system

Abstract

The system according to this embodiment aims to prevent trouble by determining in advance whether comments posted on social media may lead to defamation or libel. [Solution] The system according to this embodiment comprises a reception unit, an analysis unit, a simulation unit, and a provision unit. The reception unit receives comments posted to SNS. The analysis unit analyzes the comments received by the reception unit. The simulation unit performs a simulation based on the comments analyzed by the analysis unit. The provision unit provides the simulation results obtained by the simulation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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 prior art, it is difficult to prevent the posting of defamatory comments on SNS in advance, and there is a risk of trouble.

[0005] The system according to the embodiment aims to determine in advance whether a comment posted on SNS will lead to defamation and prevent trouble.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a simulation unit, and a provision unit. The reception unit receives comments posted to SNS. The analysis unit analyzes the comments received by the reception unit. The simulation unit performs a simulation based on the comments analyzed by the analysis unit. The provision unit provides the simulation results obtained by the simulation unit. [Effects of the Invention]

[0007] The system according to this embodiment can determine in advance whether comments posted on social media may lead to defamation or libel, thereby preventing trouble before it occurs. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between multiple computers. Examples of communication standards applicable 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 SNS comment judgment system according to an embodiment of the present invention is a system for preventing the posting of defamatory comments on social networking services (SNS). When a user posts a comment on SNS, the system inputs the comment into a generating AI, which analyzes the content of the comment and performs a simulation of what will happen after posting. The generating AI determines whether the comment is likely to lead to defamation. For example, if the comment contains slander or criticism against a specific individual or group, the generating AI determines that the comment may constitute defamation. The judgment result is provided to the user, and the user can post the comment after confirming the result. This mechanism allows users to calmly consider whether their post may hurt someone, thereby reducing trouble caused by defamation. For example, when a user posts a comment on SNS, they input the comment into the generating AI. For example, they might input a comment such as, "I cannot tolerate the actions of this group." This comment is input into the generating AI. Next, the generating AI analyzes the input comment. The generating AI understands the content of the comment and performs a simulation of what will happen after posting. For example, in response to a comment such as "I cannot tolerate the actions of this group," the generating AI simulates how other users might perceive that comment. The generating AI determines whether the comment could potentially lead to defamation. For instance, if the comment "I cannot tolerate the actions of this group" is considered criticism of a specific group, the generating AI will determine that the comment may constitute defamation. The result of this determination is provided to the user. For example, a result such as "This comment may lead to criticism of a specific group" might be displayed. Users can then post their comments after reviewing the result. For example, if a user receives a result such as "This comment may lead to criticism of a specific group," they can review their comment. This mechanism allows users to calmly consider whether their posts might hurt someone, thereby reducing trouble caused by defamation. This mechanism can prevent the posting of defamatory comments on social media.Users can refer to the AI's judgment results to check whether their comments constitute defamation or libel. This can reduce problems on social media and promote healthy communication. In this way, the social media comment judgment system can prevent the posting of defamatory comments on social media and promote healthy communication.

[0029] The SNS comment judgment system according to this embodiment comprises a reception unit, an analysis unit, a simulation unit, and a provision unit. The reception unit receives comments posted to SNS. For example, the reception unit receives comments in real time when a user posts a comment to SNS. The reception unit can also receive comments at regular intervals using batch processing. Furthermore, the reception unit can also receive comments based on filtering criteria. For example, the reception unit can prioritize receiving comments containing specific keywords. The analysis unit analyzes the comments received by the reception unit. For example, the analysis unit analyzes the content of the comments using natural language processing technology. The analysis unit can also analyze the sentiment of the comments using sentiment analysis. Furthermore, the analysis unit can extract important keywords from the comments using keyword extraction technology. For example, the analysis unit understands the context of the comments and determines whether they constitute defamation. The simulation unit performs simulations based on the comments analyzed by the analysis unit. For example, the simulation unit uses a simulation model to simulate how the comments will be received by other users. Furthermore, the simulation unit can perform simulations based on evaluation criteria. In addition, the simulation unit can perform simulations based on the data used. For example, the simulation unit can perform simulations using past reaction data. The provision unit provides the simulation results obtained by the simulation unit. The provision unit can, for example, display the simulation results to the user. The provision unit can also notify the user of the simulation results using a notification method. Furthermore, the provision unit can provide the simulation results using a feedback method to the user. For example, the provision unit can display the simulation results as a pop-up message. As a result, the SNS comment judgment system according to this embodiment can prevent the posting of defamatory comments by analyzing comments posted on SNS, performing simulations, and providing the results.

[0030] The reception unit receives comments posted on social media. For example, when a user posts a comment on social media, the reception unit receives that comment in real time. Specifically, when a user enters a comment and presses the send button, the reception unit immediately captures the comment and imports it into the system. The reception unit can also receive comments at regular intervals using batch processing. For example, by receiving newly posted comments in batches every hour, the system load can be distributed. Furthermore, the reception unit can receive comments based on filtering criteria. For example, the reception unit can prioritize comments containing specific keywords. This makes it possible to process important topics and urgent comments quickly. Filtering criteria can be set by the administrator, for example, by setting it to prioritize comments containing specific hashtags, usernames, or keywords indicating a particular sentiment. This allows the reception unit to receive comments efficiently and effectively, improving the overall system performance.

[0031] The analysis unit analyzes comments received by the reception unit. For example, the analysis unit uses natural language processing techniques to analyze the content of comments. Specifically, it performs morphological analysis to break down comments into individual words and understand the meaning and context of each word. The analysis unit can also analyze the sentiment of comments using sentiment analysis. For example, it classifies comments into sentiment categories such as positive, negative, and neutral to grasp the overall tone of the comments. Furthermore, the analysis unit can extract important keywords from comments using keyword extraction techniques. For example, it extracts keywords related to specific topics or frequently occurring words to identify the subject of the comment. The analysis unit combines these techniques to understand the context of the comment and determine whether it constitutes defamation. For example, if a comment contains language that attacks a specific user or discriminatory expressions, it flags it as a defamatory comment. Based on these analysis results, the analysis unit evaluates the appropriateness of the comment and provides information to proceed to the next processing step. This allows the analysis unit to analyze the content of comments in detail, improving the overall accuracy and reliability of the system.

[0032] The simulation unit performs simulations based on comments analyzed by the analysis unit. For example, the simulation unit uses a simulation model to simulate how comments will be received by other users. Specifically, it models the expected reactions when a particular comment is posted, based on past user reaction data. The simulation unit can also perform simulations based on evaluation criteria. For example, it evaluates the reactions that a comment may provoke by considering the sentiment score of the comment and the importance of keywords. Furthermore, the simulation unit can perform simulations based on the data used. For example, it can perform more accurate simulations using past reaction data from a specific user group or community. The simulation unit integrates this data to predict the impact of comments on other users. For example, if past data shows that comments containing certain keywords have a high probability of eliciting negative reactions, the simulation unit will rate the risk of such comments higher when they are posted. This allows the simulation unit to predict the impact of comments in advance and provide information to take appropriate countermeasures.

[0033] The service provider provides the simulation results obtained by the simulation unit. For example, the service provider displays the simulation results to the user. Specifically, it displays the simulation results as a pop-up message to users who are about to post a comment, informing them in advance how their comment will be received by other users. The service provider can also notify users of the simulation results using notification methods. For example, it can send the simulation results to the user via email or direct message on social media. Furthermore, the service provider can provide simulation results using a feedback method to the user. For example, after a comment is posted, it can compare the actual reactions to the comment with the simulation results and provide feedback to the user. This allows the user to learn how their comments are received and use that knowledge to improve future posts. Through these functions, the service provider can provide users with appropriate information and prevent the posting of defamatory comments. Furthermore, the service provider can increase the transparency of the entire system and gain user trust.

