system

Abstract

The system according to this embodiment aims to detect defamation in real time and provide prompt support to victims. [Solution] The system according to the embodiment comprises an analysis unit, a warning unit, a support unit, and a management unit. The analysis unit analyzes the content of user posts in real time. The warning unit issues warnings based on the defamation detected by the analysis unit. The support unit provides support to users who have been warned by the warning unit about defamation. The management unit coordinates the operations of the analysis unit, the warning unit, and the support unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor and includes 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 slander detection and countermeasures are not performed in real time, and sufficient mental health care for victims is not provided.

[0005] The system according to the embodiment aims to detect slander in real time and quickly provide support to the victim.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an analysis unit, a warning unit, a support unit, and a management unit. The analysis unit analyzes user posts in real time. The warning unit issues warnings based on the defamation detected by the analysis unit. The support unit provides support to users who have been warned by the warning unit about defamation. The management unit oversees the operations of the analysis unit, warning unit, and support unit. [Effects of the Invention]

[0007] The system according to this embodiment can detect defamation in real time and provide prompt support to victims. [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 labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), 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) The defamation detection system according to an embodiment of the present invention is a system that uses multimodal LLM to analyze text, image, and video content in real time, detects defamation, and automatically issues a warning. The defamation detection system provides support and mental health care to users who have been defamated through an empathy AI chatbot. For example, the defamation detection system uses multimodal LLM to analyze text, image, and video content posted by users on social networking services (SNS) and online communities in real time. Next, if defamation is detected, the defamation detection system automatically issues a warning. Furthermore, the defamation detection system provides support to users who have been defamated through an empathy AI chatbot. This system operates in conjunction with SNS platforms and news sites, and future plans include system integration with other SNS platforms and improvements to the user interface. This mechanism is expected to improve the health of social networking services (SNS) and online communities, leading to increased user engagement and the promotion of self-expression. Thus, the defamation detection system can improve the health of SNS and online communities, and achieve increased user engagement and the promotion of self-expression.

[0029] The defamation detection system according to this embodiment comprises an analysis unit, a warning unit, a support unit, and a management unit. The analysis unit analyzes user posts in real time. The analysis unit analyzes text, images, and video content in real time to detect defamation. The analysis unit can analyze text content using natural language processing technology to detect defamation. For example, the analysis unit uses text generation AI (e.g., LLM) to analyze posts and detect defamation. The analysis unit can also analyze image content using image recognition technology to detect defamation. For example, the analysis unit uses image generation AI to detect defamation within images. Furthermore, the analysis unit can analyze video content using video analysis technology to detect defamation. For example, the analysis unit uses video generation AI to detect defamation within videos. The warning unit issues warnings based on defamation detected by the analysis unit. For example, the warning unit automatically issues a warning when defamation is detected. The warning unit can also issue warnings when specific keywords are detected. For example, the warning unit can detect defamatory keywords and issue a warning. The warning unit can also issue warnings when a certain score is exceeded. For example, the warning unit issues a warning when the defamation score is high. Furthermore, the warning unit can adjust the level of detail of the warning based on the content of the defamation. For example, the warning unit issues a detailed warning for serious defamation. The support unit provides support to users who have been warned about by the warning unit. For example, the support unit provides mental health care to users who have been defamated using an empathy AI chatbot. The support unit can analyze the user's emotions using natural language processing technology and provide appropriate support. For example, the support department can use an empathy AI chatbot to analyze the user's emotions and provide appropriate support. The support department can also analyze the user's past mental health history to select the most suitable support method. For instance, it can suggest the optimal support method based on the user's past mental health history. Furthermore, the support department can customize the means of support based on the user's current living situation.For example, the support unit considers the user's current living situation and provides appropriate support. The management unit oversees the operation of the analysis unit, warning unit, and support unit. The management unit, for example, monitors, adjusts, and controls the operation of each unit. The management unit can monitor the operation status of each unit in real time and make appropriate adjustments. For example, the management unit monitors the operation status of the analysis unit and makes adjustments as needed. The management unit can also monitor the operation status of the warning unit and perform appropriate control. Furthermore, the management unit can monitor the operation status of the support unit and make appropriate adjustments. As a result, the defamation detection system according to this embodiment can analyze the user's posted content in real time, detect defamation, issue warnings, and provide support.

[0030] The analysis unit analyzes user posts in real time. For example, the analysis unit analyzes text, images, and video content in real time to detect defamation. Specifically, it uses natural language processing technology to analyze text content. Natural language processing technology includes morphological analysis, grammatical analysis, and semantic analysis, and by combining these, it can understand the context and intent of the post and detect defamation with high accuracy. For example, it uses text generation AI (e.g., LLM) to analyze the post content and detect defamation. This AI has learned from a large amount of text data and can judge defamation not only based on specific keywords and phrases, but also by considering the context and nuances. For image content, it uses image recognition technology to analyze it. Image recognition technology includes object detection, face recognition, and text recognition, and by combining these, it can detect defamation in images with high accuracy. For example, it uses image generation AI to detect defamation in images. This AI has learned from a large amount of image data and can analyze text, symbols, and facial expressions within images to determine if they contain defamation. For video content, it uses video analysis technology. This video analysis technology includes frame analysis, audio analysis, and motion analysis, and by combining these, it can detect defamation within videos with high accuracy. For example, it can use a video generation AI to detect defamation within videos. This AI has learned from a large amount of video data and can analyze audio, video, and motion within videos to determine if they contain defamation. As a result, the analysis unit can analyze text, images, and videos in real time and detect defamation with high accuracy.

[0031] The warning unit issues warnings based on defamation detected by the analysis unit. Specifically, it automatically issues a warning when defamation is detected. The warning unit can also issue a warning when specific keywords are detected. For example, it can detect defamatory keywords and issue a warning. In this case, the keyword list is updated regularly to accommodate new defamatory expressions and slang. The warning unit can also issue a warning when a certain score is exceeded. For example, it can issue a warning when the defamation score is high. This score is a numerical representation of the likelihood of defamation by the analysis unit, with a higher score indicating a higher likelihood of defamation. Furthermore, the warning unit can adjust the level of detail of the warning based on the content of the defamation. For example, it can issue a detailed warning for serious defamation. The level of detail of the warning is adjusted according to the content and impact of the defamation to prompt the user to take appropriate action. The warning system not only displays warning messages to users but also has a function to temporarily hide defamatory posts. This allows users to address defamatory posts before others see them. Furthermore, the warning system also issues warnings to users who have made defamatory posts, encouraging them to take steps to prevent recurrence. In this way, the warning system can issue swift and appropriate warnings after detecting defamation, thereby protecting user safety.

[0032] The Support Department provides support to users who have been warned by the Warning Department about defamation. Specifically, it provides mental health care to users who have been defamated using an empathy AI chatbot. The empathy AI chatbot can analyze the user's emotions using natural language processing technology and provide appropriate support. For example, the empathy AI chatbot analyzes the user's posts and conversation history to understand the user's emotional state. Based on this, it provides the user with encouraging messages and suggestions for relaxation. The empathy AI chatbot can also analyze the user's past mental health history and select the most suitable support method. For example, it can suggest the most suitable support method for the current situation based on the support the user has received in the past and its effects. Furthermore, the Support Department can customize the means of support based on the user's current living situation. For example, if the user is experiencing stress at work or school, it will provide support tailored to that situation. The Support Department can also collaborate with professional counselors and mental health specialists to provide expert support as needed. This allows the support department to provide appropriate mental health care to users who have been subjected to defamation and slander, thereby protecting the users' mental well-being.

