system

Abstract

The system according to this embodiment aims to prevent harassment. [Solution] The system according to the embodiment comprises a monitoring unit, a detection unit, an advice unit, a scoring unit, and a plug-in unit. The monitoring unit monitors the user's text and voice communications in real time. The detection unit analyzes the communications monitored by the monitoring unit and detects potentially harassing remarks or actions. The advice unit provides advice to the user when detected by the detection unit. The scoring unit scores the risk of harassment and visualizes it on a daily or monthly basis. The plug-in unit provides the system as a plug-in to the target communication tool.

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 a harassment actor may unconsciously perform harassment, and it is difficult to prevent it in advance.

[0005] The system according to the embodiment aims to prevent harassment behavior in advance.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a monitoring unit, a detection unit, an advice unit, a scoring unit, and a plug-in unit. The monitoring unit monitors the user's text and voice communications in real time. The detection unit analyzes the communications monitored by the monitoring unit and detects potentially harassing remarks or actions. The advice unit provides advice to the user when detected by the detection unit. The scoring unit scores the risk of harassment and visualizes it on a daily and monthly basis. The plug-in unit provides the system as a plug-in to the target communication tool. [Effects of the Invention]

[0007] The system according to this embodiment can prevent harassment from occurring. [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, etc. The communication I / F manages communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example of form 1) The harassment prevention system according to an embodiment of the present invention is a mechanism that uses AI to prevent harassment on everyday communication tools. This harassment prevention system provides an "AI checker" as a plug-in to the communication tools that users use on a daily basis. The AI ​​checker analyzes text and voice communication in real time and detects potentially harassing remarks and actions. If detected, the AI ​​checker immediately provides advice to the user to prevent harassing behavior. First, the "AI checker" is installed as a plug-in to the communication tool that the user uses on a daily basis. This plug-in monitors the user's text and voice communication in real time. Next, the AI ​​checker analyzes the text and voice communication. Based on a vast amount of training data, the AI ​​detects potentially harassing remarks and actions. For example, it identifies remarks and actions that fall under the three major types of harassment: sexual harassment, power harassment, and moral harassment. If detected, the AI ​​checker immediately provides advice to the user. For example, it displays a message such as, "That remark may be power harassment. Please be careful." Furthermore, by scoring the risk of harassment and visualizing it on a daily and monthly basis, users can reflect on their own behavior and make improvements. This system allows users to be aware of and prevent harassment on a daily basis. For example, by analyzing text exchanges and voice tone and providing immediate advice, harassment can be prevented. Also, by scoring and visualizing the risk of harassment, users can reflect on and improve their own behavior. In this way, the AI-powered "AI Harassment Checker" can prevent harassment and create an environment where there are no perpetrators or victims. Thus, the harassment prevention system can prevent harassment by monitoring users' text and voice communications in real time, detecting potentially harassing remarks and actions, and providing advice.

[0029] The harassment prevention system according to this embodiment comprises a monitoring unit, a detection unit, an advice unit, a scoring unit, and a plug-in unit. The monitoring unit monitors the user's text and voice communications in real time. The monitoring unit is provided, for example, as a plug-in to a communication tool that the user uses on a daily basis. The monitoring unit monitors the user's text and voice communications in real time and collects data to detect potentially harassing remarks and actions. The detection unit analyzes the communications monitored by the monitoring unit and detects potentially harassing remarks and actions. The detection unit identifies potentially harassing remarks and actions based, for example, on a vast amount of training data. The detection unit can detect remarks and actions that fall under the three major types of harassment: sexual harassment, power harassment, and moral harassment. The advice unit provides advice to the user when detected by the detection unit. The advice unit displays, for example, a message such as, "That remark may be power harassment. Please be careful." The advice unit provides advice to encourage the user to take appropriate action. The scoring unit scores the risk of harassment and visualizes it on a daily and monthly basis. For example, the scoring unit quantifies the risk of harassment and displays it as a graph or chart. The scoring unit provides information for users to reflect on their own behavior and make improvements. The plugin unit is provided as a plugin for the target communication tool. The plugin unit can be easily installed on the communication tool that the user uses on a daily basis. As a result, the harassment prevention system according to the embodiment can prevent harassment by monitoring the user's text and voice communication in real time, detecting potentially harassing remarks and actions, and providing advice.

[0030] The monitoring unit monitors users' text and voice communications in real time. The monitoring unit is provided, for example, as a plug-in to communication tools that users use daily. The monitoring unit monitors users' text and voice communications in real time and collects data to detect potentially harassing remarks and behaviors. The monitoring unit uses natural language processing technology to analyze text data and detect specific keywords and phrases. For example, it detects and records offensive language or discriminatory expressions. Similarly, it converts voice data to text using speech recognition technology and performs the same analysis. Furthermore, the monitoring unit considers the context of the user's communication, analyzing not only keywords but also the intent and nuances of the remarks. This reduces false positives and enables more accurate monitoring. The monitoring unit sends the collected data to a secure server for subsequent processing. This allows the monitoring unit to monitor user communications in real time and quickly detect potentially harassing remarks and behaviors.

[0031] The detection unit analyzes communications monitored by the monitoring unit and detects potentially harassing remarks and actions. Based on a vast amount of training data, the detection unit identifies potentially harassing remarks and actions. Specifically, it can detect remarks and actions that fall under the three major types of harassment: sexual harassment, power harassment, and moral harassment. The detection unit uses machine learning algorithms to learn from past harassment cases and perform detection with high accuracy even on new data. For example, it uses natural language processing technology to analyze text data and detect specific patterns and phrases. For audio data, it uses speech recognition technology to convert it to text and performs similar analysis. Furthermore, the detection unit considers the context of the user's communication and analyzes not only keywords but also the intent and nuances of the remarks. This reduces false positives and enables more accurate detection. The detection unit sends the detection results to the advice unit and scoring unit for use in subsequent processing. As a result, the detection unit can analyze user communications and quickly detect potentially harassing remarks and actions.

[0032] The advice unit provides advice to the user when detected by the detection unit. Specifically, it displays messages such as, "Your recent statement may be harassment. Please be careful." The advice unit provides advice to encourage appropriate behavior from the user. The advice unit has the function of displaying messages directly to the user's communication tools, providing real-time feedback. Furthermore, the advice unit can also provide individually customized advice based on the user's past behavior history. For example, it will display a stronger warning to users who have repeatedly made similar statements in the past. The advice unit also records whether the user has accepted the advice and uses this for subsequent feedback. In this way, the advice unit can encourage appropriate behavior from the user and prevent harassment.

[0033] The scoring unit scores the risk of harassment and visualizes it on a daily and monthly basis. Specifically, it quantifies the risk of harassment and displays it as graphs and charts. The scoring unit provides information to help users reflect on and improve their own behavior. Based on data from the detection unit, the scoring unit calculates each user's harassment risk. For example, it calculates a risk score by considering the frequency and content of past statements and actions. The scoring unit aggregates these scores on a daily and monthly basis and provides them to users in a visually easy-to-understand format. For example, it displays graphs and charts on the dashboard so that users can see at a glance how their risk score has changed. The scoring unit also provides specific advice to help users improve their behavior. For example, it displays a message such as, "Your risk score has increased over the past week. Please be careful about certain statements." In this way, the scoring unit provides information to help users reflect on and improve their own behavior, thereby preventing harassment.

