system

Abstract

The system according to the embodiment aims to detect fraudulent activity and inhibit fraudulent activity by imposing a disadvantage on attackers. [Solution] A system according to an embodiment includes a detection unit, a trapping unit, and a disadvantage inflicting unit. The detection unit detects fraudulent behavior. The trapping unit intentionally traps the fraudulent behavior detected by the detection unit. The disadvantage inflicting a disadvantage on the attacker based on the fraudulent behavior trapped by the trapping unit.

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] While conventional technology can detect fraudulent activity, it cannot directly harm the attacker, making it difficult to fundamentally curb fraudulent activity.

[0005] The system according to the embodiment aims to detect fraudulent activity and inhibit fraudulent activity by imposing a disadvantage on attackers. [Means for solving the problem]

[0006] A system according to an embodiment includes a detection unit, a trapping unit, and a disadvantage inflicting unit. The detection unit detects fraudulent behavior. The trapping unit intentionally traps the fraudulent behavior detected by the detection unit. The disadvantage inflicting a disadvantage on an attacker based on the fraudulent behavior trapped by the trapping unit. [Effects of the Invention]

[0007] The system according to the embodiment can detect fraudulent activity and curb fraudulent activity by penalizing the attacker. [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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[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 is a system that detects fraudulent activity and prevents fraud by inflicting a disadvantage on the attacker. This fraud prevention system detects fraudulent activity and takes actions that inflict a disadvantage on the attacker by intentionally falling for the fraud. For example, the fraud prevention system collects information about the attacker and provides it to law enforcement agencies. The fraud prevention system also provides false information to the attacker to thwart their plans. This mechanism prevents fraudulent activity and provides a deterrent to attackers. First, the fraud prevention system detects fraud. The fraud prevention system learns various fraud patterns and can detect fraud with high accuracy. For example, the fraud prevention system can handle various fraud methods, such as phishing emails and fake websites. Next, the fraud prevention system intentionally falls for the fraud. The fraud prevention system attracts attackers by taking actions that the attacker expects. For example, the fraud prevention system replies to phishing emails or enters personal information on fake websites. The fraud prevention system then takes actions that inflict a disadvantage on the attacker. For example, a fraud prevention system may collect information such as an attacker's IP address or email address and provide it to law enforcement agencies. A fraud prevention system may also provide false information to an attacker to thwart their plans. For example, providing false credit card information prevents the attacker from committing fraudulent acts. This mechanism prevents fraudulent acts before they occur and acts as a deterrent to attackers. By deliberately allowing fraud attempts to be made, attackers learn that their fraud attempts will not be successful and lose their motivation to commit fraud. A fraud prevention system may also collect information about attackers and provide it to law enforcement agencies, making it easier to detect fraudulent acts. This allows a fraud prevention system to prevent fraud before they occur and act as a deterrent to attackers.

[0029] A fraud prevention system according to an embodiment includes a detection unit, a trap unit, and a disadvantage provision unit. The detection unit detects fraudulent activities. Examples of fraudulent activities include, but are not limited to, phishing scams, credit card fraud, and online fraud. The detection unit detects fraudulent activities using, for example, an anomaly detection algorithm or pattern matching. The detection unit can also learn various fraud patterns and detect fraudulent activities with high accuracy. For example, the detection unit learns fraud patterns based on past fraud data and simulation data. The trap unit intentionally falls for fraudulent activities detected by the detection unit. The trap unit attracts attackers by, for example, taking actions that the attacker expects. For example, the trap unit may reply to phishing emails or enter personal information on fake websites. The trap unit can also learn attacker behavior patterns and select the optimal trap method. For example, the trap unit may learn the patterns of how attackers send phishing emails and select the optimal trap method. The disadvantage provision unit provides a disadvantage to the attacker based on the fraudulent act that has been caught by the trapping unit. The disadvantage provision unit collects information such as the attacker's IP address and email address, and provides it to law enforcement agencies. The disadvantage provision unit also provides false information to the attacker to thwart the attacker's plans. For example, the disadvantage provision unit provides false credit card information to prevent the attacker from fraudulently using the system. In this way, the fraud prevention system according to the embodiment can prevent fraudulent acts and act as a deterrent against attackers.

[0030] The detection unit can learn multiple fraud patterns and detect fraudulent activities with high accuracy. The detection unit learns fraud patterns based on, for example, past fraud data. For example, the detection unit can analyze data on past phishing emails and predict new phishing patterns. The detection unit can also predict the characteristics of new fake websites based on data on past fake websites. Furthermore, the detection unit can refer to data on past fraudulent calls and predict new fraudulent call patterns. In this way, the detection unit can predict new fraud patterns by utilizing past data, thereby improving detection accuracy. Some or all of the above-mentioned processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input past fraud data into AI and have the AI ​​predict new fraud patterns.

[0031] The trap unit can attract attackers by taking actions that the attacker expects. For example, the trap unit can attract attackers by replying to a phishing email. The trap unit can also attract attackers by entering personal information on a fake website. Furthermore, the trap unit can attract attackers by continuing a conversation with a scam phone call. This makes it easier for the trap unit to collect information about the attacker by attracting them. Some or all of the above-mentioned processing in the trap unit may be performed using AI, for example, or may be performed without using AI. For example, the trap unit can input actions that the attacker expects to be performed into AI and leave it to the AI ​​to execute the trapping actions.

[0032] The disadvantage provision unit can collect information including the attacker's IP address and email address and provide it to a law enforcement agency. The disadvantage provision unit, for example, collects the attacker's IP address. For example, the disadvantage provision unit extracts the IP address used by the attacker from log data and provides it to a law enforcement agency. The disadvantage provision unit can also collect the attacker's email address. For example, the disadvantage provision unit extracts the email address from the header information of a phishing email sent by the attacker and provides it to a law enforcement agency. The disadvantage provision unit can also collect the attacker's log data and provide it to a law enforcement agency. For example, the disadvantage provision unit collects log data of websites accessed by the attacker and provides it to a law enforcement agency. This allows the disadvantage provision unit to provide information about the attacker to a law enforcement agency, making it easier to detect fraudulent activities. Some or all of the above-mentioned processing in the disadvantage provision unit may be performed using, or without, AI. For example, the disadvantage provision unit can input the attacker's IP address and email address into AI and have the AI ​​collect and provide the information.

