System

Abstract

To detect signs of phishing fraud and provide warnings to users.SOLUTION: A system according to an embodiment includes an analysis unit, a detection unit, and a provision unit. The analysis unit analyzes voice data and text data. The detection unit detects signs of phishing fraud on the basis of the data analyzed by the analysis unit. The provision unit provides a warning to a user on the basis of the signs of phishing fraud detected by the detection unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional techniques have been problematic in that they are not sufficient in efficiently detecting signs of phishing scams and providing users with warnings.

[0005] The system according to the embodiment aims to detect signs of phishing scams and provide warnings to users. [Means for solving the problem]

[0006] The system according to the embodiment includes an analysis unit, a detection unit, and a provision unit. The analysis unit analyzes the voice data and the text data. The detection unit detects signs of a phishing scam based on the data analyzed by the analysis unit. The provision unit provides a warning to a user based on the signs of a phishing scam detected by the detection unit. Effect of the Invention

[0007] An embodiment of the system can detect signs of phishing and provide a warning to the user. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or 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. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the 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. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A phishing fraud prevention system according to an embodiment of the present invention is an AI-based system for mobile phone companies. The system processes voice data and text data locally on the user's device to protect the user's privacy. Next, the AI ​​analyzes the voice data to determine whether there is a possibility of phishing fraud. Furthermore, the AI ​​analyzes the text data of the email to detect signs of phishing fraud. This can provide a safe communication environment and increase the user's trust. For example, if a phone call received by a user is likely to be a phishing scam, the AI ​​issues a warning and alerts the user. Also, if an email shows signs of phishing fraud, the AI ​​automatically quarantines the email and notifies the user. In this way, the AI-based phishing fraud prevention system keeps the user's communications safe and protects them from phishing fraud. In this way, the phishing fraud prevention system can keep the user's communications safe and protect them from phishing fraud.

[0029] The phishing fraud prevention system according to the embodiment includes an analysis unit, a detection unit, and a provision unit. The analysis unit analyzes voice data. The voice data includes, but is not limited to, for example, a call recording, a voice message, and a voice file. The analysis unit, for example, analyzes the voice data using a machine learning algorithm to detect a sign of phishing fraud. The analysis unit can also extract specific keywords or phrases from the voice data to determine the possibility of phishing fraud. For example, the analysis unit detects keywords such as "bank account" and "password" in the voice data to increase the possibility of phishing fraud. The analysis unit can also analyze sender information of the voice data and evaluate reliability. For example, the analysis unit evaluates reliability based on the sender's phone number and voice characteristics. The detection unit detects a sign of phishing fraud based on the data analyzed by the analysis unit. The sign of phishing fraud includes, but is not limited to, for example, a specific keyword, a link pattern, and sender information. The detection unit detects a sign of phishing fraud based on, for example, the data provided by the analysis unit. The detection unit may also use a rule-based algorithm for detecting a sign of a phishing scam. For example, the detection unit detects a sign of a phishing scam based on a specific keyword or a pattern of a link. The provision unit provides a warning to the user based on the sign of a phishing scam detected by the detection unit. The warning may include, but is not limited to, a pop-up notification, an email notification, or a voice notification. For example, the provision unit may display a pop-up notification on the user's device to warn of the possibility of a phishing scam. The provision unit may also send an email notification to the user to inform the user of the possibility of a phishing scam. Furthermore, the provision unit may provide a warning to the user using a voice notification. For example, the provision unit may play a voice message to warn of the possibility of a phishing scam. In this way, the phishing fraud prevention system according to the embodiment can keep the user's communication safe and protect against phishing scams.

[0030] The analysis unit analyzes the voice data. The voice data includes, but is not limited to, for example, a call recording, a voice message, and a voice file. For example, the analysis unit uses a machine learning algorithm to analyze the voice data and detect signs of phishing fraud. Specifically, the analysis unit analyzes the waveform and spectrum of the voice data and extracts features. These features include voice pitch, tempo, volume fluctuation, and specific frequency components. The machine learning algorithm evaluates the possibility of phishing fraud based on these features. For example, new voice data can be evaluated using a model that has been trained on voice data of past phishing frauds. The analysis unit can also extract specific keywords and phrases from the voice data and determine the possibility of phishing fraud. For example, if keywords such as "bank account" and "password" are included, it is determined that there is a high possibility of phishing fraud. Furthermore, the analysis unit can analyze sender information of the voice data and evaluate its reliability. For example, it evaluates whether the sender is reliable based on the sender's phone number and voice features. Voice features include tone of voice, accent, speaking pattern, and the like. This enables the analysis unit to comprehensively evaluate the content of the voice data and the sender's information, and determine with a high degree of accuracy whether it is a phishing scam.

[0031] The detection unit detects a sign of a phishing scam based on the data analyzed by the analysis unit. The sign of a phishing scam includes, but is not limited to, a specific keyword, a link pattern, and sender information. The detection unit detects a sign of a phishing scam based on, for example, data provided by the analysis unit. Specifically, the analysis unit comprehensively evaluates the keywords and phrases extracted by the analysis unit, the reliability evaluation of the sender, and the feature amount of the voice data to determine the possibility of a phishing scam. The detection unit can also use a rule-based algorithm for detecting a sign of a phishing scam. For example, a rule for detecting a sign of a phishing scam can be set based on a specific keyword or a link pattern. This allows the detection unit to quickly and accurately detect a sign of a phishing scam based on the data provided by the analysis unit. Furthermore, the detection unit can also learn a pattern of a phishing scam based on data of past phishing scams and detect a new sign of a phishing scam. For example, by using a model that has learned the features of a phishing scam using voice data of past phishing scams, a new sign of a phishing scam can be detected with high accuracy. This enables the detection unit to always keep up with the latest phishing scam techniques and protect users.

[0032] The providing unit provides a warning to the user based on the signs of phishing fraud detected by the detection unit. Examples of warnings include, but are not limited to, pop-up notifications, email notifications, and voice notifications. For example, the providing unit displays a pop-up notification on the user's device to warn of the possibility of phishing fraud. Specifically, the warning message can be displayed on the screen while the user is on a call or playing a voice message. The providing unit can also send an email notification to the user to notify the user of the possibility of phishing fraud. For example, a warning email can be sent to the user's email address to notify the user of the possibility of phishing fraud. Furthermore, the providing unit can provide a warning to the user using a voice notification. For example, a voice message can be played to warn of the possibility of phishing fraud. This allows the user to receive not only visual but also auditory notifications. Furthermore, the providing unit can collect user feedback and continuously improve the accuracy and effectiveness of the warning. For example, the content and timing of the warning can be adjusted based on the user's behavior and feedback after receiving the warning. This allows the providing unit to provide a prompt and appropriate warning to the user to protect them from phishing fraud.

[0033] The phishing fraud prevention system includes a processing unit that processes voice data and text data locally. The processing unit processes the voice data and text data locally. Specific methods of local processing include, but are not limited to, processing within a device and processing without using a cloud. The processing unit, for example, analyzes the voice data and text data within a user's device to detect signs of phishing fraud. The processing unit can also protect the user's privacy by processing data within the device without using a cloud. For example, the processing unit encrypts the voice data and text data within the device and performs analysis without transmitting the data to an external device. In this way, the user's privacy can be protected by processing the voice data and text data locally.

[0034] The phishing fraud prevention system includes a protection unit that protects the privacy of the user. The protection unit protects the privacy of the user. Specific methods of privacy protection include, but are not limited to, for example, data encryption, access control, anonymization, and the like. The protection unit, for example, encrypts voice data and text data to prevent unauthorized access from the outside. The protection unit can also control access to data so that only specific users can access the data. For example, the protection unit performs access control based on the authentication information of the user to prevent unauthorized use of the data. Furthermore, the protection unit can anonymize data and protect the personal information of the user. For example, the protection unit deletes personal information from the voice data and text data, and analyzes the anonymized data. This can increase the user's trust by protecting the user's privacy.

[0035] The phishing fraud prevention system includes a quarantine unit that quarantines an email when a sign of phishing is detected. The quarantine unit quarantines an email when a sign of phishing is detected. Specific methods for quarantining an email include, but are not limited to, moving the email to a quarantine folder and notifying the user. For example, the quarantine unit moves the email in which a sign of phishing is detected to a quarantine folder and notifies the user. The quarantine unit can also automatically delete the email in which a sign of phishing is detected. For example, the quarantine unit deletes the email in which a sign of phishing is detected and notifies the user. Furthermore, the quarantine unit can analyze the email in which a sign of phishing is detected and provide detailed information. For example, the quarantine unit analyzes sender information and link patterns of the email in which a sign of phishing is detected and notifies the user. This makes it possible to ensure the safety of the user by quarantining the email when a sign of phishing is detected.

