System

Abstract

An object of a system according to an embodiment is to enable a user to effectively learn a defense skill against the latest fraud technique.SOLUTION: A system includes a simulation generation part, a fraud method analysis part, a skill acquisition support part, and a training schedule proposal part. The simulation generation unit generates a scenario based on the latest fraud method. The fraud method analysis unit analyzes the scenario generated by the simulation generation unit. The skill acquisition support unit allows the user to acquire the skill based on the fraud method analyzed by the fraud method analysis unit. The training schedule proposal unit proposes a periodic training schedule on the basis of the skill acquired by the skill acquisition support unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including 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] With conventional technology, there was a problem that, as the methods of special frauds evolve, it was difficult for users to effectively acquire defensive skills against the latest fraud methods.

[0005] The system according to the embodiment aims to enable users to effectively acquire defense skills against the latest fraudulent techniques. [Means for solving the problem]

[0006] The system according to the embodiment includes a simulation generation unit, a fraudulent method analysis unit, a skill acquisition support unit, and a training schedule proposal unit. The simulation generation unit generates a scenario based on the latest fraudulent methods. The fraudulent method analysis unit analyzes the scenario generated by the simulation generation unit. The skill acquisition support unit allows a user to acquire skills based on the fraudulent methods analyzed by the fraudulent method analysis unit. The training schedule proposal unit proposes a regular training schedule based on the skills acquired by the skill acquisition support unit. [Effects of the Invention]

[0007] The system according to the embodiment allows users to effectively acquire defense skills against the latest fraudulent techniques. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The AI ​​trainer for special fraud prevention according to an embodiment of the present invention is a system that enhances defense capabilities against special fraud through periodic simulations. This allows users to understand the latest fraud techniques and acquire skills to protect themselves from fraud.

[0029] A special fraud prevention AI trainer according to an embodiment includes a simulation generation unit, a fraud technique analysis unit, a skill acquisition support unit, and a training schedule proposal unit. The simulation generation unit generates a scenario based on the latest fraud techniques. For example, the generation AI generates a simulation scenario based on information on the latest fraud techniques. The fraud technique analysis unit analyzes the scenario generated by the simulation generation unit. For example, the generation AI analyzes the details of the fraud techniques and generates educational content for the user. The skill acquisition support unit helps the user acquire skills based on the fraud techniques analyzed by the fraud technique analysis unit. For example, the generation AI generates a training program so that the user can acquire specific skills to protect themselves from fraud. The training schedule proposal unit proposes a regular training schedule based on the skills acquired by the skill acquisition support unit. For example, the generation AI proposes a regular training schedule to the user and sends reminders. As a result, the special fraud prevention AI trainer according to an embodiment enables the user to understand the latest fraud techniques, acquire skills, and engage in regular training, thereby improving their defense against fraud.

[0030] The simulation generation unit can analyze a user's past training data and generate a simulation scenario optimized for each individual user. For example, the generation AI in the simulation generation unit analyzes a user's past training data and identifies each user's reaction patterns and weaknesses. For example, if a user responded incorrectly to a specific fraudulent method in a past simulation, the simulation generation unit generates a scenario that focuses on that method. This allows for the provision of an optimized scenario based on the user's past training data, thereby increasing the training effectiveness for each individual user.

[0031] The simulation generation unit can record the user's reaction time and options during the simulation and provide feedback on areas for improvement in the next training session. For example, the simulation generation unit can record the user's reaction time during the simulation and provide feedback on areas for improvement based on that data in the next training session. For example, if the user was unable to respond quickly to a question posed by a fraudster, that point can be emphasized and the user can practice it in the next training session. In this way, by recording the user's reaction time and options and providing feedback on areas for improvement in the next training session, the user's ability to respond can be improved.

[0032] The simulation generation unit can provide a simulation that allows a user to experience an actual fraud situation in virtual reality using VR technology. For example, the simulation generation unit can provide a simulation that allows a user to experience an actual fraud situation in virtual reality using VR technology. For example, a scene in which a fraudster makes a phone call can be reproduced in VR, and the user can respond on the spot. This allows the user to experience an actual fraud situation in virtual reality, allowing for more realistic training.