[0034] The plugin component is provided as a plugin for the target communication tool. The plugin component can be easily installed on the communication tools that users use daily. The plugin component interacts with the monitoring, detection, advice, and scoring components using the APIs of each tool. It can also collect and analyze audio data during meetings using the APIs. The plugin component is designed to integrate seamlessly with these tools, allowing users to use it without requiring any special operations. Furthermore, the plugin component is designed to flexibly adapt to version upgrades and specification changes of each tool, ensuring it always operates in the latest environment. This allows the plugin component to be easily installed on the communication tools that users use daily, maximizing the functionality of the harassment prevention system.

[0035] The monitoring unit can improve the accuracy of monitoring by referring to the user's past communication history during monitoring. For example, the monitoring unit can analyze the user's past speech patterns and adjust its sensitivity to specific keywords. The monitoring unit can also improve the accuracy of monitoring by understanding the user's speech trends during specific time periods from the user's past communication history. The monitoring unit can also adjust the accuracy of monitoring interactions with specific individuals based on the user's past communication history. This improves the accuracy of monitoring by referring to past communication history. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the user's past communication history into a generating AI and have the generating AI perform the improvement of monitoring accuracy.

[0036] The monitoring unit can prioritize monitoring for specific keywords or phrases during monitoring. For example, the monitoring unit can prioritize monitoring for specific keywords related to harassment (e.g., sexual harassment, power harassment). The monitoring unit can also prioritize monitoring for specific phrases (e.g., "You're no good") and analyze them immediately. The monitoring unit can also create a list of problematic keywords or phrases that users have used in the past and prioritize monitoring for those. This improves the accuracy of harassment detection by prioritizing monitoring for specific keywords and phrases. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or not using AI. For example, the monitoring unit can input specific keywords or phrases into a generating AI and have the generating AI perform priority monitoring.

[0037] The monitoring unit can adjust its monitoring range while taking into account the user's geographical location information. For example, if the user is in a specific location (e.g., their workplace), the monitoring unit can expand the monitoring range and perform a more detailed analysis. If the user is at home, the monitoring unit can also narrow the monitoring range and perform only a basic analysis. If the user is on the move, the monitoring unit can set the monitoring range to a moderate level and perform a moderate analysis. This allows the monitoring range to be appropriately adjusted by taking into account the user's geographical location information. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the user's geographical location information into a generating AI and have the generating AI adjust the monitoring range.

[0038] The monitoring unit can analyze a user's social media activity during monitoring and prioritize monitoring of relevant communications. For example, the monitoring unit can prioritize monitoring specific keywords used by the user on social media. The monitoring unit can also prioritize monitoring interactions with specific individuals based on the user's social media activity. Based on the user's social media activity, the monitoring unit can also understand trends in statements during specific time periods and improve the accuracy of monitoring. This allows for the prioritization of relevant communications by analyzing social media activity. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input user social media activity data into a generating AI and have the generating AI perform priority monitoring of relevant communications.

[0039] The detection unit can optimize its detection algorithm by referring to past detection data during detection. For example, the detection unit can adjust its sensitivity to specific keywords based on past detection data. The detection unit can also understand the trends in speech during specific time periods from past detection data and optimize its detection algorithm. The detection unit can also adjust its detection algorithm for interactions with specific individuals based on past detection data. In this way, the detection algorithm can be optimized by referring to past detection data. Some or all of the above processes in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input past detection data into a generating AI and have the generating AI perform the optimization of the detection algorithm.

[0040] The detection unit can prioritize detection of specific communication patterns during detection. For example, the detection unit can prioritize detection of specific communication patterns related to harassment (e.g., aggressive language). The detection unit can also prioritize detection of specific phrases (e.g., "You're no good") and analyze them immediately. The detection unit can also list problematic communication patterns that the user has used in the past and prioritize detection of those. This improves the accuracy of harassment detection by prioritizing detection of specific communication patterns. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input specific communication patterns into a generating AI and have the generating AI perform priority detection.

[0041] The detection unit can adjust its detection range when detecting a user, taking into account the user's geographical location information. For example, if the user is in a specific location (e.g., their workplace), the detection unit can widen the detection range and perform a more detailed analysis. If the user is at home, the detection unit can narrow the detection range and perform only a basic analysis. If the user is on the move, the detection unit can set the detection range to a moderate level and perform a moderate analysis. This allows the detection range to be appropriately adjusted by taking into account the user's geographical location information. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input the user's geographical location information into a generating AI and have the generating AI adjust the detection range.

[0042] The detection unit can analyze the user's social media activity during detection and prioritize the detection of relevant communications. For example, the detection unit can prioritize the detection of specific keywords used by the user on social media. The detection unit can also prioritize the detection of interactions with specific individuals from the user's social media activity. Based on the user's social media activity, the detection unit can also understand the trends in statements during specific time periods and improve the accuracy of detection. This allows for the priority detection of relevant communications by analyzing social media activity. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input the user's social media activity data into a generating AI and have the generating AI perform the priority detection of relevant communications.

[0043] The advice unit can improve the accuracy of its advice by referring to past advice history when providing advice. For example, the advice unit can adjust the accuracy of advice for a specific keyword based on past advice history. The advice unit can also improve the accuracy of advice by understanding the trend of advice during a specific time period from past advice history. The advice unit can also adjust the accuracy of advice for interactions with a specific person based on past advice history. In this way, the accuracy of advice is improved by referring to past advice history. Some or all of the above processes in the advice unit may be performed using AI, for example, or without using AI. For example, the advice unit can input past advice history into a generating AI and have the generating AI perform the improvement of advice accuracy.

[0044] The advice unit can provide different advice depending on the specific situation. For example, it can provide appropriate advice for a specific situation related to harassment (e.g., during a meeting). The advice unit can also provide situation-appropriate advice for interactions with a specific person. The advice unit can also provide appropriate advice based on specific situations the user has experienced in the past. This allows for more appropriate advice by providing different advice depending on the specific situation. Some or all of the above processing in the advice unit may be performed using AI, for example, or not using AI. For example, the advice unit can input specific situation data into a generating AI and have the generating AI perform the task of providing different advice.

[0045] The advice unit can adjust the content of its advice by taking into account the user's geographical location. For example, if the user is in a specific location (e.g., their workplace), the advice unit can provide appropriate advice. If the user is at home, the advice unit can also provide relaxed advice. If the user is on the move, the advice unit can provide concise and easy-to-understand advice. This allows the advice to be appropriately adjusted by taking into account the user's geographical location. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input the user's geographical location into a generating AI and have the generating AI adjust the content of the advice.

[0046] The advice unit can analyze the user's social media activity and provide relevant advice when giving advice. For example, the advice unit can provide appropriate advice for specific keywords the user uses on social media. The advice unit can also provide appropriate advice for interactions with specific individuals based on the user's social media activity. Based on the user's social media activity, the advice unit can also understand the trends in advice during specific time periods and provide appropriate advice. In this way, relevant advice can be provided by analyzing social media activity. Some or all of the above processes in the advice unit may be performed using AI, for example, or not using AI. For example, the advice unit can input the user's social media activity data into a generating AI and have the generating AI perform the task of providing relevant advice.