[0036] The analysis unit can analyze the voice data and determine whether or not it is a phishing scam. For example, the analysis unit can analyze the voice data using a machine learning algorithm to detect signs of a phishing scam. For example, the analysis unit can detect keywords such as "bank account" or "password" in the voice data to increase the possibility of a phishing scam. The analysis unit can also analyze sender information of the voice data and evaluate reliability. For example, the analysis unit evaluates reliability based on the sender's phone number and voice characteristics. This makes it possible to detect phishing scams early by analyzing the voice data and determining the possibility of a phishing scam.

[0037] The analysis unit can analyze the text data and detect signs of phishing fraud. For example, the analysis unit can analyze the text data using a text analysis algorithm and detect signs of phishing fraud. For example, the analysis unit can detect keywords such as "bank account" and "password" in the text data to increase the possibility of phishing fraud. The analysis unit can also analyze sender information of the text data and evaluate reliability. For example, the analysis unit can evaluate reliability based on the sender's email address and link patterns. In this way, by analyzing the text data and detecting signs of phishing fraud, it is possible to prevent phishing fraud.

[0038] When analyzing the voice data, the analysis unit can improve the analysis accuracy by referring to the user's past call history. The analysis unit improves the analysis accuracy of the voice data, for example, based on the user's past call history. For example, the analysis unit detects similar patterns based on the call history of phishing scams received by the user in the past. The analysis unit can also extract specific keywords or phrases from the user's past call history and use them in the analysis. Furthermore, the analysis unit can analyze the user's call history and prioritize analysis of calls that are likely to be phishing scams. In this way, the analysis accuracy is improved by referring to the user's past call history.

[0039] When analyzing the text data, the analysis unit can optimize the analysis algorithm by analyzing the user's email usage pattern. The analysis unit optimizes the text data analysis algorithm based on, for example, the user's email usage pattern. For example, the analysis unit learns phrases and words frequently used by the user and detects signs of phishing fraud. The analysis unit can also analyze the user's email sending and receiving patterns and detect abnormal patterns. Furthermore, the analysis unit can prioritize analysis of emails that are likely to be phishing frauds based on the user's email usage history. In this way, the analysis algorithm is optimized by analyzing the user's email usage pattern.

[0040] The analysis unit can perform the analysis based on the geographical location information of the user when analyzing the voice data. The analysis unit performs the analysis of the voice data based on, for example, the geographical location information of the user. For example, when the user is in a specific area, the analysis unit reflects the pattern of phishing scams that frequently occur in that area in the analysis. The analysis unit can also perform the analysis taking into account the language and dialect specific to the area based on the user's current location. Furthermore, the analysis unit can optimize the analysis taking into account the communication environment of the area based on the geographical location information of the user. As a result, the accuracy of the analysis is improved by taking into account the geographical location information of the user.

[0041] When analyzing the text data, the analysis unit can analyze the user's social media activity and prioritize analysis of related data. The analysis unit analyzes the text data based on, for example, the user's social media activity. For example, the analysis unit learns phrases and words frequently used by the user on social media and detects signs of phishing scams. The analysis unit can also extract specific keywords and topics from the user's social media activity and use them in the analysis. Furthermore, the analysis unit can prioritize analysis of text data that is likely to be a phishing scam based on the user's social media activity. This makes it possible to prioritize analysis of related data by analyzing the user's social media activity.

[0042] When detecting a sign of a phishing scam, the detection unit can improve detection accuracy by referring to past phishing scam data. The detection unit detects signs of a phishing scam, for example, based on past phishing scam data. For example, the detection unit detects similar patterns based on past phishing scam data. The detection unit can also extract specific keywords or phrases from past phishing scam data and use them for detection. Furthermore, the detection unit can analyze past phishing scam data and preferentially detect signs that are highly likely to be phishing scams. In this way, by referring to past phishing scam data, detection accuracy is improved.

[0043] When detecting signs of a phishing scam, the detection unit can optimize the detection algorithm by analyzing the user's communication pattern. The detection unit detects signs of a phishing scam, for example, based on the user's communication pattern. For example, the detection unit detects abnormal patterns based on the user's communication pattern. The detection unit can also extract specific keywords or phrases from the user's communication pattern and use them for detection. Furthermore, the detection unit can analyze the user's communication pattern and preferentially detect signs that are highly likely to be phishing scams. In this way, the detection algorithm is optimized by analyzing the user's communication pattern.

[0044] The detection unit can perform detection while taking into consideration the geographical location information of the user when detecting a sign of a phishing scam. The detection unit detects a sign of a phishing scam, for example, based on the geographical location information of the user. For example, when the user is in a specific area, the detection unit reflects the pattern of phishing scams that frequently occur in that area in the detection. The detection unit can also perform detection while taking into consideration the language or dialect specific to the area based on the user's current location. Furthermore, the detection unit can optimize detection while taking into consideration the communication environment of the area based on the geographical location information of the user. As a result, the accuracy of detection is improved by taking into consideration the geographical location information of the user.

[0045] When detecting a sign of a phishing scam, the detection unit can analyze the user's social media activity and preferentially detect related signs. The detection unit detects signs of a phishing scam, for example, based on the user's social media activity. For example, the detection unit learns phrases and words frequently used by the user on social media and detects signs of a phishing scam. The detection unit can also extract specific keywords and topics from the user's social media activity and use them for detection. Furthermore, the detection unit can preferentially detect signs that are highly likely to be phishing scams based on the user's social media activity. This makes it possible to preferentially detect related signs by analyzing the user's social media activity.

[0046] When providing a warning, the providing unit can select the optimal warning method by referring to the user's past warning history. The providing unit selects the warning method based on, for example, the user's past warning history. For example, the providing unit issues a similar warning based on the user's past warning history. The providing unit can also extract specific keywords or phrases from the user's past warning history and use them in the warning. Furthermore, the providing unit can analyze the user's warning history and select the most effective warning method. In this way, the optimal warning method can be selected by referring to the user's past warning history.

[0047] The providing unit can adjust the urgency of the warning based on the user's current situation when providing the warning. The providing unit adjusts the urgency of the warning based on the user's current situation, for example. For example, when the user is moving, the providing unit issues a warning with a high urgency. In addition, when the user is relaxed, the providing unit can also issue a warning with a low urgency. Furthermore, when the user is feeling stressed, the providing unit issues a concise and highly visible warning. This allows for more appropriate warning by adjusting the urgency of the warning based on the user's current situation.

[0048] When providing a warning, the providing unit can select an optimal warning method in consideration of the geographical location information of the user. The providing unit selects a warning method based on, for example, the geographical location information of the user. For example, when the user is in a specific area, the providing unit issues a warning based on a pattern of phishing scams that frequently occur in that area. The providing unit can also issue a warning based on the user's current location in consideration of a language or dialect specific to the area. Furthermore, the providing unit can optimize the warning in consideration of the communication environment of the area based on the geographical location information of the user. In this way, the optimal warning method can be selected by considering the geographical location information of the user.

[0049] When providing a warning, the providing unit can provide a relevant warning by analyzing the user's social media activity. The providing unit provides a warning based on, for example, the user's social media activity. For example, the providing unit learns phrases and words frequently used by the user on social media and warns of signs of phishing scams. The providing unit can also extract specific keywords and topics from the user's social media activity and use them for the warning. Furthermore, the providing unit can prioritize warnings that are likely to be phishing scams based on the user's social media activity. In this way, it is possible to provide relevant warnings by analyzing the user's social media activity.

[0050] When processing data, the processing unit can select the optimal processing method by referring to the user's past data processing history. The processing unit selects the data processing method based on, for example, the user's past data processing history. For example, the processing unit selects a similar processing method based on the user's past data processing history. The processing unit can also extract specific keywords or phrases from the user's past data processing history and use them in processing. Furthermore, the processing unit can analyze the user's data processing history and select the most efficient processing method. In this way, the optimal processing method can be selected by referring to the user's past data processing history.

[0051] The processing unit can adjust the priority of processing based on the user's current communication status during data processing. The processing unit adjusts the priority of data processing based on the user's current communication status, for example. For example, when the user is on the move, the processing unit prioritizes data processing with high urgency. Also, when the user is relaxed, the processing unit can perform all data processing equally. Furthermore, when the user is feeling stressed, the processing unit prioritizes simple data processing. This allows more appropriate data processing by adjusting the priority of processing based on the user's current communication status.