[0033] The Management Department oversees the operations of the Analysis Department, Warning Department, and Support Department. Specifically, it monitors, adjusts, and controls the operation of each department. The Management Department can monitor the operation status of each department in real time and make appropriate adjustments. For example, the Management Department monitors the operation status of the Analysis Department and makes adjustments as needed. If the Analysis Department is processing a large amount of data, it adjusts the allocation of resources to optimize processing capacity. The Management Department can also monitor the operation status of the Warning Department and make appropriate controls. If the Warning Department is issuing warnings frequently, it reviews the criteria and frequency of warnings to prevent excessive warnings to users. Furthermore, the Management Department can also monitor the operation status of the Support Department and make appropriate adjustments. If the Support Department is handling many users, it adjusts the allocation of resources to maintain the quality of support. In addition to overseeing the operation of each department, the Management Department formulates strategies to optimize the overall system performance. For example, it plans system updates and the introduction of new features and strengthens cooperation between departments. The Management Department also takes measures to ensure the security of the system. For example, it implements security protocols to prevent unauthorized access and data leaks and regularly checks for system vulnerabilities. This allows the management department to coordinate the operations of the analysis, warning, and support departments, thereby improving the overall reliability and efficiency of the system.

[0034] The data collection unit can collect user posts. The data collection unit can collect data using, for example, an API. The data collection unit can collect user posts using the API of an SNS platform. For example, the data collection unit can obtain a specific user's posts through an API. The data collection unit can also collect data using scraping techniques. For example, the data collection unit can scrape posts from a website. Furthermore, the data collection unit can collect user posts in real time. For example, the data collection unit monitors and collects posts in real time. This allows the data collection unit to collect user posts. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input data obtained through an API into a generating AI and have the generating AI perform data collection.

[0035] The analysis unit can analyze text, image, and video content in real time and detect defamation. For example, the analysis unit can analyze text content using natural language processing technology to detect defamation. The analysis unit can analyze posted content using text generation AI (e.g., LLM) and detect defamation. For example, the analysis unit can analyze keywords in posted content using text generation AI and detect defamation. The analysis unit can also analyze image content using image recognition technology and detect defamation. The analysis unit can detect defamation within images using image generation AI. For example, the analysis unit can analyze specific symbols or text within images using image generation AI and detect defamation. Furthermore, the analysis unit can analyze video content using video analysis technology and detect defamation. The analysis unit can detect defamation within videos using video generation AI. For example, the analysis unit uses video generation AI to analyze specific scenes or audio within a video and detect defamation. This allows the analysis unit to analyze text, images, and video content in real time and detect defamation. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input text, images, and video content into the generation AI and have the generation AI perform defamation detection.

[0036] The warning system can automatically issue warnings when defamation is detected. For example, it can issue warnings when specific keywords are detected. The warning system can detect defamatory keywords and issue warnings. For example, it can issue warnings for posts containing defamatory keywords. The warning system can also issue warnings when a certain score is exceeded. For example, it can issue warnings when the defamation score exceeds a certain threshold. Furthermore, the warning system can adjust the level of detail of the warning based on the content of the defamation. The warning system can issue detailed warnings for serious defamation. For example, it can display detailed warning messages for serious defamation. This allows the warning system to automatically issue warnings when defamation is detected. Some or all of the above-described processes in the warning unit may be performed using AI, for example, or without AI. For example, the warning unit can input the defamation detection results into a generating AI and cause the generating AI to issue a warning.

[0037] The support department can provide mental health care to users who have been subjected to defamation using an empathy AI chatbot. For example, the support department can analyze the user's emotions using the empathy AI chatbot and provide appropriate support. The support department can analyze the user's emotions using natural language processing technology and provide appropriate support. For example, the support department can analyze the user's emotions using the empathy AI chatbot and provide appropriate support. Furthermore, the support department can analyze the user's past mental health history and select the optimal support method. Based on the user's past mental health history, the support department can propose the optimal support method. In addition, the support department can customize the means of support based on the user's current living situation. Considering the user's current living situation, the support department can provide appropriate means of support. For example, if the user is busy, the support department can provide effective support in a short amount of time. Also, if the user is relaxed, the support department can provide detailed support. This allows the support department to provide mental health care to users who have been subjected to defamation using an empathy AI chatbot. Some or all of the above-described processes in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input user emotion data into a generating AI and have the generating AI select an appropriate support method.

[0038] The management unit can oversee the operations of the analysis unit, warning unit, and support unit. The management unit, for example, monitors, adjusts, and controls the operation of each unit. The management unit can monitor the operating status of each unit in real time and make appropriate adjustments. For example, the management unit monitors the operating status of the analysis unit and makes adjustments as needed. The management unit can also monitor the operating status of the warning unit and perform appropriate control. Furthermore, the management unit can monitor the operating status of the support unit and make appropriate adjustments. In this way, the management unit can oversee the operations of the analysis unit, warning unit, and support unit. Some or all of the above-described processes in the management unit may be performed using AI, for example, or without AI. For example, the management unit can input the operating data of each unit into a generating AI and have the generating AI perform monitoring and adjustment of the operations.

[0039] The data collection unit can analyze a user's past posting history and select the optimal collection method. The data collection unit can set collection timings based on, for example, the time periods when the user frequently posted in the past. The data collection unit can analyze the content of a user's past posts and prioritize the collection of posts related to specific themes. The data collection unit can collect posts related to specific events or situations from a user's past posting history. This allows the data collection unit to analyze a user's past posting history and select the optimal collection method. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past posting data into a generating AI and have the generating AI select the optimal collection method.

[0040] The collection unit can filter the collected posts based on the user's current activity and areas of interest. For example, if the user is currently active, the collection unit can collect posts in real time. The collection unit can prioritize collecting relevant posts based on the user's areas of interest. The collection unit can collect posts at the appropriate time, taking into account the user's current activity. This allows the collection unit to filter posts based on the user's current activity and areas of interest. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input user activity data into a generating AI and have the generating AI perform the filtering.

[0041] The collection unit can prioritize collecting posts that are highly relevant when collecting posted content, taking into account the user's geographical location information. For example, if a user is in a specific region, the collection unit will prioritize collecting posts related to that region. Based on the user's geographical location information, the collection unit can collect posts related to nearby events and news. The collection unit can also collect posts related to region-specific topics, taking into account the user's location information. As a result, the collection unit can prioritize collecting posts that are highly relevant, taking into account the user's geographical location information. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's geographical location data into a generating AI and have the generating AI collect highly relevant posts.

[0042] The collection unit can analyze a user's social media activity and collect relevant posts when collecting posted content. For example, if a user frequently uses a particular hashtag, the collection unit can collect posts related to that hashtag. The collection unit can prioritize collecting posts from accounts that the user follows. The collection unit can analyze a user's social media activity and collect posts based on their interests. This allows the collection unit to analyze a user's social media activity and collect relevant posts. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's social media data into a generating AI and have the generating AI collect relevant posts.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the posted content during the analysis. For example, the analysis unit can perform a detailed analysis on posts with high importance, and a simplified analysis on posts with low importance. The analysis unit can adjust the depth of the analysis according to the importance of the posted content. In this way, the analysis unit can adjust the level of detail of the analysis based on the importance of the posted content. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the importance data of the posted content into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0044] The analysis unit can apply different analysis algorithms depending on the category of the posted content during analysis. For example, the analysis unit can apply a natural language processing algorithm to text content. For image content, it can apply an image recognition algorithm. For video content, it can apply a video analysis algorithm. This allows the analysis unit to apply different analysis algorithms depending on the category of the posted content. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input category data of the posted content into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0045] The analysis unit can determine the priority of analysis based on the submission date of the submitted content during the analysis. For example, the analysis unit may prioritize the analysis of the most recent submitted content. The analysis unit may postpone the analysis of older submitted content. The analysis unit can adjust the order of analysis based on the submission date. This allows the analysis unit to determine the priority of analysis based on the submission date of the submitted content. 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 submission date data of the submitted content into a generating AI and have the generating AI perform the determination of the analysis priority.

