system

Abstract

The system according to the embodiment aims to provide an effective countermeasure against fraudulent emails. [Solution] A system according to an embodiment includes an analysis unit, a determination unit, a provision unit, and a transmission unit. The analysis unit analyzes the content of the email or the sender information. The determination unit determines whether the email is a fraudulent email based on the information analyzed by the analysis unit. The provision unit provides false personal information if the determination unit determines that the email is a fraudulent email. The transmission unit sends a request to the attacker's server if the determination unit determines that the email is a fraudulent email.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem that they are inadequate in their countermeasures against fraudulent emails and lack effective countermeasures against attackers.

[0005] The system according to the embodiment aims to provide an effective countermeasure against fraudulent emails. [Means for solving the problem]

[0006] The system according to the embodiment includes an analysis unit, a determination unit, a provision unit, and a transmission unit. The analysis unit analyzes the content of the email or the sender information. The determination unit determines whether the email is a fraudulent email based on the information analyzed by the analysis unit. The provision unit provides false personal information if the determination unit determines that the email is a fraudulent email. The transmission unit sends a request to the attacker's server if the determination unit determines that the email is a fraudulent email. [Effects of the Invention]

[0007] The system according to the embodiment can provide an effective countermeasure against fraudulent emails. [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) An anti-fraud system according to an embodiment of the present invention develops an AI that intentionally falls for fraudulent emails, thereby putting attackers at a disadvantage and eradicating fraud. The anti-fraud system analyzes emails received by the AI ​​and determines whether they are fraudulent. If a fraudulent email is determined to be fraudulent, the AI ​​intentionally falls for the email and takes actions that disadvantage the attacker. For example, it may provide false personal information or send requests that overload the attacker's server. This exhausts the attacker's resources and neutralizes the fraudulent activity. For example, the anti-fraud system analyzes emails received by the AI ​​and determines whether they are fraudulent. In this case, the AI ​​performs a detailed analysis of the email content and sender information to identify emails with fraudulent characteristics. For example, it references databases of fraudulent methods, such as phishing scams and spam emails, to accurately identify fraudulent emails. Next, if the anti-fraud system determines that an email is fraudulent, the AI ​​intentionally falls for the email. Specifically, it may provide false personal information or send requests that overload the attacker's server. For example, by entering fake credit card information or passwords, attackers are provided with meaningless data, neutralizing fraudulent activities. Furthermore, by sending a large number of requests to the attacker's server, the fraud prevention system exhausts the server's resources and disrupts the attacker's activities. Furthermore, the fraud prevention system collects information about fraudulent emails and stores it in a database. This enables it to accurately identify similar fraudulent emails in the future. For example, by analyzing the sender and content patterns of fraudulent emails, it is possible to take measures against new fraudulent methods. This is expected to eradicate fraudulent activities by reducing the risk of falling for fraudulent emails and imposing penalties on attackers. By reducing the risk of falling for fraudulent emails and imposing penalties on attackers, the fraud prevention system can eradicate fraudulent activities. For example, it can provide an environment where companies and individuals can safely use email without falling for fraudulent emails. Furthermore, by exhausting attackers' resources, it increases the cost of fraudulent activities and reduces their motivation.

[0029] The fraud prevention system according to the embodiment includes an analysis unit, a determination unit, a providing unit, and a sending unit. The analysis unit analyzes the content of an email or sender information. For example, the analysis unit analyzes the content of the email using text analysis technology. The analysis unit can also analyze sender information using pattern recognition technology. The analysis unit can also refer to a database of fraudulent methods to identify emails with characteristics of fraudulent emails. For example, the analysis unit identifies emails with characteristics of phishing scams or spam emails. The determination unit determines whether the email is fraudulent based on the information analyzed by the analysis unit. The determination unit determines whether the email is fraudulent based on, for example, the content of the email or sender information provided by the analysis unit. The determination unit can also use AI to accurately identify emails with characteristics of fraudulent emails. For example, the determination unit uses an AI model to accurately identify emails with characteristics of fraudulent emails. The providing unit provides fake personal information when the determination unit determines the email to be fraudulent. The providing unit provides, for example, fake credit card information or password. The providing unit can also use AI to provide the fake personal information. For example, the providing unit generates fake personal information using an AI model. The sending unit sends a request to the attacker's server when the determination unit determines that the email is a fraudulent email. The sending unit, for example, sends a large number of requests to the attacker's server. The sending unit can also use AI to send requests to the attacker's server. For example, the sending unit uses an AI model to send requests to the attacker's server. This allows the fraud prevention system according to the embodiment to effectively take measures against fraudulent emails and disadvantage the attacker.

[0030] The analysis unit can perform a detailed analysis of the email content or sender information to identify emails that have characteristics of fraud. The analysis unit can perform a detailed analysis of the email content using, for example, keyword extraction technology. For example, the analysis unit can extract specific keywords from the email content to identify emails that have characteristics of fraud. The analysis unit can also perform a detailed analysis of the email content using grammatical analysis technology. For example, the analysis unit can analyze the grammatical structure of the email to identify emails that have characteristics of fraud. The analysis unit can also perform a detailed analysis of sender information using pattern recognition technology. For example, the analysis unit can analyze the sender's IP address or domain information to identify emails that have characteristics of fraud. This improves the accuracy of identifying fraudulent emails. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the email content and sender information into an AI model and have the AI ​​identify emails that have characteristics of fraud.

[0031] The providing unit can provide fake credit card information or passwords. The providing unit provides, for example, fake credit card information. For example, the providing unit generates fake card numbers and fake expiration dates and provides them to an attacker. The providing unit can also provide fake passwords. For example, the providing unit generates fake passwords with random character strings or specific patterns and provides them to an attacker. The providing unit can also use AI to provide fake personal information. For example, the providing unit generates fake credit card information or fake passwords using an AI model. This provides meaningless data to an attacker, neutralizing fraudulent activity. Some or all of the above-described processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can cause AI to generate fake personal information.

[0032] The sending unit can send multiple requests to the attacker's server. For example, the sending unit can send consecutive requests to the attacker's server. For example, the sending unit can send consecutive requests at regular intervals to overload the attacker's server. The sending unit can also send parallel requests to the attacker's server. For example, the sending unit can send multiple requests simultaneously to overload the attacker's server. The sending unit can also use AI to send requests to the attacker's server. For example, the sending unit can use an AI model to send requests to the attacker's server. This exhausts the resources of the attacker's server and disrupts the attacker's activities. Some or all of the above-mentioned processing in the sending unit can be performed using AI, for example, or can be performed without using AI. For example, the sending unit can cause AI to generate requests to be sent to the attacker's server.

[0033] The analysis unit can collect information about fraudulent emails and store it in a database. For example, the analysis unit collects information about the senders of fraudulent emails and stores it in a database. For example, the analysis unit collects IP address and domain information about the senders of fraudulent emails and stores it in a database. The analysis unit can also collect content information about fraudulent emails and store it in a database. For example, the analysis unit collects the body text and contents of attachments of fraudulent emails and stores it in a database. The analysis unit can also use AI to collect information about fraudulent emails and store it in a database. For example, the analysis unit uses an AI model to collect information about fraudulent emails and store it in a database. This makes it possible to identify similar fraudulent emails with high accuracy in the future. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can have AI collect information about fraudulent emails.

[0034] The determination unit can analyze the sender and content patterns of fraudulent emails and take measures against new fraudulent methods. The determination unit, for example, analyzes the sender patterns of fraudulent emails. For example, the determination unit analyzes the patterns of the sender's IP address and domain information and takes measures against new fraudulent methods. The determination unit can also analyze the content patterns of fraudulent emails. For example, the determination unit analyzes the content patterns of the body and attachments of fraudulent emails and takes measures against new fraudulent methods. The determination unit can also use AI to analyze the patterns of fraudulent emails. For example, the determination unit uses an AI model to analyze the sender and content patterns of fraudulent emails and take measures against new fraudulent methods. This makes it possible to take measures against new fraudulent methods. Some or all of the above-mentioned processing in the determination unit may be performed using AI, for example, or may be performed without using AI. For example, the determination unit can have AI perform pattern analysis of fraudulent emails.