[0047] The scoring unit can optimize its scoring algorithm by referring to past score data during the scoring process. For example, the scoring unit can adjust the sensitivity of scoring for specific keywords based on past score data. The scoring unit can also optimize its scoring algorithm by understanding the trends in speech during specific time periods from past score data. The scoring unit can also adjust its scoring algorithm for interactions with specific individuals based on past score data. In this way, the scoring algorithm can be optimized by referring to past score data. Some or all of the above-described processes in the scoring unit may be performed using AI, for example, or without AI. For example, the scoring unit can input past score data into a generating AI and have the generating AI perform the optimization of the scoring algorithm.

[0048] The scoring unit can prioritize scoring specific communication patterns during the scoring process. For example, the scoring unit can prioritize scoring specific communication patterns related to harassment (e.g., aggressive language). The scoring unit can also prioritize scoring specific phrases (e.g., "You're no good") and analyze them immediately. The scoring unit can also list problematic communication patterns that the user has used in the past and prioritize scoring those. This improves the accuracy of harassment scoring by prioritizing scoring specific communication patterns. Some or all of the above processing in the scoring unit may be performed using AI, for example, or not using AI. For example, the scoring unit can input specific communication patterns into a generating AI and have the generating AI perform the prioritized scoring.

[0049] The scoring unit can adjust the score range by considering the user's geographical location information during the scoring process. For example, if the user is in a specific location (e.g., their workplace), the scoring unit can broaden the score range and perform a more detailed analysis. If the user is at home, the scoring unit can narrow the score range and perform only a basic analysis. If the user is on the move, the scoring unit can set the score range to a moderate level and perform a moderate analysis. This allows for appropriate adjustment of the score range by considering the user's geographical location information. Some or all of the above processing in the scoring unit may be performed using AI, for example, or without AI. For example, the scoring unit can input the user's geographical location information into a generating AI and have the generating AI adjust the score range.

[0050] The scoring unit can analyze the user's social media activity during the scoring process and provide relevant scores. For example, the scoring unit can provide scores for specific keywords used by the user on social media. The scoring unit can also provide scores for interactions with specific individuals based on the user's social media activity. Based on the user's social media activity, the scoring unit can also identify trends in statements during specific time periods and provide scores. In this way, relevant scores can be provided by analyzing social media activity. Some or all of the above processing in the scoring unit may be performed using AI, for example, or without AI. For example, the scoring unit can input the user's social media activity data into a generating AI and have the generating AI perform the task of providing relevant scores.

[0051] The plugin unit can provide optimal settings by referring to the user's past communication tool usage history during plugin installation. For example, the plugin unit can provide optimal settings based on the settings of communication tools the user has used in the past. The plugin unit can also prioritize providing specific functions based on the user's past communication tool usage history. The plugin unit can also provide settings for specific time periods based on the user's past communication tool usage history. In this way, optimal settings can be provided by referring to past communication tool usage history. Some or all of the above processing in the plugin unit may be performed using AI, for example, or without AI. For example, the plugin unit can input the user's past communication tool usage history into a generating AI and have the generating AI perform the task of providing optimal settings.

[0052] The plugin unit can provide optimal settings by considering the user's geographical location during plugin installation. For example, the plugin unit can provide optimal settings if the user is in a specific location (e.g., their workplace). If the user is at home, the plugin unit can also provide relaxed settings. If the user is on the move, the plugin unit can provide concise and easy-to-understand settings. This allows the plugin unit to provide optimal settings by considering the user's geographical location. Some or all of the above processing in the plugin unit may be performed using AI, for example, or without AI. For example, the plugin unit can input the user's geographical location information into a generating AI and have the generating AI perform the task of providing optimal settings.

[0053] The plugin unit can analyze the user's social media activity and provide relevant settings during plugin installation. For example, the plugin unit can provide optimal settings for specific keywords the user uses on social media. The plugin unit can also provide optimal settings for interactions with specific individuals based on the user's social media activity. The plugin unit can also provide settings for specific time periods based on the user's social media activity. In this way, relevant settings can be provided by analyzing social media activity. Some or all of the above processing in the plugin unit may be performed using AI, for example, or without AI. For example, the plugin unit can input the user's social media activity data into a generating AI and have the generating AI perform the provision of relevant settings.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The harassment prevention system can learn the user's communication style and provide individually optimized advice. For example, if a user habitually uses polite language, the advice system will provide advice tailored to that style. Conversely, if a user prefers casual language, the advice system can provide advice using casual expressions. Furthermore, if a user frequently uses specific industry jargon, the system can understand that terminology and provide appropriate advice. This allows for advice tailored to the user's communication style, resulting in more effective harassment prevention.

[0056] A harassment prevention system can analyze a user's communication history, detect specific patterns, and provide advice. For example, if a user tends to use aggressive language towards certain individuals, it can provide advice to encourage them to be extra careful in their interactions with those individuals. If a user is prone to stress during certain times of the day, it can offer advice on how to relax during those times. It can also provide advice on topics that users react to sensitively. This ensures that appropriate advice is provided based on the user's communication history, improving the effectiveness of harassment prevention.

[0057] The harassment prevention system can adjust the content of its advice based on the user's geographical location. For example, if the user is at work, it can provide advice appropriate to the work environment. If the user is at home, it can provide advice suitable for a relaxed environment. If the user is on the go, it can provide concise and easily visible advice. This ensures that appropriate advice is provided according to the user's geographical location, improving the effectiveness of harassment prevention.

[0058] A harassment prevention system can analyze a user's social media activity and provide relevant advice. For example, it can provide appropriate advice regarding specific keywords a user uses on social media. It can also provide appropriate advice regarding interactions with specific individuals based on a user's social media activity. Based on a user's social media activity, it can identify trends in advice given at specific times of day and provide appropriate advice accordingly. In this way, analyzing social media activity allows for the provision of relevant advice, increasing the effectiveness of harassment prevention.

[0059] The harassment prevention system can provide optimal settings by referring to the user's past communication tool usage history. For example, it can provide optimal settings based on the settings of communication tools the user has used in the past. It can also prioritize certain functions based on the user's past communication tool usage history. It can also provide settings for specific time periods based on the user's past communication tool usage history. As a result, by referring to past communication tool usage history, optimal settings are provided, improving the effectiveness of harassment prevention.

[0060] A harassment prevention system can analyze a user's social media activity and provide relevant settings. For example, it can provide optimal settings for specific keywords a user uses on social media. It can also provide optimal settings for interactions with specific individuals based on the user's social media activity. It can even provide settings for specific time periods based on the user's social media activity. In this way, analyzing social media activity allows relevant settings to be provided, improving the effectiveness of harassment prevention.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The monitoring unit monitors the user's text and voice communications in real time. The monitoring unit is provided, for example, as a plug-in to the communication tools the user uses on a daily basis. The monitoring unit monitors the user's text and voice communications in real time and collects data to detect potentially harassing remarks or actions. Step 2: The detection unit analyzes the communication monitored by the monitoring unit and detects potentially harassing remarks and actions. For example, the detection unit identifies potentially harassing remarks and actions based on a vast amount of training data. The detection unit can detect remarks and actions that fall under the three major types of harassment: sexual harassment, power harassment, and moral harassment. Step 3: The advice unit provides advice to the user if detected by the detection unit. The advice unit displays a message such as, "Your recent statement may be considered harassment. Please be careful." The advice unit provides advice to encourage the user to take appropriate action. Step 4: The scoring unit scores the risk of harassment and visualizes it on a daily and monthly basis. For example, the scoring unit quantifies the risk of harassment and displays it as a graph or chart. The scoring unit provides information for users to reflect on their own behavior and make improvements. Step 5: The plugin component is provided as a plugin for the target communication tool. The plugin component can be easily installed on the communication tool that the user uses on a daily basis.

