System

Abstract

An object of a system according to an embodiment is to detect a risk of fraudulent remittance in advance and take appropriate measures.SOLUTION: A system includes an illegal remittance detection part, a warning display part, and a police cooperation part. The fraudulent remittance detection unit analyzes the remittance request and determines the possibility of a fraudulent remittance. The warning display unit displays a warning in a pop-up when the fraudulent remittance detection unit determines that there is a possibility of fraudulent remittance. After the warning is displayed by the warning display unit, the police cooperation unit notifies the police as necessary.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the problem of not being able to adequately detect the risk of fraudulent remittances in advance and take appropriate measures.

[0005] The system according to the embodiment aims to detect the risk of fraudulent remittance in advance and take appropriate measures. [Means for solving the problem]

[0006] The system according to the embodiment includes a fraudulent remittance detection unit, a warning display unit, and a police cooperation unit. The fraudulent remittance detection unit analyzes remittance requests and determines the possibility of fraudulent remittance. The warning display unit displays a warning in a pop-up when the fraudulent remittance detection unit determines that there is a possibility of fraudulent remittance. After the warning display unit displays the warning, the police cooperation unit notifies the police as necessary. [Effects of the Invention]

[0007] The system according to the embodiment can detect the risk of fraudulent remittance in advance and take appropriate measures. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

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

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The fraudulent remittance prevention system according to the embodiment of the present invention uses AI to determine the possibility of fraud before remittance, and if there is a risk, displays a warning in a pop-up and cooperates with the police as necessary. This allows the fraudulent remittance prevention system to provide users with peace of mind and trust.

[0029] The fraudulent remittance prevention system according to the embodiment includes a fraudulent remittance detection unit, a warning display unit, and a police cooperation unit. The fraudulent remittance detection unit analyzes remittance requests and determines whether the request is fraudulent. For example, the fraudulent remittance detection unit analyzes the remittance recipient's information, the remittance amount, and past transaction history to determine whether the request matches a fraudulent pattern. The fraudulent remittance detection unit can also detect abnormal patterns by taking into account the remittance recipient's geographical information and time of day. Furthermore, the fraudulent remittance detection unit can analyze the remittance recipient's social media activity to determine the possibility of fraud. The warning display unit displays a pop-up warning when the fraudulent remittance detection unit determines that a fraudulent remittance is likely. For example, the warning display unit displays a message such as "This remittance may be fraudulent. Do you want to continue with the remittance?" to request the user's confirmation. The warning display unit can also specifically indicate past fraudulent remittance cases when displaying the pop-up warning to make the user aware of specific risks. The warning display unit can also display detailed information about the remittance recipient (such as their address and contact information) for the user to review. After the warning display unit displays a warning, the police cooperation unit notifies the police as necessary. For example, the police cooperation unit automatically notifies the police of detailed information about the fraudulent remittance, enabling a prompt response. As a result, the fraudulent remittance prevention system according to the embodiment can detect the risk of fraudulent remittance in advance, display a warning to the user, and notify the police as necessary, thereby preventing damage from fraudulent remittances.

[0030] The fraudulent remittance detection unit can analyze the remittance destination information, remittance amount, and past transaction history to check whether it matches a fraudulent pattern. For example, the fraudulent remittance detection unit analyzes the remittance destination information and detects anomalies when there is a significant deviation from normal transaction patterns. The fraudulent remittance detection unit can also analyze the remittance amount and detect abnormally large remittances. Furthermore, the fraudulent remittance detection unit can analyze past transaction history and detect remittances to remittance destinations where fraud has been reported in the past. In this way, by analyzing the remittance destination information, remittance amount, and past transaction history, the possibility of fraudulent remittances can be determined with high accuracy.

[0031] When receiving a remittance request, the fraudulent remittance detection unit can detect abnormal patterns by taking into account the geographical information and time zone of the remittance destination. For example, the fraudulent remittance detection unit analyzes the geographical information of the remittance destination and detects abnormalities when there is a significant deviation from normal transaction patterns. The fraudulent remittance detection unit can also analyze the time zone of the remittance request and detect abnormalities when there is a deviation from normal transaction hours. In this way, taking into account the geographical information and time zone of the remittance destination makes it easier to detect abnormal patterns.

