system

Abstract

The system according to this embodiment aims to detect fraudulent activities on social media early and to take appropriate action. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, an IP analysis unit, a detection unit, and a warning unit. The reception unit receives reports from users. The analysis unit analyzes the report information received by the reception unit. The IP analysis unit analyzes the IP addresses analyzed by the analysis unit. The detection unit detects the poster's behavior based on the information analyzed by the IP analysis unit. The warning unit issues warnings for suspicious accounts and posts detected by the detection unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method 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 prior art, there is a problem that it is difficult to detect fraud on SNS at an early stage and deal with it appropriately.

[0005] The system according to the embodiment aims to detect fraud on SNS at an early stage and deal with it appropriately.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, an IP analysis unit, a detection unit, and a warning unit. The reception unit receives reports from users. The analysis unit analyzes the report information received by the reception unit. The IP analysis unit analyzes the IP addresses analyzed by the analysis unit. The detection unit detects the poster's behavior based on the information analyzed by the IP analysis unit. The warning unit issues warnings for suspicious accounts and posts detected by the detection unit. [Effects of the Invention]

[0007] The system according to this embodiment can detect fraudulent activity on social media early and take appropriate action. [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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F manages communication between a plurality of computers. Examples of communication standards applicable to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". 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 bound 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also 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) An AI system according to an embodiment of the present invention is a system that detects fraudulent activity on social networking services (SNS) in real time and provides a safe SNS environment. This AI system receives reports from users, analyzes the reported information, analyzes IP addresses, detects the poster's behavior, and evaluates reliability. Based on these analysis results, it issues warnings about suspicious accounts and posts. This AI system will create a safe SNS environment and realize a society where users can share information with peace of mind. It will also create a mechanism for the entire community to confront fraudulent activity. For example, if a user finds a suspicious account or post, they report that information to the system. This reported information is input into the AI ​​system. Next, the AI ​​system analyzes the reported information and analyzes the IP address. By analyzing the IP address, the system identifies the sender's location information and past activity history. For example, if multiple suspicious accounts are created from the same IP address, that IP address will be judged as suspicious. Furthermore, the AI ​​system detects the poster's behavior. It analyzes the poster's past activity history and post content and evaluates reliability. For example, accounts that have committed fraudulent activities in the past or accounts that have made a large number of posts in a short period of time will be judged as having low reliability. Based on these analysis results, the AI ​​system issues warnings for suspicious accounts and posts. For example, it displays warning messages for suspicious accounts to alert users. It also takes measures such as temporarily hiding suspicious posts. This AI system helps create a safe SNS environment and realizes a society where users can share information with peace of mind. It also creates a mechanism for the entire community to combat fraudulent activities. For example, by having users report suspicious accounts and posts, fraudulent activities can be detected early, minimizing damage. In this way, by utilizing the AI ​​system, fraudulent activities on SNS can be detected in real time, and a safe SNS environment can be provided. This will allow all users, especially the elderly, to use SNS with peace of mind.

[0029] The AI ​​system according to this embodiment comprises a reception unit, an analysis unit, an IP analysis unit, a detection unit, and a warning unit. The reception unit receives reports from users. Reports from users include, but are not limited to, text, images, and videos. The reception unit receives reports, for example, through an online form. The reception unit can also receive reports via telephone or email. Furthermore, the reception unit can provide a reporting function on an SNS platform. For example, the reception unit provides a dedicated button for users to report suspicious accounts or posts. The analysis unit analyzes the report information received by the reception unit. The analysis unit analyzes the content of the report, for example, using text analysis technology. The analysis unit can also analyze images included in the report using image analysis technology. Furthermore, the analysis unit can also analyze videos included in the report using video analysis technology. For example, the analysis unit analyzes the intent of the report using natural language processing technology. Image analysis technology recognizes objects and text in images and analyzes the content of the report. Video analysis technology analyzes movement and sound in videos and analyzes the content of the report. The IP analysis unit analyzes the IP addresses analyzed by the analysis unit. The IP analysis unit can, for example, identify the location information of an IP address. It can also identify past activity history. Furthermore, the IP analysis unit can detect the creation of multiple accounts from the same IP address. For example, the IP analysis unit identifies the geographical location of an IP address and understands the location information of the sender. Past activity history is analyzed by examining the access history from the same IP address and identifying the sender's behavior patterns. The creation of multiple accounts from the same IP address is detected as an indication of spam or fraudulent activity. The detection unit detects the poster's behavior based on the information analyzed by the IP analysis unit. The detection unit can, for example, analyze the poster's past activity history. It can also analyze the content of posts. Furthermore, the detection unit can evaluate the reliability of the poster. For example, the detection unit detects accounts that have committed fraudulent activities in the past. Analysis of post content analyzes the frequency and trends of posts and detects suspicious behavior. Reliability is evaluated based on the poster's past activity history and post content.The warning unit issues warnings for suspicious accounts and posts detected by the detection unit. For example, the warning unit displays a warning message for suspicious accounts. The warning unit can also temporarily hide suspicious posts. Furthermore, the warning unit can send messages to users to alert them. For example, the warning unit displays a warning message for suspicious accounts and alerts the user. Suspicious posts are temporarily hidden, and a warning is issued to the user. The alert message informs the user of the possibility of fraudulent activity. As a result, the AI ​​system according to this embodiment can detect fraudulent activity on social media in real time and provide a safe social media environment.

[0030] The reception department receives reports from users. These reports may include, but are not limited to, text, images, and videos. The reception department accepts reports, for example, through online forms. These online forms are designed for easy user access and include fields for detailed reporting. Furthermore, the reception department can also accept reports via telephone and email. For telephone reports, speech recognition technology can be used to transcribe the report into text and input it into the system. For email reports, the system automatically analyzes the email content and extracts necessary information. The reception department can also provide reporting functionality on social networking services (SNS) platforms. For example, the reception department provides a dedicated button for users to report suspicious accounts or posts. Clicking this button automatically sends the report to the reception department. Reporting functionality on SNS platforms is easily accessible within SNS applications that users use daily, thus lowering the barrier to reporting. This allows the reception department to provide diverse reporting methods and create an environment where users can report quickly and easily. Furthermore, the reception department has a system in place to centrally manage reports and process them appropriately according to their type and content. For example, if a report contains information that is highly urgent, there is a system in place to immediately notify the analysis and warning departments. This provides the reception department with a foundation for prompt and appropriate responses, improving the overall efficiency and effectiveness of the system.

[0031] The analysis unit analyzes the information received by the reception unit. For example, the analysis unit analyzes the content of the report using text analysis technology. Text analysis technology includes natural language processing (NLP) technology, which can analyze the intent and sentiment of the report. For example, it analyzes the frequency of keyword occurrences and context to determine whether the report suggests fraudulent activity. The analysis unit can also analyze images included in the report using image analysis technology. Image analysis technology includes object detection and facial recognition technology, which can identify specific objects or people in an image. For example, it analyzes whether the reported image contains suspicious objects or actions. Furthermore, the analysis unit can also analyze videos included in the report using video analysis technology. Video analysis technology includes motion detection and audio analysis technology, which can analyze movement and sound in the video. For example, it analyzes whether suspicious actions or statements are being made in the video. In this way, the analysis unit can comprehensively analyze various data such as text, images, and videos to determine the truthfulness and urgency of the report. Furthermore, the analysis unit can utilize past reporting data and statistical information to analyze patterns and trends in reporting content. For example, it can analyze increasing reporting trends in specific regions or time periods to provide information for taking preventative measures. The analysis unit can also use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing warnings early. As a result, the analysis unit can not only grasp the situation in real time but also handle long-term risk management and anomaly detection, improving the reliability and security of the entire system.

[0032] The IP analysis unit analyzes the IP addresses analyzed by the analysis unit. For example, the IP analysis unit identifies the location information of an IP address. Identifying location information includes techniques that use a Geographic Information System (GIS) to determine the geographical location of the IP address's origin. This allows for understanding the physical location of the informant and conducting risk assessments for each region. The IP analysis unit can also identify past behavioral history. Identifying behavioral history includes techniques that analyze access history from the same IP address to identify the informant's behavioral patterns. For example, if there are frequent accesses from a particular IP address, that IP address may be associated with suspicious activity. Furthermore, the IP analysis unit can detect the creation of multiple accounts from the same IP address. Detecting multiple account creation includes techniques that match IP addresses with account information to identify the creation of multiple accounts from the same IP address. This allows for early detection of signs of spamming or fraudulent activity and the implementation of appropriate countermeasures. For example, the IP analysis unit identifies the geographical location of an IP address and understands the sender's location information. Past behavioral history is analyzed by examining access history from the same IP address to identify the sender's behavioral patterns. Creating multiple accounts from the same IP address is detected as an indication of spam or fraudulent activity. This allows the IP analysis department to comprehensively analyze information related to the IP address and evaluate the reliability and risk of the reported content. Furthermore, the IP analysis department can share the analysis results with other departments to strengthen risk management across the entire system. For example, the analysis results from the IP analysis department can be provided to the detection and warning departments, enabling prompt and appropriate responses. In this way, the IP analysis department can play a crucial role in improving the reliability and security of the entire system.

