System

Abstract

An object of a system according to an embodiment is to increase awareness of special fraud and suppress damage.SOLUTION: A system includes a fraud scenario generation unit, a voice reproduction unit, a mail generation unit, and an invitation message generation unit. The fraud scenario generator generates a fraud scenario. The voice reproduction unit reproduces voice on the basis of the fraud scenario generated by the fraud scenario generation unit. The mail generator generates an e-mail based on the fraud scenario. The invitation message generator generates an invitation message based on the fraud scenario.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies lack effective means for raising awareness of special frauds, and there is room for improvement in reducing damage.

[0005] The system according to the embodiment aims to raise awareness of special frauds and reduce damage caused by them. [Means for solving the problem]

[0006] The system according to the embodiment includes a fraud scenario generation unit, an audio reproduction unit, an email generation unit, and a solicitation message generation unit. The fraud scenario generation unit generates a fraud scenario. The audio reproduction unit reproduces audio based on the fraud scenario generated by the fraud scenario generation unit. The email generation unit generates an email based on the fraud scenario. The solicitation message generation unit generates a solicitation message based on the fraud scenario. [Effects of the Invention]

[0007] The system according to the embodiment can raise awareness of special frauds and reduce damage. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example 1) The fraud simulation system according to an embodiment of the present invention is a system in which a generation AI that has learned human voices simulates a phone call from a fraud perpetrator. This allows the fraud simulation system to raise users' awareness of fraud and reduce damage.

[0029] The special fraud simulation experience system according to the embodiment includes a generation AI, a fraud scenario generation unit, a voice reproduction unit, an email generation unit, and a solicitation message generation unit. The generation AI learns the voices and speaking styles of special fraud perpetrators and reproduces realistic fraudulent phone calls. For example, the generation AI generates fraudulent phone calls based on prompts including instructions for a fraud scenario. The fraud scenario generation unit generates a fraud scenario. For example, the fraud scenario generation unit automatically generates a scenario including methods commonly used by fraudsters. The voice reproduction unit reproduces voices based on the fraud scenario generated by the fraud scenario generation unit. For example, the voice reproduction unit plays the voice generated by the generation AI to the user. The email generation unit generates emails based on the fraud scenario. For example, the email generation unit automatically generates phishing emails and sends them to the user. The solicitation message generation unit generates solicitation messages based on the fraud scenario. For example, the solicitation message generation unit automatically generates messages soliciting illegal part-time work and sends them to the user. This allows the special fraud simulation experience system according to the embodiment to raise users' awareness of special frauds and reduce damage.

[0030] The fraud scenario generation unit learns the voices of family members and can simulate special frauds using voices that sound just like family members. The fraud scenario generation unit, for example, learns the voices of family members and recreates fraudulent phone calls using voices that sound just like family members. For example, the fraud scenario generation unit automatically generates a fraud scenario using the voices of family members and plays it back to the user. This allows for simulated experiences using the voices of family members to reduce more sophisticated special frauds.

[0031] The fraud scenario generation unit can reproduce fraud attacks by email. The fraud scenario generation unit reproduces, for example, fraud attacks by email. For example, the fraud scenario generation unit automatically generates phishing emails and sends them to the user. This allows the user to experience a simulated fraud attack by email, thereby raising their awareness of fraudulent methods.

[0032] The fraud scenario generation unit can reproduce a scenario of a solicitation for an illegal part-time job. The fraud scenario generation unit reproduces, for example, a scenario of a solicitation for an illegal part-time job. For example, the fraud scenario generation unit automatically generates a message of a solicitation for an illegal part-time job and sends it to the user. This allows the user to increase their awareness of fraudulent methods by experiencing a simulated solicitation for an illegal part-time job.

[0033] The fraud scenario generation unit can periodically generate new fraud scenarios and provide simulated experiences. The fraud scenario generation unit, for example, periodically generates new fraud scenarios and provides simulated experiences. For example, the fraud scenario generation unit automatically generates new fraud scenarios every month and provides them to the user. This allows the user to constantly increase their awareness of fraudulent methods through regular simulated experiences.

[0034] The fraud scenario generation unit can dynamically change the scenario depending on the user's reaction. The fraud scenario generation unit dynamically changes the scenario depending on the user's reaction, for example. For example, if the user has doubts, the fraud scenario generation unit provides the fraudster with convincing additional information. In this way, by dynamically changing the scenario depending on the user's reaction, a more realistic simulated experience can be provided.

[0035] The fraud scenario generation unit can generate scenarios that reflect regionally specific fraud methods and cultural backgrounds. The fraud scenario generation unit generates scenarios that reflect regionally specific fraud methods and cultural backgrounds, for example. For example, the fraud scenario generation unit automatically generates scenarios that incorporate fraud methods that are prevalent in a particular region. This allows users to experience fraud methods that they may actually encounter using scenarios that reflect regionally specific fraud methods and cultural backgrounds.