[0033] The simulation generation unit provides customized scenarios according to different age groups and occupations, making it possible to cater to a wide range of users. The simulation generation unit provides customized scenarios according to different age groups, for example. For example, it simulates "I'm your son" fraud and "bank transfer fraud" for the elderly, and "phishing fraud" and "SNS fraud" for younger people. This makes it possible to cater to a wide range of users by providing customized scenarios according to different age groups and occupations.

[0034] The fraud technique analysis unit uses the generation AI to analyze the evolution of fraud techniques over time and visualize the changes from the past to the present. For example, the generation AI analyzes past fraud technique data and visualizes the changes over time. For example, the fraud technique analysis unit displays the evolution of fraud techniques in graphs and charts, allowing users to understand the changes at a glance. In this way, by analyzing and visualizing the evolution of fraud techniques over time, users can easily understand the changes.

[0035] The fraud method analysis unit can use the generation AI to generate case studies of actual fraud cases and provide them to users. For example, the generation AI generates case studies based on data from actual fraud cases and provides them to users. For example, the details of specific fraud cases are explained in the form of a scenario, allowing users to understand the methods used. In this way, providing case studies of actual fraud cases makes it easier for users to understand fraud methods through specific examples.

[0036] The fraudulent method analysis unit allows users to learn about the latest fraudulent methods in an interactive quiz format, thereby enhancing the user's understanding. The fraudulent method analysis unit, for example, provides the latest fraudulent methods in an interactive quiz format, allowing the user to learn while having fun. For example, by answering questions about fraudulent methods, the user can learn the correct countermeasures. In this way, the user can learn about the latest fraudulent methods in an interactive quiz format, thereby enhancing the user's understanding.

[0037] The fraudulent method analysis unit translates information about fraudulent methods into different languages, making it possible to accommodate international users. The fraudulent method analysis unit, for example, translates information about the latest fraudulent methods into different languages, making it possible to accommodate international users. For example, it translates into multiple languages, such as English, French, and Chinese. In this way, by translating information about fraudulent methods into different languages, it is possible to accommodate international users.

[0038] The skill acquisition support unit can use generation AI to analyze a user's past behavioral data and generate a skill training program that is optimal for each individual user. For example, the skill acquisition support unit uses generation AI to analyze a user's past behavioral data and generate a skill training program that is optimal for each individual user. For example, the skill acquisition support unit can provide training that is specialized in fraudulent methods that the user has handled incorrectly in the past. This can increase the training effectiveness for each individual user by providing an optimal skill training program based on the user's past behavioral data.

[0039] The skill acquisition support unit can monitor the progress of skill acquisition in real time and adjust the training content as needed. The skill acquisition support unit adds, for example, a function to monitor the progress of skill acquisition in real time and adjust the training content as needed. For example, if a user is taking a long time to acquire a particular skill, training specialized for that skill can be provided. In this way, by monitoring the progress of skill acquisition in real time and adjusting the training content as needed, the effectiveness of the user's skill acquisition can be improved.

[0040] The skill acquisition support unit can gamify skill training so that users can learn while having fun. For example, the skill acquisition support unit gamifies skill training so that users can learn while having fun. For example, it provides quizzes and mini-games themed on fraudulent methods to teach correct countermeasures. In this way, by gamifying skill training, users can acquire skills while having fun.

[0041] The skill acquisition support unit provides composite training that combines different fraud scenarios, allowing the user to acquire practical skills. For example, the skill acquisition support unit provides composite training that combines different fraud scenarios, allowing the user to acquire practical skills. For example, a scenario that combines "It's me" fraud and "phishing fraud" is provided. In this way, by providing composite training that combines different fraud scenarios, the user can acquire practical skills.

[0042] The training schedule proposal unit can use the generation AI to analyze past training data and visualize the impact that regular training has on improving the user's defense capabilities. For example, the training schedule proposal unit uses the generation AI to analyze past training data and visualize the impact that regular training has on improving the user's defense capabilities. For example, the training schedule proposal unit displays the user's response ability before and after training in graphs and charts. This makes it easier for the user to realize the effects of training by visualizing the impact that regular training has on improving the user's defense capabilities.

