System

Abstract

An object of a system according to an embodiment is to promote sound communication in an online comment platform.SOLUTION: A system includes an emotion detection unit, a feedback provision unit, an erroneous information detection unit, and a tone evaluation unit. The emotion detection unit detects emotional wording of the comment. The feedback providing unit provides feedback for prompting cooldown on the basis of the emotional wording detected by the emotion detecting unit. The error information detection unit detects error information of the comment. The feedback providing unit provides accurate data or information based on the error information detected by the error information detection unit. The tone evaluation unit evaluates the tone and manner of the comment. The feedback providing unit provides feedback to prompt improvement based on the tone or manner evaluated by the tone evaluation unit.SELECTED DRAWING: Figure 1

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] Existing technologies do not adequately prevent emotional exchanges and the spread of misinformation on online comment platforms, and there is room for improvement.

[0005] The system according to the embodiment aims to promote healthy communication in online comment platforms. [Means for solving the problem]

[0006] The system according to the embodiment includes an emotion detection unit, a feedback providing unit, a misinformation detection unit, and a tone evaluation unit. The emotion detection unit detects emotional language in a comment. The feedback providing unit provides feedback encouraging the commenter to cool down based on the emotional language detected by the emotion detection unit. The misinformation detection unit detects misinformation in the comment. The feedback providing unit provides accurate data or information based on the misinformation detected by the misinformation detection unit. The tone evaluation unit evaluates the tone and manner of the comment. The feedback providing unit provides feedback encouraging improvement based on the tone and manner evaluated by the tone evaluation unit. [Effects of the Invention]

[0007] A system according to an embodiment can promote healthy communication in online comment platforms. [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 nonvolatile storage devices that store various programs, various parameters, etc. Examples of nonvolatile 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 comment quality improvement system according to an embodiment of the present invention is a system in which comment posting is performed in the form of a chat with an AI, and the AI ​​provides constructive feedback to the poster. This enables the comment quality improvement system to improve the quality of posted comments and promote healthy communication.

[0029] A comment quality improvement system according to an embodiment includes an emotion detection unit, a misinformation detection unit, a tone evaluation unit, and a feedback provision unit. The emotion detection unit detects emotional language in comments. For example, the emotion detection unit detects aggressive or insulting language. The emotion detection unit can also detect emotional expressions. The misinformation detection unit detects misinformation in comments. For example, the misinformation detection unit detects information that is not factual. The misinformation detection unit can also detect misleading information. The tone evaluation unit evaluates the tone and manner of the comments. For example, the tone evaluation unit evaluates politeness. The tone evaluation unit can also evaluate the presence or absence of aggression. The tone evaluation unit can also evaluate expressions of respect. The feedback provision unit provides feedback based on information detected by the emotion detection unit, the misinformation detection unit, and the tone evaluation unit. For example, the feedback provision unit provides feedback encouraging the commenter to cool down based on emotional language. The feedback provision unit can also provide feedback providing accurate data or information based on misinformation. Furthermore, the feedback providing unit can provide feedback that encourages improvement based on tone and manners. This allows the comment quality improvement system according to the embodiment to improve the quality of comments and promote healthy communication.

[0030] The emotion detection unit detects offensive language, and the feedback providing unit can provide feedback encouraging the user to cool down based on the offensive language. The emotion detection unit detects, for example, offensive language. For example, the emotion detection unit detects offensive language such as "idiot" or "die." The emotion detection unit can also detect insulting language or threatening language. The feedback providing unit provides, for example, feedback encouraging the user to cool down based on the offensive language. For example, the feedback providing unit provides feedback such as "This word may be too emotional. Why don't you express it more calmly?" Furthermore, when the feedback providing unit detects offensive language, it can also suggest a short meditation or breathing exercise to help the user relax. In this way, by detecting offensive language and encouraging the user to cool down, healthy communication can be promoted.