[0036] The fraud scenario generation unit can generate fraud scenarios that correspond to different languages ​​and cultural spheres. The fraud scenario generation unit generates fraud scenarios that correspond to different languages ​​and cultural spheres, for example. For example, the fraud scenario generation unit automatically generates scenarios that correspond to multiple languages, such as English and Chinese. This makes it possible to provide a simulated experience of international fraud methods using fraud scenarios that correspond to different languages ​​and cultural spheres.

[0037] The fraud scenario generation unit can recreate a fraud scenario in a visual novel format and add visual elements. The fraud scenario generation unit can recreate a fraud scenario in a visual novel format and add visual elements. For example, the fraud scenario generation unit displays a fraudster's character and background images. By recreating a fraud scenario in a visual novel format, a visually realistic experience can be provided.

[0038] The fraud scenario generation unit can generate a fraud scenario that reflects the individual speaking styles and catchphrases of family members. The fraud scenario generation unit, for example, learns the individual speaking styles and catchphrases of family members and generates a fraud scenario that reflects them. For example, it automatically generates a scenario that incorporates phrases and intonations unique to each family member. This makes it possible to provide a more realistic simulated experience with a fraud scenario that reflects the individual speaking styles and catchphrases of family members.

[0039] The fraud scenario generation unit can generate a fraud scenario that combines family photos and video messages. The fraud scenario generation unit generates a fraud scenario that combines, for example, family photos and video messages. For example, the fraud scenario generation unit displays family photos and plays video messages during a fraudulent phone scenario. This makes it possible to provide a more realistic simulated experience by using a fraud scenario that combines family photos and video messages.

[0040] The fraud scenario generation unit can reproduce a fraud scenario in the form of a family video call. The fraud scenario generation unit reproduces a fraud scenario in the form of, for example, a family video call. For example, the fraud scenario generation unit imitates a family video call and attempts to defraud a user. In this way, by reproducing a fraud scenario in the form of a family video call, a visually realistic experience can be provided.

[0041] The fraud scenario generation unit can provide a fraud scenario in a message format that imitates a family member's SNS account. The fraud scenario generation unit provides a fraud scenario in a message format that imitates a family member's SNS account, for example. For example, the fraud scenario generation unit sends a message pretending to be a family member's SNS account. By providing a fraud scenario in a message format that imitates a family member's SNS account, a more realistic simulated experience can be provided.

[0042] The fraud scenario generation unit can automatically generate the content of a phishing email and dynamically change the content depending on the user's reaction. For example, the fraud scenario generation unit automatically generates the content of a phishing email and dynamically changes the content based on the user's reaction when opening the email. For example, if the user clicks on a link, more detailed fraud information is provided. In this way, by dynamically changing the content of the phishing email, a more realistic simulated experience can be provided.

[0043] The fraud scenario generation unit can generate the content of phishing emails that reflect the latest fraud methods and trends. For example, the fraud scenario generation unit learns the latest fraud methods and trends and generates the content of phishing emails that reflects them. For example, it creates fraudulent emails based on recent news or incidents. This allows users to experience fraud methods that they may actually encounter by using phishing email content that reflects the latest fraud methods and trends.

[0044] The fraud scenario generation unit can generate phishing email content that corresponds to different languages ​​and cultural areas. The fraud scenario generation unit generates phishing email content that corresponds to different languages ​​and cultural areas, for example. For example, the fraud scenario generation unit automatically generates phishing emails in multiple languages, such as English and Chinese. This makes it possible to provide a simulated experience of international fraud methods by using phishing email content that corresponds to different languages ​​and cultural areas.

[0045] The deception scenario generation unit can generate the content of the phishing email by adding visual content and interactive elements. The deception scenario generation unit generates the content of the phishing email by adding, for example, visual content and interactive elements. For example, the deception scenario generation unit embeds images and videos in the deception email. This allows the content of the phishing email to be enhanced with visual content and interactive elements, providing a visually realistic experience.

[0046] The fraud scenario generation unit can automatically generate a scenario for soliciting illegal part-time work and dynamically change the scenario depending on the user's reaction. For example, the fraud scenario generation unit automatically generates a scenario for soliciting illegal part-time work and dynamically changes the scenario based on the user's reaction when reading the message. For example, if the user shows interest, more detailed information is provided. In this way, by dynamically changing the scenario for soliciting illegal part-time work, a more realistic simulated experience can be provided.

[0047] The fraud scenario generation unit can generate a scenario for soliciting illegal part-time work that reflects regionally specific methods and cultural backgrounds. The fraud scenario generation unit generates, for example, a scenario for soliciting illegal part-time work that reflects regionally specific methods and cultural backgrounds. For example, the fraud scenario generation unit automatically generates a scenario that incorporates solicitation methods that are popular in a specific region. This allows the user to experience methods that they may actually encounter through a scenario for soliciting illegal part-time work that reflects regionally specific methods and cultural backgrounds.