[0043] The training schedule suggestion unit can analyze the effects of regular training in association with the user's lifestyle habits and behavior patterns and propose an individual training schedule. For example, the training schedule suggestion unit uses a generation AI to analyze the user's lifestyle habits and behavior patterns and display the effects of regular training in association with them. For example, it proposes a training schedule that matches the user's lifestyle rhythm. In this way, the effects of regular training can be analyzed in association with the user's lifestyle habits and behavior patterns and an individual training schedule can be proposed, thereby maximizing the user's training effects.

[0044] The training schedule suggestion unit can provide a group training function that allows regular training to be conducted together with family and friends. The training schedule suggestion unit provides, for example, a group training function that allows regular training to be conducted together with family and friends. For example, it provides a fraud prevention simulation that the entire family can participate in. By providing a group training function that allows regular training to be conducted together with family and friends, users can train while supporting each other.

[0045] The training schedule suggestion unit can generate content that emphasizes the importance of training through comments from influencers and experts. The training schedule suggestion unit generates content that emphasizes the importance of training through comments from influencers and experts, for example. For example, the training schedule suggestion unit provides a video in which an anti-fraud expert talks about the importance of training. In this way, by generating content that emphasizes the importance of training through comments from influencers and experts, it is possible to increase the user's motivation to train.

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

[0047] The AI ​​trainer for special fraud prevention can analyze a user's past training data and generate simulation scenarios optimized for each individual user. For example, if a user made an incorrect response to a specific fraud technique in a past simulation, it will generate a scenario that focuses on that technique. It can also identify the user's reaction patterns and weaknesses and customize the scenario based on them. Furthermore, it provides feedback on areas for improvement in the next training session based on the user's training history. This can increase the effectiveness of training for each individual user.

[0048] The AI ​​trainer for special fraud prevention can use VR technology to provide simulations that allow users to experience actual fraud situations in virtual reality. For example, a scene in which a fraudster calls can be recreated in VR, allowing the user to respond on the spot. A scene in which a fraudster visits the customer can also be recreated in VR, allowing the user to respond on the spot. Furthermore, a scene in which a fraudster sends an email can be recreated in VR, allowing the user to respond on the spot. This allows users to experience actual fraud situations in virtual reality, making training more realistic.

[0049] The Special Fraud Prevention AI Trainer can accommodate a wide range of users by providing customized scenarios for different age groups and occupations. For example, it simulates "I'm your son" fraud and "transfer fraud" for the elderly, and "phishing fraud" and "SNS fraud" for younger people. It also provides scenarios customized by occupation, such as simulating "internal information leak fraud" for bank employees and "medical fraud" for medical professionals. It also provides scenarios customized by region, such as simulating "real estate fraud" for urban areas and "agricultural fraud" for rural areas. This allows it to accommodate a wide range of users by providing customized scenarios for different age groups and occupations.

[0050] The Special Fraud Prevention AI Trainer can use generative AI to generate case studies of actual fraud cases and provide them to users. For example, the generative AI can generate case studies based on data from actual fraud cases and provide them to users. It also explains the details of specific fraud cases in the form of scenarios, allowing users to understand the methods used. It also analyzes data from past fraud cases and visualizes the evolution of fraud methods over time. By providing case studies of actual fraud cases, it makes it easier for users to understand fraud methods through concrete examples.