[0033] The detection unit detects the poster's behavior based on information analyzed by the IP analysis unit. For example, the detection unit analyzes the poster's past activity history. This analysis includes techniques to analyze the poster's past posting content, frequency, and behavioral patterns. For example, to detect accounts that have engaged in fraudulent activities in the past, the detection unit analyzes the frequency of appearance of specific keywords or phrases. The detection unit can also analyze the content of posts. This analysis includes text analysis and image analysis techniques to analyze the frequency and content trends of posts and detect suspicious behavior. For example, if posts are concentrated in a specific time period, that account may be engaging in automated spamming. Furthermore, the detection unit can also evaluate the reliability of the poster. This reliability evaluation includes techniques to calculate a reliability score based on the poster's past activity history and posting content. For example, accounts with a history of many suspicious activities are assigned a low reliability score. This allows the detection unit to comprehensively evaluate the poster's behavior and detect suspicious accounts and posts at an early stage. Furthermore, the detection unit can continuously correct its detection results based on real-time updated data, enabling it to respond to the latest situation. For example, if a new suspicious behavior pattern is detected, it can update the detection algorithm to perform more accurate detection. The detection unit can also use anomaly detection algorithms to detect patterns that are different from the norm or abnormal data, and issue warnings early. As a result, the detection unit can not only grasp the situation in real time but also handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0034] The warning unit issues warnings for suspicious accounts and posts detected by the detection unit. For example, the warning unit displays a warning message for suspicious accounts. The warning message informs the account owner that suspicious activity has been detected and prompts them to take appropriate action. The warning unit can also temporarily hide suspicious posts. If a suspicious post is detected, it is temporarily hidden and the user is warned. Furthermore, the warning unit can send a cautionary message to the user. The cautionary message informs the user of the possibility of fraudulent activity and draws their attention to it. For example, the warning unit displays a warning message for suspicious accounts and draws the user's attention to it. Suspicious posts are temporarily hidden and the user is warned. The cautionary message informs the user of the possibility of fraudulent activity. This allows the warning unit to provide users with quick and appropriate warnings and prevent damage. Furthermore, the warning unit has a feedback system to continuously improve the accuracy and effectiveness of the warnings. For example, it collects feedback from users who have received warning messages and uses it to improve the warnings. Furthermore, the warning unit can reliably transmit information using multiple communication methods. For example, it can reliably deliver important information not only through smartphone notifications but also through voice calls, SMS, and email. This allows the warning unit to quickly and reliably warn users and minimize damage.

[0035] The reception department can analyze a user's past reporting history when receiving a report and prioritize processing reports that are considered highly reliable. For example, the reception department can prioritize reports from users who have previously made accurate reports. It can also prioritize reports from users who have previously made false reports. Furthermore, the reception department can prioritize reports that exhibit specific patterns based on past reporting history. This allows for effective detection of fraudulent activity by prioritizing the processing of highly reliable reports. The analysis of reporting history may be performed using AI or not. For example, the reception department can input a user's past reporting history into a generating AI and have the generating AI perform a reliability assessment.

[0036] The reception department can adjust the reception method based on the level of detail of the report received. For example, if the report is detailed, the reception department can automatically prioritize it for a quicker response. If the report is vague, the reception department can also display a prompt for additional information. Furthermore, if the report is concise, the reception department can process it with the normal priority. This allows for an appropriate response by adjusting the reception method based on the level of detail of the report. The evaluation of the level of detail of the report may be performed using AI, or it may be performed without AI. For example, the reception department can input the report content into a generating AI and have the generating AI perform the evaluation of the level of detail.

[0037] The reception unit can prioritize processing highly relevant reports by considering the user's geographical location information when receiving a report. For example, if a user reports from a specific region, the reception unit will prioritize reports from that region. The reception unit can also prioritize nearby reports based on the user's location information. Furthermore, if the user is on the move, the reception unit can prioritize reports based on their current location. This enables region-specific responses by considering the user's geographical location information. The acquisition of geographical location information may be done using AI, for example, or without using AI. For example, the reception unit can input the user's location information into a generating AI and have the generating AI perform a relevance evaluation.

[0038] The reception desk can analyze a user's social media activity upon receiving a report and prioritize processing relevant reports. For example, the reception desk can prioritize reports based on the content a user frequently posts. It can also determine the priority of reports based on a user's follower count and influence. Furthermore, the reception desk can analyze a user's past social media activity and prioritize processing reports that are deemed reliable. This allows for the prioritization of reliable reports by analyzing a user's social media activity. The analysis of social media activity may be performed using AI or without AI. For example, the reception desk can input a user's social media activity into a generating AI and have the generating AI perform a relevance assessment.

[0039] The analysis unit can improve the accuracy of its analysis by referring to a database of past fraud cases when analyzing reported information. For example, the analysis unit can detect similar patterns based on the database of past fraud cases. The analysis unit can also identify signs of fraud by referring to the database of past fraud cases. Furthermore, the analysis unit can optimize its analysis algorithm by utilizing the database of past fraud cases. As a result, by referring to the database of past fraud cases, the accuracy of the analysis is improved, and fraudulent activity can be detected effectively. The referencing of the fraud case database may be done using AI, for example, or without using AI. For example, the analysis unit can input the database of past fraud cases into a generating AI and have the generating AI perform the task of improving the accuracy of the analysis.

[0040] The analysis unit can apply different analysis algorithms depending on the category of the reported information. For example, the analysis unit can apply a specific analysis algorithm to reports of financial fraud. It can also apply a different analysis algorithm to reports of phishing scams. Furthermore, it can apply a dedicated analysis algorithm to reports of false information. By applying different analysis algorithms depending on the category of the reported information, the accuracy of the analysis is improved. The application of analysis algorithms may be performed using AI, for example, or without using AI. For example, the analysis unit can input the reported information into a generating AI and have the generating AI perform analysis according to the category.

[0041] The analysis unit can determine the priority of analysis based on the submission date of the report when analyzing the report information. For example, the analysis unit can prioritize the analysis of recently submitted reports. It can also prioritize the analysis of reports submitted within a specific time period. Furthermore, it can prioritize the analysis of reports with high urgency. This allows for a rapid response to high-urgency reports by determining the priority of analysis based on the submission date of the report. The evaluation of the submission date may be performed using AI, for example, or without using AI. For example, the analysis unit can input the submission date of the report into a generating AI and have the generating AI perform the priority determination.

[0042] The analysis unit can adjust the order of analysis based on the relevance of the reported information. For example, the analysis unit can group similar reported information and analyze them together. The analysis unit can also determine priorities based on the relevance of the reported information. Furthermore, the analysis unit can adjust the order of analysis based on the importance of the reported information. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the reported information. The evaluation of relevance may be performed using AI, for example, or without using AI. For example, the analysis unit can input the reported information into a generating AI and have the generating AI perform the evaluation of relevance.

[0043] The IP analysis unit can improve the accuracy of IP address analysis by referring to a historical IP address database. For example, the IP analysis unit can detect similar patterns based on the historical IP address database. The IP analysis unit can also identify suspicious IP addresses by referring to the historical IP address database. Furthermore, the IP analysis unit can optimize its analysis algorithm by utilizing the historical IP address database. As a result, by referring to the historical IP address database, the accuracy of the analysis is improved, and suspicious IP addresses can be identified effectively. The referencing of the IP address database may be performed using AI, for example, or without using AI. For example, the IP analysis unit can input the historical IP address database into a generating AI and have the generating AI perform the task of improving the analysis accuracy.

[0044] The IP analysis unit can apply different analysis algorithms to IP addresses based on the caller's behavior patterns. For example, if the caller frequently changes their IP address, the IP analysis unit will apply a specific analysis algorithm. It can also apply a different analysis algorithm if the caller is active during specific time periods. Furthermore, the IP analysis unit can select the optimal analysis algorithm based on the caller's behavior patterns. This improves analysis accuracy by applying different analysis algorithms based on the caller's behavior patterns. The application of analysis algorithms may be performed using AI, or without AI. For example, the IP analysis unit can input the caller's behavior patterns into a generating AI and have the generating AI select the optimal analysis algorithm.