[0046] The analysis unit can adjust the order of analysis based on the relevance of the posts during analysis. For example, the analysis unit may prioritize the analysis of highly relevant posts. The analysis unit may postpone the analysis of less relevant posts. The analysis unit can adjust the order of analysis based on the relevance of the posts. In this way, the analysis unit can adjust the order of analysis based on the relevance of the posts. 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 relevance data of the posts into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0047] The warning unit can adjust the level of detail of the warning based on the severity of the defamation when issuing a warning. For example, the warning unit can provide a detailed warning for serious defamation. For minor defamation, the warning unit can provide a simplified warning. The warning unit can adjust the level of detail of the warning according to the severity of the defamation. This allows the warning unit to adjust the level of detail of the warning based on the severity of the defamation. Some or all of the above processing in the warning unit may be performed using AI, for example, or without AI. For example, the warning unit can input defamation severity data into a generating AI and have the generating AI perform the adjustment of the level of detail of the warning.

[0048] The warning unit can apply different warning algorithms depending on the category of defamation when issuing a warning. For example, the warning unit can apply a natural language processing algorithm to text defamation, an image recognition algorithm to image defamation, and a video analysis algorithm to video defamation. This allows the warning unit to apply different warning algorithms depending on the category of defamation. Some or all of the above processing in the warning unit may be performed using AI, for example, or without AI. For example, the warning unit can input defamation category data into a generating AI and have the generating AI execute the application of a warning algorithm.

[0049] The warning unit can determine the priority of warnings based on when the defamation occurred. For example, the warning unit will issue a warning preferentially for the most recent defamation. The warning unit can issue warnings for older defamation at a later date. The warning unit can adjust the order of warnings based on when they occurred. This allows the warning unit to determine the priority of warnings based on when the defamation occurred. Some or all of the above processing in the warning unit may be performed using AI, for example, or without AI. For example, the warning unit can input data on when the defamation occurred into a generating AI and have the generating AI determine the priority of warnings.

[0050] The warning unit can adjust the order of warnings based on the relevance of the defamatory content when issuing a warning. For example, the warning unit will issue a warning preferentially for highly relevant defamatory content. The warning unit can issue a warning later for less relevant defamatory content. The warning unit can adjust the order of warnings based on the relevance of the defamatory content. In this way, the warning unit can adjust the order of warnings based on the relevance of the defamatory content. Some or all of the above processing in the warning unit may be performed using AI, for example, or not using AI. For example, the warning unit can input defamatory relevance data into a generating AI and have the generating AI perform the adjustment of the warning order.

[0051] The support unit can analyze the user's past mental health history to select the optimal support method during support. For example, the support unit can propose the optimal support method based on the user's past mental health history. The support unit can prioritize providing a specific support method based on the user's past mental health history. The support unit can analyze the user's past mental health history and select the most effective support method. This allows the support unit to analyze the user's past mental health history and select the optimal support method. Some or all of the above processes in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the user's mental health history data into a generating AI and have the generating AI select the optimal support method.

[0052] The support unit can customize the means of support based on the user's current living situation during support. For example, the support unit can consider the user's current living situation and provide appropriate support. If the user is busy, the support unit can provide effective support in a short amount of time. If the user is relaxed, the support unit can provide detailed support. In this way, the support unit can customize the means of support based on the user's current living situation. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input user living situation data into a generating AI and have the generating AI perform the customization of the support means.

[0053] The support unit can select the optimal support method by considering the user's geographical location information during support. For example, if the user is in a specific region, the support unit can provide support relevant to that region. Based on the user's geographical location information, the support unit can suggest nearby support resources. The support unit can provide region-specific support by considering the user's location information. This allows the support unit to select the optimal support method by considering the user's geographical location information. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the user's geographical location data into a generating AI and have the generating AI select the optimal support method.

[0054] The support unit can analyze a user's social media activity and suggest support measures when providing support. For example, if a user frequently uses a particular hashtag, the support unit can provide support related to that hashtag. The support unit can suggest appropriate support based on the activity of accounts the user follows. The support unit can analyze a user's social media activity and provide support based on their interests. This allows the support unit to analyze a user's social media activity and suggest support measures. Some or all of the above processes in the support unit may be performed using AI, for example, or not. For example, the support unit can input the user's social media data into a generating AI and have the generating AI suggest support measures.

[0055] The management department can analyze the past operational history of each unit during management to select the optimal management method. For example, the management department can propose the optimal management method based on the past operational history of each unit. The management department can prioritize providing a specific management method based on the past operational history of each unit. The management department can analyze the past operational history of each unit and select the most effective management method. In this way, the management department can analyze the past operational history of each unit and select the optimal management method. Some or all of the above processes in the management department may be performed using AI, for example, or without AI. For example, the management department can input operational history data for each unit into a generating AI and have the generating AI select the optimal management method.

[0056] The management unit can customize management methods based on the operating status of each unit during management. For example, the management unit can consider the operating status of each unit and provide appropriate management methods. The management unit can propose the optimal management method based on the operating status of each unit. The management unit can analyze the operating status of each unit and select effective management methods. This allows the management unit to customize management methods based on the operating status of each unit. Some or all of the above processes in the management unit may be performed using AI, for example, or without AI. For example, the management unit can input operating status data for each unit into a generating AI and have the generating AI perform the customization of management methods.

[0057] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0058] The analysis unit can improve the accuracy of its analysis by considering the poster's past behavior history when analyzing user posts. For example, the analysis unit can prioritize the analysis of posts by users who have previously engaged in defamation, in order to prevent recurrence. Furthermore, the analysis unit can specially monitor posts by users who have previously been subjected to defamation, enabling early intervention. In addition, the analysis unit can analyze the frequency of occurrence of specific keywords and phrases based on past behavior history, enabling early detection of signs of defamation. This allows the analysis unit to improve the accuracy of its analysis by considering the user's past behavior history.

[0059] The data collection unit can adjust its collection method based on the category of user-submitted content. For example, it can use natural language processing technology to collect text content, image recognition technology to collect image content, and video analysis technology to collect video content. This allows the data collection unit to adjust its collection method based on the category of user-submitted content.

[0060] The warning system can adjust its warning method based on the user's past response history when defamation is detected. For example, it can issue stronger warnings to users who have ignored warnings in the past, and gentler warnings to users who have accepted warnings in the past. Furthermore, the warning system can adjust the frequency and timing of warnings based on past response history. This allows the warning system to adjust its warning method based on the user's past response history.

[0061] The management department can implement a feedback loop to improve operational efficiency when monitoring the operating status of each unit. For example, it can monitor the operation of the analysis unit and make adjustments to improve the accuracy and speed of analysis. It can monitor the operation of the warning unit and make adjustments to maximize the effectiveness of warnings. It can monitor the operation of the support unit and make adjustments to improve user satisfaction. In this way, the management department can monitor the operating status of each unit and implement a feedback loop to improve operational efficiency.