[0048] The fraud scenario generation unit can generate scenarios for soliciting illegal part-time jobs that correspond to different languages ​​and cultural areas. The fraud scenario generation unit generates, for example, scenarios for soliciting illegal part-time jobs that correspond to different languages ​​and cultural areas. For example, the fraud scenario generation unit automatically generates scenarios that correspond to multiple languages, such as English and Chinese. This makes it possible to provide a simulated experience of international methods using scenarios for soliciting illegal part-time jobs that correspond to different languages ​​and cultural areas.

[0049] The fraud scenario generation unit can recreate a scenario of soliciting a shady part-time job in a visual novel format and add visual elements. The fraud scenario generation unit can recreate a scenario of soliciting a shady part-time job in a visual novel format and add visual elements. For example, the fraud scenario generation unit displays a solicitor character and background images. By recreating the scenario of soliciting a shady part-time job in a visual novel format, it is possible to provide a visually realistic experience.

[0050] The fraud scenario generation unit can automatically generate new fraud scenarios periodically and dynamically change the scenarios depending on the user's reactions. For example, the fraud scenario generation unit can automatically generate new fraud scenarios periodically and dynamically change the scenarios based on the user's reactions as they experience the scenarios. For example, if the user has doubts, the fraudster can provide convincing additional information. In this way, by dynamically changing new fraud scenarios periodically, a more realistic simulated experience can be provided.

[0051] The fraud scenario generation unit can provide regular simulated experiences that reflect the latest fraud methods and trends. The fraud scenario generation unit, for example, learns the latest fraud methods and trends and provides regular simulated experiences that reflect these. For example, the fraud scenario generation unit creates fraud scenarios based on recent news or incidents. This allows users to constantly increase their awareness of the latest fraud methods through regular simulated experiences that reflect the latest fraud methods and trends.

[0052] The fraud scenario generation unit can provide periodic simulated experiences corresponding to different languages ​​and cultural spheres. The fraud scenario generation unit provides periodic simulated experiences corresponding to different languages ​​and cultural spheres, for example. For example, the fraud scenario generation unit automatically generates fraud scenarios corresponding to multiple languages, such as English and Chinese. This makes it possible to provide simulated experiences of international fraud methods through periodic simulated experiences corresponding to different languages ​​and cultural spheres.

[0053] The fraud scenario generation unit can provide a periodic simulated experience that includes visual content and interactive elements. The fraud scenario generation unit can provide a periodic simulated experience that includes visual content and interactive elements. For example, the fraud scenario generation unit can embed images and videos into the fraud scenario. This can provide a visually realistic experience through the periodic simulated experience that includes visual content and interactive elements.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The special fraud simulation experience system can further include a behavior analysis unit that analyzes the user's behavioral history. The behavior analysis unit can, for example, analyze what types of fraud schemes the user was prone to fall for in the past and customize fraud scenarios based on the results. This makes it possible to provide a more effective simulation experience based on the user's past behavioral history. The behavior analysis unit can also identify what time of day a user is most vulnerable to fraud and provide a scenario tailored to that time of day. Furthermore, the behavior analysis unit can analyze what device the user is using and generate a fraud scenario optimized for that device.

[0056] The special fraud simulation experience system may further include a social media monitoring unit that monitors the user's social media activity. The social media monitoring unit may analyze, for example, the types of posts the user makes and the types of friendships the user has, and customize fraud scenarios based on that information. This allows for a more realistic simulation experience based on the user's social media activity. The social media monitoring unit may also identify topics in which the user is interested and generate fraud scenarios related to those topics. The social media monitoring unit may also analyze the types of advertisements the user responds to and adjust the fraud scenarios based on that information.

[0057] The special fraud simulation experience system can further include a location information acquisition unit that acquires the user's location information. The location information acquisition unit can, for example, identify the area where the user lives and the places the user has visited, and customize the fraud scenario based on that information. This makes it possible to provide a more realistic simulation experience based on the user's location information. Furthermore, if the user is in a specific area, the location information acquisition unit can also provide a scenario that reflects fraud methods that are prevalent in that area. Furthermore, if the user is on the move, the location information acquisition unit can also recreate fraud methods that the user may encounter while traveling.

[0058] The special fraud simulation experience system can further include a purchase history analysis unit that analyzes the user's purchase history. The purchase history analysis unit can, for example, analyze what products the user has purchased in the past and customize a fraud scenario based on that information. This makes it possible to provide a more effective simulation based on the user's purchase history. The purchase history analysis unit can also identify the user's interest in specific brands or products and generate fraud scenarios related to those brands or products. Furthermore, the purchase history analysis unit can analyze what payment methods the user uses and reproduce fraud schemes related to those payment methods.

[0059] The special fraud simulation experience system can further include a learning history analysis unit that analyzes the user's learning history. The learning history analysis unit can, for example, analyze what fraud techniques the user has learned in the past and customize fraud scenarios based on that information. This makes it possible to provide a more effective simulation experience based on the user's learning history. The learning history analysis unit can also identify the user's preferred learning method and generate fraud scenarios optimized for that learning method. Furthermore, the learning history analysis unit can analyze the user's learning pace and provide fraud scenarios that match that pace.

