system

Abstract

The system according to this embodiment aims to efficiently identify bot accounts that post negative comments. [Solution] The system according to the embodiment comprises a detection unit, a question generation unit, an analysis unit, and an identification unit. The detection unit detects anti-comments. The question generation unit generates questions unrelated to the account that posted the anti-comment detected by the detection unit. The analysis unit analyzes the answers to the questions generated by the question generation unit. The identification unit identifies bot accounts based on the answers analyzed by the analysis unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a 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 in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to efficiently identify a bot account that posts anti-comments.

[0005] The system according to the embodiment aims to efficiently identify a bot account that posts anti-comments.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a detection unit, a question generation unit, an analysis unit, and an identification unit. The detection unit detects anti-comments. The question generation unit generates questions unrelated to the account that posted the anti-comment detected by the detection unit. The analysis unit analyzes the answers to the questions generated by the question generation unit. The identification unit identifies bot accounts based on the answers analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently identify bot accounts that post negative comments. [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, and the like. The communication I / F manages communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

[0019] The smart device 14 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 anti-comment detection and bot account identification system on an SNS platform according to an embodiment of the present invention is a tool in which an AI automatically asks irrelevant questions and determines whether or not an account is a bot based on whether or not it answers. This system detects anti-comments, generates irrelevant questions, and identifies bot accounts by analyzing the answers. For example, the AI ​​detects anti-comments. In this process, the AI ​​uses natural language processing technology to analyze the content of the comments and determine whether or not there is intent to defame or slander. For example, comments such as "XX is incompetent" or "XX should disappear" are determined to have intent to defame or slander. Next, the AI ​​automatically asks irrelevant questions to the account that posted the detected anti-comment. For example, it asks questions such as "What is your favorite color?" or "What book have you read recently?". Since these questions are completely unrelated to the anti-comment, it is difficult for a bot to answer them automatically. If an account automatically answers the questions, that account is determined to be a bot. For example, if an account immediately answers a question with "It's blue" or "It's Harry Potter," there is a high probability that the account is a bot. This mechanism allows for the identification and reporting of bot accounts. Users can reduce the damage caused by defamation by reporting identified bot accounts. Talent agencies and companies can also use this tool to raise awareness about defamation against their artists. For example, fans of certain artists report defamatory posts on social media platforms, but recently, the number of bot accounts posting negative comments has increased, making the reporting process increasingly difficult. This tool allows for the efficient identification and reporting of bot accounts, reducing the burden of reporting. As a result, systems for detecting negative comments and identifying bot accounts on social media platforms can reduce the damage caused by defamation.

[0029] The anti-comment detection and bot account identification system on an SNS platform according to this embodiment comprises a detection unit, a question generation unit, an analysis unit, and an identification unit. The detection unit detects anti-comments. The detection unit analyzes the content of the comments using, for example, natural language processing technology and determines whether there is intent to defame or slander. For example, the detection unit determines that comments such as "XX is incompetent" or "XX should disappear" have intent to defame or slander. The question generation unit generates questions unrelated to the account that posted the anti-comment detected by the detection unit. For example, the question generation unit generates questions such as "What is your favorite color?" or "What book have you read recently?". Since the question generation unit generates questions with content completely unrelated to anti-comments, it is difficult for a bot to answer them automatically. The analysis unit analyzes the answers to the questions generated by the question generation unit. For example, if the analysis unit receives an immediate answer to a question such as "It's blue" or "It's Harry Potter," it determines that the account is a bot. The identification unit identifies bot accounts based on the responses analyzed by the analysis unit. The identification unit can, for example, identify and report bot accounts. Thus, the anti-comment detection and bot account identification system on the SNS platform according to the embodiment can detect anti-comments, generate irrelevant questions, analyze the responses, and identify bot accounts.

[0030] The detection unit detects negative comments. For example, the detection unit analyzes the content of comments using natural language processing techniques to determine whether there is intent to defame or slander. Specifically, it uses a combination of natural language processing techniques such as morphological analysis, contextual analysis, and sentiment analysis. Morphological analysis breaks down the comment into individual words and analyzes the meaning and role of each word. Contextual analysis understands the context of the entire comment and determines intent to defame or slander from the combination of words and sentence structure. Sentiment analysis analyzes the emotional tone of the comment and determines whether it contains negative emotions. For example, for comments such as "XX is incompetent" or "XX should disappear," morphological analysis extracts negative words such as "incompetent" and "disappear," contextual analysis understands how these words are used, and sentiment analysis confirms that the overall tone is negative. As a result, the detection unit can detect comments with defamatory intent with high accuracy. Furthermore, the detection unit uses machine learning models to learn patterns of defamatory comments based on past data, enabling it to make highly accurate judgments on new comments. For example, it can use algorithms such as neural networks and support vector machines to learn the characteristics of defamatory comments and perform real-time detection. As a result, the detection unit can quickly and accurately detect defamatory comments on SNS platforms, protecting user safety.