[0033] The disadvantage provision unit can provide false information to an attacker to thwart the attacker's plans. The disadvantage provision unit, for example, provides false credit card information to an attacker. For example, the disadvantage provision unit generates false credit card numbers and expiration dates and provides them to the attacker to prevent the attacker from fraudulently using the credit card. The disadvantage provision unit can also provide a false login page to the attacker. For example, the disadvantage provision unit creates a false login page and lures the attacker into accessing the page. The disadvantage provision unit can also provide a false error message to the attacker. For example, the disadvantage provision unit displays a false error message on a website accessed by the attacker to thwart the attacker's plans. In this way, the disadvantage provision unit can prevent fraud by thwarting the attacker's plans. Some or all of the above-described processing in the disadvantage provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the disadvantage provision unit can entrust the process of generating false information to AI.

[0034] The detection unit can detect fraudulent activities in real time and respond immediately. For example, the detection unit can detect a phishing email the moment it is received and display a warning to the user. The detection unit can also immediately detect when a fake website is accessed and block access. Furthermore, the detection unit can detect when a fraudulent call is received in real time and terminate the call. In this way, the detection unit can detect fraudulent activities in real time and respond immediately, thereby minimizing damage. Some or all of the above-mentioned processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can entrust the detection and response to fraudulent activities in real time to AI.

[0035] The detection unit can predict new fraud patterns by referring to past fraud data, thereby improving detection accuracy. The detection unit, for example, analyzes past phishing email data and predicts new phishing patterns. The detection unit can also predict the characteristics of new fake websites based on past data on fake websites. Furthermore, the detection unit can predict new fraud call patterns by referring to past fraud call data. In this way, the detection unit can predict new fraud patterns by utilizing past data, thereby improving detection accuracy. Some or all of the above-mentioned processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input past fraud data into AI and have the AI ​​predict new fraud patterns.

[0036] The detection unit can detect region-specific fraud patterns based on the user's geographical location information. For example, the detection unit can learn fraud patterns that frequently occur in a specific region and detect fraud in that region. Furthermore, if the user is traveling, the detection unit can also detect region-specific fraud patterns based on the user's place of residence. By detecting region-specific fraud patterns, the detection unit can take appropriate measures according to the region. Some or all of the above-described processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input the user's geographical location information into AI and have the AI ​​detect region-specific fraud patterns.

[0037] The detection unit can analyze the user's social media activity and detect related fraudulent activities. For example, the detection unit can analyze the user's social media posts to detect signs of fraudulent activities. The detection unit can also monitor the activities of accounts the user follows to detect fraudulent activities. Furthermore, the detection unit can analyze the user's social media messages to detect fraudulent activities. In this way, the detection unit can detect related fraudulent activities early by analyzing social media activities. Some or all of the above-mentioned processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input the user's social media data into AI and have the AI ​​perform fraudulent activity detection.

[0038] The trapping unit can learn the attacker's behavioral patterns and select the optimal trapping method. For example, the trapping unit can learn the pattern in which the attacker sends phishing emails and select the optimal trapping method. The trapping unit can also learn the pattern in which the attacker creates fake websites and select the optimal trapping method. Furthermore, the trapping unit can also learn the pattern in which the attacker makes fraudulent phone calls and select the optimal trapping method. In this way, the trapping unit can select the optimal trapping method by learning the attacker's behavioral patterns. Some or all of the above-mentioned processing in the trapping unit may be performed using, for example, AI, or may be performed without using AI. For example, the trapping unit can input the attacker's behavioral pattern data into AI and have the AI ​​select the optimal trapping method.

[0039] The trapping unit can take different trapping actions depending on different fraud methods. For example, the trapping unit can take the trapping action of replying to a phishing email. The trapping unit can also take the trapping action of entering personal information in response to a fake website. Furthermore, the trapping unit can take the trapping action of continuing the conversation in response to a fraudulent phone call. This allows the trapping unit to take more effective trapping actions depending on different fraud methods. Some or all of the above-mentioned processing in the trapping unit may be performed using, for example, AI, or may be performed without using AI. For example, the trapping unit can leave the selection of trapping actions depending on different fraud methods to AI.

[0040] The trapping unit can take the optimal trapping action by taking into account the geographical location information of the attacker. For example, if the attacker is in a specific area, the trapping unit can take a trapping action specific to that area. Also, if the attacker is traveling, the trapping unit can take a trapping action specific to the area to which the attacker is traveling. Furthermore, the trapping unit can take the optimal trapping action based on the attacker's place of residence. In this way, the trapping unit can take the optimal trapping action by taking into account the geographical location information of the attacker. Some or all of the above-mentioned processing in the trapping unit may be performed using AI, for example, or may be performed without using AI. For example, the trapping unit can input the geographical location information of the attacker into AI and have the AI ​​select the optimal trapping action.

[0041] The trapping unit can analyze the attacker's social media activity and take relevant trapping actions. The trapping unit can, for example, analyze the attacker's social media posts and take optimal trapping actions. The trapping unit can also monitor the activity of accounts followed by the attacker and take relevant trapping actions. Furthermore, the trapping unit can analyze the attacker's social media messages and take optimal trapping actions. In this way, the trapping unit can take relevant trapping actions by analyzing the attacker's social media activity. Some or all of the above-mentioned processing in the trapping unit may be performed using, for example, AI, or may be performed without using AI. For example, the trapping unit can input the attacker's social media data into AI and have the AI ​​select the trapping action.

[0042] The disadvantage provision unit can analyze the behavioral patterns of an attacker and select the optimal disadvantage provision method. For example, the disadvantage provision unit can analyze the pattern in which an attacker sends phishing emails and select the optimal disadvantage provision method. The disadvantage provision unit can also analyze the pattern in which an attacker creates fake websites and select the optimal disadvantage provision method. Furthermore, the disadvantage provision unit can analyze the pattern in which an attacker makes fraudulent phone calls and select the optimal disadvantage provision method. In this way, the disadvantage provision unit can select the optimal disadvantage provision method by analyzing the behavioral patterns of an attacker. Some or all of the above-mentioned processing in the disadvantage provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the disadvantage provision unit can input the behavioral pattern data of an attacker into AI and have the AI ​​select the optimal disadvantage provision method.