[0031] The misinformation detection unit detects misinformation, and the feedback providing unit can provide accurate data or information based on the misinformation. The misinformation detection unit, for example, detects misinformation. For example, the misinformation detection unit detects information that is not factual. The misinformation detection unit can also detect misleading information. The feedback providing unit, for example, provides accurate data or information based on the misinformation. For example, the feedback providing unit provides feedback such as, "This information is incorrect. The correct information is as follows." The feedback providing unit can also provide data from a reliable information source. Furthermore, when misinformation is detected, the feedback providing unit can prompt the user to check the source and basis of the information. In this way, by detecting misinformation and providing accurate data or information, it is possible to promote reliable communication.

[0032] The tone evaluation unit evaluates the tone and manner of the comment, and the feedback providing unit can provide feedback to encourage improvement based on the tone and manner. The tone evaluation unit, for example, evaluates the tone and manner of the comment. For example, the tone evaluation unit evaluates politeness. The tone evaluation unit can also evaluate the presence or absence of aggression. Furthermore, the tone evaluation unit can evaluate expressions of respect. The feedback providing unit provides feedback to encourage improvement based on the tone and manner, for example. For example, the feedback providing unit provides feedback such as, "This expression may be a little too strong. Why not try expressing it a little more softly?" The feedback providing unit can also provide feedback taking into account the user's past posting history. Furthermore, the feedback providing unit can provide feedback taking into account the manners of different cultures and regions. In this way, by evaluating the tone and manner of the comment and encouraging improvement, it is possible to promote higher quality communication.

[0033] The feedback providing unit can analyze the user's past comment history and provide individually optimized feedback. The feedback providing unit, for example, analyzes the user's past comment history. For example, the feedback providing unit analyzes the content, posting date and time, and frequency of past comments. The feedback providing unit can also extract specific patterns and trends. For example, the feedback providing unit provides feedback encouraging a calmer expression to a user who has made many emotional comments in the past. The feedback providing unit can also present specific examples of improvement to the user based on the past comment history. In this way, by analyzing the user's past comment history and providing individually optimized feedback, more effective communication can be promoted.

[0034] The feedback providing unit can present related news articles and reference materials in real time according to the content of the user's comments. The feedback providing unit, for example, analyzes the content of the user's comments. For example, the feedback providing unit analyzes the content of the comments and automatically collects related news articles and reference materials. The feedback providing unit can also present the latest news articles on a specific topic. Furthermore, the feedback providing unit can provide information from reliable news sites and academic papers. This makes it possible to promote deeper discussions by presenting related news articles and reference materials in real time according to the content of the user's comments.

[0035] The feedback providing unit can support voice input when posting a comment and convert it into text using voice recognition technology. The feedback providing unit can, for example, support voice input when posting a comment. For example, the feedback providing unit can use a microphone on a smartphone to input voice. The feedback providing unit can also convert voice into text using voice recognition technology. For example, voice recognition software can automatically analyze the voice and save it as text. Furthermore, the feedback providing unit can also build a system in which text is displayed on a screen simultaneously with voice input. This allows users to easily post comments by supporting voice input and converting it into text using voice recognition technology.

[0036] The feedback providing unit can provide the same AI-assisted function on different platforms, thereby realizing a unified user experience. The feedback providing unit, for example, introduces the AI-assisted function on different platforms. For example, the feedback providing unit makes the same AI-assisted function available on social media and forums. The feedback providing unit can also provide a unified user experience on different platforms. For example, the feedback providing unit provides similar feedback for comments posted on different platforms. This allows the same AI-assisted function to be provided on different platforms, thereby realizing a unified user experience and improving user convenience.

[0037] The misinformation detection unit can automatically collect data from reliable sources when detecting misinformation, and the feedback providing unit can present the data to the user. The misinformation detection unit, for example, automatically collects data from reliable sources when detecting misinformation. For example, the misinformation detection unit acquires information from databases of government agencies or academic institutions. The feedback providing unit, for example, presents data from reliable sources to the user. For example, the feedback providing unit provides feedback such as, "This information is incorrect. The correct information is as follows." The feedback providing unit can also provide accurate information based on the data from reliable sources. In this way, accurate information can be provided by automatically collecting data from reliable sources when detecting misinformation and presenting it to the user.