[0052] The processing unit can select an optimal processing method in consideration of the geographical location information of the user when processing data. The processing unit selects a data processing method based on, for example, the geographical location information of the user. For example, when the user is in a specific area, the processing unit performs processing based on a data processing pattern that occurs frequently in that area. The processing unit can also perform processing based on the user's current location and taking into account a communication environment specific to the area. Furthermore, the processing unit can optimize processing based on the geographical location information of the user and taking into account the communication environment of the area. This allows the optimal processing method to be selected by taking into account the geographical location information of the user.

[0053] During data processing, the processing unit can analyze the user's social media activity and prioritize processing of related data. The processing unit processes data based on, for example, the user's social media activity. For example, the processing unit learns phrases and words frequently used by the user on social media and uses them in data processing. The processing unit can also extract specific keywords and topics from the user's social media activity and use them in data processing. Furthermore, the processing unit can also prioritize processing of related data based on the user's social media activity. As a result, by analyzing the user's social media activity, related data can be prioritized for processing.

[0054] During privacy protection, the protection unit can select an optimal protection method by referring to the user's past privacy protection history. The protection unit selects a privacy protection method based on, for example, the user's past privacy protection history. For example, the protection unit selects a similar protection method based on the user's past privacy protection history. The protection unit can also extract specific keywords or phrases from the user's past privacy protection history and use them for protection. Furthermore, the protection unit can analyze the user's privacy protection history and select the most effective protection method. In this way, the optimal protection method can be selected by referring to the user's past privacy protection history.

[0055] The protection unit can select an optimal protection method in consideration of the geographical location information of the user during privacy protection. The protection unit selects a privacy protection method based on, for example, the geographical location information of the user. For example, when the user is in a specific area, the protection unit performs protection based on a pattern of privacy violations that frequently occur in that area. The protection unit can also perform protection in consideration of a privacy protection method specific to the area based on the user's current location. Furthermore, the protection unit can optimize protection in consideration of the communication environment of the area based on the geographical location information of the user. In this way, the optimal protection method can be selected by considering the geographical location information of the user.

[0056] When isolating an email, the quarantine unit can select the optimal quarantine method by referring to the user's past email quarantine history. The quarantine unit selects the email quarantine method based on, for example, the user's past email quarantine history. For example, the quarantine unit quarantines similar emails based on the history of emails that the user has previously quarantined. The quarantine unit can also extract specific keywords or phrases from the user's past email quarantine history and use them for quarantine. Furthermore, the quarantine unit can analyze the user's email quarantine history and select the most effective quarantine method. In this way, the optimal quarantine method can be selected by referring to the user's past email quarantine history.

[0057] When isolating email, the quarantine unit can select the optimal quarantine method by taking into consideration the geographical location information of the user. The quarantine unit selects the email quarantine method based on, for example, the geographical location information of the user. For example, when the user is in a specific area, the quarantine unit quarantines email based on the pattern of phishing scams that frequently occur in that area. The quarantine unit can also quarantine email by taking into consideration the language or dialect specific to the area based on the user's current location. Furthermore, the quarantine unit can also quarantine email by taking into consideration the communication environment of the area based on the geographical location information of the user. In this way, the optimal quarantine method can be selected by taking into consideration the geographical location information of the user.

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

[0059] When analyzing the voice data, the analysis unit can improve the accuracy of the analysis by referring to the user's past voice data. For example, the analysis unit can detect similar patterns based on the user's history of phishing scam calls received in the past. The analysis unit can also extract specific keywords or phrases from the user's past voice data and use them in the analysis. Furthermore, the analysis unit can analyze the user's voice data and prioritize the analysis of calls that are likely to be phishing scams. In this way, the analysis accuracy can be improved by referring to the user's past voice data.

[0060] When analyzing the voice data, the analysis unit can perform the analysis based on the geographical location information of the user. For example, when the user is in a specific area, the analysis unit reflects the phishing scam patterns that occur frequently in that area in the analysis. The analysis unit can also perform the analysis taking into account the language and dialect specific to the area based on the user's current location. Furthermore, the analysis unit can optimize the analysis by taking into account the communication environment of the area based on the geographical location information of the user. In this way, the accuracy of the analysis is improved by taking into account the geographical location information of the user.

[0061] When analyzing the text data, the analysis unit can optimize the analysis algorithm by analyzing the user's email usage patterns. For example, the analysis unit can learn phrases and words frequently used by the user and detect signs of phishing scams. The analysis unit can also analyze the user's email sending and receiving patterns and detect abnormal patterns. Furthermore, the analysis unit can prioritize the analysis of emails that are likely to be phishing scams based on the user's email usage history. In this way, the analysis algorithm is optimized by analyzing the user's email usage patterns.

[0062] When detecting signs of phishing fraud, the detection unit can improve detection accuracy by referring to past phishing fraud data. For example, the detection unit detects similar patterns based on past phishing fraud data. The detection unit can also extract specific keywords or phrases from past phishing fraud data and use them for detection. Furthermore, the detection unit can analyze past phishing fraud data and preferentially detect signs that are likely to be phishing frauds. In this way, by referring to past phishing fraud data, detection accuracy is improved.

[0063] When providing a warning, the providing unit can select the optimal warning method by referring to the user's past warning history. For example, the providing unit issues a similar warning based on the user's past warning history. The providing unit can also extract specific keywords or phrases from the user's past warning history and use them in the warning. Furthermore, the providing unit can analyze the user's warning history and select the most effective warning method. In this way, the optimal warning method can be selected by referring to the user's past warning history.

[0064] The process flow of the first embodiment will be briefly described below.

[0065] Step 1: The analysis unit analyzes the voice data. Voice data includes call recordings, voice messages, and audio files. The analysis unit uses machine learning algorithms to analyze the voice data and detect signs of phishing scams. It also extracts specific keywords and phrases from the voice data to determine whether they are phishing scams. It also analyzes sender information and evaluates their credibility. Step 2: The analysis unit analyzes the text data. The text data includes emails, chat messages, document files, etc. The analysis unit extracts specific keywords and phrases from the text data and determines whether it is a phishing scam. It also analyzes the sender information and evaluates its credibility. Step 3: The detection unit detects signs of phishing based on the data analyzed by the analysis unit. Signs of phishing include specific keywords, link patterns, sender information, etc. The detection unit uses a rule-based algorithm to detect signs of phishing based on the data provided by the analysis unit. Step 4: The providing unit provides a warning to the user based on the indications of a phishing scam detected by the detection unit. The warning may be a pop-up notification, an email notification, a voice notification, or the like. The providing unit displays a pop-up notification on the user's device to warn of the possibility of a phishing scam. The providing unit may also provide a warning to the user using an email notification or a voice notification.

[0066] (Example 2) A phishing fraud prevention system according to an embodiment of the present invention is an AI-based system for mobile phone companies. The system processes voice data and text data locally on the user's device to protect the user's privacy. Next, the AI ​​analyzes the voice data to determine whether there is a possibility of phishing fraud. Furthermore, the AI ​​analyzes the text data of the email to detect signs of phishing fraud. This can provide a safe communication environment and increase the user's trust. For example, if a phone call received by a user is likely to be a phishing scam, the AI ​​issues a warning and alerts the user. Also, if an email shows signs of phishing fraud, the AI ​​automatically quarantines the email and notifies the user. In this way, the AI-based phishing fraud prevention system keeps the user's communications safe and protects them from phishing fraud. In this way, the phishing fraud prevention system can keep the user's communications safe and protect them from phishing fraud.

[0067] The phishing fraud prevention system according to the embodiment includes an analysis unit, a detection unit, and a provision unit. The analysis unit analyzes voice data. The voice data includes, but is not limited to, for example, a call recording, a voice message, and a voice file. The analysis unit, for example, analyzes the voice data using a machine learning algorithm to detect a sign of phishing fraud. The analysis unit can also extract specific keywords or phrases from the voice data to determine the possibility of phishing fraud. For example, the analysis unit detects keywords such as "bank account" and "password" in the voice data to increase the possibility of phishing fraud. The analysis unit can also analyze sender information of the voice data and evaluate reliability. For example, the analysis unit evaluates reliability based on the sender's phone number and voice characteristics. The detection unit detects a sign of phishing fraud based on the data analyzed by the analysis unit. The sign of phishing fraud includes, but is not limited to, for example, a specific keyword, a link pattern, and sender information. The detection unit detects a sign of phishing fraud based on, for example, the data provided by the analysis unit. The detection unit may also use a rule-based algorithm for detecting a sign of a phishing scam. For example, the detection unit detects a sign of a phishing scam based on a specific keyword or a pattern of a link. The provision unit provides a warning to the user based on the sign of a phishing scam detected by the detection unit. The warning may include, but is not limited to, a pop-up notification, an email notification, or a voice notification. For example, the provision unit may display a pop-up notification on the user's device to warn of the possibility of a phishing scam. The provision unit may also send an email notification to the user to inform the user of the possibility of a phishing scam. Furthermore, the provision unit may provide a warning to the user using a voice notification. For example, the provision unit may play a voice message to warn of the possibility of a phishing scam. In this way, the phishing fraud prevention system according to the embodiment can keep the user's communication safe and protect against phishing scams.