[0043] The disadvantage provision unit can customize the means of providing a disadvantage taking into account the attribute information of the attacker. The disadvantage provision unit customizes the means of providing a disadvantage based on, for example, the age and gender of the attacker. The disadvantage provision unit can also customize the means of providing a disadvantage based on the occupation and career of the attacker. Furthermore, the disadvantage provision unit can also customize the means of providing a disadvantage based on the attacker's past criminal history. In this way, the disadvantage provision unit can customize the means of providing a disadvantage based on the attribute information of the attacker, thereby enabling more effective disadvantage provision. Some or all of the above-mentioned processing in the disadvantage provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the disadvantage provision unit can input the attribute information of the attacker into AI and cause the AI ​​to customize the means of providing a disadvantage.

[0044] The disadvantage provision unit can select the optimal disadvantage provision method by taking into account the geographical location information of the attacker. For example, if the attacker is in a specific area, the disadvantage provision unit selects a disadvantage provision method specific to that area. Furthermore, if the attacker is traveling, the disadvantage provision unit can also select a disadvantage provision method specific to the area of ​​the attacker's destination. Furthermore, the disadvantage provision unit can select the optimal disadvantage provision method based on the attacker's place of residence. In this way, the disadvantage provision unit can select the optimal disadvantage provision method by taking into account the geographical location information of the attacker. Some or all of the above-mentioned processing in the disadvantage provision unit may be performed using AI, for example, or may be performed without using AI. For example, the disadvantage provision unit can input the geographical location information of the attacker into AI and cause the AI ​​to select the optimal disadvantage provision method.

[0045] The disadvantage provision unit can analyze the attacker's social media activity and suggest related disadvantage provision measures. The disadvantage provision unit can, for example, analyze the attacker's social media posts and suggest optimal disadvantage provision measures. The disadvantage provision unit can also monitor the activity of accounts followed by the attacker and suggest related disadvantage provision measures. Furthermore, the disadvantage provision unit can analyze the attacker's social media messages and suggest optimal disadvantage provision measures. In this way, the disadvantage provision unit can suggest related disadvantage provision measures by analyzing the attacker's social media activity. Some or all of the above-mentioned processing in the disadvantage provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the disadvantage provision unit can input the attacker's social media data into AI and have the AI ​​execute the suggestion of disadvantage provision measures.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The fraud prevention system may further include a behavioral analysis unit that analyzes a user's behavioral history. The behavioral analysis unit analyzes a user's past online behavior and assesses the risk of fraud. For example, the behavioral analysis unit can predict the risk of fraud by analyzing the websites a user frequently visits or patterns of emails received in the past. The behavioral analysis unit can also record the types of fraudulent activities a user has encountered in the past and assess risk based on that information. Furthermore, the behavioral analysis unit can monitor changes in a user's online behavior in real time, and if abnormal behavior is detected, determine that the risk of fraud is increasing. This allows the fraud prevention system to assess the risk of fraud based on a user's behavioral history and take more effective fraud prevention measures.

[0048] The fraud prevention system may further include a geographic information evaluation unit that evaluates the risk of fraud by taking into account the user's geographic location information. The geographic information evaluation unit, for example, learns fraud patterns that frequently occur in a particular region and evaluates the risk of fraud in that region. Furthermore, if the user is traveling, the geographic information evaluation unit may also evaluate the risk by taking into account fraud patterns specific to the region of the user's travel destination. Furthermore, the geographic information evaluation unit may also evaluate the risk of fraud specific to the region based on the user's place of residence. This allows the geographic information evaluation unit to take into account region-specific fraud patterns, enabling appropriate risk evaluation according to the region. Some or all of the above-described processing in the geographic information evaluation unit may be performed using, or without, AI. For example, the geographic information evaluation unit may input the user's geographic location information into AI and have the AI ​​perform a risk evaluation of region-specific fraud patterns.

[0049] The fraud prevention system may further include a social media analysis unit that analyzes the user's social media activities. The social media analysis unit, for example, analyzes the user's social media posts and assesses the risk of fraud. The social media analysis unit may also monitor the activities of accounts the user follows and assess the risk of fraud. The social media analysis unit may also analyze the user's social media messages and assess the risk of fraud. In this way, the social media analysis unit can analyze the social media activities and assess the risk of related fraud at an early stage. Some or all of the above-described processing in the social media analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the social media analysis unit may input the user's social media data into AI and have the AI ​​perform a fraud risk assessment.

[0050] The fraud prevention system may further include a behavioral analysis unit that analyzes a user's behavioral history. The behavioral analysis unit analyzes a user's past online behavior and assesses the risk of fraud. For example, the behavioral analysis unit can predict the risk of fraud by analyzing the websites a user frequently visits or patterns of emails received in the past. The behavioral analysis unit can also record the types of fraudulent activities a user has encountered in the past and assess risk based on that information. Furthermore, the behavioral analysis unit can monitor changes in a user's online behavior in real time, and if abnormal behavior is detected, determine that the risk of fraud is increasing. This allows the fraud prevention system to assess the risk of fraud based on a user's behavioral history and take more effective fraud prevention measures.

[0051] The fraud prevention system may further include a geographic information evaluation unit that evaluates the risk of fraud by taking into account the user's geographic location information. The geographic information evaluation unit, for example, learns fraud patterns that frequently occur in a particular region and evaluates the risk of fraud in that region. Furthermore, if the user is traveling, the geographic information evaluation unit may also evaluate the risk by taking into account fraud patterns specific to the region of the user's travel destination. Furthermore, the geographic information evaluation unit may also evaluate the risk of fraud specific to the region based on the user's place of residence. This allows the geographic information evaluation unit to take into account region-specific fraud patterns, enabling appropriate risk evaluation according to the region. Some or all of the above-described processing in the geographic information evaluation unit may be performed using, or without, AI. For example, the geographic information evaluation unit may input the user's geographic location information into AI and have the AI ​​perform a risk evaluation of region-specific fraud patterns.

