system

Abstract

The system according to the embodiment aims to identify the risk of a social media outrage in advance and deal with it appropriately. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, an identification unit, and a suggestion unit. The reception unit receives content posted by a user. The analysis unit analyzes the content posted by the reception unit and analyzes the context or sentiment. The identification unit identifies potential controversial elements based on the analysis results by the analysis unit. The suggestion unit makes correction suggestions based on the controversial elements identified by the identification unit.

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has the problem that it is difficult to identify the risk of social media outrage in advance and deal with it appropriately.

[0005] The system according to the embodiment aims to identify the risk of a social media outrage in advance and deal with it appropriately. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, an identification unit, and a suggestion unit. The reception unit receives content posted by a user. The analysis unit analyzes the content posted by the reception unit and analyzes the context or sentiment. The identification unit identifies potential controversial elements based on the analysis results by the analysis unit. The suggestion unit makes correction suggestions based on the controversial elements identified by the identification unit. [Effects of the Invention]

[0007] The system according to the embodiment can identify the risk of a social media outrage in advance and deal with it appropriately. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A flame war prevention system according to an embodiment of the present invention automatically accepts user posts, analyzes them using a generation AI, identifies potential flame war elements, and makes correction suggestions. The flame war prevention system accepts user posts, analyzes them using a generation AI, and considers context and sentiment to identify potential flame war elements and make correction suggestions. For example, when a user inputs a post, the generation AI uses advanced natural language processing technology to perform a detailed analysis of the content. This analysis includes the context and sentiment of the post, as well as the choice of words used. The generation AI then identifies potential flame war elements and makes correction suggestions to the user. These correction suggestions take context and sentiment into account, supporting safe and balanced communication. For example, if a user uses emotional language, the generation AI suggests replacing that language with a more neutral expression. Furthermore, if a post is likely to be misleading in a specific context, the generation AI makes correction suggestions to clarify the context. This allows users to review their posts and reduce the risk of flame wars. The flame war prevention system also learns from past posting data and analyzes flame war patterns, enabling more accurate analysis and correction suggestions. For example, if a particular topic or word tends to cause an uproar, the AI ​​can use that information to warn users. This allows the system to prevent social media outrages before they occur, protecting the trust and reputation of companies and individuals. For example, users can review their posts to reduce the risk of an outrage. Furthermore, by learning from past posting data and analyzing outrage patterns, the AI ​​can provide more accurate analysis and suggest corrections.

[0029] The flame war prevention system according to the embodiment includes a reception unit, an analysis unit, an identification unit, and a suggestion unit. The reception unit receives posts from users. The posts include, but are not limited to, text, images, and videos. The reception unit receives, for example, text entered by a user. The reception unit can also receive images and videos. The reception unit can also receive voice input. For example, the reception unit converts voice input from a user into text and receives the text. The analysis unit uses a generation AI to analyze the posts received by the reception unit. The analysis may involve, for example, analyzing the context and sentiment of the posts, but is not limited to, this example. For example, the generation AI analyzes the posts using a text generation AI (e.g., LLM). The analysis unit can also use a multimodal generation AI to analyze the context and sentiment of the posts. The analysis unit can also use the generation AI to extract and analyze important parts of the posts. For example, the text generation AI has learned a large amount of text data and has advanced natural language processing capabilities. The multimodal generation AI can handle multiple modalities, including not only text but also images and audio. The generation AI uses keyword extraction technology to select particularly important information from the post content and performs analysis based on that information. The identification unit identifies potential controversial elements based on the results of the analysis by the analysis unit. Identification is performed, for example, based on specific keywords or past controversial cases, but is not limited to these examples. The identification unit, for example, identifies controversial elements using specific keywords. The identification unit can also identify controversial elements based on past controversial cases. The identification unit can also identify controversial elements using AI. For example, the identification unit can identify controversial elements using an AI model that inputs the results of the analysis by the analysis unit and outputs controversial elements. The suggestion unit makes correction suggestions based on the controversial elements identified by the identification unit. The correction suggestions are performed, for example, based on specific correction suggestions or the form of the suggestion, but are not limited to these examples. The suggestion unit provides specific correction suggestions based on the identified controversial elements. The suggestion unit can also make correction suggestions based on the form of the suggestion. The suggestion unit can also make correction suggestions using AI.For example, the suggestion unit can make correction suggestions using an AI model that receives the controversial elements identified by the identification unit as input and outputs correction suggestions, thereby enabling the controversy prevention system according to the embodiment to efficiently analyze the content posted by users, identify potential controversial elements, and make correction suggestions.

[0030] The suggestion unit can learn from past posting data and analyze flaming patterns. Past posting data includes, but is not limited to, data from a specific period or data from a specific user. The suggestion unit, for example, learns from past posting data and analyzes flaming patterns. For example, the suggestion unit uses past posting data to analyze the tendency of specific topics or words to flaming. The suggestion unit can also analyze frequently occurring flaming keywords and posting times based on past posting data. By learning from past posting data, flaming patterns can be analyzed and more accurate correction suggestions can be made. Some or all of the above-described processing by the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input past posting data into a generation AI and cause the generation AI to analyze flaming patterns.

[0031] The suggestion unit can warn the user if a specific topic or word is likely to cause an uproar. Specific topics include, but are not limited to, politics and social issues. Specific words include, but are not limited to, discriminatory language and offensive language. For example, the suggestion unit can warn the user if a specific topic is likely to cause an uproar. The suggestion unit can also warn the user if a specific word is likely to cause an uproar. For example, the suggestion unit can analyze the tendency of a specific topic or word to cause an uproar and warn the user based on that information. By issuing a warning if a specific topic or word is likely to cause an uproar, the risk of an uproar can be reduced. Some or all of the above-described processing by the suggestion unit can be performed, for example, using AI or without AI. For example, the suggestion unit can input information about a specific topic or word into a generation AI and cause the generation AI to execute a warning suggestion.

[0032] The reception unit can analyze a user's past posting history and select the optimal reception method. Past posting history includes, but is not limited to, a history for a specific period or a history for a specific user. Appropriate reception methods include, but are not limited to, text input and voice input. The reception unit, for example, preferentially suggests posting methods (text, image, video, etc.) that the user has frequently used in the past. The reception unit can also analyze a user's past posting history to determine whether they tend to post during specific time periods and encourage them to post during those time periods. The reception unit can also prioritize receiving related posts based on tags and keywords used by the user in the past. This allows the analysis of a user's past posting history to select the optimal reception method and efficiently receive posts. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or without AI. For example, the reception unit can input a user's past posting history into a generation AI and have the generation AI select the optimal reception method.

[0033] When receiving the posted content, the reception unit may filter the posted content based on the user's current areas of interest and activity status. Areas of interest include, for example, but are not limited to, the user's past posted content and survey results. Activity status includes, for example, but is not limited to, the user's login frequency and posting frequency. The reception unit, for example, preferentially receives posted content related to topics in which the user is currently interested. The reception unit may also analyze the user's recent activity status (event participation, purchase history, etc.) and filter related posted content. The reception unit may also preferentially receive related posted content based on the activity status of accounts and groups the user follows. In this way, by filtering based on the user's current areas of interest and activity status, highly relevant posted content can be preferentially received. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input data on the user's areas of interest and activity status into a generation AI and cause the generation AI to perform filtering.