[0051] The anti-specialty fraud AI trainer can gamify skill training, allowing users to learn while having fun. For example, skill training can be gamified so that users can learn while having fun. It can also provide quizzes and mini-games themed around fraud methods to teach correct countermeasures. Furthermore, a reward system can be introduced that gives users a sense of accomplishment, offering badges and points. By gamifying skill training, users can acquire skills while having fun.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The simulation generation unit generates a scenario based on the latest fraud methods. For example, the generation AI generates a simulation scenario based on information about the latest fraud methods. Step 2: The fraud analysis unit analyzes the scenario generated by the simulation generation unit. For example, the generation AI analyzes the details of the fraudulent methods and generates educational content for the user. Step 3: The skill acquisition support unit helps the user acquire skills based on the fraudulent techniques analyzed by the fraud technique analysis unit. For example, the generation AI generates a training program that allows the user to acquire specific skills to protect themselves from fraud. Step 4: The training schedule suggestion unit suggests a regular training schedule based on the skills acquired by the skill acquisition support unit. For example, the generation AI suggests a regular training schedule to the user and sends reminders.

[0054] (Example 2) The AI ​​trainer for special fraud prevention according to an embodiment of the present invention is a system that enhances defense capabilities against special fraud through periodic simulations. This allows users to understand the latest fraud techniques and acquire skills to protect themselves from fraud.

[0055] A special fraud prevention AI trainer according to an embodiment includes a simulation generation unit, a fraud technique analysis unit, a skill acquisition support unit, and a training schedule proposal unit. The simulation generation unit generates a scenario based on the latest fraud techniques. For example, the generation AI generates a simulation scenario based on information on the latest fraud techniques. The fraud technique analysis unit analyzes the scenario generated by the simulation generation unit. For example, the generation AI analyzes the details of the fraud techniques and generates educational content for the user. The skill acquisition support unit helps the user acquire skills based on the fraud techniques analyzed by the fraud technique analysis unit. For example, the generation AI generates a training program so that the user can acquire specific skills to protect themselves from fraud. The training schedule proposal unit proposes a regular training schedule based on the skills acquired by the skill acquisition support unit. For example, the generation AI proposes a regular training schedule to the user and sends reminders. As a result, the special fraud prevention AI trainer according to an embodiment enables the user to understand the latest fraud techniques, acquire skills, and engage in regular training, thereby improving their defense against fraud.

[0056] The simulation generation unit can analyze a user's past training data and generate a simulation scenario optimized for each individual user. For example, the generation AI in the simulation generation unit analyzes a user's past training data and identifies each user's reaction patterns and weaknesses. For example, if a user responded incorrectly to a specific fraudulent method in a past simulation, the simulation generation unit generates a scenario that focuses on that method. This allows for the provision of an optimized scenario based on the user's past training data, thereby increasing the training effectiveness for each individual user.

[0057] The simulation generation unit can record the user's reaction time and options during the simulation and provide feedback on areas for improvement in the next training session. For example, the simulation generation unit can record the user's reaction time during the simulation and provide feedback on areas for improvement based on that data in the next training session. For example, if the user was unable to respond quickly to a question posed by a fraudster, that point can be emphasized and the user can practice it in the next training session. In this way, by recording the user's reaction time and options and providing feedback on areas for improvement in the next training session, the user's ability to respond can be improved.

[0058] The simulation generation unit can use the emotion estimation function to monitor the user's emotional state during the simulation in real time and adjust the scenario according to the stress level. For example, the simulation generation unit uses the emotion estimation function to analyze the user's facial expressions and voice during the simulation and monitor the emotional state in real time. For example, if the user is feeling high stress, the difficulty level of the scenario can be temporarily lowered. In this way, by monitoring the user's emotional state in real time and adjusting the scenario according to the stress level, the training effect for the user can be improved.

[0059] The simulation generation unit can provide a simulation that allows a user to experience an actual fraud situation in virtual reality using VR technology. For example, the simulation generation unit can provide a simulation that allows a user to experience an actual fraud situation in virtual reality using VR technology. For example, a scene in which a fraudster makes a phone call can be reproduced in VR, and the user can respond on the spot. This allows the user to experience an actual fraud situation in virtual reality, allowing for more realistic training.

[0060] The simulation generation unit provides customized scenarios according to different age groups and occupations, making it possible to cater to a wide range of users. The simulation generation unit provides customized scenarios according to different age groups, for example. For example, it simulates "I'm your son" fraud and "bank transfer fraud" for the elderly, and "phishing fraud" and "SNS fraud" for younger people. This makes it possible to cater to a wide range of users by providing customized scenarios according to different age groups and occupations.

