System

Abstract

An object of a system according to an embodiment is to detect fraud, identify an attacker, and collect evidence.SOLUTION: A system according to an embodiment includes a fraud act detection unit, an intentional hooking unit, an attacker specification unit, and an evidence collection unit. The fraud act detection unit detects a fraud act. The intentional catching unit intentionally catches a fraudulent act detected by the fraudulent act detection unit. The attacker specification unit specifies an attacker on the basis of a fraudulent act caught by the intentional catching unit. The evidence collection unit collects evidence about the attacker identified by the attacker identification unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of making it difficult not only to detect fraudulent activity, but also to identify attackers and collect evidence.

[0005] The system according to the embodiment aims to detect fraudulent activity, identify attackers and collect evidence. [Means for solving the problem]

[0006] A system according to an embodiment includes a fraud detection unit, an intentional trap unit, an attacker identification unit, and an evidence collection unit. The fraud detection unit detects fraud. The intentional trap unit intentionally falls for the fraud detected by the fraud detection unit. The attacker identification unit identifies an attacker based on the fraud that was caught by the intentional trap unit. The evidence collection unit collects evidence related to the attacker identified by the attacker identification unit. [Effects of the Invention]

[0007] Embodiments of the system can detect fraudulent activity, identify attackers, and collect evidence. [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) A fraud prevention system according to an embodiment of the present invention uses a generative AI that intentionally falls for fraudulent activity to penalize attackers. This system detects fraudulent activity, identifies attackers by intentionally falling for the fraud, and collects evidence for legal action. This allows the fraud prevention system to effectively respond to fraudulent activity and eliminate fraud by penalizing attackers.

[0029] A fraud prevention system according to an embodiment includes a fraud detection unit, an intentional trap unit, an attacker identification unit, and an evidence collection unit. The fraud detection unit detects fraud. For example, the generation AI detects fraudulent activities committed over the Internet or through a communication network. The generation AI analyzes phishing emails, fake websites, scam phone calls, and other content to identify potentially fraudulent content. The generation AI receives prompts containing instructions for detecting fraud, and the generation AI detects fraud based on the prompts. The intentional trap unit intentionally falls for the detected fraudulent activity. For example, the generation AI clicks on a link in a phishing email or enters personal information on a fake website. This behavior leads the user to mistakenly believe that the attacker has successfully committed a fraudulent act and prompts the user to proceed to the next step. The attacker identification unit identifies the attacker during the process of falling for the fraud. For example, the generation AI records the IP addresses and communication logs used by the attacker, the methods used to commit the fraud, and other information. This allows the attacker to be identified and evidence to be collected for legal action. The evidence collection unit supports legal action based on the collected evidence. For example, generative AI can provide detailed reports of fraudulent activity to police and law enforcement agencies, helping them arrest and prosecute perpetrators. Generative AI can also provide appropriate advice to victims to prevent further damage. This allows fraud prevention systems to effectively respond to fraudulent activity and eliminate it by penalizing attackers. For example, by intentionally falling for phishing emails, attackers can be identified and legal action can be taken. Furthermore, by learning patterns of fraudulent activity, future fraud can be prevented.

[0030] The fraud detection unit analyzes the psychological triggers behind fraudulent acts, enabling a deeper understanding of fraudulent methods. For example, when the generative AI detects fraudulent acts, the fraud detection unit analyzes the psychological triggers behind the fraudulent acts. For example, it identifies the psychological inducements contained in the text of fraudulent emails and learns their patterns. This allows a deeper understanding of fraudulent methods by analyzing the psychological triggers behind fraudulent acts.

[0031] The fraud detection unit can identify the source of fraud and map its geographical distribution. For example, the fraud detection unit uses a generative AI to identify the source of fraud and map its geographical distribution. For example, it analyzes the IP address of the sender of a fraudulent email to identify the area where the fraud is occurring. This makes it possible to identify the source of fraud and map its geographical distribution, thereby understanding the area where the fraud is occurring.

[0032] The fraud detection unit can simultaneously analyze fraudulent activities in different languages ​​and cultural spheres, making it possible to grasp global trends in fraudulent activities. For example, the generation AI can simultaneously analyze fraudulent activities in different languages ​​and cultural spheres, making it possible to grasp global trends in fraudulent activities. For example, it can analyze fraudulent emails in multiple languages ​​and identify the fraudulent methods used in each language. This makes it possible to simultaneously analyze fraudulent activities in different languages ​​and cultural spheres, making it possible to grasp global trends in fraudulent activities.