[0045] The IP analysis unit can determine the priority of IP address analysis by considering the caller's geographical location information. For example, if the caller is operating from a specific region, the IP analysis unit will prioritize the analysis of IP addresses in that region. The IP analysis unit can also prioritize the analysis of nearby IP addresses based on the caller's location information. Furthermore, if the caller is on the move, the IP analysis unit can prioritize the analysis of IP addresses based on their current location. This allows for region-specific responses by considering the caller's geographical location information. The acquisition of geographical location information may be performed using AI, for example, or without using AI. For example, the IP analysis unit can input the caller's location information into a generating AI and have the generating AI perform the priority determination.

[0046] The IP analysis unit can analyze the sender's social media activity when analyzing IP addresses and prioritize the analysis of relevant information. For example, the IP analysis unit can prioritize the analysis of relevant IP addresses based on the content the sender frequently posts. The IP analysis unit can also determine the priority of IP addresses based on the sender's number of followers and influence. Furthermore, the IP analysis unit can analyze the sender's past social media activity and prioritize the analysis of relevant IP addresses. This allows for the prioritization of relevant information by analyzing the sender's social media activity. The analysis of social media activity may be performed using AI or without AI. For example, the IP analysis unit can input the sender's social media activity into a generating AI and have the generating AI perform a relevance evaluation.

[0047] The detection unit can improve detection accuracy by referring to a database of past fraudulent activities when detecting the poster's behavior. For example, the detection unit can detect similar patterns based on the database of past fraudulent activities. The detection unit can also identify signs of fraud by referring to the database of past fraudulent activities. Furthermore, the detection unit can optimize its detection algorithm by utilizing the database of past fraudulent activities. As a result, by referring to the database of past fraudulent activities, detection accuracy is improved and fraudulent activities can be detected effectively. The referencing of the fraudulent activity database may be performed using AI, for example, or without using AI. For example, the detection unit can input the database of past fraudulent activities into a generating AI and have the generating AI perform the task of improving detection accuracy.

[0048] The detection unit can apply different detection algorithms depending on the category of the post content when detecting the poster's behavior. For example, the detection unit can apply a specific detection algorithm to posts related to financial fraud. It can also apply a different detection algorithm to posts related to phishing scams. Furthermore, the detection unit can apply a dedicated detection algorithm to posts related to false information. By applying different detection algorithms depending on the category of the post content, detection accuracy is improved. The application of detection algorithms may be performed using AI, for example, or without using AI. For example, the detection unit can input the post content into a generation AI and have the generation AI perform detection according to the category.

[0049] The detection unit can determine detection priorities based on the submission date of posts when detecting the poster's behavior. For example, the detection unit can prioritize detecting recently submitted posts. It can also prioritize detecting posts submitted within a specific time period. Furthermore, the detection unit can prioritize detecting posts of high urgency. This allows for a quick response to urgent posts by determining detection priorities based on the submission date. The evaluation of submission date may be performed using AI, for example, or without using AI. For example, the detection unit can input the submission date of posts into a generating AI and have the generating AI determine the priority.

[0050] The detection unit can adjust the detection order based on the relevance of the posts when detecting the poster's behavior. For example, the detection unit can group similar posts together and detect them all at once. The detection unit can also determine priority based on the relevance of the posts. Furthermore, the detection unit can adjust the detection order based on the importance of the posts. This allows for efficient detection by adjusting the detection order based on the relevance of the posts. The evaluation of relevance may be performed using AI, for example, or without using AI. For example, the detection unit can input the posts into a generation AI and have the generation AI perform the relevance evaluation.

[0051] The warning unit can improve warning accuracy by referring to past warning history when issuing warnings for suspicious accounts or posts. For example, the warning unit can detect similar patterns based on past warning history. The warning unit can also refer to past warning history to determine the appropriate timing for warnings. Furthermore, the warning unit can optimize the warning algorithm by utilizing past warning history. This improves warning accuracy and enables appropriate warnings by referring to past warning history. The referencing of warning history may be done using AI, for example, or without using AI. For example, the warning unit can input past warning history into a generating AI and have the generating AI perform the task of improving warning accuracy.

[0052] The warning unit can apply different warning algorithms depending on the category of the warning when issuing warnings about suspicious accounts or posts. For example, the warning unit can apply a specific warning algorithm to warnings about financial fraud. It can also apply a different warning algorithm to warnings about phishing scams. Furthermore, the warning unit can apply a dedicated warning algorithm to warnings about misinformation. This improves warning accuracy by applying different warning algorithms depending on the category of the warning. The application of warning algorithms may be done using AI, for example, or without using AI. For example, the warning unit can input the warning content into a generation AI and have the generation AI issue a warning according to the category.

[0053] The warning system can prioritize warnings based on when they are submitted, when issuing warnings about suspicious accounts or posts. For example, it can prioritize recently submitted warnings. It can also prioritize warnings submitted within a specific time period. Furthermore, it can prioritize high-urgency warnings. This allows for a quick response to urgent warnings by prioritizing them based on when they are submitted. The evaluation of submission timing may be performed using AI or not. For example, the warning system can input the submission timing of warnings into a generating AI and have the generating AI determine the prioritization.

[0054] The warning unit can adjust the order of warnings based on the relevance of the warning content when issuing warnings about suspicious accounts or posts. For example, the warning unit can group similar warning content and display them together. The warning unit can also determine priority based on the relevance of the warning content. Furthermore, the warning unit can adjust the order of warnings based on the importance of the warning content. This allows for efficient warnings by adjusting the order of warnings based on the relevance of the warning content. The evaluation of relevance may be performed using AI, for example, or without using AI. For example, the warning unit can input warning content into a generation AI and have the generation AI perform the evaluation of relevance.

[0055] The warning unit can display different warning messages depending on the category of the warning when it warns about suspicious accounts or posts. For example, the warning unit can display a specific warning message for financial fraud. It can also display a different warning message for phishing scams. Furthermore, it can display a dedicated warning message for false information. This allows for appropriate warnings by displaying different warning messages depending on the category of the warning. The display of warning messages may be done using AI, or it may be done without AI. For example, the warning unit can input the warning content into a generation AI and have the generation AI generate warning messages according to the category.

[0056] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0057] The analysis unit can improve the accuracy of its analysis by referring to a database of past fraud cases when analyzing reported information. For example, it can detect similar patterns based on the database of past fraud cases. It can also identify signs of fraud by referring to the database of past fraud cases. Furthermore, it can optimize the analysis algorithm by utilizing the database of past fraud cases. As a result, by referring to the database of past fraud cases, the accuracy of the analysis is improved, and fraudulent activity can be detected effectively. The referencing of the fraud case database may be done using AI, for example, or without using AI. For example, the analysis unit can input the database of past fraud cases into a generating AI and have the generating AI perform the task of improving the accuracy of the analysis.

[0058] The reception department can analyze a user's past reporting history when receiving a report and prioritize processing reports that are considered highly reliable. For example, it can prioritize reports from users who have previously made accurate reports. It can also prioritize reports from users who have previously made false reports. Furthermore, it can prioritize reports that exhibit specific patterns based on past reporting history. By prioritizing highly reliable reports, this enables effective detection of fraudulent activity. The analysis of reporting history may be performed using AI, or it may be performed without AI. For example, the reception department can input a user's past reporting history into a generating AI and have the generating AI perform a reliability evaluation.

[0059] The analysis unit can apply different analysis algorithms depending on the category of the reported information. For example, a specific analysis algorithm can be applied to reports of financial fraud. Another analysis algorithm can be applied to reports of phishing scams. Furthermore, a dedicated analysis algorithm can be applied to reports of false information. By applying different analysis algorithms depending on the category of the reported information, the accuracy of the analysis is improved. The application of analysis algorithms may be performed using AI, for example, or without using AI. For example, the analysis unit can input the reported information into a generating AI and have the generating AI perform analysis according to the category.

[0060] The IP analysis unit can improve the accuracy of IP address analysis by referring to a historical IP address database. For example, it can detect similar patterns based on the historical IP address database. It can also identify suspicious IP addresses by referring to the historical IP address database. Furthermore, it can optimize the analysis algorithm by utilizing the historical IP address database. As a result, by referring to the historical IP address database, the accuracy of the analysis is improved, and suspicious IP addresses can be identified effectively. The referencing of the IP address database may be performed using AI, for example, or without using AI. For example, the IP analysis unit can input the historical IP address database into a generating AI and have the generating AI perform the task of improving the analysis accuracy.

[0061] The detection unit can improve detection accuracy by referring to a database of past fraudulent activities when detecting the poster's behavior. For example, it can detect similar patterns based on the database of past fraudulent activities. It can also identify signs of fraud by referring to the database of past fraudulent activities. Furthermore, it can optimize the detection algorithm by utilizing the database of past fraudulent activities. As a result, by referring to the database of past fraudulent activities, detection accuracy is improved and fraudulent activities can be detected effectively. The referencing of the fraudulent activity database may be done using AI, for example, or without using AI. For example, the detection unit can input the database of past fraudulent activities into a generating AI and have the generating AI perform the task of improving detection accuracy.