[0038] When the misinformation detection unit detects misinformation, the feedback providing unit can prompt the user to confirm the source and basis of the information. For example, when misinformation is detected, the misinformation detection unit prompts the user to confirm the source and basis of the information. For example, the misinformation detection unit displays a message to the user such as "Please tell us the source of this information." The feedback providing unit provides feedback prompting the user to confirm the source and basis of the information. For example, the feedback providing unit provides feedback such as "Please check the source of this information." The feedback providing unit can also present specific steps for confirming the source and basis of the information. In this way, when misinformation is detected, the user is prompted to confirm the source and basis of the information, thereby enabling the user to provide accurate information.

[0039] When the misinformation detection unit detects misinformation, the feedback providing unit can explain the background and historical background of the information. For example, the misinformation detection unit explains the background and historical background of the information when it detects misinformation. For example, the misinformation detection unit explains how the misinformation spread. For example, the feedback providing unit provides feedback explaining the background and historical background of the information. For example, the feedback providing unit provides feedback such as, "This information is incorrect. The correct information is as follows." The feedback providing unit can also present specific steps for explaining the background and historical background of the information. In this way, by explaining the background and historical background of the information when misinformation is detected, the user can understand the correct information.

[0040] The misinformation detection unit is configured to provide accurate information in advance for topics where misinformation is frequently posted, thereby preventing the spread of misinformation. The misinformation detection unit, for example, identifies topics where misinformation is frequently posted. For example, the misinformation detection unit analyzes past posting history and evaluates the frequency of misinformation. The feedback providing unit, for example, provides accurate information in advance. For example, the feedback providing unit provides feedback such as, "The accurate information regarding this topic is as follows." The feedback providing unit can also present specific steps for preventing the spread of misinformation. This makes it possible to provide accurate information in advance for topics where misinformation is frequently posted, thereby preventing the spread of misinformation and promoting more reliable communication.

[0041] The tone evaluation unit can take past posting history into consideration when evaluating tone and manner, and the feedback providing unit can provide feedback. The tone evaluation unit, for example, takes past posting history into consideration when evaluating tone and manner. For example, the tone evaluation unit analyzes the content, posting date and time, and frequency of past comments. The tone evaluation unit can also extract specific patterns and trends. The feedback providing unit provides feedback based on the past posting history, for example. For example, the feedback providing unit provides feedback encouraging calmer expression to a user who has made many aggressive comments in the past. The feedback providing unit can also present specific examples of improvement to the user based on the past posting history. In this way, by providing feedback taking past posting history into consideration when evaluating tone and manner, more effective communication can be promoted.

[0042] If the tone evaluation unit determines that the tone or manner is inappropriate, the feedback providing unit can present specific examples for improvement. The tone evaluation unit, for example, evaluates whether the tone or manner is inappropriate. For example, the tone evaluation unit evaluates whether the tone or manner is polite or aggressive. The feedback providing unit, for example, presents specific examples for improvement. For example, the feedback providing unit provides feedback such as, "This expression may be a little too strong. Why not try expressing it a little more softly?" The feedback providing unit can also present specific examples of sentences and advice for improvement. In this way, by presenting specific examples of improvement when the tone or manner is inappropriate, the user's communication skills can be improved.

[0043] The tone evaluation unit can provide feedback by taking into account cultural and regional manners when evaluating tone and manners. The tone evaluation unit can provide feedback by taking into account cultural and regional manners, for example. For example, the tone evaluation unit can evaluate differences in etiquette in a specific culture and regional manners. The feedback providing unit can provide feedback by taking into account cultural and regional manners, for example. For example, the feedback providing unit can provide appropriate feedback to users from different cultural backgrounds. The feedback providing unit can also present specific examples of improvement based on cultural and regional manners. As a result, providing feedback by taking into account cultural and regional manners when evaluating tone and manners can promote more appropriate communication.

