system

Abstract

The system according to this embodiment aims to warn users when there is a high possibility of investment fraud using social media. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, and a warning unit. The reception unit receives input from the user. The analysis unit analyzes the content of the conversation based on the information received by the reception unit. The warning unit issues a warning if there is a high possibility of fraud based on the results of the analysis by the analysis unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult to prevent damage caused by investment fraud using SNS, and there is a risk of damaging user trust.

[0005] The system according to the embodiment aims to warn a user when there is a high possibility of investment fraud using SNS.

Means for Solving the Problems

[0006] The system according to the embodiment includes a reception unit, an analysis unit, and a warning unit. The reception unit receives an input from a user. The analysis unit analyzes the conversation content of a message application based on the information received by the reception unit. The warning unit issues a warning when there is a high possibility of fraud based on the result analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can warn users when there is a high possibility of investment fraud using social media. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The fraud prevention system according to an embodiment of the present invention is a system for preventing investment fraud using social media. This fraud prevention system uses a generating AI agent to analyze the content of conversations and issue a warning to the user if there is a high probability of fraud. First, the generating AI agent analyzes the content of conversations and detects messages that are likely to be fraudulent. Next, if the generating AI agent determines that there is a high probability of fraud, it issues a warning to the user. For example, if a scheme is detected in which a user is invited to a group, a fake poster claims to be making money, and instructs them to transfer money to a specified account, the system will issue a warning to the user. The generating AI agent will also issue a warning if the official website of the economic analyst hosting the group states that the group does not exist, or if a personal account rather than a bank account is specified as the transfer destination. This allows even users with low financial literacy to spot fraud and prevent them from becoming victims. For example, the generating AI agent analyzes the content of conversations in real time and immediately detects messages that are likely to be fraudulent. The generating AI agent also learns from past fraud cases and is able to respond to new fraudulent methods. Furthermore, the generating AI agent considers user attribute information and past chat history to determine the likelihood of fraud with high accuracy. This allows the fraud prevention system to prevent a decrease in the number of users and advertising revenue. In this way, the fraud prevention system can proactively prevent investment fraud using social media.

[0029] The fraud prevention system according to this embodiment comprises a reception unit, an analysis unit, and a warning unit. The reception unit receives input from the user. User input includes, but is not limited to, text messages, voice input, and images. The reception unit receives, for example, messages sent by the user. The reception unit can also receive content entered by the user via voice. Furthermore, the reception unit can also receive images sent by the user. For example, the reception unit receives messages sent by the user in real time. In the case of voice input, the reception unit converts the voice to text using voice recognition technology. In the case of image input, the reception unit analyzes the image using image recognition technology. The analysis unit analyzes the content of the conversation based on the information received by the reception unit using generative AI. The analysis is performed using, for example, natural language processing technology and machine learning algorithms, but is not limited to these examples. For example, the analysis unit analyzes the content of the conversation using generative AI and detects messages that are highly likely to be fraudulent. The analysis unit can also analyze the frequency of keyword occurrences in the conversation content using generative AI. Furthermore, the analysis unit can also use a generation AI to analyze similarities with past fraud cases. For example, the analysis unit can use a generation AI to analyze the content of a conversation and analyze the frequency of occurrence of specific keywords. The analysis unit can also use a generation AI to analyze the similarity between the content of the conversation and past fraud cases. Some or all of the above-described processes in the analysis unit may be performed using a generation AI, or they may not be performed using a generation AI. For example, the analysis unit can input the content of a conversation into a generation AI and have the generation AI perform the detection of messages that are likely to be fraudulent. The warning unit issues a warning if there is a high probability of fraud based on the results analyzed by the analysis unit. The warning may be, but is not limited to, a pop-up notification or an audio alert. For example, if a message that is likely to be fraudulent is detected, the warning unit will issue a pop-up notification to the user. The warning unit may also issue an audio alert if a message that is likely to be fraudulent is detected. The warning unit may also issue a warning by email if a message that is likely to be fraudulent is detected.For example, if the warning unit detects a message that is highly likely to be fraudulent, it will issue a pop-up notification to the user to warn them of the potential fraud. If the warning unit detects a message that is highly likely to be fraudulent, it will issue an audio alert to draw the user's attention. If the warning unit detects a message that is highly likely to be fraudulent, it will issue a warning via email to provide the user with detailed information. In this way, the fraud prevention system according to the embodiment can prevent fraud by analyzing the user's input and issuing a warning when there is a high possibility of fraud.

[0030] The reception desk receives input from users. User input includes, but is not limited to, text messages, voice input, and images. For example, the reception desk receives messages sent by users. The reception desk can also receive content entered by users via voice. Furthermore, the reception desk can receive images sent by users. For example, the reception desk receives messages sent by users in real time. In the case of voice input, the reception desk uses speech recognition technology to convert the speech to text. In the case of image input, the reception desk uses image recognition technology to analyze the image. The reception desk employs advanced technology to process these inputs quickly and accurately. For example, the speech recognition technology uses a deep learning-based speech model to convert user speech into text with high accuracy. This allows for accurate analysis of content entered by users via voice. The image recognition technology uses computer vision technology to extract important information from images sent by users. For example, it can read text in images using OCR technology to detect signs of fraud. Furthermore, the reception desk has high-speed data processing capabilities to process user input in real time. This allows for the immediate transmission of user-submitted messages, images, and audio to the analysis unit, enabling a rapid response. The reception unit also implements security measures such as data encryption and access control to protect user privacy. This prevents unauthorized access to users' personal information and ensures secure data processing. The reception unit plays a crucial role in enhancing the overall effectiveness of the fraud prevention system by efficiently receiving diverse user inputs and quickly providing data to the analysis unit.

[0031] The analysis unit uses a generative AI to analyze the content of the conversation based on the information received by the reception unit. The analysis is performed using, for example, natural language processing techniques and machine learning algorithms, but is not limited to these examples. For example, the analysis unit uses a generative AI to analyze the content of the conversation and detect messages that are likely to be fraudulent. The analysis unit can also use a generative AI to analyze the frequency of occurrence of keywords in the conversation content. The analysis unit can also use a generative AI to analyze the similarity to past fraud cases. For example, the analysis unit uses a generative AI to analyze the content of the conversation and analyze the frequency of occurrence of specific keywords. The analysis unit uses a generative AI to analyze the similarity between the conversation content and past fraud cases. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the conversation content into a generative AI and have the generative AI detect messages that are likely to be fraudulent. The analysis unit uses a generative AI to analyze text messages received from users using natural language processing techniques and detect signs of fraud. For example, the system detects keywords and phrases specific to fraudulent messages and analyzes their frequency of occurrence to assess the likelihood of fraud. The analysis unit also uses generative AI to analyze the similarity between the user's message and past fraud cases. By referring to a database of past fraud cases and detecting similar patterns and phrases, the likelihood of fraud is increased. Furthermore, the analysis unit uses generative AI to analyze the context of the user's message and detect signs of fraud. For example, if the message content suddenly changes or contains unnatural requests, it is judged to have a high probability of being fraudulent. Based on these analysis results, the analysis unit identifies messages with a high probability of fraud and notifies the warning unit. By using generative AI, the analysis unit possesses advanced analytical capabilities and can quickly and accurately detect signs of fraud. This allows for warnings to be issued before users become victims of fraud, preventing them from becoming victims.