[0052] The fraud prevention system may further include a social media analysis unit that analyzes the user's social media activities. The social media analysis unit, for example, analyzes the user's social media posts and assesses the risk of fraud. The social media analysis unit may also monitor the activities of accounts the user follows and assess the risk of fraud. The social media analysis unit may also analyze the user's social media messages and assess the risk of fraud. In this way, the social media analysis unit can analyze the social media activities and assess the risk of related fraud at an early stage. Some or all of the above-described processing in the social media analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the social media analysis unit may input the user's social media data into AI and have the AI ​​perform a fraud risk assessment.

[0053] The fraud prevention system may further include a behavioral analysis unit that analyzes a user's behavioral history. The behavioral analysis unit analyzes a user's past online behavior and assesses the risk of fraud. For example, the behavioral analysis unit can predict the risk of fraud by analyzing the websites a user frequently visits or patterns of emails received in the past. The behavioral analysis unit can also record the types of fraudulent activities a user has encountered in the past and assess risk based on that information. Furthermore, the behavioral analysis unit can monitor changes in a user's online behavior in real time, and if abnormal behavior is detected, determine that the risk of fraud is increasing. This allows the fraud prevention system to assess the risk of fraud based on a user's behavioral history and take more effective fraud prevention measures.

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

[0055] Step 1: The detection unit detects fraudulent activity. Fraudulent activity includes phishing, credit card fraud, online fraud, etc. The detection unit detects fraudulent activity using anomaly detection algorithms and pattern matching, and learns fraud patterns based on past fraud data and simulation data. Step 2: The trapping unit intentionally falls for the fraudulent behavior detected by the detection unit. The trapping unit attracts attackers by taking actions that the attacker expects. For example, replying to a phishing email or entering personal information on a fake website. The trapping unit also learns the attacker's behavior patterns and selects the optimal trapping method. Step 3: The Disadvantage Unit penalizes the attacker based on the fraudulent activity caught by the Trap Unit. The Disadvantage Unit collects information such as the attacker's IP address and email address and provides it to law enforcement agencies. The Disadvantage Unit also provides false information to the attacker to thwart their plans. For example, by providing false credit card information, the attacker is prevented from committing fraudulent acts.

[0056] (Example 2) A fraud prevention system according to an embodiment of the present invention is a system that detects fraudulent activity and prevents fraud by inflicting a disadvantage on the attacker. This fraud prevention system detects fraudulent activity and takes actions that inflict a disadvantage on the attacker by intentionally falling for the fraud. For example, the fraud prevention system collects information about the attacker and provides it to law enforcement agencies. The fraud prevention system also provides false information to the attacker to thwart their plans. This mechanism prevents fraudulent activity and provides a deterrent to attackers. First, the fraud prevention system detects fraud. The fraud prevention system learns various fraud patterns and can detect fraud with high accuracy. For example, the fraud prevention system can handle various fraud methods, such as phishing emails and fake websites. Next, the fraud prevention system intentionally falls for the fraud. The fraud prevention system attracts attackers by taking actions that the attacker expects. For example, the fraud prevention system replies to phishing emails or enters personal information on fake websites. The fraud prevention system then takes actions that inflict a disadvantage on the attacker. For example, a fraud prevention system may collect information such as an attacker's IP address or email address and provide it to law enforcement agencies. A fraud prevention system may also provide false information to an attacker to thwart their plans. For example, providing false credit card information prevents the attacker from committing fraudulent acts. This mechanism prevents fraudulent acts before they occur and acts as a deterrent to attackers. By deliberately allowing fraud attempts to be made, attackers learn that their fraud attempts will not be successful and lose their motivation to commit fraud. A fraud prevention system may also collect information about attackers and provide it to law enforcement agencies, making it easier to detect fraudulent acts. This allows a fraud prevention system to prevent fraud before they occur and act as a deterrent to attackers.

[0057] A fraud prevention system according to an embodiment includes a detection unit, a trap unit, and a disadvantage provision unit. The detection unit detects fraudulent activities. Examples of fraudulent activities include, but are not limited to, phishing scams, credit card fraud, and online fraud. The detection unit detects fraudulent activities using, for example, an anomaly detection algorithm or pattern matching. The detection unit can also learn various fraud patterns and detect fraudulent activities with high accuracy. For example, the detection unit learns fraud patterns based on past fraud data and simulation data. The trap unit intentionally falls for fraudulent activities detected by the detection unit. The trap unit attracts attackers by, for example, taking actions that the attacker expects. For example, the trap unit may reply to phishing emails or enter personal information on fake websites. The trap unit can also learn attacker behavior patterns and select the optimal trap method. For example, the trap unit may learn the patterns of how attackers send phishing emails and select the optimal trap method. The disadvantage provision unit provides a disadvantage to the attacker based on the fraudulent act that has been caught by the trapping unit. The disadvantage provision unit collects information such as the attacker's IP address and email address, and provides it to law enforcement agencies. The disadvantage provision unit also provides false information to the attacker to thwart the attacker's plans. For example, the disadvantage provision unit provides false credit card information to prevent the attacker from fraudulently using the system. In this way, the fraud prevention system according to the embodiment can prevent fraudulent acts and act as a deterrent against attackers.

[0058] The detection unit can learn multiple fraud patterns and detect fraudulent activities with high accuracy. The detection unit learns fraud patterns based on, for example, past fraud data. For example, the detection unit can analyze data on past phishing emails and predict new phishing patterns. The detection unit can also predict the characteristics of new fake websites based on data on past fake websites. Furthermore, the detection unit can refer to data on past fraudulent calls and predict new fraudulent call patterns. In this way, the detection unit can predict new fraud patterns by utilizing past data, thereby improving detection accuracy. Some or all of the above-mentioned processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input past fraud data into AI and have the AI ​​predict new fraud patterns.

[0059] The trap unit can attract attackers by taking actions that the attacker expects. For example, the trap unit can attract attackers by replying to a phishing email. The trap unit can also attract attackers by entering personal information on a fake website. Furthermore, the trap unit can attract attackers by continuing a conversation with a scam phone call. This makes it easier for the trap unit to collect information about the attacker by attracting them. Some or all of the above-mentioned processing in the trap unit may be performed using AI, for example, or may be performed without using AI. For example, the trap unit can input actions that the attacker expects to be performed into AI and leave it to the AI ​​to execute the trapping actions.