[0031] The question generation unit generates questions unrelated to the account that posted the anti-comment detected by the detection unit. For example, the question generation unit generates questions such as "What is your favorite color?" or "What book have you read recently?" Specifically, the question generation unit either randomly selects a question from a pre-prepared list or generates a new question using a generation AI. The generation AI can generate grammatically correct questions using natural language generation technology. For example, the generation AI generates contextually appropriate questions based on a large amount of pre-trained question data. Because the question generation unit generates questions that are completely unrelated to anti-comments, it is difficult for a bot to answer them automatically. For example, to the question "What is your favorite color?", a bot can only return pre-programmed answers, making it difficult to give a natural response. In addition, the question generation unit makes it difficult for bots to learn patterns by increasing the variations in questions. For example, even for the same question, using different expressions such as "What is your favorite color?" or "Please tell me your favorite color" makes it difficult for bots to answer accurately. This allows the question generation unit to effectively generate questions for accounts that have posted negative comments, thereby assisting in the identification of bot accounts.

[0032] The analysis unit analyzes the answers to questions generated by the question generation unit. For example, if an account immediately responds to a question with an answer like "It's blue" or "It's Harry Potter," the analysis unit determines that the account is a bot. Specifically, the analysis unit analyzes the content, timing, and grammatical accuracy of the answer. For example, if an answer is returned immediately after a question, it is determined that there is a high probability that it is a bot. It also determines that an answer is a bot if the content is unrelated to the question or grammatically unnatural. The analysis unit uses natural language processing technology to analyze the content of the answer and evaluate its relevance to the question. For example, if the answer to the question "What is your favorite color?" is "It's blue," it is determined that the relevance is high, but if the answer is "It's Harry Potter," it is determined that the relevance is low. Furthermore, the analysis unit analyzes the timing of the answer, and if it is returned immediately, it is determined that there is a high probability that it is a bot. For example, if an answer is returned within one second of the question being sent, it is determined that it is a bot because it is too short a time for a human to answer. Furthermore, the analysis unit also evaluates the grammatical accuracy of the answers, and determines that an answer is likely from a bot if it contains unnatural grammar or numerous typos. This allows the analysis unit to analyze answers to questions from multiple angles and identify bot accounts with high accuracy.

[0033] The identification unit identifies bot accounts based on the responses analyzed by the analysis unit. The identification unit can, for example, identify and report bot accounts. Specifically, the identification unit analyzes the characteristics of bot accounts based on the information provided by the analysis unit and finds specific patterns. For example, the identification unit identifies accounts that post a large number of comments in a short period of time or accounts that frequently use specific keywords as bots. The identification unit also identifies accounts as bots if multiple accounts are accessing from the same IP address or operating from the same device, based on the information from the analysis unit. The identification unit can report the identified bot accounts to the administrators of the SNS platform and take appropriate measures. For example, the identification unit can temporarily freeze or completely delete bot accounts. The identification unit can also store information on identified bot accounts in a database and use it as training data for future bot detection. This allows the identification unit to quickly and effectively identify and counter bot accounts on SNS platforms. Furthermore, the identification unit can continuously improve the accuracy of bot account identification based on reports and feedback from users. For example, by analyzing user reports and learning new bot patterns, more sophisticated bot detection becomes possible. This allows the system to maintain the integrity of the SNS platform and protect user safety.

[0034] The identification unit includes a reporting unit that reports identified bot accounts. The reporting unit, for example, reports identified bot accounts to the administrator of the SNS platform. The reporting unit collects information about identified bot accounts and sends it as a report. The reporting unit includes, for example, the username of the identified bot account, the content of posts, and details of detected anti-comments as part of the report. This makes it possible to report identified bot accounts. Some or all of the above processing in the reporting unit may be performed using AI, for example, or without AI. For example, the reporting unit can input information about identified bot accounts into AI and have the AI ​​generate the report content.

[0035] The detection unit can change the detection criteria for anti-comments when detecting them, taking into account the time of day the comment was posted. For example, the detection unit might assume that comments posted late at night tend to be more aggressive and tighten the detection criteria. For example, it might assume that comments posted during the day generally tend to be milder and loosen the detection criteria. For example, it might assume that comments posted on weekends tend to be more emotional and adjust the detection criteria. This allows the detection criteria for anti-comments to be changed based on the time of day the comment was posted. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input comment posting time data into a generating AI and have the generating AI perform the changes to the detection criteria.

[0036] The detection unit can improve the accuracy of anti-comment detection by referring to the commenter's past posting history during detection. For example, the detection unit strictly detects comments from users who have posted many aggressive comments in the past. For example, the detection unit loosely detects comments from users who have posted many mild comments in the past. For example, the detection unit prioritizes detecting comments containing specific trigger words from past posting history. This improves the accuracy of anti-comment detection based on the commenter's past posting history. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input the commenter's past posting history data into a generating AI and have the generating AI perform the improvement of detection accuracy.

[0037] The question generation unit can generate the most suitable questions by referring to past question history when generating questions. For example, the question generation unit can prioritize generating questions that were effective in the past. For example, the question generation unit can generate questions related to a specific topic from past question history. For example, the question generation unit can analyze past question history and generate the most effective question patterns. This makes it possible to generate the most suitable questions based on past question history. Some or all of the above processes in the question generation unit may be performed using AI, for example, or without AI. For example, the question generation unit can input past question history data into a generation AI and have the generation AI perform the generation of the most suitable questions.