[0035] When analyzing emails, the analysis unit can improve the accuracy of the analysis by referring to patterns of past fraudulent emails. For example, the analysis unit can refer to a database of fraudulent emails detected in the past and identify emails with similar patterns. For example, the analysis unit can learn the characteristics of past fraudulent emails and extract the characteristics of new fraudulent emails. The analysis unit can also refer to sender information of past fraudulent emails and prioritize analysis of emails from the same sender. For example, the analysis unit can identify emails that are likely to be fraudulent based on sender information of past fraudulent emails. In this way, by referring to patterns of past fraudulent emails, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can have AI learn the patterns of past fraudulent emails to improve the accuracy of the analysis.

[0036] When analyzing emails, the analysis unit can take into account the geographical information of the sender. For example, the analysis unit obtains geographical information from the sender's IP address and prioritizes analysis of emails from specific regions. For example, the analysis unit identifies emails from regions with a high probability of being fraudulent based on the geographical information of the sender. The analysis unit can also refer to the geographical information of the sender and issue a warning about emails from abnormal regions. For example, the analysis unit identifies emails from abnormal regions based on the geographical information of the sender and issues a warning. By taking the geographical information of the sender into consideration, the accuracy of identifying fraudulent emails is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can have AI analyze the geographical information of the sender to improve the accuracy of identifying fraudulent emails.

[0037] When analyzing emails, the analysis unit can take into account the language and cultural background of the email content. The analysis unit, for example, automatically detects the language of the email and analyzes fraud patterns specific to that language. For example, the analysis unit evaluates the likelihood of fraud by considering the cultural background contained in the email content. The analysis unit can also identify fraudulent methods based on the language and cultural background of the email. For example, the analysis unit identifies emails with fraudulent characteristics based on the language and cultural background of the email. This improves the accuracy of identifying fraudulent emails by taking into account the language and cultural background of the email content. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can use AI to analyze the language and cultural background of the email content to improve the accuracy of identifying fraudulent emails.

[0038] When analyzing emails, the analysis unit can improve the accuracy of the analysis by referring to the user's past email history. The analysis unit, for example, analyzes the user's past email history to identify similar fraudulent emails. For example, the analysis unit prioritizes analyzing emails that are likely to be fraudulent from the user's past email history. The analysis unit can also extract characteristics of fraudulent emails based on the user's past email history. For example, the analysis unit identifies emails with fraudulent characteristics based on the user's past email history. In this way, by referring to the user's past email history, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can have AI analyze the user's past email history to improve the accuracy of the analysis.

[0039] When analyzing emails, the analysis unit can analyze the sender's IP address. For example, the analysis unit can identify emails from regions where there is a high possibility of fraud based on the sender's IP address. For example, the analysis unit can refer to the sender's IP address and warn of emails from abnormal regions. The analysis unit can also analyze the sender's IP address to identify emails that have characteristics of fraud. For example, the analysis unit can identify emails that have characteristics of fraud based on the sender's IP address. By analyzing the sender's IP address, the accuracy of identifying fraudulent emails is improved. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can have AI analyze the sender's IP address to improve the accuracy of identifying fraudulent emails.

[0040] When analyzing an email, the analysis unit can analyze the contents of email attachments to identify characteristics of fraud. The analysis unit, for example, automatically analyzes email attachments to identify files that have characteristics of fraud. For example, the analysis unit analyzes the contents of email attachments to evaluate the possibility of fraud. The analysis unit can also identify fraudulent methods based on email attachments. For example, the analysis unit identifies emails that have characteristics of fraud based on the contents of email attachments. In this way, analyzing the contents of email attachments improves the accuracy of identifying fraudulent emails. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can have AI analyze the contents of email attachments to improve the accuracy of identifying fraudulent emails.

[0041] When determining whether an email is fraudulent, the determination unit can improve the accuracy of the determination by referring to past determination results. The determination unit, for example, refers to past determination results to identify similar fraudulent emails. For example, the determination unit determines emails with fraudulent characteristics with high accuracy based on past determination results. The determination unit can also analyze past determination results and take measures against new fraudulent methods. For example, the determination unit takes measures against new fraudulent methods based on past determination results. In this way, by referring to past determination results, the accuracy of the determination is improved. Some or all of the above-mentioned processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can have AI analyze past determination results to improve the accuracy of the determination.

[0042] When determining whether an email is fraudulent, the determination unit can make the determination by taking into account the sender's domain information. For example, the determination unit identifies emails that are likely to be fraudulent based on the sender's domain information. For example, the determination unit references the sender's domain information and warns of emails from abnormal domains. The determination unit can also analyze the sender's domain information to identify emails that have characteristics of fraud. For example, the determination unit identifies emails that have characteristics of fraud based on the sender's domain information. By taking the sender's domain information into consideration, the accuracy of identifying fraudulent emails is improved. Some or all of the above-mentioned processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can have AI analyze the sender's domain information to improve the accuracy of identifying fraudulent emails.

[0043] When determining whether an email is fraudulent, the determination unit can analyze the topics and keywords in the email content to make the determination. The determination unit, for example, evaluates the possibility of fraud based on the topics and keywords contained in the email content. For example, the determination unit analyzes the email content and identifies topics and keywords that are characteristic of fraud. The determination unit can also determine whether an email is fraudulent with high accuracy by referring to the topics and keywords in the email content. For example, the determination unit identifies emails that are characteristic of fraud based on the topics and keywords in the email content. In this way, analyzing the topics and keywords in the email content improves the accuracy of identifying fraudulent emails. Some or all of the above-mentioned processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can have AI analyze the topics and keywords in the email content to improve the accuracy of identifying fraudulent emails.

[0044] When determining whether an email is fraudulent, the determination unit can improve the accuracy of the determination by referring to the user's past email exchanges. The determination unit, for example, analyzes the user's past email exchanges and identifies similar fraudulent emails. For example, the determination unit prioritizes determining emails that are likely to be fraudulent based on the user's past email exchanges. The determination unit can also extract characteristics of fraudulent emails based on the user's past email exchanges. For example, the determination unit identifies emails that have characteristics of fraud based on the user's past email exchanges. This improves the accuracy of the determination by referring to the user's past email exchanges. Some or all of the above-described processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can have AI analyze the user's past email exchanges to improve the accuracy of the determination.

[0045] When determining whether an email is fraudulent, the determination unit can evaluate the reliability of the sender's email server and make the determination. The determination unit, for example, identifies emails that are likely to be fraudulent based on the reliability of the sender's email server. For example, the determination unit references the reliability of the sender's email server and warns of emails from abnormal servers. The determination unit can also evaluate the reliability of the sender's email server and identify emails that have characteristics of fraud. For example, the determination unit identifies emails that have characteristics of fraud based on the reliability of the sender's email server. This improves the accuracy of identifying fraudulent emails by evaluating the reliability of the sender's email server. Some or all of the above-described processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can have AI evaluate the reliability of the sender's email server to improve the accuracy of identifying fraudulent emails.

[0046] When determining whether an email is fraudulent, the determination unit can analyze the format and style of the email content to make the determination. The determination unit, for example, evaluates the possibility of fraud based on the format and style of the email content. For example, the determination unit analyzes the email content and identifies formats and styles that are characteristic of fraud. The determination unit can also refer to the format and style of the email content to determine whether an email is fraudulent with high accuracy. For example, the determination unit identifies emails that are characteristic of fraud based on the format and style of the email content. In this way, analyzing the format and style of the email content improves the accuracy of identifying fraudulent emails. Some or all of the above-mentioned processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can have AI analyze the format and style of the email content to improve the accuracy of identifying fraudulent emails.

[0047] When providing false information, the providing unit can improve the accuracy of the provision by referring to past provision results. For example, the providing unit refers to past provision results and provides false information that is optimal for similar fraudulent emails. For example, the providing unit adjusts the content of the false information based on past provision results to improve accuracy. The providing unit can also analyze past provision results and provide false information that is optimal for new fraudulent methods. For example, the providing unit provides false information that is optimal for new fraudulent methods based on past provision results. In this way, by referring to past provision results, the accuracy of providing false information is improved. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can have AI analyze past provision results to improve the accuracy of providing false information.