[0032] When receiving a remittance request, the fraudulent remittance detection unit can analyze the social media activity of the remittance recipient to determine the possibility of fraud. The fraudulent remittance detection unit, for example, analyzes the remittance recipient's social media account and evaluates the account's reliability. For example, it issues a warning if the account is newly created. The fraudulent remittance detection unit can also analyze the content of the remittance recipient's social media posts to detect suspicious activity. In this way, by analyzing the remittance recipient's social media activity, it is possible to determine the possibility of fraud with a high degree of accuracy.

[0033] When displaying the pop-up, the warning display unit specifically shows past cases of fraudulent remittances, making the user aware of specific risks. When displaying the pop-up, the warning display unit, for example, specifically shows past cases of fraudulent remittances, making the user aware of specific risks. For example, it displays a message such as "There have been cases in the past where similar remittances have been used for fraud." The warning display unit can also display details of past cases of fraudulent remittances, making the user aware of specific risks. In this way, by specifically showing past cases of fraudulent remittances, the user can be made aware of specific risks.

[0034] The warning display unit can display detailed information about the remittance destination when the pop-up is displayed, allowing the user to check it. The warning display unit can, for example, display the address of the remittance destination when the pop-up is displayed, allowing the user to check it. For example, it can display a message such as "The address of the remittance destination is Shibuya-ku, Tokyo." The warning display unit can also display contact information about the remittance destination, allowing the user to check it. In this way, detailed information about the remittance destination is displayed, allowing the user to check it.

[0035] The warning display unit can display the remittance destination's past transaction history and reputation when displaying the pop-up, allowing the user to make a decision. The warning display unit can, for example, display the remittance destination's past transaction history when displaying the pop-up, allowing the user to make a decision. For example, it can display a message such as "The remittance destination has had 10 transactions in the past, all of which were completed successfully." The warning display unit can also display the remittance destination's reputation, allowing the user to make a decision. By displaying the remittance destination's past transaction history and reputation, the user can make a decision.

[0036] The warning display unit may display the social media activity and online presence of the remittance recipient when the pop-up is displayed, allowing the user to check them. For example, the warning display unit may display a link to the remittance recipient's social media account when the pop-up is displayed, allowing the user to check them. For example, the warning display unit may display a message such as "Here is the remittance recipient's social media account." The warning display unit may also display the remittance recipient's online presence, allowing the user to check them. By displaying the remittance recipient's social media activity and online presence, the user may check them.

[0037] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0038] The fraudulent remittance prevention system can further include a behavioral analysis unit that analyzes the behavioral history of the remitter. The behavioral analysis unit, for example, analyzes the remitter's past online behavior and detects abnormalities if the behavior deviates from normal behavioral patterns. For example, a warning can be issued if the remitter accesses the system during times when they normally do not access the system or if they make a remittance request from a device they normally do not use. The behavioral analysis unit can also analyze the remitter's browsing history and search history to detect suspicious activity. In this way, by analyzing the remitter's behavioral history, the possibility of fraudulent remittance can be determined with high accuracy.

[0039] The fraudulent remittance prevention system can further include a biometric authentication unit that analyzes the biometric authentication information of the remitter. The biometric authentication unit, for example, analyzes the fingerprint or facial authentication data of the remitter to confirm whether the remitter is the real person. For example, it can issue a warning if the remitter's fingerprint does not match the registered data. The biometric authentication unit can also analyze the remitter's facial authentication data and issue a warning if it does not match past data. In this way, by analyzing the remitter's biometric authentication information, it is possible to accurately determine the possibility of fraudulent remittance.

[0040] The fraudulent remittance prevention system can further include an environmental analysis unit that analyzes the remitter's environmental information. The environmental analysis unit, for example, analyzes the remitter's IP address and device information and detects abnormalities if the environment deviates from the normal environment. For example, it can issue a warning if the remitter is accessing the system from an IP address that is not normally used or if the remitter is making a remittance request from a device that is not normally used. The environmental analysis unit can also analyze the remitter's location information and issue a warning if the remitter is significantly away from their normal location. In this way, by analyzing the remitter's environmental information, it is possible to accurately determine the possibility of fraudulent remittance.