[0060] The special fraud simulation experience system can further include a health monitoring unit that monitors the user's health condition. The health monitoring unit can, for example, measure the user's blood pressure and heart rate and monitor the user's health condition in real time. This makes it possible to temporarily suspend the fraud scenario and provide health advice if the user has a health problem. Furthermore, if the user has a specific health problem, the health monitoring unit can also reproduce fraud methods related to that problem. Furthermore, the health monitoring unit can identify what fraud methods the user is vulnerable to depending on their health condition and provide scenarios related to those methods.

[0061] The special fraud simulation experience system can further include a lifestyle analysis unit that analyzes the user's lifestyle. The lifestyle analysis unit can analyze, for example, what time of day the user is active and what kind of meals the user eats, and customize a fraud scenario based on that information. This makes it possible to provide a more realistic simulation experience based on the user's lifestyle. The lifestyle analysis unit can also identify the user's hobbies and interests and generate a fraud scenario related to those hobbies and interests. Furthermore, the lifestyle analysis unit can analyze the type of stress the user is experiencing and reproduce fraud techniques related to that stress.

[0062] The processing flow of the first embodiment will be briefly explained below.

[0063] Step 1: The fraud scenario generation unit generates a fraud scenario. For example, the fraud scenario generation unit automatically generates a scenario including methods commonly used by fraudsters. Step 2: The voice reproducing unit reproduces the voice based on the fraud scenario generated by the fraud scenario generating unit. For example, the voice reproducing unit plays back the voice generated by the generation AI to the user. Step 3: The email generator generates an email based on a fraud scenario. For example, the email generator automatically generates a phishing email and sends it to the user. Step 4: The solicitation message generator generates a solicitation message based on the fraud scenario. For example, the solicitation message generator automatically generates a message soliciting illegal part-time work and sends it to the user.

[0064] (Example 2) The fraud simulation system according to an embodiment of the present invention is a system in which a generation AI that has learned human voices simulates a phone call from a fraud perpetrator. This allows the fraud simulation system to raise users' awareness of fraud and reduce damage.

[0065] The special fraud simulation experience system according to the embodiment includes a generation AI, a fraud scenario generation unit, a voice reproduction unit, an email generation unit, and a solicitation message generation unit. The generation AI learns the voices and speaking styles of special fraud perpetrators and reproduces realistic fraudulent phone calls. For example, the generation AI generates fraudulent phone calls based on prompts including instructions for a fraud scenario. The fraud scenario generation unit generates a fraud scenario. For example, the fraud scenario generation unit automatically generates a scenario including methods commonly used by fraudsters. The voice reproduction unit reproduces voices based on the fraud scenario generated by the fraud scenario generation unit. For example, the voice reproduction unit plays the voice generated by the generation AI to the user. The email generation unit generates emails based on the fraud scenario. For example, the email generation unit automatically generates phishing emails and sends them to the user. The solicitation message generation unit generates solicitation messages based on the fraud scenario. For example, the solicitation message generation unit automatically generates messages soliciting illegal part-time work and sends them to the user. This allows the special fraud simulation experience system according to the embodiment to raise users' awareness of special frauds and reduce damage.

[0066] The fraud scenario generation unit learns the voices of family members and can simulate special frauds using voices that sound just like family members. The fraud scenario generation unit, for example, learns the voices of family members and recreates fraudulent phone calls using voices that sound just like family members. For example, the fraud scenario generation unit automatically generates a fraud scenario using the voices of family members and plays it back to the user. This allows for simulated experiences using the voices of family members to reduce more sophisticated special frauds.

[0067] The fraud scenario generation unit can reproduce fraud attacks by email. The fraud scenario generation unit reproduces, for example, fraud attacks by email. For example, the fraud scenario generation unit automatically generates phishing emails and sends them to the user. This allows the user to experience a simulated fraud attack by email, thereby raising their awareness of fraudulent methods.

[0068] The fraud scenario generation unit can reproduce a scenario of a solicitation for an illegal part-time job. The fraud scenario generation unit reproduces, for example, a scenario of a solicitation for an illegal part-time job. For example, the fraud scenario generation unit automatically generates a message of a solicitation for an illegal part-time job and sends it to the user. This allows the user to increase their awareness of fraudulent methods by experiencing a simulated solicitation for an illegal part-time job.

[0069] The fraud scenario generation unit can periodically generate new fraud scenarios and provide simulated experiences. The fraud scenario generation unit, for example, periodically generates new fraud scenarios and provides simulated experiences. For example, the fraud scenario generation unit automatically generates new fraud scenarios every month and provides them to the user. This allows the user to constantly increase their awareness of fraudulent methods through regular simulated experiences.

[0070] The fraud scenario generation unit can dynamically change the scenario depending on the user's reaction. The fraud scenario generation unit dynamically changes the scenario depending on the user's reaction, for example. For example, if the user has doubts, the fraud scenario generation unit provides the fraudster with convincing additional information. In this way, by dynamically changing the scenario depending on the user's reaction, a more realistic simulated experience can be provided.