[0038] The analysis unit can adjust the level of detail of the analysis based on the content of the response during the analysis. For example, if the response is detailed, the AI ​​will perform a detailed analysis. For example, if the response is concise, the AI ​​will perform a concise analysis. The analysis unit adjusts the optimal level of detail of the analysis based on the content of the response. This allows the level of detail of the analysis to be adjusted based on the content of the response. 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 content data of the response into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0039] The identification unit can improve the accuracy of identification by referring to past identification history during the identification process. For example, the identification unit can refer to patterns of previously identified bot accounts and identify accounts with similar patterns. For example, the identification unit can prioritize the identification of accounts containing specific trigger words from past identification history. For example, the identification unit can analyze past identification history and apply the most effective identification method. This allows for improved accuracy of identification based on past identification history. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input past identification history data into a generating AI and have the generating AI perform the improvement of identification accuracy.

[0040] The reporting unit can improve the accuracy of reports by referring to past reporting history when a report is made. For example, the reporting unit can refer to patterns of bot accounts that have been reported in the past and report accounts that have similar patterns. For example, the reporting unit can prioritize reporting accounts that contain specific trigger words based on past reporting history. For example, the reporting unit can analyze past reporting history and apply the most effective reporting method. This allows for improved reporting accuracy based on past reporting history. Some or all of the above processes in the reporting unit may be performed using AI, for example, or without AI. For example, the reporting unit can input past reporting history data into a generating AI and have the generating AI perform the task of improving reporting accuracy.

[0041] The reporting department can determine the priority of reports based on when they are submitted. For example, the reporting department may prioritize reports submitted recently. For example, the reporting department may prioritize reports submitted within a specific time period. For example, the reporting department may determine the optimal priority of reports based on the submission time. This allows the reporting department to determine the priority of reports based on when they are submitted. Some or all of the above processing in the reporting department may be performed using AI, for example, or not using AI. For example, the reporting department may input report submission time data into a generating AI and have the generating AI perform the determination of report priority.

[0042] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0043] A system for detecting anti-comments and identifying bot accounts on social networking platforms may further include a behavioral analysis unit that analyzes user behavior patterns. This unit, for example, analyzes a user's posting frequency, comment content, and interaction patterns with other users. For instance, it can identify users who post frequently during specific time periods or who comment only on specific topics. Furthermore, if a user's interactions with other users are one-sided, the unit can determine that the user is likely a bot. This improves the accuracy of identifying bot accounts based on user behavior patterns.

[0044] A system for detecting anti-comments and identifying bot accounts on social media platforms may further include a profile analysis unit that analyzes user profile information. The profile analysis unit, for example, analyzes the user's profile picture, self-introduction, and number of followers. For instance, the profile analysis unit can determine that users without a profile picture or with short self-introductions are likely to be bots. It can also identify users with extremely low follower counts or low follower activity. This improves the accuracy of identifying bot accounts based on user profile information.

[0045] A system for detecting anti-comments and identifying bot accounts on social networking platforms may further include a behavioral analysis unit that analyzes user behavior patterns. This unit, for example, analyzes a user's posting frequency, comment content, and interaction patterns with other users. For instance, it can identify users who post frequently during specific time periods or who comment only on specific topics. Furthermore, if a user's interactions with other users are one-sided, the unit can determine that the user is likely a bot. This improves the accuracy of identifying bot accounts based on user behavior patterns.

[0046] A system for detecting anti-comments and identifying bot accounts on social media platforms may further include a profile analysis unit that analyzes user profile information. The profile analysis unit, for example, analyzes the user's profile picture, self-introduction, and number of followers. For instance, the profile analysis unit can determine that users without a profile picture or with short self-introductions are likely to be bots. It can also identify users with extremely low follower counts or low follower activity. This improves the accuracy of identifying bot accounts based on user profile information.

[0047] The following briefly describes the processing flow for example form 1.

[0048] Step 1: The detection unit detects anti-comments. The detection unit analyzes the content of the comments using natural language processing technology and determines whether there is intent to defame or slander. For example, comments such as "XX is incompetent" or "XX should disappear" are determined to have intent to defame or slander. Step 2: The question generation unit generates questions unrelated to the account that posted the anti-comment detected by the detection unit. For example, it generates questions such as "What is your favorite color?" or "What book have you read recently?". As a result, it generates questions that are completely unrelated to the anti-comment, making it difficult for a bot to answer them automatically. Step 3: The analysis unit analyzes the answers to the questions generated by the question generation unit. For example, if the answer to a question is immediately "It's blue" or "It's Harry Potter," the unit determines that the account is a bot. Step 4: The identification unit identifies the bot account based on the response analyzed by the analysis unit. For example, it can identify and report the bot account.