[0063] (Example of form 2) The harassment prevention system according to an embodiment of the present invention is a mechanism that uses AI to prevent harassment on everyday communication tools. This harassment prevention system provides an "AI checker" as a plug-in to the communication tools that users use on a daily basis. The AI ​​checker analyzes text and voice communication in real time and detects potentially harassing remarks and actions. If detected, the AI ​​checker immediately provides advice to the user to prevent harassing behavior. First, the "AI checker" is installed as a plug-in to the communication tool that the user uses on a daily basis. This plug-in monitors the user's text and voice communication in real time. Next, the AI ​​checker analyzes the text and voice communication. Based on a vast amount of training data, the AI ​​detects potentially harassing remarks and actions. For example, it identifies remarks and actions that fall under the three major types of harassment: sexual harassment, power harassment, and moral harassment. If detected, the AI ​​checker immediately provides advice to the user. For example, it displays a message such as, "That remark may be power harassment. Please be careful." Furthermore, by scoring the risk of harassment and visualizing it on a daily and monthly basis, users can reflect on their own behavior and make improvements. This system allows users to be aware of and prevent harassment on a daily basis. For example, by analyzing text exchanges and voice tone and providing immediate advice, harassment can be prevented. Also, by scoring and visualizing the risk of harassment, users can reflect on and improve their own behavior. In this way, the AI-powered "AI Harassment Checker" can prevent harassment and create an environment where there are no perpetrators or victims. Thus, the harassment prevention system can prevent harassment by monitoring users' text and voice communications in real time, detecting potentially harassing remarks and actions, and providing advice.

[0064] The harassment prevention system according to this embodiment comprises a monitoring unit, a detection unit, an advice unit, a scoring unit, and a plug-in unit. The monitoring unit monitors the user's text and voice communications in real time. The monitoring unit is provided, for example, as a plug-in to a communication tool that the user uses on a daily basis. The monitoring unit monitors the user's text and voice communications in real time and collects data to detect potentially harassing remarks and actions. The detection unit analyzes the communications monitored by the monitoring unit and detects potentially harassing remarks and actions. The detection unit identifies potentially harassing remarks and actions based, for example, on a vast amount of training data. The detection unit can detect remarks and actions that fall under the three major types of harassment: sexual harassment, power harassment, and moral harassment. The advice unit provides advice to the user when detected by the detection unit. The advice unit displays, for example, a message such as, "That remark may be power harassment. Please be careful." The advice unit provides advice to encourage the user to take appropriate action. The scoring unit scores the risk of harassment and visualizes it on a daily and monthly basis. For example, the scoring unit quantifies the risk of harassment and displays it as a graph or chart. The scoring unit provides information for users to reflect on their own behavior and make improvements. The plugin unit is provided as a plugin for the target communication tool. The plugin unit can be easily installed on the communication tool that the user uses on a daily basis. As a result, the harassment prevention system according to the embodiment can prevent harassment by monitoring the user's text and voice communication in real time, detecting potentially harassing remarks and actions, and providing advice.

[0065] The monitoring unit monitors users' text and voice communications in real time. The monitoring unit is provided, for example, as a plug-in to communication tools that users use daily. The monitoring unit monitors users' text and voice communications in real time and collects data to detect potentially harassing remarks and behaviors. The monitoring unit uses natural language processing technology to analyze text data and detect specific keywords and phrases. For example, it detects and records offensive language or discriminatory expressions. Similarly, it converts voice data to text using speech recognition technology and performs the same analysis. Furthermore, the monitoring unit considers the context of the user's communication, analyzing not only keywords but also the intent and nuances of the remarks. This reduces false positives and enables more accurate monitoring. The monitoring unit sends the collected data to a secure server for subsequent processing. This allows the monitoring unit to monitor user communications in real time and quickly detect potentially harassing remarks and behaviors.

[0066] The detection unit analyzes communications monitored by the monitoring unit and detects potentially harassing remarks and actions. Based on a vast amount of training data, the detection unit identifies potentially harassing remarks and actions. Specifically, it can detect remarks and actions that fall under the three major types of harassment: sexual harassment, power harassment, and moral harassment. The detection unit uses machine learning algorithms to learn from past harassment cases and perform detection with high accuracy even on new data. For example, it uses natural language processing technology to analyze text data and detect specific patterns and phrases. For audio data, it uses speech recognition technology to convert it to text and performs similar analysis. Furthermore, the detection unit considers the context of the user's communication and analyzes not only keywords but also the intent and nuances of the remarks. This reduces false positives and enables more accurate detection. The detection unit sends the detection results to the advice unit and scoring unit for use in subsequent processing. As a result, the detection unit can analyze user communications and quickly detect potentially harassing remarks and actions.

[0067] The advice unit provides advice to the user when detected by the detection unit. Specifically, it displays messages such as, "Your recent statement may be harassment. Please be careful." The advice unit provides advice to encourage appropriate behavior from the user. The advice unit has the function of displaying messages directly to the user's communication tools, providing real-time feedback. Furthermore, the advice unit can also provide individually customized advice based on the user's past behavior history. For example, it will display a stronger warning to users who have repeatedly made similar statements in the past. The advice unit also records whether the user has accepted the advice and uses this for subsequent feedback. In this way, the advice unit can encourage appropriate behavior from the user and prevent harassment.

[0068] The scoring unit scores the risk of harassment and visualizes it on a daily and monthly basis. Specifically, it quantifies the risk of harassment and displays it as graphs and charts. The scoring unit provides information to help users reflect on and improve their own behavior. Based on data from the detection unit, the scoring unit calculates each user's harassment risk. For example, it calculates a risk score by considering the frequency and content of past statements and actions. The scoring unit aggregates these scores on a daily and monthly basis and provides them to users in a visually easy-to-understand format. For example, it displays graphs and charts on the dashboard so that users can see at a glance how their risk score has changed. The scoring unit also provides specific advice to help users improve their behavior. For example, it displays a message such as, "Your risk score has increased over the past week. Please be careful about certain statements." In this way, the scoring unit provides information to help users reflect on and improve their own behavior, thereby preventing harassment.

[0069] The plugin component is provided as a plugin for the target communication tool. The plugin component can be easily installed on the communication tools that users use daily. The plugin component interacts with the monitoring, detection, advice, and scoring components using the APIs of each tool. It can also collect and analyze audio data during meetings using the APIs. The plugin component is designed to integrate seamlessly with these tools, allowing users to use it without requiring any special operations. Furthermore, the plugin component is designed to flexibly adapt to version upgrades and specification changes of each tool, ensuring it always operates in the latest environment. This allows the plugin component to be easily installed on the communication tools that users use daily, maximizing the functionality of the harassment prevention system.