[0071] The fraud scenario generation unit can generate scenarios that reflect regionally specific fraud methods and cultural backgrounds. The fraud scenario generation unit generates scenarios that reflect regionally specific fraud methods and cultural backgrounds, for example. For example, the fraud scenario generation unit automatically generates scenarios that incorporate fraud methods that are prevalent in a particular region. This allows users to experience fraud methods that they may actually encounter using scenarios that reflect regionally specific fraud methods and cultural backgrounds.

[0072] The fraud scenario generation unit can use the emotion estimation function to analyze the user's emotional reactions in real time and adjust the fraud scenario. The fraud scenario generation unit can, for example, use the emotion estimation function to analyze the user's emotional reactions in real time and adjust the fraud scenario. For example, if the user feels anxious, the fraudster can add words to reassure the user. In this way, by adjusting the scenario based on the user's emotional reactions, a more realistic simulated experience can be provided.

[0073] The fraud scenario generation unit can generate fraud scenarios that correspond to different languages ​​and cultural spheres. The fraud scenario generation unit generates fraud scenarios that correspond to different languages ​​and cultural spheres, for example. For example, the fraud scenario generation unit automatically generates scenarios that correspond to multiple languages, such as English and Chinese. This makes it possible to provide a simulated experience of international fraud methods using fraud scenarios that correspond to different languages ​​and cultural spheres.

[0074] The fraud scenario generation unit can recreate a fraud scenario in a visual novel format and add visual elements. The fraud scenario generation unit can recreate a fraud scenario in a visual novel format and add visual elements. For example, the fraud scenario generation unit displays a fraudster's character and background images. By recreating a fraud scenario in a visual novel format, a visually realistic experience can be provided.

[0075] The fraud scenario generation unit can use the emotion estimation function to provide a customized fraud scenario based on the user's emotions. The fraud scenario generation unit, for example, uses the emotion estimation function to provide a customized fraud scenario based on the user's emotions. For example, if the user feels anxious, the fraudster can add words to reassure the user. This allows the customized fraud scenario based on the user's emotions to provide a more personalized simulated experience.

[0076] The fraud scenario generation unit can generate a fraud scenario that reflects the individual speaking styles and catchphrases of family members. The fraud scenario generation unit, for example, learns the individual speaking styles and catchphrases of family members and generates a fraud scenario that reflects them. For example, it automatically generates a scenario that incorporates phrases and intonations unique to each family member. This makes it possible to provide a more realistic simulated experience with a fraud scenario that reflects the individual speaking styles and catchphrases of family members.

[0077] The fraud scenario generation unit can generate a fraud scenario that combines family photos and video messages. The fraud scenario generation unit generates a fraud scenario that combines, for example, family photos and video messages. For example, the fraud scenario generation unit displays family photos and plays video messages during a fraudulent phone scenario. This makes it possible to provide a more realistic simulated experience by using a fraud scenario that combines family photos and video messages.

[0078] The fraud scenario generation unit can use the emotion estimation function to analyze the user's emotional reaction when hearing the voices of family members and adjust the scenario. For example, the fraud scenario generation unit uses the emotion estimation function to analyze the user's emotional reaction when hearing the voices of family members in real time and adjust the scenario. For example, if the user feels anxious, the fraudster can add words to reassure the user. In this way, a more realistic simulated experience can be provided by adjusting the scenario based on the user's emotional reaction when hearing the voices of family members.

[0079] The fraud scenario generation unit can reproduce a fraud scenario in the form of a family video call. The fraud scenario generation unit reproduces a fraud scenario in the form of, for example, a family video call. For example, the fraud scenario generation unit imitates a family video call and attempts to defraud a user. In this way, by reproducing a fraud scenario in the form of a family video call, a visually realistic experience can be provided.

[0080] The fraud scenario generation unit can provide a fraud scenario in a message format that imitates a family member's SNS account. The fraud scenario generation unit provides a fraud scenario in a message format that imitates a family member's SNS account, for example. For example, the fraud scenario generation unit sends a message pretending to be a family member's SNS account. By providing a fraud scenario in a message format that imitates a family member's SNS account, a more realistic simulated experience can be provided.

[0081] The fraud scenario generation unit can use the emotion estimation function to provide a customized scenario based on the user's emotions when hearing the voices of family members. The fraud scenario generation unit, for example, uses the emotion estimation function to provide a customized scenario based on the user's emotions when hearing the voices of family members. For example, if the user feels anxious, the fraudster can add words to reassure the user. This makes it possible to provide a more personalized simulated experience through a customized scenario based on the user's emotions when hearing the voices of family members.

[0082] The fraud scenario generation unit can automatically generate the content of a phishing email and dynamically change the content depending on the user's reaction. For example, the fraud scenario generation unit automatically generates the content of a phishing email and dynamically changes the content based on the user's reaction when opening the email. For example, if the user clicks on a link, more detailed fraud information is provided. In this way, by dynamically changing the content of the phishing email, a more realistic simulated experience can be provided.