[0034] The analysis unit can understand the content of a comment and determine whether it constitutes defamation. The analysis unit can analyze the content of a comment using, for example, natural language processing technology. For example, the analysis unit can understand the context of the comment and determine whether it constitutes defamation. The analysis unit can also analyze the sentiment of a comment using sentiment analysis. For example, the analysis unit can analyze the sentiment of a comment and determine whether it constitutes defamation. The analysis unit can also extract important keywords from a comment using keyword extraction technology. For example, the analysis unit can extract important keywords from a comment and determine whether it constitutes defamation. In this way, by understanding the content of a comment and determining whether it constitutes defamation, it is possible to prevent the posting of defamatory comments. Some or all of the above processing in the analysis unit may be performed using generative AI, or it may be performed without using generative AI. For example, the analysis unit can input comments into a generation AI, which can analyze the content of the comments and determine whether or not they constitute defamation.

[0035] The simulation unit can simulate how comments will be received by other users. For example, the simulation unit can use a simulation model to simulate how comments will be received by other users. For example, the simulation unit can use sentiment analysis to simulate how comments will be received by other users. The simulation unit can also use user profiles to simulate how comments will be received by other users. For example, the simulation unit can use user profiles to simulate how comments will be received by other users. The simulation unit can also use past reaction data to simulate how comments will be received by other users. For example, the simulation unit can use past reaction data to simulate how comments will be received by other users. This makes it possible to prevent the posting of defamatory comments by simulating how comments will be received by other users. Some or all of the above processing in the simulation unit may be performed using a generative AI, or it may be performed without using a generative AI. For example, the simulation unit can input a comment into a generative AI, and the generative AI can simulate how the comment will be received by other users.

[0036] The service provider can provide the user with simulation results and give the user an opportunity to review their comments. The service provider can, for example, display the simulation results to the user. For example, the service provider can display the simulation results as a pop-up message. The service provider can also notify the user of the simulation results using a notification method. For example, the service provider can notify the user of the simulation results via email. The service provider can also provide the simulation results to the user using a feedback method. For example, the service provider can provide the simulation results as a feedback message. By providing the user with simulation results and giving them an opportunity to review their comments, it is possible to prevent the posting of defamatory comments. Some or all of the above processing in the service provider may be performed using a generation AI, or it may be performed without a generation AI. For example, the service provider can input the simulation results into a generation AI, and the generation AI can provide the simulation results to the user.

[0037] The reception unit can receive comments when users post them on social media. For example, the reception unit can receive comments in real time when users post them on social media. For example, the reception unit can receive comments as soon as the user types them. The reception unit can also receive comments at regular intervals using batch processing. For example, the reception unit can receive comments in batches every hour. The reception unit can also receive comments based on filtering criteria. For example, the reception unit can prioritize receiving comments that contain specific keywords. This helps prevent the posting of defamatory comments by receiving comments when users post them on social media. Some or all of the above processing in the reception unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the reception unit can input comments into a generation AI, and the generation AI can receive the comments.

[0038] The analysis unit can determine whether a comment contains slander or criticism against a specific individual or group. For example, the analysis unit may use natural language processing technology to analyze the content of a comment and determine whether it contains slander or criticism against a specific individual or group. For example, the analysis unit may understand the context of the comment and determine whether it contains slander or criticism against a specific individual or group. The analysis unit may also use sentiment analysis to analyze the sentiment of a comment and determine whether it contains slander or criticism against a specific individual or group. For example, the analysis unit may analyze the sentiment of a comment and determine whether it contains slander or criticism against a specific individual or group. The analysis unit may also use keyword extraction technology to extract important keywords from a comment and determine whether it contains slander or criticism against a specific individual or group. For example, the analysis unit may extract important keywords from a comment and determine whether it contains slander or criticism against a specific individual or group. This makes it possible to prevent the posting of defamatory comments by determining whether a comment contains slander or criticism against a specific individual or group. Some or all of the above-described processes in the analysis unit may be performed using generative AI, or they may be performed without using generative AI. For example, the analysis unit can input comments into a generation AI, which can analyze the content of the comments and determine whether they contain slander or criticism against a specific individual or group.

[0039] The reception system can analyze a user's past comment history and select the most appropriate reception method. For example, if a user has a history of posting defamatory comments, the reception system will strictly process their comments and display a message prompting them to reconfirm. The reception system can also process comments quickly if a user has a history of posting constructive comments. For example, if a user has a history of posting constructive comments, the reception system will process their comments quickly. The reception system can also analyze a user's past comment history and select the most appropriate reception method for a given time period if there are many posts during that period. For example, if a user has a history of posting comments during a given time period, the reception system will select the most appropriate reception method for that time period if there are many posts during that period. By analyzing a user's past comment history and selecting the most appropriate reception method, it is possible to prevent the posting of defamatory comments. Some or all of the above processing in the reception system may be performed using or without a generation AI. For example, the reception desk can input the user's past comment history data into a generating AI, which can then select the most suitable reception method.

[0040] The reception system can prioritize receiving comments that are highly relevant, taking into account the user's geographical location. For example, if the user is in a specific region, the reception system can prioritize receiving comments related to that region. Furthermore, if the user is traveling, the reception system can prioritize receiving comments related to their travel destination. Also, if the user is at home, the reception system can prioritize receiving comments related to local news and events. This allows for the prevention of defamatory comments by considering the user's geographical location when receiving comments. Some or all of the above processing in the reception system may be performed using a generation AI, or without one. For example, the reception system can input the user's geographical location data into a generation AI, which can then accept comments while considering the user's geographical location.

[0041] The reception desk can analyze a user's social media activity when receiving comments and accept relevant comments. For example, if a user frequently posts about a particular topic, the reception desk can prioritize accepting comments related to that topic. The reception desk can also prioritize accepting comments related to a particular hashtag if the user frequently uses that hashtag. The reception desk can also prioritize accepting comments related to a particular group or community if the user belongs to that group or community. By analyzing a user's social media activity and accepting comments accordingly, it is possible to prevent the posting of defamatory comments. Some or all of the above processing in the reception desk may be performed using or without generative AI. For example, the reception desk can input the user's social media activity data into a generating AI, which can then analyze the user's social media activity and receive relevant comments.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the comments during the analysis. For example, the analysis unit can perform a detailed analysis on comments of high importance. The analysis unit can also perform a simplified analysis on comments of low importance. The analysis unit can also perform an analysis with an appropriate level of detail on comments of medium importance. By adjusting the level of detail of the analysis based on the importance of the comments, it is possible to prevent the posting of defamatory comments. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input comment importance data into a generation AI, and the generation AI can adjust the level of detail of the analysis based on the importance of the comments.

[0043] The analysis unit can apply different analysis algorithms depending on the category of the comment during analysis. For example, the analysis unit can apply an algorithm that analyzes specific political keywords to comments related to politics. The analysis unit can also apply an algorithm that analyzes specific entertainment-related keywords to comments related to entertainment. The analysis unit can also apply an algorithm that analyzes specific sports-related keywords to comments related to sports. By applying different analysis algorithms depending on the category of the comment, it is possible to prevent the posting of defamatory comments. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input comment category data into a generation AI, and the generation AI can apply different analysis algorithms depending on the category of the comment.