[0041] The fraudulent remittance prevention system can further include a pattern analysis unit that analyzes the remitter's past remittance patterns. The pattern analysis unit, for example, analyzes the remitter's past remittance history and detects abnormalities if there is a deviation from the normal pattern. For example, it can issue a warning if the remitter is sending a large amount of money to a recipient to whom the remitter normally does not send money. The pattern analysis unit can also analyze the remitter's past remittance frequency and remittance amount to detect abnormal patterns. In this way, by analyzing the remitter's past remittance patterns, it is possible to accurately determine the possibility of fraudulent remittances.

[0042] The fraudulent remittance prevention system can further include a network analysis unit that analyzes the remitter's social network. The network analysis unit, for example, analyzes the remitter's social media friendships and followers and detects anomalies if they deviate from the normal network. For example, it can issue a warning if the remitter is sending money to someone with whom the remitter does not normally interact. The network analysis unit can also analyze the remitter's social media activity history and detect suspicious activity. In this way, by analyzing the remitter's social network, it is possible to accurately determine the possibility of fraudulent remittances.

[0043] The fraudulent remittance prevention system can further include a security analysis unit that analyzes the security status of the remitter's device. The security analysis unit can, for example, check whether the latest security patches have been applied to the remitter's device and issue a warning if they have not. The security analysis unit can also analyze applications installed on the remitter's device and issue a warning if a suspicious application has been installed. In this way, by analyzing the security status of the remitter's device, the risk of fraudulent remittance can be reduced.

[0044] The processing flow of the first embodiment will be briefly explained below.

[0045] Step 1: The fraudulent remittance detection unit analyzes the remittance request and determines whether it is fraudulent. For example, it analyzes the recipient's information, the remittance amount, and past transaction history to see if it matches any fraudulent patterns. It can also detect abnormal patterns by taking into account the recipient's geographical information and time of day. It can also analyze the recipient's social media activity to determine whether it is fraudulent. Step 2: The warning display unit displays a pop-up warning if the fraudulent remittance detection unit determines that a remittance may be fraudulent. For example, it displays a message such as "This remittance may be fraudulent. Do you want to continue with the remittance?", asking the user for confirmation. It can also show specific examples of past fraudulent remittances to make the user aware of specific risks. It can also display detailed information about the remittance recipient (address and contact information) so that the user can check it. Step 3: After the warning display unit displays a warning, the police cooperation unit notifies the police as necessary. For example, it can automatically notify the police of details of the fraudulent transfer, enabling a prompt response.

[0046] (Example 2) The fraudulent remittance prevention system according to the embodiment of the present invention uses AI to determine the possibility of fraud before remittance, and if there is a risk, displays a warning in a pop-up and cooperates with the police as necessary. This allows the fraudulent remittance prevention system to provide users with peace of mind and trust.

[0047] The fraudulent remittance prevention system according to the embodiment includes a fraudulent remittance detection unit, a warning display unit, and a police cooperation unit. The fraudulent remittance detection unit analyzes remittance requests and determines whether the request is fraudulent. For example, the fraudulent remittance detection unit analyzes the remittance recipient's information, the remittance amount, and past transaction history to determine whether the request matches a fraudulent pattern. The fraudulent remittance detection unit can also detect abnormal patterns by taking into account the remittance recipient's geographical information and time of day. Furthermore, the fraudulent remittance detection unit can analyze the remittance recipient's social media activity to determine the possibility of fraud. The warning display unit displays a pop-up warning when the fraudulent remittance detection unit determines that a fraudulent remittance is likely. For example, the warning display unit displays a message such as "This remittance may be fraudulent. Do you want to continue with the remittance?" to request the user's confirmation. The warning display unit can also specifically indicate past fraudulent remittance cases when displaying the pop-up warning to make the user aware of specific risks. The warning display unit can also display detailed information about the remittance recipient (such as their address and contact information) for the user to review. After the warning display unit displays a warning, the police cooperation unit notifies the police as necessary. For example, the police cooperation unit automatically notifies the police of detailed information about the fraudulent remittance, enabling a prompt response. As a result, the fraudulent remittance prevention system according to the embodiment can detect the risk of fraudulent remittance in advance, display a warning to the user, and notify the police as necessary, thereby preventing damage from fraudulent remittances.