[0062] The analysis unit can adjust its analysis algorithm based on the language of the posted content during analysis. For example, for English posts, it can apply an English-specific natural language processing algorithm. For Japanese posts, it can apply a Japanese-specific natural language processing algorithm. For multilingual posts, it can apply analysis algorithms corresponding to each language. This allows the analysis unit to adjust its analysis algorithm based on the language of the posted content.

[0063] The following briefly describes the processing flow for example form 1.

[0064] Step 1: The analysis unit analyzes user posts in real time. The analysis unit analyzes text, image, and video content in real time to detect defamation. For example, the analysis unit uses natural language processing technology to analyze text content and detect defamation. Furthermore, it can also use image recognition technology to analyze image content and detect defamation. It can also use video analysis technology to analyze video content and detect defamation. Step 2: The warning unit issues a warning based on the defamation detected by the analysis unit. The warning unit automatically issues a warning when defamation is detected. It can also issue a warning when specific keywords are detected or when the defamation score is high. Furthermore, the warning unit can adjust the level of detail of the warning based on the content of the defamation. Step 3: The support department provides support to users who have been warned by the warning department about defamation. The support department provides mental health care to users who have been defamated using an empathy AI chatbot. It analyzes the user's emotions using natural language processing technology and provides appropriate support. Furthermore, it can customize the means of support based on the user's past mental health history and current living situation. Step 4: The Management Department oversees the operation of the Analysis Department, Warning Department, and Support Department. The Management Department monitors, adjusts, and controls the operation of each department. For example, it monitors the operation status of the Analysis Department and makes adjustments as needed. It monitors the operation status of the Warning Department and provides appropriate control. It monitors the operation status of the Support Department and makes appropriate adjustments.

[0065] (Example of form 2) The defamation detection system according to an embodiment of the present invention is a system that uses multimodal LLM to analyze text, image, and video content in real time, detects defamation, and automatically issues a warning. The defamation detection system provides support and mental health care to users who have been defamated through an empathy AI chatbot. For example, the defamation detection system uses multimodal LLM to analyze text, image, and video content posted by users on social networking services (SNS) and online communities in real time. Next, if defamation is detected, the defamation detection system automatically issues a warning. Furthermore, the defamation detection system provides support to users who have been defamated through an empathy AI chatbot. This system operates in conjunction with SNS platforms and news sites, and future plans include system integration with other SNS platforms and improvements to the user interface. This mechanism is expected to improve the health of social networking services (SNS) and online communities, leading to increased user engagement and the promotion of self-expression. Thus, the defamation detection system can improve the health of SNS and online communities, and achieve increased user engagement and the promotion of self-expression.

[0066] The defamation detection system according to this embodiment comprises an analysis unit, a warning unit, a support unit, and a management unit. The analysis unit analyzes user posts in real time. The analysis unit analyzes text, images, and video content in real time to detect defamation. The analysis unit can analyze text content using natural language processing technology to detect defamation. For example, the analysis unit uses text generation AI (e.g., LLM) to analyze posts and detect defamation. The analysis unit can also analyze image content using image recognition technology to detect defamation. For example, the analysis unit uses image generation AI to detect defamation within images. Furthermore, the analysis unit can analyze video content using video analysis technology to detect defamation. For example, the analysis unit uses video generation AI to detect defamation within videos. The warning unit issues warnings based on defamation detected by the analysis unit. For example, the warning unit automatically issues a warning when defamation is detected. The warning unit can also issue warnings when specific keywords are detected. For example, the warning unit can detect defamatory keywords and issue a warning. The warning unit can also issue warnings when a certain score is exceeded. For example, the warning unit issues a warning when the defamation score is high. Furthermore, the warning unit can adjust the level of detail of the warning based on the content of the defamation. For example, the warning unit issues a detailed warning for serious defamation. The support unit provides support to users who have been warned about by the warning unit. For example, the support unit provides mental health care to users who have been defamated using an empathy AI chatbot. The support unit can analyze the user's emotions using natural language processing technology and provide appropriate support. For example, the support department can use an empathy AI chatbot to analyze the user's emotions and provide appropriate support. The support department can also analyze the user's past mental health history to select the most suitable support method. For instance, it can suggest the optimal support method based on the user's past mental health history. Furthermore, the support department can customize the means of support based on the user's current living situation.For example, the support unit considers the user's current living situation and provides appropriate support. The management unit oversees the operation of the analysis unit, warning unit, and support unit. The management unit, for example, monitors, adjusts, and controls the operation of each unit. The management unit can monitor the operation status of each unit in real time and make appropriate adjustments. For example, the management unit monitors the operation status of the analysis unit and makes adjustments as needed. The management unit can also monitor the operation status of the warning unit and perform appropriate control. Furthermore, the management unit can monitor the operation status of the support unit and make appropriate adjustments. As a result, the defamation detection system according to this embodiment can analyze the user's posted content in real time, detect defamation, issue warnings, and provide support.

[0067] The analysis unit analyzes user posts in real time. For example, the analysis unit analyzes text, images, and video content in real time to detect defamation. Specifically, it uses natural language processing technology to analyze text content. Natural language processing technology includes morphological analysis, grammatical analysis, and semantic analysis, and by combining these, it can understand the context and intent of the post and detect defamation with high accuracy. For example, it uses text generation AI (e.g., LLM) to analyze the post content and detect defamation. This AI has learned from a large amount of text data and can judge defamation not only based on specific keywords and phrases, but also by considering the context and nuances. For image content, it uses image recognition technology to analyze it. Image recognition technology includes object detection, face recognition, and text recognition, and by combining these, it can detect defamation in images with high accuracy. For example, it uses image generation AI to detect defamation in images. This AI has learned from a large amount of image data and can analyze text, symbols, and facial expressions within images to determine if they contain defamation. For video content, it uses video analysis technology. This video analysis technology includes frame analysis, audio analysis, and motion analysis, and by combining these, it can detect defamation within videos with high accuracy. For example, it can use a video generation AI to detect defamation within videos. This AI has learned from a large amount of video data and can analyze audio, video, and motion within videos to determine if they contain defamation. As a result, the analysis unit can analyze text, images, and videos in real time and detect defamation with high accuracy.

[0068] The warning unit issues warnings based on defamation detected by the analysis unit. Specifically, it automatically issues a warning when defamation is detected. The warning unit can also issue a warning when specific keywords are detected. For example, it can detect defamatory keywords and issue a warning. In this case, the keyword list is updated regularly to accommodate new defamatory expressions and slang. The warning unit can also issue a warning when a certain score is exceeded. For example, it can issue a warning when the defamation score is high. This score is a numerical representation of the likelihood of defamation by the analysis unit, with a higher score indicating a higher likelihood of defamation. Furthermore, the warning unit can adjust the level of detail of the warning based on the content of the defamation. For example, it can issue a detailed warning for serious defamation. The level of detail of the warning is adjusted according to the content and impact of the defamation to prompt the user to take appropriate action. The warning system not only displays warning messages to users but also has a function to temporarily hide defamatory posts. This allows users to address defamatory posts before others see them. Furthermore, the warning system also issues warnings to users who have made defamatory posts, encouraging them to take steps to prevent recurrence. In this way, the warning system can issue swift and appropriate warnings after detecting defamation, thereby protecting user safety.