[0044] The tone evaluation unit can encourage improvement of tone and manners, and the feedback providing unit can periodically provide advice for improving communication skills. The tone evaluation unit, for example, performs evaluation to encourage improvement of tone and manners. For example, the tone evaluation unit evaluates whether or not there is politeness or aggression. The feedback providing unit, for example, periodically provides advice for improving communication skills. For example, the feedback providing unit provides advice such as "Learn the trick to expressing yourself calmly." The feedback providing unit can also present specific areas for improvement and practice methods. In this way, by periodically providing advice for improving communication skills to encourage improvement of tone and manners, the user's communication skills can be improved.

[0045] In order to protect the user's freedom of expression of opinion, the feedback providing unit can emphasize that the feedback is suggestive and give the user the final decision-making power. The feedback providing unit, for example, emphasizes that the feedback is suggestive. For example, the feedback providing unit displays a message such as, "This feedback is merely a suggestion. The final decision rests with you." The feedback providing unit can also present specific steps for giving the user the final decision-making power. In this way, by emphasizing that the feedback is suggestive and giving the user the final decision-making power, the user's freedom of expression of opinion can be protected.

[0046] In order to protect the user's freedom of expression, the feedback providing unit can make the content of the feedback transparent, allowing the user to confirm the basis for the feedback. The feedback providing unit, for example, makes the content of the feedback transparent. For example, the feedback providing unit displays data or information sources that are the basis for the feedback. The feedback providing unit can also present specific steps for allowing the user to confirm the basis for the feedback. In this way, the content of the feedback is made transparent, allowing the user to confirm the basis, thereby protecting the user's freedom of expression.

[0047] The feedback providing unit may provide the user with an option to customize the content of the feedback in order to protect the user's freedom of expression. The feedback providing unit, for example, provides the user with an option to customize the content of the feedback. For example, the feedback providing unit may allow the user to select the content of the feedback. The feedback providing unit may also adjust the content of the feedback to suit the user's preferences. Thus, by providing the user with an option to customize the content of the feedback, the user's freedom of expression can be protected.

[0048] The feedback providing unit can explicitly present to the user the right to choose to accept the feedback in order to protect the freedom of expression of opinions. The feedback providing unit, for example, explicitly presents the user the right to choose to accept the feedback. For example, the feedback providing unit displays a message such as "It is your choice whether to accept this feedback." The feedback providing unit can also present specific steps for allowing the user to choose to accept the feedback. In this way, by explicitly presenting the right to choose to accept the feedback, the freedom of expression of opinions of the user can be protected.

[0049] The feedback providing unit can make effective suggestions by taking into account the user's past feedback acceptance history when suggesting feedback. The feedback providing unit, for example, analyzes the user's past feedback acceptance history. For example, the feedback providing unit analyzes the content of past feedback and the frequency of acceptance. The feedback providing unit can also extract specific patterns and trends. The feedback providing unit makes effective suggestions based on, for example, patterns of feedback that have been accepted in the past. For example, the feedback providing unit presents examples of feedback that have been successful in the past. The feedback providing unit can also present specific examples of improvement based on the user's past feedback acceptance history. In this way, the user's acceptance rate can be improved by making more effective suggestions by taking into account the user's past feedback acceptance history when suggesting feedback.

[0050] The feedback providing unit can present specific examples and success stories to show that the feedback proposal is beneficial to the user. The feedback providing unit, for example, presents specific examples and success stories to show that the feedback proposal is beneficial to the user. For example, the feedback providing unit displays past success stories. The feedback providing unit can also present specific examples of improvement. In this way, by presenting specific examples and success stories to show that the feedback proposal is beneficial to the user, it is possible to improve the acceptance rate of users.

[0051] The feedback providing unit may provide different perspectives or approaches to diversify the feedback suggestions. For example, the feedback providing unit may present multiple feedback options. The feedback providing unit may also provide perspectives from different cultures or backgrounds. This may improve user acceptance rates by providing different perspectives or approaches to diversify the feedback suggestions.