[0070] The monitoring unit can estimate the user's emotions and adjust the monitoring intensity based on the estimated emotions. For example, if the user is stressed, the monitoring unit can increase the monitoring intensity and perform a more detailed analysis. If the user is relaxed, the monitoring unit can also lower the monitoring intensity and perform only a basic analysis. If the user is excited, the monitoring unit can set the monitoring intensity to a moderate level and perform a moderate analysis. This allows for more appropriate monitoring by adjusting the monitoring intensity according to 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 monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0071] The monitoring unit can improve the accuracy of monitoring by referring to the user's past communication history during monitoring. For example, the monitoring unit can analyze the user's past speech patterns and adjust its sensitivity to specific keywords. The monitoring unit can also improve the accuracy of monitoring by understanding the user's speech trends during specific time periods from the user's past communication history. The monitoring unit can also adjust the accuracy of monitoring interactions with specific individuals based on the user's past communication history. This improves the accuracy of monitoring by referring to past communication history. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the user's past communication history into a generating AI and have the generating AI perform the improvement of monitoring accuracy.

[0072] The monitoring unit can prioritize monitoring for specific keywords or phrases during monitoring. For example, the monitoring unit can prioritize monitoring for specific keywords related to harassment (e.g., sexual harassment, power harassment). The monitoring unit can also prioritize monitoring for specific phrases (e.g., "You're no good") and analyze them immediately. The monitoring unit can also create a list of problematic keywords or phrases that users have used in the past and prioritize monitoring for those. This improves the accuracy of harassment detection by prioritizing monitoring for specific keywords and phrases. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or not using AI. For example, the monitoring unit can input specific keywords or phrases into a generating AI and have the generating AI perform priority monitoring.

[0073] The monitoring unit can estimate the user's emotions and adjust the monitoring time based on the estimated emotions. For example, the monitoring unit can intensify monitoring and perform detailed analysis during times when the user is stressed. It can also ease monitoring and perform only basic analysis during times when the user is relaxed. It can also set monitoring to a moderate level and perform appropriate analysis during times when the user is excited. This allows for more appropriate monitoring by adjusting the monitoring time according to 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 monitoring unit may be performed using AI or not using AI. For example, the monitoring unit can input user emotion data into a generative AI and have the generative AI adjust the monitoring time.

[0074] The monitoring unit can adjust its monitoring range while taking into account the user's geographical location information. For example, if the user is in a specific location (e.g., their workplace), the monitoring unit can expand the monitoring range and perform a more detailed analysis. If the user is at home, the monitoring unit can also narrow the monitoring range and perform only a basic analysis. If the user is on the move, the monitoring unit can set the monitoring range to a moderate level and perform a moderate analysis. This allows the monitoring range to be appropriately adjusted by taking into account the user's geographical location information. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the user's geographical location information into a generating AI and have the generating AI adjust the monitoring range.

[0075] The monitoring unit can analyze a user's social media activity during monitoring and prioritize monitoring of relevant communications. For example, the monitoring unit can prioritize monitoring specific keywords used by the user on social media. The monitoring unit can also prioritize monitoring interactions with specific individuals based on the user's social media activity. Based on the user's social media activity, the monitoring unit can also understand trends in statements during specific time periods and improve the accuracy of monitoring. This allows for the prioritization of relevant communications by analyzing social media activity. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input user social media activity data into a generating AI and have the generating AI perform priority monitoring of relevant communications.

[0076] The detection unit can estimate the user's emotions and adjust the detection criteria based on the estimated emotions. For example, if the user is stressed, the detection unit can tighten the detection criteria and perform a detailed analysis. If the user is relaxed, the detection unit can also loosen the detection criteria and perform only a basic analysis. If the user is excited, the detection unit can also set the detection criteria to a moderate level and perform a moderate analysis. By adjusting the detection criteria according to the user's emotions, more appropriate detection becomes possible. 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 detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input user emotion data into the generative AI and have the generative AI adjust the detection criteria.

[0077] The detection unit can optimize its detection algorithm by referring to past detection data during detection. For example, the detection unit can adjust its sensitivity to specific keywords based on past detection data. The detection unit can also understand the trends in speech during specific time periods from past detection data and optimize its detection algorithm. The detection unit can also adjust its detection algorithm for interactions with specific individuals based on past detection data. In this way, the detection algorithm can be optimized by referring to past detection data. Some or all of the above processes in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input past detection data into a generating AI and have the generating AI perform the optimization of the detection algorithm.

[0078] The detection unit can prioritize detection of specific communication patterns during detection. For example, the detection unit can prioritize detection of specific communication patterns related to harassment (e.g., aggressive language). The detection unit can also prioritize detection of specific phrases (e.g., "You're no good") and analyze them immediately. The detection unit can also list problematic communication patterns that the user has used in the past and prioritize detection of those. This improves the accuracy of harassment detection by prioritizing detection of specific communication patterns. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input specific communication patterns into a generating AI and have the generating AI perform priority detection.

[0079] The detection unit can estimate the user's emotions and adjust the display method of the detection results based on the estimated user emotions. For example, if the user is nervous, the detection unit can provide a simple and highly visible display method. If the user is relaxed, the detection unit can also provide a display method that includes detailed information. If the user is in a hurry, the detection unit can also provide a display method that gets straight to the point. By adjusting the display method of the detection results according to the user's emotions, a more appropriate display becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input the user's emotion data into the generative AI and have the generative AI adjust the display method of the detection results.

[0080] The detection unit can adjust its detection range when detecting a user, taking into account the user's geographical location information. For example, if the user is in a specific location (e.g., their workplace), the detection unit can widen the detection range and perform a more detailed analysis. If the user is at home, the detection unit can narrow the detection range and perform only a basic analysis. If the user is on the move, the detection unit can set the detection range to a moderate level and perform a moderate analysis. This allows the detection range to be appropriately adjusted by taking into account the user's geographical location information. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input the user's geographical location information into a generating AI and have the generating AI adjust the detection range.

[0081] The detection unit can analyze the user's social media activity during detection and prioritize the detection of relevant communications. For example, the detection unit can prioritize the detection of specific keywords used by the user on social media. The detection unit can also prioritize the detection of interactions with specific individuals from the user's social media activity. Based on the user's social media activity, the detection unit can also understand the trends in statements during specific time periods and improve the accuracy of detection. This allows for the priority detection of relevant communications by analyzing social media activity. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input the user's social media activity data into a generating AI and have the generating AI perform the priority detection of relevant communications.

[0082] The advice unit can estimate the user's emotions and adjust the way it expresses advice based on the estimated emotions. For example, if the user is nervous, the advice unit can provide advice in a calm tone. If the user is relaxed, the advice unit can also provide advice in a cheerful tone. If the user is in a hurry, the advice unit can provide advice in a quick and concise tone. By adjusting the way advice is expressed according to the user's emotions, more appropriate advice can be provided. 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 advice unit may be performed using AI, for example, or not using AI. For example, the advice unit can input user emotion data into the generative AI and have the generative AI adjust the way advice is expressed.