[0033] The fraud detection unit can notify the user of the fraud detection results in real time and encourage immediate action. The fraud detection unit, for example, builds a system that notifies the user of the fraud detection results in real time and encourages immediate action. For example, when a fraudulent email is detected, a warning message is sent to the user. This allows the user to be notified of the fraud detection results in real time and encourages immediate action.

[0034] The intentional trapping unit can analyze the attacker's reaction in real time when they intentionally fall for a scam and predict their next action. For example, when the generation AI intentionally falls for a scam, the intentional trapping unit can analyze the attacker's reaction in real time and predict their next action. For example, it can analyze the attacker's reaction when they click on a link in a phishing email. This allows the attacker's reaction to be analyzed in real time when they intentionally fall for a scam and predict their next action.

[0035] The intentional trapping unit can analyze the attacker's communication patterns and identify the attacker's behavioral patterns when the AI ​​intentionally falls for a scam. For example, the intentional trapping unit can analyze the attacker's communication patterns and identify the attacker's behavioral patterns when the AI ​​intentionally falls for a scam. For example, it can analyze the source IP address and communication logs of phishing emails. This allows the attacker's communication patterns to be analyzed and the attacker's behavioral patterns to be identified when the AI ​​falls for a scam.

[0036] The intentional trapping section applies the same technique to different fraudulent methods, making it possible to deal with a wide range of fraudulent activities. For example, when the generation AI intentionally falls for a fraud, it applies the same technique to different fraudulent methods, making it possible to deal with a wide range of fraudulent activities. For example, it applies the same technique to phishing emails, fake websites, fraudulent phone calls, etc. This makes it possible to apply the same technique to different fraudulent methods, making it possible to deal with a wide range of fraudulent activities.

[0037] The intentional trapping unit can simultaneously analyze multiple fraudulent attempts and identify the attacker's network when the AI ​​falls for a fraudulent attempt. For example, when the generation AI intentionally falls for a fraud, the intentional trapping unit can simultaneously analyze multiple fraudulent attempts and identify the attacker's network. For example, it can analyze the senders of multiple phishing emails and identify the attacker's network. This makes it possible to simultaneously analyze multiple fraudulent attempts and identify the attacker's network when the AI ​​falls for a fraudulent attempt.

[0038] The attacker identification unit can analyze the attacker's past behavioral history and evaluate the possibility of recidivism. For example, when the generation AI identifies an attacker, the attacker identification unit analyzes the attacker's past behavioral history and evaluates the possibility of recidivism. For example, it analyzes the history of phishing emails sent in the past and evaluates the possibility of recidivism. This makes it possible to analyze the attacker's past behavioral history and evaluate the possibility of recidivism.

[0039] The attacker identification unit can analyze the content of the attacker's communications collected by the evidence collection unit and clarify the attacker's intentions and goals. For example, when collecting evidence, the attacker identification unit uses a generation AI to analyze the content of the attacker's communications and clarify the attacker's intentions and goals. For example, it analyzes the content of a phishing email and identifies the attacker's intentions. This makes it possible to analyze the content of the attacker's communications and clarify the attacker's intentions and goals.

[0040] The attacker identification unit can integrate information from different data sources to understand the overall picture of the attacker. For example, when the generative AI identifies an attacker, the attacker identification unit integrates information from different data sources (e.g., social media and the dark web) to understand the overall picture of the attacker. For example, it analyzes the content of social media posts to help identify the attacker. This makes it possible to integrate information from different data sources to understand the overall picture of the attacker.

[0041] The Attacker Identification Unit can store evidence collected by the Evidence Collection Unit in the cloud in real time and share it immediately with law enforcement agencies. For example, when collecting evidence, the Attacker Identification Unit will build a system in which the generation AI stores the evidence in the cloud in real time and shares it immediately with law enforcement agencies. For example, the communication log of phishing emails will be stored in the cloud in real time. This allows evidence to be stored in the cloud in real time and shared immediately with law enforcement agencies.

[0042] When supporting legal action, the evidence collection unit can analyze past precedents and propose optimal legal strategies. For example, when the generative AI supports legal action, the evidence collection unit can analyze past precedents and propose optimal legal strategies. For example, it can analyze past precedents in fraud cases and propose optimal legal strategies. This makes it possible to analyze past precedents and propose optimal legal strategies.