[0034] When accepting a post, the acceptance unit can select an appropriate acceptance means depending on the user's input method. Examples of input methods include, but are not limited to, text input, voice input, and image input. For example, if the user uses voice input, the acceptance unit automatically converts the post into text using voice recognition technology and accepts the text. Furthermore, if the user posts an image, the acceptance unit can analyze the content using image analysis technology and automatically assign appropriate tags before accepting the post. Furthermore, if the user uses text input, the acceptance unit can analyze the input content in real time and accept the post while providing appropriate feedback. This allows posts to be accepted efficiently by selecting the optimal acceptance means depending on the user's input method. Some or all of the above-described processing in the acceptance unit may be performed using, for example, AI, or may be performed without AI. For example, the acceptance unit can input data on the user's input method into a generation AI and have the generation AI select the optimal acceptance means.

[0035] When receiving post content, the reception unit can prioritize receiving posts that are highly relevant based on the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, IP address, etc. For example, if the user is in a specific area, the reception unit can prioritize receiving post content related to that area. Furthermore, if the user is traveling, the reception unit can prioritize receiving tourist information and event information related to the user's current location. Furthermore, if the user is in a specific location, the reception unit can prioritize receiving news and information related to that location. In this way, by taking the user's geographical location information into consideration, highly relevant posts can be prioritized. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's geographical location information to the generation AI and cause the generation AI to prioritize receiving highly relevant posts.

[0036] The reception unit may analyze the user's social media activity when receiving the post content and receive related posts. Social media activity may include, but is not limited to, posting frequency and the number of followers. For example, the reception unit may analyze hashtags frequently used by the user on social media and prioritize receiving related posts. The reception unit may also analyze the activity status of accounts and groups followed by the user and prioritize receiving related posts. The reception unit may also analyze the user's social media posting history and prioritize receiving related posts. In this way, by analyzing the user's social media activity, highly relevant posts can be prioritized. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input the user's social media activity data into the generation AI and cause the generation AI to prioritize receiving related posts.

[0037] The reception unit can customize the reception method based on the user's past feedback when receiving the post content. Past feedback includes, but is not limited to, the user's evaluation comments and survey results. The reception unit, for example, suggests an optimal reception method based on the user's past feedback. The reception unit can also preferentially receive posting formats (text, image, video, etc.) that the user has previously preferred. The reception unit can also analyze the user's past feedback and provide appropriate feedback when receiving the post content. This makes it possible to provide an optimal reception method by reflecting the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the reception method.

[0038] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the posted content. Importance includes, but is not limited to, the impact of the posted content and the user's level of interest. For example, the analysis unit can provide detailed analysis results for posted content with high importance. The analysis unit can also provide concise analysis results for posted content with low importance. The analysis unit can also adjust the priority of the analysis according to the importance of the posted content. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the posted content. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input importance data of the posted content to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0039] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the posted content. Examples of categories include, but are not limited to, politics, economics, and entertainment. For example, the analysis unit can apply an analysis algorithm based on a highly reliable information source to posted content in the news category. Furthermore, the analysis unit can also apply an analysis algorithm that emphasizes sentiment analysis to posted content in the entertainment category. Furthermore, the analysis unit can also apply an analysis algorithm that takes into account industry-specific terminology and trends to posted content in the business category. By applying different analysis algorithms depending on the category of the posted content, more accurate analysis can be performed. Some or all of the above-described processing by the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input category data of the posted content into the generation AI and have the generation AI apply the analysis algorithm.

[0040] During analysis, the analysis unit can improve the accuracy of the analysis based on the user's past analysis results. Past analysis results include, but are not limited to, analysis results for a specific period of time and analysis results for a specific user. The analysis unit can adjust the analysis algorithm based on, for example, feedback provided by the user in the past. The analysis unit can also analyze the user's past analysis results and perform highly accurate analysis of similar posted content. The analysis unit can also adjust the analysis priority by referring to the user's past analysis results. This allows the accuracy of the analysis to be improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0041] During analysis, the analysis unit can determine the priority of analysis based on the submission time of the posted content. The submission time includes, but is not limited to, for example, the time period of posting and the frequency of posting. For example, the analysis unit prioritizes analysis of the most recent posted content. The analysis unit can also lower the analysis priority of older posted content. The analysis unit can also adjust the analysis schedule based on the submission time. This allows for efficient analysis by determining the analysis priority based on the submission time of the posted content. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the submission time of the posted content to the generation AI and have the generation AI determine the analysis priority.

[0042] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the posted content. Relevance includes, but is not limited to, for example, the similarity of the posted content and related topics. For example, the analysis unit prioritizes analysis of highly relevant posted content. The analysis unit can also postpone the order of analysis of less relevant posted content. The analysis unit can also adjust the analysis schedule based on the relevance of the posted content. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the posted content. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input relevance data of the posted content to the generation AI and cause the generation AI to adjust the order of analysis.

[0043] During analysis, the analysis unit can adjust the use of technical terms in the analysis based on the user's level of expertise. Examples of technical terminology include, but are not limited to, the user's occupation and educational background. For example, if the user has technical expertise, the analysis unit can provide analysis results that use a lot of technical terms. Furthermore, if the user does not have technical expertise, the analysis unit can provide concise and easy-to-understand analysis results. The analysis unit can also adjust the way the analysis results are presented based on the user's level of expertise. This allows for more appropriate analysis results to be provided by adjusting the use of technical terms in the analysis based on the user's level of expertise. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and have the generation AI adjust the use of technical terms.

[0044] During classification, the identification unit can improve the accuracy of classification based on the interrelationships between posted contents. Interrelationships include, but are not limited to, for example, the relevance of posted contents and the relationship between posters. The identification unit, for example, analyzes the context of posted contents and improves the accuracy of classification based on the context. The identification unit can also consider the relevance of multiple posted contents and understand the overall context to perform classification. The identification unit can also analyze the interrelationships between posted contents and identify expressions that may be misleading. This improves the accuracy of classification based on the interrelationships between posted contents. Some or all of the above-described processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input interrelationship data between posted contents to the generation AI and cause the generation AI to improve the accuracy of classification.

[0045] The identification unit can perform identification by taking into account attribute information of the submitter of the posted content. Attribute information includes, but is not limited to, for example, age, gender, and occupation. The identification unit can apply appropriate identification criteria, for example, by taking into account the submitter's age and gender. The identification unit can also improve the accuracy of identification by taking into account the submitter's occupation and expertise. The identification unit can also adjust the identification criteria by referring to the submitter's past posting history. This allows for more appropriate identification by taking into account the submitter's attribute information. Some or all of the above-described processing in the identification unit can be performed using, for example, AI, or can be performed without using AI. For example, the identification unit can input the submitter's attribute information data into the generation AI and cause the generation AI to adjust the identification criteria.

[0046] During classification, the classification unit can assign classification weights based on the submission frequency of the posted content. Submission frequency includes, but is not limited to, for example, the number of posts and the interval between posts. For example, the classification unit assigns a high classification weight to posted content that is submitted frequently. The classification unit can also assign a low classification weight to posted content that is submitted infrequently. The classification unit can also adjust the classification priority based on the submission frequency. By assigning classification weights based on the submission frequency, more appropriate classification can be performed. Some or all of the above-described processing in the classification unit may be performed using, for example, AI, or without AI. For example, the classification unit can input submission frequency data to a generation AI and cause the generation AI to assign classification weights.