[0083] The advice unit can improve the accuracy of its advice by referring to past advice history when providing advice. For example, the advice unit can adjust the accuracy of advice for a specific keyword based on past advice history. The advice unit can also improve the accuracy of advice by understanding the trend of advice during a specific time period from past advice history. The advice unit can also adjust the accuracy of advice for interactions with a specific person based on past advice history. In this way, the accuracy of advice is improved by referring to past advice history. Some or all of the above processes in the advice unit may be performed using AI, for example, or without using AI. For example, the advice unit can input past advice history into a generating AI and have the generating AI perform the improvement of advice accuracy.

[0084] The advice unit can provide different advice depending on the specific situation. For example, it can provide appropriate advice for a specific situation related to harassment (e.g., during a meeting). The advice unit can also provide situation-appropriate advice for interactions with a specific person. The advice unit can also provide appropriate advice based on specific situations the user has experienced in the past. This allows for more appropriate advice by providing different advice depending on the specific situation. Some or all of the above processing in the advice unit may be performed using AI, for example, or not using AI. For example, the advice unit can input specific situation data into a generating AI and have the generating AI perform the task of providing different advice.

[0085] The advice unit can estimate the user's emotions and adjust the timing of advice based on the estimated emotions. For example, if the user is nervous, the advice unit can delay the timing of the advice. If the user is relaxed, the advice unit can also provide advice earlier. If the user is in a hurry, the advice unit can provide advice immediately. By adjusting the timing of advice according to the user's emotions, advice can be provided at a more appropriate time. 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 advice unit may be performed using AI, for example, or not using AI. For example, the advice unit can input user emotion data into the generative AI and have the generative AI adjust the timing of the advice.

[0086] The advice unit can adjust the content of its advice by taking into account the user's geographical location. For example, if the user is in a specific location (e.g., their workplace), the advice unit can provide appropriate advice. If the user is at home, the advice unit can also provide relaxed advice. If the user is on the move, the advice unit can provide concise and easy-to-understand advice. This allows the advice to be appropriately adjusted by taking into account the user's geographical location. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input the user's geographical location into a generating AI and have the generating AI adjust the content of the advice.

[0087] The advice unit can analyze the user's social media activity and provide relevant advice when giving advice. For example, the advice unit can provide appropriate advice for specific keywords the user uses on social media. The advice unit can also provide appropriate advice for interactions with specific individuals based on the user's social media activity. Based on the user's social media activity, the advice unit can also understand the trends in advice during specific time periods and provide appropriate advice. In this way, relevant advice can be provided by analyzing social media activity. Some or all of the above processes in the advice unit may be performed using AI, for example, or not using AI. For example, the advice unit can input the user's social media activity data into a generating AI and have the generating AI perform the task of providing relevant advice.

[0088] The scoring unit can estimate the user's emotions and adjust the scoring criteria based on the estimated emotions. For example, if the user is stressed, the scoring unit can tighten the scoring criteria and perform a more detailed analysis. If the user is relaxed, the scoring unit can also loosen the scoring criteria and perform only a basic analysis. If the user is excited, the scoring unit can also set the scoring criteria to a moderate level and perform a moderate analysis. This allows for more appropriate scoring by adjusting the scoring criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the scoring unit may be performed using AI, or not. For example, the scoring unit can input user emotion data into a generative AI and have the generative AI adjust the scoring criteria.

[0089] The scoring unit can optimize its scoring algorithm by referring to past score data during the scoring process. For example, the scoring unit can adjust the sensitivity of scoring for specific keywords based on past score data. The scoring unit can also optimize its scoring algorithm by understanding the trends in speech during specific time periods from past score data. The scoring unit can also adjust its scoring algorithm for interactions with specific individuals based on past score data. In this way, the scoring algorithm can be optimized by referring to past score data. Some or all of the above-described processes in the scoring unit may be performed using AI, for example, or without AI. For example, the scoring unit can input past score data into a generating AI and have the generating AI perform the optimization of the scoring algorithm.

[0090] The scoring unit can prioritize scoring specific communication patterns during the scoring process. For example, the scoring unit can prioritize scoring specific communication patterns related to harassment (e.g., aggressive language). The scoring unit can also prioritize scoring specific phrases (e.g., "You're no good") and analyze them immediately. The scoring unit can also list problematic communication patterns that the user has used in the past and prioritize scoring those. This improves the accuracy of harassment scoring by prioritizing scoring specific communication patterns. Some or all of the above processing in the scoring unit may be performed using AI, for example, or not using AI. For example, the scoring unit can input specific communication patterns into a generating AI and have the generating AI perform the prioritized scoring.

[0091] The scoring unit can estimate the user's emotions and adjust the score display method based on the estimated emotions. For example, if the user is nervous, the scoring unit can provide a simple and highly visible display method. If the user is relaxed, the scoring unit can also provide a display method that includes detailed information. If the user is in a hurry, the scoring unit can also provide a concise display method. By adjusting the score display method according to the user's emotions, a more appropriate display becomes possible. 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 scoring unit may be performed using AI, for example, or not using AI. For example, the scoring unit can input user emotion data into the generative AI and have the generative AI adjust the score display method.

[0092] The scoring unit can adjust the score range by considering the user's geographical location information during the scoring process. For example, if the user is in a specific location (e.g., their workplace), the scoring unit can broaden the score range and perform a more detailed analysis. If the user is at home, the scoring unit can narrow the score range and perform only a basic analysis. If the user is on the move, the scoring unit can set the score range to a moderate level and perform a moderate analysis. This allows for appropriate adjustment of the score range by considering the user's geographical location information. Some or all of the above processing in the scoring unit may be performed using AI, for example, or without AI. For example, the scoring unit can input the user's geographical location information into a generating AI and have the generating AI adjust the score range.

[0093] The scoring unit can analyze the user's social media activity during the scoring process and provide relevant scores. For example, the scoring unit can provide scores for specific keywords used by the user on social media. The scoring unit can also provide scores for interactions with specific individuals based on the user's social media activity. Based on the user's social media activity, the scoring unit can also identify trends in statements during specific time periods and provide scores. In this way, relevant scores can be provided by analyzing social media activity. Some or all of the above processing in the scoring unit may be performed using AI, for example, or without AI. For example, the scoring unit can input the user's social media activity data into a generating AI and have the generating AI perform the task of providing relevant scores.

[0094] The plugin unit can estimate the user's emotions and adjust the plugin's behavior based on the estimated emotions. For example, if the user is stressed, the plugin unit may simplify the plugin's behavior and provide only basic functions. If the user is relaxed, the plugin unit may also make the plugin's behavior more detailed and provide additional functions. If the user is excited, the plugin unit may also set the plugin's behavior to a moderate level and provide appropriate functions. This allows for more appropriate operation by adjusting the plugin's behavior according to 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 plugin unit may be performed using AI, or not using AI. For example, the plugin unit can input user emotion data into a generative AI and have the generative AI adjust the plugin's behavior.

[0095] The plugin unit can provide optimal settings by referring to the user's past communication tool usage history during plugin installation. For example, the plugin unit can provide optimal settings based on the settings of communication tools the user has used in the past. The plugin unit can also prioritize providing specific functions based on the user's past communication tool usage history. The plugin unit can also provide settings for specific time periods based on the user's past communication tool usage history. In this way, optimal settings can be provided by referring to past communication tool usage history. Some or all of the above processing in the plugin unit may be performed using AI, for example, or without AI. For example, the plugin unit can input the user's past communication tool usage history into a generating AI and have the generating AI perform the task of providing optimal settings.