[0061] The simulation generation unit is equipped with an emotion estimation function and can introduce relaxation techniques to reduce the anxiety and fear felt by the user during the simulation. For example, the simulation generation unit uses the emotion estimation function to detect the anxiety and fear felt by the user during the simulation in real time and introduces relaxation techniques, such as providing deep breathing exercises or relaxing music. This reduces the anxiety and fear felt by the user during the simulation, allowing for more effective training.

[0062] The fraud technique analysis unit uses the generation AI to analyze the evolution of fraud techniques over time and visualize the changes from the past to the present. For example, the generation AI analyzes past fraud technique data and visualizes the changes over time. For example, the fraud technique analysis unit displays the evolution of fraud techniques in graphs and charts, allowing users to understand the changes at a glance. In this way, by analyzing and visualizing the evolution of fraud techniques over time, users can easily understand the changes.

[0063] The fraud method analysis unit can use the generation AI to generate case studies of actual fraud cases and provide them to users. For example, the generation AI generates case studies based on data from actual fraud cases and provides them to users. For example, the details of specific fraud cases are explained in the form of a scenario, allowing users to understand the methods used. In this way, providing case studies of actual fraud cases makes it easier for users to understand fraud methods through specific examples.

[0064] The fraudulent method analysis unit can use the emotion estimation function to detect in real time any doubts or anxieties the user may have while the fraudulent method is being explained, and can instantly provide supplementary explanations. For example, the fraudulent method analysis unit can use the emotion estimation function to detect in real time any doubts or anxieties the user may have while the fraudulent method is being explained, and can instantly provide supplementary explanations. For example, if the user feels anxious, a detailed explanation of the method is added. This allows the fraudulent method analysis unit to detect in real time any doubts or anxieties the user may have while the fraudulent method is being explained, and can instantly provide supplementary explanations, thereby deepening the user's understanding.

[0065] The fraudulent method analysis unit allows users to learn about the latest fraudulent methods in an interactive quiz format, thereby enhancing the user's understanding. The fraudulent method analysis unit, for example, provides the latest fraudulent methods in an interactive quiz format, allowing the user to learn while having fun. For example, by answering questions about fraudulent methods, the user can learn the correct countermeasures. In this way, the user can learn about the latest fraudulent methods in an interactive quiz format, thereby enhancing the user's understanding.

[0066] The fraudulent method analysis unit translates information about fraudulent methods into different languages, making it possible to accommodate international users. The fraudulent method analysis unit, for example, translates information about the latest fraudulent methods into different languages, making it possible to accommodate international users. For example, it translates into multiple languages, such as English, French, and Chinese. In this way, by translating information about fraudulent methods into different languages, it is possible to accommodate international users.

[0067] The fraudulent method analysis unit can use the emotion estimation function to identify the part of the fraudulent method that the user is most interested in while the fraudulent method is being explained, and can provide a focused explanation of that part. For example, the fraudulent method analysis unit can use the emotion estimation function to identify the part of the fraudulent method that the user is most interested in in real time while the fraudulent method is being explained, and can provide a focused explanation of that part. For example, the fraudulent method analysis unit can provide additional details of the fraudulent method that the user is particularly interested in. This allows the user's understanding to be deepened by identifying the part that the user is most interested in and providing a focused explanation of that part.

[0068] The skill acquisition support unit can use generation AI to analyze a user's past behavioral data and generate a skill training program that is optimal for each individual user. For example, the skill acquisition support unit uses generation AI to analyze a user's past behavioral data and generate a skill training program that is optimal for each individual user. For example, the skill acquisition support unit can provide training that is specialized in fraudulent methods that the user has handled incorrectly in the past. This can increase the training effectiveness for each individual user by providing an optimal skill training program based on the user's past behavioral data.