[0032] The warning unit issues a warning if there is a high probability of fraud based on the results of analysis by the analysis unit. Warnings may be issued in the form of, for example, pop-up notifications or audio alerts, but are not limited to these examples. For example, if the warning unit detects a message that is likely to be fraudulent, it will issue a pop-up notification to the user. The warning unit may also issue an audio alert if a message that is likely to be fraudulent is detected. The warning unit may also issue a warning via email if a message that is likely to be fraudulent is detected. For example, if the warning unit detects a message that is likely to be fraudulent, it will issue a pop-up notification to the user to warn of the possibility of fraud. If the warning unit detects a message that is likely to be fraudulent, it will issue an audio alert to draw the user's attention. If the warning unit detects a message that is likely to be fraudulent, it will issue a warning via email to provide the user with more detailed information. The warning unit uses multiple notification methods to warn the user quickly and effectively. For example, pop-up notifications are displayed immediately when the user is using the app to warn of the possibility of fraud. Audio alerts are effective in drawing the user's attention even when the user is not using the app. Email notifications are used to provide detailed information that users can review later. The warning unit can reliably deliver warnings to users by combining these notification methods. Furthermore, the warning unit can collect user feedback and continuously improve the accuracy and effectiveness of warnings. For example, it can analyze user behavior after receiving a warning and optimize the content and timing of the warning. The warning unit can also customize notification methods and warning frequency according to user settings. This allows for flexible responses tailored to user needs. Based on information from the analysis unit, the warning unit plays a role in issuing warnings quickly and accurately, alerting users before they become victims of fraud. As a result, the fraud prevention system according to this embodiment can ensure user safety and prevent fraud.

[0033] The analysis unit can analyze the content of conversations using a generation AI and detect messages that are highly likely to be fraudulent. For example, the analysis unit can analyze the content of conversations using a generation AI and detect messages that are highly likely to be fraudulent. For example, the analysis unit can also analyze the frequency of keyword occurrences in the conversation content using a generation AI. Furthermore, the analysis unit can also analyze the similarity to past fraud cases using a generation AI. For example, the analysis unit can analyze the content of conversations using a generation AI and analyze the frequency of occurrences of specific keywords. The analysis unit can analyze the similarity between the conversation content and past fraud cases using a generation AI. As a result, by using a generation AI, messages that are highly likely to be fraudulent can be detected with high accuracy. Some or all of the above-described processes in the analysis unit may be performed using a generation AI, for example, or without a generation AI. For example, the analysis unit can input the conversation content into a generation AI and have the generation AI perform the detection of messages that are highly likely to be fraudulent.

[0034] The warning unit can issue a warning if the official website of the economic analyst sponsoring the group states that the group does not exist. For example, the warning unit can issue a warning if the official website of the economic analyst sponsoring the group states that the group does not exist. For example, the warning unit can refer to the information on the official website and confirm the existence of the group. The warning unit can also periodically update the information on the official website and issue a warning based on the latest information. For example, the warning unit can refer to the URL of the official website and confirm the existence of the group. The warning unit evaluates the information on the official website based on reliability verification methods and issues a warning. This allows the warning unit to issue a warning if there is a high probability of fraud by referring to the information on the official website. Official websites include, but are not limited to, the official websites of economic analysts and reliable sources. Some or all of the above processes in the warning unit may be performed using, for example, AI, or not using AI. For example, the warning unit can input the information on the official website into AI and have the AI ​​perform the process of verifying the existence of the group.

[0035] The warning unit can issue a warning if a personal account is specified as the recipient of a transfer instead of a financial institution account. For example, the warning unit will issue a warning if a personal account is specified as the recipient of a transfer instead of a financial institution account. For example, the warning unit will check the recipient information and determine whether it is a financial institution account or a personal account. The warning unit can also periodically update the recipient information and issue a warning based on the latest information. For example, the warning unit will refer to the recipient information and determine whether it is a financial institution account or a personal account. The warning unit will evaluate the recipient information based on a reliability verification method and issue a warning. This allows the system to issue a warning if there is a high probability of fraud by checking the recipient information. Financial institution accounts include, but are not limited to, bank accounts and credit union accounts. Personal accounts include, but are not limited to, bank accounts in an individual's name and individual electronic money accounts. Some or all of the above processing in the warning unit may be performed using, for example, AI, or not using AI. For example, the warning unit can input the recipient's information into the AI ​​and have the AI ​​perform a process to determine whether it is a bank account or a personal account.

[0036] The reception desk can analyze the user's past input history and select the optimal reception method. For example, the reception desk may prioritize suggesting input methods (voice, text, etc.) that the user has frequently used in the past. For example, the reception desk may predict and suggest input methods to be used during specific time periods based on the user's past input history. The reception desk may also suggest relevant input methods by referring to content that the user has entered in the past. For example, the reception desk may prioritize suggesting input methods that the user has frequently used in the past. The reception desk may predict and suggest input methods to be used during specific time periods based on the user's past input history. The reception desk may suggest relevant input methods by referring to content that the user has entered in the past. In this way, the optimal reception method can be selected by analyzing the user's past input history. The optimal reception method may include, but is not limited to, the user's past input patterns and preferences. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk may input the user's past input history into AI and have the AI ​​select the optimal reception method.

[0037] The reception unit can filter input based on the user's current areas of interest and activity. For example, the reception unit prioritizes receiving information related to topics the user has recently been interested in. For example, the reception unit filters appropriate information according to the user's current activity status (e.g., at work, on vacation). The reception unit can also filter highly relevant information by referring to the user's past activity history. For example, the reception unit prioritizes receiving information related to topics the user has recently been interested in. The reception unit filters appropriate information according to the user's current activity status. The reception unit filters highly relevant information by referring to the user's past activity history. This allows the reception unit to prioritize receiving highly relevant information by filtering based on the user's areas of interest and activity status. Areas of interest include, but are not limited to, the user's past search history and social media activity. Activity status includes, but is not limited to, current location information and recent activity history. Some or all of the above processing in the reception unit may be performed using, for example, AI, or not using AI. For example, the reception desk can input the user's areas of interest and activity status into the AI ​​and have the AI ​​perform filtering.

[0038] The reception unit can prioritize receiving highly relevant information by considering the user's geographical location information when receiving input. For example, the reception unit can prioritize receiving information related to the user's current location. For example, if the user is traveling, the reception unit can prioritize receiving information related to the travel destination. The reception unit can also prioritize receiving information related to a specific region if the user is interested in that region. For example, the reception unit can prioritize receiving information related to the user's current location. If the user is traveling, the reception unit can prioritize receiving information related to the travel destination. If the user is interested in a specific region, the reception unit can prioritize receiving information related to that region. This allows the reception unit to prioritize receiving highly relevant information by considering the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and IP addresses. Some or all of the processing described above in the reception unit may be performed using, for example, AI, or not using AI. For example, the reception unit can input the user's geographical location information into AI and have AI perform the reception of highly relevant information.

[0039] The reception unit can analyze the user's social media activity and receive relevant information when input is received. For example, the reception unit can receive information related to topics the user has recently shown interest in on social media. For example, the reception unit can analyze the user's social media activity history and receive highly relevant information. The reception unit can also receive relevant information by referring to the content of posts from accounts the user follows. For example, the reception unit can receive information related to topics the user has recently shown interest in on social media. The reception unit can analyze the user's social media activity history and receive highly relevant information. The reception unit can receive relevant information by referring to the content of posts from accounts the user follows. This allows the reception unit to prioritize receiving highly relevant information by analyzing the user's social media activity. Social media activity includes, but is not limited to, posts and follower reactions. Some or all of the above processing in the reception unit may be performed using, for example, AI, or not using AI. For example, the reception unit can input the user's social media activity into AI and have AI perform the reception of relevant information.

[0040] The analysis unit can adjust the level of detail of the analysis based on the importance of the messages during analysis. For example, the analysis unit can analyze high-importance messages in detail and low-importance messages simply. For example, the analysis unit can adjust the display order of the analysis results according to the importance of the messages. The analysis unit can also provide additional analysis information to high-importance messages. For example, the analysis unit can analyze high-importance messages in detail and low-importance messages simply. The analysis unit can adjust the display order of the analysis results according to the importance of the messages. The analysis unit can provide additional analysis information to high-importance messages. This allows important messages to be analyzed in detail by adjusting the level of detail of the analysis based on the importance of the messages. Message importance includes, but is not limited to, the frequency of occurrence of specific keywords or the reliability of the sender. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the importance of the messages into the AI ​​and have the AI ​​adjust the level of detail of the analysis.