[0047] During identification, the identification unit can perform identification based on the geographical distribution of the posted content. Examples of geographical distribution include, but are not limited to, the number of posts per region and topics specific to the region. For example, the identification unit applies region-specific identification criteria to posted content related to a specific region. Furthermore, the identification unit can also perform identification that reflects regional trends and culture by taking geographical distribution into consideration. Furthermore, the identification unit can adjust the priority of identification based on the geographical distribution. In this way, region-specific identification criteria can be applied by taking geographical distribution into consideration. Some or all of the above-described processing in the identification unit may be performed using, or without, AI. For example, the identification unit can input geographical distribution data to a generation AI and have the generation AI perform the identification.

[0048] During classification, the identification unit can improve the accuracy of classification by referring to related literature of the posted content. Related literature includes, but is not limited to, academic papers, news articles, etc. For example, the identification unit can refer to the related literature to evaluate the reliability of the posted content. The identification unit can also perform classification by complementing background information of the posted content based on the related literature. The identification unit can also analyze the related literature to identify the possibility that the posted content may be misleading. Thus, by referring to the related literature, the accuracy of classification can be improved. Some or all of the above-described processing in the identification unit may be performed using, or without, AI. For example, the identification unit can input related literature data into the generation AI and cause the generation AI to improve the accuracy of classification.

[0049] The identification unit can perform identification based on the market value of the posted content during identification. Market value includes, but is not limited to, sales forecasts and demand forecasts. For example, the identification unit can assign a high identification weight to posted content with a high market value. The identification unit can also assign a low identification weight to posted content with a low market value. The identification unit can also adjust the priority of identification based on market value. This allows for more appropriate identification by taking market value into consideration. Some or all of the above-described processing in the identification unit can be performed using, for example, AI, or can be performed without using AI. For example, the identification unit can input market value data to a generation AI and have the generation AI perform the identification.

[0050] When making a proposal, the suggestion unit can adjust the level of detail of the proposal based on the importance of the controversial element. Importance includes, but is not limited to, the influence of the controversial element and the user's level of interest. For example, the suggestion unit can provide detailed proposal content for a controversial element with high importance. The suggestion unit can also provide concise proposal content for a controversial element with low importance. The suggestion unit can also adjust the priority of the proposal according to the importance of the controversial element. This allows for efficient proposals by adjusting the level of detail of the proposal based on the importance of the controversial element. Some or all of the above-described processing by the suggestion unit may be performed using, or without, an AI. For example, the suggestion unit can input importance data of the controversial element into the generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0051] When making a proposal, the suggestion unit can apply different proposal algorithms depending on the category of the controversy element. Examples of categories include, but are not limited to, politics, economics, and entertainment. For example, the suggestion unit can apply a proposal algorithm based on a reliable information source to controversy elements in the news category. The suggestion unit can also apply a proposal algorithm that emphasizes sentiment analysis to controversy elements in the entertainment category. The suggestion unit can also apply a proposal algorithm that takes into account industry-specific terms and trends to controversy elements in the business category. By applying different proposal algorithms depending on the category of the controversy element, more accurate proposals can be made. Some or all of the above-described processing by the suggestion unit can be performed using, for example, AI, or without AI. For example, the suggestion unit can input category data of the controversy element into a generation AI and cause the generation AI to apply a proposal algorithm.

[0052] When making a suggestion, the suggestion unit can improve the accuracy of the suggestion based on the user's past suggestion results. Past suggestion results include, but are not limited to, suggestion results from a specific period or a specific user. The suggestion unit, for example, adjusts the suggestion algorithm based on feedback provided by the user in the past. The suggestion unit can also analyze the user's past suggestion results and make highly accurate suggestions for similar controversial topics. The suggestion unit can also adjust the priority of suggestions by referring to the user's past suggestion results. This allows the accuracy of suggestions to be improved by referring to the user's past suggestion results. Some or all of the above-described processing by the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's past suggestion result data into the generation AI and cause the generation AI to improve the accuracy of the suggestions.

[0053] When making a proposal, the suggestion unit can determine the priority of the proposal based on the submission time of the controversial element. The submission time includes, but is not limited to, for example, the time period of posting and the frequency of submission. For example, the suggestion unit can prioritize the proposal for the most recent controversial element. The suggestion unit can also lower the priority of the proposal for the oldest controversial element. The suggestion unit can also adjust the proposal schedule based on the submission time. This allows for efficient proposals by determining the priority of the proposal based on the submission time of the controversial element. Some or all of the above-described processing by the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input submission time data of the controversial element into the generation AI and cause the generation AI to determine the priority of the proposal.

[0054] The suggestion unit can adjust the order of suggestions based on the relevance of the controversial elements when making suggestions. Relevance includes, but is not limited to, the similarity of the controversial elements and related topics. For example, the suggestion unit prioritizes suggestions for controversial elements with high relevance. The suggestion unit can also postpone the order of suggestions for controversial elements with low relevance. The suggestion unit can also adjust the schedule of suggestions based on the relevance of the controversial elements. By adjusting the order of suggestions based on the relevance of the controversial elements, suggestions can be made efficiently. Some or all of the above-described processing by the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input relevance data of controversial elements into a generation AI and cause the generation AI to adjust the order of suggestions.

[0055] When making a proposal, the suggestion unit can adjust the use of technical terminology in the proposal based on the user's level of expertise. Examples of the level of expertise include, but are not limited to, the user's occupation and educational background. For example, if the user has technical expertise, the suggestion unit can provide proposal content that uses a lot of technical terminology. Furthermore, if the user does not have technical expertise, the suggestion unit can provide proposal content that is concise and easy to understand. The suggestion unit can also adjust the way the proposal content is expressed based on the user's level of expertise. This allows for more appropriate proposals by adjusting the use of technical terminology in the proposal based on the user's level of expertise. Some or all of the above-described processing by the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terminology.

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

[0057] The reception unit can analyze the user's past posts and customize the method for receiving posts based on the user's posting tendencies. For example, it can prioritize recognition of words and phrases that the user has frequently used in the past, allowing for smooth reception of posts. Also, if a user tends to post during a specific time period, the reception unit's response speed can be optimized to match that time period. Furthermore, if a user has shown interest in a specific topic in the past, posts related to that topic can be prioritized for reception. This makes it possible to provide a more personalized reception method by taking into account the user's past posting tendencies.

[0058] The analysis unit can analyze images and videos included in users' posts and identify inflammatory elements by taking visual elements into consideration. For example, image analysis technology can be used to detect offensive symbols and inappropriate language in images included in posts. Video analysis technology can also be used to identify inflammatory elements from audio and video within videos. Furthermore, the content of images and videos can be converted into text and combined with text analysis for more accurate identification. By taking visual elements into consideration, it is possible to detect inflammatory elements that cannot be identified from text alone.

[0059] The identification unit can analyze metadata included in the user's post content and identify flame war elements by taking into account the background information of the post. For example, flame war elements related to a specific event or situation can be identified based on the date and time the post was created and the poster's location information. It can also analyze information on links included in the post content and identify flame war elements by taking into account the reliability and content of the links. Furthermore, it can analyze hashtags and mentions included in the post content and identify flame war elements based on related topics and people. By taking metadata into account, highly accurate identification based on the background information of the post can be performed.