[0062] The warning unit can improve warning accuracy by referring to past warning history when issuing warnings about suspicious accounts or posts. For example, it can detect similar patterns based on past warning history. It can also identify the appropriate timing for warnings by referring to past warning history. Furthermore, it can optimize the warning algorithm by utilizing past warning history. As a result, by referring to past warning history, warning accuracy is improved and appropriate warnings can be issued. The referencing of warning history may be performed using AI, for example, or without using AI. For example, the warning unit can input past warning history into a generation AI and have the generation AI perform the task of improving warning accuracy.

[0063] The warning unit can display different warning messages depending on the category of the warning when issuing a warning about suspicious accounts or posts. For example, a specific warning message can be displayed for financial fraud warnings. A different warning message can also be displayed for phishing scams. Furthermore, a dedicated warning message can be displayed for false information warnings. This allows for appropriate warnings by displaying different warning messages depending on the category of the warning. The display of warning messages may be done using AI, or it may be done without AI. For example, the warning unit can input the warning content into a generation AI and have the generation AI generate warning messages according to the category.

[0064] The following briefly describes the processing flow for example form 1.

[0065] Step 1: The reception desk receives reports from users. Reports can include text, images, videos, etc., and can be received through online forms, phone calls, emails, or reporting functions on social media platforms. For example, a dedicated button can be provided for users to report suspicious accounts or posts. Step 2: The analysis unit analyzes the information received by the reception unit. It analyzes the content of the report using text analysis technology, image analysis technology, and video analysis technology, and analyzes the intent of the report using natural language processing technology. Image analysis technology recognizes objects and text within images, and video analysis technology analyzes movement and sound within videos. Step 3: The IP analysis unit analyzes the IP addresses analyzed by the analysis unit. It identifies the location information of the IP address and its past activity history. It detects the creation of multiple accounts from the same IP address and identifies signs of spam or fraudulent activity. Step 4: The detection unit detects the poster's behavior based on the information analyzed by the IP analysis unit. It analyzes the poster's past activity history and posting content to evaluate the poster's trustworthiness. It detects accounts that have engaged in fraudulent activities in the past and analyzes the frequency and content trends of their posts to detect suspicious behavior. Step 5: The warning unit issues warnings for suspicious accounts and posts detected by the detection unit. It displays a warning message for suspicious accounts and temporarily hides suspicious posts. It sends a message to the user to alert them to the possibility of fraudulent activity.

[0066] (Example of form 2) An AI system according to an embodiment of the present invention is a system that detects fraudulent activity on social networking services (SNS) in real time and provides a safe SNS environment. This AI system receives reports from users, analyzes the reported information, analyzes IP addresses, detects the poster's behavior, and evaluates reliability. Based on these analysis results, it issues warnings about suspicious accounts and posts. This AI system will create a safe SNS environment and realize a society where users can share information with peace of mind. It will also create a mechanism for the entire community to confront fraudulent activity. For example, if a user finds a suspicious account or post, they report that information to the system. This reported information is input into the AI ​​system. Next, the AI ​​system analyzes the reported information and analyzes the IP address. By analyzing the IP address, the system identifies the sender's location information and past activity history. For example, if multiple suspicious accounts are created from the same IP address, that IP address will be judged as suspicious. Furthermore, the AI ​​system detects the poster's behavior. It analyzes the poster's past activity history and post content and evaluates reliability. For example, accounts that have committed fraudulent activities in the past or accounts that have made a large number of posts in a short period of time will be judged as having low reliability. Based on these analysis results, the AI ​​system issues warnings for suspicious accounts and posts. For example, it displays warning messages for suspicious accounts to alert users. It also takes measures such as temporarily hiding suspicious posts. This AI system helps create a safe SNS environment and realizes a society where users can share information with peace of mind. It also creates a mechanism for the entire community to combat fraudulent activities. For example, by having users report suspicious accounts and posts, fraudulent activities can be detected early, minimizing damage. In this way, by utilizing the AI ​​system, fraudulent activities on SNS can be detected in real time, and a safe SNS environment can be provided. This will allow all users, especially the elderly, to use SNS with peace of mind.

[0067] The AI ​​system according to this embodiment comprises a reception unit, an analysis unit, an IP analysis unit, a detection unit, and a warning unit. The reception unit receives reports from users. Reports from users include, but are not limited to, text, images, and videos. The reception unit receives reports, for example, through an online form. The reception unit can also receive reports via telephone or email. Furthermore, the reception unit can provide a reporting function on an SNS platform. For example, the reception unit provides a dedicated button for users to report suspicious accounts or posts. The analysis unit analyzes the report information received by the reception unit. The analysis unit analyzes the content of the report, for example, using text analysis technology. The analysis unit can also analyze images included in the report using image analysis technology. Furthermore, the analysis unit can also analyze videos included in the report using video analysis technology. For example, the analysis unit analyzes the intent of the report using natural language processing technology. Image analysis technology recognizes objects and text in images and analyzes the content of the report. Video analysis technology analyzes movement and sound in videos and analyzes the content of the report. The IP analysis unit analyzes the IP addresses analyzed by the analysis unit. The IP analysis unit can, for example, identify the location information of an IP address. It can also identify past activity history. Furthermore, the IP analysis unit can detect the creation of multiple accounts from the same IP address. For example, the IP analysis unit identifies the geographical location of an IP address and understands the location information of the sender. Past activity history is analyzed by examining the access history from the same IP address and identifying the sender's behavior patterns. The creation of multiple accounts from the same IP address is detected as an indication of spam or fraudulent activity. The detection unit detects the poster's behavior based on the information analyzed by the IP analysis unit. The detection unit can, for example, analyze the poster's past activity history. It can also analyze the content of posts. Furthermore, the detection unit can evaluate the reliability of the poster. For example, the detection unit detects accounts that have committed fraudulent activities in the past. Analysis of post content analyzes the frequency and trends of posts and detects suspicious behavior. Reliability is evaluated based on the poster's past activity history and post content.The warning unit issues warnings for suspicious accounts and posts detected by the detection unit. For example, the warning unit displays a warning message for suspicious accounts. The warning unit can also temporarily hide suspicious posts. Furthermore, the warning unit can send messages to users to alert them. For example, the warning unit displays a warning message for suspicious accounts and alerts the user. Suspicious posts are temporarily hidden, and a warning is issued to the user. The alert message informs the user of the possibility of fraudulent activity. As a result, the AI ​​system according to this embodiment can detect fraudulent activity on social media in real time and provide a safe social media environment.

[0068] The reception department receives reports from users. These reports may include, but are not limited to, text, images, and videos. The reception department accepts reports, for example, through online forms. These online forms are designed for easy user access and include fields for detailed reporting. Furthermore, the reception department can also accept reports via telephone and email. For telephone reports, speech recognition technology can be used to transcribe the report into text and input it into the system. For email reports, the system automatically analyzes the email content and extracts necessary information. The reception department can also provide reporting functionality on social networking services (SNS) platforms. For example, the reception department provides a dedicated button for users to report suspicious accounts or posts. Clicking this button automatically sends the report to the reception department. Reporting functionality on SNS platforms is easily accessible within SNS applications that users use daily, thus lowering the barrier to reporting. This allows the reception department to provide diverse reporting methods and create an environment where users can report quickly and easily. Furthermore, the reception department has a system in place to centrally manage reports and process them appropriately according to their type and content. For example, if a report contains information that is highly urgent, there is a system in place to immediately notify the analysis and warning departments. This provides the reception department with a foundation for prompt and appropriate responses, improving the overall efficiency and effectiveness of the system.

[0069] The analysis unit analyzes the information received by the reception unit. For example, the analysis unit analyzes the content of the report using text analysis technology. Text analysis technology includes natural language processing (NLP) technology, which can analyze the intent and sentiment of the report. For example, it analyzes the frequency of keyword occurrences and context to determine whether the report suggests fraudulent activity. The analysis unit can also analyze images included in the report using image analysis technology. Image analysis technology includes object detection and facial recognition technology, which can identify specific objects or people in an image. For example, it analyzes whether the reported image contains suspicious objects or actions. Furthermore, the analysis unit can also analyze videos included in the report using video analysis technology. Video analysis technology includes motion detection and audio analysis technology, which can analyze movement and sound in the video. For example, it analyzes whether suspicious actions or statements are being made in the video. In this way, the analysis unit can comprehensively analyze various data such as text, images, and videos to determine the truthfulness and urgency of the report. Furthermore, the analysis unit can utilize past reporting data and statistical information to analyze patterns and trends in reporting content. For example, it can analyze increasing reporting trends in specific regions or time periods to provide information for taking preventative measures. The analysis unit can also use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing warnings early. As a result, the analysis unit can not only grasp the situation in real time but also handle long-term risk management and anomaly detection, improving the reliability and security of the entire system.