[0041] The analysis unit can apply different analysis algorithms depending on the message category during analysis. For example, the analysis unit can apply a specialized financial analysis algorithm to investment-related messages. For example, the analysis unit can apply a specialized medical analysis algorithm to health-related messages. The analysis unit can also apply a specialized education analysis algorithm to education-related messages. For example, the analysis unit can apply a specialized financial analysis algorithm to investment-related messages. For example, the analysis unit can apply a specialized medical analysis algorithm to health-related messages. For example, the analysis unit can apply a specialized education analysis algorithm to education-related messages. By applying different analysis algorithms depending on the message category, more appropriate analysis results can be provided. Message categories include, for example, business, private, and urgent, but are not limited to these examples. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the message category into the AI ​​and have the AI ​​perform the application of the analysis algorithm.

[0042] The analysis unit can determine the priority of analysis based on the message transmission date during analysis. For example, the analysis unit may prioritize the analysis of recently sent messages. For example, the analysis unit may prioritize the analysis of messages sent immediately before an important event. The analysis unit can also prioritize the analysis of messages received by a user during a specific time period. For example, the analysis unit may prioritize the analysis of recently sent messages. For example, the analysis unit may prioritize the analysis of messages sent immediately before an important event. For example, the analysis unit may prioritize the analysis of messages received by a user during a specific time period. This allows for the prioritization of analysis based on the message transmission date, thereby prioritizing the analysis of messages at important timings. The message transmission date includes, but is not limited to, timestamps and transmission history. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit may input the message transmission date into AI and have AI determine the analysis priority.

[0043] The analysis unit can adjust the order of analysis based on the relevance of messages during analysis. For example, the analysis unit may prioritize analyzing highly relevant messages. For example, the analysis unit may prioritize analyzing messages related to the user's interests. The analysis unit can also group highly relevant messages based on their content and analyze them. For example, the analysis unit may prioritize analyzing highly relevant messages. The analysis unit may prioritize analyzing messages related to the user's interests. The analysis unit may group highly relevant messages based on their content and analyze them. This allows the analysis unit to prioritize the analysis of highly relevant messages by adjusting the order of analysis based on the relevance of messages. Message relevance includes, but is not limited to, similarity of content or relationship between senders. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the relevance of messages into AI and have AI perform the adjustment of the analysis order.

[0044] The warning unit can adjust the level of detail of a warning based on the content of a message that is likely to be fraudulent. For example, the warning unit will issue a detailed warning for a message that is likely to be fraudulent. For example, the warning unit will issue a simplified warning for a message that is unlikely to be fraudulent. The warning unit can also adjust the level of detail of a warning depending on the content of the message. For example, the warning unit will issue a detailed warning for a message that is likely to be fraudulent. The warning unit will issue a simplified warning for a message that is unlikely to be fraudulent. The warning unit adjusts the level of detail of a warning depending on the content of the message. This allows for the issuance of more appropriate warnings by adjusting the level of detail of a warning based on the content of a message that is likely to be fraudulent. The level of detail of a warning includes, but is not limited to, the likelihood of fraud and the severity of the damage. Some or all of the above processing in the warning unit may be performed using, for example, AI, or not using AI. For example, the warning unit can input the content of a message that is likely to be fraudulent into AI and have AI perform the adjustment of the level of detail of the warning.

[0045] The warning unit can apply different warning algorithms depending on the type of fraud when issuing a warning. For example, the warning unit can apply a financial warning algorithm to investment fraud. For example, the warning unit can apply a medical warning algorithm to health fraud. The warning unit can also apply an education-specific warning algorithm to education fraud. For example, the warning unit can apply a financial warning algorithm to investment fraud. For example, the warning unit can apply a medical warning algorithm to health fraud. For example, the warning unit can apply an education-specific warning algorithm to education fraud. By applying different warning algorithms depending on the type of fraud, more appropriate warnings can be issued. Fraudulent methods include, but are not limited to, phishing, impersonation, and investment fraud. Some or all of the above processing in the warning unit may be performed using, for example, AI, or not using AI. For example, the warning unit can input the fraudulent method into AI and have the AI ​​perform the application of the warning algorithm.

[0046] The warning unit can issue a warning when it issues one, taking into account the attribute information of the sender of a message that is likely to be fraudulent. For example, the warning unit will issue a warning if the sender of a message that is likely to be fraudulent is a new account. For example, the warning unit will issue a warning if the sender of a message that is likely to be fraudulent has a history of fraudulent activity. The warning unit can also issue a warning if the sender of a message that is likely to be fraudulent is a source of unreliable information. For example, the warning unit will issue a warning if the sender of a message that is likely to be fraudulent is a new account. For example, the warning unit will issue a warning if the sender of a message that is likely to be fraudulent has a history of fraudulent activity source of unreliable information. This allows for more accurate warnings to be issued by taking into account the attribute information of the sender of a message that is likely to be fraudulent. Sender attribute information includes, but is not limited to, age, occupation, and past message history. Some or all of the processing described above in the warning unit may be performed using, for example, AI, or not using AI. For example, the warning unit can input the sender's attribute information into the AI ​​and have the AI ​​execute the warning.

[0047] The warning unit can improve the accuracy of its warnings by referring to relevant literature related to messages that are likely to be fraudulent. For example, the warning unit can improve the accuracy of its warnings by referring to relevant literature related to messages that are likely to be fraudulent. For example, the warning unit can improve the accuracy of its warnings by referring to the latest literature on fraudulent methods. The warning unit can also improve the accuracy of its warnings by referring to past cases related to messages that are likely to be fraudulent. For example, the warning unit can improve the accuracy of its warnings by referring to relevant literature related to messages that are likely to be fraudulent. The warning unit can improve the accuracy of its warnings by referring to the latest literature on fraudulent methods. The warning unit can improve the accuracy of its warnings by referring to past cases related to messages that are likely to be fraudulent. This allows the warning unit to improve the accuracy of its warnings by referring to relevant literature related to messages that are likely to be fraudulent. Relevant literature includes, but is not limited to, academic papers and past fraud cases. Some or all of the above processing in the warning unit may be performed using, for example, AI, or not using AI. For example, the warning unit can input relevant literature into AI and have the AI ​​perform the improvement of warning accuracy.

[0048] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0049] The reception desk can estimate the user's intent based on their input and take appropriate action based on that estimation. For example, if the user is asking a question about investment, the reception desk can provide information about investment. The reception desk can also provide advice on fraud prevention if the user suspects potential fraud. Furthermore, if the user is requesting a specific action, the reception desk can guide them through the steps to perform that action. This improves user satisfaction by providing appropriate responses tailored to the user's intent. Intent estimation may, but is not limited to, natural language processing techniques or machine learning algorithms. Some or all of the processing described above in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's input into an AI and have the AI ​​perform intent estimation.

[0050] The reception desk can automatically search for and provide relevant information based on the user's input. For example, if the user asks about a specific stock, the reception desk can provide the latest news and analysis reports on that stock. If the user asks about a specific fraud scheme, the reception desk can also provide past cases and preventative measures related to that scheme. Furthermore, if the user asks about a specific financial product, the reception desk can provide detailed information and risk assessments of that product. This improves user satisfaction by providing quick and appropriate information in response to user inquiries. Information retrieval may use, but is not limited to, search engines or databases. Some or all of the processing described above in the reception desk may be performed using, for example, AI, or not. For example, the reception desk can input the user's input into an AI and have the AI ​​perform the information retrieval.

[0051] The reception desk can automatically search for and provide relevant past cases based on the user's input. For example, if the reception desk asks about a specific fraud scheme, it can provide past cases related to that scheme. It can also provide past performance and risk assessments of a specific financial product if the user asks about that product. Furthermore, if the reception desk asks about a specific investment strategy, it can provide past success and failure stories related to that strategy. This allows users to gain a deeper understanding by providing past examples in response to their questions. Searching for past cases may involve, but is not limited to, databases or archives. Some or all of the processing described above in the reception desk may be performed using, for example, AI, or not. For example, the reception desk can input the user's input into an AI and have the AI ​​perform a search for past cases.