[0060] The reception unit can automatically detect the language included in the user's posted content and accept the post in the appropriate language. For example, if the user posts in English, the post is accepted in English, and if the user posts in Japanese, the post is accepted in Japanese. The reception unit can also appropriately recognize and respond to posts that contain a mixture of multiple languages. Furthermore, the reception unit can translate the posted content based on the language detection results and support acceptance in other languages. This allows for more multilingual reception by taking into account the language included in the user's posted content.

[0061] The identification unit can analyze the voice data included in the user's post and identify inflammatory elements by taking into account the tone and emotion of the voice. For example, voice analysis technology can be used to detect aggressive tones and inappropriate expressions in the voice included in the post. It can also perform emotion analysis of the voice and identify inflammatory elements when emotional expressions are included. Furthermore, the voice data can be converted into text and combined with text analysis for more accurate identification. By taking voice data into account, it is possible to detect inflammatory elements that cannot be identified by text alone.

[0062] The reception unit can evaluate the reliability of links included in user posts and restrict the reception of posts that include unreliable links. For example, the reception unit can evaluate reliability based on the domain of the link destination and past evaluations, and temporarily suspend the reception of posts that include unreliable links. The reception unit can also analyze the content of the link destination and restrict the reception of posts that include inappropriate information. Furthermore, the reception unit can suggest link corrections to the user based on the reliability of the link destination. In this way, by taking the reliability of the link destination into consideration, the spread of unreliable information can be prevented.

[0063] The processing flow of the first embodiment will be briefly explained below.

[0064] Step 1: The reception unit receives content posted by a user. The content posted may include text, images, videos, and voice input. For example, the reception unit converts the text or voice input by the user into text and receives it. Step 2: The analysis unit uses the generation AI to analyze the content of the post received by the reception unit. The analysis analyzes the context and sentiment of the post. For example, it uses text generation AI (LLM) or multimodal generation AI to extract and analyze important parts of the post. Step 3: The identification unit identifies potential controversial elements based on the results of the analysis by the analysis unit. Identification is performed based on specific keywords and past controversial cases. For example, an AI model is used to input the analysis results and output controversial elements. Step 4: The suggestion unit makes correction suggestions based on the controversial elements identified by the identification unit. The correction suggestions are made based on specific correction plans and proposal formats. For example, the suggestion unit inputs the controversial elements identified using an AI model and outputs correction suggestions.

[0065] (Example 2) A flame war prevention system according to an embodiment of the present invention automatically accepts user posts, analyzes them using a generation AI, identifies potential flame war elements, and makes correction suggestions. The flame war prevention system accepts user posts, analyzes them using a generation AI, and considers context and sentiment to identify potential flame war elements and make correction suggestions. For example, when a user inputs a post, the generation AI uses advanced natural language processing technology to perform a detailed analysis of the content. This analysis includes the context and sentiment of the post, as well as the choice of words used. The generation AI then identifies potential flame war elements and makes correction suggestions to the user. These correction suggestions take context and sentiment into account, supporting safe and balanced communication. For example, if a user uses emotional language, the generation AI suggests replacing that language with a more neutral expression. Furthermore, if a post is likely to be misleading in a specific context, the generation AI makes correction suggestions to clarify the context. This allows users to review their posts and reduce the risk of flame wars. The flame war prevention system also learns from past posting data and analyzes flame war patterns, enabling more accurate analysis and correction suggestions. For example, if a particular topic or word tends to cause an uproar, the AI ​​can use that information to warn users. This allows the system to prevent social media outrages before they occur, protecting the trust and reputation of companies and individuals. For example, users can review their posts to reduce the risk of an outrage. Furthermore, by learning from past posting data and analyzing outrage patterns, the AI ​​can provide more accurate analysis and suggest corrections.

[0066] The flame war prevention system according to the embodiment includes a reception unit, an analysis unit, an identification unit, and a suggestion unit. The reception unit receives posts from users. The posts include, but are not limited to, text, images, and videos. The reception unit receives, for example, text entered by a user. The reception unit can also receive images and videos. The reception unit can also receive voice input. For example, the reception unit converts voice input from a user into text and receives the text. The analysis unit uses a generation AI to analyze the posts received by the reception unit. The analysis may involve, for example, analyzing the context and sentiment of the posts, but is not limited to, this example. For example, the generation AI analyzes the posts using a text generation AI (e.g., LLM). The analysis unit can also use a multimodal generation AI to analyze the context and sentiment of the posts. The analysis unit can also use the generation AI to extract and analyze important parts of the posts. For example, the text generation AI has learned a large amount of text data and has advanced natural language processing capabilities. The multimodal generation AI can handle multiple modalities, including not only text but also images and audio. The generation AI uses keyword extraction technology to select particularly important information from the post content and performs analysis based on that information. The identification unit identifies potential controversial elements based on the results of the analysis by the analysis unit. Identification is performed, for example, based on specific keywords or past controversial cases, but is not limited to these examples. The identification unit, for example, identifies controversial elements using specific keywords. The identification unit can also identify controversial elements based on past controversial cases. The identification unit can also identify controversial elements using AI. For example, the identification unit can identify controversial elements using an AI model that inputs the results of the analysis by the analysis unit and outputs controversial elements. The suggestion unit makes correction suggestions based on the controversial elements identified by the identification unit. The correction suggestions are performed, for example, based on specific correction suggestions or the form of the suggestion, but are not limited to these examples. The suggestion unit provides specific correction suggestions based on the identified controversial elements. The suggestion unit can also make correction suggestions based on the form of the suggestion. The suggestion unit can also make correction suggestions using AI.For example, the suggestion unit can make correction suggestions using an AI model that receives the controversial elements identified by the identification unit as input and outputs correction suggestions, thereby enabling the controversy prevention system according to the embodiment to efficiently analyze the content posted by users, identify potential controversial elements, and make correction suggestions.

[0067] The suggestion unit can learn from past posting data and analyze flaming patterns. Past posting data includes, but is not limited to, data from a specific period or data from a specific user. The suggestion unit, for example, learns from past posting data and analyzes flaming patterns. For example, the suggestion unit uses past posting data to analyze the tendency of specific topics or words to flaming. The suggestion unit can also analyze frequently occurring flaming keywords and posting times based on past posting data. By learning from past posting data, flaming patterns can be analyzed and more accurate correction suggestions can be made. Some or all of the above-described processing by the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input past posting data into a generation AI and cause the generation AI to analyze flaming patterns.

[0068] The suggestion unit can warn the user if a specific topic or word is likely to cause an uproar. Specific topics include, but are not limited to, politics and social issues. Specific words include, but are not limited to, discriminatory language and offensive language. For example, the suggestion unit can warn the user if a specific topic is likely to cause an uproar. The suggestion unit can also warn the user if a specific word is likely to cause an uproar. For example, the suggestion unit can analyze the tendency of a specific topic or word to cause an uproar and warn the user based on that information. By issuing a warning if a specific topic or word is likely to cause an uproar, the risk of an uproar can be reduced. Some or all of the above-described processing by the suggestion unit can be performed, for example, using AI or without AI. For example, the suggestion unit can input information about a specific topic or word into a generation AI and cause the generation AI to execute a warning suggestion.