[0083] The fraud scenario generation unit can generate the content of phishing emails that reflect the latest fraud methods and trends. For example, the fraud scenario generation unit learns the latest fraud methods and trends and generates the content of phishing emails that reflects them. For example, it creates fraudulent emails based on recent news or incidents. This allows users to experience fraud methods that they may actually encounter by using phishing email content that reflects the latest fraud methods and trends.

[0084] The fraud scenario generation unit can use the emotion estimation function to analyze the user's emotional response when reading a phishing email and adjust the content accordingly. For example, the fraud scenario generation unit can use the emotion estimation function to analyze the user's emotional response when reading a phishing email in real time and adjust the content accordingly. For example, if the user feels anxious, the fraudster can add words to reassure the user. This allows the content to be adjusted based on the user's emotional response when reading a phishing email, providing a more realistic simulated experience.

[0085] The fraud scenario generation unit can generate phishing email content that corresponds to different languages ​​and cultural areas. The fraud scenario generation unit generates phishing email content that corresponds to different languages ​​and cultural areas, for example. For example, the fraud scenario generation unit automatically generates phishing emails in multiple languages, such as English and Chinese. This makes it possible to provide a simulated experience of international fraud methods by using phishing email content that corresponds to different languages ​​and cultural areas.

[0086] The deception scenario generation unit can generate the content of the phishing email by adding visual content and interactive elements. The deception scenario generation unit generates the content of the phishing email by adding, for example, visual content and interactive elements. For example, the deception scenario generation unit embeds images and videos in the deception email. This allows the content of the phishing email to be enhanced with visual content and interactive elements, providing a visually realistic experience.

[0087] The fraud scenario generation unit can use the emotion estimation function to provide customized content based on the user's emotions when reading a phishing email. The fraud scenario generation unit can, for example, use the emotion estimation function to provide customized content based on the user's emotions when reading a phishing email. For example, if the user feels anxious, the fraudster can add words to reassure the user. This allows the user to be provided with a more personalized simulated experience through customized content based on the user's emotions when reading a phishing email.

[0088] The fraud scenario generation unit can automatically generate a scenario for soliciting illegal part-time work and dynamically change the scenario depending on the user's reaction. For example, the fraud scenario generation unit automatically generates a scenario for soliciting illegal part-time work and dynamically changes the scenario based on the user's reaction when reading the message. For example, if the user shows interest, more detailed information is provided. In this way, by dynamically changing the scenario for soliciting illegal part-time work, a more realistic simulated experience can be provided.

[0089] The fraud scenario generation unit can generate a scenario for soliciting illegal part-time work that reflects regionally specific methods and cultural backgrounds. The fraud scenario generation unit generates, for example, a scenario for soliciting illegal part-time work that reflects regionally specific methods and cultural backgrounds. For example, the fraud scenario generation unit automatically generates a scenario that incorporates solicitation methods that are popular in a specific region. This allows the user to experience methods that they may actually encounter through a scenario for soliciting illegal part-time work that reflects regionally specific methods and cultural backgrounds.

[0090] The fraud scenario generation unit can use the emotion estimation function to analyze the user's emotional reactions in real time and adjust the scenario. The fraud scenario generation unit can, for example, use the emotion estimation function to analyze the user's emotional reactions in real time and adjust the scenario. For example, if the user feels anxious, the solicitor can add words to reassure the user. In this way, by adjusting the scenario based on the user's emotional reactions, a more realistic simulated experience can be provided.

[0091] The fraud scenario generation unit can generate scenarios for soliciting illegal part-time jobs that correspond to different languages ​​and cultural areas. The fraud scenario generation unit generates, for example, scenarios for soliciting illegal part-time jobs that correspond to different languages ​​and cultural areas. For example, the fraud scenario generation unit automatically generates scenarios that correspond to multiple languages, such as English and Chinese. This makes it possible to provide a simulated experience of international methods using scenarios for soliciting illegal part-time jobs that correspond to different languages ​​and cultural areas.

[0092] The fraud scenario generation unit can recreate a scenario of soliciting a shady part-time job in a visual novel format and add visual elements. The fraud scenario generation unit can recreate a scenario of soliciting a shady part-time job in a visual novel format and add visual elements. For example, the fraud scenario generation unit displays a solicitor character and background images. By recreating the scenario of soliciting a shady part-time job in a visual novel format, it is possible to provide a visually realistic experience.

[0093] The fraud scenario generation unit can use the emotion estimation function to provide a customized solicitation scenario for an illegal part-time job based on the user's emotions. The fraud scenario generation unit, for example, uses the emotion estimation function to provide a customized solicitation scenario based on the user's emotions. For example, if the user feels anxious, the solicitor can add words to reassure the user. This makes it possible to provide a more personalized simulated experience with a customized solicitation scenario for an illegal part-time job based on the user's emotions.

[0094] The fraud scenario generation unit can automatically generate new fraud scenarios periodically and dynamically change the scenarios depending on the user's reactions. For example, the fraud scenario generation unit can automatically generate new fraud scenarios periodically and dynamically change the scenarios based on the user's reactions as they experience the scenarios. For example, if the user has doubts, the fraudster can provide convincing additional information. In this way, by dynamically changing new fraud scenarios periodically, a more realistic simulated experience can be provided.