[0043] In supporting legal action, the evidence collection unit can analyze the attacker's behavioral patterns and propose measures to prevent recidivism. For example, in supporting legal action, the evidence collection unit can analyze the attacker's behavioral patterns and propose measures to prevent recidivism using a generation AI. For example, it can analyze the sending patterns of phishing emails and propose measures to prevent recidivism. This makes it possible to analyze the attacker's behavioral patterns and propose measures to prevent recidivism.

[0044] When supporting legal action, the evidence collection unit can analyze the laws of different jurisdictions and support international legal action. For example, when generative AI supports legal action, the evidence collection unit can analyze the laws of different jurisdictions and support international legal action. For example, it can analyze the laws of the country where a fraudulent act occurred and propose the most appropriate legal action. This makes it possible to analyze the laws of different jurisdictions and support international legal action.

[0045] The evidence collection unit can automatically organize victim testimony and generate legal documents in support of legal action. For example, in support of legal action, the evidence collection unit can automatically organize victim testimony and generate legal documents using a generation AI. For example, the evidence collection unit can collect victim testimony as text data and automatically create legal documents. This makes it possible to automatically organize victim testimony and generate legal documents.

[0046] When preventing fraud, the fraud detection unit can analyze data on past fraud and predict new fraud methods. For example, when the generation AI prevents fraud, the fraud detection unit analyzes data on past fraud and predicts new fraud methods. For example, it analyzes data on past phishing emails and predicts new fraud methods. This makes it possible to analyze data on past fraud and predict new fraud methods.

[0047] The fraud detection unit can analyze user behavior patterns and identify users who are likely to fall for fraud. For example, when the generation AI prevents fraud, the fraud detection unit analyzes user behavior patterns and identifies users who are likely to fall for fraud. For example, it analyzes past internet usage history and identifies users who are likely to fall for fraud. This allows the user behavior patterns to be analyzed and users who are likely to fall for fraud to be identified.

[0048] The fraud detection unit can issue fraud alerts in real time and encourage immediate action. The fraud detection unit, for example, builds a system in which the generation AI issues fraud alerts in real time and encourages immediate action. For example, when a fraudulent email is detected, a warning message is sent to the user. This makes it possible to issue fraud alerts in real time and encourage immediate action.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] The fraud detection unit analyzes the psychological triggers behind fraudulent acts, enabling a deeper understanding of fraudulent methods. For example, it identifies psychological inducements contained in the text of fraudulent emails and learns their patterns. This allows for a deeper understanding of fraudulent methods by analyzing the psychological triggers behind fraudulent acts.

[0051] The fraud detection unit can identify the source of fraud and map its geographical distribution. For example, it can analyze the IP addresses of fraudulent emails to identify the areas where fraud is occurring. This allows us to identify the source of fraud and map its geographical distribution, thereby understanding the areas where fraud is occurring.

[0052] The fraud detection unit can simultaneously analyze fraudulent activities in different languages ​​and cultures, allowing it to grasp global trends in fraud. For example, it can analyze fraudulent emails in multiple languages ​​and identify the fraudulent methods used in each language. This allows it to simultaneously analyze fraudulent activities in different languages ​​and cultures, allowing it to grasp global trends in fraud.

[0053] The fraud detection unit can notify the user of the fraud detection results in real time and encourage immediate action. For example, when a fraudulent email is detected, a warning message is sent to the user. This allows the user to be notified of the fraud detection results in real time and encourage immediate action.

[0054] The intentional fall-for-fraud unit analyzes the attacker's reaction in real time when a user intentionally falls for a scam and can predict the user's next action. For example, it analyzes the attacker's reaction when a user clicks on a link in a phishing email. This allows the attacker's reaction to be analyzed in real time when a user intentionally falls for a scam and can predict the user's next action.

[0055] The intentional trapping unit analyzes the attacker's communication patterns and identifies their behavioral patterns when they fall for a fraudulent attempt. For example, it analyzes the source IP address and communication logs of phishing emails. This allows it to analyze the attacker's communication patterns and identify their behavioral patterns when they fall for a fraudulent attempt.