[0069] The reception unit can estimate the user's emotions and adjust the timing of accepting the posted content based on the estimated user emotions. The emotion estimation is performed, for example, using an emotion analysis algorithm, but is not limited to such an example. The acceptance timing can include, for example, the time of posting and the user's activity status. For example, if the user is feeling angry, the reception unit temporarily delays the acceptance of the posted content to give the user time to calm down. Furthermore, if the user is excited, the reception unit can quickly accept the posted content and complete the posting while the user's emotions are high. Furthermore, if the user is sad, the reception unit can flexibly adjust the acceptance of the posted content and wait until the user's emotions have calmed down. By adjusting the acceptance timing of the posted content based on the user's emotions, the post can be accepted at a more appropriate time. The emotion estimation is realized, for example, using an emotion estimation function using an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input user emotion data to the generation AI and cause the generation AI to estimate the emotion.

[0070] The reception unit can analyze a user's past posting history and select the optimal reception method. Past posting history includes, but is not limited to, a history for a specific period or a history for a specific user. Appropriate reception methods include, but are not limited to, text input and voice input. The reception unit, for example, preferentially suggests posting methods (text, image, video, etc.) that the user has frequently used in the past. The reception unit can also analyze a user's past posting history to determine whether they tend to post during specific time periods and encourage them to post during those time periods. The reception unit can also prioritize receiving related posts based on tags and keywords used by the user in the past. This allows the analysis of a user's past posting history to select the optimal reception method and efficiently receive posts. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or without AI. For example, the reception unit can input a user's past posting history into a generation AI and have the generation AI select the optimal reception method.

[0071] When receiving the posted content, the reception unit may filter the posted content based on the user's current areas of interest and activity status. Areas of interest include, for example, but are not limited to, the user's past posted content and survey results. Activity status includes, for example, but is not limited to, the user's login frequency and posting frequency. The reception unit, for example, preferentially receives posted content related to topics in which the user is currently interested. The reception unit may also analyze the user's recent activity status (event participation, purchase history, etc.) and filter related posted content. The reception unit may also preferentially receive related posted content based on the activity status of accounts and groups the user follows. In this way, by filtering based on the user's current areas of interest and activity status, highly relevant posted content can be preferentially received. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input data on the user's areas of interest and activity status into a generation AI and cause the generation AI to perform filtering.

[0072] When accepting a post, the acceptance unit can select an appropriate acceptance means depending on the user's input method. Examples of input methods include, but are not limited to, text input, voice input, and image input. For example, if the user uses voice input, the acceptance unit automatically converts the post into text using voice recognition technology and accepts the text. Furthermore, if the user posts an image, the acceptance unit can analyze the content using image analysis technology and automatically assign appropriate tags before accepting the post. Furthermore, if the user uses text input, the acceptance unit can analyze the input content in real time and accept the post while providing appropriate feedback. This allows posts to be accepted efficiently by selecting the optimal acceptance means depending on the user's input method. Some or all of the above-described processing in the acceptance unit may be performed using, for example, AI, or may be performed without AI. For example, the acceptance unit can input data on the user's input method into a generation AI and have the generation AI select the optimal acceptance means.

[0073] The reception unit can estimate the user's emotions and determine the priority of the posts to be received based on the estimated user emotions. The emotion estimation can be performed, for example, using an emotion analysis algorithm, but is not limited to this example. The priority can include, for example, the importance of the post and the user's emotional state. For example, if the user is feeling angry, the reception unit can lower the priority of the post to give the user time to calm down. Furthermore, if the user is excited, the reception unit can also raise the priority of the post and complete the post while the user's emotions are high. Furthermore, if the user is sad, the reception unit can flexibly adjust the priority of the post and wait until the user's emotions have calmed down. This allows posts to be received in a more appropriate order by determining the priority of the post based on the user's emotions. The emotion estimation can be achieved, for example, using an emotion engine or a generation AI with an emotion estimation function. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to this example. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input user emotion data to the generation AI and cause the generation AI to estimate the emotion.

[0074] When receiving post content, the reception unit can prioritize receiving posts that are highly relevant based on the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, IP address, etc. For example, if the user is in a specific area, the reception unit can prioritize receiving post content related to that area. Furthermore, if the user is traveling, the reception unit can prioritize receiving tourist information and event information related to the user's current location. Furthermore, if the user is in a specific location, the reception unit can prioritize receiving news and information related to that location. In this way, by taking the user's geographical location information into consideration, highly relevant posts can be prioritized. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's geographical location information to the generation AI and cause the generation AI to prioritize receiving highly relevant posts.

[0075] The reception unit may analyze the user's social media activity when receiving the post content and receive related posts. Social media activity may include, but is not limited to, posting frequency and the number of followers. For example, the reception unit may analyze hashtags frequently used by the user on social media and prioritize receiving related posts. The reception unit may also analyze the activity status of accounts and groups followed by the user and prioritize receiving related posts. The reception unit may also analyze the user's social media posting history and prioritize receiving related posts. In this way, by analyzing the user's social media activity, highly relevant posts can be prioritized. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input the user's social media activity data into the generation AI and cause the generation AI to prioritize receiving related posts.

[0076] The reception unit can customize the reception method based on the user's past feedback when receiving the post content. Past feedback includes, but is not limited to, the user's evaluation comments and survey results. The reception unit, for example, suggests an optimal reception method based on the user's past feedback. The reception unit can also preferentially receive posting formats (text, image, video, etc.) that the user has previously preferred. The reception unit can also analyze the user's past feedback and provide appropriate feedback when receiving the post content. This makes it possible to provide an optimal reception method by reflecting the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the reception method.

[0077] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. The emotion estimation can be performed, for example, using an emotion analysis algorithm, but is not limited to this example. The presentation method can include, for example, but is not limited to, wording and tone of expression. For example, if the user is angry, the analysis unit can provide the analysis results in a calm and neutral manner. Furthermore, if the user is excited, the analysis unit can provide the analysis results quickly and concisely. Furthermore, if the user is sad, the analysis unit can provide the analysis results in a gentle and considerate manner. This allows for adjusting the way the analysis is presented based on the user's emotions, thereby providing more appropriate analysis results. The emotion estimation can be achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to this example. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without using an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0078] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the posted content. Importance includes, but is not limited to, the impact of the posted content and the user's level of interest. For example, the analysis unit can provide detailed analysis results for posted content with high importance. The analysis unit can also provide concise analysis results for posted content with low importance. The analysis unit can also adjust the priority of the analysis according to the importance of the posted content. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the posted content. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input importance data of the posted content to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0079] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the posted content. Examples of categories include, but are not limited to, politics, economics, and entertainment. For example, the analysis unit can apply an analysis algorithm based on a highly reliable information source to posted content in the news category. Furthermore, the analysis unit can also apply an analysis algorithm that emphasizes sentiment analysis to posted content in the entertainment category. Furthermore, the analysis unit can also apply an analysis algorithm that takes into account industry-specific terminology and trends to posted content in the business category. By applying different analysis algorithms depending on the category of the posted content, more accurate analysis can be performed. Some or all of the above-described processing by the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input category data of the posted content into the generation AI and have the generation AI apply the analysis algorithm.