[0052] The feedback providing unit can refer to the feedback acceptance histories of other users to show that the feedback proposal is beneficial to the user. The feedback providing unit, for example, analyzes the feedback acceptance histories of other users. For example, the feedback providing unit analyzes success stories and acceptance frequencies of other users. The feedback providing unit can also extract specific patterns and trends. The feedback providing unit makes effective suggestions based on the feedback acceptance histories of other users. For example, the feedback providing unit presents examples of feedback that have been successful in the past. The feedback providing unit can also present specific examples of improvement based on the feedback acceptance histories of other users. In this way, by referring to the feedback acceptance histories of other users to show that the feedback proposal is beneficial to the user, the user acceptance rate can be improved.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The comment quality improvement system may further include a learning tracking unit that tracks the user's learning progress. The learning tracking unit records the feedback the user has received in the past and subsequent changes in the quality of their comments, thereby visualizing the user's growth. For example, the learning tracking unit may record what kind of feedback the user has received and display, in a graph or chart, how the quality of their comments has improved since then. The learning tracking unit may also evaluate how much the user has improved in response to specific feedback and provide advice for further learning. This allows the user to realize their own growth and increase their motivation.

[0055] The comment quality improvement system may further include a learning resource providing unit that provides relevant learning resources based on the content of a user's comment. For example, when a user posts a comment on a specific topic, the learning resource providing unit may suggest online courses, articles, or videos related to the topic. The learning resource providing unit may also provide customized learning resources based on the user's interests. This allows users to gain new knowledge through their comments and promote deeper discussions.

[0056] The comment quality improvement system may further include a community suggestion unit that suggests related communities and forums based on the content of a user's comment. For example, when a user posts a comment on a specific topic, the community suggestion unit suggests online communities and forums related to the topic. The community suggestion unit may also suggest customized communities based on the user's interests. This allows the user to connect with other users who share the same interests and engage in deeper discussions.

[0057] The comment quality improvement system may further include an event suggestion unit that suggests related events and seminars based on the content of a user's comment. For example, when a user posts a comment on a specific topic, the event suggestion unit suggests online events and seminars related to the topic. The event suggestion unit may also suggest customized events based on the user's interests. This increases the user's opportunities to gain new knowledge and promotes deeper discussions.

[0058] The comment quality improvement system may further include a book suggestion unit that suggests related books and papers based on the content of a user's comment. For example, when a user posts a comment on a specific topic, the book suggestion unit may suggest books and academic papers related to the topic. The book suggestion unit may also suggest books customized based on the user's interests. This increases the user's opportunities to gain new knowledge and promotes deeper discussions.

[0059] The processing flow of the first embodiment will be briefly explained below.

[0060] Step 1: The emotion detection unit detects emotional language in comments, such as offensive or insulting words and emotional expressions. Step 2: The misinformation detector detects misinformation in comments, such as information that is factually incorrect or misleading. Step 3: The tone evaluator evaluates the tone and manner of the comments, for example, whether they are polite, aggressive, or respectful. Step 4: The feedback providing unit provides feedback based on the information detected by the emotion detection unit, misinformation detection unit, and tone evaluation unit. For example, feedback encouraging cooling down based on emotional language, feedback providing accurate data or information based on misinformation, and feedback encouraging improvement based on tone and manner are provided.

[0061] (Example 2) A comment quality improvement system according to an embodiment of the present invention is a system in which comment posting is performed in the form of a chat with an AI, and the AI ​​provides constructive feedback to the poster. This enables the comment quality improvement system to improve the quality of posted comments and promote healthy communication.