[0056] The intentional trapping unit can apply the same techniques to different fraudulent methods, making it possible to deal with a wide range of fraudulent activities. For example, it applies the same techniques to phishing emails, fake websites, fraudulent phone calls, etc. This makes it possible to apply the same techniques to different fraudulent methods, making it possible to deal with a wide range of fraudulent activities.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The fraud detection unit detects fraudulent activity. For example, the generation AI detects fraudulent activity occurring over the internet or through communication networks. The generation AI analyzes phishing emails, fake websites, scam phone calls, etc. to identify potentially fraudulent content. The input to the generation AI is a prompt containing instructions for detecting fraudulent activity, and the generation AI detects fraudulent activity based on the prompt. Step 2: The intentionally falling for the scam is intentional. For example, the generation AI clicks on a link in a phishing email or enters personal information on a fake website. This action leads the attacker to mistakenly believe that the scam has been successful and prompts the user to proceed to the next step. Step 3: The attacker identification unit identifies the attacker in the process of falling for the fraud. For example, the generation AI records the IP addresses and communication logs used by the attacker, as well as the methods used to commit the fraud. This allows the attacker to be identified and evidence to be collected for legal action. Step 4: The evidence collection unit supports legal action based on the collected evidence. For example, the generative AI can provide detailed reports of fraudulent activity to police and law enforcement agencies, helping them arrest and prosecute attackers. The generative AI can also provide appropriate advice to victims to prevent further damage.

[0059] (Example 2) A fraud prevention system according to an embodiment of the present invention uses a generative AI that intentionally falls for fraudulent activity to penalize attackers. This system detects fraudulent activity, identifies attackers by intentionally falling for the fraud, and collects evidence for legal action. This allows the fraud prevention system to effectively respond to fraudulent activity and eliminate fraud by penalizing attackers.

[0060] A fraud prevention system according to an embodiment includes a fraud detection unit, an intentional trap unit, an attacker identification unit, and an evidence collection unit. The fraud detection unit detects fraud. For example, the generation AI detects fraudulent activities committed over the Internet or through a communication network. The generation AI analyzes phishing emails, fake websites, scam phone calls, and other content to identify potentially fraudulent content. The generation AI receives prompts containing instructions for detecting fraud, and the generation AI detects fraud based on the prompts. The intentional trap unit intentionally falls for the detected fraudulent activity. For example, the generation AI clicks on a link in a phishing email or enters personal information on a fake website. This behavior leads the user to mistakenly believe that the attacker has successfully committed a fraudulent act and prompts the user to proceed to the next step. The attacker identification unit identifies the attacker during the process of falling for the fraud. For example, the generation AI records the IP addresses and communication logs used by the attacker, the methods used to commit the fraud, and other information. This allows the attacker to be identified and evidence to be collected for legal action. The evidence collection unit supports legal action based on the collected evidence. For example, generative AI can provide detailed reports of fraudulent activity to police and law enforcement agencies, helping them arrest and prosecute perpetrators. Generative AI can also provide appropriate advice to victims to prevent further damage. This allows fraud prevention systems to effectively respond to fraudulent activity and eliminate it by penalizing attackers. For example, by intentionally falling for phishing emails, attackers can be identified and legal action can be taken. Furthermore, by learning patterns of fraudulent activity, future fraud can be prevented.

[0061] The fraud detection unit analyzes the psychological triggers behind fraudulent acts, enabling a deeper understanding of fraudulent methods. For example, when the generative AI detects fraudulent acts, the fraud detection unit analyzes the psychological triggers behind the fraudulent acts. For example, it identifies the psychological inducements contained in the text of fraudulent emails and learns their patterns. This allows a deeper understanding of fraudulent methods by analyzing the psychological triggers behind fraudulent acts.

[0062] The fraud detection unit can identify the source of fraud and map its geographical distribution. For example, the fraud detection unit uses a generative AI to identify the source of fraud and map its geographical distribution. For example, it analyzes the IP address of the sender of a fraudulent email to identify the area where the fraud is occurring. This makes it possible to identify the source of fraud and map its geographical distribution, thereby understanding the area where the fraud is occurring.

[0063] The fraud detection unit can use the emotion estimation function to analyze the user's emotional response to fraudulent content and preferentially detect content that makes the user feel emotionally uneasy. The fraud detection unit can, for example, use the emotion estimation function to analyze the user's emotional response to fraudulent content. For example, the emotion estimation function can analyze the facial expression and voice of a user who has read a fraudulent email and identify content that makes the user feel uneasy. This allows the emotion estimation function to analyze the user's emotional response to fraudulent content and preferentially detect content that makes the user feel emotionally uneasy.