[0080] During analysis, the analysis unit can improve the accuracy of the analysis based on the user's past analysis results. Past analysis results include, but are not limited to, analysis results for a specific period of time and analysis results for a specific user. The analysis unit can adjust the analysis algorithm based on, for example, feedback provided by the user in the past. The analysis unit can also analyze the user's past analysis results and perform highly accurate analysis of similar posted content. The analysis unit can also adjust the analysis priority by referring to the user's past analysis results. This allows the accuracy of the analysis to be improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0081] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. Emotion estimation is performed, for example, using an emotion analysis algorithm, but is not limited to this example. The length of the analysis includes, for example, the level of analysis detail and the analysis time, but is not limited to this example. For example, if the user is in a hurry, the analysis unit can provide a short and concise analysis result. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is excited, the analysis unit can provide an analysis result with a visually stimulating effect. By adjusting the length of the analysis based on the user's emotions, more appropriate analysis results can be provided. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to this example. Some or all of the above-described processing in the analysis unit may be performed using AI, or without AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0082] During analysis, the analysis unit can determine the priority of analysis based on the submission time of the posted content. The submission time includes, but is not limited to, for example, the time period of posting and the frequency of posting. For example, the analysis unit prioritizes analysis of the most recent posted content. The analysis unit can also lower the analysis priority of older posted content. The analysis unit can also adjust the analysis schedule based on the submission time. This allows for efficient analysis by determining the analysis priority based on the submission time of the posted content. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the submission time of the posted content to the generation AI and have the generation AI determine the analysis priority.

[0083] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the posted content. Relevance includes, but is not limited to, for example, the similarity of the posted content and related topics. For example, the analysis unit prioritizes analysis of highly relevant posted content. The analysis unit can also postpone the order of analysis of less relevant posted content. The analysis unit can also adjust the analysis schedule based on the relevance of the posted content. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the posted content. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input relevance data of the posted content to the generation AI and cause the generation AI to adjust the order of analysis.

[0084] During analysis, the analysis unit can adjust the use of technical terms in the analysis based on the user's level of expertise. Examples of technical terminology include, but are not limited to, the user's occupation and educational background. For example, if the user has technical expertise, the analysis unit can provide analysis results that use a lot of technical terms. Furthermore, if the user does not have technical expertise, the analysis unit can provide concise and easy-to-understand analysis results. The analysis unit can also adjust the way the analysis results are presented based on the user's level of expertise. This allows for more appropriate analysis results to be provided by adjusting the use of technical terms in the analysis based on the user's level of expertise. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and have the generation AI adjust the use of technical terms.

[0085] The identification unit can estimate the user's emotions and adjust the identification criteria based on the estimated user emotions. The emotion estimation is performed using, for example, an emotion analysis algorithm, but is not limited to this example. The identification criteria include, for example, identification accuracy and identification importance, but are not limited to this example. For example, when the user is angry, the identification unit tightens the identification criteria to strictly identify elements with a high risk of causing a stir. Furthermore, when the user is excited, the identification unit can relax the identification criteria to allow emotional expressions. Furthermore, when the user is sad, the identification unit can flexibly adjust the identification criteria to perform identification that takes emotions into consideration. This allows for more appropriate identification by adjusting the identification criteria based on the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to this example. Some or all of the above-described processing in the identification unit may be performed using, for example, AI, or without AI. For example, the identification unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0086] During classification, the identification unit can improve the accuracy of classification based on the interrelationships between posted contents. Interrelationships include, but are not limited to, for example, the relevance of posted contents and the relationship between posters. The identification unit, for example, analyzes the context of posted contents and improves the accuracy of classification based on the context. The identification unit can also consider the relevance of multiple posted contents and understand the overall context to perform classification. The identification unit can also analyze the interrelationships between posted contents and identify expressions that may be misleading. This improves the accuracy of classification based on the interrelationships between posted contents. Some or all of the above-described processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input interrelationship data between posted contents to the generation AI and cause the generation AI to improve the accuracy of classification.

[0087] The identification unit can perform identification by taking into account attribute information of the submitter of the posted content. Attribute information includes, but is not limited to, for example, age, gender, and occupation. The identification unit can apply appropriate identification criteria, for example, by taking into account the submitter's age and gender. The identification unit can also improve the accuracy of identification by taking into account the submitter's occupation and expertise. The identification unit can also adjust the identification criteria by referring to the submitter's past posting history. This allows for more appropriate identification by taking into account the submitter's attribute information. Some or all of the above-described processing in the identification unit can be performed using, for example, AI, or can be performed without using AI. For example, the identification unit can input the submitter's attribute information data into the generation AI and cause the generation AI to adjust the identification criteria.

[0088] During classification, the classification unit can assign classification weights based on the submission frequency of the posted content. Submission frequency includes, but is not limited to, for example, the number of posts and the interval between posts. For example, the classification unit assigns a high classification weight to posted content that is submitted frequently. The classification unit can also assign a low classification weight to posted content that is submitted infrequently. The classification unit can also adjust the classification priority based on the submission frequency. By assigning classification weights based on the submission frequency, more appropriate classification can be performed. Some or all of the above-described processing in the classification unit may be performed using, for example, AI, or without AI. For example, the classification unit can input submission frequency data to a generation AI and cause the generation AI to assign classification weights.

[0089] The identification unit can estimate the user's emotion and adjust the display order of the identification results based on the estimated user emotion. The emotion estimation is performed, for example, using an emotion analysis algorithm, but is not limited to this example. The display order can include, for example, importance, relevance, etc., but is not limited to this example. For example, if the user is angry, the identification unit can display the identification results in a calm and neutral order. Furthermore, if the user is excited, the identification unit can also display the identification results quickly and concisely. Furthermore, if the user is sad, the identification unit can also display the identification results in a gentle and considerate order. By adjusting the display order of the identification results based on the user's emotion, the results can be provided in a more appropriate order. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to this example. Some or all of the above-described processing in the identification unit may be performed using, for example, AI, or without AI. For example, the identification unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0090] During identification, the identification unit can perform identification based on the geographical distribution of the posted content. Examples of geographical distribution include, but are not limited to, the number of posts per region and topics specific to the region. For example, the identification unit applies region-specific identification criteria to posted content related to a specific region. Furthermore, the identification unit can also perform identification that reflects regional trends and culture by taking geographical distribution into consideration. Furthermore, the identification unit can adjust the priority of identification based on the geographical distribution. In this way, region-specific identification criteria can be applied by taking geographical distribution into consideration. Some or all of the above-described processing in the identification unit may be performed using, or without, AI. For example, the identification unit can input geographical distribution data to a generation AI and have the generation AI perform the identification.

[0091] During classification, the identification unit can improve the accuracy of classification by referring to related literature of the posted content. Related literature includes, but is not limited to, academic papers, news articles, etc. For example, the identification unit can refer to the related literature to evaluate the reliability of the posted content. The identification unit can also perform classification by complementing background information of the posted content based on the related literature. The identification unit can also analyze the related literature to identify the possibility that the posted content may be misleading. Thus, by referring to the related literature, the accuracy of classification can be improved. Some or all of the above-described processing in the identification unit may be performed using, or without, AI. For example, the identification unit can input related literature data into the generation AI and cause the generation AI to improve the accuracy of classification.

[0092] The identification unit can perform identification based on the market value of the posted content during identification. Market value includes, but is not limited to, sales forecasts and demand forecasts. For example, the identification unit can assign a high identification weight to posted content with a high market value. The identification unit can also assign a low identification weight to posted content with a low market value. The identification unit can also adjust the priority of identification based on market value. This allows for more appropriate identification by taking market value into consideration. Some or all of the above-described processing in the identification unit can be performed using, for example, AI, or can be performed without using AI. For example, the identification unit can input market value data to a generation AI and have the generation AI perform the identification.