[0062] A comment quality improvement system according to an embodiment includes an emotion detection unit, a misinformation detection unit, a tone evaluation unit, and a feedback provision unit. The emotion detection unit detects emotional language in comments. For example, the emotion detection unit detects aggressive or insulting language. The emotion detection unit can also detect emotional expressions. The misinformation detection unit detects misinformation in comments. For example, the misinformation detection unit detects information that is not factual. The misinformation detection unit can also detect misleading information. The tone evaluation unit evaluates the tone and manner of the comments. For example, the tone evaluation unit evaluates politeness. The tone evaluation unit can also evaluate the presence or absence of aggression. The tone evaluation unit can also evaluate expressions of respect. The feedback provision unit provides feedback based on information detected by the emotion detection unit, the misinformation detection unit, and the tone evaluation unit. For example, the feedback provision unit provides feedback encouraging the commenter to cool down based on emotional language. The feedback provision unit can also provide feedback providing accurate data or information based on misinformation. Furthermore, the feedback providing unit can provide feedback that encourages improvement based on tone and manners. This allows the comment quality improvement system according to the embodiment to improve the quality of comments and promote healthy communication.

[0063] The emotion detection unit detects offensive language, and the feedback providing unit can provide feedback encouraging the user to cool down based on the offensive language. The emotion detection unit detects, for example, offensive language. For example, the emotion detection unit detects offensive language such as "idiot" or "die." The emotion detection unit can also detect insulting language or threatening language. The feedback providing unit provides, for example, feedback encouraging the user to cool down based on the offensive language. For example, the feedback providing unit provides feedback such as "This word may be too emotional. Why don't you express it more calmly?" Furthermore, when the feedback providing unit detects offensive language, it can also suggest a short meditation or breathing exercise to help the user relax. In this way, by detecting offensive language and encouraging the user to cool down, healthy communication can be promoted.

[0064] The misinformation detection unit detects misinformation, and the feedback providing unit can provide accurate data or information based on the misinformation. The misinformation detection unit, for example, detects misinformation. For example, the misinformation detection unit detects information that is not factual. The misinformation detection unit can also detect misleading information. The feedback providing unit, for example, provides accurate data or information based on the misinformation. For example, the feedback providing unit provides feedback such as, "This information is incorrect. The correct information is as follows." The feedback providing unit can also provide data from a reliable information source. Furthermore, when misinformation is detected, the feedback providing unit can prompt the user to check the source and basis of the information. In this way, by detecting misinformation and providing accurate data or information, it is possible to promote reliable communication.

[0065] The tone evaluation unit evaluates the tone and manner of the comment, and the feedback providing unit can provide feedback to encourage improvement based on the tone and manner. The tone evaluation unit, for example, evaluates the tone and manner of the comment. For example, the tone evaluation unit evaluates politeness. The tone evaluation unit can also evaluate the presence or absence of aggression. Furthermore, the tone evaluation unit can evaluate expressions of respect. The feedback providing unit provides feedback to encourage improvement based on the tone and manner, for example. For example, the feedback providing unit provides feedback such as, "This expression may be a little too strong. Why not try expressing it a little more softly?" The feedback providing unit can also provide feedback taking into account the user's past posting history. Furthermore, the feedback providing unit can provide feedback taking into account the manners of different cultures and regions. In this way, by evaluating the tone and manner of the comment and encouraging improvement, it is possible to promote higher quality communication.

[0066] The feedback providing unit can analyze the user's past comment history and provide individually optimized feedback. The feedback providing unit, for example, analyzes the user's past comment history. For example, the feedback providing unit analyzes the content, posting date and time, and frequency of past comments. The feedback providing unit can also extract specific patterns and trends. For example, the feedback providing unit provides feedback encouraging a calmer expression to a user who has made many emotional comments in the past. The feedback providing unit can also present specific examples of improvement to the user based on the past comment history. In this way, by analyzing the user's past comment history and providing individually optimized feedback, more effective communication can be promoted.

[0067] The feedback providing unit can present related news articles and reference materials in real time according to the content of the user's comments. The feedback providing unit, for example, analyzes the content of the user's comments. For example, the feedback providing unit analyzes the content of the comments and automatically collects related news articles and reference materials. The feedback providing unit can also present the latest news articles on a specific topic. Furthermore, the feedback providing unit can provide information from reliable news sites and academic papers. This makes it possible to promote deeper discussions by presenting related news articles and reference materials in real time according to the content of the user's comments.