[0049] (Example of form 2) An anti-comment detection and bot account identification system on an SNS platform according to an embodiment of the present invention is a tool in which an AI automatically asks irrelevant questions and determines whether or not an account is a bot based on whether or not it answers. This system detects anti-comments, generates irrelevant questions, and identifies bot accounts by analyzing the answers. For example, the AI ​​detects anti-comments. In this process, the AI ​​uses natural language processing technology to analyze the content of the comments and determine whether or not there is intent to defame or slander. For example, comments such as "XX is incompetent" or "XX should disappear" are determined to have intent to defame or slander. Next, the AI ​​automatically asks irrelevant questions to the account that posted the detected anti-comment. For example, it asks questions such as "What is your favorite color?" or "What book have you read recently?". Since these questions are completely unrelated to the anti-comment, it is difficult for a bot to answer them automatically. If an account automatically answers the questions, that account is determined to be a bot. For example, if an account immediately answers a question with "It's blue" or "It's Harry Potter," there is a high probability that the account is a bot. This mechanism allows for the identification and reporting of bot accounts. Users can reduce the damage caused by defamation by reporting identified bot accounts. Talent agencies and companies can also use this tool to raise awareness about defamation against their artists. For example, fans of certain artists report defamatory posts on social media platforms, but recently, the number of bot accounts posting negative comments has increased, making the reporting process increasingly difficult. This tool allows for the efficient identification and reporting of bot accounts, reducing the burden of reporting. As a result, systems for detecting negative comments and identifying bot accounts on social media platforms can reduce the damage caused by defamation.

[0050] The anti-comment detection and bot account identification system on an SNS platform according to this embodiment comprises a detection unit, a question generation unit, an analysis unit, and an identification unit. The detection unit detects anti-comments. The detection unit analyzes the content of the comments using, for example, natural language processing technology and determines whether there is intent to defame or slander. For example, the detection unit determines that comments such as "XX is incompetent" or "XX should disappear" have intent to defame or slander. The question generation unit generates questions unrelated to the account that posted the anti-comment detected by the detection unit. For example, the question generation unit generates questions such as "What is your favorite color?" or "What book have you read recently?". Since the question generation unit generates questions with content completely unrelated to anti-comments, it is difficult for a bot to answer them automatically. The analysis unit analyzes the answers to the questions generated by the question generation unit. For example, if the analysis unit receives an immediate answer to a question such as "It's blue" or "It's Harry Potter," it determines that the account is a bot. The identification unit identifies bot accounts based on the responses analyzed by the analysis unit. The identification unit can, for example, identify and report bot accounts. Thus, the anti-comment detection and bot account identification system on the SNS platform according to the embodiment can detect anti-comments, generate irrelevant questions, analyze the responses, and identify bot accounts.

[0051] The detection unit detects negative comments. For example, the detection unit analyzes the content of comments using natural language processing techniques to determine whether there is intent to defame or slander. Specifically, it uses a combination of natural language processing techniques such as morphological analysis, contextual analysis, and sentiment analysis. Morphological analysis breaks down the comment into individual words and analyzes the meaning and role of each word. Contextual analysis understands the context of the entire comment and determines intent to defame or slander from the combination of words and sentence structure. Sentiment analysis analyzes the emotional tone of the comment and determines whether it contains negative emotions. For example, for comments such as "XX is incompetent" or "XX should disappear," morphological analysis extracts negative words such as "incompetent" and "disappear," contextual analysis understands how these words are used, and sentiment analysis confirms that the overall tone is negative. As a result, the detection unit can detect comments with defamatory intent with high accuracy. Furthermore, the detection unit uses machine learning models to learn patterns of defamatory comments based on past data, enabling it to make highly accurate judgments on new comments. For example, it can use algorithms such as neural networks and support vector machines to learn the characteristics of defamatory comments and perform real-time detection. As a result, the detection unit can quickly and accurately detect defamatory comments on SNS platforms, protecting user safety.

[0052] The question generation unit generates questions unrelated to the account that posted the anti-comment detected by the detection unit. For example, the question generation unit generates questions such as "What is your favorite color?" or "What book have you read recently?" Specifically, the question generation unit either randomly selects a question from a pre-prepared list or generates a new question using a generation AI. The generation AI can generate grammatically correct questions using natural language generation technology. For example, the generation AI generates contextually appropriate questions based on a large amount of pre-trained question data. Because the question generation unit generates questions that are completely unrelated to anti-comments, it is difficult for a bot to answer them automatically. For example, to the question "What is your favorite color?", a bot can only return pre-programmed answers, making it difficult to give a natural response. In addition, the question generation unit makes it difficult for bots to learn patterns by increasing the variations in questions. For example, even for the same question, using different expressions such as "What is your favorite color?" or "Please tell me your favorite color" makes it difficult for bots to answer accurately. This allows the question generation unit to effectively generate questions for accounts that have posted negative comments, thereby assisting in the identification of bot accounts.