[0069] The skill acquisition support unit can monitor the progress of skill acquisition in real time and adjust the training content as needed. The skill acquisition support unit adds, for example, a function to monitor the progress of skill acquisition in real time and adjust the training content as needed. For example, if a user is taking a long time to acquire a particular skill, training specialized for that skill can be provided. In this way, by monitoring the progress of skill acquisition in real time and adjusting the training content as needed, the effectiveness of the user's skill acquisition can be improved.

[0070] The skill acquisition support unit can use the emotion estimation function to provide feedback to maintain the user's motivation during training. For example, the skill acquisition support unit uses the emotion estimation function to monitor the user's emotional state during training in real time and provide feedback to maintain motivation. For example, if the user feels tired, an encouraging message is displayed. This provides feedback to maintain the user's motivation during training, thereby improving the effectiveness of the user's skill acquisition.

[0071] The skill acquisition support unit can gamify skill training so that users can learn while having fun. For example, the skill acquisition support unit gamifies skill training so that users can learn while having fun. For example, it provides quizzes and mini-games themed on fraudulent methods to teach correct countermeasures. In this way, by gamifying skill training, users can acquire skills while having fun.

[0072] The skill acquisition support unit provides composite training that combines different fraud scenarios, allowing the user to acquire practical skills. For example, the skill acquisition support unit provides composite training that combines different fraud scenarios, allowing the user to acquire practical skills. For example, a scenario that combines "It's me" fraud and "phishing fraud" is provided. In this way, by providing composite training that combines different fraud scenarios, the user can acquire practical skills.

[0073] The skill acquisition support unit can use the emotion estimation function to introduce a reward system for enhancing the sense of accomplishment felt by the user during training. The skill acquisition support unit, for example, uses the emotion estimation function to monitor the sense of accomplishment felt by the user during training in real time and introduces a reward system. For example, if the user feels a high sense of accomplishment, badges or points are provided. In this way, by introducing a reward system for enhancing the sense of accomplishment felt by the user during training, it is possible to maintain the user's motivation and improve the effectiveness of skill acquisition.

[0074] The training schedule proposal unit can use the generation AI to analyze past training data and visualize the impact that regular training has on improving the user's defense capabilities. For example, the training schedule proposal unit uses the generation AI to analyze past training data and visualize the impact that regular training has on improving the user's defense capabilities. For example, the training schedule proposal unit displays the user's response ability before and after training in graphs and charts. This makes it easier for the user to realize the effects of training by visualizing the impact that regular training has on improving the user's defense capabilities.

[0075] The training schedule suggestion unit can analyze the effects of regular training in association with the user's lifestyle habits and behavior patterns and propose an individual training schedule. For example, the training schedule suggestion unit uses a generation AI to analyze the user's lifestyle habits and behavior patterns and display the effects of regular training in association with them. For example, it proposes a training schedule that matches the user's lifestyle rhythm. In this way, the effects of regular training can be analyzed in association with the user's lifestyle habits and behavior patterns and an individual training schedule can be proposed, thereby maximizing the user's training effects.

[0076] The training schedule suggestion unit can use the emotion estimation function to generate a personalized message for emphasizing the importance of training to the user. The training schedule suggestion unit, for example, uses the emotion estimation function to generate a personalized message for emphasizing the importance of training to the user. For example, if the user is not feeling motivated to train, the training schedule suggestion unit sends an encouraging message. In this way, by generating a personalized message for emphasizing the importance of training to the user, the user's motivation to train can be increased.

[0077] The training schedule suggestion unit can provide a group training function that allows regular training to be conducted together with family and friends. The training schedule suggestion unit provides, for example, a group training function that allows regular training to be conducted together with family and friends. For example, it provides a fraud prevention simulation that the entire family can participate in. By providing a group training function that allows regular training to be conducted together with family and friends, users can train while supporting each other.

[0078] The training schedule suggestion unit can generate content that emphasizes the importance of training through comments from influencers and experts. The training schedule suggestion unit generates content that emphasizes the importance of training through comments from influencers and experts, for example. For example, the training schedule suggestion unit provides a video in which an anti-fraud expert talks about the importance of training. In this way, by generating content that emphasizes the importance of training through comments from influencers and experts, it is possible to increase the user's motivation to train.