[0069] The Support Department provides support to users who have been warned by the Warning Department about defamation. Specifically, it provides mental health care to users who have been defamated using an empathy AI chatbot. The empathy AI chatbot can analyze the user's emotions using natural language processing technology and provide appropriate support. For example, the empathy AI chatbot analyzes the user's posts and conversation history to understand the user's emotional state. Based on this, it provides the user with encouraging messages and suggestions for relaxation. The empathy AI chatbot can also analyze the user's past mental health history and select the most suitable support method. For example, it can suggest the most suitable support method for the current situation based on the support the user has received in the past and its effects. Furthermore, the Support Department can customize the means of support based on the user's current living situation. For example, if the user is experiencing stress at work or school, it will provide support tailored to that situation. The Support Department can also collaborate with professional counselors and mental health specialists to provide expert support as needed. This allows the support department to provide appropriate mental health care to users who have been subjected to defamation and slander, thereby protecting the users' mental well-being.

[0070] The Management Department oversees the operations of the Analysis Department, Warning Department, and Support Department. Specifically, it monitors, adjusts, and controls the operation of each department. The Management Department can monitor the operation status of each department in real time and make appropriate adjustments. For example, the Management Department monitors the operation status of the Analysis Department and makes adjustments as needed. If the Analysis Department is processing a large amount of data, it adjusts the allocation of resources to optimize processing capacity. The Management Department can also monitor the operation status of the Warning Department and make appropriate controls. If the Warning Department is issuing warnings frequently, it reviews the criteria and frequency of warnings to prevent excessive warnings to users. Furthermore, the Management Department can also monitor the operation status of the Support Department and make appropriate adjustments. If the Support Department is handling many users, it adjusts the allocation of resources to maintain the quality of support. In addition to overseeing the operation of each department, the Management Department formulates strategies to optimize the overall system performance. For example, it plans system updates and the introduction of new features and strengthens cooperation between departments. The Management Department also takes measures to ensure the security of the system. For example, it implements security protocols to prevent unauthorized access and data leaks and regularly checks for system vulnerabilities. This allows the management department to coordinate the operations of the analysis, warning, and support departments, thereby improving the overall reliability and efficiency of the system.

[0071] The data collection unit can collect user posts. The data collection unit can collect data using, for example, an API. The data collection unit can collect user posts using the API of an SNS platform. For example, the data collection unit can obtain a specific user's posts through an API. The data collection unit can also collect data using scraping techniques. For example, the data collection unit can scrape posts from a website. Furthermore, the data collection unit can collect user posts in real time. For example, the data collection unit monitors and collects posts in real time. This allows the data collection unit to collect user posts. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input data obtained through an API into a generating AI and have the generating AI perform data collection.

[0072] The analysis unit can analyze text, image, and video content in real time and detect defamation. For example, the analysis unit can analyze text content using natural language processing technology to detect defamation. The analysis unit can analyze posted content using text generation AI (e.g., LLM) and detect defamation. For example, the analysis unit can analyze keywords in posted content using text generation AI and detect defamation. The analysis unit can also analyze image content using image recognition technology and detect defamation. The analysis unit can detect defamation within images using image generation AI. For example, the analysis unit can analyze specific symbols or text within images using image generation AI and detect defamation. Furthermore, the analysis unit can analyze video content using video analysis technology and detect defamation. The analysis unit can detect defamation within videos using video generation AI. For example, the analysis unit uses video generation AI to analyze specific scenes or audio within a video and detect defamation. This allows the analysis unit to analyze text, images, and video content in real time and detect defamation. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input text, images, and video content into the generation AI and have the generation AI perform defamation detection.

[0073] The warning system can automatically issue warnings when defamation is detected. For example, it can issue warnings when specific keywords are detected. The warning system can detect defamatory keywords and issue warnings. For example, it can issue warnings for posts containing defamatory keywords. The warning system can also issue warnings when a certain score is exceeded. For example, it can issue warnings when the defamation score exceeds a certain threshold. Furthermore, the warning system can adjust the level of detail of the warning based on the content of the defamation. The warning system can issue detailed warnings for serious defamation. For example, it can display detailed warning messages for serious defamation. This allows the warning system to automatically issue warnings when defamation is detected. Some or all of the above-described processes in the warning unit may be performed using AI, for example, or without AI. For example, the warning unit can input the defamation detection results into a generating AI and cause the generating AI to issue a warning.

[0074] The support department can provide mental health care to users who have been subjected to defamation using an empathy AI chatbot. For example, the support department can analyze the user's emotions using the empathy AI chatbot and provide appropriate support. The support department can analyze the user's emotions using natural language processing technology and provide appropriate support. For example, the support department can analyze the user's emotions using the empathy AI chatbot and provide appropriate support. Furthermore, the support department can analyze the user's past mental health history and select the optimal support method. Based on the user's past mental health history, the support department can propose the optimal support method. In addition, the support department can customize the means of support based on the user's current living situation. Considering the user's current living situation, the support department can provide appropriate means of support. For example, if the user is busy, the support department can provide effective support in a short amount of time. Also, if the user is relaxed, the support department can provide detailed support. This allows the support department to provide mental health care to users who have been subjected to defamation using an empathy AI chatbot. Some or all of the above-described processes in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input user emotion data into a generating AI and have the generating AI select an appropriate support method.

[0075] The management unit can oversee the operations of the analysis unit, warning unit, and support unit. The management unit, for example, monitors, adjusts, and controls the operation of each unit. The management unit can monitor the operating status of each unit in real time and make appropriate adjustments. For example, the management unit monitors the operating status of the analysis unit and makes adjustments as needed. The management unit can also monitor the operating status of the warning unit and perform appropriate control. Furthermore, the management unit can monitor the operating status of the support unit and make appropriate adjustments. In this way, the management unit can oversee the operations of the analysis unit, warning unit, and support unit. Some or all of the above-described processes in the management unit may be performed using AI, for example, or without AI. For example, the management unit can input the operating data of each unit into a generating AI and have the generating AI perform monitoring and adjustment of the operations.

[0076] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the collection frequency to lessen the user's burden. If the user is relaxed, the data collection unit can increase the collection frequency to collect more data. If the user is excited, the data collection unit can adjust the collection timing to collect data at the appropriate time. In this way, the data collection unit can adjust the timing of data collection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into the generative AI and have the generative AI adjust the collection timing.

[0077] The data collection unit can analyze a user's past posting history and select the optimal collection method. The data collection unit can set collection timings based on, for example, the time periods when the user frequently posted in the past. The data collection unit can analyze the content of a user's past posts and prioritize the collection of posts related to specific themes. The data collection unit can collect posts related to specific events or situations from a user's past posting history. This allows the data collection unit to analyze a user's past posting history and select the optimal collection method. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past posting data into a generating AI and have the generating AI select the optimal collection method.

[0078] The collection unit can filter the collected posts based on the user's current activity and areas of interest. For example, if the user is currently active, the collection unit can collect posts in real time. The collection unit can prioritize collecting relevant posts based on the user's areas of interest. The collection unit can collect posts at the appropriate time, taking into account the user's current activity. This allows the collection unit to filter posts based on the user's current activity and areas of interest. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input user activity data into a generating AI and have the generating AI perform the filtering.

[0079] The collection unit can estimate the user's emotions and determine the priority of posts to collect based on the estimated emotions. For example, if the user is stressed, the collection unit will prioritize collecting posts with positive content. If the user is relaxed, the collection unit can collect posts with a wide range of content. If the user is excited, the collection unit can prioritize collecting posts with high relevance. In this way, the collection unit can determine the priority of posts to collect based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the collection unit may be performed using AI, for example, or not using AI. For example, the collection unit can input user emotion data into a generative AI and have the generative AI determine the priority of posts.