[0068] The analysis unit analyzes the voice data. The voice data includes, but is not limited to, for example, a call recording, a voice message, and a voice file. For example, the analysis unit uses a machine learning algorithm to analyze the voice data and detect signs of phishing fraud. Specifically, the analysis unit analyzes the waveform and spectrum of the voice data and extracts features. These features include voice pitch, tempo, volume fluctuation, and specific frequency components. The machine learning algorithm evaluates the possibility of phishing fraud based on these features. For example, new voice data can be evaluated using a model that has been trained on voice data of past phishing frauds. The analysis unit can also extract specific keywords and phrases from the voice data and determine the possibility of phishing fraud. For example, if keywords such as "bank account" and "password" are included, it is determined that there is a high possibility of phishing fraud. Furthermore, the analysis unit can analyze sender information of the voice data and evaluate its reliability. For example, it evaluates whether the sender is reliable based on the sender's phone number and voice features. Voice features include tone of voice, accent, speaking pattern, and the like. This enables the analysis unit to comprehensively evaluate the content of the voice data and the sender's information, and determine with a high degree of accuracy whether it is a phishing scam.

[0069] The detection unit detects a sign of a phishing scam based on the data analyzed by the analysis unit. The sign of a phishing scam includes, but is not limited to, a specific keyword, a link pattern, and sender information. The detection unit detects a sign of a phishing scam based on, for example, data provided by the analysis unit. Specifically, the analysis unit comprehensively evaluates the keywords and phrases extracted by the analysis unit, the reliability evaluation of the sender, and the feature amount of the voice data to determine the possibility of a phishing scam. The detection unit can also use a rule-based algorithm for detecting a sign of a phishing scam. For example, a rule for detecting a sign of a phishing scam can be set based on a specific keyword or a link pattern. This allows the detection unit to quickly and accurately detect a sign of a phishing scam based on the data provided by the analysis unit. Furthermore, the detection unit can also learn a pattern of a phishing scam based on data of past phishing scams and detect a new sign of a phishing scam. For example, by using a model that has learned the features of a phishing scam using voice data of past phishing scams, a new sign of a phishing scam can be detected with high accuracy. This enables the detection unit to always keep up with the latest phishing scam techniques and protect users.

[0070] The providing unit provides a warning to the user based on the signs of phishing fraud detected by the detection unit. Examples of warnings include, but are not limited to, pop-up notifications, email notifications, and voice notifications. For example, the providing unit displays a pop-up notification on the user's device to warn of the possibility of phishing fraud. Specifically, the warning message can be displayed on the screen while the user is on a call or playing a voice message. The providing unit can also send an email notification to the user to notify the user of the possibility of phishing fraud. For example, a warning email can be sent to the user's email address to notify the user of the possibility of phishing fraud. Furthermore, the providing unit can provide a warning to the user using a voice notification. For example, a voice message can be played to warn of the possibility of phishing fraud. This allows the user to receive not only visual but also auditory notifications. Furthermore, the providing unit can collect user feedback and continuously improve the accuracy and effectiveness of the warning. For example, the content and timing of the warning can be adjusted based on the user's behavior and feedback after receiving the warning. This allows the providing unit to provide a prompt and appropriate warning to the user to protect them from phishing fraud.

[0071] The phishing fraud prevention system includes a processing unit that processes voice data and text data locally. The processing unit processes the voice data and text data locally. Specific methods of local processing include, but are not limited to, processing within a device and processing without using a cloud. The processing unit, for example, analyzes the voice data and text data within a user's device to detect signs of phishing fraud. The processing unit can also protect the user's privacy by processing data within the device without using a cloud. For example, the processing unit encrypts the voice data and text data within the device and performs analysis without transmitting the data to an external device. In this way, the user's privacy can be protected by processing the voice data and text data locally.

[0072] The phishing fraud prevention system includes a protection unit that protects the privacy of the user. The protection unit protects the privacy of the user. Specific methods of privacy protection include, but are not limited to, for example, data encryption, access control, anonymization, and the like. The protection unit, for example, encrypts voice data and text data to prevent unauthorized access from the outside. The protection unit can also control access to data so that only specific users can access the data. For example, the protection unit performs access control based on the authentication information of the user to prevent unauthorized use of the data. Furthermore, the protection unit can anonymize data and protect the personal information of the user. For example, the protection unit deletes personal information from the voice data and text data, and analyzes the anonymized data. This can increase the user's trust by protecting the user's privacy.

[0073] The phishing fraud prevention system includes a quarantine unit that quarantines an email when a sign of phishing is detected. The quarantine unit quarantines an email when a sign of phishing is detected. Specific methods for quarantining an email include, but are not limited to, moving the email to a quarantine folder and notifying the user. For example, the quarantine unit moves the email in which a sign of phishing is detected to a quarantine folder and notifies the user. The quarantine unit can also automatically delete the email in which a sign of phishing is detected. For example, the quarantine unit deletes the email in which a sign of phishing is detected and notifies the user. Furthermore, the quarantine unit can analyze the email in which a sign of phishing is detected and provide detailed information. For example, the quarantine unit analyzes sender information and link patterns of the email in which a sign of phishing is detected and notifies the user. This makes it possible to ensure the safety of the user by quarantining the email when a sign of phishing is detected.

[0074] The analysis unit can analyze the voice data and determine whether or not it is a phishing scam. For example, the analysis unit can analyze the voice data using a machine learning algorithm to detect signs of a phishing scam. For example, the analysis unit can detect keywords such as "bank account" or "password" in the voice data to increase the possibility of a phishing scam. The analysis unit can also analyze sender information of the voice data and evaluate reliability. For example, the analysis unit evaluates reliability based on the sender's phone number and voice characteristics. This makes it possible to detect phishing scams early by analyzing the voice data and determining the possibility of a phishing scam.

[0075] The analysis unit can analyze the text data and detect signs of phishing fraud. For example, the analysis unit can analyze the text data using a text analysis algorithm and detect signs of phishing fraud. For example, the analysis unit can detect keywords such as "bank account" and "password" in the text data to increase the possibility of phishing fraud. The analysis unit can also analyze sender information of the text data and evaluate reliability. For example, the analysis unit can evaluate reliability based on the sender's email address and link patterns. In this way, by analyzing the text data and detecting signs of phishing fraud, it is possible to prevent phishing fraud.

[0076] The analysis unit can estimate the user's emotion and adjust the analysis method of the voice data based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion using voice tone analysis or facial expression recognition. For example, the analysis unit analyzes the user's voice tone and determines whether the user is nervous or relaxed. The analysis unit can also capture the user's facial expression with a camera and estimate the emotion using a facial expression recognition algorithm. For example, the analysis unit analyzes changes in the user's facial expression and calculates an emotion score. Furthermore, the analysis unit can adjust the analysis method of the voice data based on the user's emotion. For example, if the user is nervous, the analysis unit quickly analyzes the voice data and provides the result immediately. Also, if the user is relaxed, the analysis unit can perform a detailed analysis and provide more information. Furthermore, if the user is stressed, the analysis unit summarizes the analysis results concisely so as not to burden the user. As a result, by adjusting the analysis method of the voice data based on the user's emotion, more appropriate analysis is possible. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0077] When analyzing the voice data, the analysis unit can improve the analysis accuracy by referring to the user's past call history. The analysis unit improves the analysis accuracy of the voice data, for example, based on the user's past call history. For example, the analysis unit detects similar patterns based on the call history of phishing scams received by the user in the past. The analysis unit can also extract specific keywords or phrases from the user's past call history and use them in the analysis. Furthermore, the analysis unit can analyze the user's call history and prioritize analysis of calls that are likely to be phishing scams. In this way, the analysis accuracy is improved by referring to the user's past call history.

[0078] When analyzing the text data, the analysis unit can optimize the analysis algorithm by analyzing the user's email usage pattern. The analysis unit optimizes the text data analysis algorithm based on, for example, the user's email usage pattern. For example, the analysis unit learns phrases and words frequently used by the user and detects signs of phishing fraud. The analysis unit can also analyze the user's email sending and receiving patterns and detect abnormal patterns. Furthermore, the analysis unit can prioritize analysis of emails that are likely to be phishing frauds based on the user's email usage history. In this way, the analysis algorithm is optimized by analyzing the user's email usage pattern.