[0053] The analysis unit analyzes the answers to questions generated by the question generation unit. For example, if an account immediately responds to a question with an answer like "It's blue" or "It's Harry Potter," the analysis unit determines that the account is a bot. Specifically, the analysis unit analyzes the content, timing, and grammatical accuracy of the answer. For example, if an answer is returned immediately after a question, it is determined that there is a high probability that it is a bot. It also determines that an answer is a bot if the content is unrelated to the question or grammatically unnatural. The analysis unit uses natural language processing technology to analyze the content of the answer and evaluate its relevance to the question. For example, if the answer to the question "What is your favorite color?" is "It's blue," it is determined that the relevance is high, but if the answer is "It's Harry Potter," it is determined that the relevance is low. Furthermore, the analysis unit analyzes the timing of the answer, and if it is returned immediately, it is determined that there is a high probability that it is a bot. For example, if an answer is returned within one second of the question being sent, it is determined that it is a bot because it is too short a time for a human to answer. Furthermore, the analysis unit also evaluates the grammatical accuracy of the answers, and determines that an answer is likely from a bot if it contains unnatural grammar or numerous typos. This allows the analysis unit to analyze answers to questions from multiple angles and identify bot accounts with high accuracy.

[0054] The identification unit identifies bot accounts based on the responses analyzed by the analysis unit. The identification unit can, for example, identify and report bot accounts. Specifically, the identification unit analyzes the characteristics of bot accounts based on the information provided by the analysis unit and finds specific patterns. For example, the identification unit identifies accounts that post a large number of comments in a short period of time or accounts that frequently use specific keywords as bots. The identification unit also identifies accounts as bots if multiple accounts are accessing from the same IP address or operating from the same device, based on the information from the analysis unit. The identification unit can report the identified bot accounts to the administrators of the SNS platform and take appropriate measures. For example, the identification unit can temporarily freeze or completely delete bot accounts. The identification unit can also store information on identified bot accounts in a database and use it as training data for future bot detection. This allows the identification unit to quickly and effectively identify and counter bot accounts on SNS platforms. Furthermore, the identification unit can continuously improve the accuracy of bot account identification based on reports and feedback from users. For example, by analyzing user reports and learning new bot patterns, more sophisticated bot detection becomes possible. This allows the system to maintain the integrity of the SNS platform and protect user safety.

[0055] The identification unit includes a reporting unit that reports identified bot accounts. The reporting unit, for example, reports identified bot accounts to the administrator of the SNS platform. The reporting unit collects information about identified bot accounts and sends it as a report. The reporting unit includes, for example, the username of the identified bot account, the content of posts, and details of detected anti-comments as part of the report. This makes it possible to report identified bot accounts. Some or all of the above processing in the reporting unit may be performed using AI, for example, or without AI. For example, the reporting unit can input information about identified bot accounts into AI and have the AI ​​generate the report content.

[0056] The detection unit can estimate the user's emotions and adjust the accuracy of anti-comment detection based on the estimated user emotions. For example, if the user is showing anger, the AI ​​will detect anti-comments using stricter criteria. For example, if the user is showing sadness, the AI ​​will react more sensitively to emotional trigger words. For example, if the user is showing neutral emotions, the AI ​​will detect anti-comments using normal criteria. This allows the accuracy of anti-comment detection to be adjusted based on the user's emotions. 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 user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0057] The detection unit can change the detection criteria for anti-comments when detecting them, taking into account the time of day the comment was posted. For example, the detection unit might assume that comments posted late at night tend to be more aggressive and tighten the detection criteria. For example, it might assume that comments posted during the day generally tend to be milder and loosen the detection criteria. For example, it might assume that comments posted on weekends tend to be more emotional and adjust the detection criteria. This allows the detection criteria for anti-comments to be changed based on the time of day the comment was posted. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input comment posting time data into a generating AI and have the generating AI perform the changes to the detection criteria.

[0058] The detection unit can improve the accuracy of anti-comment detection by referring to the commenter's past posting history during detection. For example, the detection unit strictly detects comments from users who have posted many aggressive comments in the past. For example, the detection unit loosely detects comments from users who have posted many mild comments in the past. For example, the detection unit prioritizes detecting comments containing specific trigger words from past posting history. This improves the accuracy of anti-comment detection based on the commenter's past posting history. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input the commenter's past posting history data into a generating AI and have the generating AI perform the improvement of detection accuracy.

[0059] The question generation unit can estimate the user's emotions and adjust the content of the question based on the estimated emotions. For example, if the user is angry, the AI ​​will generate a calm question. If the user is sad, the AI ​​will generate an encouraging question. If the user is neutral, the AI ​​will generate a normal question. This allows the content of the question to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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 question generation unit may be performed using an AI, or not using an AI. For example, the question generation unit can input user emotion data into a generative AI and have the generative AI adjust the content of the question.

[0060] The question generation unit can generate the most suitable questions by referring to past question history when generating questions. For example, the question generation unit can prioritize generating questions that were effective in the past. For example, the question generation unit can generate questions related to a specific topic from past question history. For example, the question generation unit can analyze past question history and generate the most effective question patterns. This makes it possible to generate the most suitable questions based on past question history. Some or all of the above processes in the question generation unit may be performed using AI, for example, or without AI. For example, the question generation unit can input past question history data into a generation AI and have the generation AI perform the generation of the most suitable questions.

[0061] The analysis unit can estimate the user's emotions and adjust the response analysis method based on the estimated user emotions. For example, if the user is expressing anger, the AI ​​will analyze the response using strict criteria. For example, if the user is expressing sadness, the AI ​​will be highly sensitive to emotional trigger words. For example, if the user is expressing neutral emotions, the AI ​​will analyze the response using normal criteria. This allows the response analysis method to be adjusted based on the user's emotions. 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 analysis unit may be performed using AI or not using AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the response analysis method.