[0044] The analysis unit can determine the priority of analysis based on when the comments were submitted. For example, the analysis unit may prioritize the analysis of the most recent comments. The analysis unit may also prioritize the analysis of comments submitted during a specific time period. The analysis unit may also prioritize the analysis of comments that are of high urgency. By determining the priority of analysis based on when the comments were submitted, it is possible to prevent the posting of defamatory comments. Some or all of the above processing in the analysis unit may be performed using a generation AI, or not. For example, the analysis unit may input comment submission time data into a generation AI, and the generation AI may determine the priority of analysis based on when the comments were submitted.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the comments during analysis. For example, the analysis unit may prioritize the analysis of highly relevant comments. The analysis unit may also postpone the analysis of less relevant comments. The analysis unit may also prioritize the analysis of comments related to a specific topic. By adjusting the order of analysis based on the relevance of the comments, it is possible to prevent the posting of defamatory comments. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit may input comment relevance data into a generation AI, and the generation AI may adjust the order of analysis based on the relevance of the comments.

[0046] The simulation unit can improve the accuracy of the simulation by considering the interrelationships of comments during the simulation. For example, if multiple comments are related, the simulation unit simulates them all at once. The simulation unit can also perform the simulation considering the context of the comments. The simulation unit can also understand the context of the comments and perform the simulation considering their interrelationships. By improving the accuracy of the simulation by considering the interrelationships of comments, it is possible to prevent the posting of defamatory comments. Some or all of the above processing in the simulation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the simulation unit can input comment interrelationship data into a generation AI, and the generation AI can improve the accuracy of the simulation by considering the interrelationships of the comments.

[0047] The simulation unit can perform simulations while considering the attribute information of the comment submitter. For example, the simulation unit can perform simulations while considering the submitter's age and gender. The simulation unit can also perform simulations while considering the submitter's occupation and interests. The simulation unit can also perform simulations while considering the submitter's past posting history. By performing simulations while considering the attribute information of the comment submitter, it is possible to prevent the posting of defamatory comments. Some or all of the above processing in the simulation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the simulation unit can input the submitter's attribute information data into a generation AI, and the generation AI can perform simulations while considering the submitter's attribute information.

[0048] The simulation unit can perform simulations while considering the geographical distribution of comments. For example, the simulation unit can prioritize simulating comments related to a specific region. The simulation unit can also simulate geographically close comments in a batch. The simulation unit can also simulate highly relevant comments while considering their geographical distribution. By performing simulations while considering the geographical distribution of comments, it is possible to prevent the posting of defamatory comments. Some or all of the above processing in the simulation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the simulation unit can input geographical distribution data of comments into a generation AI, and the generation AI can perform simulations while considering the geographical distribution of comments.

[0049] The simulation unit can improve the accuracy of the simulation by referring to relevant literature for the comments during the simulation. For example, the simulation unit can perform the simulation by referring to relevant academic papers. For example, the simulation unit can perform the simulation by referring to relevant news articles. For example, the simulation unit can perform the simulation by referring to relevant news articles. For example, the simulation unit can perform the simulation by referring to relevant books and reports. For example, the simulation unit can perform the simulation by referring to relevant books and reports. By improving the accuracy of the simulation by referring to relevant literature for the comments, it is possible to prevent the posting of defamatory comments. Some or all of the above processing in the simulation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the simulation unit can input the relevant literature data for the comments into a generation AI, and the generation AI can improve the accuracy of the simulation by referring to the relevant literature for the comments.

[0050] The service provider can select the optimal display method by referring to the user's past operation history when providing the service. For example, the service provider can prioritize providing the display method previously selected by the user. The service provider can also suggest the optimal display method based on the user's past operation history. The service provider can also provide a customized display method based on the display method previously used by the user. By selecting the optimal display method by referring to the user's past operation history, it is possible to prevent the posting of defamatory comments. Some or all of the above processing in the service provider may be performed using a generation AI, or it may be performed without a generation AI. For example, the service provider can input the user's past operation history data into a generation AI, and the generation AI can select the optimal display method by referring to the user's past operation history.

[0051] The service provider can select the optimal display method at the time of delivery, taking into account the user's device information. For example, if the user is using a smartphone, the service provider can provide a display method that matches the screen size. For example, if the user is using a smartphone, the service provider can provide a display method that matches the screen size. The service provider can also provide a display method optimized for larger screens if the user is using a tablet. For example, if the user is using a tablet, the service provider can provide a display method optimized for larger screens. The service provider can also provide a display method that includes detailed information if the user is using a desktop. For example, if the user is using a desktop, the service provider can provide a display method that includes detailed information. By selecting the optimal display method considering the user's device information, it is possible to prevent the posting of defamatory comments. Some or all of the above processing in the service provider may be performed using a generation AI, or it may be performed without a generation AI. For example, the service provider can input the user's device information data into a generation AI, and the generation AI can select the optimal display method considering the user's device information.

[0052] The service provider can provide multilingual information according to the user's language settings at the time of delivery. For example, the service provider can automatically set the information based on the language settings of the user's device. The service provider can also provide a language switching function if the user uses multiple languages. The service provider can also provide information in a specific language if the user selects that language. By providing multilingual information according to the user's language settings, it is possible to prevent the posting of defamatory comments. Some or all of the above processing in the service provider may be performed using a generation AI, or not. For example, the service provider can input the user's language setting data into a generation AI, and the generation AI can provide multilingual information according to the user's language settings.

[0053] The service provider can adjust how information is displayed based on past user feedback at the time of delivery. For example, the service provider can customize the display method based on feedback previously provided by the user. The service provider can also suggest the optimal display method based on past user feedback. The service provider can also improve how information is displayed based on feedback previously provided by the user. By adjusting how information is displayed based on past user feedback, it is possible to prevent the posting of defamatory comments. Some or all of the above processing in the service provider may be performed using a generation AI, or it may be performed without a generation AI. For example, the service provider can input past user feedback data into a generation AI, and the generation AI can adjust how information is displayed based on past user feedback.

[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 analysis unit can analyze a user's past comment history and select the most suitable analysis method. For example, if a user has a history of posting defamatory comments, the analysis can be performed more rigorously. Conversely, if a user has a history of posting constructive comments, the analysis can be performed more quickly. Furthermore, if a user's past comment history shows a high volume of posts during specific time periods, the system can select the most suitable analysis method for those periods. By analyzing a user's past comment history and selecting the most appropriate analysis method, the system can prevent the posting of defamatory comments.

[0056] The simulation unit can improve the accuracy of the simulation by considering the interrelationships between comments. For example, if multiple comments are related, they can be simulated together. It can also perform simulations considering the context of comments. Furthermore, it can understand the context of comments and perform simulations considering their interrelationships. By improving the accuracy of the simulation by considering the interrelationships between comments, it is possible to prevent the posting of defamatory comments.

[0057] The reception system can prioritize comments that are highly relevant to the user's geographical location. For example, if a user is in a specific region, comments related to that region can be prioritized. Similarly, if a user is traveling, comments related to their travel destination can be prioritized. Furthermore, if a user is at home, comments related to local news and events can be prioritized. By considering the user's geographical location when receiving comments, this system can prevent the posting of defamatory or abusive comments.

[0058] The simulation unit can perform simulations while considering the attribute information of the commenter. For example, it can perform simulations while considering the commenter's age and gender. It can also perform simulations while considering the commenter's occupation and interests. Furthermore, it can perform simulations while considering the commenter's past posting history. In this way, by performing simulations while considering the attribute information of the commenter, it is possible to prevent the posting of defamatory comments.