[0048] When providing false information, the providing unit can select information to provide by analyzing the behavioral patterns of the attacker. The providing unit, for example, provides optimal false information based on the behavioral patterns of the attacker. For example, the providing unit references the attacker's past behavioral patterns to provide effective false information. The providing unit can also analyze the attacker's behavioral patterns to provide optimal false information for new fraud methods. For example, the providing unit provides optimal false information based on the attacker's behavioral patterns. In this way, effective false information can be provided by analyzing the attacker's behavioral patterns. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can have AI analyze the attacker's behavioral patterns to improve the accuracy of providing false information.

[0049] The providing unit can adjust the form or format of the information to be provided when providing false information. For example, the providing unit adjusts the form of the false information to be provided to match the attacker's expectations. For example, the providing unit optimizes the format of the false information to be provided based on the attacker's past behavioral patterns. The providing unit can also adjust the content of the false information to be provided in a way that goes against the attacker's expectations, thereby causing confusion. For example, the providing unit adjusts the content of the false information to be provided in a way that goes against the attacker's expectations, thereby causing confusion. In this way, adjusting the form or format of the information to be provided improves the effectiveness against the attacker. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can cause AI to adjust the form or format of the false information to be provided.

[0050] When providing false information, the providing unit can select information to provide by taking into consideration the geographical information of the attacker. The providing unit, for example, provides optimal false information based on the geographical information of the attacker. For example, the providing unit references the geographical information of the attacker and provides false information specific to a region. The providing unit can also analyze the geographical information of the attacker to provide effective false information. For example, the providing unit provides optimal false information based on the geographical information of the attacker. In this way, effective false information can be provided by taking the geographical information of the attacker into consideration. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can have AI analyze the geographical information of the attacker to improve the accuracy of providing false information.

[0051] When providing false information, the providing unit can customize the information to be provided by referring to the attacker's past behavioral history. The providing unit, for example, provides optimal false information based on the attacker's past behavioral history. For example, the providing unit refers to the attacker's past behavioral history to provide effective false information. The providing unit can also analyze the attacker's past behavioral history to provide optimal false information for new fraud methods. For example, the providing unit provides optimal false information based on the attacker's past behavioral history. In this way, effective false information can be provided by referring to the attacker's past behavioral history. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can have AI analyze the attacker's past behavioral history to improve the accuracy of providing false information.

[0052] When providing false information, the providing unit can adjust the language and cultural background of the information to be provided. For example, the providing unit adjusts the language of the false information to match the attacker's native language. For example, the providing unit optimizes the cultural background of the false information to match the attacker's expectations. The providing unit can also adjust the content of the false information to be contrary to the attacker's cultural background, thereby causing confusion. For example, the providing unit adjusts the content of the false information to be contrary to the attacker's cultural background, thereby causing confusion. In this way, by taking the language and cultural background of the information to be provided into consideration, effective false information can be provided. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can cause AI to adjust the language and cultural background of the false information to be provided.

[0053] When sending a request, the sending unit can improve the accuracy of the sending by referring to past sending results. For example, the sending unit refers to past sending results and sends an optimal request for similar fraudulent emails. For example, the sending unit adjusts the content of the request based on past sending results to improve accuracy. The sending unit can also analyze past sending results and send an optimal request for a new fraudulent method. For example, the sending unit sends an optimal request for a new fraudulent method based on past sending results. In this way, the accuracy of sending the request is improved by referring to past sending results. Some or all of the above-mentioned processing in the sending unit may be performed using, for example, AI, or may be performed without using AI. For example, the sending unit can have AI analyze past sending results to improve the accuracy of sending the request.

[0054] When transmitting a request, the transmission unit can analyze the load status of the attacker's server and adjust the timing of transmission. The transmission unit, for example, transmits the request at the optimal timing based on the load status of the attacker's server. For example, the transmission unit refers to the load status of the attacker's server and transmits the request at an effective timing. The transmission unit can also analyze the load status of the attacker's server and optimize the timing of transmitting the request. For example, the transmission unit transmits the request at the optimal timing based on the load status of the attacker's server. In this way, by analyzing the load status of the attacker's server, it is possible to provide an effective timing for transmitting the request. Some or all of the above-described processing in the transmission unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmission unit can have AI analyze the load status of the attacker's server and optimize the timing of transmitting the request.

[0055] The transmitting unit can adjust the content and format of the request to be sent when sending the request. For example, the transmitting unit adjusts the content of the request to be sent to match the expectations of the attacker. For example, the transmitting unit optimizes the format of the request to be sent based on the attacker's past behavioral patterns. The transmitting unit can also adjust the content of the request to be sent in a manner that goes against the attacker's expectations, causing confusion. For example, the transmitting unit adjusts the content of the request to be sent in a manner that goes against the attacker's expectations, causing confusion. In this way, by adjusting the content and format of the request to be sent, an effective request can be sent. Some or all of the above-mentioned processing in the transmitting unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmitting unit can cause AI to adjust the content and format of the request to be sent.

[0056] When transmitting a request, the transmission unit can transmit the request while taking into account the geographical information of the attacker's server. The transmission unit, for example, transmits an optimal request based on the geographical information of the attacker's server. For example, the transmission unit references the geographical information of the attacker's server and transmits a region-specific request. The transmission unit can also analyze the geographical information of the attacker's server and transmit an effective request. For example, the transmission unit transmits an optimal request based on the geographical information of the attacker's server. In this way, an effective request can be transmitted by taking into account the geographical information of the attacker's server. Some or all of the above-described processing in the transmission unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmission unit can have AI analyze the geographical information of the attacker's server to improve the accuracy of request transmission.

[0057] When transmitting a request, the transmission unit can analyze the security measures of the attacker's server and select a transmission method. The transmission unit, for example, selects the optimal transmission method based on the security measures of the attacker's server. For example, the transmission unit refers to the security measures of the attacker's server and selects an effective transmission method. The transmission unit can also analyze the security measures of the attacker's server and optimize the transmission method. For example, the transmission unit selects the optimal transmission method based on the security measures of the attacker's server. In this way, an effective transmission method can be provided by analyzing the security measures of the attacker's server. Some or all of the above-described processing in the transmission unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmission unit can have AI analyze the security measures of the attacker's server and optimize the transmission method.

[0058] When transmitting a request, the transmission unit can customize the content of the request to be transmitted based on the behavioral patterns of the attacker. The transmission unit, for example, transmits an optimal request based on the behavioral patterns of the attacker. For example, the transmission unit refers to the attacker's past behavioral patterns and transmits an effective request. The transmission unit can also analyze the attacker's behavioral patterns and transmit an optimal request for a new fraud scheme. For example, the transmission unit transmits an optimal request based on the attacker's behavioral patterns. In this way, an effective request can be transmitted by customizing the request based on the attacker's behavioral patterns. Some or all of the above-described processing in the transmission unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmission unit can have AI analyze the attacker's behavioral patterns to improve the accuracy of request transmission.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The analysis unit can also take into account the time the email was sent when analyzing the content of the email. For example, the analysis unit may prioritize analyzing emails sent late at night or early in the morning and determine that they are more likely to be fraudulent. The analysis unit can also perform detailed analysis of emails sent on specific days of the week or on holidays. For example, the analysis unit may identify emails sent on weekends or holidays and assess the risk of fraud. Furthermore, the analysis unit can classify the content of emails based on the time they were sent and identify emails that have the characteristics of fraud. By taking the time the email was sent into account, the accuracy of identifying fraudulent emails is improved.

[0061] When determining whether an email is fraudulent, the determination unit can improve the accuracy of the determination by referring to the sender's past behavioral history. For example, the determination unit analyzes the sender's past email sending history to identify senders who are likely to be fraudulent. The determination unit can also accurately determine emails that have the characteristics of fraudulent emails based on the sender's past behavioral patterns. Furthermore, the determination unit can also refer to the sender's past behavioral history and take measures against new fraudulent methods. In this way, by referring to the sender's past behavioral history, the accuracy of the determination is improved.