[0070] The IP analysis unit analyzes the IP addresses analyzed by the analysis unit. For example, the IP analysis unit identifies the location information of an IP address. Identifying location information includes techniques that use a Geographic Information System (GIS) to determine the geographical location of the IP address's origin. This allows for understanding the physical location of the informant and conducting risk assessments for each region. The IP analysis unit can also identify past behavioral history. Identifying behavioral history includes techniques that analyze access history from the same IP address to identify the informant's behavioral patterns. For example, if there are frequent accesses from a particular IP address, that IP address may be associated with suspicious activity. Furthermore, the IP analysis unit can detect the creation of multiple accounts from the same IP address. Detecting multiple account creation includes techniques that match IP addresses with account information to identify the creation of multiple accounts from the same IP address. This allows for early detection of signs of spamming or fraudulent activity and the implementation of appropriate countermeasures. For example, the IP analysis unit identifies the geographical location of an IP address and understands the sender's location information. Past behavioral history is analyzed by examining access history from the same IP address to identify the sender's behavioral patterns. Creating multiple accounts from the same IP address is detected as an indication of spam or fraudulent activity. This allows the IP analysis department to comprehensively analyze information related to the IP address and evaluate the reliability and risk of the reported content. Furthermore, the IP analysis department can share the analysis results with other departments to strengthen risk management across the entire system. For example, the analysis results from the IP analysis department can be provided to the detection and warning departments, enabling prompt and appropriate responses. In this way, the IP analysis department can play a crucial role in improving the reliability and security of the entire system.

[0071] The detection unit detects the poster's behavior based on information analyzed by the IP analysis unit. For example, the detection unit analyzes the poster's past activity history. This analysis includes techniques to analyze the poster's past posting content, frequency, and behavioral patterns. For example, to detect accounts that have engaged in fraudulent activities in the past, the detection unit analyzes the frequency of appearance of specific keywords or phrases. The detection unit can also analyze the content of posts. This analysis includes text analysis and image analysis techniques to analyze the frequency and content trends of posts and detect suspicious behavior. For example, if posts are concentrated in a specific time period, that account may be engaging in automated spamming. Furthermore, the detection unit can also evaluate the reliability of the poster. This reliability evaluation includes techniques to calculate a reliability score based on the poster's past activity history and posting content. For example, accounts with a history of many suspicious activities are assigned a low reliability score. This allows the detection unit to comprehensively evaluate the poster's behavior and detect suspicious accounts and posts at an early stage. Furthermore, the detection unit can continuously correct its detection results based on real-time updated data, enabling it to respond to the latest situation. For example, if a new suspicious behavior pattern is detected, it can update the detection algorithm to perform more accurate detection. The detection unit can also use anomaly detection algorithms to detect patterns that are different from the norm or abnormal data, and issue warnings early. As a result, the detection unit can not only grasp the situation in real time but also handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0072] The warning unit issues warnings for suspicious accounts and posts detected by the detection unit. For example, the warning unit displays a warning message for suspicious accounts. The warning message informs the account owner that suspicious activity has been detected and prompts them to take appropriate action. The warning unit can also temporarily hide suspicious posts. If a suspicious post is detected, it is temporarily hidden and the user is warned. Furthermore, the warning unit can send a cautionary message to the user. The cautionary message informs the user of the possibility of fraudulent activity and draws their attention to it. For example, the warning unit displays a warning message for suspicious accounts and draws the user's attention to it. Suspicious posts are temporarily hidden and the user is warned. The cautionary message informs the user of the possibility of fraudulent activity. This allows the warning unit to provide users with quick and appropriate warnings and prevent damage. Furthermore, the warning unit has a feedback system to continuously improve the accuracy and effectiveness of the warnings. For example, it collects feedback from users who have received warning messages and uses it to improve the warnings. Furthermore, the warning unit can reliably transmit information using multiple communication methods. For example, it can reliably deliver important information not only through smartphone notifications but also through voice calls, SMS, and email. This allows the warning unit to quickly and reliably warn users and minimize damage.

[0073] The reception desk can estimate the user's emotions and determine the priority of reports based on the estimated emotions. For example, if the user is feeling highly anxious, the reception desk will process the report with the highest priority. If the user is calm, the reception desk can process the report with the normal priority. Furthermore, if the user is angry, the reception desk can process the report with a high priority in order to respond quickly. This enables a quick and appropriate response by determining the priority of reports based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, 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 desk may be performed using AI or not using AI. For example, the reception desk can input the content of the user's report into a generative AI and have the generative AI perform emotion estimation.

[0074] The reception department can analyze a user's past reporting history when receiving a report and prioritize processing reports that are considered highly reliable. For example, the reception department can prioritize reports from users who have previously made accurate reports. It can also prioritize reports from users who have previously made false reports. Furthermore, the reception department can prioritize reports that exhibit specific patterns based on past reporting history. This allows for effective detection of fraudulent activity by prioritizing the processing of highly reliable reports. The analysis of reporting history may be performed using AI or not. For example, the reception department can input a user's past reporting history into a generating AI and have the generating AI perform a reliability assessment.

[0075] The reception department can adjust the reception method based on the level of detail of the report received. For example, if the report is detailed, the reception department can automatically prioritize it for a quicker response. If the report is vague, the reception department can also display a prompt for additional information. Furthermore, if the report is concise, the reception department can process it with the normal priority. This allows for an appropriate response by adjusting the reception method based on the level of detail of the report. The evaluation of the level of detail of the report may be performed using AI, or it may be performed without AI. For example, the reception department can input the report content into a generating AI and have the generating AI perform the evaluation of the level of detail.

[0076] The reception desk can estimate the user's emotions and adjust the method of receiving the report based on the estimated emotions. For example, if the user is nervous, the reception desk can provide a simple and easy-to-understand interface. If the user is relaxed, the reception desk can also provide detailed input options. Furthermore, if the user is in a hurry, the reception desk can prioritize voice input and receive the report quickly. This allows for a user-friendly interface by adjusting the method of receiving reports based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, 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 desk may be performed using AI or not using AI. For example, the reception desk can input the user's report content into a generative AI and have the generative AI perform emotion estimation.

[0077] The reception unit can prioritize processing highly relevant reports by considering the user's geographical location information when receiving a report. For example, if a user reports from a specific region, the reception unit will prioritize reports from that region. The reception unit can also prioritize nearby reports based on the user's location information. Furthermore, if the user is on the move, the reception unit can prioritize reports based on their current location. This enables region-specific responses by considering the user's geographical location information. The acquisition of geographical location information may be done using AI, for example, or without using AI. For example, the reception unit can input the user's location information into a generating AI and have the generating AI perform a relevance evaluation.

[0078] The reception desk can analyze a user's social media activity upon receiving a report and prioritize processing relevant reports. For example, the reception desk can prioritize reports based on the content a user frequently posts. It can also determine the priority of reports based on a user's follower count and influence. Furthermore, the reception desk can analyze a user's past social media activity and prioritize processing reports that are deemed reliable. This allows for the prioritization of reliable reports by analyzing a user's social media activity. The analysis of social media activity may be performed using AI or without AI. For example, the reception desk can input a user's social media activity into a generating AI and have the generating AI perform a relevance assessment.

[0079] 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. It can also provide concise analysis results if the user is relaxed. Furthermore, if the user is in a hurry, the analysis unit can provide quick analysis results. By adjusting the level of detail of the analysis based on the user's emotions, the system can provide analysis results that are appropriate for the user. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative 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 above-described processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input the user's report content into a generative AI and have the generative AI perform emotion estimation.

[0080] The analysis unit can improve the accuracy of its analysis by referring to a database of past fraud cases when analyzing reported information. For example, the analysis unit can detect similar patterns based on the database of past fraud cases. The analysis unit can also identify signs of fraud by referring to the database of past fraud cases. Furthermore, the analysis unit can optimize its analysis algorithm by utilizing the database of past fraud cases. As a result, by referring to the database of past fraud cases, the accuracy of the analysis is improved, and fraudulent activity can be detected effectively. The referencing of the fraud case database may be done using AI, for example, or without using AI. For example, the analysis unit can input the database of past fraud cases into a generating AI and have the generating AI perform the task of improving the accuracy of the analysis.

[0081] The analysis unit can apply different analysis algorithms depending on the category of the reported information. For example, the analysis unit can apply a specific analysis algorithm to reports of financial fraud. It can also apply a different analysis algorithm to reports of phishing scams. Furthermore, it can apply a dedicated analysis algorithm to reports of false information. By applying different analysis algorithms depending on the category of the reported information, the accuracy of the analysis is improved. The application of analysis algorithms may be performed using AI, for example, or without using AI. For example, the analysis unit can input the reported information into a generating AI and have the generating AI perform analysis according to the category.