[0096] The plugin unit can estimate the user's emotions and adjust the plugin installation timing based on the estimated emotions. For example, if the user is nervous, the plugin unit can delay the plugin installation. If the user is relaxed, the plugin unit can also accelerate the plugin installation. If the user is in a hurry, the plugin unit can install the plugin immediately. By adjusting the plugin installation timing according to the user's emotions, installation can be performed at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, such as 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 plugin unit may be performed using AI, or not using AI. For example, the plugin unit can input user emotion data into the generative AI and have the generative AI adjust the plugin installation timing.

[0097] The plugin unit can provide optimal settings by considering the user's geographical location during plugin installation. For example, the plugin unit can provide optimal settings if the user is in a specific location (e.g., their workplace). If the user is at home, the plugin unit can also provide relaxed settings. If the user is on the move, the plugin unit can provide concise and easy-to-understand settings. This allows the plugin unit to provide optimal settings by considering the user's geographical location. Some or all of the above processing in the plugin unit may be performed using AI, for example, or without AI. For example, the plugin unit can input the user's geographical location information into a generating AI and have the generating AI perform the task of providing optimal settings.

[0098] The plugin unit can analyze the user's social media activity and provide relevant settings during plugin installation. For example, the plugin unit can provide optimal settings for specific keywords the user uses on social media. The plugin unit can also provide optimal settings for interactions with specific individuals based on the user's social media activity. The plugin unit can also provide settings for specific time periods based on the user's social media activity. In this way, relevant settings can be provided by analyzing social media activity. Some or all of the above processing in the plugin unit may be performed using AI, for example, or without AI. For example, the plugin unit can input the user's social media activity data into a generating AI and have the generating AI perform the provision of relevant settings.

[0099] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0100] The harassment prevention system can learn the user's communication style and provide individually optimized advice. For example, if a user habitually uses polite language, the advice system will provide advice tailored to that style. Conversely, if a user prefers casual language, the advice system can provide advice using casual expressions. Furthermore, if a user frequently uses specific industry jargon, the system can understand that terminology and provide appropriate advice. This allows for advice tailored to the user's communication style, resulting in more effective harassment prevention.

[0101] The harassment prevention system can estimate the user's emotions and adjust the content of the advice based on those emotions. For example, if the user is angry, the advice unit will provide advice encouraging them to calm down. If the user is sad, it can also provide advice that includes words of encouragement. Furthermore, if the user is happy, it can provide positive advice to help maintain that feeling. This ensures that appropriate advice is provided according to the user's emotions, increasing the effectiveness of harassment prevention.

[0102] A harassment prevention system can analyze a user's communication history, detect specific patterns, and provide advice. For example, if a user tends to use aggressive language towards certain individuals, it can provide advice to encourage them to be extra careful in their interactions with those individuals. If a user is prone to stress during certain times of the day, it can offer advice on how to relax during those times. It can also provide advice on topics that users react to sensitively. This ensures that appropriate advice is provided based on the user's communication history, improving the effectiveness of harassment prevention.

[0103] The harassment prevention system can estimate the user's emotions and adjust the timing of advice based on those emotions. For example, if the user is tense, the advice unit will delay providing advice. If the user is relaxed, the advice can be provided earlier. If the user is in a hurry, advice can be provided immediately. This ensures that advice is provided at the appropriate time according to the user's emotions, increasing the effectiveness of harassment prevention.

[0104] The harassment prevention system can adjust the content of its advice based on the user's geographical location. For example, if the user is at work, it can provide advice appropriate to the work environment. If the user is at home, it can provide advice suitable for a relaxed environment. If the user is on the go, it can provide concise and easily visible advice. This ensures that appropriate advice is provided according to the user's geographical location, improving the effectiveness of harassment prevention.

[0105] A harassment prevention system can analyze a user's social media activity and provide relevant advice. For example, it can provide appropriate advice regarding specific keywords a user uses on social media. It can also provide appropriate advice regarding interactions with specific individuals based on a user's social media activity. Based on a user's social media activity, it can identify trends in advice given at specific times of day and provide appropriate advice accordingly. In this way, analyzing social media activity allows for the provision of relevant advice, increasing the effectiveness of harassment prevention.

[0106] The harassment prevention system can estimate the user's emotions and adjust the scoring criteria based on those emotions. For example, if the user is stressed, the scoring criteria can be made stricter and a more detailed analysis performed. If the user is relaxed, the scoring criteria can be relaxed and only a basic analysis can be performed. If the user is agitated, the scoring criteria can be set to a moderate level and an appropriate analysis can be performed. This ensures that the scoring is appropriate to the user's emotions, improving the effectiveness of harassment prevention.

[0107] The harassment prevention system can provide optimal settings by referring to the user's past communication tool usage history. For example, it can provide optimal settings based on the settings of communication tools the user has used in the past. It can also prioritize certain functions based on the user's past communication tool usage history. It can also provide settings for specific time periods based on the user's past communication tool usage history. As a result, by referring to past communication tool usage history, optimal settings are provided, improving the effectiveness of harassment prevention.

[0108] The harassment prevention system can estimate the user's emotions and adjust the plugin's behavior based on those emotions. For example, if the user is stressed, the plugin's behavior can be simplified, providing only basic functions. If the user is relaxed, the plugin's behavior can be made more detailed, providing additional functions. If the user is agitated, the plugin's behavior can be set to a moderate level, providing appropriate functionality. This ensures that the plugin behaves appropriately according to the user's emotions, improving the effectiveness of harassment prevention.

[0109] A harassment prevention system can analyze a user's social media activity and provide relevant settings. For example, it can provide optimal settings for specific keywords a user uses on social media. It can also provide optimal settings for interactions with specific individuals based on the user's social media activity. It can even provide settings for specific time periods based on the user's social media activity. In this way, analyzing social media activity allows relevant settings to be provided, improving the effectiveness of harassment prevention.

[0110] The following briefly describes the processing flow for example form 2.