[0062] When transmitting a request, the transmission unit can adjust the timing of transmission by taking into account the response time of the attacker's server. For example, the transmission unit transmits the request at the optimal timing based on the response time of the attacker's server. The transmission unit can also transmit the request at an effective timing by referring to the response time of the attacker's server. Furthermore, the transmission unit can analyze the response time of the attacker's server and optimize the timing of transmitting the request. In this way, it is possible to provide an effective timing for transmitting the request by taking into account the response time of the attacker's server.

[0063] When providing false information, the providing unit can select the information to be provided taking into consideration the type of device used by the attacker. For example, if the attacker is using a smartphone, the providing unit can provide false information for mobile devices. Also, if the attacker is using a desktop computer, the providing unit can provide false information for desktop computers. Furthermore, if the attacker is using a tablet computer, the providing unit can provide false information for tablets. This makes it possible to provide effective false information by providing false information according to the device used by the attacker.

[0064] When sending a request, the sending unit can adjust the sending method by taking into account the network bandwidth of the attacker's server. For example, the sending unit sends an optimal request based on the network bandwidth of the attacker's server. The sending unit can also refer to the network bandwidth of the attacker's server and send an effective request. Furthermore, the sending unit can analyze the network bandwidth of the attacker's server and optimize the request sending method. In this way, an effective request can be sent by taking into account the network bandwidth of the attacker's server.

[0065] When determining whether an email is fraudulent, the determination unit can analyze images and videos in the email content to make the determination. For example, the determination unit evaluates the possibility of fraud based on the content of images and videos attached to the email. The determination unit can also refer to the images and videos in the email content to determine whether the email is fraudulent with high accuracy. Furthermore, the determination unit can identify emails that have fraudulent characteristics based on the images and videos in the email content. This improves the accuracy of identifying fraudulent emails by analyzing the images and videos in the email content.

[0066] The processing flow of the first embodiment will be briefly explained below.

[0067] Step 1: The analysis unit analyzes the content of the email or the sender information. For example, the analysis unit can analyze the content of the email using text analysis technology and analyze the sender information using pattern recognition technology. It can also refer to a database of fraudulent methods to identify emails that have the characteristics of fraudulent emails. For example, it can identify emails that have the characteristics of phishing scams or spam emails. Step 2: The determination unit determines whether the email is fraudulent based on the information analyzed by the analysis unit. The determination unit determines whether the email is fraudulent, for example, based on the content of the email and sender information provided by the analysis unit. AI can also be used to accurately determine whether emails have the characteristics of fraudulent emails. For example, an AI model can be used to accurately determine whether emails have the characteristics of fraudulent emails. Step 3: The providing unit provides false personal information if the determining unit determines that the email is a fraudulent email. For example, the providing unit provides false credit card information or a password. AI can also be used to provide the false personal information. For example, an AI model can be used to generate false personal information. Step 4: If the determination unit determines that the email is a fraudulent email, the sending unit sends a request to the attacker's server. For example, the sending unit sends a large number of requests to the attacker's server. AI can also be used to send requests to the attacker's server. For example, an AI model is used to send requests to the attacker's server.

[0068] (Example 2) An anti-fraud system according to an embodiment of the present invention develops an AI that intentionally falls for fraudulent emails, thereby putting attackers at a disadvantage and eradicating fraud. The anti-fraud system analyzes emails received by the AI ​​and determines whether they are fraudulent. If a fraudulent email is determined to be fraudulent, the AI ​​intentionally falls for the email and takes actions that disadvantage the attacker. For example, it may provide false personal information or send requests that overload the attacker's server. This exhausts the attacker's resources and neutralizes the fraudulent activity. For example, the anti-fraud system analyzes emails received by the AI ​​and determines whether they are fraudulent. In this case, the AI ​​performs a detailed analysis of the email content and sender information to identify emails with fraudulent characteristics. For example, it references databases of fraudulent methods, such as phishing scams and spam emails, to accurately identify fraudulent emails. Next, if the anti-fraud system determines that an email is fraudulent, the AI ​​intentionally falls for the email. Specifically, it may provide false personal information or send requests that overload the attacker's server. For example, by entering fake credit card information or passwords, attackers are provided with meaningless data, neutralizing fraudulent activities. Furthermore, by sending a large number of requests to the attacker's server, the fraud prevention system exhausts the server's resources and disrupts the attacker's activities. Furthermore, the fraud prevention system collects information about fraudulent emails and stores it in a database. This enables it to accurately identify similar fraudulent emails in the future. For example, by analyzing the sender and content patterns of fraudulent emails, it is possible to take measures against new fraudulent methods. This is expected to eradicate fraudulent activities by reducing the risk of falling for fraudulent emails and imposing penalties on attackers. By reducing the risk of falling for fraudulent emails and imposing penalties on attackers, the fraud prevention system can eradicate fraudulent activities. For example, it can provide an environment where companies and individuals can safely use email without falling for fraudulent emails. Furthermore, by exhausting attackers' resources, it increases the cost of fraudulent activities and reduces their motivation.

[0069] The fraud prevention system according to the embodiment includes an analysis unit, a determination unit, a providing unit, and a sending unit. The analysis unit analyzes the content of an email or sender information. For example, the analysis unit analyzes the content of the email using text analysis technology. The analysis unit can also analyze sender information using pattern recognition technology. The analysis unit can also refer to a database of fraudulent methods to identify emails with characteristics of fraudulent emails. For example, the analysis unit identifies emails with characteristics of phishing scams or spam emails. The determination unit determines whether the email is fraudulent based on the information analyzed by the analysis unit. The determination unit determines whether the email is fraudulent based on, for example, the content of the email or sender information provided by the analysis unit. The determination unit can also use AI to accurately identify emails with characteristics of fraudulent emails. For example, the determination unit uses an AI model to accurately identify emails with characteristics of fraudulent emails. The providing unit provides fake personal information when the determination unit determines the email to be fraudulent. The providing unit provides, for example, fake credit card information or password. The providing unit can also use AI to provide the fake personal information. For example, the providing unit generates fake personal information using an AI model. The sending unit sends a request to the attacker's server when the determination unit determines that the email is a fraudulent email. The sending unit, for example, sends a large number of requests to the attacker's server. The sending unit can also use AI to send requests to the attacker's server. For example, the sending unit uses an AI model to send requests to the attacker's server. This allows the fraud prevention system according to the embodiment to effectively take measures against fraudulent emails and disadvantage the attacker.

[0070] The analysis unit can perform a detailed analysis of the email content or sender information to identify emails that have characteristics of fraud. The analysis unit can perform a detailed analysis of the email content using, for example, keyword extraction technology. For example, the analysis unit can extract specific keywords from the email content to identify emails that have characteristics of fraud. The analysis unit can also perform a detailed analysis of the email content using grammatical analysis technology. For example, the analysis unit can analyze the grammatical structure of the email to identify emails that have characteristics of fraud. The analysis unit can also perform a detailed analysis of sender information using pattern recognition technology. For example, the analysis unit can analyze the sender's IP address or domain information to identify emails that have characteristics of fraud. This improves the accuracy of identifying fraudulent emails. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the email content and sender information into an AI model and have the AI ​​identify emails that have characteristics of fraud.

[0071] The providing unit can provide fake credit card information or passwords. The providing unit provides, for example, fake credit card information. For example, the providing unit generates fake card numbers and fake expiration dates and provides them to an attacker. The providing unit can also provide fake passwords. For example, the providing unit generates fake passwords with random character strings or specific patterns and provides them to an attacker. The providing unit can also use AI to provide fake personal information. For example, the providing unit generates fake credit card information or fake passwords using an AI model. This provides meaningless data to an attacker, neutralizing fraudulent activity. Some or all of the above-described processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can cause AI to generate fake personal information.

[0072] The sending unit can send multiple requests to the attacker's server. For example, the sending unit can send consecutive requests to the attacker's server. For example, the sending unit can send consecutive requests at regular intervals to overload the attacker's server. The sending unit can also send parallel requests to the attacker's server. For example, the sending unit can send multiple requests simultaneously to overload the attacker's server. The sending unit can also use AI to send requests to the attacker's server. For example, the sending unit can use an AI model to send requests to the attacker's server. This exhausts the resources of the attacker's server and disrupts the attacker's activities. Some or all of the above-mentioned processing in the sending unit can be performed using AI, for example, or can be performed without using AI. For example, the sending unit can cause AI to generate requests to be sent to the attacker's server.