[0060] The disadvantage provision unit can collect information including the attacker's IP address and email address and provide it to a law enforcement agency. The disadvantage provision unit, for example, collects the attacker's IP address. For example, the disadvantage provision unit extracts the IP address used by the attacker from log data and provides it to a law enforcement agency. The disadvantage provision unit can also collect the attacker's email address. For example, the disadvantage provision unit extracts the email address from the header information of a phishing email sent by the attacker and provides it to a law enforcement agency. The disadvantage provision unit can also collect the attacker's log data and provide it to a law enforcement agency. For example, the disadvantage provision unit collects log data of websites accessed by the attacker and provides it to a law enforcement agency. This allows the disadvantage provision unit to provide information about the attacker to a law enforcement agency, making it easier to detect fraudulent activities. Some or all of the above-mentioned processing in the disadvantage provision unit may be performed using, or without, AI. For example, the disadvantage provision unit can input the attacker's IP address and email address into AI and have the AI ​​collect and provide the information.

[0061] The disadvantage provision unit can provide false information to an attacker to thwart the attacker's plans. The disadvantage provision unit, for example, provides false credit card information to an attacker. For example, the disadvantage provision unit generates false credit card numbers and expiration dates and provides them to the attacker to prevent the attacker from fraudulently using the credit card. The disadvantage provision unit can also provide a false login page to the attacker. For example, the disadvantage provision unit creates a false login page and lures the attacker into accessing the page. The disadvantage provision unit can also provide a false error message to the attacker. For example, the disadvantage provision unit displays a false error message on a website accessed by the attacker to thwart the attacker's plans. In this way, the disadvantage provision unit can prevent fraud by thwarting the attacker's plans. Some or all of the above-described processing in the disadvantage provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the disadvantage provision unit can entrust the process of generating false information to AI.

[0062] The detection unit can estimate the user's emotions and adjust the fraud detection accuracy based on the estimated user emotions. For example, when the user is feeling anxious, the detection unit can increase the detection accuracy and quickly detect fraud. Furthermore, when the user is relaxed, the detection unit can also detect fraud with normal detection accuracy. Furthermore, when the user is in a hurry, the detection unit can prioritize detection speed and quickly detect fraud. This allows the detection unit to adjust the detection accuracy according to the user's emotions, enabling more appropriate fraud detection. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the detection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the detection unit can input the user's emotion data into an AI and have the AI ​​adjust the detection accuracy.

[0063] The detection unit can detect fraudulent activities in real time and respond immediately. For example, the detection unit can detect a phishing email the moment it is received and display a warning to the user. The detection unit can also immediately detect when a fake website is accessed and block access. Furthermore, the detection unit can detect when a fraudulent call is received in real time and terminate the call. In this way, the detection unit can detect fraudulent activities in real time and respond immediately, thereby minimizing damage. Some or all of the above-mentioned processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can entrust the detection and response to fraudulent activities in real time to AI.

[0064] The detection unit can predict new fraud patterns by referring to past fraud data, thereby improving detection accuracy. The detection unit, for example, analyzes past phishing email data and predicts new phishing patterns. The detection unit can also predict the characteristics of new fake websites based on past data on fake websites. Furthermore, the detection unit can predict new fraud call patterns by referring to past fraud call data. In this way, the detection unit can predict new fraud patterns by utilizing past data, thereby improving detection accuracy. Some or all of the above-mentioned processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input past fraud data into AI and have the AI ​​predict new fraud patterns.

[0065] The detection unit can estimate the user's emotions and adjust the display method of the detection results based on the estimated user emotions. For example, if the user is feeling anxious, the detection unit can display detailed detection results to provide a sense of security. The detection unit can also display concise detection results if the user is relaxed. Furthermore, if the user is in a hurry, the detection unit can display detection results that are concise. This allows the detection unit to adjust the display method of the detection results according to the user's emotions, thereby enabling a display that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the detection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the detection unit can input user emotion data into an AI and have the AI ​​adjust the display method of the detection results.

[0066] The detection unit can detect region-specific fraud patterns based on the user's geographical location information. For example, the detection unit can learn fraud patterns that frequently occur in a specific region and detect fraud in that region. Furthermore, if the user is traveling, the detection unit can also detect region-specific fraud patterns based on the user's place of residence. By detecting region-specific fraud patterns, the detection unit can take appropriate measures according to the region. Some or all of the above-described processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input the user's geographical location information into AI and have the AI ​​detect region-specific fraud patterns.

[0067] The detection unit can analyze the user's social media activity and detect related fraudulent activities. For example, the detection unit can analyze the user's social media posts to detect signs of fraudulent activities. The detection unit can also monitor the activities of accounts the user follows to detect fraudulent activities. Furthermore, the detection unit can analyze the user's social media messages to detect fraudulent activities. In this way, the detection unit can detect related fraudulent activities early by analyzing social media activities. Some or all of the above-mentioned processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input the user's social media data into AI and have the AI ​​perform fraudulent activity detection.

[0068] The trapping unit can estimate the user's emotions and adjust the timing of the trapping behavior based on the estimated user's emotions. For example, if the user is feeling anxious, the trapping unit can quickly initiate the trapping behavior. Furthermore, if the user is relaxed, the trapping unit can also initiate the trapping behavior at a normal timing. Furthermore, if the user is in a hurry, the trapping unit can also initiate the trapping behavior immediately. This allows the trapping unit to adjust the timing of the trapping behavior according to the user's emotions, thereby enabling more effective trapping behavior. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the trapping unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the trapping unit can input the user's emotion data into an AI and have the AI ​​adjust the timing of the trapping behavior.