[0059] The simulation unit can improve the accuracy of its simulations by referring to relevant literature for comments. For example, it can perform simulations by referring to relevant academic papers. It can also perform simulations by referring to relevant news articles. Furthermore, it can perform simulations by referring to relevant books and reports. By improving the accuracy of simulations by referring to relevant literature for comments, it is possible to prevent the posting of defamatory comments.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The reception desk receives comments posted on social media. The reception desk receives comments in real time as users post them on social media. It can also receive comments at regular intervals using batch processing. Furthermore, it can prioritize receiving comments that contain specific keywords based on filtering criteria. Step 2: The analysis unit analyzes the comments received by the reception unit. The analysis unit uses natural language processing technology to analyze the content of the comments and can also analyze the sentiment of the comments using sentiment analysis. In addition, it uses keyword extraction technology to extract important keywords from the comments and understand the context of the comments to determine whether they constitute defamation or libel. Step 3: The simulation unit performs a simulation based on the comments analyzed by the analysis unit. The simulation unit can use a simulation model to simulate how the comments will be received by other users and can also perform simulations based on evaluation criteria. It can also perform simulations using past response data. Step 4: The provisioning unit provides the simulation results obtained by the simulation unit. The provisioning unit can also display the simulation results to the user and notify the user of the simulation results using a notification method. Alternatively, the simulation results can be provided using a feedback method, for example, by displaying them as a pop-up message.

[0062] (Example of form 2) The SNS comment judgment system according to an embodiment of the present invention is a system for preventing the posting of defamatory comments on social networking services (SNS). When a user posts a comment on SNS, the system inputs the comment into a generating AI, which analyzes the content of the comment and performs a simulation of what will happen after posting. The generating AI determines whether the comment is likely to lead to defamation. For example, if the comment contains slander or criticism against a specific individual or group, the generating AI determines that the comment may constitute defamation. The judgment result is provided to the user, and the user can post the comment after confirming the result. This mechanism allows users to calmly consider whether their post may hurt someone, thereby reducing trouble caused by defamation. For example, when a user posts a comment on SNS, they input the comment into the generating AI. For example, they might input a comment such as, "I cannot tolerate the actions of this group." This comment is input into the generating AI. Next, the generating AI analyzes the input comment. The generating AI understands the content of the comment and performs a simulation of what will happen after posting. For example, in response to a comment such as "I cannot tolerate the actions of this group," the generating AI simulates how other users might perceive that comment. The generating AI determines whether the comment could potentially lead to defamation. For instance, if the comment "I cannot tolerate the actions of this group" is considered criticism of a specific group, the generating AI will determine that the comment may constitute defamation. The result of this determination is provided to the user. For example, a result such as "This comment may lead to criticism of a specific group" might be displayed. Users can then post their comments after reviewing the result. For example, if a user receives a result such as "This comment may lead to criticism of a specific group," they can review their comment. This mechanism allows users to calmly consider whether their posts might hurt someone, thereby reducing trouble caused by defamation. This mechanism can prevent the posting of defamatory comments on social media.Users can refer to the AI's judgment results to check whether their comments constitute defamation or libel. This can reduce problems on social media and promote healthy communication. In this way, the social media comment judgment system can prevent the posting of defamatory comments on social media and promote healthy communication.

[0063] The SNS comment judgment system according to this embodiment comprises a reception unit, an analysis unit, a simulation unit, and a provision unit. The reception unit receives comments posted to SNS. For example, the reception unit receives comments in real time when a user posts a comment to SNS. The reception unit can also receive comments at regular intervals using batch processing. Furthermore, the reception unit can also receive comments based on filtering criteria. For example, the reception unit can prioritize receiving comments containing specific keywords. The analysis unit analyzes the comments received by the reception unit. For example, the analysis unit analyzes the content of the comments using natural language processing technology. The analysis unit can also analyze the sentiment of the comments using sentiment analysis. Furthermore, the analysis unit can extract important keywords from the comments using keyword extraction technology. For example, the analysis unit understands the context of the comments and determines whether they constitute defamation. The simulation unit performs simulations based on the comments analyzed by the analysis unit. For example, the simulation unit uses a simulation model to simulate how the comments will be received by other users. Furthermore, the simulation unit can perform simulations based on evaluation criteria. In addition, the simulation unit can perform simulations based on the data used. For example, the simulation unit can perform simulations using past reaction data. The provision unit provides the simulation results obtained by the simulation unit. The provision unit can, for example, display the simulation results to the user. The provision unit can also notify the user of the simulation results using a notification method. Furthermore, the provision unit can provide the simulation results using a feedback method to the user. For example, the provision unit can display the simulation results as a pop-up message. As a result, the SNS comment judgment system according to this embodiment can prevent the posting of defamatory comments by analyzing comments posted on SNS, performing simulations, and providing the results.

[0064] The reception unit receives comments posted on social media. For example, when a user posts a comment on social media, the reception unit receives that comment in real time. Specifically, when a user enters a comment and presses the send button, the reception unit immediately captures the comment and imports it into the system. The reception unit can also receive comments at regular intervals using batch processing. For example, by receiving newly posted comments in batches every hour, the system load can be distributed. Furthermore, the reception unit can receive comments based on filtering criteria. For example, the reception unit can prioritize comments containing specific keywords. This makes it possible to process important topics and urgent comments quickly. Filtering criteria can be set by the administrator, for example, by setting it to prioritize comments containing specific hashtags, usernames, or keywords indicating a particular sentiment. This allows the reception unit to receive comments efficiently and effectively, improving the overall system performance.

[0065] The analysis unit analyzes comments received by the reception unit. For example, the analysis unit uses natural language processing techniques to analyze the content of comments. Specifically, it performs morphological analysis to break down comments into individual words and understand the meaning and context of each word. The analysis unit can also analyze the sentiment of comments using sentiment analysis. For example, it classifies comments into sentiment categories such as positive, negative, and neutral to grasp the overall tone of the comments. Furthermore, the analysis unit can extract important keywords from comments using keyword extraction techniques. For example, it extracts keywords related to specific topics or frequently occurring words to identify the subject of the comment. The analysis unit combines these techniques to understand the context of the comment and determine whether it constitutes defamation. For example, if a comment contains language that attacks a specific user or discriminatory expressions, it flags it as a defamatory comment. Based on these analysis results, the analysis unit evaluates the appropriateness of the comment and provides information to proceed to the next processing step. This allows the analysis unit to analyze the content of comments in detail, improving the overall accuracy and reliability of the system.

[0066] The simulation unit performs simulations based on comments analyzed by the analysis unit. For example, the simulation unit uses a simulation model to simulate how comments will be received by other users. Specifically, it models the expected reactions when a particular comment is posted, based on past user reaction data. The simulation unit can also perform simulations based on evaluation criteria. For example, it evaluates the reactions that a comment may provoke by considering the sentiment score of the comment and the importance of keywords. Furthermore, the simulation unit can perform simulations based on the data used. For example, it can perform more accurate simulations using past reaction data from a specific user group or community. The simulation unit integrates this data to predict the impact of comments on other users. For example, if past data shows that comments containing certain keywords have a high probability of eliciting negative reactions, the simulation unit will rate the risk of such comments higher when they are posted. This allows the simulation unit to predict the impact of comments in advance and provide information to take appropriate countermeasures.

[0067] The service provider provides the simulation results obtained by the simulation unit. For example, the service provider displays the simulation results to the user. Specifically, it displays the simulation results as a pop-up message to users who are about to post a comment, informing them in advance how their comment will be received by other users. The service provider can also notify users of the simulation results using notification methods. For example, it can send the simulation results to the user via email or direct message on social media. Furthermore, the service provider can provide simulation results using a feedback method to the user. For example, after a comment is posted, it can compare the actual reactions to the comment with the simulation results and provide feedback to the user. This allows the user to learn how their comments are received and use that knowledge to improve future posts. Through these functions, the service provider can provide users with appropriate information and prevent the posting of defamatory comments. Furthermore, the service provider can increase the transparency of the entire system and gain user trust.