[0073] The analysis unit can collect information about fraudulent emails and store it in a database. For example, the analysis unit collects information about the senders of fraudulent emails and stores it in a database. For example, the analysis unit collects IP address and domain information about the senders of fraudulent emails and stores it in a database. The analysis unit can also collect content information about fraudulent emails and store it in a database. For example, the analysis unit collects the body text and contents of attachments of fraudulent emails and stores it in a database. The analysis unit can also use AI to collect information about fraudulent emails and store it in a database. For example, the analysis unit uses an AI model to collect information about fraudulent emails and store it in a database. This makes it possible to identify similar fraudulent emails with high accuracy in the future. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can have AI collect information about fraudulent emails.

[0074] The determination unit can analyze the sender and content patterns of fraudulent emails and take measures against new fraudulent methods. The determination unit, for example, analyzes the sender patterns of fraudulent emails. For example, the determination unit analyzes the patterns of the sender's IP address and domain information and takes measures against new fraudulent methods. The determination unit can also analyze the content patterns of fraudulent emails. For example, the determination unit analyzes the content patterns of the body and attachments of fraudulent emails and takes measures against new fraudulent methods. The determination unit can also use AI to analyze the patterns of fraudulent emails. For example, the determination unit uses an AI model to analyze the sender and content patterns of fraudulent emails and take measures against new fraudulent methods. This makes it possible to take measures against new fraudulent methods. Some or all of the above-mentioned processing in the determination unit may be performed using AI, for example, or may be performed without using AI. For example, the determination unit can have AI perform pattern analysis of fraudulent emails.

[0075] The analysis unit can estimate the user's emotions and adjust the email analysis method based on the estimated user emotions. The analysis unit, for example, estimates the user's emotions using an emotion analysis algorithm. For example, the analysis unit analyzes the user's facial expressions and voice to estimate emotions. The analysis unit can also estimate the user's emotions using survey results. For example, the analysis unit conducts a survey of the user and estimates emotions based on the results. The analysis unit then adjusts the email analysis method based on the estimated user emotions. For example, if the user is feeling anxious, the analysis unit can perform a detailed analysis to improve analysis accuracy. If the user is relaxed, the analysis unit can prioritize speed and provide results quickly. Furthermore, if the user is in a hurry, the analysis can be focused on important points to quickly identify fraudulent emails. This improves analysis accuracy by providing an analysis method that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generative AI can 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 analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may cause AI to estimate the user's emotions.

[0076] When analyzing emails, the analysis unit can improve the accuracy of the analysis by referring to patterns of past fraudulent emails. For example, the analysis unit can refer to a database of fraudulent emails detected in the past and identify emails with similar patterns. For example, the analysis unit can learn the characteristics of past fraudulent emails and extract the characteristics of new fraudulent emails. The analysis unit can also refer to sender information of past fraudulent emails and prioritize analysis of emails from the same sender. For example, the analysis unit can identify emails that are likely to be fraudulent based on sender information of past fraudulent emails. In this way, by referring to patterns of past fraudulent emails, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can have AI learn the patterns of past fraudulent emails to improve the accuracy of the analysis.

[0077] When analyzing emails, the analysis unit can take into account the geographical information of the sender. For example, the analysis unit obtains geographical information from the sender's IP address and prioritizes analysis of emails from specific regions. For example, the analysis unit identifies emails from regions with a high probability of being fraudulent based on the geographical information of the sender. The analysis unit can also refer to the geographical information of the sender and issue a warning about emails from abnormal regions. For example, the analysis unit identifies emails from abnormal regions based on the geographical information of the sender and issues a warning. By taking the geographical information of the sender into consideration, the accuracy of identifying fraudulent emails is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can have AI analyze the geographical information of the sender to improve the accuracy of identifying fraudulent emails.

[0078] When analyzing emails, the analysis unit can take into account the language and cultural background of the email content. The analysis unit, for example, automatically detects the language of the email and analyzes fraud patterns specific to that language. For example, the analysis unit evaluates the likelihood of fraud by considering the cultural background contained in the email content. The analysis unit can also identify fraudulent methods based on the language and cultural background of the email. For example, the analysis unit identifies emails with fraudulent characteristics based on the language and cultural background of the email. This improves the accuracy of identifying fraudulent emails by taking into account the language and cultural background of the email content. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can use AI to analyze the language and cultural background of the email content to improve the accuracy of identifying fraudulent emails.

[0079] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. The analysis unit, for example, estimates the user's emotions using an emotion analysis algorithm. For example, the analysis unit analyzes the user's facial expressions and voice to estimate emotions. The analysis unit can also estimate the user's emotions using survey results. For example, the analysis unit conducts a survey of the user and estimates emotions based on the results. The analysis unit then adjusts the display method of the analysis results based on the estimated user emotions. For example, if the user is feeling anxious, the analysis unit can provide detailed analysis results to give a sense of security. If the user is relaxed, the analysis unit can provide concise analysis results to quickly convey information. Furthermore, if the user is in a hurry, the analysis unit can provide analysis results that focus on the important points. This allows the user to deepen their understanding by providing a display method of the analysis results that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can 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 analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may cause AI to estimate the user's emotions.

[0080] When analyzing emails, the analysis unit can improve the accuracy of the analysis by referring to the user's past email history. The analysis unit, for example, analyzes the user's past email history to identify similar fraudulent emails. For example, the analysis unit prioritizes analyzing emails that are likely to be fraudulent from the user's past email history. The analysis unit can also extract characteristics of fraudulent emails based on the user's past email history. For example, the analysis unit identifies emails with fraudulent characteristics based on the user's past email history. In this way, by referring to the user's past email history, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can have AI analyze the user's past email history to improve the accuracy of the analysis.

[0081] When analyzing emails, the analysis unit can analyze the sender's IP address. For example, the analysis unit can identify emails from regions where there is a high possibility of fraud based on the sender's IP address. For example, the analysis unit can refer to the sender's IP address and warn of emails from abnormal regions. The analysis unit can also analyze the sender's IP address to identify emails that have characteristics of fraud. For example, the analysis unit can identify emails that have characteristics of fraud based on the sender's IP address. By analyzing the sender's IP address, the accuracy of identifying fraudulent emails is improved. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can have AI analyze the sender's IP address to improve the accuracy of identifying fraudulent emails.

[0082] When analyzing an email, the analysis unit can analyze the contents of email attachments to identify characteristics of fraud. The analysis unit, for example, automatically analyzes email attachments to identify files that have characteristics of fraud. For example, the analysis unit analyzes the contents of email attachments to evaluate the possibility of fraud. The analysis unit can also identify fraudulent methods based on email attachments. For example, the analysis unit identifies emails that have characteristics of fraud based on the contents of email attachments. In this way, analyzing the contents of email attachments improves the accuracy of identifying fraudulent emails. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can have AI analyze the contents of email attachments to improve the accuracy of identifying fraudulent emails.

[0083] The determination unit can estimate the user's emotions and adjust the criteria for determining whether an email is fraudulent based on the estimated user's emotions. The determination unit, for example, estimates the user's emotions using an emotion analysis algorithm. For example, the determination unit analyzes the user's facial expressions and voice to estimate the user's emotions. The determination unit can also estimate the user's emotions using survey results. For example, the determination unit conducts a survey of the user and estimates the user's emotions based on the results. The determination unit then adjusts the criteria for determining whether an email is fraudulent based on the estimated user's emotions. For example, if the user is feeling anxious, strict criteria can be applied to identify fraudulent emails with high accuracy. If the user is relaxed, a quick determination can be made and the results can be provided quickly. Furthermore, if the user is in a hurry, a determination can be made that focuses on important points, allowing for quick identification of fraudulent emails. This improves the accuracy of the determination by providing criteria that correspond to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can 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 determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit may cause AI to estimate the user's emotions.