[0064] The fraud detection unit can simultaneously analyze fraudulent activities in different languages ​​and cultural spheres, making it possible to grasp global trends in fraudulent activities. For example, the generation AI can simultaneously analyze fraudulent activities in different languages ​​and cultural spheres, making it possible to grasp global trends in fraudulent activities. For example, it can analyze fraudulent emails in multiple languages ​​and identify the fraudulent methods used in each language. This makes it possible to simultaneously analyze fraudulent activities in different languages ​​and cultural spheres, making it possible to grasp global trends in fraudulent activities.

[0065] The fraud detection unit can notify the user of the fraud detection results in real time and encourage immediate action. The fraud detection unit, for example, builds a system that notifies the user of the fraud detection results in real time and encourages immediate action. For example, when a fraudulent email is detected, a warning message is sent to the user. This allows the user to be notified of the fraud detection results in real time and encourages immediate action.

[0066] The fraud detection unit can use the emotion estimation function to monitor the user's emotional state when fraud is detected and provide advice to reduce stress. The fraud detection unit can, for example, use the emotion estimation function to monitor the user's emotional state when fraud is detected and provide advice to reduce stress. For example, when a fraudulent email is detected, the fraud detection unit can analyze the user's facial expression and voice and display advice to relax. In this way, the emotion estimation function can be used to monitor the user's emotional state when fraud is detected and provide advice to reduce stress.

[0067] The intentional trapping unit can analyze the attacker's reaction in real time when they intentionally fall for a scam and predict their next action. For example, when the generation AI intentionally falls for a scam, the intentional trapping unit can analyze the attacker's reaction in real time and predict their next action. For example, it can analyze the attacker's reaction when they click on a link in a phishing email. This allows the attacker's reaction to be analyzed in real time when they intentionally fall for a scam and predict their next action.

[0068] The intentional trapping unit can analyze the attacker's communication patterns and identify the attacker's behavioral patterns when the AI ​​intentionally falls for a scam. For example, the intentional trapping unit can analyze the attacker's communication patterns and identify the attacker's behavioral patterns when the AI ​​intentionally falls for a scam. For example, it can analyze the source IP address and communication logs of phishing emails. This allows the attacker's communication patterns to be analyzed and the attacker's behavioral patterns to be identified when the AI ​​falls for a scam.

[0069] The intentional trap unit can use the emotion estimation function to estimate the emotional state of the attacker when they fall for the fraudulent act and identify the attacker's psychological weaknesses. The intentional trap unit can, for example, use the emotion estimation function to estimate the emotional state of the attacker when they fall for the fraudulent act and identify the attacker's psychological weaknesses. For example, the emotional state of the sender of a phishing email is analyzed. This allows the emotion estimation function to estimate the emotional state of the attacker when they fall for the fraudulent act and identify the attacker's psychological weaknesses.

[0070] The intentional trapping section applies the same technique to different fraudulent methods, making it possible to deal with a wide range of fraudulent activities. For example, when the generation AI intentionally falls for a fraud, it applies the same technique to different fraudulent methods, making it possible to deal with a wide range of fraudulent activities. For example, it applies the same technique to phishing emails, fake websites, fraudulent phone calls, etc. This makes it possible to apply the same technique to different fraudulent methods, making it possible to deal with a wide range of fraudulent activities.

[0071] The intentional trapping unit can simultaneously analyze multiple fraudulent attempts and identify the attacker's network when the AI ​​falls for a fraudulent attempt. For example, when the generation AI intentionally falls for a fraud, the intentional trapping unit can simultaneously analyze multiple fraudulent attempts and identify the attacker's network. For example, it can analyze the senders of multiple phishing emails and identify the attacker's network. This makes it possible to simultaneously analyze multiple fraudulent attempts and identify the attacker's network when the AI ​​falls for a fraudulent attempt.

[0072] The attacker identification unit can analyze the attacker's past behavioral history and evaluate the possibility of recidivism. For example, when the generation AI identifies an attacker, the attacker identification unit analyzes the attacker's past behavioral history and evaluates the possibility of recidivism. For example, it analyzes the history of phishing emails sent in the past and evaluates the possibility of recidivism. This makes it possible to analyze the attacker's past behavioral history and evaluate the possibility of recidivism.