[0068] The analysis unit can understand the content of a comment and determine whether it constitutes defamation. The analysis unit can analyze the content of a comment using, for example, natural language processing technology. For example, the analysis unit can understand the context of the comment and determine whether it constitutes defamation. The analysis unit can also analyze the sentiment of a comment using sentiment analysis. For example, the analysis unit can analyze the sentiment of a comment and determine whether it constitutes defamation. The analysis unit can also extract important keywords from a comment using keyword extraction technology. For example, the analysis unit can extract important keywords from a comment and determine whether it constitutes defamation. In this way, by understanding the content of a comment and determining whether it constitutes defamation, it is possible to prevent the posting of defamatory comments. Some or all of the above processing in the analysis unit may be performed using generative AI, or it may be performed without using generative AI. For example, the analysis unit can input comments into a generation AI, which can analyze the content of the comments and determine whether or not they constitute defamation.

[0069] The simulation unit can simulate how comments will be received by other users. For example, the simulation unit can use a simulation model to simulate how comments will be received by other users. For example, the simulation unit can use sentiment analysis to simulate how comments will be received by other users. The simulation unit can also use user profiles to simulate how comments will be received by other users. For example, the simulation unit can use user profiles to simulate how comments will be received by other users. The simulation unit can also use past reaction data to simulate how comments will be received by other users. For example, the simulation unit can use past reaction data to simulate how comments will be received by other users. This makes it possible to prevent the posting of defamatory comments by simulating how comments will be received by other users. Some or all of the above processing in the simulation unit may be performed using a generative AI, or it may be performed without using a generative AI. For example, the simulation unit can input a comment into a generative AI, and the generative AI can simulate how the comment will be received by other users.

[0070] The service provider can provide the user with simulation results and give the user an opportunity to review their comments. The service provider can, for example, display the simulation results to the user. For example, the service provider can display the simulation results as a pop-up message. The service provider can also notify the user of the simulation results using a notification method. For example, the service provider can notify the user of the simulation results via email. The service provider can also provide the simulation results to the user using a feedback method. For example, the service provider can provide the simulation results as a feedback message. By providing the user with simulation results and giving them an opportunity to review their comments, it is possible to prevent the posting of defamatory comments. Some or all of the above processing in the service provider may be performed using a generation AI, or it may be performed without a generation AI. For example, the service provider can input the simulation results into a generation AI, and the generation AI can provide the simulation results to the user.

[0071] The reception unit can receive comments when users post them on social media. For example, the reception unit can receive comments in real time when users post them on social media. For example, the reception unit can receive comments as soon as the user types them. The reception unit can also receive comments at regular intervals using batch processing. For example, the reception unit can receive comments in batches every hour. The reception unit can also receive comments based on filtering criteria. For example, the reception unit can prioritize receiving comments that contain specific keywords. This helps prevent the posting of defamatory comments by receiving comments when users post them on social media. Some or all of the above processing in the reception unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the reception unit can input comments into a generation AI, and the generation AI can receive the comments.

[0072] The analysis unit can determine whether a comment contains slander or criticism against a specific individual or group. For example, the analysis unit may use natural language processing technology to analyze the content of a comment and determine whether it contains slander or criticism against a specific individual or group. For example, the analysis unit may understand the context of the comment and determine whether it contains slander or criticism against a specific individual or group. The analysis unit may also use sentiment analysis to analyze the sentiment of a comment and determine whether it contains slander or criticism against a specific individual or group. For example, the analysis unit may analyze the sentiment of a comment and determine whether it contains slander or criticism against a specific individual or group. The analysis unit may also use keyword extraction technology to extract important keywords from a comment and determine whether it contains slander or criticism against a specific individual or group. For example, the analysis unit may extract important keywords from a comment and determine whether it contains slander or criticism against a specific individual or group. This makes it possible to prevent the posting of defamatory comments by determining whether a comment contains slander or criticism against a specific individual or group. Some or all of the above-described processes in the analysis unit may be performed using generative AI, or they may be performed without using generative AI. For example, the analysis unit can input comments into a generation AI, which can analyze the content of the comments and determine whether they contain slander or criticism against a specific individual or group.

[0073] The reception system can estimate the user's emotions and adjust the timing of comment submission based on the estimated emotions. For example, if the user is angry, the reception system may temporarily delay comment submission until the user calms down. For example, if the user is angry, the reception system may delay comment submission for a certain period of time. The reception system can also temporarily hold off on comment submission if the user is agitated and prompt the user to reconsider. For example, if the user is agitated, the reception system may hold off on comment submission and display a message prompting reconsideration. The reception system can also temporarily hold off on comment submission if the user is sad and wait until the user's emotions have calmed down. For example, if the user is sad, the reception system may delay comment submission for a certain period of time. By adjusting the timing of comment submission based on the user's emotions, it is possible to prevent the posting of defamatory comments. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the reception area may be performed using a generative AI, or they may not be performed using a generative AI. For example, the reception area can input user emotion data into a generative AI, which can then estimate the user's emotion and adjust the timing of comment reception.

[0074] The reception system can analyze a user's past comment history and select the most appropriate reception method. For example, if a user has a history of posting defamatory comments, the reception system will strictly process their comments and display a message prompting them to reconfirm. The reception system can also process comments quickly if a user has a history of posting constructive comments. For example, if a user has a history of posting constructive comments, the reception system will process their comments quickly. The reception system can also analyze a user's past comment history and select the most appropriate reception method for a given time period if there are many posts during that period. For example, if a user has a history of posting comments during a given time period, the reception system will select the most appropriate reception method for that time period if there are many posts during that period. By analyzing a user's past comment history and selecting the most appropriate reception method, it is possible to prevent the posting of defamatory comments. Some or all of the above processing in the reception system may be performed using or without a generation AI. For example, the reception desk can input the user's past comment history data into a generating AI, which can then select the most suitable reception method.

[0075] The reception system can filter comments based on the user's current psychological state and areas of interest. For example, if the user is stressed, the reception system will filter out extreme comments and display a message prompting the user to reconsider. The reception system can also prioritize filtering comments related to a user's strong opinions on a particular area of ​​interest. For example, if the user is relaxed, the reception system will prioritize filtering comments related to that area. The reception system can also relax filtering and accept free comments if the user is relaxed. For example, if the user is relaxed, the reception system will relax filtering and accept free comments. This allows for the prevention of defamatory comments by filtering based on the user's psychological state and areas of interest. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the reception area may be performed using a generation AI, or it may be performed without using a generation AI. For example, the reception area can input user psychological state data into a generation AI, which can then estimate the user's psychological state and filter comments.

[0076] The reception system can estimate the user's emotions and determine the priority of comments to be received based on the estimated emotions. For example, if the user is angry, the reception system may set a low priority for the comment and wait until the user calms down. The reception system may also set a low priority for the comment if the user is agitated and prompt them to reconsider. The reception system may also set a high priority for the comment if the user is relaxed and process it quickly. By determining the priority of comments based on the user's emotions, it is possible to prevent the posting of defamatory comments. Some or all of the above processing in the reception system may be performed using generative AI, or without using generative AI. For example, the reception desk can input user emotion data into a generating AI, which can then estimate the user's emotions and determine the priority of comments.

[0077] The reception system can prioritize receiving comments that are highly relevant, taking into account the user's geographical location. For example, if the user is in a specific region, the reception system can prioritize receiving comments related to that region. Furthermore, if the user is traveling, the reception system can prioritize receiving comments related to their travel destination. Also, if the user is at home, the reception system can prioritize receiving comments related to local news and events. This allows for the prevention of defamatory comments by considering the user's geographical location when receiving comments. Some or all of the above processing in the reception system may be performed using a generation AI, or without one. For example, the reception system can input the user's geographical location data into a generation AI, which can then accept comments while considering the user's geographical location.