[0069] The trapping unit can learn the attacker's behavioral patterns and select the optimal trapping method. For example, the trapping unit can learn the pattern in which the attacker sends phishing emails and select the optimal trapping method. The trapping unit can also learn the pattern in which the attacker creates fake websites and select the optimal trapping method. Furthermore, the trapping unit can also learn the pattern in which the attacker makes fraudulent phone calls and select the optimal trapping method. In this way, the trapping unit can select the optimal trapping method by learning the attacker's behavioral patterns. Some or all of the above-mentioned processing in the trapping unit may be performed using, for example, AI, or may be performed without using AI. For example, the trapping unit can input the attacker's behavioral pattern data into AI and have the AI ​​select the optimal trapping method.

[0070] The trapping unit can take different trapping actions depending on different fraud methods. For example, the trapping unit can take the trapping action of replying to a phishing email. The trapping unit can also take the trapping action of entering personal information in response to a fake website. Furthermore, the trapping unit can take the trapping action of continuing the conversation in response to a fraudulent phone call. This allows the trapping unit to take more effective trapping actions depending on different fraud methods. Some or all of the above-mentioned processing in the trapping unit may be performed using, for example, AI, or may be performed without using AI. For example, the trapping unit can leave the selection of trapping actions depending on different fraud methods to AI.

[0071] The trapping unit can estimate the user's emotions and prioritize the trapping behaviors based on the estimated user's emotions. For example, if the user is feeling anxious, the trapping unit prioritizes the most important trapping behaviors. Furthermore, if the user is relaxed, the trapping unit can also determine the trapping behaviors in normal priority order. Furthermore, if the user is in a hurry, the trapping unit can quickly determine the trapping behaviors. This allows the trapping unit to prioritize the trapping behaviors according to the user's emotions, enabling more effective trapping behaviors. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the trapping unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the trapping unit can input the user's emotion data into an AI and have the AI ​​determine the priority of the trapping behaviors.

[0072] The trapping unit can take the optimal trapping action by taking into account the geographical location information of the attacker. For example, if the attacker is in a specific area, the trapping unit can take a trapping action specific to that area. Also, if the attacker is traveling, the trapping unit can take a trapping action specific to the area to which the attacker is traveling. Furthermore, the trapping unit can take the optimal trapping action based on the attacker's place of residence. In this way, the trapping unit can take the optimal trapping action by taking into account the geographical location information of the attacker. Some or all of the above-mentioned processing in the trapping unit may be performed using AI, for example, or may be performed without using AI. For example, the trapping unit can input the geographical location information of the attacker into AI and have the AI ​​select the optimal trapping action.

[0073] The trapping unit can analyze the attacker's social media activity and take relevant trapping actions. The trapping unit can, for example, analyze the attacker's social media posts and take optimal trapping actions. The trapping unit can also monitor the activity of accounts followed by the attacker and take relevant trapping actions. Furthermore, the trapping unit can analyze the attacker's social media messages and take optimal trapping actions. In this way, the trapping unit can take relevant trapping actions by analyzing the attacker's social media activity. Some or all of the above-mentioned processing in the trapping unit may be performed using, for example, AI, or may be performed without using AI. For example, the trapping unit can input the attacker's social media data into AI and have the AI ​​select the trapping action.

[0074] The disadvantage provision unit can estimate the user's emotions and adjust the method of providing a disadvantage based on the estimated user's emotions. For example, if the user is feeling anxious, the disadvantage provision unit can quickly provide a disadvantage. Furthermore, if the user is relaxed, the disadvantage provision unit can also provide a disadvantage in a normal manner. Furthermore, if the user is in a hurry, the disadvantage provision unit can quickly provide a disadvantage. This allows the disadvantage provision unit to adjust the method of providing a disadvantage according to the user's emotions, thereby enabling more effective disadvantage provision. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the disadvantage provision unit may be performed using an AI, for example, or without an AI. For example, the disadvantage provision unit can input the user's emotion data into an AI and have the AI ​​adjust the method of providing a disadvantage.

[0075] The disadvantage provision unit can analyze the behavioral patterns of an attacker and select the optimal disadvantage provision method. For example, the disadvantage provision unit can analyze the pattern in which an attacker sends phishing emails and select the optimal disadvantage provision method. The disadvantage provision unit can also analyze the pattern in which an attacker creates fake websites and select the optimal disadvantage provision method. Furthermore, the disadvantage provision unit can analyze the pattern in which an attacker makes fraudulent phone calls and select the optimal disadvantage provision method. In this way, the disadvantage provision unit can select the optimal disadvantage provision method by analyzing the behavioral patterns of an attacker. Some or all of the above-mentioned processing in the disadvantage provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the disadvantage provision unit can input the behavioral pattern data of an attacker into AI and have the AI ​​select the optimal disadvantage provision method.

[0076] The disadvantage provision unit can customize the means of providing a disadvantage taking into account the attribute information of the attacker. The disadvantage provision unit customizes the means of providing a disadvantage based on, for example, the age and gender of the attacker. The disadvantage provision unit can also customize the means of providing a disadvantage based on the occupation and career of the attacker. Furthermore, the disadvantage provision unit can also customize the means of providing a disadvantage based on the attacker's past criminal history. In this way, the disadvantage provision unit can customize the means of providing a disadvantage based on the attribute information of the attacker, thereby enabling more effective disadvantage provision. Some or all of the above-mentioned processing in the disadvantage provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the disadvantage provision unit can input the attribute information of the attacker into AI and cause the AI ​​to customize the means of providing a disadvantage.

[0077] The disadvantage provision unit can estimate the user's emotions and determine the priority of disadvantage provision based on the estimated user's emotions. For example, if the user is feeling anxious, the disadvantage provision unit prioritizes the most important disadvantage provision. Furthermore, if the user is relaxed, the disadvantage provision unit can also determine disadvantage provision in normal priority order. Furthermore, if the user is in a hurry, the disadvantage provision unit can quickly determine disadvantage provision. This enables the disadvantage provision unit to prioritize disadvantage provision according to the user's emotions, thereby enabling more effective disadvantage provision. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the disadvantage provision unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the disadvantage provision unit can input the user's emotion data into an AI and have the AI ​​determine the priority of disadvantage provision.