[0062] The analysis unit can adjust the level of detail of the analysis based on the content of the response during the analysis. For example, if the response is detailed, the AI ​​will perform a detailed analysis. For example, if the response is concise, the AI ​​will perform a concise analysis. The analysis unit adjusts the optimal level of detail of the analysis based on the content of the response. This allows the level of detail of the analysis to be adjusted based on the content of the response. 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 content data of the response into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0063] The identification unit can estimate the user's emotions and adjust the method of identifying bot accounts based on the estimated user emotions. For example, if the user is angry, the AI ​​will identify bot accounts using strict criteria. For example, if the user is sad, the AI ​​will be sensitive to emotional trigger words. For example, if the user is neutral, the AI ​​will identify bot accounts using normal criteria. This allows the method of identifying bot accounts to be adjusted based on the user's emotions. 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 identification unit may be performed using AI or not using AI. For example, the identification unit can input user emotion data into a generative AI and have the generative AI adjust the method of identifying bot accounts.

[0064] The identification unit can improve the accuracy of identification by referring to past identification history during the identification process. For example, the identification unit can refer to patterns of previously identified bot accounts and identify accounts with similar patterns. For example, the identification unit can prioritize the identification of accounts containing specific trigger words from past identification history. For example, the identification unit can analyze past identification history and apply the most effective identification method. This allows for improved accuracy of identification based on past identification history. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input past identification history data into a generating AI and have the generating AI perform the improvement of identification accuracy.

[0065] The reporting unit can estimate the user's emotions and adjust the reporting method based on the estimated emotions. For example, if the user is showing anger, the AI ​​will quickly submit a report. If the user is showing sadness, the AI ​​will respond sensitively to emotional trigger words and submit a report. If the user is showing neutral emotions, the AI ​​will submit a report using normal criteria. This allows the reporting method to be adjusted based on the user's emotions. 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 reporting unit may be performed using AI or not using AI. For example, the reporting unit can input user emotion data into a generative AI and have the generative AI adjust the reporting method.

[0066] The reporting unit can improve the accuracy of reports by referring to past reporting history when a report is made. For example, the reporting unit can refer to patterns of bot accounts that have been reported in the past and report accounts that have similar patterns. For example, the reporting unit can prioritize reporting accounts that contain specific trigger words based on past reporting history. For example, the reporting unit can analyze past reporting history and apply the most effective reporting method. This allows for improved reporting accuracy based on past reporting history. Some or all of the above processes in the reporting unit may be performed using AI, for example, or without AI. For example, the reporting unit can input past reporting history data into a generating AI and have the generating AI perform the task of improving reporting accuracy.

[0067] The reporting unit can estimate a user's emotions and determine the priority of reports based on the estimated emotions. For example, if a user is expressing anger, the AI ​​will prioritize that user's report. If a user is expressing sadness, the AI ​​will prioritize that user's report. If a user is expressing neutral emotions, the AI ​​will report the report with normal priority. This allows the reporting unit to determine 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 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 reporting unit may be performed using AI or not using AI. For example, the reporting unit can input user emotion data into a generative AI and have the generative AI determine the priority of reports.

[0068] The reporting department can determine the priority of reports based on when they are submitted. For example, the reporting department may prioritize reports submitted recently. For example, the reporting department may prioritize reports submitted within a specific time period. For example, the reporting department may determine the optimal priority of reports based on the submission time. This allows the reporting department to determine the priority of reports based on when they are submitted. Some or all of the above processing in the reporting department may be performed using AI, for example, or not using AI. For example, the reporting department may input report submission time data into a generating AI and have the generating AI perform the determination of report priority.

[0069] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0070] A system for detecting anti-comments and identifying bot accounts on social networking platforms may further include a behavioral analysis unit that analyzes user behavior patterns. This unit, for example, analyzes a user's posting frequency, comment content, and interaction patterns with other users. For instance, it can identify users who post frequently during specific time periods or who comment only on specific topics. Furthermore, if a user's interactions with other users are one-sided, the unit can determine that the user is likely a bot. This improves the accuracy of identifying bot accounts based on user behavior patterns.

[0071] A system for detecting anti-comments and identifying bot accounts on social media platforms may further include a profile analysis unit that analyzes user profile information. The profile analysis unit, for example, analyzes the user's profile picture, self-introduction, and number of followers. For instance, the profile analysis unit can determine that users without a profile picture or with short self-introductions are likely to be bots. It can also identify users with extremely low follower counts or low follower activity. This improves the accuracy of identifying bot accounts based on user profile information.

[0072] The anti-comment detection and bot account identification system on social media platforms can further estimate the user's emotions and adjust the accuracy of anti-comment detection based on the estimated user emotions. For example, if the user is expressing anger, the AI ​​will detect anti-comments using stricter criteria. For example, if the user is expressing sadness, the AI ​​will react more sensitively to emotional trigger words. For example, if the user is expressing neutral emotions, the AI ​​will detect anti-comments using normal criteria. This allows the accuracy of anti-comment detection to be adjusted based on the user's emotions.