[0073] The attacker identification unit can analyze the content of the attacker's communications collected by the evidence collection unit and clarify the attacker's intentions and goals. For example, when collecting evidence, the attacker identification unit uses a generation AI to analyze the content of the attacker's communications and clarify the attacker's intentions and goals. For example, it analyzes the content of a phishing email and identifies the attacker's intentions. This makes it possible to analyze the content of the attacker's communications and clarify the attacker's intentions and goals.

[0074] The attacker identification unit can use the emotion estimation function to analyze the emotional tone included in the content of the attacker's communications and identify the attacker's psychological state. The attacker identification unit, for example, uses the emotion estimation function to analyze the emotional tone included in the content of the attacker's communications and identify the attacker's psychological state. For example, the attacker identification unit analyzes the emotional tone included in the text of a phishing email. In this way, the emotion estimation function can be used to analyze the emotional tone included in the content of the attacker's communications and identify the attacker's psychological state.

[0075] The attacker identification unit can integrate information from different data sources to understand the overall picture of the attacker. For example, when the generative AI identifies an attacker, the attacker identification unit integrates information from different data sources (e.g., social media and the dark web) to understand the overall picture of the attacker. For example, it analyzes the content of social media posts to help identify the attacker. This makes it possible to integrate information from different data sources to understand the overall picture of the attacker.

[0076] The Attacker Identification Unit can store evidence collected by the Evidence Collection Unit in the cloud in real time and share it immediately with law enforcement agencies. For example, when collecting evidence, the Attacker Identification Unit will build a system in which the generation AI stores the evidence in the cloud in real time and shares it immediately with law enforcement agencies. For example, the communication log of phishing emails will be stored in the cloud in real time. This allows evidence to be stored in the cloud in real time and shared immediately with law enforcement agencies.

[0077] The attacker identification unit can use the emotion estimation function to analyze the user's emotional reaction when identifying an attacker and provide feedback that puts the user at ease. The attacker identification unit can, for example, use the emotion estimation function to analyze the user's emotional reaction when identifying an attacker and provide feedback that puts the user at ease. For example, when a phishing email is detected, the attacker identification unit can analyze the user's facial expression and voice and display a reassuring message. In this way, the emotion estimation function can be used to analyze the user's emotional reaction when identifying an attacker and provide feedback that puts the user at ease.

[0078] When supporting legal action, the evidence collection unit can analyze past precedents and propose optimal legal strategies. For example, when the generative AI supports legal action, the evidence collection unit can analyze past precedents and propose optimal legal strategies. For example, it can analyze past precedents in fraud cases and propose optimal legal strategies. This makes it possible to analyze past precedents and propose optimal legal strategies.

[0079] In supporting legal action, the evidence collection unit can analyze the attacker's behavioral patterns and propose measures to prevent recidivism. For example, in supporting legal action, the evidence collection unit can analyze the attacker's behavioral patterns and propose measures to prevent recidivism using a generation AI. For example, it can analyze the sending patterns of phishing emails and propose measures to prevent recidivism. This makes it possible to analyze the attacker's behavioral patterns and propose measures to prevent recidivism.

[0080] The evidence collection unit can use the emotion estimation function to monitor the user's emotional state during the legal action and provide support for reducing stress. The evidence collection unit can, for example, use the emotion estimation function to monitor the user's emotional state during the legal action and provide support for reducing stress. For example, the evidence collection unit can analyze the user's facial expressions and voice during the legal proceedings and display advice on how to relax. This allows the emotion estimation function to monitor the user's emotional state during the legal action and provide support for reducing stress.

[0081] When supporting legal action, the evidence collection unit can analyze the laws of different jurisdictions and support international legal action. For example, when generative AI supports legal action, the evidence collection unit can analyze the laws of different jurisdictions and support international legal action. For example, it can analyze the laws of the country where a fraudulent act occurred and propose the most appropriate legal action. This makes it possible to analyze the laws of different jurisdictions and support international legal action.

[0082] The evidence collection unit can automatically organize victim testimony and generate legal documents in support of legal action. For example, in support of legal action, the evidence collection unit can automatically organize victim testimony and generate legal documents using a generation AI. For example, the evidence collection unit can collect victim testimony as text data and automatically create legal documents. This makes it possible to automatically organize victim testimony and generate legal documents.