[0068] The feedback providing unit can use the emotion estimation function to provide feedback according to the user's emotional state, thereby promoting positive communication. The feedback providing unit, for example, uses the emotion estimation function to grasp the user's emotional state. For example, the feedback providing unit analyzes the user's facial expressions and voice to estimate the emotion. The feedback providing unit can also use the emotion estimation function to provide feedback according to the user's emotional state. For example, if the user feels strong anger or sadness, feedback encouraging the user to express themselves calmly is provided. In this way, by providing feedback according to the user's emotional state, positive communication can be promoted.

[0069] The feedback providing unit can support voice input when posting a comment and convert it into text using voice recognition technology. The feedback providing unit can, for example, support voice input when posting a comment. For example, the feedback providing unit can use a microphone on a smartphone to input voice. The feedback providing unit can also convert voice into text using voice recognition technology. For example, voice recognition software can automatically analyze the voice and save it as text. Furthermore, the feedback providing unit can also build a system in which text is displayed on a screen simultaneously with voice input. This allows users to easily post comments by supporting voice input and converting it into text using voice recognition technology.

[0070] The feedback providing unit can provide the same AI-assisted function on different platforms, thereby realizing a unified user experience. The feedback providing unit, for example, introduces the AI-assisted function on different platforms. For example, the feedback providing unit makes the same AI-assisted function available on social media and forums. The feedback providing unit can also provide a unified user experience on different platforms. For example, the feedback providing unit provides similar feedback for comments posted on different platforms. This allows the same AI-assisted function to be provided on different platforms, thereby realizing a unified user experience and improving user convenience.

[0071] The feedback providing unit can use the emotion estimation function to analyze the emotion of the user when posting a comment in real time and provide appropriate feedback. The feedback providing unit, for example, uses the emotion estimation function to analyze the emotion of the user in real time. For example, the feedback providing unit estimates the emotion by analyzing the user's facial expression or voice. The feedback providing unit can also use the emotion estimation function to analyze the emotion of the user when posting a comment in real time. For example, appropriate feedback is provided according to the user's emotional state. In this way, by analyzing the emotion of the user when posting a comment in real time and providing appropriate feedback, positive communication can be promoted.

[0072] The emotion detection unit can provide more accurate feedback by taking context into consideration when detecting emotional language. The emotion detection unit, for example, analyzes the context of a comment. For example, the emotion detection unit analyzes surrounding sentences and related topics. The emotion detection unit can also consider context when detecting emotional language. For example, even if an offensive word is used, the emotion detection unit may determine that the expression is appropriate depending on the context. The feedback providing unit, for example, provides feedback by taking context into consideration. For example, the feedback providing unit provides appropriate feedback based on the context. In this way, by taking context into consideration when detecting emotional language, more accurate feedback can be provided.

[0073] When the emotion detection unit detects emotional language, the feedback providing unit can suggest a short meditation or breathing exercise to help users relax. The emotion detection unit, for example, analyzes the user's emotional state when emotional language is detected. For example, the emotion detection unit estimates the user's emotion by analyzing the user's facial expressions and voice. The feedback providing unit, for example, suggests a short meditation or breathing exercise to help users relax. For example, the feedback providing unit provides feedback such as, "Would you like to try a few minutes of guided meditation?" The feedback providing unit can also present specific steps for the breathing exercise. In this way, by suggesting a short meditation or breathing exercise to help users relax when emotional language is detected, it is possible to promote calm communication among users.