[0048] The fraudulent remittance detection unit can analyze the remittance destination information, remittance amount, and past transaction history to check whether it matches a fraudulent pattern. For example, the fraudulent remittance detection unit analyzes the remittance destination information and detects anomalies when there is a significant deviation from normal transaction patterns. The fraudulent remittance detection unit can also analyze the remittance amount and detect abnormally large remittances. Furthermore, the fraudulent remittance detection unit can analyze past transaction history and detect remittances to remittance destinations where fraud has been reported in the past. In this way, by analyzing the remittance destination information, remittance amount, and past transaction history, the possibility of fraudulent remittances can be determined with high accuracy.

[0049] When receiving a remittance request, the fraudulent remittance detection unit can detect abnormal patterns by taking into account the geographical information and time zone of the remittance destination. For example, the fraudulent remittance detection unit analyzes the geographical information of the remittance destination and detects abnormalities when there is a significant deviation from normal transaction patterns. The fraudulent remittance detection unit can also analyze the time zone of the remittance request and detect abnormalities when there is a deviation from normal transaction hours. In this way, taking into account the geographical information and time zone of the remittance destination makes it easier to detect abnormal patterns.

[0050] When receiving a remittance request, the fraudulent remittance detection unit can analyze the social media activity of the remittance recipient to determine the possibility of fraud. The fraudulent remittance detection unit, for example, analyzes the remittance recipient's social media account and evaluates the account's reliability. For example, it issues a warning if the account is newly created. The fraudulent remittance detection unit can also analyze the content of the remittance recipient's social media posts to detect suspicious activity. In this way, by analyzing the remittance recipient's social media activity, it is possible to determine the possibility of fraud with a high degree of accuracy.

[0051] The fraudulent remittance detection unit uses the emotion estimation function to analyze the emotional state of the remitter, and can increase the possibility of fraud if abnormal stress or impatience is observed. The fraudulent remittance detection unit, for example, analyzes the facial expressions of the remitter and issues a warning if abnormal stress or impatience is observed. For example, the possibility of fraud increases if the remitter appears more nervous than usual. The fraudulent remittance detection unit can also analyze the remitter's voice and issue a warning if abnormal stress or impatience is observed. In this way, by analyzing the emotional state of the remitter, the possibility of fraud can be determined with high accuracy.

[0052] When displaying the pop-up, the warning display unit specifically shows past cases of fraudulent remittances, making the user aware of specific risks. When displaying the pop-up, the warning display unit, for example, specifically shows past cases of fraudulent remittances, making the user aware of specific risks. For example, it displays a message such as "There have been cases in the past where similar remittances have been used for fraud." The warning display unit can also display details of past cases of fraudulent remittances, making the user aware of specific risks. In this way, by specifically showing past cases of fraudulent remittances, the user can be made aware of specific risks.

[0053] The warning display unit can display detailed information about the remittance destination when the pop-up is displayed, allowing the user to check it. The warning display unit can, for example, display the address of the remittance destination when the pop-up is displayed, allowing the user to check it. For example, it can display a message such as "The address of the remittance destination is Shibuya-ku, Tokyo." The warning display unit can also display contact information about the remittance destination, allowing the user to check it. In this way, detailed information about the remittance destination is displayed, allowing the user to check it.

[0054] The warning display unit can use the emotion estimation function to analyze the user's emotional state and adjust the content and tone of the warning message. For example, when displaying a pop-up, the warning display unit analyzes the user's facial expression and adjusts the content and tone of the warning message according to the user's emotional state. For example, if the user is nervous, a more polite message is displayed. The warning display unit can also analyze the user's voice and adjust the content and tone of the warning message according to the user's emotional state. This allows for more effective warnings by adjusting the content and tone of the warning message according to the user's emotional state.

[0055] The warning display unit can display the remittance destination's past transaction history and reputation when displaying the pop-up, allowing the user to make a decision. The warning display unit can, for example, display the remittance destination's past transaction history when displaying the pop-up, allowing the user to make a decision. For example, it can display a message such as "The remittance destination has had 10 transactions in the past, all of which were completed successfully." The warning display unit can also display the remittance destination's reputation, allowing the user to make a decision. By displaying the remittance destination's past transaction history and reputation, the user can make a decision.

[0056] The warning display unit may display the social media activity and online presence of the remittance recipient when the pop-up is displayed, allowing the user to check them. For example, the warning display unit may display a link to the remittance recipient's social media account when the pop-up is displayed, allowing the user to check them. For example, the warning display unit may display a message such as "Here is the remittance recipient's social media account." The warning display unit may also display the remittance recipient's online presence, allowing the user to check them. By displaying the remittance recipient's social media activity and online presence, the user may check them.