[0078] The disadvantage provision unit can select the optimal disadvantage provision method by taking into account the geographical location information of the attacker. For example, if the attacker is in a specific area, the disadvantage provision unit selects a disadvantage provision method specific to that area. Furthermore, if the attacker is traveling, the disadvantage provision unit can also select a disadvantage provision method specific to the area of ​​the attacker's destination. Furthermore, the disadvantage provision unit can select the optimal disadvantage provision method based on the attacker's place of residence. In this way, the disadvantage provision unit can select the optimal disadvantage provision method by taking into account the geographical location information of the attacker. Some or all of the above-mentioned processing in the disadvantage provision unit may be performed using AI, for example, or may be performed without using AI. For example, the disadvantage provision unit can input the geographical location information of the attacker into AI and cause the AI ​​to select the optimal disadvantage provision method.

[0079] The disadvantage provision unit can analyze the attacker's social media activity and suggest related disadvantage provision measures. The disadvantage provision unit can, for example, analyze the attacker's social media posts and suggest optimal disadvantage provision measures. The disadvantage provision unit can also monitor the activity of accounts followed by the attacker and suggest related disadvantage provision measures. Furthermore, the disadvantage provision unit can analyze the attacker's social media messages and suggest optimal disadvantage provision measures. In this way, the disadvantage provision unit can suggest related disadvantage provision measures by analyzing the attacker's social media activity. Some or all of the above-mentioned processing in the disadvantage provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the disadvantage provision unit can input the attacker's social media data into AI and have the AI ​​execute the suggestion of disadvantage provision measures. === Hard Collateral 1-1 === Each of the multiple elements, including the detection unit, trap unit, and disadvantage provision unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the detection unit is realized by the control unit 46A of the smart device 14 and detects fraudulent activity using an anomaly detection algorithm or pattern matching. The trap unit is realized, for example, by the control unit 46A of the smart device 14 and attracts attackers by taking actions that the attackers expect. The disadvantage provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and collects information on attackers and provides it to law enforcement agencies. === Hard Collateral 1-2 === Each of the multiple elements, including the detection unit, the trapping unit, and the disadvantage provision unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the detection unit is realized by the control unit 46A of the smart glasses 214 and detects fraudulent activity using an anomaly detection algorithm or pattern matching. The trapping unit is realized, for example, by the control unit 46A of the smart glasses 214 and attracts attackers by taking actions that the attackers expect. The disadvantage provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and collects information on attackers and provides it to law enforcement agencies. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned detection unit, trap unit, and disadvantage provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the detection unit is realized by the control unit 46A of the headset type terminal 314 and detects fraudulent activity using an anomaly detection algorithm or pattern matching. The trap unit is realized, for example, by the control unit 46A of the headset type terminal 314 and attracts attackers by taking actions that the attackers expect. The disadvantage provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and collects information on attackers and provides it to law enforcement agencies. === Hard Collateral 1-4 === Each of the multiple elements including the detection unit, the trap unit, and the disadvantage provision unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the detection unit is realized by the control unit 46A of the robot 414 and detects fraudulent activity using an anomaly detection algorithm or pattern matching. The trap unit is realized, for example, by the control unit 46A of the robot 414 and attracts attackers by taking actions that the attackers expect. The disadvantage provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and collects information about attackers and provides it to law enforcement agencies.

[0080] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0081] The fraud prevention system may further include a behavioral analysis unit that analyzes a user's behavioral history. The behavioral analysis unit analyzes a user's past online behavior and assesses the risk of fraud. For example, the behavioral analysis unit can predict the risk of fraud by analyzing the websites a user frequently visits or patterns of emails received in the past. The behavioral analysis unit can also record the types of fraudulent activities a user has encountered in the past and assess risk based on that information. Furthermore, the behavioral analysis unit can monitor changes in a user's online behavior in real time, and if abnormal behavior is detected, determine that the risk of fraud is increasing. This allows the fraud prevention system to assess the risk of fraud based on a user's behavioral history and take more effective fraud prevention measures.

[0082] The fraud prevention system may further include an emotion evaluation unit that estimates a user's emotion and evaluates the risk of fraud based on the estimated user emotion. For example, if the user feels anxious, the emotion evaluation unit may determine that the risk of fraud is increased and issue a stronger warning. The emotion evaluation unit may also perform a normal risk evaluation if the user is relaxed. Furthermore, if the user is in a hurry, the emotion evaluation unit may quickly perform a risk evaluation and determine that the risk of fraud is increased. This allows the emotion evaluation unit to evaluate the risk of fraud according to the user's emotion and take more appropriate fraud prevention measures. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0083] The fraud prevention system may further include a geographic information evaluation unit that evaluates the risk of fraud by taking into account the user's geographic location information. The geographic information evaluation unit, for example, learns fraud patterns that frequently occur in a particular region and evaluates the risk of fraud in that region. Furthermore, if the user is traveling, the geographic information evaluation unit may also evaluate the risk by taking into account fraud patterns specific to the region of the user's travel destination. Furthermore, the geographic information evaluation unit may also evaluate the risk of fraud specific to the region based on the user's place of residence. This allows the geographic information evaluation unit to take into account region-specific fraud patterns, enabling appropriate risk evaluation according to the region. Some or all of the above-described processing in the geographic information evaluation unit may be performed using, or without, AI. For example, the geographic information evaluation unit may input the user's geographic location information into AI and have the AI ​​perform a risk evaluation of region-specific fraud patterns.

[0084] The fraud prevention system may further include a social media analysis unit that analyzes the user's social media activities. The social media analysis unit, for example, analyzes the user's social media posts and assesses the risk of fraud. The social media analysis unit may also monitor the activities of accounts the user follows and assess the risk of fraud. The social media analysis unit may also analyze the user's social media messages and assess the risk of fraud. In this way, the social media analysis unit can analyze the social media activities and assess the risk of related fraud at an early stage. Some or all of the above-described processing in the social media analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the social media analysis unit may input the user's social media data into AI and have the AI ​​perform a fraud risk assessment.