[0078] The reception desk can analyze a user's social media activity when receiving comments and accept relevant comments. For example, if a user frequently posts about a particular topic, the reception desk can prioritize accepting comments related to that topic. The reception desk can also prioritize accepting comments related to a particular hashtag if the user frequently uses that hashtag. The reception desk can also prioritize accepting comments related to a particular group or community if the user belongs to that group or community. By analyzing a user's social media activity and accepting comments accordingly, it is possible to prevent the posting of defamatory comments. Some or all of the above processing in the reception desk may be performed using or without generative AI. For example, the reception desk can input the user's social media activity data into a generating AI, which can then analyze the user's social media activity and receive relevant comments.

[0079] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated emotions. For example, if the user is angry, the analysis unit can present the analysis results in calm language. For example, if the user is angry, the analysis unit can present the analysis results in calm language. For example, if the user is excited, the analysis unit can present the analysis results in calm language. For example, if the user is sad, the analysis unit can present the analysis results in gentle language. By adjusting the way the analysis is presented based on the user's emotions, it is possible to prevent the posting of defamatory comments. Emotion estimation is achieved using an emotion estimation function, such as 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-described processing in the analysis unit may be performed using a generative AI or not. For example, the analysis unit can input user emotion data into a generating AI, which can then estimate the user's emotions and adjust the method of expressing the analysis.

[0080] The analysis unit can adjust the level of detail of the analysis based on the importance of the comments during the analysis. For example, the analysis unit can perform a detailed analysis on comments of high importance. The analysis unit can also perform a simplified analysis on comments of low importance. The analysis unit can also perform an analysis with an appropriate level of detail on comments of medium importance. By adjusting the level of detail of the analysis based on the importance of the comments, it is possible to prevent the posting of defamatory comments. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input comment importance data into a generation AI, and the generation AI can adjust the level of detail of the analysis based on the importance of the comments.

[0081] The analysis unit can apply different analysis algorithms depending on the category of the comment during analysis. For example, the analysis unit can apply an algorithm that analyzes specific political keywords to comments related to politics. The analysis unit can also apply an algorithm that analyzes specific entertainment-related keywords to comments related to entertainment. The analysis unit can also apply an algorithm that analyzes specific sports-related keywords to comments related to sports. By applying different analysis algorithms depending on the category of the comment, it is possible to prevent the posting of defamatory comments. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input comment category data into a generation AI, and the generation AI can apply different analysis algorithms depending on the category of the comment.

[0082] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis. The analysis unit can also provide a detailed analysis if the user is relaxed. For example, if the user is relaxed, the analysis unit can provide a detailed analysis. The analysis unit can also provide a concise and easy-to-understand analysis if the user is excited. For example, if the user is excited, the analysis unit can provide a concise and easy-to-understand analysis. By adjusting the length of the analysis based on the user's emotions, it is possible to prevent the posting of defamatory comments. Emotion estimation is achieved using an emotion estimation function, such as 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-described processing in the analysis unit may be performed using a generative AI or not. For example, the analysis unit can input user emotion data into a generating AI, which can then estimate the user's emotions and adjust the length of the analysis.

[0083] The analysis unit can determine the priority of analysis based on when the comments were submitted. For example, the analysis unit may prioritize the analysis of the most recent comments. The analysis unit may also prioritize the analysis of comments submitted during a specific time period. The analysis unit may also prioritize the analysis of comments that are of high urgency. By determining the priority of analysis based on when the comments were submitted, it is possible to prevent the posting of defamatory comments. Some or all of the above processing in the analysis unit may be performed using a generation AI, or not. For example, the analysis unit may input comment submission time data into a generation AI, and the generation AI may determine the priority of analysis based on when the comments were submitted.

[0084] The analysis unit can adjust the order of analysis based on the relevance of the comments during analysis. For example, the analysis unit may prioritize the analysis of highly relevant comments. The analysis unit may also postpone the analysis of less relevant comments. The analysis unit may also prioritize the analysis of comments related to a specific topic. By adjusting the order of analysis based on the relevance of the comments, it is possible to prevent the posting of defamatory comments. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit may input comment relevance data into a generation AI, and the generation AI may adjust the order of analysis based on the relevance of the comments.

[0085] The simulation unit can estimate the user's emotions and adjust the simulation criteria based on the estimated user emotions. For example, if the user is angry, the simulation unit can perform the simulation from a calm perspective. For example, if the user is angry, the simulation unit can perform the simulation from a calm perspective. For example, if the user is excited, the simulation unit can perform the simulation from a calm perspective. For example, if the user is sad, the simulation unit can perform the simulation from a gentle perspective. By adjusting the simulation criteria based on the user's emotions, it is possible to prevent the posting of defamatory comments. Emotion estimation is achieved using an emotion estimation function, such as 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-described processing in the simulation unit may be performed using generative AI or not. For example, the simulation unit can input user emotion data into a generating AI, which can then estimate the user's emotions and adjust the simulation criteria.

[0086] The simulation unit can improve the accuracy of the simulation by considering the interrelationships of comments during the simulation. For example, if multiple comments are related, the simulation unit simulates them all at once. The simulation unit can also perform the simulation considering the context of the comments. The simulation unit can also understand the context of the comments and perform the simulation considering their interrelationships. By improving the accuracy of the simulation by considering the interrelationships of comments, it is possible to prevent the posting of defamatory comments. Some or all of the above processing in the simulation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the simulation unit can input comment interrelationship data into a generation AI, and the generation AI can improve the accuracy of the simulation by considering the interrelationships of the comments.

[0087] The simulation unit can perform simulations while considering the attribute information of the comment submitter. For example, the simulation unit can perform simulations while considering the submitter's age and gender. The simulation unit can also perform simulations while considering the submitter's occupation and interests. The simulation unit can also perform simulations while considering the submitter's past posting history. By performing simulations while considering the attribute information of the comment submitter, it is possible to prevent the posting of defamatory comments. Some or all of the above processing in the simulation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the simulation unit can input the submitter's attribute information data into a generation AI, and the generation AI can perform simulations while considering the submitter's attribute information.

[0088] The simulation unit can estimate the user's emotions and adjust the order in which the simulation results are displayed based on the estimated user's emotions. For example, if the user is angry, the simulation unit can prioritize displaying calm results. For example, if the user is excited, the simulation unit can prioritize displaying calm results. For example, if the user is sad, the simulation unit can prioritize displaying gentle results. By adjusting the order in which the simulation results are displayed based on the user's emotions, it is possible to prevent the posting of defamatory comments. Emotion estimation is achieved using an emotion estimation function, such as 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-described processing in the simulation unit may be performed using or without generative AI. For example, the simulation unit can input user emotion data into a generating AI, which then estimates the user's emotions and adjusts the order in which the simulation results are displayed.

[0089] The simulation unit can perform simulations while considering the geographical distribution of comments. For example, the simulation unit can prioritize simulating comments related to a specific region. The simulation unit can also simulate geographically close comments in a batch. The simulation unit can also simulate highly relevant comments while considering their geographical distribution. By performing simulations while considering the geographical distribution of comments, it is possible to prevent the posting of defamatory comments. Some or all of the above processing in the simulation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the simulation unit can input geographical distribution data of comments into a generation AI, and the generation AI can perform simulations while considering the geographical distribution of comments.