[0082] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is nervous, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a concise display method. By adjusting the display method of the analysis results based on the user's emotions, a user-friendly display can be achieved. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is 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 report content into the generative AI and have the generative AI perform emotion estimation.

[0083] The analysis unit can determine the priority of analysis based on the submission date of the report when analyzing the report information. For example, the analysis unit can prioritize the analysis of recently submitted reports. It can also prioritize the analysis of reports submitted within a specific time period. Furthermore, it can prioritize the analysis of reports with high urgency. This allows for a rapid response to high-urgency reports by determining the priority of analysis based on the submission date of the report. The evaluation of the submission date may be performed using AI, for example, or without using AI. For example, the analysis unit can input the submission date of the report into a generating AI and have the generating AI perform the priority determination.

[0084] The analysis unit can adjust the order of analysis based on the relevance of the reported information. For example, the analysis unit can group similar reported information and analyze them together. The analysis unit can also determine priorities based on the relevance of the reported information. Furthermore, the analysis unit can adjust the order of analysis based on the importance of the reported information. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the reported information. The evaluation of relevance may be performed using AI, for example, or without using AI. For example, the analysis unit can input the reported information into a generating AI and have the generating AI perform the evaluation of relevance.

[0085] The IP analysis unit can estimate the user's emotions and adjust the level of detail in the IP analysis based on the estimated emotions. For example, if the user is feeling anxious, the IP analysis unit can provide detailed IP analysis results. It can also provide concise IP analysis results if the user is relaxed. Furthermore, if the user is in a hurry, the IP analysis unit can provide quick IP analysis results. By adjusting the level of detail in the IP analysis based on the user's emotions, it is possible to provide analysis results that are appropriate for the user. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the IP analysis unit may be performed using AI, or not. For example, the IP analysis unit can input the user's message content into a generative AI and have the generative AI perform emotion estimation.

[0086] The IP analysis unit can improve the accuracy of IP address analysis by referring to a historical IP address database. For example, the IP analysis unit can detect similar patterns based on the historical IP address database. The IP analysis unit can also identify suspicious IP addresses by referring to the historical IP address database. Furthermore, the IP analysis unit can optimize its analysis algorithm by utilizing the historical IP address database. As a result, by referring to the historical IP address database, the accuracy of the analysis is improved, and suspicious IP addresses can be identified effectively. The referencing of the IP address database may be performed using AI, for example, or without using AI. For example, the IP analysis unit can input the historical IP address database into a generating AI and have the generating AI perform the task of improving the analysis accuracy.

[0087] The IP analysis unit can apply different analysis algorithms to IP addresses based on the caller's behavior patterns. For example, if the caller frequently changes their IP address, the IP analysis unit will apply a specific analysis algorithm. It can also apply a different analysis algorithm if the caller is active during specific time periods. Furthermore, the IP analysis unit can select the optimal analysis algorithm based on the caller's behavior patterns. This improves analysis accuracy by applying different analysis algorithms based on the caller's behavior patterns. The application of analysis algorithms may be performed using AI, or without AI. For example, the IP analysis unit can input the caller's behavior patterns into a generating AI and have the generating AI select the optimal analysis algorithm.

[0088] The IP analysis unit can estimate the user's emotions and adjust the display method of the IP analysis results based on the estimated emotions. For example, if the user is tense, the IP analysis unit can provide a simple and highly visible display method. If the user is relaxed, the IP analysis unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the IP analysis unit can provide a concise display method. By adjusting the display method of the IP analysis results based on the user's emotions, a user-friendly display is made possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the IP analysis unit may be performed using AI, for example, or without AI. For example, the IP analysis unit can input the user's report content into a generative AI and have the generative AI perform emotion estimation.

[0089] The IP analysis unit can determine the priority of IP address analysis by considering the caller's geographical location information. For example, if the caller is operating from a specific region, the IP analysis unit will prioritize the analysis of IP addresses in that region. The IP analysis unit can also prioritize the analysis of nearby IP addresses based on the caller's location information. Furthermore, if the caller is on the move, the IP analysis unit can prioritize the analysis of IP addresses based on their current location. This allows for region-specific responses by considering the caller's geographical location information. The acquisition of geographical location information may be performed using AI, for example, or without using AI. For example, the IP analysis unit can input the caller's location information into a generating AI and have the generating AI perform the priority determination.

[0090] The IP analysis unit can analyze the sender's social media activity when analyzing IP addresses and prioritize the analysis of relevant information. For example, the IP analysis unit can prioritize the analysis of relevant IP addresses based on the content the sender frequently posts. The IP analysis unit can also determine the priority of IP addresses based on the sender's number of followers and influence. Furthermore, the IP analysis unit can analyze the sender's past social media activity and prioritize the analysis of relevant IP addresses. This allows for the prioritization of relevant information by analyzing the sender's social media activity. The analysis of social media activity may be performed using AI or without AI. For example, the IP analysis unit can input the sender's social media activity into a generating AI and have the generating AI perform a relevance evaluation.

[0091] The detection unit can estimate the user's emotions and adjust the behavior detection criteria based on the estimated emotions. For example, if the user is feeling anxious, the detection unit can apply strict behavior detection criteria. It can also apply normal behavior detection criteria if the user is relaxed. Furthermore, if the user is in a hurry, the detection unit can adjust the criteria to perform rapid behavior detection. This allows for appropriate detection by adjusting the behavior detection criteria based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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 detection unit may be performed using AI, or not. For example, the detection unit can input the user's report content into the generative AI and have the generative AI perform emotion estimation.

[0092] The detection unit can improve detection accuracy by referring to a database of past fraudulent activities when detecting the poster's behavior. For example, the detection unit can detect similar patterns based on the database of past fraudulent activities. The detection unit can also identify signs of fraud by referring to the database of past fraudulent activities. Furthermore, the detection unit can optimize its detection algorithm by utilizing the database of past fraudulent activities. As a result, by referring to the database of past fraudulent activities, detection accuracy is improved and fraudulent activities can be detected effectively. The referencing of the fraudulent activity database may be performed using AI, for example, or without using AI. For example, the detection unit can input the database of past fraudulent activities into a generating AI and have the generating AI perform the task of improving detection accuracy.

[0093] The detection unit can apply different detection algorithms depending on the category of the post content when detecting the poster's behavior. For example, the detection unit can apply a specific detection algorithm to posts related to financial fraud. It can also apply a different detection algorithm to posts related to phishing scams. Furthermore, the detection unit can apply a dedicated detection algorithm to posts related to false information. By applying different detection algorithms depending on the category of the post content, detection accuracy is improved. The application of detection algorithms may be performed using AI, for example, or without using AI. For example, the detection unit can input the post content into a generation AI and have the generation AI perform detection according to the category.

[0094] The detection unit can estimate the user's emotions and adjust the display method of the detection results based on the estimated emotions. For example, if the user is tense, the detection unit can provide a simple and highly visible display method. If the user is relaxed, the detection unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the detection unit can provide a concise display method. By adjusting the display method of the detection results based on the user's emotions, a user-friendly display is possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the detection unit may be performed using AI, or not using AI. For example, the detection unit can input the user's report content into the generative AI and have the generative AI perform emotion estimation.

[0095] The detection unit can determine detection priorities based on the submission date of posts when detecting the poster's behavior. For example, the detection unit can prioritize detecting recently submitted posts. It can also prioritize detecting posts submitted within a specific time period. Furthermore, the detection unit can prioritize detecting posts of high urgency. This allows for a quick response to urgent posts by determining detection priorities based on the submission date. The evaluation of submission date may be performed using AI, for example, or without using AI. For example, the detection unit can input the submission date of posts into a generating AI and have the generating AI determine the priority.

[0096] The detection unit can adjust the detection order based on the relevance of the posts when detecting the poster's behavior. For example, the detection unit can group similar posts together and detect them all at once. The detection unit can also determine priority based on the relevance of the posts. Furthermore, the detection unit can adjust the detection order based on the importance of the posts. This allows for efficient detection by adjusting the detection order based on the relevance of the posts. The evaluation of relevance may be performed using AI, for example, or without using AI. For example, the detection unit can input the posts into a generation AI and have the generation AI perform the relevance evaluation.

[0097] The warning unit can estimate the user's emotions and adjust the way the warning message is expressed based on the estimated emotions. For example, if the user is feeling anxious, the warning unit can display a warning message in gentle language. It can also display a normal warning message if the user is relaxed. Furthermore, if the user is feeling angry, the warning unit can display a warning message in strong language to encourage a quick response. This allows for appropriate warnings by adjusting the way the warning message is expressed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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-described processing in the warning unit may be performed using AI or not. For example, the warning unit can input the user's report content into a generative AI and have the generative AI perform emotion estimation.