[0085] The fraud prevention system may further include an emotion evaluation unit that estimates a user's emotion and evaluates the risk of fraud based on the estimated user emotion. For example, if the user feels anxious, the emotion evaluation unit may determine that the risk of fraud is increased and issue a stronger warning. The emotion evaluation unit may also perform a normal risk evaluation if the user is relaxed. Furthermore, if the user is in a hurry, the emotion evaluation unit may quickly perform a risk evaluation and determine that the risk of fraud is increased. This allows the emotion evaluation unit to evaluate the risk of fraud according to the user's emotion and take more appropriate fraud prevention measures. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0086] The fraud prevention system may further include a behavioral analysis unit that analyzes a user's behavioral history. The behavioral analysis unit analyzes a user's past online behavior and assesses the risk of fraud. For example, the behavioral analysis unit can predict the risk of fraud by analyzing the websites a user frequently visits or patterns of emails received in the past. The behavioral analysis unit can also record the types of fraudulent activities a user has encountered in the past and assess risk based on that information. Furthermore, the behavioral analysis unit can monitor changes in a user's online behavior in real time, and if abnormal behavior is detected, determine that the risk of fraud is increasing. This allows the fraud prevention system to assess the risk of fraud based on a user's behavioral history and take more effective fraud prevention measures.

[0087] The fraud prevention system may further include a geographic information evaluation unit that evaluates the risk of fraud by taking into account the user's geographic location information. The geographic information evaluation unit, for example, learns fraud patterns that frequently occur in a particular region and evaluates the risk of fraud in that region. Furthermore, if the user is traveling, the geographic information evaluation unit may also evaluate the risk by taking into account fraud patterns specific to the region of the user's travel destination. Furthermore, the geographic information evaluation unit may also evaluate the risk of fraud specific to the region based on the user's place of residence. This allows the geographic information evaluation unit to take into account region-specific fraud patterns, enabling appropriate risk evaluation according to the region. Some or all of the above-described processing in the geographic information evaluation unit may be performed using, or without, AI. For example, the geographic information evaluation unit may input the user's geographic location information into AI and have the AI ​​perform a risk evaluation of region-specific fraud patterns.

[0088] The fraud prevention system may further include a social media analysis unit that analyzes the user's social media activities. The social media analysis unit, for example, analyzes the user's social media posts and assesses the risk of fraud. The social media analysis unit may also monitor the activities of accounts the user follows and assess the risk of fraud. The social media analysis unit may also analyze the user's social media messages and assess the risk of fraud. In this way, the social media analysis unit can analyze the social media activities and assess the risk of related fraud at an early stage. Some or all of the above-described processing in the social media analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the social media analysis unit may input the user's social media data into AI and have the AI ​​perform a fraud risk assessment.

[0089] The fraud prevention system may further include an emotion evaluation unit that estimates a user's emotion and evaluates the risk of fraud based on the estimated user emotion. For example, if the user feels anxious, the emotion evaluation unit may determine that the risk of fraud is increased and issue a stronger warning. The emotion evaluation unit may also perform a normal risk evaluation if the user is relaxed. Furthermore, if the user is in a hurry, the emotion evaluation unit may quickly perform a risk evaluation and determine that the risk of fraud is increased. This allows the emotion evaluation unit to evaluate the risk of fraud according to the user's emotion and take more appropriate fraud prevention measures. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0090] The fraud prevention system may further include a behavioral analysis unit that analyzes a user's behavioral history. The behavioral analysis unit analyzes a user's past online behavior and assesses the risk of fraud. For example, the behavioral analysis unit can predict the risk of fraud by analyzing the websites a user frequently visits or patterns of emails received in the past. The behavioral analysis unit can also record the types of fraudulent activities a user has encountered in the past and assess risk based on that information. Furthermore, the behavioral analysis unit can monitor changes in a user's online behavior in real time, and if abnormal behavior is detected, determine that the risk of fraud is increasing. This allows the fraud prevention system to assess the risk of fraud based on a user's behavioral history and take more effective fraud prevention measures.

[0091] The processing flow of the second embodiment will be briefly explained below.

[0092] Step 1: The detection unit detects fraudulent activity. Fraudulent activity includes phishing, credit card fraud, online fraud, etc. The detection unit detects fraudulent activity using anomaly detection algorithms and pattern matching, and learns fraud patterns based on past fraud data and simulation data. Step 2: The trapping unit intentionally falls for the fraudulent behavior detected by the detection unit. The trapping unit attracts attackers by taking actions that the attacker expects. For example, replying to a phishing email or entering personal information on a fake website. The trapping unit also learns the attacker's behavior patterns and selects the optimal trapping method. Step 3: The Disadvantage Unit penalizes the attacker based on the fraudulent activity caught by the Trap Unit. The Disadvantage Unit collects information such as the attacker's IP address and email address and provides it to law enforcement agencies. The Disadvantage Unit also provides false information to the attacker to thwart their plans. For example, by providing false credit card information, the attacker is prevented from committing fraudulent acts.

[0093] 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.

[0094] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0095] 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.

[0096] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0097] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0098] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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).

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0107] 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. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0108] 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.

[0109] 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.

[0110] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0111] 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.

[0112] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0113] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0114] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0123] 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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0124] 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.

[0125] 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.

[0126] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0127] 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.

[0128] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

[0130] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0131] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0132] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

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

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0135] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0136] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0137] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0138] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0139] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0140] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. 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 the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

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

[0142] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0143] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

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

[0145] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] 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."

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] [Explanation of symbols]

[0165] 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 detection unit that detects fraudulent activity; a trapping unit that intentionally traps fraudulent acts detected by the detection unit; a disadvantage inflicting a disadvantage on the attacker based on the fraudulent act caught by the trapping unit; Equipped with A system characterized by:

2. The detection unit Learns multiple fraud patterns and detects fraudulent activity with high accuracy 2. The system of claim 1.

3. The catch portion is Attract attackers by taking the actions they expect 2. The system of claim 1.

4. The disadvantage provision unit Collect information, including the attacker's IP address and email address, and provide it to law enforcement agencies 2. The system of claim 1.

5. The disadvantage provision unit Providing false information to attackers to disrupt their plans 2. The system of claim 1.

6. The detection unit Estimate user emotions and adjust fraud detection accuracy based on the estimated user emotions 2. The system of claim 1.

7. The detection unit Detect fraud in real time and respond immediately 2. The system of claim 1.

8. The detection unit References historical fraud data to predict new fraud patterns and improve detection accuracy 2. The system of claim 1.

Images