[0090] The simulation unit can improve the accuracy of the simulation by referring to relevant literature for the comments during the simulation. For example, the simulation unit can perform the simulation by referring to relevant academic papers. For example, the simulation unit can perform the simulation by referring to relevant news articles. For example, the simulation unit can perform the simulation by referring to relevant news articles. For example, the simulation unit can perform the simulation by referring to relevant books and reports. For example, the simulation unit can perform the simulation by referring to relevant books and reports. By improving the accuracy of the simulation by referring to relevant literature for the comments, it is possible to prevent the posting of defamatory comments. Some or all of the above processing in the simulation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the simulation unit can input the relevant literature data for the comments into a generation AI, and the generation AI can improve the accuracy of the simulation by referring to the relevant literature for the comments.

[0091] The information provider can estimate the user's emotions and adjust how the information is displayed based on the estimated emotions. For example, if the user is angry, the information provider can provide information in calm language. For example, if the user is angry, the information provider can provide information in calm language. For example, if the user is excited, the information provider can provide information in calm language. For example, if the user is sad, the information provider can provide information in gentle language. By adjusting how the information is displayed based on the user's emotions, it is possible to prevent the posting of defamatory comments. Emotion estimation is achieved using an emotion estimation function, such as 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 information provider may be performed using generative AI or not. For example, the service provider can input user emotion data into a generating AI, which can then estimate the user's emotions and adjust how the information is displayed.

[0092] The service provider can select the optimal display method by referring to the user's past operation history when providing the service. For example, the service provider can prioritize providing the display method previously selected by the user. The service provider can also suggest the optimal display method based on the user's past operation history. The service provider can also provide a customized display method based on the display method previously used by the user. By selecting the optimal display method by referring to the user's past operation history, it is possible to prevent the posting of defamatory comments. Some or all of the above processing in the service provider may be performed using a generation AI, or it may be performed without a generation AI. For example, the service provider can input the user's past operation history data into a generation AI, and the generation AI can select the optimal display method by referring to the user's past operation history.

[0093] The service provider can adjust how information is displayed at the time of delivery, taking into account the user's current psychological state. For example, if the user is stressed, the service provider can provide a simple and highly visible display method. For example, if the user is stressed, the service provider can provide a simple and highly visible display method. The service provider can also provide a display method that includes detailed information if the user is relaxed. For example, if the user is relaxed, the service provider can provide a display method that includes detailed information. The service provider can also provide a display method that gets straight to the point if the user is in a hurry. For example, if the user is in a hurry, the service provider can provide a display method that gets straight to the point. By adjusting how information is displayed considering the user's current psychological state, it is possible to prevent the posting of defamatory comments. Emotion estimation is achieved using an emotion estimation function, such as 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 service provider may be performed using a generative AI or not. For example, the service provider can input user psychological state data into a generating AI, which can then estimate the user's psychological state and adjust how the information is displayed.

[0094] The service provider can estimate the user's emotions and adjust the instructions for operating the information provided based on the estimated emotions. For example, if the user is angry, the service provider can simplify the instructions. For example, if the user is angry, the service provider can simplify the instructions. For example, if the user is agitated, the service provider can provide instructions in a calm manner. For example, if the user is agitated, the service provider can provide instructions in a calm manner. For example, if the user is sad, the service provider can provide instructions in a gentle manner. For example, if the user is sad, the service provider can provide instructions in a gentle manner. By adjusting the instructions for operating the information provided based on the user's emotions, it is possible to prevent the posting of defamatory comments. Emotion estimation is achieved using an emotion estimation function, such as 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 service provider may be performed using generative AI or not. For example, the service provider can input user emotion data into a generating AI, which can then estimate the user's emotions and adjust the procedure for handling the information provided.

[0095] The service provider can select the optimal display method at the time of delivery, taking into account the user's device information. For example, if the user is using a smartphone, the service provider can provide a display method that matches the screen size. For example, if the user is using a smartphone, the service provider can provide a display method that matches the screen size. The service provider can also provide a display method optimized for larger screens if the user is using a tablet. For example, if the user is using a tablet, the service provider can provide a display method optimized for larger screens. The service provider can also provide a display method that includes detailed information if the user is using a desktop. For example, if the user is using a desktop, the service provider can provide a display method that includes detailed information. By selecting the optimal display method considering the user's device information, it is possible to prevent the posting of defamatory comments. Some or all of the above processing in the service provider may be performed using a generation AI, or it may be performed without a generation AI. For example, the service provider can input the user's device information data into a generation AI, and the generation AI can select the optimal display method considering the user's device information.

[0096] The service provider can provide multilingual information according to the user's language settings at the time of delivery. For example, the service provider can automatically set the information based on the language settings of the user's device. The service provider can also provide a language switching function if the user uses multiple languages. The service provider can also provide information in a specific language if the user selects that language. By providing multilingual information according to the user's language settings, it is possible to prevent the posting of defamatory comments. Some or all of the above processing in the service provider may be performed using a generation AI, or not. For example, the service provider can input the user's language setting data into a generation AI, and the generation AI can provide multilingual information according to the user's language settings.

[0097] The service provider can adjust how information is displayed based on past user feedback at the time of delivery. For example, the service provider can customize the display method based on feedback previously provided by the user. The service provider can also suggest the optimal display method based on past user feedback. The service provider can also improve how information is displayed based on feedback previously provided by the user. By adjusting how information is displayed based on past user feedback, it is possible to prevent the posting of defamatory comments. Some or all of the above processing in the service provider may be performed using a generation AI, or it may be performed without a generation AI. For example, the service provider can input past user feedback data into a generation AI, and the generation AI can adjust how information is displayed based on past user feedback.

[0098] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0099] The reception system can estimate the user's emotions and adjust the timing of comment submission based on those emotions. For example, if a user is angry, the system can temporarily delay comment submission until they calm down. If a user is agitated, the system can temporarily hold the comment submission and display a message prompting them to reconsider. Furthermore, if a user is sad, the system can temporarily delay comment submission and wait until their emotions have subsided. By adjusting the timing of comment submission based on the user's emotions, the system can prevent the posting of defamatory comments.

[0100] The analysis unit can analyze a user's past comment history and select the most suitable analysis method. For example, if a user has a history of posting defamatory comments, the analysis can be performed more rigorously. Conversely, if a user has a history of posting constructive comments, the analysis can be performed more quickly. Furthermore, if a user's past comment history shows a high volume of posts during specific time periods, the system can select the most suitable analysis method for those periods. By analyzing a user's past comment history and selecting the most appropriate analysis method, the system can prevent the posting of defamatory comments.

[0101] The simulation unit can improve the accuracy of the simulation by considering the interrelationships between comments. For example, if multiple comments are related, they can be simulated together. It can also perform simulations considering the context of comments. Furthermore, it can understand the context of comments and perform simulations considering their interrelationships. By improving the accuracy of the simulation by considering the interrelationships between comments, it is possible to prevent the posting of defamatory comments.

[0102] The information provider can estimate the user's emotions and adjust how the information is displayed based on those emotions. For example, if the user is angry, the information can be presented in a calm manner. If the user is excited, the information can be presented in a more subdued manner. Furthermore, if the user is sad, the information can be presented in a gentle manner. By adjusting how the information is presented based on the user's emotions, it is possible to prevent the posting of defamatory comments.

[0103] The reception system can prioritize comments that are highly relevant to the user's geographical location. For example, if a user is in a specific region, comments related to that region can be prioritized. Similarly, if a user is traveling, comments related to their travel destination can be prioritized. Furthermore, if a user is at home, comments related to local news and events can be prioritized. By considering the user's geographical location when receiving comments, this system can prevent the posting of defamatory or abusive comments.

[0104] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on those emotions. For example, if the user is angry, the analysis results can be presented in a calm manner. If the user is excited, the results can be presented in a calm manner. Furthermore, if the user is sad, the results can be presented in a gentle manner. By adjusting the presentation of the analysis based on the user's emotions, it is possible to prevent the posting of defamatory comments.