[0080] The collection unit can prioritize collecting posts that are highly relevant when collecting posted content, taking into account the user's geographical location information. For example, if a user is in a specific region, the collection unit will prioritize collecting posts related to that region. Based on the user's geographical location information, the collection unit can collect posts related to nearby events and news. The collection unit can also collect posts related to region-specific topics, taking into account the user's location information. As a result, the collection unit can prioritize collecting posts that are highly relevant, taking into account the user's geographical location information. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's geographical location data into a generating AI and have the generating AI collect highly relevant posts.

[0081] The collection unit can analyze a user's social media activity and collect relevant posts when collecting posted content. For example, if a user frequently uses a particular hashtag, the collection unit can collect posts related to that hashtag. The collection unit can prioritize collecting posts from accounts that the user follows. The collection unit can analyze a user's social media activity and collect posts based on their interests. This allows the collection unit to analyze a user's social media activity and collect relevant posts. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's social media data into a generating AI and have the generating AI collect relevant posts.

[0082] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit can provide simple and easy-to-understand analysis results. If the user is relaxed, the analysis unit can provide detailed analysis results. If the user is excited, the analysis unit can provide visually stimulating analysis results. This allows the analysis unit to adjust the presentation of the analysis 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, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the presentation of the analysis.

[0083] The analysis unit can adjust the level of detail of the analysis based on the importance of the posted content during the analysis. For example, the analysis unit can perform a detailed analysis on posts with high importance, and a simplified analysis on posts with low importance. The analysis unit can adjust the depth of the analysis according to the importance of the posted content. In this way, the analysis unit can adjust the level of detail of the analysis based on the importance of the posted content. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the importance data of the posted content into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0084] The analysis unit can apply different analysis algorithms depending on the category of the posted content during analysis. For example, the analysis unit can apply a natural language processing algorithm to text content. For image content, it can apply an image recognition algorithm. For video content, it can apply a video analysis algorithm. This allows the analysis unit to apply different analysis algorithms depending on the category of the posted content. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input category data of the posted content into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0085] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis result. If the user is relaxed, the analysis unit can provide a detailed analysis result. If the user is excited, the analysis unit can provide a visually stimulating analysis result. Thus, the analysis unit can adjust the length of the analysis 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, for example, or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the length of the analysis.

[0086] The analysis unit can determine the priority of analysis based on the submission date of the submitted content during the analysis. For example, the analysis unit may prioritize the analysis of the most recent submitted content. The analysis unit may postpone the analysis of older submitted content. The analysis unit can adjust the order of analysis based on the submission date. This allows the analysis unit to determine the priority of analysis based on the submission date of the submitted content. 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 submission date data of the submitted content into a generating AI and have the generating AI perform the determination of the analysis priority.

[0087] The analysis unit can adjust the order of analysis based on the relevance of the posts during analysis. For example, the analysis unit may prioritize the analysis of highly relevant posts. The analysis unit may postpone the analysis of less relevant posts. The analysis unit can adjust the order of analysis based on the relevance of the posts. In this way, the analysis unit can adjust the order of analysis based on the relevance of the posts. 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 relevance data of the posts into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0088] The warning unit can estimate the user's emotions and adjust the way it expresses the warning based on those emotions. For example, if the user is stressed, the warning unit may issue a warning in gentle language. If the user is relaxed, the warning unit may provide a detailed warning. If the user is agitated, the warning unit may issue a visually stimulating warning. This allows the warning unit to adjust the way it expresses the warning based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the warning unit may be performed using AI, for example, or not using AI. For example, the warning unit can input user emotion data into the generative AI and have the generative AI adjust the way it expresses the warning.

[0089] The warning unit can adjust the level of detail of the warning based on the severity of the defamation when issuing a warning. For example, the warning unit can provide a detailed warning for serious defamation. For minor defamation, the warning unit can provide a simplified warning. The warning unit can adjust the level of detail of the warning according to the severity of the defamation. This allows the warning unit to adjust the level of detail of the warning based on the severity of the defamation. Some or all of the above processing in the warning unit may be performed using AI, for example, or without AI. For example, the warning unit can input defamation severity data into a generating AI and have the generating AI perform the adjustment of the level of detail of the warning.

[0090] The warning unit can apply different warning algorithms depending on the category of defamation when issuing a warning. For example, the warning unit can apply a natural language processing algorithm to text defamation, an image recognition algorithm to image defamation, and a video analysis algorithm to video defamation. This allows the warning unit to apply different warning algorithms depending on the category of defamation. Some or all of the above processing in the warning unit may be performed using AI, for example, or without AI. For example, the warning unit can input defamation category data into a generating AI and have the generating AI execute the application of a warning algorithm.

[0091] The warning unit can estimate the user's emotions and adjust the length of the warning based on the estimated emotions. For example, if the user is in a hurry, the warning unit can issue a short, concise warning. If the user is relaxed, the warning unit can provide a detailed warning. If the user is excited, the warning unit can issue a visually stimulating warning. This allows the warning unit to adjust the length of the warning based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the warning unit may be performed using AI or not using AI. For example, the warning unit can input user emotion data into the generative AI and have the generative AI adjust the length of the warning.

[0092] The warning unit can determine the priority of warnings based on when the defamation occurred. For example, the warning unit will issue a warning preferentially for the most recent defamation. The warning unit can issue warnings for older defamation at a later date. The warning unit can adjust the order of warnings based on when they occurred. This allows the warning unit to determine the priority of warnings based on when the defamation occurred. Some or all of the above processing in the warning unit may be performed using AI, for example, or without AI. For example, the warning unit can input data on when the defamation occurred into a generating AI and have the generating AI determine the priority of warnings.

[0093] The warning unit can adjust the order of warnings based on the relevance of the defamatory content when issuing a warning. For example, the warning unit will issue a warning preferentially for highly relevant defamatory content. The warning unit can issue a warning later for less relevant defamatory content. The warning unit can adjust the order of warnings based on the relevance of the defamatory content. In this way, the warning unit can adjust the order of warnings based on the relevance of the defamatory content. Some or all of the above processing in the warning unit may be performed using AI, for example, or not using AI. For example, the warning unit can input defamatory relevance data into a generating AI and have the generating AI perform the adjustment of the warning order.

[0094] The support unit can estimate the user's emotions and adjust the way it expresses support based on those emotions. For example, if the user is stressed, the support unit can provide support in gentle words. If the user is relaxed, the support unit can provide detailed support. If the user is excited, the support unit can provide visually stimulating support. In this way, the support unit can adjust the way it expresses support based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the support unit may be performed using AI, for example, or not using AI. For example, the support unit can input user emotion data into a generative AI and have the generative AI adjust the way it expresses support.

[0095] The support unit can analyze the user's past mental health history to select the optimal support method during support. For example, the support unit can propose the optimal support method based on the user's past mental health history. The support unit can prioritize providing a specific support method based on the user's past mental health history. The support unit can analyze the user's past mental health history and select the most effective support method. This allows the support unit to analyze the user's past mental health history and select the optimal support method. Some or all of the above processes in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the user's mental health history data into a generating AI and have the generating AI select the optimal support method.

[0096] The support unit can customize the means of support based on the user's current living situation during support. For example, the support unit can consider the user's current living situation and provide appropriate support. If the user is busy, the support unit can provide effective support in a short amount of time. If the user is relaxed, the support unit can provide detailed support. In this way, the support unit can customize the means of support based on the user's current living situation. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input user living situation data into a generating AI and have the generating AI perform the customization of the support means.