[0093] The suggestion unit can estimate the user's emotions and adjust the way suggestions are expressed based on the estimated user emotions. The estimation of emotions is performed, for example, using an emotion analysis algorithm, but is not limited to this example. The expression method can include, for example, but is not limited to, wording and tone of expression. For example, if the user is angry, the suggestion unit can provide the suggestion content in a calm and neutral manner. Furthermore, if the user is excited, the suggestion unit can provide the suggestion content quickly and concisely. Furthermore, if the user is sad, the suggestion unit can provide the suggestion content in a gentle and considerate manner. This allows for more appropriate suggestions by adjusting the way suggestions are expressed based on the user's emotions. The estimation of emotions is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to this example. Some or all of the above-described processing in the suggestion unit can be performed, for example, using AI or without AI. For example, the suggestion unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0094] When making a proposal, the suggestion unit can adjust the level of detail of the proposal based on the importance of the controversial element. Importance includes, but is not limited to, the influence of the controversial element and the user's level of interest. For example, the suggestion unit can provide detailed proposal content for a controversial element with high importance. The suggestion unit can also provide concise proposal content for a controversial element with low importance. The suggestion unit can also adjust the priority of the proposal according to the importance of the controversial element. This allows for efficient proposals by adjusting the level of detail of the proposal based on the importance of the controversial element. Some or all of the above-described processing by the suggestion unit may be performed using, or without, an AI. For example, the suggestion unit can input importance data of the controversial element into the generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0095] When making a proposal, the suggestion unit can apply different proposal algorithms depending on the category of the controversy element. Examples of categories include, but are not limited to, politics, economics, and entertainment. For example, the suggestion unit can apply a proposal algorithm based on a reliable information source to controversy elements in the news category. The suggestion unit can also apply a proposal algorithm that emphasizes sentiment analysis to controversy elements in the entertainment category. The suggestion unit can also apply a proposal algorithm that takes into account industry-specific terms and trends to controversy elements in the business category. By applying different proposal algorithms depending on the category of the controversy element, more accurate proposals can be made. Some or all of the above-described processing by the suggestion unit can be performed using, for example, AI, or without AI. For example, the suggestion unit can input category data of the controversy element into a generation AI and cause the generation AI to apply a proposal algorithm.

[0096] When making a suggestion, the suggestion unit can improve the accuracy of the suggestion based on the user's past suggestion results. Past suggestion results include, but are not limited to, suggestion results from a specific period or a specific user. The suggestion unit, for example, adjusts the suggestion algorithm based on feedback provided by the user in the past. The suggestion unit can also analyze the user's past suggestion results and make highly accurate suggestions for similar controversial topics. The suggestion unit can also adjust the priority of suggestions by referring to the user's past suggestion results. This allows the accuracy of suggestions to be improved by referring to the user's past suggestion results. Some or all of the above-described processing by the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's past suggestion result data into the generation AI and cause the generation AI to improve the accuracy of the suggestions.

[0097] The suggestion unit can estimate the user's emotion and adjust the length of the suggestion based on the estimated user's emotion. The emotion estimation can be performed, for example, using an emotion analysis algorithm, but is not limited to this example. The length of the suggestion can include, for example, the level of detail of the suggestion and the duration of the suggestion, but is not limited to this example. For example, if the user is in a hurry, the suggestion unit can provide a short and concise suggestion. For example, if the user is relaxed, the suggestion unit can provide a detailed suggestion. For example, if the user is excited, the suggestion unit can provide a suggestion with a visually stimulating effect. This allows for more appropriate suggestions to be made by adjusting the length of the suggestion based on the user's emotion. The emotion estimation can be achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to this example. Some or all of the above-described processing in the suggestion unit can be performed using AI, or without AI. For example, the suggestion unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0098] When making a proposal, the suggestion unit can determine the priority of the proposal based on the submission time of the controversial element. The submission time includes, but is not limited to, for example, the time period of posting and the frequency of submission. For example, the suggestion unit can prioritize the proposal for the most recent controversial element. The suggestion unit can also lower the priority of the proposal for the oldest controversial element. The suggestion unit can also adjust the proposal schedule based on the submission time. This allows for efficient proposals by determining the priority of the proposal based on the submission time of the controversial element. Some or all of the above-described processing by the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input submission time data of the controversial element into the generation AI and cause the generation AI to determine the priority of the proposal.

[0099] The suggestion unit can adjust the order of suggestions based on the relevance of the controversial elements when making suggestions. Relevance includes, but is not limited to, the similarity of the controversial elements and related topics. For example, the suggestion unit prioritizes suggestions for controversial elements with high relevance. The suggestion unit can also postpone the order of suggestions for controversial elements with low relevance. The suggestion unit can also adjust the schedule of suggestions based on the relevance of the controversial elements. By adjusting the order of suggestions based on the relevance of the controversial elements, suggestions can be made efficiently. Some or all of the above-described processing by the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input relevance data of controversial elements into a generation AI and cause the generation AI to adjust the order of suggestions.

[0100] When making a proposal, the suggestion unit can adjust the use of technical terminology in the proposal based on the user's level of expertise. Examples of the level of expertise include, but are not limited to, the user's occupation and educational background. For example, if the user has technical expertise, the suggestion unit can provide proposal content that uses a lot of technical terminology. Furthermore, if the user does not have technical expertise, the suggestion unit can provide proposal content that is concise and easy to understand. The suggestion unit can also adjust the way the proposal content is expressed based on the user's level of expertise. This allows for more appropriate proposals by adjusting the use of technical terminology in the proposal based on the user's level of expertise. Some or all of the above-described processing by the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terminology. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, identification unit, and suggestion unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14 and accepts content posted by a user. The analysis unit is realized by the identification processing unit 290 of the data processing device 12 and analyzes the content posted using a generation AI. The identification unit is realized by the identification processing unit 290 of the data processing device 12 and identifies potential controversial elements based on the analysis results. The suggestion unit is realized by the identification processing unit 290 of the data processing device 12 and makes correction suggestions based on the identified controversial elements. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, identification unit, and suggestion unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and receives content posted by a user. The analysis unit is realized by the identification processing unit 290 of the data processing device 12 and analyzes the content posted using a generation AI. The identification unit is realized by the identification processing unit 290 of the data processing device 12 and identifies potential controversial elements based on the analysis results. The suggestion unit is realized by the identification processing unit 290 of the data processing device 12 and makes correction suggestions based on the identified controversial elements. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, identification unit, and suggestion unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset-type terminal 314 and accepts content posted by a user. The analysis unit is realized by the identification processing unit 290 of the data processing device 12 and analyzes the content posted using a generation AI. The identification unit is realized by the identification processing unit 290 of the data processing device 12 and identifies potential flaming elements based on the analysis results. The suggestion unit is realized by the identification processing unit 290 of the data processing device 12 and makes correction suggestions based on the identified flaming elements. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, identification unit, and suggestion unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414 and receives content posted by a user. The analysis unit is realized by the identification processing unit 290 of the data processing device 12 and analyzes the content posted using a generation AI. The identification unit is realized by the identification processing unit 290 of the data processing device 12 and identifies potential inflammatory elements based on the analysis results. The suggestion unit is realized by the identification processing unit 290 of the data processing device 12 and makes correction suggestions based on the identified inflammatory elements.