[0079] The training schedule proposal unit can use the emotion estimation function to identify the most effective timing for communicating the importance of training to the user and provide a notification. The training schedule proposal unit, for example, uses the emotion estimation function to identify the most effective timing for communicating the importance of training to the user and provide a notification. For example, the training schedule proposal unit can send a training reminder during a time period when the user is relaxed. In this way, the most effective timing for communicating the importance of training to the user can be identified and a notification can be provided, thereby increasing the user's motivation to train.

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

[0081] The AI ​​trainer for special fraud prevention can estimate the user's emotions and adjust the training content based on the estimated emotions. For example, if the user feels anxious during training, it can temporarily change the training content to something easier. If the user feels high stress, it can introduce relaxation techniques to reduce stress. Furthermore, if the user is not feeling motivated to train, it can display encouraging messages. This makes it possible to adjust the training content according to the user's emotional state, resulting in more effective training.

[0082] The AI ​​trainer for special fraud prevention can analyze a user's past training data and generate simulation scenarios optimized for each individual user. For example, if a user made an incorrect response to a specific fraud technique in a past simulation, it will generate a scenario that focuses on that technique. It can also identify the user's reaction patterns and weaknesses and customize the scenario based on them. Furthermore, it provides feedback on areas for improvement in the next training session based on the user's training history. This can increase the effectiveness of training for each individual user.

[0083] The AI ​​trainer for special fraud prevention uses an emotion estimation function to monitor the user's emotional state in real time during the simulation and adjust the scenario according to the user's stress level. For example, if the user is feeling highly stressed, the difficulty of the scenario can be temporarily lowered. Alternatively, if the user is relaxed, the difficulty of the scenario can be increased. Furthermore, relaxation techniques can be introduced according to the user's emotional state to reduce stress. This makes it possible to adjust the scenario according to the user's emotional state, resulting in more effective training.

[0084] The AI ​​trainer for special fraud prevention can use VR technology to provide simulations that allow users to experience actual fraud situations in virtual reality. For example, a scene in which a fraudster calls can be recreated in VR, allowing the user to respond on the spot. A scene in which a fraudster visits the customer can also be recreated in VR, allowing the user to respond on the spot. Furthermore, a scene in which a fraudster sends an email can be recreated in VR, allowing the user to respond on the spot. This allows users to experience actual fraud situations in virtual reality, making training more realistic.

[0085] The AI ​​trainer for special fraud prevention can use its emotion estimation function to introduce relaxation techniques to reduce the anxiety and fear felt by the user during the simulation. For example, it can use the emotion estimation function to detect the anxiety and fear felt by the user in real time during the simulation and provide deep breathing exercises or relaxing music. It can also temporarily lower the difficulty of the scenario if the user is feeling high stress. Furthermore, it can increase the difficulty of the scenario if the user is relaxed. This reduces the anxiety and fear felt by the user during the simulation, allowing for more effective training.

[0086] The Special Fraud Prevention AI Trainer can accommodate a wide range of users by providing customized scenarios for different age groups and occupations. For example, it simulates "I'm your son" fraud and "transfer fraud" for the elderly, and "phishing fraud" and "SNS fraud" for younger people. It also provides scenarios customized by occupation, such as simulating "internal information leak fraud" for bank employees and "medical fraud" for medical professionals. It also provides scenarios customized by region, such as simulating "real estate fraud" for urban areas and "agricultural fraud" for rural areas. This allows it to accommodate a wide range of users by providing customized scenarios for different age groups and occupations.

[0087] The Special Fraud Prevention AI Trainer uses its emotion estimation function to detect any doubts or anxieties the user may have in real time while a fraud method is being explained, and can instantly provide supplementary explanations. For example, the emotion estimation function can be used to detect any doubts or anxieties the user may have in real time while a fraud method is being explained, and add detailed explanations of the method. It can also provide additional details on fraud methods that the user is particularly interested in. It can also focus on explaining parts that the user finds difficult to understand. This allows the AI ​​Trainer to detect any doubts or anxieties the user may have in real time while a fraud method is being explained, and instantly provide supplementary explanations, deepening the user's understanding.