[0097] The support unit can estimate the user's emotions and determine the priority of support based on the estimated emotions. For example, if the user is stressed, the support unit can provide priority support. If the user is relaxed, the support unit can provide normal support. If the user is agitated, the support unit can provide rapid support. In this way, the support unit can determine the priority of support based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the support unit may be performed using AI, for example, or not using AI. For example, the support unit can input user emotion data into a generative AI and have the generative AI determine the priority of support.

[0098] The support unit can select the optimal support method by considering the user's geographical location information during support. For example, if the user is in a specific region, the support unit can provide support relevant to that region. Based on the user's geographical location information, the support unit can suggest nearby support resources. The support unit can provide region-specific support by considering the user's location information. This allows the support unit to select the optimal support method by considering the user's geographical location information. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the user's geographical location data into a generating AI and have the generating AI select the optimal support method.

[0099] The support unit can analyze a user's social media activity and suggest support measures when providing support. For example, if a user frequently uses a particular hashtag, the support unit can provide support related to that hashtag. The support unit can suggest appropriate support based on the activity of accounts the user follows. The support unit can analyze a user's social media activity and provide support based on their interests. This allows the support unit to analyze a user's social media activity and suggest support measures. Some or all of the above processes in the support unit may be performed using AI, for example, or not. For example, the support unit can input the user's social media data into a generating AI and have the generating AI suggest support measures.

[0100] The management unit can estimate the user's emotions and adjust its management methods based on those emotions. For example, if the user is stressed, the management unit can provide a simple and highly visual management method. If the user is relaxed, the management unit can provide a detailed management method. If the user is excited, the management unit can provide a visually stimulating management method. This allows the management unit to adjust its management methods based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the management unit may be performed using AI, for example, or not using AI. For example, the management unit can input user emotion data into a generative AI and have the generative AI adjust the management method.

[0101] The management department can analyze the past operational history of each unit during management to select the optimal management method. For example, the management department can propose the optimal management method based on the past operational history of each unit. The management department can prioritize providing a specific management method based on the past operational history of each unit. The management department can analyze the past operational history of each unit and select the most effective management method. In this way, the management department can analyze the past operational history of each unit and select the optimal management method. Some or all of the above processes in the management department may be performed using AI, for example, or without AI. For example, the management department can input operational history data for each unit into a generating AI and have the generating AI select the optimal management method.

[0102] The management unit can estimate the user's emotions and determine management priorities based on the estimated emotions. For example, if the user is stressed, the management unit will prioritize management. If the user is relaxed, the management unit can perform normal management. If the user is agitated, the management unit can perform management quickly. This allows the management unit to determine management priorities based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the management unit may be performed using AI, for example, or not using AI. For example, the management unit can input user emotion data into a generative AI and have the generative AI determine management priorities.

[0103] The management unit can customize management methods based on the operating status of each unit during management. For example, the management unit can consider the operating status of each unit and provide appropriate management methods. The management unit can propose the optimal management method based on the operating status of each unit. The management unit can analyze the operating status of each unit and select effective management methods. This allows the management unit to customize management methods based on the operating status of each unit. Some or all of the above processes in the management unit may be performed using AI, for example, or without AI. For example, the management unit can input operating status data for each unit into a generating AI and have the generating AI perform the customization of management methods.

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

[0105] The analysis unit can improve the accuracy of its analysis by considering the poster's past behavior history when analyzing user posts. For example, the analysis unit can prioritize the analysis of posts by users who have previously engaged in defamation, in order to prevent recurrence. Furthermore, the analysis unit can specially monitor posts by users who have previously been subjected to defamation, enabling early intervention. In addition, the analysis unit can analyze the frequency of occurrence of specific keywords and phrases based on past behavior history, enabling early detection of signs of defamation. This allows the analysis unit to improve the accuracy of its analysis by considering the user's past behavior history.

[0106] The data collection unit can adjust its collection method based on the category of user-submitted content. For example, it can use natural language processing technology to collect text content, image recognition technology to collect image content, and video analysis technology to collect video content. This allows the data collection unit to adjust its collection method based on the category of user-submitted content.

[0107] The analysis unit can estimate the user's emotions and determine the priority of analysis based on those emotions. For example, if a user is stressed, it will prioritize analyzing posts with positive content to alleviate stress. If a user is relaxed, it can analyze posts with a wide range of content. If a user is excited, it can prioritize analyzing posts with high relevance. In this way, the analysis unit can determine the priority of analysis based on the user's emotions.

[0108] The warning system can adjust its warning method based on the user's past response history when defamation is detected. For example, it can issue stronger warnings to users who have ignored warnings in the past, and gentler warnings to users who have accepted warnings in the past. Furthermore, the warning system can adjust the frequency and timing of warnings based on past response history. This allows the warning system to adjust its warning method based on the user's past response history.

[0109] The support department can estimate the user's emotions and customize the support content based on those estimates. For example, if the user is stressed, it can provide relaxing content. If the user is relaxed, it can provide content that helps with self-improvement or learning. If the user is excited, it can provide highly entertaining content. In this way, the support department can customize the support content based on the user's emotions.

[0110] The management department can implement a feedback loop to improve operational efficiency when monitoring the operating status of each unit. For example, it can monitor the operation of the analysis unit and make adjustments to improve the accuracy and speed of analysis. It can monitor the operation of the warning unit and make adjustments to maximize the effectiveness of warnings. It can monitor the operation of the support unit and make adjustments to improve user satisfaction. In this way, the management department can monitor the operating status of each unit and implement a feedback loop to improve operational efficiency.

[0111] The data collection unit can estimate the user's emotions and adjust the types of data collected based on those emotions. For example, if the user is stressed, it can prioritize collecting positive data. If the user is relaxed, it can collect a wide range of data. If the user is excited, it can prioritize collecting highly relevant data. In this way, the data collection unit can adjust the types of data collected based on the user's emotions.

[0112] The analysis unit can adjust its analysis algorithm based on the language of the posted content during analysis. For example, for English posts, it can apply an English-specific natural language processing algorithm. For Japanese posts, it can apply a Japanese-specific natural language processing algorithm. For multilingual posts, it can apply analysis algorithms corresponding to each language. This allows the analysis unit to adjust its analysis algorithm based on the language of the posted content.

[0113] The warning unit can estimate the user's emotions and adjust the timing of the warning based on those emotions. For example, if the user is stressed, the warning can be delayed. If the user is relaxed, the warning can be issued immediately. If the user is excited, the warning can be issued at an appropriate time. In this way, the warning unit can adjust the timing of the warning based on the user's emotions.

[0114] The support unit can estimate the user's emotions and adjust the frequency of support based on those estimates. For example, if the user is stressed, support can be provided frequently. If the user is relaxed, support can be provided at a normal frequency. If the user is agitated, support can be provided quickly. In this way, the support unit can adjust the frequency of support based on the user's emotions.

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

[0116] Step 1: The analysis unit analyzes user posts in real time. The analysis unit analyzes text, image, and video content in real time to detect defamation. For example, the analysis unit uses natural language processing technology to analyze text content and detect defamation. Furthermore, it can also use image recognition technology to analyze image content and detect defamation. It can also use video analysis technology to analyze video content and detect defamation. Step 2: The warning unit issues a warning based on the defamation detected by the analysis unit. The warning unit automatically issues a warning when defamation is detected. It can also issue a warning when specific keywords are detected or when the defamation score is high. Furthermore, the warning unit can adjust the level of detail of the warning based on the content of the defamation. Step 3: The support department provides support to users who have been warned by the warning department about defamation. The support department provides mental health care to users who have been defamated using an empathy AI chatbot. It analyzes the user's emotions using natural language processing technology and provides appropriate support. Furthermore, it can customize the means of support based on the user's past mental health history and current living situation. Step 4: The Management Department oversees the operation of the Analysis Department, Warning Department, and Support Department. The Management Department monitors, adjusts, and controls the operation of each department. For example, it monitors the operation status of the Analysis Department and makes adjustments as needed. It monitors the operation status of the Warning Department and provides appropriate control. It monitors the operation status of the Support Department and makes appropriate adjustments.