[0052] The reception desk can automatically search for and provide expert opinions based on the user's input. For example, if the user asks about a specific investment strategy, the reception desk can provide expert opinions on that strategy. It can also provide expert opinions on the risk assessment of a specific financial product if the user asks about that product. Furthermore, if the user asks about a specific fraudulent scheme, the reception desk can provide expert advice on that scheme. This allows the user to deepen their understanding by providing expert opinions in response to their questions. Expert opinions can be searched using, but are not limited to, expert databases or online forums. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not. For example, the reception desk can input the user's input into an AI and have the AI ​​perform the search for expert opinions.

[0053] The following briefly describes the processing flow for example form 1.

[0054] Step 1: The reception desk receives input from the user. User input includes text messages, voice input, and images. For example, the reception desk receives messages sent by the user in real time. In the case of voice input, the reception desk uses speech recognition technology to convert the voice to text. In the case of image input, the reception desk uses image recognition technology to analyze the image. Step 2: The analysis unit analyzes the content of the conversation based on the information received by the reception unit. The analysis is performed using generative AI, employing natural language processing techniques and machine learning algorithms. For example, the analysis unit uses generative AI to analyze the conversation content and detect messages that are highly likely to be fraudulent. It also analyzes the frequency of keyword occurrences and similarities to past fraud cases. Step 3: The warning unit issues a warning if it determines that a message is likely to be fraudulent based on the results of the analysis by the analysis unit. The warning may be delivered in the form of a pop-up notification, audio alert, or email. For example, if the warning unit detects a message that is likely to be fraudulent, it may send a pop-up notification, an audio alert, or an email warning to the user.

[0055] (Example of form 2) The fraud prevention system according to an embodiment of the present invention is a system for preventing investment fraud using social networking services (SNS). This fraud prevention system uses a generating AI agent to analyze the content of conversations and issue a warning to the user if there is a high probability of fraud. First, the generating AI agent analyzes the content of conversations and detects messages that are likely to be fraudulent. Next, if the generating AI agent determines that there is a high probability of fraud, it issues a warning to the user. For example, if a scheme is detected in which a user is invited to a group, a fake poster claims to be making money, and instructs them to transfer money to a specified account, the system will issue a warning to the user. The generating AI agent will also issue a warning if the official website of the economic analyst hosting the group states that the group does not exist, or if a personal account rather than a bank account is specified as the transfer destination. This allows even users with low financial literacy to spot fraud and prevent them from becoming victims. For example, the generating AI agent analyzes the content of conversations in real time and immediately detects messages that are likely to be fraudulent. The generating AI agent also learns from past fraud cases and is able to respond to new fraudulent methods. Furthermore, the generating AI agent considers user attribute information and past chat history to determine the likelihood of fraud with high accuracy. This allows the fraud prevention system to prevent a decrease in the number of users and advertising revenue. In this way, the fraud prevention system can proactively prevent investment fraud using social media.

[0056] The fraud prevention system according to this embodiment comprises a reception unit, an analysis unit, and a warning unit. The reception unit receives input from the user. User input includes, but is not limited to, text messages, voice input, and images. The reception unit receives, for example, messages sent by the user. The reception unit can also receive content entered by the user via voice. Furthermore, the reception unit can also receive images sent by the user. For example, the reception unit receives messages sent by the user in real time. In the case of voice input, the reception unit converts the voice to text using voice recognition technology. In the case of image input, the reception unit analyzes the image using image recognition technology. The analysis unit analyzes the content of the conversation based on the information received by the reception unit using generative AI. The analysis is performed using, for example, natural language processing technology and machine learning algorithms, but is not limited to these examples. For example, the analysis unit analyzes the content of the conversation using generative AI and detects messages that are highly likely to be fraudulent. The analysis unit can also analyze the frequency of keyword occurrences in the conversation content using generative AI. Furthermore, the analysis unit can also use a generation AI to analyze similarities with past fraud cases. For example, the analysis unit can use a generation AI to analyze the content of a conversation and analyze the frequency of occurrence of specific keywords. The analysis unit can also use a generation AI to analyze the similarity between the content of the conversation and past fraud cases. Some or all of the above-described processes in the analysis unit may be performed using a generation AI, or they may not be performed using a generation AI. For example, the analysis unit can input the content of a conversation into a generation AI and have the generation AI perform the detection of messages that are likely to be fraudulent. The warning unit issues a warning if there is a high probability of fraud based on the results analyzed by the analysis unit. The warning may be, but is not limited to, a pop-up notification or an audio alert. For example, if a message that is likely to be fraudulent is detected, the warning unit will issue a pop-up notification to the user. The warning unit may also issue an audio alert if a message that is likely to be fraudulent is detected. The warning unit may also issue a warning by email if a message that is likely to be fraudulent is detected.For example, if the warning unit detects a message that is highly likely to be fraudulent, it will issue a pop-up notification to the user to warn them of the potential fraud. If the warning unit detects a message that is highly likely to be fraudulent, it will issue an audio alert to draw the user's attention. If the warning unit detects a message that is highly likely to be fraudulent, it will issue a warning via email to provide the user with detailed information. In this way, the fraud prevention system according to the embodiment can prevent fraud by analyzing the user's input and issuing a warning when there is a high possibility of fraud.

[0057] The reception desk receives input from users. User input includes, but is not limited to, text messages, voice input, and images. For example, the reception desk receives messages sent by users. The reception desk can also receive content entered by users via voice. Furthermore, the reception desk can receive images sent by users. For example, the reception desk receives messages sent by users in real time. In the case of voice input, the reception desk uses speech recognition technology to convert the speech to text. In the case of image input, the reception desk uses image recognition technology to analyze the image. The reception desk employs advanced technology to process these inputs quickly and accurately. For example, the speech recognition technology uses a deep learning-based speech model to convert user speech into text with high accuracy. This allows for accurate analysis of content entered by users via voice. The image recognition technology uses computer vision technology to extract important information from images sent by users. For example, it can read text in images using OCR technology to detect signs of fraud. Furthermore, the reception desk has high-speed data processing capabilities to process user input in real time. This allows for the immediate transmission of user-submitted messages, images, and audio to the analysis unit, enabling a rapid response. The reception unit also implements security measures such as data encryption and access control to protect user privacy. This prevents unauthorized access to users' personal information and ensures secure data processing. The reception unit plays a crucial role in enhancing the overall effectiveness of the fraud prevention system by efficiently receiving diverse user inputs and quickly providing data to the analysis unit.

[0058] The analysis unit uses a generative AI to analyze the content of the conversation based on the information received by the reception unit. The analysis is performed using, for example, natural language processing techniques and machine learning algorithms, but is not limited to these examples. For example, the analysis unit uses a generative AI to analyze the content of the conversation and detect messages that are likely to be fraudulent. The analysis unit can also use a generative AI to analyze the frequency of occurrence of keywords in the conversation content. The analysis unit can also use a generative AI to analyze the similarity to past fraud cases. For example, the analysis unit uses a generative AI to analyze the content of the conversation and analyze the frequency of occurrence of specific keywords. The analysis unit uses a generative AI to analyze the similarity between the conversation content and past fraud cases. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the conversation content into a generative AI and have the generative AI detect messages that are likely to be fraudulent. The analysis unit uses a generative AI to analyze text messages received from users using natural language processing techniques and detect signs of fraud. For example, the system detects keywords and phrases specific to fraudulent messages and analyzes their frequency of occurrence to assess the likelihood of fraud. The analysis unit also uses generative AI to analyze the similarity between the user's message and past fraud cases. By referring to a database of past fraud cases and detecting similar patterns and phrases, the likelihood of fraud is increased. Furthermore, the analysis unit uses generative AI to analyze the context of the user's message and detect signs of fraud. For example, if the message content suddenly changes or contains unnatural requests, it is judged to have a high probability of being fraudulent. Based on these analysis results, the analysis unit identifies messages with a high probability of fraud and notifies the warning unit. By using generative AI, the analysis unit possesses advanced analytical capabilities and can quickly and accurately detect signs of fraud. This allows for warnings to be issued before users become victims of fraud, preventing them from becoming victims.