[0084] When determining whether an email is fraudulent, the determination unit can improve the accuracy of the determination by referring to past determination results. The determination unit, for example, refers to past determination results to identify similar fraudulent emails. For example, the determination unit determines emails with fraudulent characteristics with high accuracy based on past determination results. The determination unit can also analyze past determination results and take measures against new fraudulent methods. For example, the determination unit takes measures against new fraudulent methods based on past determination results. In this way, by referring to past determination results, the accuracy of the determination is improved. Some or all of the above-mentioned processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can have AI analyze past determination results to improve the accuracy of the determination.

[0085] When determining whether an email is fraudulent, the determination unit can make the determination by taking into account the sender's domain information. For example, the determination unit identifies emails that are likely to be fraudulent based on the sender's domain information. For example, the determination unit references the sender's domain information and warns of emails from abnormal domains. The determination unit can also analyze the sender's domain information to identify emails that have characteristics of fraud. For example, the determination unit identifies emails that have characteristics of fraud based on the sender's domain information. By taking the sender's domain information into consideration, the accuracy of identifying fraudulent emails is improved. Some or all of the above-mentioned processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can have AI analyze the sender's domain information to improve the accuracy of identifying fraudulent emails.

[0086] When determining whether an email is fraudulent, the determination unit can analyze the topics and keywords in the email content to make the determination. The determination unit, for example, evaluates the possibility of fraud based on the topics and keywords contained in the email content. For example, the determination unit analyzes the email content and identifies topics and keywords that are characteristic of fraud. The determination unit can also determine whether an email is fraudulent with high accuracy by referring to the topics and keywords in the email content. For example, the determination unit identifies emails that are characteristic of fraud based on the topics and keywords in the email content. In this way, analyzing the topics and keywords in the email content improves the accuracy of identifying fraudulent emails. Some or all of the above-mentioned processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can have AI analyze the topics and keywords in the email content to improve the accuracy of identifying fraudulent emails.

[0087] The determination unit can estimate the user's emotion and adjust the display method of the determination result based on the estimated user's emotion. The determination unit, for example, estimates the user's emotion using an emotion analysis algorithm. For example, the determination unit analyzes the user's facial expressions and voice to estimate the emotion. The determination unit can also estimate the user's emotion using survey results. For example, the determination unit conducts a survey of the user and estimates the emotion based on the results. The determination unit then adjusts the display method of the determination result based on the estimated user's emotion. For example, if the user is feeling anxious, the determination unit can provide a detailed determination result to give a sense of security. If the user is relaxed, the determination unit can provide a concise determination result to quickly convey information. Furthermore, if the user is in a hurry, the determination unit can provide a determination result that focuses on the important points. This allows the user to deepen their understanding by providing a display method of the determination result that corresponds to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can 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 determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit may cause AI to estimate the user's emotions.

[0088] When determining whether an email is fraudulent, the determination unit can improve the accuracy of the determination by referring to the user's past email exchanges. The determination unit, for example, analyzes the user's past email exchanges and identifies similar fraudulent emails. For example, the determination unit prioritizes determining emails that are likely to be fraudulent based on the user's past email exchanges. The determination unit can also extract characteristics of fraudulent emails based on the user's past email exchanges. For example, the determination unit identifies emails that have characteristics of fraud based on the user's past email exchanges. This improves the accuracy of the determination by referring to the user's past email exchanges. Some or all of the above-described processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can have AI analyze the user's past email exchanges to improve the accuracy of the determination.

[0089] When determining whether an email is fraudulent, the determination unit can evaluate the reliability of the sender's email server and make the determination. The determination unit, for example, identifies emails that are likely to be fraudulent based on the reliability of the sender's email server. For example, the determination unit references the reliability of the sender's email server and warns of emails from abnormal servers. The determination unit can also evaluate the reliability of the sender's email server and identify emails that have characteristics of fraud. For example, the determination unit identifies emails that have characteristics of fraud based on the reliability of the sender's email server. This improves the accuracy of identifying fraudulent emails by evaluating the reliability of the sender's email server. Some or all of the above-described processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can have AI evaluate the reliability of the sender's email server to improve the accuracy of identifying fraudulent emails.

[0090] When determining whether an email is fraudulent, the determination unit can analyze the format and style of the email content to make the determination. The determination unit, for example, evaluates the possibility of fraud based on the format and style of the email content. For example, the determination unit analyzes the email content and identifies formats and styles that are characteristic of fraud. The determination unit can also refer to the format and style of the email content to determine whether an email is fraudulent with high accuracy. For example, the determination unit identifies emails that are characteristic of fraud based on the format and style of the email content. In this way, analyzing the format and style of the email content improves the accuracy of identifying fraudulent emails. Some or all of the above-mentioned processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can have AI analyze the format and style of the email content to improve the accuracy of identifying fraudulent emails.

[0091] The providing unit can estimate the user's emotions and adjust the content of the false information to be provided based on the estimated user's emotions. The providing unit, for example, estimates the user's emotions using an emotion analysis algorithm. For example, the providing unit analyzes the user's facial expressions and voice to estimate the user's emotions. The providing unit can also estimate the user's emotions using survey results. For example, the providing unit conducts a survey of the user and estimates the user's emotions based on the results. The providing unit further adjusts the content of the false information to be provided based on the estimated user's emotions. For example, if the user is feeling anxious, detailed false information can be provided to give a sense of security. If the user is relaxed, concise false information can be provided to quickly convey information. Furthermore, if the user is in a hurry, false information that focuses on important points can be provided. This improves the effectiveness against attackers by providing false information that matches the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can 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 providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may cause AI to estimate the user's emotions.

[0092] When providing false information, the providing unit can improve the accuracy of the provision by referring to past provision results. For example, the providing unit refers to past provision results and provides false information that is optimal for similar fraudulent emails. For example, the providing unit adjusts the content of the false information based on past provision results to improve accuracy. The providing unit can also analyze past provision results and provide false information that is optimal for new fraudulent methods. For example, the providing unit provides false information that is optimal for new fraudulent methods based on past provision results. In this way, by referring to past provision results, the accuracy of providing false information is improved. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can have AI analyze past provision results to improve the accuracy of providing false information.

[0093] When providing false information, the providing unit can select information to provide by analyzing the behavioral patterns of the attacker. The providing unit, for example, provides optimal false information based on the behavioral patterns of the attacker. For example, the providing unit references the attacker's past behavioral patterns to provide effective false information. The providing unit can also analyze the attacker's behavioral patterns to provide optimal false information for new fraud methods. For example, the providing unit provides optimal false information based on the attacker's behavioral patterns. In this way, effective false information can be provided by analyzing the attacker's behavioral patterns. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can have AI analyze the attacker's behavioral patterns to improve the accuracy of providing false information.

[0094] The providing unit can adjust the form or format of the information to be provided when providing false information. For example, the providing unit adjusts the form of the false information to be provided to match the attacker's expectations. For example, the providing unit optimizes the format of the false information to be provided based on the attacker's past behavioral patterns. The providing unit can also adjust the content of the false information to be provided in a way that goes against the attacker's expectations, thereby causing confusion. For example, the providing unit adjusts the content of the false information to be provided in a way that goes against the attacker's expectations, thereby causing confusion. In this way, adjusting the form or format of the information to be provided improves the effectiveness against the attacker. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can cause AI to adjust the form or format of the false information to be provided.

[0095] The providing unit can estimate the user's emotions and prioritize the false information to be provided based on the estimated user's emotions. The providing unit, for example, estimates the user's emotions using an emotion analysis algorithm. For example, the providing unit analyzes the user's facial expressions and voice to estimate the user's emotions. The providing unit can also estimate the user's emotions using survey results. For example, the providing unit conducts a survey of the user and estimates the user's emotions based on the results. The providing unit then prioritizes the false information to be provided based on the estimated user's emotions. For example, if the user is feeling anxious, the most effective false information can be provided first. Also, if the user is relaxed, false information that can be provided quickly can be prioritized. Furthermore, if the user is in a hurry, false information that focuses on important points can be provided first. This allows for effective false information to be provided by prioritizing false information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can 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 providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may cause AI to estimate the user's emotions.