[0057] The warning display unit can use the emotion estimation function to monitor the user's emotional state in real time and optimize the timing of displaying the warning message. For example, when displaying a pop-up, the warning display unit monitors the user's facial expression in real time and optimizes the timing of displaying the warning message according to the user's emotional state. For example, the warning display unit displays the warning when the user is relaxed. The warning display unit can also monitor the user's voice in real time and optimize the timing of displaying the warning message according to the user's emotional state. In this way, by monitoring the user's emotional state in real time and optimizing the timing of displaying the warning message, more effective warnings can be provided.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] The fraudulent remittance prevention system can further include a behavioral analysis unit that analyzes the behavioral history of the remitter. The behavioral analysis unit, for example, analyzes the remitter's past online behavior and detects abnormalities if the behavior deviates from normal behavioral patterns. For example, a warning can be issued if the remitter accesses the system during times when they normally do not access the system or if they make a remittance request from a device they normally do not use. The behavioral analysis unit can also analyze the remitter's browsing history and search history to detect suspicious activity. In this way, by analyzing the remitter's behavioral history, the possibility of fraudulent remittance can be determined with high accuracy.

[0060] The fraudulent remittance prevention system can further include a biometric authentication unit that analyzes the biometric authentication information of the remitter. The biometric authentication unit, for example, analyzes the fingerprint or facial authentication data of the remitter to confirm whether the remitter is the real person. For example, it can issue a warning if the remitter's fingerprint does not match the registered data. The biometric authentication unit can also analyze the remitter's facial authentication data and issue a warning if it does not match past data. In this way, by analyzing the remitter's biometric authentication information, it is possible to accurately determine the possibility of fraudulent remittance.

[0061] The fraudulent remittance prevention system can further include a psychology analysis unit that analyzes the psychology of the remitter. The psychology analysis unit, for example, analyzes the remitter's typing speed and mouse movements to detect abnormal psychology. For example, it can issue a warning if the remitter is typing more quickly than usual or if the mouse movements are unnatural. The psychology analysis unit can also analyze the input content of the remitter and detect abnormal language or writing style. In this way, by analyzing the remitter's psychology, it is possible to accurately determine the possibility of fraudulent remittance.

[0062] The fraudulent remittance prevention system can further include an environmental analysis unit that analyzes the remitter's environmental information. The environmental analysis unit, for example, analyzes the remitter's IP address and device information and detects abnormalities if the environment deviates from the normal environment. For example, it can issue a warning if the remitter is accessing the system from an IP address that is not normally used or if the remitter is making a remittance request from a device that is not normally used. The environmental analysis unit can also analyze the remitter's location information and issue a warning if the remitter is significantly away from their normal location. In this way, by analyzing the remitter's environmental information, it is possible to accurately determine the possibility of fraudulent remittance.

[0063] The fraudulent remittance prevention system can further include an emotion analysis unit that analyzes the emotional state of the remitter. The emotion analysis unit, for example, analyzes the remitter's tone of voice and choice of words to detect an abnormal emotional state. For example, it can issue a warning if the remitter appears more nervous or impatient than usual. The emotion analysis unit can also analyze the remitter's facial expressions to detect an abnormal emotional state. In this way, by analyzing the remitter's emotional state, it is possible to accurately determine the possibility of fraudulent remittance.

[0064] The fraudulent remittance prevention system can further include a pattern analysis unit that analyzes the remitter's past remittance patterns. The pattern analysis unit, for example, analyzes the remitter's past remittance history and detects abnormalities if there is a deviation from the normal pattern. For example, it can issue a warning if the remitter is sending a large amount of money to a recipient to whom the remitter normally does not send money. The pattern analysis unit can also analyze the remitter's past remittance frequency and remittance amount to detect abnormal patterns. In this way, by analyzing the remitter's past remittance patterns, it is possible to accurately determine the possibility of fraudulent remittances.

[0065] The fraudulent remittance prevention system can also monitor the sender's emotional state in real time to detect abnormalities throughout the remittance process. For example, it can monitor the sender's facial expressions and tone of voice in real time and issue a warning if an abnormal emotional state persists. It can also monitor the sender's typing speed and mouse movements in real time and issue a warning if abnormal behavior persists. In this way, by monitoring the sender's emotional state in real time throughout the remittance process, it is possible to accurately determine the possibility of fraudulent remittances.