[0073] The anti-comment detection and bot account identification system on social media platforms can further estimate the user's emotions and adjust the content of questions based on those emotions. For example, if the user is expressing anger, the AI ​​will generate a calm question. If the user is expressing sadness, the AI ​​will generate an encouraging question. If the user is expressing neutral emotions, the AI ​​will generate a normal question. This allows the content of questions to be adjusted based on the user's emotions.

[0074] The anti-comment detection and bot account identification system on social media platforms can further estimate the user's emotions and adjust the response analysis method based on the estimated user emotions. For example, if the user is expressing anger, the AI ​​will analyze the response using strict criteria. For example, if the user is expressing sadness, the AI ​​will react sensitively to emotional trigger words. For example, if the user is expressing neutral emotions, the AI ​​will analyze the response using normal criteria. This allows the response analysis method to be adjusted based on the user's emotions.

[0075] The anti-comment detection and bot account identification system on social media platforms can further estimate the user's emotions and adjust the bot account identification method based on the estimated user emotions. For example, if the user is expressing anger, the AI ​​will identify bot accounts using strict criteria. For example, if the user is expressing sadness, the AI ​​will react sensitively to emotional trigger words. For example, if the user is expressing neutral emotions, the AI ​​will identify bot accounts using normal criteria. This allows the bot account identification method to be adjusted based on the user's emotions.

[0076] The anti-comment detection and bot account identification system on social media platforms can further estimate the user's emotions and adjust the reporting method based on those emotions. For example, if a user is expressing anger, the AI ​​will quickly file a report. If a user is expressing sadness, the AI ​​will react sensitively to emotional trigger words and file a report. If a user is expressing neutral emotions, the AI ​​will file a report using normal criteria. This allows the reporting method to be adjusted based on the user's emotions.

[0077] The anti-comment detection and bot account identification system on social media platforms can further estimate the user's emotions and determine the priority of reports based on those emotions. For example, if a user is expressing anger, the AI ​​will prioritize that user's report. If a user is expressing sadness, the AI ​​will next prioritize that user's report. If a user is expressing neutral emotions, the AI ​​will report the content with normal priority. This allows the system to determine the priority of reports based on the user's emotions.

[0078] A system for detecting anti-comments and identifying bot accounts on social networking platforms may further include a behavioral analysis unit that analyzes user behavior patterns. This unit, for example, analyzes a user's posting frequency, comment content, and interaction patterns with other users. For instance, it can identify users who post frequently during specific time periods or who comment only on specific topics. Furthermore, if a user's interactions with other users are one-sided, the unit can determine that the user is likely a bot. This improves the accuracy of identifying bot accounts based on user behavior patterns.

[0079] A system for detecting anti-comments and identifying bot accounts on social media platforms may further include a profile analysis unit that analyzes user profile information. The profile analysis unit, for example, analyzes the user's profile picture, self-introduction, and number of followers. For instance, the profile analysis unit can determine that users without a profile picture or with short self-introductions are likely to be bots. It can also identify users with extremely low follower counts or low follower activity. This improves the accuracy of identifying bot accounts based on user profile information.

[0080] The following briefly describes the processing flow for example form 2.

[0081] Step 1: The detection unit detects anti-comments. The detection unit analyzes the content of the comments using natural language processing technology and determines whether there is intent to defame or slander. For example, comments such as "XX is incompetent" or "XX should disappear" are determined to have intent to defame or slander. Step 2: The question generation unit generates questions unrelated to the account that posted the anti-comment detected by the detection unit. For example, it generates questions such as "What is your favorite color?" or "What book have you read recently?". As a result, it generates questions that are completely unrelated to the anti-comment, making it difficult for a bot to answer them automatically. Step 3: The analysis unit analyzes the answers to the questions generated by the question generation unit. For example, if the answer to a question is immediately "It's blue" or "It's Harry Potter," the unit determines that the account is a bot. Step 4: The identification unit identifies the bot account based on the response analyzed by the analysis unit. For example, it can identify and report the bot account.

[0082] 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.

[0083] 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.

[0084] 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.

[0085] Each of the multiple elements described above, including the detection unit, question generation unit, analysis unit, identification unit, and reporting unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the detection unit is implemented by the control unit 46A of the smart device 14, which analyzes the content of a comment and determines whether there is intent to defame or slander. The question generation unit is implemented by the identification processing unit 290 of the data processing device 12, which generates an irrelevant question. The analysis unit is implemented by the control unit 46A of the smart device 14, which analyzes the answer to the question. The identification unit is implemented by the identification processing unit 290 of the data processing device 12, which identifies a bot account. The reporting unit is implemented by the control unit 46A of the smart device 14, which reports the identified bot account to the administrator of the SNS platform. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0086] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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).

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.).

[0098] 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.

[0099] 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.

[0100] 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.