[0096] When providing false information, the providing unit can select information to provide by taking into consideration the geographical information of the attacker. The providing unit, for example, provides optimal false information based on the geographical information of the attacker. For example, the providing unit references the geographical information of the attacker and provides false information specific to a region. The providing unit can also analyze the geographical information of the attacker to provide effective false information. For example, the providing unit provides optimal false information based on the geographical information of the attacker. In this way, effective false information can be provided by taking the geographical information of the attacker into consideration. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can have AI analyze the geographical information of the attacker to improve the accuracy of providing false information.

[0097] When providing false information, the providing unit can customize the information to be provided by referring to the attacker's past behavioral history. The providing unit, for example, provides optimal false information based on the attacker's past behavioral history. For example, the providing unit refers to the attacker's past behavioral history to provide effective false information. The providing unit can also analyze the attacker's past behavioral history to provide optimal false information for new fraud methods. For example, the providing unit provides optimal false information based on the attacker's past behavioral history. In this way, effective false information can be provided by referring to the attacker's past behavioral history. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can have AI analyze the attacker's past behavioral history to improve the accuracy of providing false information.

[0098] When providing false information, the providing unit can adjust the language and cultural background of the information to be provided. For example, the providing unit adjusts the language of the false information to match the attacker's native language. For example, the providing unit optimizes the cultural background of the false information to match the attacker's expectations. The providing unit can also adjust the content of the false information to be contrary to the attacker's cultural background, thereby causing confusion. For example, the providing unit adjusts the content of the false information to be contrary to the attacker's cultural background, thereby causing confusion. In this way, by taking the language and cultural background of the information to be provided into consideration, effective false information can be provided. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can cause AI to adjust the language and cultural background of the false information to be provided.

[0099] The transmission unit can estimate the user's emotions and adjust the request transmission method based on the estimated user emotions. The transmission unit, for example, estimates the user's emotions using an emotion analysis algorithm. For example, the transmission unit analyzes the user's facial expressions and voice to estimate the user's emotions. The transmission unit can also estimate the user's emotions using survey results. For example, the transmission unit conducts a survey of the user and estimates the user's emotions based on the results. The transmission unit then adjusts the request transmission method based on the estimated user emotions. For example, if the user is feeling anxious, the transmission unit can send a detailed request to provide a sense of security. If the user is relaxed, the transmission unit can send a quick request to quickly convey information. Furthermore, if the user is in a hurry, the transmission unit can send a request that focuses on the important points. This allows for effective request transmission by providing a request transmission method that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can 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 transmission unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmission unit may cause AI to estimate the user's emotions.

[0100] When sending a request, the sending unit can improve the accuracy of the sending by referring to past sending results. For example, the sending unit refers to past sending results and sends an optimal request for similar fraudulent emails. For example, the sending unit adjusts the content of the request based on past sending results to improve accuracy. The sending unit can also analyze past sending results and send an optimal request for a new fraudulent method. For example, the sending unit sends an optimal request for a new fraudulent method based on past sending results. In this way, the accuracy of sending the request is improved by referring to past sending results. Some or all of the above-mentioned processing in the sending unit may be performed using, for example, AI, or may be performed without using AI. For example, the sending unit can have AI analyze past sending results to improve the accuracy of sending the request.

[0101] When transmitting a request, the transmission unit can analyze the load status of the attacker's server and adjust the timing of transmission. The transmission unit, for example, transmits the request at the optimal timing based on the load status of the attacker's server. For example, the transmission unit refers to the load status of the attacker's server and transmits the request at an effective timing. The transmission unit can also analyze the load status of the attacker's server and optimize the timing of transmitting the request. For example, the transmission unit transmits the request at the optimal timing based on the load status of the attacker's server. In this way, by analyzing the load status of the attacker's server, it is possible to provide an effective timing for transmitting the request. Some or all of the above-described processing in the transmission unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmission unit can have AI analyze the load status of the attacker's server and optimize the timing of transmitting the request.

[0102] The transmitting unit can adjust the content and format of the request to be sent when sending the request. For example, the transmitting unit adjusts the content of the request to be sent to match the expectations of the attacker. For example, the transmitting unit optimizes the format of the request to be sent based on the attacker's past behavioral patterns. The transmitting unit can also adjust the content of the request to be sent in a manner that goes against the attacker's expectations, causing confusion. For example, the transmitting unit adjusts the content of the request to be sent in a manner that goes against the attacker's expectations, causing confusion. In this way, by adjusting the content and format of the request to be sent, an effective request can be sent. Some or all of the above-mentioned processing in the transmitting unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmitting unit can cause AI to adjust the content and format of the request to be sent.

[0103] The transmission unit can estimate the user's emotions and adjust the frequency of request transmission based on the estimated user emotions. The transmission unit, for example, estimates the user's emotions using an emotion analysis algorithm. For example, the transmission unit analyzes the user's facial expressions and voice to estimate the emotions. The transmission unit can also estimate the user's emotions using survey results. For example, the transmission unit conducts a survey of the user and estimates the emotions based on the results. The transmission unit then adjusts the frequency of request transmission based on the estimated user emotions. For example, if the user feels anxious, the transmission unit can send requests frequently to provide a sense of security. If the user feels relaxed, the transmission unit can send requests at an appropriate frequency. Furthermore, if the user is in a hurry, the transmission unit can send requests frequently that focus on important points. This allows for effective request transmission by providing a request transmission frequency that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can 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 transmission unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmission unit may cause AI to estimate the user's emotions.

[0104] When transmitting a request, the transmission unit can transmit the request while taking into account the geographical information of the attacker's server. The transmission unit, for example, transmits an optimal request based on the geographical information of the attacker's server. For example, the transmission unit references the geographical information of the attacker's server and transmits a region-specific request. The transmission unit can also analyze the geographical information of the attacker's server and transmit an effective request. For example, the transmission unit transmits an optimal request based on the geographical information of the attacker's server. In this way, an effective request can be transmitted by taking into account the geographical information of the attacker's server. Some or all of the above-described processing in the transmission unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmission unit can have AI analyze the geographical information of the attacker's server to improve the accuracy of request transmission.

[0105] When transmitting a request, the transmission unit can analyze the security measures of the attacker's server and select a transmission method. The transmission unit, for example, selects the optimal transmission method based on the security measures of the attacker's server. For example, the transmission unit refers to the security measures of the attacker's server and selects an effective transmission method. The transmission unit can also analyze the security measures of the attacker's server and optimize the transmission method. For example, the transmission unit selects the optimal transmission method based on the security measures of the attacker's server. In this way, an effective transmission method can be provided by analyzing the security measures of the attacker's server. Some or all of the above-described processing in the transmission unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmission unit can have AI analyze the security measures of the attacker's server and optimize the transmission method.

[0106] When transmitting a request, the transmission unit can customize the content of the request to be transmitted based on the behavioral patterns of the attacker. The transmission unit, for example, transmits an optimal request based on the behavioral patterns of the attacker. For example, the transmission unit refers to the attacker's past behavioral patterns and transmits an effective request. The transmission unit can also analyze the attacker's behavioral patterns and transmit an optimal request for a new fraud scheme. For example, the transmission unit transmits an optimal request based on the attacker's behavioral patterns. In this way, an effective request can be transmitted by customizing the request based on the attacker's behavioral patterns. Some or all of the above-described processing in the transmission unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmission unit can have AI analyze the attacker's behavioral patterns to improve the accuracy of request transmission. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned analysis unit, determination unit, provision unit, and transmission unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the analysis unit is realized by the processor 46 of the smart device 14 and analyzes the content of the email and sender information. The determination unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and determines whether the email is fraudulent based on the analyzed information. The provision unit is realized, for example, by the control unit 46A of the smart device 14 and provides false personal information. The transmission unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and sends a request to the attacker's server. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned analysis unit, determination unit, providing unit, and transmission unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the analysis unit is realized by the processor 46 of the smart glasses 214 and analyzes the content of the email and sender information. The determination unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and determines whether the email is fraudulent based on the analyzed information. The providing unit is realized, for example, by the control unit 46A of the smart glasses 214 and provides false personal information. The transmission unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and sends a request to the attacker's server. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned analysis unit, determination unit, provision unit, and transmission unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the analysis unit is realized by the processor 46 of the headset type terminal 314 and analyzes the content of the email and sender information. The determination unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and determines whether the email is a fraudulent email based on the analyzed information. The provision unit is realized, for example, by the control unit 46A of the headset type terminal 314 and provides false personal information. The transmission unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and sends a request to the attacker's server. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned analysis unit, determination unit, provision unit, and transmission unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the analysis unit is realized by the processor 46 of the robot 414 and analyzes the content of the email and information about the sender. The determination unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and determines whether the email is a fraudulent email based on the analyzed information. The provision unit is realized, for example, by the control unit 46A of the robot 414 and provides false personal information. The transmission unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and sends a request to the attacker's server.