[0079] The analysis unit can estimate the user's emotion and determine the analysis priority of the text data based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion using voice tone analysis or facial expression recognition. For example, the analysis unit analyzes the user's voice tone and determines whether the user is nervous or relaxed. The analysis unit can also capture the user's facial expression with a camera and estimate the emotion using a facial expression recognition algorithm. For example, the analysis unit analyzes changes in the user's facial expression and calculates an emotion score. Furthermore, the analysis unit can determine the analysis priority of the text data based on the user's emotion. For example, if the user is nervous, the analysis unit prioritizes analysis of important text data. Also, if the user is relaxed, the analysis unit can perform a detailed analysis and analyze all text data evenly. Furthermore, if the user is stressed, the analysis unit performs a concise analysis and provides only important information. This enables more appropriate analysis by determining the analysis priority of the text data based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0080] The analysis unit can perform the analysis based on the geographical location information of the user when analyzing the voice data. The analysis unit performs the analysis of the voice data based on, for example, the geographical location information of the user. For example, when the user is in a specific area, the analysis unit reflects the pattern of phishing scams that frequently occur in that area in the analysis. The analysis unit can also perform the analysis taking into account the language and dialect specific to the area based on the user's current location. Furthermore, the analysis unit can optimize the analysis taking into account the communication environment of the area based on the geographical location information of the user. As a result, the accuracy of the analysis is improved by taking into account the geographical location information of the user.

[0081] When analyzing the text data, the analysis unit can analyze the user's social media activity and prioritize analysis of related data. The analysis unit analyzes the text data based on, for example, the user's social media activity. For example, the analysis unit learns phrases and words frequently used by the user on social media and detects signs of phishing scams. The analysis unit can also extract specific keywords and topics from the user's social media activity and use them in the analysis. Furthermore, the analysis unit can prioritize analysis of text data that is likely to be a phishing scam based on the user's social media activity. This makes it possible to prioritize analysis of related data by analyzing the user's social media activity.

[0082] The detection unit can estimate the user's emotions and adjust the phishing sign detection criteria based on the estimated user's emotions. The detection unit estimates the user's emotions using, for example, voice tone analysis or facial expression recognition. For example, the detection unit analyzes the user's voice tone and determines whether the user is nervous or relaxed. The detection unit can also capture the user's facial expression with a camera and estimate the emotion using a facial expression recognition algorithm. For example, the detection unit analyzes changes in the user's facial expression and calculates an emotion score. Furthermore, the detection unit can adjust the phishing sign detection criteria based on the user's emotions. For example, if the user is nervous, the detection unit detects the phishing sign with strict criteria. Also, if the user is relaxed, the detection unit can detect the phishing sign with flexible criteria. Furthermore, if the user is stressed, the detection unit detects the phishing sign with simple criteria. As a result, more appropriate detection is possible by adjusting the phishing sign detection criteria based on the user's emotions. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0083] When detecting a sign of a phishing scam, the detection unit can improve detection accuracy by referring to past phishing scam data. The detection unit detects signs of a phishing scam, for example, based on past phishing scam data. For example, the detection unit detects similar patterns based on past phishing scam data. The detection unit can also extract specific keywords or phrases from past phishing scam data and use them for detection. Furthermore, the detection unit can analyze past phishing scam data and preferentially detect signs that are highly likely to be phishing scams. In this way, by referring to past phishing scam data, detection accuracy is improved.

[0084] When detecting signs of a phishing scam, the detection unit can optimize the detection algorithm by analyzing the user's communication pattern. The detection unit detects signs of a phishing scam, for example, based on the user's communication pattern. For example, the detection unit detects abnormal patterns based on the user's communication pattern. The detection unit can also extract specific keywords or phrases from the user's communication pattern and use them for detection. Furthermore, the detection unit can analyze the user's communication pattern and preferentially detect signs that are highly likely to be phishing scams. In this way, the detection algorithm is optimized by analyzing the user's communication pattern.

[0085] The detection unit can estimate the user's emotion and adjust the display method of the phishing sign detection result based on the estimated user's emotion. The detection unit estimates the user's emotion using, for example, voice tone analysis or facial expression recognition. For example, the detection unit analyzes the user's voice tone and determines whether the user is nervous or relaxed. The detection unit can also capture the user's facial expression with a camera and estimate the emotion using a facial expression recognition algorithm. For example, the detection unit analyzes changes in the user's facial expression and calculates an emotion score. Furthermore, the detection unit can adjust the display method of the phishing sign detection result based on the user's emotion. For example, when the user is nervous, the detection unit provides a simple and highly visible display method. Furthermore, when the user is relaxed, the detection unit can also provide a display method including detailed information. Furthermore, when the user is stressed, the detection unit provides a simple display method and displays only important information. This allows for more appropriate display by adjusting the display method based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0086] The detection unit can perform detection while taking into consideration the geographical location information of the user when detecting a sign of a phishing scam. The detection unit detects a sign of a phishing scam, for example, based on the geographical location information of the user. For example, when the user is in a specific area, the detection unit reflects the pattern of phishing scams that frequently occur in that area in the detection. The detection unit can also perform detection while taking into consideration the language or dialect specific to the area based on the user's current location. Furthermore, the detection unit can optimize detection while taking into consideration the communication environment of the area based on the geographical location information of the user. As a result, the accuracy of detection is improved by taking into consideration the geographical location information of the user.

[0087] When detecting a sign of a phishing scam, the detection unit can analyze the user's social media activity and preferentially detect related signs. The detection unit detects signs of a phishing scam, for example, based on the user's social media activity. For example, the detection unit learns phrases and words frequently used by the user on social media and detects signs of a phishing scam. The detection unit can also extract specific keywords and topics from the user's social media activity and use them for detection. Furthermore, the detection unit can preferentially detect signs that are highly likely to be phishing scams based on the user's social media activity. This makes it possible to preferentially detect related signs by analyzing the user's social media activity.

[0088] The providing unit can estimate the user's emotion and adjust the way of expressing the warning based on the estimated user's emotion. The providing unit, for example, estimates the user's emotion using voice tone analysis or facial expression recognition. For example, the providing unit analyzes the user's voice tone and determines whether the user is tense or relaxed. The providing unit can also capture the user's facial expression with a camera and estimate the emotion using a facial expression recognition algorithm. For example, the providing unit analyzes changes in the user's facial expression and calculates an emotion score. Furthermore, the providing unit can adjust the way of expressing the warning based on the user's emotion. For example, if the user is tense, the providing unit issues a warning in a calm tone. Also, if the user is relaxed, the providing unit can issue a warning in a bright tone. Furthermore, if the user is feeling stressed, the providing unit issues a concise and highly visible warning. This enables a more appropriate warning by adjusting the way of expressing the warning based on the user's emotion. The estimation of the emotion is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0089] When providing a warning, the providing unit can select the optimal warning method by referring to the user's past warning history. The providing unit selects the warning method based on, for example, the user's past warning history. For example, the providing unit issues a similar warning based on the user's past warning history. The providing unit can also extract specific keywords or phrases from the user's past warning history and use them in the warning. Furthermore, the providing unit can analyze the user's warning history and select the most effective warning method. In this way, the optimal warning method can be selected by referring to the user's past warning history.

[0090] The providing unit can adjust the urgency of the warning based on the user's current situation when providing the warning. The providing unit adjusts the urgency of the warning based on the user's current situation, for example. For example, when the user is moving, the providing unit issues a warning with a high urgency. In addition, when the user is relaxed, the providing unit can also issue a warning with a low urgency. Furthermore, when the user is feeling stressed, the providing unit issues a concise and highly visible warning. This allows for more appropriate warning by adjusting the urgency of the warning based on the user's current situation.

[0091] The providing unit can estimate the user's emotion and determine the priority of warnings based on the estimated user's emotion. The providing unit, for example, estimates the user's emotion using voice tone analysis or facial expression recognition. For example, the providing unit analyzes the user's voice tone and determines whether the user is tense or relaxed. The providing unit can also capture the user's facial expression with a camera and estimate the emotion using a facial expression recognition algorithm. For example, the providing unit analyzes changes in the user's facial expression and calculates an emotion score. Furthermore, the providing unit can determine the priority of warnings based on the user's emotion. For example, when the user is tense, the providing unit issues important warnings with priority. Also, when the user is relaxed, the providing unit can issue all warnings equally. Furthermore, when the user is stressed, the providing unit issues concise warnings with priority. This enables more appropriate warnings by determining the priority of warnings based on the user's emotion. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (for example, LLM) or a multimodal generation AI.