[0111] Step 1: The monitoring unit monitors the user's text and voice communications in real time. The monitoring unit is provided, for example, as a plug-in to the communication tools the user uses on a daily basis. The monitoring unit monitors the user's text and voice communications in real time and collects data to detect potentially harassing remarks or actions. Step 2: The detection unit analyzes the communication monitored by the monitoring unit and detects potentially harassing remarks and actions. For example, the detection unit identifies potentially harassing remarks and actions based on a vast amount of training data. The detection unit can detect remarks and actions that fall under the three major types of harassment: sexual harassment, power harassment, and moral harassment. Step 3: The advice unit provides advice to the user if detected by the detection unit. The advice unit displays a message such as, "Your recent statement may be considered harassment. Please be careful." The advice unit provides advice to encourage the user to take appropriate action. Step 4: The scoring unit scores the risk of harassment and visualizes it on a daily and monthly basis. For example, the scoring unit quantifies the risk of harassment and displays it as a graph or chart. The scoring unit provides information for users to reflect on their own behavior and make improvements. Step 5: The plugin component is provided as a plugin for the target communication tool. The plugin component can be easily installed on the communication tool that the user uses on a daily basis.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] Each of the multiple elements described above, including the monitoring unit, detection unit, advice unit, scoring unit, and plug-in unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the monitoring unit is implemented by the control unit 46A of the smart device 14 and monitors the user's text and voice communications in real time. The detection unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the data collected by the monitoring unit to detect potentially harassing remarks or actions. The advice unit is implemented by the control unit 46A of the smart device 14 and provides advice to the user when detected. The scoring unit is implemented by the identification processing unit 290 of the data processing unit 12 and scores the risk of harassment, making it visible on a daily or monthly basis. The plug-in unit is implemented by the control unit 46A of the smart device 14 and is provided as a plug-in to the target communication tool. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0116] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] Each of the multiple elements described above, including the monitoring unit, detection unit, advice unit, scoring unit, and plug-in unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the monitoring unit is implemented by the control unit 46A of the smart glasses 214 and monitors the user's text and voice communications in real time. The detection unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the data collected by the monitoring unit to detect potentially harassing remarks or actions. The advice unit is implemented by the control unit 46A of the smart glasses 214 and provides advice to the user when detected. The scoring unit is implemented by the identification processing unit 290 of the data processing unit 12 and scores the risk of harassment, visualizing it on a daily or monthly basis. The plug-in unit is implemented by the control unit 46A of the smart glasses 214 and is provided as a plug-in to the target communication tool. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0132] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] Each of the multiple elements described above, including the monitoring unit, detection unit, advice unit, scoring unit, and plug-in unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the monitoring unit is implemented by the control unit 46A of the headset terminal 314 and monitors the user's text and voice communications in real time. The detection unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the data collected by the monitoring unit to detect potentially harassing remarks or actions. The advice unit is implemented by the control unit 46A of the headset terminal 314 and provides advice to the user when detected. The scoring unit is implemented by the identification processing unit 290 of the data processing unit 12 and scores the risk of harassment, making it visible on a daily or monthly basis. The plug-in unit is implemented by the control unit 46A of the headset terminal 314 and is provided as a plug-in to the target communication tool. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0148] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] Each of the multiple elements described above, including the monitoring unit, detection unit, advice unit, scoring unit, and plug-in unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the monitoring unit is implemented by the control unit 46A of the robot 414 and monitors the user's text and voice communications in real time. The detection unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the data collected by the monitoring unit to detect potentially harassing remarks or actions. The advice unit is implemented by the control unit 46A of the robot 414 and provides advice to the user when detected. The scoring unit is implemented by the identification processing unit 290 of the data processing unit 12 and scores the risk of harassment, visualizing it on a daily or monthly basis. The plug-in unit is implemented by the control unit 46A of the robot 414 and is provided as a plug-in to the target communication tool. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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."

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] (Note 1) A monitoring unit that monitors users' text and voice communications in real time, A detection unit analyzes the communication monitored by the aforementioned monitoring unit and detects potentially harassing remarks or actions. An advice unit that provides advice to the user when detected by the detection unit, A scoring unit that scores the risk of harassment and visualizes it on a daily and monthly basis, It comprises a plugin component that is provided as a plugin for the target communication tool. A system characterized by the following features. (Note 2) The aforementioned monitoring unit, It estimates the user's emotions and adjusts the monitoring intensity based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned monitoring unit, During monitoring, the accuracy of monitoring is improved by referring to the user's past communication history. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned monitoring unit, During monitoring, prioritize monitoring for specific keywords or phrases. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned monitoring unit, It estimates the user's emotions and adjusts the monitoring time based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned monitoring unit, During monitoring, the monitoring range is adjusted considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned monitoring unit, During monitoring, the system analyzes users' social media activity and prioritizes monitoring relevant communications. The system described in Appendix 1, characterized by the features described herein. (Note 8) The detection unit is It estimates the user's emotions and adjusts the detection criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The detection unit is During detection, the detection algorithm is optimized by referring to past detection data. The system described in Appendix 1, characterized by the features described herein. (Note 10) The detection unit is During detection, prioritize detection based on specific communication patterns. The system described in Appendix 1, characterized by the features described herein. (Note 11) The detection unit is It estimates the user's emotions and adjusts how the detection results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The detection unit is During detection, the detection range is adjusted considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The detection unit is During detection, the system analyzes the user's social media activity and prioritizes the detection of relevant communications. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned advice section, It estimates the user's emotions and adjusts the way advice is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned advice section, When giving advice, we refer to past advice history to improve the accuracy of the advice. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned advice section, When giving advice, provide different advice depending on the specific situation. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned advice section, It estimates the user's emotions and adjusts the timing of advice based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned advice section, When providing advice, we adjust the content of the advice to take into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned advice section, When providing advice, we analyze the user's social media activity and offer relevant advice. The system described in Appendix 1, characterized by the features described herein. (Note 20) The scoring unit, It estimates the user's emotions and adjusts the scoring criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The scoring unit, When scoring, the scoring algorithm is optimized by referring to past score data. The system described in Appendix 1, characterized by the features described herein. (Note 22) The scoring unit, When scoring, prioritize scoring for specific communication patterns. The system described in Appendix 1, characterized by the features described herein. (Note 23) The scoring unit, The system estimates the user's emotions and adjusts how the score is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The scoring unit, When calculating scores, the score range is adjusted to take into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 25) The scoring unit, When scoring, the system analyzes the user's social media activity and provides relevant scores. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned plug-in section is It estimates the user's emotions and adjusts the plugin's behavior based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned plug-in section is During plugin installation, the system provides optimal settings by referencing the user's past communication tool usage history. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned plug-in section is It estimates the user's emotions and adjusts the timing of plugin installation based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned plug-in section is When installing a plugin, it takes the user's geographical location into account to provide optimal settings. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned plug-in section is During plugin installation, the system analyzes the user's social media activity and provides relevant settings. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0184] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A monitoring unit that monitors users' text and voice communications in real time, A detection unit analyzes the communication monitored by the aforementioned monitoring unit and detects potentially harassing remarks or actions. An advice unit that provides advice to the user when detected by the detection unit, A scoring unit that scores the risk of harassment and visualizes it on a daily and monthly basis, It comprises a plugin component that is provided as a plugin for the target communication tool. A system characterized by the following features.

2. The aforementioned monitoring unit, It estimates the user's emotions and adjusts the monitoring intensity based on the estimated user emotions. The system according to feature 1.

3. The aforementioned monitoring unit, During monitoring, the accuracy of monitoring is improved by referring to the user's past communication history. The system according to feature 1.

4. The aforementioned monitoring unit, During monitoring, prioritize monitoring for specific keywords or phrases. The system according to feature 1.

5. The aforementioned monitoring unit, It estimates the user's emotions and adjusts the monitoring time based on the estimated user emotions. The system according to feature 1.

6. The aforementioned monitoring unit, During monitoring, the monitoring range is adjusted considering the user's geographical location. The system according to feature 1.

7. The aforementioned monitoring unit, During monitoring, the system analyzes users' social media activity and prioritizes monitoring relevant communications. The system according to feature 1.

8. The detection unit is It estimates the user's emotions and adjusts the detection criteria based on the estimated user emotions. The system according to feature 1.

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