[0059] The warning unit issues a warning if there is a high probability of fraud based on the results of analysis by the analysis unit. Warnings may be issued in the form of, for example, pop-up notifications or audio alerts, but are not limited to these examples. For example, if the warning unit detects a message that is likely to be fraudulent, it will issue a pop-up notification to the user. The warning unit may also issue an audio alert if a message that is likely to be fraudulent is detected. The warning unit may also issue a warning via email if a message that is likely to be fraudulent is detected. For example, if the warning unit detects a message that is likely to be fraudulent, it will issue a pop-up notification to the user to warn of the possibility of fraud. If the warning unit detects a message that is likely to be fraudulent, it will issue an audio alert to draw the user's attention. If the warning unit detects a message that is likely to be fraudulent, it will issue a warning via email to provide the user with more detailed information. The warning unit uses multiple notification methods to warn the user quickly and effectively. For example, pop-up notifications are displayed immediately when the user is using the app to warn of the possibility of fraud. Audio alerts are effective in drawing the user's attention even when the user is not using the app. Email notifications are used to provide detailed information that users can review later. The warning unit can reliably deliver warnings to users by combining these notification methods. Furthermore, the warning unit can collect user feedback and continuously improve the accuracy and effectiveness of warnings. For example, it can analyze user behavior after receiving a warning and optimize the content and timing of the warning. The warning unit can also customize notification methods and warning frequency according to user settings. This allows for flexible responses tailored to user needs. Based on information from the analysis unit, the warning unit plays a role in issuing warnings quickly and accurately, alerting users before they become victims of fraud. As a result, the fraud prevention system according to this embodiment can ensure user safety and prevent fraud.

[0060] The analysis unit can analyze the content of conversations using a generation AI and detect messages that are highly likely to be fraudulent. For example, the analysis unit can analyze the content of conversations using a generation AI and detect messages that are highly likely to be fraudulent. For example, the analysis unit can also analyze the frequency of keyword occurrences in the conversation content using a generation AI. Furthermore, the analysis unit can also analyze the similarity to past fraud cases using a generation AI. For example, the analysis unit can analyze the content of conversations using a generation AI and analyze the frequency of occurrences of specific keywords. The analysis unit can analyze the similarity between the conversation content and past fraud cases using a generation AI. As a result, by using a generation AI, messages that are highly likely to be fraudulent can be detected with high accuracy. Some or all of the above-described processes in the analysis unit may be performed using a generation AI, for example, or without a generation AI. For example, the analysis unit can input the conversation content into a generation AI and have the generation AI perform the detection of messages that are highly likely to be fraudulent.

[0061] The warning unit can issue a warning if the official website of the economic analyst sponsoring the group states that the group does not exist. For example, the warning unit can issue a warning if the official website of the economic analyst sponsoring the group states that the group does not exist. For example, the warning unit can refer to the information on the official website and confirm the existence of the group. The warning unit can also periodically update the information on the official website and issue a warning based on the latest information. For example, the warning unit can refer to the URL of the official website and confirm the existence of the group. The warning unit evaluates the information on the official website based on reliability verification methods and issues a warning. This allows the warning unit to issue a warning if there is a high probability of fraud by referring to the information on the official website. Official websites include, but are not limited to, the official websites of economic analysts and reliable sources. Some or all of the above processes in the warning unit may be performed using, for example, AI, or not using AI. For example, the warning unit can input the information on the official website into AI and have the AI ​​perform the process of verifying the existence of the group.

[0062] The warning unit can issue a warning if a personal account is specified as the recipient of a transfer instead of a financial institution account. For example, the warning unit will issue a warning if a personal account is specified as the recipient of a transfer instead of a financial institution account. For example, the warning unit will check the recipient information and determine whether it is a financial institution account or a personal account. The warning unit can also periodically update the recipient information and issue a warning based on the latest information. For example, the warning unit will refer to the recipient information and determine whether it is a financial institution account or a personal account. The warning unit will evaluate the recipient information based on a reliability verification method and issue a warning. This allows the system to issue a warning if there is a high probability of fraud by checking the recipient information. Financial institution accounts include, but are not limited to, bank accounts and credit union accounts. Personal accounts include, but are not limited to, bank accounts in an individual's name and individual electronic money accounts. Some or all of the above processing in the warning unit may be performed using, for example, AI, or not using AI. For example, the warning unit can input the recipient's information into the AI ​​and have the AI ​​perform a process to determine whether it is a bank account or a personal account.

[0063] The reception system can estimate the user's emotions and adjust the timing of input acceptance based on the estimated emotions. For example, if the user is feeling anxious, the reception system can delay the timing of input acceptance and wait until the user calms down. For example, if the user is excited, the reception system can speed up the timing of input acceptance and respond quickly. The reception system can also accept input at the normal timing if the user is relaxed. For example, the reception system can capture the user's facial expression with a camera and estimate their emotions using an emotion estimation algorithm. The reception system can also record the user's voice and estimate their emotions using voice analysis technology. For example, the reception system can analyze the tone and speed of the user's voice to estimate their emotions. Furthermore, the reception system can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, the reception system can estimate emotions based on fluctuations in heart rate. By adjusting the timing of input acceptance according to the user's emotions, input can be accepted at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception area may be performed using AI or not using AI. For example, the reception area can input user image data captured by a camera into a generative AI and have the generative AI perform emotion estimation of the user.

[0064] The reception desk can analyze the user's past input history and select the optimal reception method. For example, the reception desk may prioritize suggesting input methods (voice, text, etc.) that the user has frequently used in the past. For example, the reception desk may predict and suggest input methods to be used during specific time periods based on the user's past input history. The reception desk may also suggest relevant input methods by referring to content that the user has entered in the past. For example, the reception desk may prioritize suggesting input methods that the user has frequently used in the past. The reception desk may predict and suggest input methods to be used during specific time periods based on the user's past input history. The reception desk may suggest relevant input methods by referring to content that the user has entered in the past. In this way, the optimal reception method can be selected by analyzing the user's past input history. The optimal reception method may include, but is not limited to, the user's past input patterns and preferences. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk may input the user's past input history into AI and have the AI ​​select the optimal reception method.

[0065] The reception unit can filter input based on the user's current areas of interest and activity. For example, the reception unit prioritizes receiving information related to topics the user has recently been interested in. For example, the reception unit filters appropriate information according to the user's current activity status (e.g., at work, on vacation). The reception unit can also filter highly relevant information by referring to the user's past activity history. For example, the reception unit prioritizes receiving information related to topics the user has recently been interested in. The reception unit filters appropriate information according to the user's current activity status. The reception unit filters highly relevant information by referring to the user's past activity history. This allows the reception unit to prioritize receiving highly relevant information by filtering based on the user's areas of interest and activity status. Areas of interest include, but are not limited to, the user's past search history and social media activity. Activity status includes, but is not limited to, current location information and recent activity history. Some or all of the above processing in the reception unit may be performed using, for example, AI, or not using AI. For example, the reception desk can input the user's areas of interest and activity status into the AI ​​and have the AI ​​perform filtering.