[0092] When providing a warning, the providing unit can select an optimal warning method in consideration of the geographical location information of the user. The providing unit selects a warning method based on, for example, the geographical location information of the user. For example, when the user is in a specific area, the providing unit issues a warning based on a pattern of phishing scams that frequently occur in that area. The providing unit can also issue a warning based on the user's current location in consideration of a language or dialect specific to the area. Furthermore, the providing unit can optimize the warning in consideration of the communication environment of the area based on the geographical location information of the user. In this way, the optimal warning method can be selected by considering the geographical location information of the user.

[0093] When providing a warning, the providing unit can provide a relevant warning by analyzing the user's social media activity. The providing unit provides a warning based on, for example, the user's social media activity. For example, the providing unit learns phrases and words frequently used by the user on social media and warns of signs of phishing scams. The providing unit can also extract specific keywords and topics from the user's social media activity and use them for the warning. Furthermore, the providing unit can prioritize warnings that are likely to be phishing scams based on the user's social media activity. In this way, it is possible to provide relevant warnings by analyzing the user's social media activity.

[0094] The processing unit can estimate the user's emotion and adjust the timing of data processing based on the estimated user's emotion. The processing unit, for example, estimates the user's emotion using voice tone analysis or facial expression recognition. For example, the processing unit analyzes the user's voice tone and determines whether the user is nervous or relaxed. The processing unit can also capture the user's facial expression with a camera and estimate the emotion using a facial expression recognition algorithm. For example, the processing unit analyzes changes in the user's facial expression and calculates an emotion score. Furthermore, the processing unit can adjust the timing of data processing based on the user's emotion. For example, if the user is nervous, the processing unit can quickly perform data processing and provide results immediately. Also, if the user is relaxed, the processing unit can perform detailed data processing and provide more information. Furthermore, if the user is stressed, the processing unit can concisely summarize the data processing results so as not to burden the user. As a result, more appropriate data processing is possible by adjusting the timing of data processing based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0095] When processing data, the processing unit can select the optimal processing method by referring to the user's past data processing history. The processing unit selects the data processing method based on, for example, the user's past data processing history. For example, the processing unit selects a similar processing method based on the user's past data processing history. The processing unit can also extract specific keywords or phrases from the user's past data processing history and use them in processing. Furthermore, the processing unit can analyze the user's data processing history and select the most efficient processing method. In this way, the optimal processing method can be selected by referring to the user's past data processing history.

[0096] The processing unit can adjust the priority of processing based on the user's current communication status during data processing. The processing unit adjusts the priority of data processing based on the user's current communication status, for example. For example, when the user is on the move, the processing unit prioritizes data processing with high urgency. Also, when the user is relaxed, the processing unit can perform all data processing equally. Furthermore, when the user is feeling stressed, the processing unit prioritizes simple data processing. This allows more appropriate data processing by adjusting the priority of processing based on the user's current communication status.

[0097] The processing unit can estimate the user's emotion and determine the priority of data processing based on the estimated user's emotion. The processing unit, for example, estimates the user's emotion using voice tone analysis or facial expression recognition. For example, the processing unit analyzes the user's voice tone and determines whether the user is nervous or relaxed. The processing unit can also capture the user's facial expression with a camera and estimate the emotion using a facial expression recognition algorithm. For example, the processing unit analyzes changes in the user's facial expression and calculates an emotion score. Furthermore, the processing unit can determine the priority of data processing based on the user's emotion. For example, when the user is nervous, the processing unit prioritizes important data processing. Also, when the user is relaxed, the processing unit can equally perform all data processing. Furthermore, when the user is stressed, the processing unit prioritizes simple data processing. As a result, more appropriate data processing is possible by determining the priority of data processing based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (for example, LLM) or a multimodal generation AI.

[0098] The processing unit can select an optimal processing method in consideration of the geographical location information of the user when processing data. The processing unit selects a data processing method based on, for example, the geographical location information of the user. For example, when the user is in a specific area, the processing unit performs processing based on a data processing pattern that occurs frequently in that area. The processing unit can also perform processing based on the user's current location and taking into account a communication environment specific to the area. Furthermore, the processing unit can optimize processing based on the geographical location information of the user and taking into account the communication environment of the area. This allows the optimal processing method to be selected by taking into account the geographical location information of the user.

[0099] During data processing, the processing unit can analyze the user's social media activity and prioritize processing of related data. The processing unit processes data based on, for example, the user's social media activity. For example, the processing unit learns phrases and words frequently used by the user on social media and uses them in data processing. The processing unit can also extract specific keywords and topics from the user's social media activity and use them in data processing. Furthermore, the processing unit can also prioritize processing of related data based on the user's social media activity. As a result, by analyzing the user's social media activity, related data can be prioritized for processing.

[0100] The protection unit can estimate the user's emotion and adjust the privacy protection method based on the estimated user's emotion. The protection unit estimates the user's emotion using, for example, voice tone analysis or facial expression recognition. For example, the protection unit analyzes the user's voice tone and determines whether the user is tense or relaxed. The protection unit can also capture the user's facial expression with a camera and estimate the emotion using a facial expression recognition algorithm. For example, the protection unit analyzes changes in the user's facial expression and calculates an emotion score. Furthermore, the protection unit can adjust the privacy protection method based on the user's emotion. For example, when the user is tense, the protection unit performs strict privacy protection. Also, when the user is relaxed, the protection unit can perform flexible privacy protection. Furthermore, when the user is stressed, the protection unit performs simple privacy protection. As a result, by adjusting the privacy protection method based on the user's emotion, more appropriate privacy protection is possible. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0101] During privacy protection, the protection unit can select an optimal protection method by referring to the user's past privacy protection history. The protection unit selects a privacy protection method based on, for example, the user's past privacy protection history. For example, the protection unit selects a similar protection method based on the user's past privacy protection history. The protection unit can also extract specific keywords or phrases from the user's past privacy protection history and use them for protection. Furthermore, the protection unit can analyze the user's privacy protection history and select the most effective protection method. In this way, the optimal protection method can be selected by referring to the user's past privacy protection history.

[0102] The protection unit can estimate the user's emotion and determine the priority of privacy protection based on the estimated user's emotion. The protection unit, for example, estimates the user's emotion using voice tone analysis or facial expression recognition. For example, the protection unit analyzes the user's voice tone and determines whether the user is tense or relaxed. The protection unit can also capture the user's facial expression with a camera and estimate the emotion using a facial expression recognition algorithm. For example, the protection unit analyzes changes in the user's facial expression and calculates an emotion score. Furthermore, the protection unit can determine the priority of privacy protection based on the user's emotion. For example, if the user is tense, the protection unit prioritizes important privacy protection. Also, if the user is relaxed, the protection unit can equally perform all privacy protection. Furthermore, if the user is stressed, the protection unit prioritizes simple privacy protection. As a result, more appropriate privacy protection is possible by determining the priority of privacy protection based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0103] The protection unit can select an optimal protection method in consideration of the geographical location information of the user during privacy protection. The protection unit selects a privacy protection method based on, for example, the geographical location information of the user. For example, when the user is in a specific area, the protection unit performs protection based on a pattern of privacy violations that frequently occur in that area. The protection unit can also perform protection in consideration of a privacy protection method specific to the area based on the user's current location. Furthermore, the protection unit can optimize protection in consideration of the communication environment of the area based on the geographical location information of the user. In this way, the optimal protection method can be selected by considering the geographical location information of the user.

[0104] The quarantine unit can estimate the user's emotions and adjust the quarantine method of the email based on the estimated user's emotions. The quarantine unit, for example, estimates the user's emotions using voice tone analysis or facial expression recognition. For example, the quarantine unit analyzes the user's voice tone and determines whether the user is nervous or relaxed. The quarantine unit can also capture the user's facial expression with a camera and estimate the emotion using a facial expression recognition algorithm. For example, the quarantine unit analyzes changes in the user's facial expression and calculates an emotion score. Furthermore, the quarantine unit can adjust the quarantine method of the email based on the user's emotions. For example, if the user is nervous, the quarantine unit quickly quarantines the email and notifies the user immediately. Also, if the user is relaxed, the quarantine unit can provide detailed information and explain the reason for the quarantine. Furthermore, if the user is stressed, the quarantine unit provides a concise notification and provides only important information. This allows for more appropriate quarantine by adjusting the quarantine method of the email based on the user's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0105] When isolating an email, the quarantine unit can select the optimal quarantine method by referring to the user's past email quarantine history. The quarantine unit selects the email quarantine method based on, for example, the user's past email quarantine history. For example, the quarantine unit quarantines similar emails based on the history of emails that the user has previously quarantined. The quarantine unit can also extract specific keywords or phrases from the user's past email quarantine history and use them for quarantine. Furthermore, the quarantine unit can analyze the user's email quarantine history and select the most effective quarantine method. In this way, the optimal quarantine method can be selected by referring to the user's past email quarantine history.