[0095] The fraud scenario generation unit can provide regular simulated experiences that reflect the latest fraud methods and trends. The fraud scenario generation unit, for example, learns the latest fraud methods and trends and provides regular simulated experiences that reflect these. For example, the fraud scenario generation unit creates fraud scenarios based on recent news or incidents. This allows users to constantly increase their awareness of the latest fraud methods through regular simulated experiences that reflect the latest fraud methods and trends.

[0096] The fraud scenario generation unit can use the emotion estimation function to analyze the user's emotional reactions during the periodic simulated experiences and adjust the scenario. For example, the fraud scenario generation unit can use the emotion estimation function to analyze the user's emotional reactions during the periodic simulated experiences in real time and adjust the scenario. For example, if the user feels anxious, the fraudster can add words to reassure them. In this way, by adjusting the scenario based on the emotional reactions during the periodic simulated experiences, a more realistic simulated experience can be provided.

[0097] The fraud scenario generation unit can provide periodic simulated experiences corresponding to different languages ​​and cultural spheres. The fraud scenario generation unit provides periodic simulated experiences corresponding to different languages ​​and cultural spheres, for example. For example, the fraud scenario generation unit automatically generates fraud scenarios corresponding to multiple languages, such as English and Chinese. This makes it possible to provide simulated experiences of international fraud methods through periodic simulated experiences corresponding to different languages ​​and cultural spheres.

[0098] The fraud scenario generation unit can provide a periodic simulated experience that includes visual content and interactive elements. The fraud scenario generation unit can provide a periodic simulated experience that includes visual content and interactive elements. For example, the fraud scenario generation unit can embed images and videos into the fraud scenario. This can provide a visually realistic experience through the periodic simulated experience that includes visual content and interactive elements.

[0099] The fraud scenario generation unit can use the emotion estimation function to provide a customized scenario based on the user's emotions during the periodic simulated experiences. The fraud scenario generation unit can, for example, use the emotion estimation function to provide a customized scenario based on the user's emotions during the periodic simulated experiences. For example, if the user feels anxious, the fraudster can add words to reassure the user. This makes it possible to provide a more personalized simulated experience through a customized scenario based on the user's emotions during the periodic simulated experiences.

[0100] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0101] The special fraud simulation experience system can further include a behavior analysis unit that analyzes the user's behavioral history. The behavior analysis unit can, for example, analyze what types of fraud schemes the user was prone to fall for in the past and customize fraud scenarios based on the results. This makes it possible to provide a more effective simulation experience based on the user's past behavioral history. The behavior analysis unit can also identify what time of day a user is most vulnerable to fraud and provide a scenario tailored to that time of day. Furthermore, the behavior analysis unit can analyze what device the user is using and generate a fraud scenario optimized for that device.

[0102] The fraud simulation system may further include a biometric information acquisition unit that acquires the user's biometric information. The biometric information acquisition unit can, for example, measure the user's heart rate and electrodermal activity to monitor the user's stress level in real time. This allows the fraud scenario to be temporarily interrupted and advice to relax can be provided if the user feels high stress. The biometric information acquisition unit can also monitor the user's pupil movement to identify which part of the body the user is focusing on. Furthermore, the biometric information acquisition unit can analyze the tone and speed of the user's voice to estimate the user's emotional state.

[0103] The special fraud simulation experience system may further include a social media monitoring unit that monitors the user's social media activity. The social media monitoring unit may analyze, for example, the types of posts the user makes and the types of friendships the user has, and customize fraud scenarios based on that information. This allows for a more realistic simulation experience based on the user's social media activity. The social media monitoring unit may also identify topics in which the user is interested and generate fraud scenarios related to those topics. The social media monitoring unit may also analyze the types of advertisements the user responds to and adjust the fraud scenarios based on that information.

[0104] The special fraud simulation experience system can further include a location information acquisition unit that acquires the user's location information. The location information acquisition unit can, for example, identify the area where the user lives and the places the user has visited, and customize the fraud scenario based on that information. This makes it possible to provide a more realistic simulation experience based on the user's location information. Furthermore, if the user is in a specific area, the location information acquisition unit can also provide a scenario that reflects fraud methods that are prevalent in that area. Furthermore, if the user is on the move, the location information acquisition unit can also recreate fraud methods that the user may encounter while traveling.

[0105] The special fraud simulation experience system can further include a purchase history analysis unit that analyzes the user's purchase history. The purchase history analysis unit can, for example, analyze what products the user has purchased in the past and customize a fraud scenario based on that information. This makes it possible to provide a more effective simulation based on the user's purchase history. The purchase history analysis unit can also identify the user's interest in specific brands or products and generate fraud scenarios related to those brands or products. Furthermore, the purchase history analysis unit can analyze what payment methods the user uses and reproduce fraud schemes related to those payment methods.