[0101] Each of the multiple elements described above, including the detection unit, question generation unit, analysis unit, identification unit, and reporting unit, is implemented in at least one of the smart glasses 214 and the data processing device 12. For example, the detection unit is implemented by the control unit 46A of the smart glasses 214, which analyzes the content of a comment and determines whether there is intent to defame or slander. The question generation unit is implemented by the identification processing unit 290 of the data processing device 12, which generates an irrelevant question. The analysis unit is implemented by the control unit 46A of the smart glasses 214, which analyzes the answer to the question. The identification unit is implemented by the identification processing unit 290 of the data processing device 12, which identifies a bot account. The reporting unit is implemented by the control unit 46A of the smart glasses 214, which reports the identified bot account to the administrator of the SNS platform. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0102] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] Each of the multiple elements described above, including the detection unit, question generation unit, analysis unit, identification unit, and reporting unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the detection unit is implemented by the control unit 46A of the headset terminal 314, which analyzes the content of a comment and determines whether there is intent to defame or slander. The question generation unit is implemented by the identification processing unit 290 of the data processing unit 12, which generates an irrelevant question. The analysis unit is implemented by the control unit 46A of the headset terminal 314, which analyzes the answer to the question. The identification unit is implemented by the identification processing unit 290 of the data processing unit 12, which identifies a bot account. The reporting unit is implemented by the control unit 46A of the headset terminal 314, which reports the identified bot account to the administrator of the SNS platform. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0118] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] Each of the multiple elements described above, including the detection unit, question generation unit, analysis unit, identification unit, and reporting unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the detection unit is implemented by the control unit 46A of the robot 414, which analyzes the content of a comment and determines whether there is intent to defame or slander. The question generation unit is implemented by the identification processing unit 290 of the data processing unit 12, which generates an irrelevant question. The analysis unit is implemented by the control unit 46A of the robot 414, which analyzes the answer to the question. The identification unit is implemented by the identification processing unit 290 of the data processing unit 12, which identifies a bot account. The reporting unit is implemented by the control unit 46A of the robot 414, which reports the identified bot account to the administrator of the SNS platform. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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."

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] (Note 1) A detection unit for detecting negative comments, A question generation unit generates a question unrelated to the account that posted the anti-comment detected by the detection unit, An analysis unit analyzes the answers to the questions generated by the question generation unit, The system comprises an identification unit that identifies a bot account based on the response analyzed by the analysis unit. A system characterized by the following features. (Note 2) It has a reporting function for reporting identified bot accounts. The system described in Appendix 1, characterized by the features described herein. (Note 3) The detection unit is The system estimates the user's sentiment and adjusts the accuracy of anti-comment detection based on the estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 4) The detection unit is When detecting malicious comments, the criteria for detecting anti-comments will be changed to take into account the time the comment was posted. The system described in Appendix 1, characterized by the features described herein. (Note 5) The detection unit is When detecting malicious comments, the system improves the accuracy of anti-comment detection by referencing the commenter's past posting history. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned question generation unit, The system estimates the user's emotions and adjusts the content of the questions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned question generation unit, When generating a question, the system refers to past question history to generate the most suitable question. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, We estimate the user's emotions and adjust the response analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, During analysis, the level of detail of the analysis is adjusted based on the content of the responses. The system described in Appendix 1, characterized by the features described herein. (Note 10) The specified part is, We estimate the user's sentiment and adjust the method of identifying bot accounts based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 11) The specified part is, When a specific location is identified, past identification history is referenced to improve the accuracy of that identification. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reporting unit, We estimate the user's emotions and adjust the reporting method based on the estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 13) The aforementioned reporting unit, When making a report, past reporting history is referenced to improve the accuracy of the report. The system described in Appendix 2, characterized by the features described herein. (Note 14) The aforementioned reporting unit, The system estimates the user's emotions and prioritizes reports based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 15) The aforementioned reporting unit, When a report is submitted, the priority of the report is determined based on when it was submitted. The system described in Appendix 2, characterized by the features described herein. [Explanation of Symbols]

[0154] 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 detection unit for detecting negative comments, A question generation unit generates a question unrelated to the account that posted the anti-comment detected by the detection unit, An analysis unit analyzes the answers to the questions generated by the question generation unit, The system comprises an identification unit that identifies a bot account based on the response analyzed by the analysis unit. A system characterized by the following features.

2. It has a reporting function for reporting identified bot accounts. The system according to feature 1.

3. The detection unit is The system estimates the user's sentiment and adjusts the accuracy of anti-comment detection based on the estimated sentiment. The system according to feature 1.

4. The detection unit is When detecting malicious comments, the criteria for detecting anti-comments will be changed to take into account the time the comment was posted. The system according to feature 1.

5. The detection unit is When detecting malicious comments, the system improves the accuracy of anti-comment detection by referencing the commenter's past posting history. The system according to feature 1.

6. The aforementioned question generation unit, The system estimates the user's emotions and adjusts the content of the questions based on those estimated emotions. The system according to feature 1.

7. The aforementioned question generation unit, When generating a question, the system refers to past question history to generate the most suitable question. The system according to feature 1.

8. The aforementioned analysis unit, We estimate the user's emotions and adjust the response analysis method based on the estimated user emotions. The system according to feature 1.

9. The aforementioned analysis unit, During analysis, the level of detail of the analysis is adjusted based on the content of the responses. The system according to feature 1.

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