[0098] The warning unit can improve warning accuracy by referring to past warning history when issuing warnings for suspicious accounts or posts. For example, the warning unit can detect similar patterns based on past warning history. The warning unit can also refer to past warning history to determine the appropriate timing for warnings. Furthermore, the warning unit can optimize the warning algorithm by utilizing past warning history. This improves warning accuracy and enables appropriate warnings by referring to past warning history. The referencing of warning history may be done using AI, for example, or without using AI. For example, the warning unit can input past warning history into a generating AI and have the generating AI perform the task of improving warning accuracy.

[0099] The warning unit can apply different warning algorithms depending on the category of the warning when issuing warnings about suspicious accounts or posts. For example, the warning unit can apply a specific warning algorithm to warnings about financial fraud. It can also apply a different warning algorithm to warnings about phishing scams. Furthermore, the warning unit can apply a dedicated warning algorithm to warnings about misinformation. This improves warning accuracy by applying different warning algorithms depending on the category of the warning. The application of warning algorithms may be done using AI, for example, or without using AI. For example, the warning unit can input the warning content into a generation AI and have the generation AI issue a warning according to the category.

[0100] The warning unit can estimate the user's emotions and adjust how warning messages are displayed based on those emotions. For example, if the user is stressed, the warning unit can display a simple and highly visible warning message. If the user is relaxed, the warning unit can also display a warning message with more detailed information. Furthermore, if the user is in a hurry, the warning unit can display a concise warning message. By adjusting how warning messages are displayed based on the user's emotions, a user-friendly display is possible. Emotion estimation is achieved using an emotion estimation function, such as 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 warning unit may be performed using AI or not. For example, the warning unit can input the user's notification content into a generative AI and have the generative AI perform emotion estimation.

[0101] The warning system can prioritize warnings based on when they are submitted, when issuing warnings about suspicious accounts or posts. For example, it can prioritize recently submitted warnings. It can also prioritize warnings submitted within a specific time period. Furthermore, it can prioritize high-urgency warnings. This allows for a quick response to urgent warnings by prioritizing them based on when they are submitted. The evaluation of submission timing may be performed using AI or not. For example, the warning system can input the submission timing of warnings into a generating AI and have the generating AI determine the prioritization.

[0102] The warning unit can adjust the order of warnings based on the relevance of the warning content when issuing warnings about suspicious accounts or posts. For example, the warning unit can group similar warning content and display them together. The warning unit can also determine priority based on the relevance of the warning content. Furthermore, the warning unit can adjust the order of warnings based on the importance of the warning content. This allows for efficient warnings by adjusting the order of warnings based on the relevance of the warning content. The evaluation of relevance may be performed using AI, for example, or without using AI. For example, the warning unit can input warning content into a generation AI and have the generation AI perform the evaluation of relevance.

[0103] The warning unit can display different warning messages depending on the category of the warning when it warns about suspicious accounts or posts. For example, the warning unit can display a specific warning message for financial fraud. It can also display a different warning message for phishing scams. Furthermore, it can display a dedicated warning message for false information. This allows for appropriate warnings by displaying different warning messages depending on the category of the warning. The display of warning messages may be done using AI, or it may be done without AI. For example, the warning unit can input the warning content into a generation AI and have the generation AI generate warning messages according to the category.

[0104] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0105] The reception desk can estimate the user's emotions and determine the priority of reports based on the estimated emotions. For example, if the user is feeling highly anxious, the report will be processed with the highest priority. If the user is calm, the report can be processed with the normal priority. Furthermore, if the user is angry, the report can be processed with a high priority in order to respond quickly. This allows for a quick and appropriate response by determining the priority of reports based on the user's emotions. Emotion estimation can be achieved using, for example, an emotion engine or generative AI. Generative AI is, 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 desk may be performed using AI, or not using AI. For example, the reception desk can input the content of the user's report into a generative AI and have the generative AI perform emotion estimation.

[0106] The analysis unit can improve the accuracy of its analysis by referring to a database of past fraud cases when analyzing reported information. For example, it can detect similar patterns based on the database of past fraud cases. It can also identify signs of fraud by referring to the database of past fraud cases. Furthermore, it can optimize the analysis algorithm by utilizing the database of past fraud cases. As a result, by referring to the database of past fraud cases, the accuracy of the analysis is improved, and fraudulent activity can be detected effectively. The referencing of the fraud case database may be done using AI, for example, or without using AI. For example, the analysis unit can input the database of past fraud cases into a generating AI and have the generating AI perform the task of improving the accuracy of the analysis.

[0107] The detection unit can estimate the user's emotions and adjust the behavior detection criteria based on the estimated emotions. For example, if the user is feeling anxious, a strict behavior detection criterion may be applied. If the user is relaxed, a normal behavior detection criterion may be applied. Furthermore, if the user is in a hurry, the criteria may be adjusted to perform behavior detection quickly. This allows for appropriate detection by adjusting the behavior detection criteria based on the user's emotions. Emotion estimation is achieved using, for example, an emotion engine or a generative AI. The generative 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 above processing in the detection unit may be performed using, for example, AI, or not using AI. For example, the detection unit can input the user's report content into the generative AI and have the generative AI perform emotion estimation.

[0108] The warning unit can estimate the user's emotions and adjust the way the warning message is expressed based on the estimated emotions. For example, if the user is feeling anxious, the warning message can be displayed in gentle language. If the user is relaxed, a normal warning message can be displayed. Furthermore, if the user is feeling angry, the warning message can be displayed in strong language to encourage a quick response. By adjusting the way the warning message is expressed based on the user's emotions, appropriate warnings can be provided to the user. Emotion estimation can be achieved using, for example, 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 warning unit may be performed using AI, or not using AI. For example, the warning unit can input the user's report content into the generative AI and have the generative AI perform emotion estimation.

[0109] The reception department can analyze a user's past reporting history when receiving a report and prioritize processing reports that are considered highly reliable. For example, it can prioritize reports from users who have previously made accurate reports. It can also prioritize reports from users who have previously made false reports. Furthermore, it can prioritize reports that exhibit specific patterns based on past reporting history. By prioritizing highly reliable reports, this enables effective detection of fraudulent activity. The analysis of reporting history may be performed using AI, or it may be performed without AI. For example, the reception department can input a user's past reporting history into a generating AI and have the generating AI perform a reliability evaluation.

[0110] The analysis unit can apply different analysis algorithms depending on the category of the reported information. For example, a specific analysis algorithm can be applied to reports of financial fraud. Another analysis algorithm can be applied to reports of phishing scams. Furthermore, a dedicated analysis algorithm can be applied to reports of false information. By applying different analysis algorithms depending on the category of the reported information, the accuracy of the analysis is improved. The application of analysis algorithms may be performed using AI, for example, or without using AI. For example, the analysis unit can input the reported information into a generating AI and have the generating AI perform analysis according to the category.

[0111] The IP analysis unit can improve the accuracy of IP address analysis by referring to a historical IP address database. For example, it can detect similar patterns based on the historical IP address database. It can also identify suspicious IP addresses by referring to the historical IP address database. Furthermore, it can optimize the analysis algorithm by utilizing the historical IP address database. As a result, by referring to the historical IP address database, the accuracy of the analysis is improved, and suspicious IP addresses can be identified effectively. The referencing of the IP address database may be performed using AI, for example, or without using AI. For example, the IP analysis unit can input the historical IP address database into a generating AI and have the generating AI perform the task of improving the analysis accuracy.

[0112] The detection unit can improve detection accuracy by referring to a database of past fraudulent activities when detecting the poster's behavior. For example, it can detect similar patterns based on the database of past fraudulent activities. It can also identify signs of fraud by referring to the database of past fraudulent activities. Furthermore, it can optimize the detection algorithm by utilizing the database of past fraudulent activities. As a result, by referring to the database of past fraudulent activities, detection accuracy is improved and fraudulent activities can be detected effectively. The referencing of the fraudulent activity database may be done using AI, for example, or without using AI. For example, the detection unit can input the database of past fraudulent activities into a generating AI and have the generating AI perform the task of improving detection accuracy.

[0113] The warning unit can improve warning accuracy by referring to past warning history when issuing warnings about suspicious accounts or posts. For example, it can detect similar patterns based on past warning history. It can also identify the appropriate timing for warnings by referring to past warning history. Furthermore, it can optimize the warning algorithm by utilizing past warning history. As a result, by referring to past warning history, warning accuracy is improved and appropriate warnings can be issued. The referencing of warning history may be performed using AI, for example, or without using AI. For example, the warning unit can input past warning history into a generation AI and have the generation AI perform the task of improving warning accuracy.