[0105] The simulation unit can perform simulations while considering the attribute information of the commenter. For example, it can perform simulations while considering the commenter's age and gender. It can also perform simulations while considering the commenter's occupation and interests. Furthermore, it can perform simulations while considering the commenter's past posting history. In this way, by performing simulations while considering the attribute information of the commenter, it is possible to prevent the posting of defamatory comments.

[0106] The service provider can estimate the user's emotions and adjust the instructions for using the information provided based on those emotions. For example, if the user is angry, the instructions can be simplified. If the user is agitated, the instructions can be presented in a calmer manner. Furthermore, if the user is sad, gentle instructions can be provided. By adjusting the instructions for using the information provided based on the user's emotions, it is possible to prevent the posting of defamatory comments.

[0107] The simulation unit can improve the accuracy of its simulations by referring to relevant literature for comments. For example, it can perform simulations by referring to relevant academic papers. It can also perform simulations by referring to relevant news articles. Furthermore, it can perform simulations by referring to relevant books and reports. By improving the accuracy of simulations by referring to relevant literature for comments, it is possible to prevent the posting of defamatory comments.

[0108] The information provider can estimate the user's emotions and adjust how the information is displayed based on those emotions. For example, if the user is angry, the information can be presented in a calm manner. If the user is excited, the information can be presented in a more subdued manner. Furthermore, if the user is sad, the information can be presented in a gentle manner. By adjusting how the information is presented based on the user's emotions, it is possible to prevent the posting of defamatory comments.

[0109] The following briefly describes the processing flow for example form 2.

[0110] Step 1: The reception desk receives comments posted on social media. The reception desk receives comments in real time as users post them on social media. It can also receive comments at regular intervals using batch processing. Furthermore, it can prioritize receiving comments that contain specific keywords based on filtering criteria. Step 2: The analysis unit analyzes the comments received by the reception unit. The analysis unit uses natural language processing technology to analyze the content of the comments and can also analyze the sentiment of the comments using sentiment analysis. In addition, it uses keyword extraction technology to extract important keywords from the comments and understand the context of the comments to determine whether they constitute defamation or libel. Step 3: The simulation unit performs a simulation based on the comments analyzed by the analysis unit. The simulation unit can use a simulation model to simulate how the comments will be received by other users and can also perform simulations based on evaluation criteria. It can also perform simulations using past response data. Step 4: The provisioning unit provides the simulation results obtained by the simulation unit. The provisioning unit can also display the simulation results to the user and notify the user of the simulation results using a notification method. Alternatively, the simulation results can be provided using a feedback method, for example, by displaying them as a pop-up message.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] Each of the multiple elements described above, including the reception unit, analysis unit, simulation unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and receives comments in real time when a user posts a comment on SNS. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the content of the comment using natural language processing technology. The simulation unit is implemented by the specific processing unit 290 of the data processing unit 12 and simulates how the comment will be received by other users. The provision unit is implemented by the control unit 46A of the smart device 14 and displays the simulation results to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0115] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] Each of the multiple elements described above, including the reception unit, analysis unit, simulation unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and receives comments in real time when a user posts a comment on social media. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the content of the comment using natural language processing technology. The simulation unit is implemented by the specific processing unit 290 of the data processing unit 12 and simulates how the comment will be received by other users. The provision unit is implemented by the control unit 46A of the smart glasses 214 and displays the simulation results to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0131] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0140] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0141] In 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.

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

[0143] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0145] The data processing system 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.

[0146] Each of the multiple elements described above, including the reception unit, analysis unit, simulation unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and receives comments in real time when a user posts a comment on SNS. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the content of the comment using natural language processing technology. The simulation unit is implemented by the specific processing unit 290 of the data processing unit 12 and simulates how the comment will be received by other users. The provision unit is implemented by the control unit 46A of the headset terminal 314 and displays the simulation results to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0147] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.).

[0160] 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.

[0161] 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.

[0162] 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.

[0163] Each of the multiple elements described above, including the reception unit, analysis unit, simulation unit, and provision unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and receives comments in real time when a user posts a comment on SNS. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the content of the comment using natural language processing technology. The simulation unit is implemented by the specific processing unit 290 of the data processing unit 12 and simulates how the comment will be received by other users. The provision unit is implemented by the control unit 46A of the robot 414 and displays the simulation results to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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."

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] (Note 1) A reception desk that accepts comments posted on social media, An analysis unit analyzes the comments received by the aforementioned reception unit, A simulation unit performs a simulation based on the comments analyzed by the aforementioned analysis unit, The system includes a providing unit that provides simulation results obtained by the simulation unit. A system characterized by the following features. (Note 2) The aforementioned analysis unit, We will understand the content of the comment and determine whether it constitutes defamation or libel. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned simulation unit, Simulate how comments will be received by other users. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, Provide users with simulation results and give them an opportunity to review their comments. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is When a user posts a comment on social media, the system accepts that comment. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit, Determining whether it contains slander or criticism against a specific individual or group. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of comment submissions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is Analyze the user's past comment history and select the most suitable method for receiving comments. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When receiving comments, filtering is performed based on the user's current psychological state and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's sentiment and determines the priority of comments to accept based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When receiving comments, the system prioritizes accepting comments that are highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When receiving comments, the system analyzes the user's social media activity and accepts relevant comments. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the comments. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of the comment. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of the analysis will be determined based on when the comments were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the comments. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned simulation unit, It estimates the user's emotions and adjusts the simulation criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned simulation unit, During simulation, consider the interrelationships of comments to improve the accuracy of the simulation. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned simulation unit, During the simulation, the attribute information of the commenter will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned simulation unit, It estimates the user's emotions and adjusts the order in which the simulation results are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned simulation unit, During the simulation, the geographical distribution of comments will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned simulation unit, During simulations, refer to the relevant literature in the comments to improve the accuracy of the simulation. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, It estimates the user's emotions and adjusts how information is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing the service, the system selects the optimal display method by referring to the user's past operation history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing the service, we adjust the way information is displayed, taking into account the user's current psychological state. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, It estimates the user's emotions and adjusts the instructions for interacting with the information provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing the service, the optimal display method is selected considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, When providing the service, we will provide information in multiple languages ​​according to the user's language settings. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned supply unit is, When providing the service, we will adjust how information is displayed based on past user feedback. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0183] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk that accepts comments posted on social media, An analysis unit analyzes the comments received by the aforementioned reception unit, A simulation unit performs a simulation based on the comments analyzed by the aforementioned analysis unit, The system includes a providing unit that provides simulation results obtained by the simulation unit. A system characterized by the following features.

2. The aforementioned analysis unit, We will understand the content of the comment and determine whether it constitutes defamation or libel. The system according to feature 1.

3. The aforementioned simulation unit, Simulate how comments will be received by other users. The system according to feature 1.

4. The aforementioned supply unit is, Provide users with simulation results and give them an opportunity to review their comments. The system according to feature 1.

5. The aforementioned reception unit is When a user posts a comment on social media, the system accepts that comment. The system according to feature 1.

6. The aforementioned analysis unit, Determining whether it contains slander or criticism against a specific individual or group. The system according to feature 1.

7. The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of comment submissions based on those estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is Analyze the user's past comment history and select the most suitable method for receiving comments. The system according to feature 1.

9. The aforementioned reception unit is When receiving comments, filtering is performed based on the user's current psychological state and areas of interest. The system according to feature 1.

10. The aforementioned reception unit is It estimates the user's sentiment and determines the priority of comments to accept based on the estimated user sentiment. The system according to feature 1.

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