[0083] The evidence collection unit can use the emotion estimation function to analyze the user's emotional reactions during the legal action process and provide feedback that will reassure the user. The evidence collection unit can, for example, use the emotion estimation function to analyze the user's emotional reactions during the legal action process and provide feedback that will reassure the user. For example, the evidence collection unit can analyze the user's facial expressions and voice during the legal proceedings and display a reassuring message. In this way, the emotion estimation function can be used to analyze the user's emotional reactions during the legal action process and provide feedback that will reassure the user.

[0084] When preventing fraud, the fraud detection unit can analyze data on past fraud and predict new fraud methods. For example, when the generation AI prevents fraud, the fraud detection unit analyzes data on past fraud and predicts new fraud methods. For example, it analyzes data on past phishing emails and predicts new fraud methods. This makes it possible to analyze data on past fraud and predict new fraud methods.

[0085] The fraud detection unit can analyze user behavior patterns and identify users who are likely to fall for fraud. For example, when the generation AI prevents fraud, the fraud detection unit analyzes user behavior patterns and identifies users who are likely to fall for fraud. For example, it analyzes past internet usage history and identifies users who are likely to fall for fraud. This allows the user behavior patterns to be analyzed and users who are likely to fall for fraud to be identified.

[0086] The fraud detection unit can use the emotion estimation function to monitor the user's emotional state in order to prevent fraud and provide advice to increase vigilance against fraud. The fraud detection unit can, for example, use the emotion estimation function to monitor the user's emotional state in order to prevent fraud and provide advice to increase vigilance against fraud. For example, the fraud detection unit can analyze the user's facial expressions and voice while using the Internet and display advice to increase vigilance. In this way, the emotion estimation function can be used to monitor the user's emotional state in order to prevent fraud and provide advice to increase vigilance against fraud.

[0087] The fraud detection unit can issue fraud alerts in real time and encourage immediate action. The fraud detection unit, for example, builds a system in which the generation AI issues fraud alerts in real time and encourages immediate action. For example, when a fraudulent email is detected, a warning message is sent to the user. This makes it possible to issue fraud alerts in real time and encourage immediate action.

[0088] The fraud detection unit can use the emotion estimation function to analyze the user's emotional response in preventing fraud and provide feedback that reassures the user. The fraud detection unit can, for example, use the emotion estimation function to analyze the user's emotional response in preventing fraud and provide feedback that reassures the user. For example, when a fraudulent email is detected, the fraud detection unit can analyze the user's facial expression and voice and display a reassuring message. In this way, the emotion estimation function can be used to analyze the user's emotional response in preventing fraud and provide feedback that reassures the user.

[0089] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0090] The fraud detection unit analyzes the psychological triggers behind fraudulent acts, enabling a deeper understanding of fraudulent methods. For example, it identifies psychological inducements contained in the text of fraudulent emails and learns their patterns. This allows for a deeper understanding of fraudulent methods by analyzing the psychological triggers behind fraudulent acts.

[0091] The fraud detection unit can identify the source of fraud and map its geographical distribution. For example, it can analyze the IP addresses of fraudulent emails to identify the areas where fraud is occurring. This allows us to identify the source of fraud and map its geographical distribution, thereby understanding the areas where fraud is occurring.

[0092] The fraud detection unit can use the emotion estimation function to analyze the user's emotional response to fraudulent content and prioritize detecting content that makes the user feel emotionally uneasy. For example, the emotion estimation function can analyze the user's facial expression and voice after reading a fraudulent email and identify content that makes the user feel uneasy. This allows the emotion estimation function to analyze the user's emotional response to fraudulent content and prioritize detecting content that makes the user feel emotionally uneasy.

[0093] The fraud detection unit can simultaneously analyze fraudulent activities in different languages ​​and cultures, allowing it to grasp global trends in fraud. For example, it can analyze fraudulent emails in multiple languages ​​and identify the fraudulent methods used in each language. This allows it to simultaneously analyze fraudulent activities in different languages ​​and cultures, allowing it to grasp global trends in fraud.

[0094] The fraud detection unit can notify the user of the fraud detection results in real time and encourage immediate action. For example, when a fraudulent email is detected, a warning message is sent to the user. This allows the user to be notified of the fraud detection results in real time and encourage immediate action.