[0066] The reception desk can estimate the user's emotions and prioritize the information to be received based on those emotions. For example, if the user is feeling anxious, the reception desk will prioritize receiving important information. For example, if the user is relaxed, the reception desk will prioritize information according to normal priorities. The reception desk can also prioritize receiving information that requires a quick response if the user is excited. For example, the reception desk can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The reception desk can also record the user's voice and estimate their emotions using voice analysis technology. For example, the reception desk can analyze the tone and speed of the user's voice to estimate their emotions. The reception desk can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, the reception desk can estimate emotions based on fluctuations in heart rate. This allows the reception desk to prioritize information according to the user's emotions, thereby prioritizing the receipt of important information. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. The generative AI may be, but is not limited to, text-generating AI (e.g., LLM) or multimodal generative AI. Some or all of the processing described above in the reception area may be performed using AI, or not using AI. For example, the reception area may input image data of the user captured by a camera into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0067] The reception unit can prioritize receiving highly relevant information by considering the user's geographical location information when receiving input. For example, the reception unit can prioritize receiving information related to the user's current location. For example, if the user is traveling, the reception unit can prioritize receiving information related to the travel destination. The reception unit can also prioritize receiving information related to a specific region if the user is interested in that region. For example, the reception unit can prioritize receiving information related to the user's current location. If the user is traveling, the reception unit can prioritize receiving information related to the travel destination. If the user is interested in a specific region, the reception unit can prioritize receiving information related to that region. This allows the reception unit to prioritize receiving highly relevant information by considering the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and IP addresses. Some or all of the processing described above in the reception unit may be performed using, for example, AI, or not using AI. For example, the reception unit can input the user's geographical location information into AI and have AI perform the reception of highly relevant information.

[0068] The reception unit can analyze the user's social media activity and receive relevant information when input is received. For example, the reception unit can receive information related to topics the user has recently shown interest in on social media. For example, the reception unit can analyze the user's social media activity history and receive highly relevant information. The reception unit can also receive relevant information by referring to the content of posts from accounts the user follows. For example, the reception unit can receive information related to topics the user has recently shown interest in on social media. The reception unit can analyze the user's social media activity history and receive highly relevant information. The reception unit can receive relevant information by referring to the content of posts from accounts the user follows. This allows the reception unit to prioritize receiving highly relevant information by analyzing the user's social media activity. Social media activity includes, but is not limited to, posts and follower reactions. Some or all of the above processing in the reception unit may be performed using, for example, AI, or not using AI. For example, the reception unit can input the user's social media activity into AI and have AI perform the reception of relevant information.

[0069] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is feeling anxious, the analysis unit provides the analysis results in a simple and easy-to-understand format. For example, if the user is relaxed, the analysis unit provides detailed analysis results. Furthermore, if the user is excited, the analysis unit can also provide the analysis results in a visually appealing format. For example, the analysis unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The analysis unit can also record the user's voice and estimate their emotions using voice analysis technology. For example, the analysis unit can analyze the tone and speed of the user's voice to estimate their emotions. The analysis unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, the analysis unit can estimate emotions based on fluctuations in heart rate. This allows the analysis results to be provided in a more appropriate format by adjusting the presentation of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the processing described above in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input user image data captured by a camera into the generation AI and have the generation AI perform the estimation of the user's emotions.

[0070] The analysis unit can adjust the level of detail of the analysis based on the importance of the messages during analysis. For example, the analysis unit can analyze high-importance messages in detail and low-importance messages simply. For example, the analysis unit can adjust the display order of the analysis results according to the importance of the messages. The analysis unit can also provide additional analysis information to high-importance messages. For example, the analysis unit can analyze high-importance messages in detail and low-importance messages simply. The analysis unit can adjust the display order of the analysis results according to the importance of the messages. The analysis unit can provide additional analysis information to high-importance messages. This allows important messages to be analyzed in detail by adjusting the level of detail of the analysis based on the importance of the messages. Message importance includes, but is not limited to, the frequency of occurrence of specific keywords or the reliability of the sender. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the importance of the messages into the AI ​​and have the AI ​​adjust the level of detail of the analysis.

[0071] The analysis unit can apply different analysis algorithms depending on the message category during analysis. For example, the analysis unit can apply a specialized financial analysis algorithm to investment-related messages. For example, the analysis unit can apply a specialized medical analysis algorithm to health-related messages. The analysis unit can also apply a specialized education analysis algorithm to education-related messages. For example, the analysis unit can apply a specialized financial analysis algorithm to investment-related messages. For example, the analysis unit can apply a specialized medical analysis algorithm to health-related messages. For example, the analysis unit can apply a specialized education analysis algorithm to education-related messages. By applying different analysis algorithms depending on the message category, more appropriate analysis results can be provided. Message categories include, for example, business, private, and urgent, but are not limited to these examples. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the message category into the AI ​​and have the AI ​​perform the application of the analysis algorithm.

[0072] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit will provide a short, concise analysis. For example, if the user is relaxed, the analysis unit will provide a detailed analysis. Furthermore, if the user is excited, the analysis unit can provide the analysis results in a visually appealing format. For example, the analysis unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The analysis unit can also record the user's voice and estimate their emotions using voice analysis technology. For example, the analysis unit can analyze the tone and speed of the user's voice to estimate their emotions. The analysis unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, the analysis unit can estimate emotions based on fluctuations in heart rate. This allows the analysis unit to adjust the length of the analysis according to the user's emotions, thereby providing analysis results of a more appropriate length. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the processing described above in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input user image data captured by a camera into the generation AI and have the generation AI perform the estimation of the user's emotions.

[0073] The analysis unit can determine the priority of analysis based on the message transmission date during analysis. For example, the analysis unit may prioritize the analysis of recently sent messages. For example, the analysis unit may prioritize the analysis of messages sent immediately before an important event. The analysis unit can also prioritize the analysis of messages received by a user during a specific time period. For example, the analysis unit may prioritize the analysis of recently sent messages. For example, the analysis unit may prioritize the analysis of messages sent immediately before an important event. For example, the analysis unit may prioritize the analysis of messages received by a user during a specific time period. This allows for the prioritization of analysis based on the message transmission date, thereby prioritizing the analysis of messages at important timings. The message transmission date includes, but is not limited to, timestamps and transmission history. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit may input the message transmission date into AI and have AI determine the analysis priority.

[0074] The analysis unit can adjust the order of analysis based on the relevance of messages during analysis. For example, the analysis unit may prioritize analyzing highly relevant messages. For example, the analysis unit may prioritize analyzing messages related to the user's interests. The analysis unit can also group highly relevant messages based on their content and analyze them. For example, the analysis unit may prioritize analyzing highly relevant messages. The analysis unit may prioritize analyzing messages related to the user's interests. The analysis unit may group highly relevant messages based on their content and analyze them. This allows the analysis unit to prioritize the analysis of highly relevant messages by adjusting the order of analysis based on the relevance of messages. Message relevance includes, but is not limited to, similarity of content or relationship between senders. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the relevance of messages into AI and have AI perform the adjustment of the analysis order.

[0075] The warning unit can estimate the user's emotions and adjust the way it expresses the warning based on those emotions. For example, if the user is feeling anxious, the warning unit will issue a warning in a calm tone. For example, if the user is relaxed, the warning unit will issue a warning in a normal tone. The warning unit can also issue a warning quickly if the user is excited. For example, the warning unit can capture the user's facial expression with a camera and estimate their emotions using an emotion estimation algorithm. The warning unit can also record the user's voice and estimate their emotions using voice analysis technology. For example, the warning unit can analyze the tone and speed of the user's voice to estimate their emotions. The warning unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, the warning unit can estimate emotions based on fluctuations in heart rate. This allows the warning unit to adjust the way it expresses the warning according to the user's emotions, enabling it to issue warnings in a more appropriate format. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the processing described above in the warning unit may be performed using AI, or not using AI. For example, the warning unit may input user image data captured by a camera into the generation AI and have the generation AI perform the estimation of the user's emotions.