[0106] The quarantine unit can estimate the user's emotions and determine the quarantine priority of emails based on the estimated user's emotions. The quarantine unit, for example, estimates the user's emotions using voice tone analysis or facial expression recognition. For example, the quarantine unit analyzes the user's voice tone and determines whether the user is nervous or relaxed. The quarantine unit can also capture the user's facial expression with a camera and estimate the emotion using a facial expression recognition algorithm. For example, the quarantine unit analyzes changes in the user's facial expression and calculates an emotion score. Furthermore, the quarantine unit can determine the quarantine priority of emails based on the user's emotions. For example, if the user is nervous, the quarantine unit prioritizes quarantining important emails. Also, if the user is relaxed, the quarantine unit can evenly quarantine all emails. Furthermore, if the user is stressed, the quarantine unit prioritizes quarantining concise emails. This enables more appropriate quarantine by determining the quarantine priority of emails based on the user's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0107] When isolating email, the quarantine unit can select the optimal quarantine method by taking into consideration the geographical location information of the user. The quarantine unit selects the email quarantine method based on, for example, the geographical location information of the user. For example, when the user is in a specific area, the quarantine unit quarantines email based on the pattern of phishing scams that frequently occur in that area. The quarantine unit can also quarantine email by taking into consideration the language or dialect specific to the area based on the user's current location. Furthermore, the quarantine unit can also quarantine email by taking into consideration the communication environment of the area based on the geographical location information of the user. In this way, the optimal quarantine method can be selected by taking into consideration the geographical location information of the user.

[0108] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0109] When analyzing the voice data, the analysis unit can improve the accuracy of the analysis by referring to the user's past voice data. For example, the analysis unit can detect similar patterns based on the user's history of phishing scam calls received in the past. The analysis unit can also extract specific keywords or phrases from the user's past voice data and use them in the analysis. Furthermore, the analysis unit can analyze the user's voice data and prioritize the analysis of calls that are likely to be phishing scams. In this way, the analysis accuracy can be improved by referring to the user's past voice data.

[0110] When analyzing the voice data, the analysis unit can perform the analysis based on the geographical location information of the user. For example, when the user is in a specific area, the analysis unit reflects the phishing scam patterns that occur frequently in that area in the analysis. The analysis unit can also perform the analysis taking into account the language and dialect specific to the area based on the user's current location. Furthermore, the analysis unit can optimize the analysis by taking into account the communication environment of the area based on the geographical location information of the user. In this way, the accuracy of the analysis is improved by taking into account the geographical location information of the user.

[0111] The analysis unit can estimate the user's emotions and adjust the analysis method of the voice data based on the estimated user's emotions. For example, the analysis unit can analyze the user's voice tone and determine whether the user is tense or relaxed. The analysis unit can also capture the user's facial expression with a camera and estimate the emotion using a facial expression recognition algorithm. Furthermore, if the user is feeling stressed, the analysis unit summarizes the analysis results concisely so as not to burden the user. This allows for more appropriate analysis by adjusting the analysis method of the voice data based on the user's emotions.

[0112] When analyzing the text data, the analysis unit can optimize the analysis algorithm by analyzing the user's email usage patterns. For example, the analysis unit can learn phrases and words frequently used by the user and detect signs of phishing scams. The analysis unit can also analyze the user's email sending and receiving patterns and detect abnormal patterns. Furthermore, the analysis unit can prioritize the analysis of emails that are likely to be phishing scams based on the user's email usage history. In this way, the analysis algorithm is optimized by analyzing the user's email usage patterns.

[0113] The analysis unit can estimate the user's emotions and determine the analysis priority of the text data based on the estimated user's emotions. For example, the analysis unit can analyze the user's voice tone and determine whether the user is tense or relaxed. The analysis unit can also capture the user's facial expression with a camera and estimate the emotion using a facial expression recognition algorithm. Furthermore, if the user is feeling stressed, the analysis unit performs a concise analysis and provides only important information. This allows for more appropriate analysis by determining the analysis priority of the text data based on the user's emotions.

[0114] When detecting signs of phishing fraud, the detection unit can improve detection accuracy by referring to past phishing fraud data. For example, the detection unit detects similar patterns based on past phishing fraud data. The detection unit can also extract specific keywords or phrases from past phishing fraud data and use them for detection. Furthermore, the detection unit can analyze past phishing fraud data and preferentially detect signs that are likely to be phishing frauds. In this way, by referring to past phishing fraud data, detection accuracy is improved.

[0115] The detection unit can estimate the user's emotions and adjust the phishing sign detection criteria based on the estimated user's emotions. For example, the detection unit can analyze the user's voice tone and determine whether the user is nervous or relaxed. The detection unit can also capture the user's facial expression with a camera and estimate the emotion using a facial expression recognition algorithm. Furthermore, if the user is feeling stressed, the detection unit detects the phishing sign with simple criteria. This allows for more appropriate detection by adjusting the phishing sign detection criteria based on the user's emotions.

[0116] When providing a warning, the providing unit can select the optimal warning method by referring to the user's past warning history. For example, the providing unit issues a similar warning based on the user's past warning history. The providing unit can also extract specific keywords or phrases from the user's past warning history and use them in the warning. Furthermore, the providing unit can analyze the user's warning history and select the most effective warning method. In this way, the optimal warning method can be selected by referring to the user's past warning history.

[0117] The providing unit can estimate the user's emotion and adjust the way in which the warning is expressed based on the estimated user's emotion. For example, the providing unit can analyze the user's voice tone and determine whether the user is tense or relaxed. The providing unit can also capture the user's facial expression with a camera and estimate the emotion using a facial expression recognition algorithm. Furthermore, if the user is feeling stressed, the providing unit issues a concise and highly visible warning. This allows for more appropriate warnings by adjusting the way in which the warning is expressed based on the user's emotion.

[0118] The protection unit can estimate the user's emotion and adjust the privacy protection method based on the estimated user's emotion. For example, the protection unit can analyze the user's voice tone and determine whether the user is tense or relaxed. The protection unit can also capture the user's facial expression with a camera and estimate the emotion using a facial expression recognition algorithm. Furthermore, if the user is feeling stressed, the protection unit performs simple privacy protection. This allows for more appropriate privacy protection by adjusting the privacy protection method based on the user's emotion.

[0119] The process flow of the second embodiment will be briefly described below.

[0120] Step 1: The analysis unit analyzes the voice data. Voice data includes call recordings, voice messages, and audio files. The analysis unit uses machine learning algorithms to analyze the voice data and detect signs of phishing scams. It also extracts specific keywords and phrases from the voice data to determine whether they are phishing scams. It also analyzes sender information and evaluates their credibility. Step 2: The analysis unit analyzes the text data. The text data includes emails, chat messages, document files, etc. The analysis unit extracts specific keywords and phrases from the text data and determines whether it is a phishing scam. It also analyzes the sender information and evaluates its credibility. Step 3: The detection unit detects signs of phishing based on the data analyzed by the analysis unit. Signs of phishing include specific keywords, link patterns, sender information, etc. The detection unit uses a rule-based algorithm to detect signs of phishing based on the data provided by the analysis unit. Step 4: The providing unit provides a warning to the user based on the indications of a phishing scam detected by the detection unit. The warning may be a pop-up notification, an email notification, a voice notification, or the like. The providing unit displays a pop-up notification on the user's device to warn of the possibility of a phishing scam. The providing unit may also provide a warning to the user using an email notification or a voice notification.

[0121] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0122] 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<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. 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 a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0123] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0124] Each of the multiple elements including the above-mentioned analysis unit, detection unit, provision unit, processing unit, protection unit, and isolation 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 voice data. The detection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and detects signs of phishing fraud. The provision unit is realized, for example, by the control unit 46A of the smart device 14 and provides a warning to the user. The processing unit is realized, for example, by the processor 46 of the smart device 14 and processes voice data and text data locally. The protection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and protects the privacy of the user. The isolation unit is realized, for example, by the control unit 46A of the smart device 14 and isolates the email when a sign of phishing fraud is detected. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0125] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0126] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0127] 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, the RAM 30, and the 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.

[0128] 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.

[0129] 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 the voice according to instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0131] 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 performed in a secure state.

[0132] Fig. 4 shows an example of 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.

[0133] 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.

[0134] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0135] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 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 also perform processing similar to that of the specific processing unit 290 using these models.

[0136] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0137] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0138] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0139] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, 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, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0140] Each of the multiple elements including the above-mentioned analysis unit, detection unit, provision unit, processing unit, protection unit, and isolation 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 voice data. The detection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and detects signs of phishing fraud. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214 and provides a warning to the user. The processing unit is realized, for example, by the processor 46 of the smart glasses 214 and processes voice data and text data locally. The protection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and protects the privacy of the user. The isolation unit is realized, for example, by the control unit 46A of the smart glasses 214 and isolates the email when a sign of phishing fraud is detected. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0141] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0142] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0143] 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, the RAM 30, and the 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.