[0106] The special fraud simulation experience system can further include an emotion adjustment unit that estimates the user's emotions and adjusts the fraud scenario based on those emotions. For example, if the user feels anxious, the emotion adjustment unit can add words for the fraudster to reassure the user. Also, if the user feels angry, the emotion adjustment unit can add words for the fraudster to encourage them to stay calm. Furthermore, if the user is excited, the emotion adjustment unit can add words for the fraudster to encourage them to think calmly. This makes it possible to provide a more realistic simulation experience based on the user's emotions.

[0107] The special fraud simulation experience system can further include a learning history analysis unit that analyzes the user's learning history. The learning history analysis unit can, for example, analyze what fraud techniques the user has learned in the past and customize fraud scenarios based on that information. This makes it possible to provide a more effective simulation experience based on the user's learning history. The learning history analysis unit can also identify the user's preferred learning method and generate fraud scenarios optimized for that learning method. Furthermore, the learning history analysis unit can analyze the user's learning pace and provide fraud scenarios that match that pace.

[0108] The special fraud simulation experience system can further include a health monitoring unit that monitors the user's health condition. The health monitoring unit can, for example, measure the user's blood pressure and heart rate and monitor the user's health condition in real time. This makes it possible to temporarily suspend the fraud scenario and provide health advice if the user has a health problem. Furthermore, if the user has a specific health problem, the health monitoring unit can also reproduce fraud methods related to that problem. Furthermore, the health monitoring unit can identify what fraud methods the user is vulnerable to depending on their health condition and provide scenarios related to those methods.

[0109] The special fraud simulation experience system can further include an emotion customization unit that estimates the user's emotions and customizes the fraud scenario based on those emotions. For example, if the user feels sadness, the emotion customization unit can allow the fraudster to add sympathetic words. If the user feels joy, the emotion customization unit can also add words that the fraudster can use to take advantage of that joy. Furthermore, if the user feels surprise, the fraudster can also add words that the fraudster can use to take advantage of that surprise. This makes it possible to provide a more personalized simulation experience based on the user's emotions.

[0110] The special fraud simulation experience system can further include a lifestyle analysis unit that analyzes the user's lifestyle. The lifestyle analysis unit can analyze, for example, what time of day the user is active and what kind of meals the user eats, and customize a fraud scenario based on that information. This makes it possible to provide a more realistic simulation experience based on the user's lifestyle. The lifestyle analysis unit can also identify the user's hobbies and interests and generate a fraud scenario related to those hobbies and interests. Furthermore, the lifestyle analysis unit can analyze the type of stress the user is experiencing and reproduce fraud techniques related to that stress.

[0111] The processing flow of the second embodiment will be briefly explained below.

[0112] Step 1: The fraud scenario generation unit generates a fraud scenario. For example, the fraud scenario generation unit automatically generates a scenario including methods commonly used by fraudsters. Step 2: The voice reproducing unit reproduces the voice based on the fraud scenario generated by the fraud scenario generating unit. For example, the voice reproducing unit plays back the voice generated by the generation AI to the user. Step 3: The email generator generates an email based on a fraud scenario. For example, the email generator automatically generates a phishing email and sends it to the user. Step 4: The solicitation message generator generates a solicitation message based on the fraud scenario. For example, the solicitation message generator automatically generates a message soliciting illegal part-time work and sends it to the user.

[0113] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0114] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0115] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0116] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0117] 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.

[0118] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0119] 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.

[0120] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0122] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0123] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0124] 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.

[0125] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0126] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0127] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0128] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0129] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0130] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0131] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0132] 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.

[0133] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0134] 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.

[0135] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0137] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0138] 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.

[0139] 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.

[0140] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0141] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the 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 specific processing unit 290 using these models.

[0142] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0143] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0144] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0145] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0146] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0147] 7, the 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.

[0148] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0149] 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.

[0150] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0151] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0152] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0153] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0154] 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.

[0155] 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.

[0156] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0157] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The 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.

[0158] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0159] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0160] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0161] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0162] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0163] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0164] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0165] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0166] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0167] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0168] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0169] 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.

[0170] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0171] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0172] 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.

[0173] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0174] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0175] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0176] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0177] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0178] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0179] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0180] 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. This is a system that uses a generation AI to simulate a phone call from a special fraudster. a fraud scenario generation unit that generates a fraud scenario; a voice reproducing unit that reproduces voice based on the fraud scenario generated by the fraud scenario generating unit; an email generation unit that generates emails based on fraud scenarios; a solicitation message generation unit that generates a solicitation message based on a fraud scenario; A system characterized by:

2. The fraud scenario generation unit It learns the voices of family members and simulates special fraud using voices that sound just like the family members.

2. The system of claim 1.

3. The fraud scenario generation unit Recreate the email scam attack 2. The system of claim 1.

4. The fraud scenario generation unit Recreate the scenario of a solicitation for a shady part-time job 2. The system of claim 1.

5. The fraud scenario generation unit Periodically generating new fraud scenarios and providing the simulated experiences 2. The system of claim 1.

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