[0066] The fraudulent remittance prevention system can further include a network analysis unit that analyzes the remitter's social network. The network analysis unit, for example, analyzes the remitter's social media friendships and followers and detects anomalies if they deviate from the normal network. For example, it can issue a warning if the remitter is sending money to someone with whom the remitter does not normally interact. The network analysis unit can also analyze the remitter's social media activity history and detect suspicious activity. In this way, by analyzing the remitter's social network, it is possible to accurately determine the possibility of fraudulent remittances.

[0067] The fraudulent remittance prevention system can also analyze the sender's emotional state and provide appropriate feedback at each step of the remittance process. For example, if the sender is nervous, advice to relax can be displayed. Alternatively, if the sender is impatient, the system can pause the remittance and display a message urging them to reconfirm. In this way, by analyzing the sender's emotional state and providing appropriate feedback at each step of the remittance process, the risk of fraudulent remittances can be reduced.

[0068] The fraudulent remittance prevention system can further include a security analysis unit that analyzes the security status of the remitter's device. The security analysis unit can, for example, check whether the latest security patches have been applied to the remitter's device and issue a warning if they have not. The security analysis unit can also analyze applications installed on the remitter's device and issue a warning if a suspicious application has been installed. In this way, by analyzing the security status of the remitter's device, the risk of fraudulent remittance can be reduced.

[0069] The processing flow of the second embodiment will be briefly explained below.

[0070] Step 1: The fraudulent remittance detection unit analyzes the remittance request and determines whether it is fraudulent. For example, it analyzes the recipient's information, the remittance amount, and past transaction history to see if it matches any fraudulent patterns. It can also detect abnormal patterns by taking into account the recipient's geographical information and time of day. It can also analyze the recipient's social media activity to determine whether it is fraudulent. Step 2: The warning display unit displays a pop-up warning if the fraudulent remittance detection unit determines that a remittance may be fraudulent. For example, it displays a message such as "This remittance may be fraudulent. Do you want to continue with the remittance?", asking the user for confirmation. It can also show specific examples of past fraudulent remittances to make the user aware of specific risks. It can also display detailed information about the remittance recipient (address and contact information) so that the user can check it. Step 3: After the warning display unit displays a warning, the police cooperation unit notifies the police as necessary. For example, it can automatically notify the police of details of the fraudulent transfer, enabling a prompt response.

[0071] 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.

[0072] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0073] 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.

[0074] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0075] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0076] 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.

[0077] 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.

[0078] 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.

[0079] 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).

[0080] 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.

[0081] 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.

[0082] 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.

[0083] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0084] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0085] 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.

[0086] 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.

[0087] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0088] 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.

[0089] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0090] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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).

[0095] 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.

[0096] 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.

[0097] 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.

[0098] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0099] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0100] 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.

[0101] 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.

[0102] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0103] 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.

[0104] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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).

[0110] 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.

[0111] 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.

[0112] 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.

[0113] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0114] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0115] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0116] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0117] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0118] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0119] The data processing system 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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."

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0138] 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 fraudulent remittance detection unit that analyzes remittance requests and determines the possibility of fraudulent remittance; a warning display unit that displays a warning in a pop-up format when the fraudulent remittance detection unit determines that there is a possibility of fraudulent remittance; and a police cooperation unit that notifies the police as necessary after the warning is displayed by the warning display unit. A system characterized by:

2. The fraudulent remittance detection unit Analyze recipient information, transfer amount, and past transaction history to see if they match any fraudulent patterns 2. The system of claim 1.

3. The warning display unit When the pop-up is displayed, specific examples of past fraudulent remittances are shown to make the user aware of the specific risks.

2. The system of claim 1.

4. The warning display unit When the popup is displayed, the recipient's past transaction history and reputation will be displayed, allowing users to make a decision.

2. The system of claim 1.

5. The fraudulent remittance detection unit Analyzing the emotional state of the remitter and increasing the likelihood of fraud if unusual stress or impatience is observed 2. The system of claim 1.

6. The warning display unit Analyze the user's emotional state and adjust the content and tone of the warning message 2. The system of claim 1.

7. The warning display unit Monitor the user's emotional state in real time and optimize the timing of warning messages.

2. The system of claim 1.

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