[0107] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0108] The analysis unit can also take into account the time the email was sent when analyzing the content of the email. For example, the analysis unit may prioritize analyzing emails sent late at night or early in the morning and determine that they are more likely to be fraudulent. The analysis unit can also perform detailed analysis of emails sent on specific days of the week or on holidays. For example, the analysis unit may identify emails sent on weekends or holidays and assess the risk of fraud. Furthermore, the analysis unit can classify the content of emails based on the time they were sent and identify emails that have the characteristics of fraud. By taking the time the email was sent into account, the accuracy of identifying fraudulent emails is improved.

[0109] When determining whether an email is fraudulent, the determination unit can improve the accuracy of the determination by referring to the sender's past behavioral history. For example, the determination unit analyzes the sender's past email sending history to identify senders who are likely to be fraudulent. The determination unit can also accurately determine emails that have the characteristics of fraudulent emails based on the sender's past behavioral patterns. Furthermore, the determination unit can also refer to the sender's past behavioral history and take measures against new fraudulent methods. In this way, by referring to the sender's past behavioral history, the accuracy of the determination is improved.

[0110] When providing false information, the providing unit can estimate the attacker's psychological state and adjust the content of the false information to be provided. For example, the providing unit can analyze the content of the attacker's emails and behavioral patterns, and if the attacker is in a hurry, provide more detailed false information. Also, if the attacker is calm, it can provide concise false information. Furthermore, if the attacker is suspicious, it can provide highly credible false information. In this way, by providing false information that is appropriate for the attacker's psychological state, the effectiveness against the attacker is improved.

[0111] When transmitting a request, the transmission unit can adjust the timing of transmission by taking into account the response time of the attacker's server. For example, the transmission unit transmits the request at the optimal timing based on the response time of the attacker's server. The transmission unit can also transmit the request at an effective timing by referring to the response time of the attacker's server. Furthermore, the transmission unit can analyze the response time of the attacker's server and optimize the timing of transmitting the request. In this way, it is possible to provide an effective timing for transmitting the request by taking into account the response time of the attacker's server.

[0112] The analysis unit can estimate the user's emotions and adjust the notification method of the analysis results based on the estimated user emotions. For example, if the user is feeling anxious, the analysis unit can notify the user of detailed analysis results, providing a sense of security. If the user is relaxed, the analysis unit can notify the user of concise analysis results, providing quick information. Furthermore, if the user is in a hurry, the analysis unit can notify the user of analysis results that focus on important points. This allows the user to gain a deeper understanding by providing a notification method of analysis results that corresponds to the user's emotions.

[0113] When determining whether an email is fraudulent, the determination unit can analyze the emotional tone of the email content to make the determination. For example, the determination unit evaluates the possibility of fraud based on the emotional tone contained in the email content. The determination unit can also refer to the emotional tone of the email content to determine whether the email is fraudulent with high accuracy. Furthermore, the determination unit can identify emails that have fraudulent characteristics based on the emotional tone of the email content. In this way, analyzing the emotional tone of the email content improves the accuracy of identifying fraudulent emails.

[0114] When providing false information, the providing unit can select the information to be provided taking into consideration the type of device used by the attacker. For example, if the attacker is using a smartphone, the providing unit can provide false information for mobile devices. Also, if the attacker is using a desktop computer, the providing unit can provide false information for desktop computers. Furthermore, if the attacker is using a tablet computer, the providing unit can provide false information for tablets. This makes it possible to provide effective false information by providing false information according to the device used by the attacker.

[0115] When sending a request, the sending unit can adjust the sending method by taking into account the network bandwidth of the attacker's server. For example, the sending unit sends an optimal request based on the network bandwidth of the attacker's server. The sending unit can also refer to the network bandwidth of the attacker's server and send an effective request. Furthermore, the sending unit can analyze the network bandwidth of the attacker's server and optimize the request sending method. In this way, an effective request can be sent by taking into account the network bandwidth of the attacker's server.

[0116] The analysis unit can estimate the user's emotions and adjust the analysis priority based on the estimated user's emotions. For example, if the user is feeling anxious, the analysis unit can prioritize analyzing emails that are likely to be fraudulent. If the user is relaxed, the analysis unit can also perform analysis with normal priority. Furthermore, if the user is in a hurry, the analysis unit can prioritize analysis that focuses on important points. This allows for effective analysis by providing analysis priorities according to the user's emotions.

[0117] When determining whether an email is fraudulent, the determination unit can analyze images and videos in the email content to make the determination. For example, the determination unit evaluates the possibility of fraud based on the content of images and videos attached to the email. The determination unit can also refer to the images and videos in the email content to determine whether the email is fraudulent with high accuracy. Furthermore, the determination unit can identify emails that have fraudulent characteristics based on the images and videos in the email content. This improves the accuracy of identifying fraudulent emails by analyzing the images and videos in the email content.

[0118] The processing flow of the second embodiment will be briefly explained below.

[0119] Step 1: The analysis unit analyzes the content of the email or the sender information. For example, the analysis unit can analyze the content of the email using text analysis technology and analyze the sender information using pattern recognition technology. It can also refer to a database of fraudulent methods to identify emails that have the characteristics of fraudulent emails. For example, it can identify emails that have the characteristics of phishing scams or spam emails. Step 2: The determination unit determines whether the email is fraudulent based on the information analyzed by the analysis unit. The determination unit determines whether the email is fraudulent, for example, based on the content of the email and sender information provided by the analysis unit. AI can also be used to accurately determine whether emails have the characteristics of fraudulent emails. For example, an AI model can be used to accurately determine whether emails have the characteristics of fraudulent emails. Step 3: The providing unit provides false personal information if the determining unit determines that the email is a fraudulent email. For example, the providing unit provides false credit card information or a password. AI can also be used to provide the false personal information. For example, an AI model can be used to generate false personal information. Step 4: If the determination unit determines that the email is a fraudulent email, the sending unit sends a request to the attacker's server. For example, the sending unit sends a large number of requests to the attacker's server. AI can also be used to send requests to the attacker's server. For example, an AI model is used to send requests to the attacker's server.

[0120] 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.

[0121] 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 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.

[0122] 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.

[0123] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0124] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0140] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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).

[0177] 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.

[0178] 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."

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] [Explanation of symbols]

[0192] 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. an analysis unit that analyzes the content of the email or information about the sender; a determination unit that determines whether the email is a fraudulent email based on the information analyzed by the analysis unit; a providing unit that provides false personal information when the determining unit determines that the email is a fraudulent email; a sending unit that sends a request to an attacker's server when the determining unit determines that the email is a fraudulent email. A system characterized by:

2. The analysis unit Deeply analyze email content or sender information to identify emails with fraudulent characteristics 2. The system of claim 1.

3. The providing unit Providing false credit card information or passwords 2. The system of claim 1.

4. The transmission unit Send multiple requests to the attacker's server 2. The system of claim 1.

5. The analysis unit Collect information about fraudulent emails and store it in a database 2. The system of claim 1.

6. The determination unit Analyze the sender and content patterns of fraudulent emails and take measures against new fraud methods 2. The system of claim 1.

7. The analysis unit Infer user sentiment and adjust email analysis based on the inferred sentiment 2. The system of claim 1.

8. The analysis unit When analyzing emails, the accuracy of the analysis is improved by referring to patterns of past fraudulent emails.

2. The system of claim 1.

Images