[0076] The warning unit can adjust the level of detail of a warning based on the content of a message that is likely to be fraudulent. For example, the warning unit will issue a detailed warning for a message that is likely to be fraudulent. For example, the warning unit will issue a simplified warning for a message that is unlikely to be fraudulent. The warning unit can also adjust the level of detail of a warning depending on the content of the message. For example, the warning unit will issue a detailed warning for a message that is likely to be fraudulent. The warning unit will issue a simplified warning for a message that is unlikely to be fraudulent. The warning unit adjusts the level of detail of a warning depending on the content of the message. This allows for the issuance of more appropriate warnings by adjusting the level of detail of a warning based on the content of a message that is likely to be fraudulent. The level of detail of a warning includes, but is not limited to, the likelihood of fraud and the severity of the damage. Some or all of the above processing in the warning unit may be performed using, for example, AI, or not using AI. For example, the warning unit can input the content of a message that is likely to be fraudulent into AI and have AI perform the adjustment of the level of detail of the warning.

[0077] The warning unit can apply different warning algorithms depending on the type of fraud when issuing a warning. For example, the warning unit can apply a financial warning algorithm to investment fraud. For example, the warning unit can apply a medical warning algorithm to health fraud. The warning unit can also apply an education-specific warning algorithm to education fraud. For example, the warning unit can apply a financial warning algorithm to investment fraud. For example, the warning unit can apply a medical warning algorithm to health fraud. For example, the warning unit can apply an education-specific warning algorithm to education fraud. By applying different warning algorithms depending on the type of fraud, more appropriate warnings can be issued. Fraudulent methods include, but are not limited to, phishing, impersonation, and investment fraud. Some or all of the above processing in the warning unit may be performed using, for example, AI, or not using AI. For example, the warning unit can input the fraudulent method into AI and have the AI ​​perform the application of the warning algorithm.

[0078] The warning unit can estimate the user's emotions and adjust the timing of the warning based on those emotions. For example, if the user is feeling anxious, the warning unit can delay the warning and wait until the user calms down. For example, if the user is excited, the warning unit can speed up the warning and respond quickly. The warning unit can also issue a warning at the normal time if the user is relaxed. For example, the warning unit can capture the user's facial expression with a camera and estimate their emotions using an emotion estimation algorithm. The warning unit can also record the user's voice and estimate their emotions using voice analysis technology. For example, the warning unit can analyze the tone and speed of the user's voice to estimate their emotions. The warning unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, the warning unit can estimate emotions based on fluctuations in heart rate. This allows the warning unit to issue warnings at a more appropriate time by adjusting the timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the processing described above in the warning unit may be performed using AI, or not using AI. For example, the warning unit may input user image data captured by a camera into the generation AI and have the generation AI perform the estimation of the user's emotions.

[0079] The warning unit can issue a warning when it issues one, taking into account the attribute information of the sender of a message that is likely to be fraudulent. For example, the warning unit will issue a warning if the sender of a message that is likely to be fraudulent is a new account. For example, the warning unit will issue a warning if the sender of a message that is likely to be fraudulent has a history of fraudulent activity. The warning unit can also issue a warning if the sender of a message that is likely to be fraudulent is a source of unreliable information. For example, the warning unit will issue a warning if the sender of a message that is likely to be fraudulent is a new account. For example, the warning unit will issue a warning if the sender of a message that is likely to be fraudulent has a history of fraudulent activity source of unreliable information. This allows for more accurate warnings to be issued by taking into account the attribute information of the sender of a message that is likely to be fraudulent. Sender attribute information includes, but is not limited to, age, occupation, and past message history. Some or all of the processing described above in the warning unit may be performed using, for example, AI, or not using AI. For example, the warning unit can input the sender's attribute information into the AI ​​and have the AI ​​execute the warning.

[0080] The warning unit can improve the accuracy of its warnings by referring to relevant literature related to messages that are likely to be fraudulent. For example, the warning unit can improve the accuracy of its warnings by referring to relevant literature related to messages that are likely to be fraudulent. For example, the warning unit can improve the accuracy of its warnings by referring to the latest literature on fraudulent methods. The warning unit can also improve the accuracy of its warnings by referring to past cases related to messages that are likely to be fraudulent. For example, the warning unit can improve the accuracy of its warnings by referring to relevant literature related to messages that are likely to be fraudulent. The warning unit can improve the accuracy of its warnings by referring to the latest literature on fraudulent methods. The warning unit can improve the accuracy of its warnings by referring to past cases related to messages that are likely to be fraudulent. This allows the warning unit to improve the accuracy of its warnings by referring to relevant literature related to messages that are likely to be fraudulent. Relevant literature includes, but is not limited to, academic papers and past fraud cases. Some or all of the above processing in the warning unit may be performed using, for example, AI, or not using AI. For example, the warning unit can input relevant literature into AI and have the AI ​​perform the improvement of warning accuracy.

[0081] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0082] The reception desk can estimate the user's intent based on their input and take appropriate action based on that estimation. For example, if the user is asking a question about investment, the reception desk can provide information about investment. The reception desk can also provide advice on fraud prevention if the user suspects potential fraud. Furthermore, if the user is requesting a specific action, the reception desk can guide them through the steps to perform that action. This improves user satisfaction by providing appropriate responses tailored to the user's intent. Intent estimation may, but is not limited to, natural language processing techniques or machine learning algorithms. Some or all of the processing described above in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's input into an AI and have the AI ​​perform intent estimation.

[0083] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated emotions. For example, if the user is feeling anxious, the analysis unit will prioritize analyzing important messages. If the user is relaxed, the analysis unit can also perform analysis with normal priorities. Furthermore, if the user is excited, the analysis unit can prioritize analyzing messages that require immediate attention. This allows for the rapid analysis of important messages by determining the priority of analysis according to the user's emotions. For example, emotion engines or generative AI may be used to estimate emotions, but are not limited to these examples. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's emotions into an AI and have the AI ​​determine the priority of analysis.

[0084] The warning unit can estimate the user's emotions and adjust the content of the warning based on the estimated emotions. For example, if the user is feeling anxious, the warning unit can issue a warning with a detailed explanation. If the user is relaxed, the warning unit can also issue a concise warning. Furthermore, if the user is agitated, the warning unit can issue a warning requiring immediate attention. By adjusting the content of the warning according to the user's emotions, more appropriate warnings can be issued. Emotion estimation can be performed using, for example, an emotion engine or generative AI, but is not limited to these examples. Some or all of the processing described above in the warning unit may be performed using, for example, AI, or not using AI. For example, the warning unit can input the user's emotions into an AI and have the AI ​​adjust the content of the warning.

[0085] The reception desk can automatically search for and provide relevant information based on the user's input. For example, if the user asks about a specific stock, the reception desk can provide the latest news and analysis reports on that stock. If the user asks about a specific fraud scheme, the reception desk can also provide past cases and preventative measures related to that scheme. Furthermore, if the user asks about a specific financial product, the reception desk can provide detailed information and risk assessments of that product. This improves user satisfaction by providing quick and appropriate information in response to user inquiries. Information retrieval may use, but is not limited to, search engines or databases. Some or all of the processing described above in the reception desk may be performed using, for example, AI, or not. For example, the reception desk can input the user's input into an AI and have the AI ​​perform the information retrieval.

[0086] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is feeling anxious, the analysis unit can provide the analysis results in a simple and easy-to-understand format. If the user is relaxed, the analysis unit can also provide detailed analysis results. Furthermore, if the user is excited, the analysis unit can provide the analysis results in a visually appealing format. This allows for the provision of analysis results in a more appropriate format by adjusting the presentation of the analysis according to the user's emotions. Emotion estimation may use, but is not limited to, an emotion engine or generative AI. Some or all of the above-described processes in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's emotions into an AI and have the AI ​​adjust the presentation of the analysis.

[0087] The warning unit can estimate the user's emotions and adjust the timing of the warning based on the estimated emotions. For example, if the user is feeling anxious, the warning unit can delay the warning and wait until the user calms down. Conversely, if the user is excited, the warning unit can advance the warning to respond quickly. Furthermore, if the user is relaxed, the warning unit can issue a warning at the normal time. This allows for more appropriate timing of warnings by adjusting the timing according to the user's emotions. Emotion estimation can be performed using, but is not limited to, an emotion engine or generative AI. Some or all of the above-described processes in the warning unit may be performed using, for example, AI, or not. For example, the warning unit can input the user's emotions into an AI and have the AI ​​adjust the timing of the warning.