[0144] 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.

[0145] 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 the voice according to instructions from the processor 46.

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0147] 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 performed in a secure state.

[0148] 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.

[0149] 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.

[0150] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0151] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific 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 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.

[0152] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0153] 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 voice indicating a user input for 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.

[0154] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0155] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0156] Each of the multiple elements including the above-mentioned analysis unit, detection unit, provision unit, processing unit, protection unit, and isolation 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 voice data. The detection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and detects signs of phishing fraud. The provision unit is realized, for example, by the control unit 46A of the headset type terminal 314 and provides a warning to the user. The processing unit is realized, for example, by the processor 46 of the headset type terminal 314 and processes voice data and text data locally. The protection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and protects the privacy of the user. The isolation unit is realized, for example, by the control unit 46A of the headset type terminal 314 and isolates the email when a sign of phishing fraud is detected. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0157] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0158] 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.

[0159] 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, the RAM 30, and the 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.

[0160] 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.

[0161] 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 the voice according to instructions from the processor 46.

[0162] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0163] 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 performed in a secure state.

[0164] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0165] 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.

[0166] 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.

[0167] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0168] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 program 60 executed on the RAM 48. The robot 414 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.

[0169] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0170] 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 a voice indicating a user input for 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.

[0171] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0172] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, 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.

[0173] Each of the multiple elements including the above-mentioned analysis unit, detection unit, provision unit, processing unit, protection unit, and isolation 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 voice data. The detection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and detects signs of phishing fraud. The provision unit is realized, for example, by the control unit 46A of the robot 414 and provides a warning to the user. The processing unit is realized, for example, by the processor 46 of the robot 414 and processes voice data and text data locally. The protection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and protects the privacy of the user. The isolation unit is realized, for example, by the control unit 46A of the robot 414 and isolates the email when a sign of phishing fraud is detected. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0174] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0175] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0176] These emotions are distributed in the three o'clock direction of emotion map 400 and usually 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.

[0177] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0178] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0179] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is 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 the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0180] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0181] 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.

[0182] 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 Universal Serial Bus (USB) 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.

[0183] In addition, 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 upon request from the data processing device 12.

[0184] 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.

[0185] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0186] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0187] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[0188] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0189] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0190] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0191] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0192] (Appendix 1) an analysis unit that analyzes the voice data and the text data; a detection unit that detects signs of phishing fraud based on the data analyzed by the analysis unit; a providing unit that provides a warning to a user based on the indication of a phishing scam detected by the detection unit; Equipped system. (Appendix 2) Equipped with a processing unit for locally processing voice data and text data 2. The system of claim 1, (Appendix 3) Equipped with a protection section to protect user privacy 2. The system of claim 1, (Appendix 4) Equipped with a quarantine section that quarantines emails if signs of phishing are detected 2. The system of claim 1, (Appendix 5) The analysis unit is Analyzes voice data to determine whether it is a phishing scam 2. The system of claim 1, (Appendix 6) The analysis unit is Analyze text data to detect signs of phishing scams 2. The system of claim 1, (Appendix 7) The analysis unit is To estimate a user's emotion and adjust a method of analyzing voice data based on the estimated user's emotion. 2. The system of claim 1, (Appendix 8) The analysis unit is When analyzing voice data, the accuracy of the analysis can be improved by referring to the user's past call history. 2. The system of claim 1, (Appendix 9) The analysis unit is When analyzing text data, optimize the analysis algorithm by analyzing the user's email usage patterns. 2. The system of claim 1, (Appendix 10) The analysis unit is A user's emotions are estimated, and a priority order for analyzing text data is determined based on the estimated user's emotions. 2. The system of claim 1, (Appendix 11) The analysis unit is When analyzing voice data, the analysis is based on the user's geographic location information. 2. The system of claim 1, (Appendix 12) The analysis unit is When analyzing text data, analyze users' social media activities to prioritize analysis of relevant data. 2. The system of claim 1, (Appendix 13) The detection unit is To estimate a user's emotions and adjust a phishing scam symptom detection criterion based on the estimated user's emotions. 2. The system of claim 1, (Appendix 14) The detection unit is When detecting signs of phishing, improve detection accuracy by referencing past phishing data 2. The system of claim 1, (Appendix 15) The detection unit is When detecting signs of phishing, the detection algorithm is optimized by analyzing the user's communication patterns. 2. The system of claim 1, (Appendix 16) The detection unit is Inferring user emotions and adjusting the display method of phishing indicator detection results based on the estimated user emotions 2. The system of claim 1, (Appendix 17) The detection unit is When detecting signs of phishing scams, the detection takes into account the user's geographic location information. 2. The system of claim 1, (Appendix 18) The detection unit is When detecting signs of phishing scams, the system analyzes users' social media activity to prioritize detection of related signs. 2. The system of claim 1, (Appendix 19) The providing unit is Infer user emotions and adjust warning wording based on the inferred user emotions 2. The system of claim 1, (Appendix 20) The providing unit is When providing a warning, the most appropriate warning method is selected by referring to the user's past warning history. 2. The system of claim 1, (Appendix 21) The providing unit is When an alert is delivered, adjust the urgency of the alert based on the user's current situation 2. The system of claim 1, (Appendix 22) The providing unit is Infer user emotions and prioritize alerts based on the inferred user emotions 2. The system of claim 1, (Appendix 23) The providing unit is When providing alerts, the most appropriate alert method is selected taking into account the user's geographic location. 2. The system of claim 1, (Appendix 24) The providing unit is When providing alerts, we analyze your social media activity to provide you with relevant alerts. 2. The system of claim 1, (Appendix 25) The processing unit includes: To estimate a user's emotion and adjust the timing of data processing based on the estimated user's emotion. 3. The system of claim 2, (Appendix 26) The processing unit includes: When processing data, the optimal processing method is selected by referring to the user's past data processing history. 3. The system of claim 2, (Appendix 27) The processing unit includes: When processing data, adjust the processing priority based on the user's current communication status 3. The system of claim 2, (Appendix 28) The processing unit includes: To estimate a user's emotion and determine a priority of data processing based on the estimated user's emotion. 3. The system of claim 2, (Appendix 29) The processing unit includes: When processing data, the optimal processing method is selected taking into account the user's geographic location information. 3. The system of claim 2, (Appendix 30) The processing unit includes: When processing your data, analyze your social media activity and give priority to the relevant data 3. The system of claim 2, (Appendix 31) The protective part is Inferring user emotions and adjusting privacy protection methods based on the estimated user emotions 4. The system of claim 3. (Appendix 32) The protective part is When protecting privacy, the optimal protection method is selected by referring to the user's past privacy protection history. 4. The system of claim 3. (Appendix 33) The protective part is Estimating user emotions and determining privacy protection priorities based on the estimated user emotions 4. The system of claim 3. (Appendix 34) The protective part is When protecting privacy, select the optimal protection method taking into account the user's geographic location information 4. The system of claim 3. (Appendix 35) The isolation unit includes: Infer user sentiment and adjust email quarantine strategy based on inferred user sentiment 5. The system of claim 4. (Appendix 36) The isolation unit includes: When quarantining emails, the system selects the optimal quarantine method by referring to the user's past email quarantine history. 5. The system of claim 4. (Appendix 37) The isolation unit includes: Inferring user sentiment and prioritizing quarantine of emails based on the inferred user sentiment 5. The system of claim 4. (Appendix 38) The isolation unit includes: When quarantining emails, the geographic location of the user is taken into account to determine the optimal quarantine method 5. The system of claim 4. [Explanation of symbols]

[0193] 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 voice data and the text data; a detection unit that detects signs of phishing fraud based on the data analyzed by the analysis unit; a providing unit that provides a warning to a user based on the indication of a phishing scam detected by the detection unit; Equipped system.

2. Equipped with a processing unit for locally processing voice data and text data 2. The system of claim 1 .

3. Equipped with a protection section to protect user privacy 2. The system of claim 1 .

4. Equipped with a quarantine section that quarantines emails if signs of phishing are detected 2. The system of claim 1 .

5. The analysis unit is To estimate a user's emotion and adjust a method of analyzing voice data based on the estimated user's emotion.

2. The system of claim 1 .

6. The analysis unit is When analyzing voice data, the accuracy of the analysis can be improved by referring to the user's past call history.

2. The system of claim 1 .

7. The analysis unit is When analyzing text data, optimize the analysis algorithm by analyzing the user's email usage patterns.

2. The system of claim 1 .

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