[0117] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0118] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0119] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0120] Each of the multiple elements described above, including the analysis unit, warning unit, support unit, management unit, and collection unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the smart device 14 and analyzes the user's posts in real time. The warning unit is implemented by the identification processing unit 290 of the data processing unit 12 and automatically issues a warning when defamation is detected. The support unit is implemented by the control unit 46A of the smart device 14 and provides mental health care to the user using an empathy AI chatbot. The management unit is implemented by the identification processing unit 290 of the data processing unit 12 and oversees the operation of each unit. The collection unit is implemented by the processor 46 of the smart device 14 and collects the user's posts using an API. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

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

[0122] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0123] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0124] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0125] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0127] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0128] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0129] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0130] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0131] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0132] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0133] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0135] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0136] Each of the multiple elements described above, including the analysis unit, warning unit, support unit, management unit, and collection unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the smart glasses 214 and analyzes the user's posts in real time. The warning unit is implemented by the identification processing unit 290 of the data processing unit 12 and automatically issues a warning when defamation is detected. The support unit is implemented by the control unit 46A of the smart glasses 214 and provides mental health care to the user using an empathy AI chatbot. The management unit is implemented by the identification processing unit 290 of the data processing unit 12 and oversees the operation of each unit. The collection unit is implemented by the processor 46 of the smart glasses 214 and collects the user's posts using an API. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

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

[0138] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0139] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0140] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0141] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0143] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0144] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0145] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0146] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0147] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0148] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0149] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0150] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0151] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0152] Each of the multiple elements described above, including the analysis unit, warning unit, support unit, management unit, and collection unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the headset terminal 314 and analyzes the user's posts in real time. The warning unit is implemented by the identification processing unit 290 of the data processing unit 12 and automatically issues a warning when defamation is detected. The support unit is implemented by the control unit 46A of the headset terminal 314 and provides mental health care to the user using an empathy AI chatbot. The management unit is implemented by the identification processing unit 290 of the data processing unit 12 and oversees the operation of each unit. The collection unit is implemented by the processor 46 of the headset terminal 314 and collects the user's posts using an API. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

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

[0154] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0155] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0156] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0157] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0158] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0159] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0160] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0161] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0162] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0163] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0164] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0165] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0166] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0167] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0168] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0169] Each of the multiple elements described above, including the analysis unit, warning unit, support unit, management unit, and collection unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the robot 414 and analyzes user posts in real time. The warning unit is implemented by the identification processing unit 290 of the data processing unit 12 and automatically issues a warning when defamation is detected. The support unit is implemented by the control unit 46A of the robot 414 and provides mental health care to the user using an empathy AI chatbot. The management unit is implemented by the identification processing unit 290 of the data processing unit 12 and oversees the operation of each unit. The collection unit is implemented by the processor 46 of the robot 414 and collects user posts using an API. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0170] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0171] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0172] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0173] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0174] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0175] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0176] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0177] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0178] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0179] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0180] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0181] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0182] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0183] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0184] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0185] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0186] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0187] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0188] (Note 1) An analysis unit that analyzes user posts in real time, A warning unit that issues a warning based on the defamation detected by the aforementioned analysis unit, A support unit provides support to users who have been warned by the aforementioned warning unit about defamation and libel, The system includes a management unit that oversees the operation of the aforementioned analysis unit, warning unit, and support unit. A system characterized by the following features. (Note 2) It includes a collection unit that collects user-submitted content. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, It analyzes text, images, and video content in real time to detect defamation. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned warning unit is Automatically issues a warning if defamation is detected. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned support unit is Providing mental health care to users who have been subjected to defamation and harassment using an empathy AI chatbot. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned management department, It oversees the operation of the analysis unit, warning unit, and support unit. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is It estimates user sentiment and adjusts the timing of content collection based on the estimated user sentiment. The system described in Appendix 2, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the user's past posting history and select the optimal data collection method. The system described in Appendix 2, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting submitted content, filtering is performed based on the user's current activity and areas of interest. The system described in Appendix 2, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates user sentiment and determines the priority of posts to collect based on the estimated user sentiment. The system described in Appendix 2, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting user posts, the system prioritizes collecting posts that are highly relevant, taking into account the user's geographical location. The system described in Appendix 2, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting posted content, the system analyzes users' social media activity and collects relevant posts. The system described in Appendix 2, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, the level of detail of the analysis is adjusted based on the importance of the content of the posts. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of the posted content. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of analysis is determined based on the submission date of the submitted content. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the posted content. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned warning unit is The system estimates the user's emotions and adjusts the way warnings are presented based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned warning unit is When issuing a warning, adjust the level of detail based on the severity of the defamation. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned warning unit is When issuing a warning, different warning algorithms are applied depending on the category of defamation. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned warning unit is It estimates the user's emotions and adjusts the length of the warning based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned warning unit is When issuing a warning, the priority of the warning will be determined based on when the defamation occurred. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned warning unit is When issuing a warning, the order of warnings will be adjusted based on the relevance of the content to defamation. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned support unit is It estimates the user's emotions and adjusts the way support is expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned support unit is During support, we analyze the user's past mental health history to select the most appropriate support method. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned support unit is During support, customize the support methods based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned support unit is The system estimates the user's emotions and determines support priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned support unit is During support, the optimal support method will be selected considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned support unit is During support, we analyze the user's social media activity and suggest support methods. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned management department, It estimates user sentiment and adjusts management methods based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned management department, During management, the past operational history of each component is analyzed to select the optimal management method. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned management department, It estimates user sentiment and determines management priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned management department, During management, the management methods are customized based on the operating status of each component. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0189] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. An analysis unit that analyzes user posts in real time, A warning unit that issues a warning based on the defamation detected by the aforementioned analysis unit, A support unit provides support to users who have been warned by the aforementioned warning unit about defamation and libel, The system comprises a management unit that oversees the operation of the analysis unit, the warning unit, and the support unit. A system characterized by the following features.

2. It includes a collection unit that collects user-submitted content. The system according to feature 1.

3. The aforementioned analysis unit, It analyzes text, images, and video content in real time to detect defamation. The system according to feature 1.

4. The aforementioned warning unit is Automatically issues a warning if defamation is detected. The system according to feature 1.

5. The aforementioned support unit is Providing mental health care to users who have been subjected to defamation and slander using an empathy AI chatbot. The system according to feature 1.

6. The aforementioned management department, The operation of the analysis unit, the warning unit, and the support unit is coordinated. The system according to feature 1.

7. The aforementioned collection unit is It estimates user sentiment and adjusts the timing of content collection based on the estimated user sentiment. The system according to feature 2.

8. The aforementioned collection unit is Analyze the user's past posting history and select the optimal data collection method. The system according to feature 2.

9. The aforementioned collection unit is When collecting submitted content, filtering is performed based on the user's current activity and areas of interest. The system according to feature 2.

10. The aforementioned collection unit is It estimates user sentiment and determines the priority of posts to collect based on the estimated user sentiment. The system according to feature 2.

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