[0101] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0102] The reception unit can analyze the user's past posts and customize the method for receiving posts based on the user's posting tendencies. For example, it can prioritize recognition of words and phrases that the user has frequently used in the past, allowing for smooth reception of posts. Also, if a user tends to post during a specific time period, the reception unit's response speed can be optimized to match that time period. Furthermore, if a user has shown interest in a specific topic in the past, posts related to that topic can be prioritized for reception. This makes it possible to provide a more personalized reception method by taking into account the user's past posting tendencies.

[0103] The analysis unit can analyze images and videos included in users' posts and identify inflammatory elements by taking visual elements into consideration. For example, image analysis technology can be used to detect offensive symbols and inappropriate language in images included in posts. Video analysis technology can also be used to identify inflammatory elements from audio and video within videos. Furthermore, the content of images and videos can be converted into text and combined with text analysis for more accurate identification. By taking visual elements into consideration, it is possible to detect inflammatory elements that cannot be identified from text alone.

[0104] The suggestion unit can estimate the user's emotions and adjust the timing of suggestions based on the estimated user emotions. For example, if the user is feeling angry, the suggestion unit can temporarily delay the suggestion to provide time for the user to calm down. Also, if the user is excited, the suggestion unit can quickly make the suggestion to make the user more likely to accept it while their emotions are heightened. Furthermore, if the user is sad, the suggestion unit can flexibly adjust the suggestion to wait until the user's emotions have calmed down. In this way, by adjusting the timing of suggestions based on the user's emotions, suggestions can be made at more appropriate times.

[0105] The identification unit can analyze metadata included in the user's post content and identify flame war elements by taking into account the background information of the post. For example, flame war elements related to a specific event or situation can be identified based on the date and time the post was created and the poster's location information. It can also analyze information on links included in the post content and identify flame war elements by taking into account the reliability and content of the links. Furthermore, it can analyze hashtags and mentions included in the post content and identify flame war elements based on related topics and people. By taking metadata into account, highly accurate identification based on the background information of the post can be performed.

[0106] The suggestion unit can estimate the user's emotions and customize the content of the suggestion based on the estimated user's emotions. For example, if the user is feeling angry, the suggestion unit can change the content of the suggestion to a calm and neutral expression. Also, if the user is excited, the suggestion unit can provide the content quickly and concisely. Furthermore, if the user is sad, the suggestion unit can change the content of the suggestion to a kind and considerate expression. In this way, by customizing the content of the suggestion based on the user's emotions, more appropriate suggestions can be made.

[0107] The reception unit can automatically detect the language included in the user's posted content and accept the post in the appropriate language. For example, if the user posts in English, the post is accepted in English, and if the user posts in Japanese, the post is accepted in Japanese. The reception unit can also appropriately recognize and respond to posts that contain a mixture of multiple languages. Furthermore, the reception unit can translate the posted content based on the language detection results and support acceptance in other languages. This allows for more multilingual reception by taking into account the language included in the user's posted content.

[0108] The analysis unit can estimate the user's emotions and adjust the visual presentation of the analysis based on the estimated user's emotions. For example, if the user is feeling angry, the analysis results can be displayed in calm, neutral colors. If the user is excited, the analysis results can be displayed in vivid, visually stimulating colors. Furthermore, if the user is sad, the analysis results can be displayed in gentle, considerate colors. In this way, by adjusting the visual presentation of the analysis based on the user's emotions, more appropriate analysis results can be provided.

[0109] The identification unit can analyze the voice data included in the user's post and identify inflammatory elements by taking into account the tone and emotion of the voice. For example, voice analysis technology can be used to detect aggressive tones and inappropriate expressions in the voice included in the post. It can also perform emotion analysis of the voice and identify inflammatory elements when emotional expressions are included. Furthermore, the voice data can be converted into text and combined with text analysis for more accurate identification. By taking voice data into account, it is possible to detect inflammatory elements that cannot be identified by text alone.

[0110] The suggestion unit can estimate the user's emotions and determine the priority of suggestions based on the estimated user's emotions. For example, if the user is feeling angry, important suggestions can be given priority, and suggestions to calm the user can be postponed. Also, if the user is excited, suggestions that require a quick response can be given priority. Furthermore, if the user is sad, suggestions that take the user's emotions into consideration can be given priority. In this way, by determining the priority of suggestions based on the user's emotions, suggestions can be made in a more appropriate order.

[0111] The reception unit can evaluate the reliability of links included in user posts and restrict the reception of posts that include unreliable links. For example, the reception unit can evaluate reliability based on the domain of the link destination and past evaluations, and temporarily suspend the reception of posts that include unreliable links. The reception unit can also analyze the content of the link destination and restrict the reception of posts that include inappropriate information. Furthermore, the reception unit can suggest link corrections to the user based on the reliability of the link destination. In this way, by taking the reliability of the link destination into consideration, the spread of unreliable information can be prevented.

[0112] The processing flow of the second embodiment will be briefly explained below.

[0113] Step 1: The reception unit receives content posted by a user. The content posted may include text, images, videos, and voice input. For example, the reception unit converts the text or voice input by the user into text and receives it. Step 2: The analysis unit uses the generation AI to analyze the content of the post received by the reception unit. The analysis analyzes the context and sentiment of the post. For example, it uses text generation AI (LLM) or multimodal generation AI to extract and analyze important parts of the post. Step 3: The identification unit identifies potential controversial elements based on the results of the analysis by the analysis unit. Identification is performed based on specific keywords and past controversial cases. For example, an AI model is used to input the analysis results and output controversial elements. Step 4: The suggestion unit makes correction suggestions based on the controversial elements identified by the identification unit. The correction suggestions are made based on specific correction plans and proposal formats. For example, the suggestion unit inputs the controversial elements identified using an AI model and outputs correction suggestions.

[0114] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0115] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0116] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0117] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0118] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0119] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0120] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0121] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0122] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0123] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0124] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0125] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0126] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0127] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0128] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0129] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0130] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0131] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0132] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0133] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0134] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0135] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0136] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0137] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0138] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0139] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0140] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0141] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0142] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0143] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0144] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0145] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0146] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0147] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0148] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0149] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0150] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0151] 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.

[0152] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0153] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0154] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0155] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0156] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0157] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0158] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0159] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0160] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0161] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0162] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0163] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0164] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0165] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0166] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0167] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0168] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0169] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0170] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0171] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0172] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0173] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0174] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0175] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0176] 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.

[0177] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0178] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0179] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0180] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0181] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0182] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0183] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0184] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0185] [Explanation of symbols]

[0186] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives content posted by users; an analysis unit that analyzes the content of the post received by the reception unit and analyzes the context or sentiment; an identification unit that identifies potential flaming elements based on the results of the analysis by the analysis unit; a suggestion unit that makes a correction suggestion based on the flaming elements identified by the identification unit. A system characterized by:

2. The proposal unit Learning from past posting data and analyzing patterns of online flame wars 2. The system of claim 1.

3. The proposal unit Alert users to specific topics or words that tend to be inflammatory 2. The system of claim 1.

4. The reception unit Estimates user emotions and adjusts the timing of accepting posts based on the estimated user emotions.

2. The system of claim 1.

5. The reception unit Analyze the user's past posting history and select the appropriate reception method 2. The system of claim 1.

6. The reception unit As posts are accepted, they are filtered based on the user's current interests and activity.

2. The system of claim 1.

7. The reception unit When accepting submissions, select the appropriate method of acceptance depending on the user's input method.

2. The system of claim 1.

8. The reception unit Estimate the user's emotions and prioritize the posts to be accepted based on the estimated user emotions.

2. The system of claim 1.

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