way, by analyzing the user's emotion, the risk of fraud can be more accurately evaluated.

[0078] The fraud prevention system may further include a data integration unit that integrates information from different data sources. For example, the data integration unit may integrate data from financial institutions and social media data to assess the risk of fraud. The data integration unit may also integrate fraud data from different countries to assess the risk of fraud from a global perspective. Furthermore, the data integration unit may integrate data from different industries to identify industry-specific fraud techniques. Thus, by integrating information from different data sources, the risk of fraud can be more accurately assessed.

[0079] The fraud prevention system may further include an advice providing unit that estimates the user's emotions and provides customized advice for fraud prevention based on the estimated emotions. For example, the advice providing unit may suggest ways to relax when the user feels anxious. The advice providing unit may also provide a message that gives the user a sense of security when the user feels fear. Furthermore, the advice providing unit may also suggest ways to reduce stress when the user feels stressed. In this way, the effectiveness of fraud prevention can be improved by providing customized advice based on the user's emotions.

[0080] The fraud prevention system may further include a behavior prediction unit that predicts user behavior and assesses the risk of fraud based on the predicted behavior. The behavior prediction unit, for example, analyzes past behavior data of the user to predict future behavior. The behavior prediction unit may also analyze current behavior data of the user to predict behavior in real time. Furthermore, the behavior prediction unit may learn user behavior patterns and detect abnormal behavior. This allows for more accurate assessment of fraud risk by predicting user behavior.

[0081] The fraud prevention system may further include an educational program providing unit that estimates the user's emotions and customizes an educational program for fraud prevention based on the estimated emotions. The educational program providing unit may, for example, provide educational content that gives the user a sense of security when the user feels anxious. The educational program providing unit may also provide educational content that teaches the user how to relax when the user feels fear. The educational program providing unit may also provide educational content that teaches the user how to reduce stress when the user feels stressed. In this way, the effectiveness of fraud prevention can be enhanced by providing an educational program customized based on the user's emotions.

[0082] The fraud prevention system may further include a device linking unit that enables data sharing between different devices. The device linking unit may enable data sharing between a smartphone and a PC, for example. The device linking unit may also enable data sharing between a smart watch and a tablet. The device linking unit may also enable data sharing between a smart speaker and a smart home device. This allows data sharing between different devices, thereby improving the convenience of the fraud prevention system.

[0083] The fraud prevention system may further include an alert providing unit that estimates a user's emotions and provides real-time alerts for fraud prevention based on the estimated emotions. The alert providing unit may, for example, immediately issue an alert when the user feels anxious. The alert providing unit may also immediately issue an alert when the user feels fear. The alert providing unit may also immediately issue an alert when the user feels stress. In this way, by providing real-time alerts based on the user's emotions, the effectiveness of fraud prevention can be improved.

[0084] The fraud prevention system may further include an expert collaboration unit that collaborates with experts from different industries to develop new methods for fraud prevention. For example, the expert collaboration unit may collaborate with experts from the financial industry to develop methods for preventing financial fraud. The expert collaboration unit may also collaborate with experts from the IT industry to develop methods for preventing cyber fraud. Furthermore, the expert collaboration unit may collaborate with legal experts to develop legal methods for fraud prevention. This makes it possible to develop new methods for fraud prevention by collaborating with experts from different industries.

[0085] The fraud prevention system may further include a support providing unit that estimates the user's emotions and provides customized support for fraud prevention based on the estimated emotions. For example, the support providing unit provides counseling when the user feels anxious. The support providing unit may also provide psychological support when the user feels fear. Furthermore, the support providing unit may also provide relaxation techniques when the user feels stressed. In this way, the effectiveness of fraud prevention can be enhanced by providing customized support based on the user's emotions.

[0086] The processing flow of the second embodiment will be briefly explained below.

[0087] Step 1: The fraud detection data collection unit collects fraud detection data. For example, the fraud detection data collection unit can collect call records, text messages, and transaction history. Call records are automatically collected and stored in a database. Text messages can be collected as SMS or chat app messages, and transaction history can be collected as financial institution transaction data. Step 2: The analysis unit analyzes the fraud detection data collected by the fraud detection data collection unit. For example, the analysis unit uses data mining techniques, statistical analysis, and machine learning algorithms to identify fraud patterns, analyze fraud trends, and build fraud prediction models. This allows the analysis unit to extract common patterns from past fraud cases, analyze the frequency and timing of fraud occurrences, and detect new fraud methods. Step 3: The learning unit learns new fraud techniques based on the results of the analysis by the analysis unit. For example, the learning unit can learn new fraud techniques using supervised learning, unsupervised learning, and reinforcement learning. This allows the unit to learn new fraud techniques based on past fraud cases, cluster data, identify new fraud techniques, and learn optimal fraud prevention measures through trial and error. Step 4: The warning unit issues a warning based on the fraud techniques learned by the learning unit. For example, the warning unit issues an alert, sends a notification, or displays a warning message. This allows the unit to warn users of suspected fraud, send notifications via email or SMS, and display warning messages in applications or websites.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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).

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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."

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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]

[0155] 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. a fraud determination data collection unit that collects fraud determination data; an analysis unit that analyzes the fraud determination data collected by the fraud determination data collection unit; a learning unit that learns new fraud techniques based on the results of the analysis by the analysis unit; a warning unit that issues a warning based on the fraudulent techniques learned by the learning unit. A system characterized by:

2. The analysis unit Analyzing text and voice data contained in fraud detection data to identify fraudsters' emotional patterns 2. The system of claim 1.

3. The analysis unit Integrate fraud detection data with data from different industries to identify industry-specific fraud techniques 2. The system of claim 1.

4. The analysis unit Using generative AI to analyze social media posts about the latest fraud techniques and identify trends 2. The system of claim 1.

5. The analysis unit Using generative AI to personalize anti-fraud messages for seniors and provide effective warnings 2. The system of claim 1.

6. The analysis unit Monitor user satisfaction in real time to help improve services 2. The system of claim 1.

7. The analysis unit Analyze the emotions of fraud victims and propose measures to reduce their psychological burden 2. The system of claim 1.

8. The analysis unit Monitor the emotional state of the elderly and alert them if they are at high risk of fraud 2. The system of claim 1.

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