[0088] The Special Fraud Prevention AI Trainer can use generative AI to generate case studies of actual fraud cases and provide them to users. For example, the generative AI can generate case studies based on data from actual fraud cases and provide them to users. It also explains the details of specific fraud cases in the form of scenarios, allowing users to understand the methods used. It also analyzes data from past fraud cases and visualizes the evolution of fraud methods over time. By providing case studies of actual fraud cases, it makes it easier for users to understand fraud methods through concrete examples.

[0089] The AI ​​trainer for special fraud prevention can use its emotion estimation function to provide feedback to maintain the user's motivation during training. For example, it can use the emotion estimation function to monitor the user's emotional state in real time during training and provide feedback to maintain motivation. It can also display encouraging messages if the user feels tired. Furthermore, it can provide badges and points if the user feels a high sense of accomplishment. This provides feedback to maintain the user's motivation during training, thereby improving the effectiveness of the user's skill acquisition.

[0090] The anti-specialty fraud AI trainer can gamify skill training, allowing users to learn while having fun. For example, skill training can be gamified so that users can learn while having fun. It can also provide quizzes and mini-games themed around fraud methods to teach correct countermeasures. Furthermore, a reward system can be introduced that gives users a sense of accomplishment, offering badges and points. By gamifying skill training, users can acquire skills while having fun.

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

[0092] Step 1: The simulation generation unit generates a scenario based on the latest fraud methods. For example, the generation AI generates a simulation scenario based on information about the latest fraud methods. Step 2: The fraud analysis unit analyzes the scenario generated by the simulation generation unit. For example, the generation AI analyzes the details of the fraudulent methods and generates educational content for the user. Step 3: The skill acquisition support unit helps the user acquire skills based on the fraudulent techniques analyzed by the fraud technique analysis unit. For example, the generation AI generates a training program that allows the user to acquire specific skills to protect themselves from fraud. Step 4: The training schedule suggestion unit suggests a regular training schedule based on the skills acquired by the skill acquisition support unit. For example, the generation AI suggests a regular training schedule to the user and sends reminders.

[0093] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0094] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (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.

[0095] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0096] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0097] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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).

[0102] 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.

[0103] 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.

[0104] 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.

[0105] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0106] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0112] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0119] 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.

[0120] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0121] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0127] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0137] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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."

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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, in order to avoid confusion and to 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.

[0159] 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]

[0160] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a simulation generation unit that generates scenarios based on the latest fraud methods; a fraudulent scheme analysis unit that analyzes the scenario generated by the simulation generation unit; a skill acquisition support unit that allows a user to acquire skills based on the fraudulent methods analyzed by the fraudulent method analysis unit; a training schedule suggestion unit that suggests a periodic training schedule based on the skills acquired by the skill acquisition support unit. A system characterized by:

2. The simulation generation unit Using emotion estimation functionality, the emotional state of the user is monitored in real time during the simulation, and the scenario is adjusted according to the user's stress level.

2. The system of claim 1.

3. The fraud method analysis unit Using the generative AI, we analyze the evolution of the fraud methods over time and visualize their evolution from the past to the present.

2. The system of claim 1.

4. The skill acquisition support unit The generation AI is used to analyze the user's past behavioral data and generate an optimal skill training program for each individual user.

2. The system of claim 1.

5. The training schedule suggestion unit The generative AI is used to analyze past training data and visualize the impact of the periodic training on improving the user's defense capabilities.

2. The system of claim 1.

6. The simulation generation unit Equipped with emotion estimation functionality, it will also introduce relaxation techniques to reduce the anxiety and fear felt by the user during the simulation.

2. The system of claim 1.

7. The fraud method analysis unit Using an emotion estimation function, the system detects in real time any doubts or anxieties the user may have while explaining the fraud method, and immediately provides supplementary explanations.

2. The system of claim 1.

8. The skill acquisition support unit Using emotion estimation to provide feedback to keep the user motivated during training 2. The system of claim 1.

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