[0088] The reception desk can automatically search for and provide relevant past cases based on the user's input. For example, if the reception desk asks about a specific fraud scheme, it can provide past cases related to that scheme. It can also provide past performance and risk assessments of a specific financial product if the user asks about that product. Furthermore, if the reception desk asks about a specific investment strategy, it can provide past success and failure stories related to that strategy. This allows users to gain a deeper understanding by providing past examples in response to their questions. Searching for past cases may involve, but is not limited to, databases or archives. Some or all of the processing described above in the reception desk may be performed using, for example, AI, or not. For example, the reception desk can input the user's input into an AI and have the AI ​​perform a search for past cases.

[0089] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis. If the user is relaxed, the analysis unit can also provide a detailed analysis. Furthermore, if the user is excited, the analysis unit can provide the analysis in a visually appealing format. By adjusting the length of the analysis according to the user's emotions, it is possible to provide an analysis of a more appropriate length. For example, emotion engines or generative AI may be used to estimate emotions, but are not limited to these examples. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's emotions into an AI and have the AI ​​adjust the length of the analysis.

[0090] The reception desk can automatically search for and provide expert opinions based on the user's input. For example, if the user asks about a specific investment strategy, the reception desk can provide expert opinions on that strategy. It can also provide expert opinions on the risk assessment of a specific financial product if the user asks about that product. Furthermore, if the user asks about a specific fraudulent scheme, the reception desk can provide expert advice on that scheme. This allows the user to deepen their understanding by providing expert opinions in response to their questions. Expert opinions can be searched using, but are not limited to, expert databases or online forums. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not. For example, the reception desk can input the user's input into an AI and have the AI ​​perform the search for expert opinions.

[0091] The analysis unit can estimate the user's emotions and adjust the level of detail of the analysis based on the estimated emotions. For example, if the user is feeling anxious, the analysis unit can provide detailed analysis results. If the user is relaxed, the analysis unit can also provide concise analysis results. Furthermore, if the user is excited, the analysis unit can provide analysis results in a visually appealing format. By adjusting the level of detail of the analysis according to the user's emotions, it is possible to provide analysis results with a more appropriate level of detail. For example, emotion engines or generative AI may be used to estimate emotions, but are not limited to such examples. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's emotions into AI and have the AI ​​adjust the level of detail of the analysis.

[0092] The following briefly describes the processing flow for example form 2.

[0093] Step 1: The reception desk receives input from the user. User input includes text messages, voice input, and images. For example, the reception desk receives messages sent by the user in real time. In the case of voice input, the reception desk uses speech recognition technology to convert the voice to text. In the case of image input, the reception desk uses image recognition technology to analyze the image. Step 2: The analysis unit analyzes the content of the conversation based on the information received by the reception unit. The analysis is performed using generative AI, employing natural language processing techniques and machine learning algorithms. For example, the analysis unit uses generative AI to analyze the conversation content and detect messages that are highly likely to be fraudulent. It also analyzes the frequency of keyword occurrences and similarities to past fraud cases. Step 3: The warning unit issues a warning if it determines that a message is likely to be fraudulent based on the results of the analysis by the analysis unit. The warning may be delivered in the form of a pop-up notification, audio alert, or email. For example, if the warning unit detects a message that is likely to be fraudulent, it may send a pop-up notification, an audio alert, or an email warning to the user.

[0094] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0095] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0096] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0097] Each of the multiple elements described above, including the reception unit, analysis unit, and warning unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and receives input from the user. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the content of the conversation using a generation AI. The warning unit is implemented by the control unit 46A of the smart device 14 and issues a warning if there is a high probability of fraud. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0098] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0099] As shown in Figure 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.

[0100] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0101] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0102] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0103] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0104] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0105] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0106] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0107] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0108] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0109] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0110] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0111] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0112] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0113] Each of the multiple elements described above, including the reception unit, analysis unit, and warning unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and receives input from the user. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the content of the conversation using a generation AI. The warning unit is implemented by the control unit 46A of the smart glasses 214 and issues a warning if there is a high probability of fraud. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0114] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0115] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0116] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0117] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0118] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0120] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0121] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0122] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0123] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0124] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0125] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0126] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0127] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0128] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0129] Each of the multiple elements described above, including the reception unit, analysis unit, and warning unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and receives input from the user. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the content of the conversation using a generation AI. The warning unit is implemented by the control unit 46A of the headset terminal 314 and issues a warning if there is a high probability of fraud. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0130] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0131] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0132] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0133] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0134] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0135] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0136] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0137] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0138] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0139] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0140] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0141] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0142] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0143] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0144] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0145] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0146] Each of the multiple elements described above, including the reception unit, analysis unit, and warning unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and receives input from the user. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the content of the conversation using a generation AI. The warning unit is implemented by the control unit 46A of the robot 414 and issues a warning if there is a high probability of fraud. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0147] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0148] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0149] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0150] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0151] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0152] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0153] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0154] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0155] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0156] 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.

[0157] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0158] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0159] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0160] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0161] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0162] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0163] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0164] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0165] (Note 1) A reception area that receives input from users, An analysis unit analyzes the content of the conversation based on the information received by the reception unit, The system includes a warning unit that issues a warning if there is a high probability of fraud based on the results of the analysis performed by the aforementioned analysis unit. A system characterized by the following features. (Note 2) The aforementioned analysis unit, Using a generation AI, we analyze the content of conversations and detect messages that are highly likely to be fraudulent. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned warning unit is A warning will be issued if the official website of the economic analyst who organizes the group states that the group does not exist. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned warning unit is A warning is issued if a personal account, rather than a bank account, is specified as the recipient of the transfer. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned warning unit is One of the group members says they made money with stock 4689, but they issued a warning because the stock has recently fallen by about 20%. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of input acceptance based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is Analyze the user's past input history to select the optimal reception method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When receiving input, filtering is performed based on the user's current areas of interest and activity. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is It estimates the user's emotions and determines the priority of the information to be received based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When receiving input, the system prioritizes receiving information that is highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When receiving input, the system analyzes the user's social media activity and collects relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the messages. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the message category. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the messages were sent. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the messages. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned warning unit is The system estimates the user's emotions and adjusts the way warnings are presented based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned warning unit is When issuing a warning, adjust the level of detail based on the content of the message, which is likely to be a scam. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned warning unit is When issuing a warning, different warning algorithms are applied depending on the fraudulent scheme. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned warning unit is It estimates the user's emotions and adjusts the timing of warnings based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned warning unit is When issuing a warning, the system takes into account the attributes of the sender of the message, which is likely to be fraudulent. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned warning unit is When issuing a warning, we improve the accuracy of the warning by referring to relevant literature related to messages that are likely to be fraudulent. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0166] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception area that receives input from users, An analysis unit analyzes the content of a message app based on the information received by the reception unit, The system includes a warning unit that issues a warning if there is a high probability of fraud based on the results of the analysis performed by the aforementioned analysis unit. A system characterized by the following features.

2. The aforementioned analysis unit, Using AI-generated messages, we analyze the content of conversations and detect messages that are highly likely to be fraudulent. The system according to feature 1.

3. The aforementioned warning unit is A warning will be issued if the official website of the economic analyst who organizes the group states that the group does not exist. The system according to feature 1.

4. The aforementioned warning unit is A warning is issued if a personal account, rather than a bank account, is specified as the recipient of the transfer. The system according to feature 1.

5. The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of input acceptance based on the estimated emotions. The system according to feature 1.

6. The aforementioned reception unit is Analyze the user's past input history to select the optimal reception method. The system according to feature 1.

7. The aforementioned reception unit is When receiving input, filtering is performed based on the user's current areas of interest and activity. The system according to feature 1.

8. The aforementioned reception unit is It estimates the user's emotions and determines the priority of the information to be received based on the estimated user emotions. The system according to feature 1.

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