[0095] The fraud detection unit can use the emotion estimation function to monitor the user's emotional state when fraud is detected and provide advice to reduce stress. For example, when a fraudulent email is detected, the unit can analyze the user's facial expression and voice and display advice to help them relax. This allows the emotion estimation function to monitor the user's emotional state when fraud is detected and provide advice to reduce stress.

[0096] The intentional fall-for-fraud unit analyzes the attacker's reaction in real time when a user intentionally falls for a scam and can predict the user's next action. For example, it analyzes the attacker's reaction when a user clicks on a link in a phishing email. This allows the attacker's reaction to be analyzed in real time when a user intentionally falls for a scam and can predict the user's next action.

[0097] The intentional trapping unit analyzes the attacker's communication patterns and identifies their behavioral patterns when they fall for a fraudulent attempt. For example, it analyzes the source IP address and communication logs of phishing emails. This allows it to analyze the attacker's communication patterns and identify their behavioral patterns when they fall for a fraudulent attempt.

[0098] The intentional trap unit can use the emotion estimation function to estimate the emotional state of the attacker when they fall for the fraudulent act and identify the attacker's psychological weaknesses. For example, the emotional state of the sender of a phishing email is analyzed. This allows the emotion estimation function to estimate the emotional state of the attacker when they fall for the fraudulent act and identify the attacker's psychological weaknesses.

[0099] The intentional trapping unit can apply the same techniques to different fraudulent methods, making it possible to deal with a wide range of fraudulent activities. For example, it applies the same techniques to phishing emails, fake websites, fraudulent phone calls, etc. This makes it possible to apply the same techniques to different fraudulent methods, making it possible to deal with a wide range of fraudulent activities.

[0100] The processing flow of the second embodiment will be briefly explained below.

[0101] Step 1: The fraud detection unit detects fraudulent activity. For example, the generation AI detects fraudulent activity occurring over the internet or through communication networks. The generation AI analyzes phishing emails, fake websites, scam phone calls, etc. to identify potentially fraudulent content. The input to the generation AI is a prompt containing instructions for detecting fraudulent activity, and the generation AI detects fraudulent activity based on the prompt. Step 2: The intentionally falling for the scam is intentional. For example, the generation AI clicks on a link in a phishing email or enters personal information on a fake website. This action leads the attacker to mistakenly believe that the scam has been successful and prompts the user to proceed to the next step. Step 3: The attacker identification unit identifies the attacker in the process of falling for the fraud. For example, the generation AI records the IP addresses and communication logs used by the attacker, as well as the methods used to commit the fraud. This allows the attacker to be identified and evidence to be collected for legal action. Step 4: The evidence collection unit supports legal action based on the collected evidence. For example, the generative AI can provide detailed reports of fraudulent activity to police and law enforcement agencies, helping them arrest and prosecute attackers. The generative AI can also provide appropriate advice to victims to prevent further damage.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0106] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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).

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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 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.

[0119] 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.

[0120] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0121] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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 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.

[0134] 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.

[0135] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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 area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0156] 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."

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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]

[0169] 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 detection unit for detecting fraud; an intentional trapping unit that intentionally traps the fraudulent behavior detected by the fraud detection unit; an attacker identification unit that identifies an attacker based on the fraudulent act caught by the intentional trap unit; an evidence collection unit that collects evidence related to the attacker identified by the attacker identification unit. A system characterized by:

2. The fraud detection unit Analyze fraud in different languages ​​and cultures simultaneously to understand global fraud trends 2. The system of claim 1.

3. The intentional catch portion is When a user intentionally falls for the fraudulent activity, the attacker's reaction is analyzed in real time and their next move is predicted.

2. The system of claim 1.

4. The attacker identification unit Analyze the attacker's past behavioral history and assess the likelihood of recidivism 2. The system of claim 1.

5. The evidence collection unit When assisting with legal actions, we analyze past cases and propose optimal legal strategies.

2. The system of claim 1.

6. The fraud detection unit Analyzing users' emotional reactions to the fraudulent content and preferentially detecting emotionally disturbing content 2. The system of claim 1.

7. The intentional catch portion is Estimating the attacker's emotional state at the time of falling for the fraudulent activity and identifying the attacker's psychological weaknesses 2. The system of claim 1.

8. The attacker identification unit Analyzing the emotional tone of the attacker's communications to identify the attacker's state of mind 2. The system of claim 1.

Images