[0114] The warning unit can display different warning messages depending on the category of the warning when issuing a warning about suspicious accounts or posts. For example, a specific warning message can be displayed for financial fraud warnings. A different warning message can also be displayed for phishing scams. Furthermore, a dedicated warning message can be displayed for false information warnings. This allows for appropriate warnings by displaying different warning messages depending on the category of the warning. The display of warning messages may be done using AI, or it may be done without AI. For example, the warning unit can input the warning content into a generation AI and have the generation AI generate warning messages according to the category.

[0115] The following briefly describes the processing flow for example form 2.

[0116] Step 1: The reception desk receives reports from users. Reports can include text, images, videos, etc., and can be received through online forms, phone calls, emails, or reporting functions on social media platforms. For example, a dedicated button can be provided for users to report suspicious accounts or posts. Step 2: The analysis unit analyzes the information received by the reception unit. It analyzes the content of the report using text analysis technology, image analysis technology, and video analysis technology, and analyzes the intent of the report using natural language processing technology. Image analysis technology recognizes objects and text within images, and video analysis technology analyzes movement and sound within videos. Step 3: The IP analysis unit analyzes the IP addresses analyzed by the analysis unit. It identifies the location information of the IP address and its past activity history. It detects the creation of multiple accounts from the same IP address and identifies signs of spam or fraudulent activity. Step 4: The detection unit detects the poster's behavior based on the information analyzed by the IP analysis unit. It analyzes the poster's past activity history and posting content to evaluate the poster's trustworthiness. It detects accounts that have engaged in fraudulent activities in the past and analyzes the frequency and content trends of their posts to detect suspicious behavior. Step 5: The warning unit issues warnings for suspicious accounts and posts detected by the detection unit. It displays a warning message for suspicious accounts and temporarily hides suspicious posts. It sends a message to the user to alert them to the possibility of fraudulent activity.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] Each of the multiple elements described above, including the reception unit, analysis unit, IP analysis unit, detection 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 provides a reporting function on an online form or SNS platform. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the reporting information using text analysis technology and image analysis technology. The IP analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and identifies the location information and past behavioral history of the IP address. The detection unit is implemented by the identification processing unit 290 of the data processing unit 12 and detects the behavior of the poster and evaluates its reliability. The warning unit is implemented by the control unit 46A of the smart device 14 and displays a warning message for suspicious accounts or posts. 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.

[0121] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] Each of the multiple elements described above, including the reception unit, analysis unit, IP analysis unit, detection 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 provides a reporting function on online forms and SNS platforms. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the reporting information using text analysis technology and image analysis technology. The IP analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and identifies the location information and past behavioral history of the IP address. The detection unit is implemented by the identification processing unit 290 of the data processing unit 12 and detects the behavior of the poster and evaluates its reliability. The warning unit is implemented by the control unit 46A of the smart glasses 214 and displays a warning message for suspicious accounts or posts. 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.

[0137] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] Each of the multiple elements described above, including the reception unit, analysis unit, IP analysis unit, detection 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 provides a reporting function on online forms and SNS platforms. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the reporting information using text analysis technology and image analysis technology. The IP analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and identifies the location information and past behavioral history of the IP address. The detection unit is implemented by the identification processing unit 290 of the data processing unit 12 and detects the behavior of the poster and evaluates its reliability. The warning unit is implemented by the control unit 46A of the headset terminal 314 and displays a warning message for suspicious accounts or posts. 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.

[0153] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.).

[0166] 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.

[0167] 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.

[0168] 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.

[0169] Each of the multiple elements described above, including the reception unit, analysis unit, IP analysis unit, detection unit, and warning unit, is implemented by, for example, 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 provides a reporting function on online forms and SNS platforms. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and analyzes the reporting information using text analysis technology and image analysis technology. The IP analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and identifies the location information and past behavior history of an IP address. The detection unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and detects the behavior of the poster and evaluates its reliability. The warning unit is implemented by, for example, the control unit 46A of the robot 414 and displays a warning message for suspicious accounts or posts. 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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."

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] (Note 1) A reception desk that receives reports from users, An analysis unit that analyzes the notification information received by the reception unit, An IP analysis unit analyzes the IP address analyzed by the aforementioned analysis unit, A detection unit that detects the poster's behavior based on the information analyzed by the aforementioned IP analysis unit, The system includes a warning unit that issues warnings regarding suspicious accounts and posts detected by the aforementioned detection unit. A system characterized by the following features. (Note 2) The aforementioned reception unit is The system estimates the user's emotions and prioritizes reports based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned reception unit is When a report is received, the system analyzes the user's past reporting history and prioritizes processing reports that are deemed highly reliable. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned reception unit is When receiving a report, the method of receiving the report will be adjusted based on the level of detail of the report. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is We estimate the user's emotions and adjust the reporting process based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is When receiving a report, the system prioritizes processing reports that are highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is When a report is received, the system analyzes the user's social media activity and prioritizes processing relevant reports. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, It estimates the user's emotions and adjusts the level of detail in the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, When analyzing reported information, we improve the accuracy of the analysis by referring to a database of past fraud cases. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, When analyzing reported information, different analysis algorithms are applied depending on the category of the reported content. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, When analyzing reported information, the priority of the analysis is determined based on when the report was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, When analyzing reported information, the order of analysis is adjusted based on the relevance of the reported content. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned IP analysis unit, It estimates the user's sentiment and adjusts the level of detail in the IP analysis based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned IP analysis unit, When analyzing IP addresses, we refer to a database of past IP addresses to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned IP analysis unit, When analyzing IP addresses, different analysis algorithms are applied based on the caller's behavioral patterns. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned IP analysis unit, It estimates the user's emotions and adjusts how the IP analysis results are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned IP analysis unit, When analyzing IP addresses, the geographical location information of the caller is taken into consideration to determine the priority of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned IP analysis unit, When analyzing IP addresses, the system analyzes the sender's social media activity and prioritizes the analysis of relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 20) The detection unit is It estimates the user's emotions and adjusts the behavior detection criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The detection unit is When detecting the poster's behavior, we improve detection accuracy by referring to a database of past fraudulent activities. The system described in Appendix 1, characterized by the features described herein. (Note 22) The detection unit is When detecting the poster's behavior, different detection algorithms are applied depending on the category of the post content. The system described in Appendix 1, characterized by the features described herein. (Note 23) The detection unit is It estimates the user's emotions and adjusts how the detection results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The detection unit is When detecting the poster's behavior, the detection priority is determined based on when the post was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 25) The detection unit is When detecting the poster's behavior, the detection order is adjusted based on the relevance of the post content. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned warning unit is The system estimates the user's emotions and adjusts the way warning messages are presented based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned warning unit is When issuing warnings about suspicious accounts or posts, we improve the accuracy of warnings by referring to past warning history. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned warning unit is When issuing warnings about suspicious accounts or posts, different warning algorithms are applied depending on the category of the warning. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned warning unit is It estimates the user's emotions and adjusts how warning messages are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned warning unit is When issuing warnings about suspicious accounts or posts, the priority of the warnings will be determined based on when the warning was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned warning unit is When issuing warnings about suspicious accounts or posts, the order of warnings will be adjusted based on the relevance of the warning content. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned warning unit is When warnings are issued about suspicious accounts or posts, different warning messages are displayed depending on the category of the warning. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0189] 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 desk that receives reports from users, An analysis unit that analyzes the notification information received by the reception unit, An IP analysis unit analyzes the IP address analyzed by the aforementioned analysis unit, A detection unit that detects the poster's behavior based on the information analyzed by the aforementioned IP analysis unit, The system includes a warning unit that issues warnings regarding suspicious accounts and posts detected by the aforementioned detection unit. A system characterized by the following features.

2. The aforementioned reception unit is The system estimates the user's emotions and prioritizes reports based on those estimated emotions. The system according to feature 1.

3. The aforementioned reception unit is When a report is received, the system analyzes the user's past reporting history and prioritizes processing reports that are deemed highly reliable. The system according to feature 1.

4. The aforementioned reception unit is When receiving a report, the method of receiving the report will be adjusted based on the level of detail of the report. The system according to feature 1.

5. The aforementioned reception unit is We estimate the user's emotions and adjust the reporting process based on those estimated emotions. The system according to feature 1.

6. The aforementioned reception unit is When receiving a report, the system prioritizes processing reports that are highly relevant, taking into account the user's geographical location. The system according to feature 1.

7. The aforementioned reception unit is When a report is received, the system analyzes the user's social media activity and prioritizes processing relevant reports. The system according to feature 1.

8. The aforementioned analysis unit, It estimates the user's emotions and adjusts the level of detail in the analysis based on the estimated user emotions. The system according to feature 1.

9. The aforementioned analysis unit, When analyzing reported information, we improve the accuracy of the analysis by referring to a database of past fraud cases. The system according to feature 1.

10. The aforementioned analysis unit, When analyzing reported information, different analysis algorithms are applied depending on the category of the reported content. The system according to feature 1.

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