[0074] The emotion detection unit uses the emotion estimation function to monitor the user's emotional state in real time, and the feedback providing unit can urge the user to cool down at an appropriate timing. The emotion detection unit, for example, uses the emotion estimation function to monitor the user's emotional state in real time. For example, the emotion detection unit analyzes the user's facial expressions and voice to estimate the user's emotion. The feedback providing unit provides, for example, feedback urging the user to cool down at an appropriate timing. For example, the feedback providing unit provides feedback such as "Why don't you take a short break?" when the user's emotions become heightened. The feedback providing unit can also urge the user to cool down based on the timing at which it detects heightened emotions. In this way, calm communication can be promoted by monitoring the user's emotional state in real time and urging the user to cool down at an appropriate timing.

[0075] When the emotion detection unit detects emotional language, the feedback providing unit can suggest alternative expressions for expressing the emotion. The emotion detection unit, for example, detects emotional language. For example, the emotion detection unit detects aggressive words such as "idiot" or "die." The feedback providing unit, for example, suggests alternative expressions for expressing the emotion. For example, the feedback providing unit provides feedback such as "This expression may be a little too strong. Why not try a softer expression?" The feedback providing unit can also suggest expressions that can be used instead of aggressive words. In this way, by suggesting alternative expressions for expressing emotions when emotional language is detected, it is possible to promote calm communication.

[0076] The emotion detection unit can periodically provide advice to improve the communication skills of users in whom emotional language is frequently detected. The emotion detection unit, for example, identifies users in whom emotional language is frequently detected. For example, the emotion detection unit analyzes past comment history and evaluates the frequency of emotional language. The feedback provision unit, for example, periodically provides advice to improve communication skills. For example, the feedback provision unit provides advice such as "Learn how to express yourself calmly." The feedback provision unit can also suggest specific areas for improvement and practice methods. In this way, by periodically providing advice to improve communication skills to users in whom emotional language is frequently detected, the user's communication skills can be improved.

[0077] The emotion detection unit uses the emotion estimation function to predict before the user uses emotional language, and the feedback providing unit can urge the user to cool down in advance. The emotion detection unit, for example, predicts the user's emotional state using the emotion estimation function. For example, the emotion detection unit analyzes the user's facial expressions and voice to estimate the user's emotion. The feedback providing unit provides, for example, feedback urging the user to cool down before using emotional language. For example, the feedback providing unit provides feedback such as "Why don't you wait a little while to calm down before posting your comment?" The feedback providing unit can also urge the user to cool down based on the timing at which it predicts that emotions will rise. In this way, it is possible to predict before the user uses emotional language and urge the user to cool down in advance, thereby promoting calm communication.

[0078] The misinformation detection unit can automatically collect data from reliable sources when detecting misinformation, and the feedback providing unit can present the data to the user. The misinformation detection unit, for example, automatically collects data from reliable sources when detecting misinformation. For example, the misinformation detection unit acquires information from databases of government agencies or academic institutions. The feedback providing unit, for example, presents data from reliable sources to the user. For example, the feedback providing unit provides feedback such as, "This information is incorrect. The correct information is as follows." The feedback providing unit can also provide accurate information based on the data from reliable sources. In this way, accurate information can be provided by automatically collecting data from reliable sources when detecting misinformation and presenting it to the user.

[0079] When the misinformation detection unit detects misinformation, the feedback providing unit can prompt the user to confirm the source and basis of the information. For example, when misinformation is detected, the misinformation detection unit prompts the user to confirm the source and basis of the information. For example, the misinformation detection unit displays a message to the user such as "Please tell us the source of this information." The feedback providing unit provides feedback prompting the user to confirm the source and basis of the information. For example, the feedback providing unit provides feedback such as "Please check the source of this information." The feedback providing unit can also present specific steps for confirming the source and basis of the information. In this way, when misinformation is detected, the user is prompted to confirm the source and basis of the information, thereby enabling the user to provide accurate information.

[0080] The misinformation detection unit uses the emotion estimation function to analyze the user's emotional reaction to the misinformation, and the feedback providing unit can provide appropriate feedback. The misinformation detection unit, for example, uses the emotion estimation function to analyze the user's emotional reaction to the misinformation. For example, the misinformation detection unit analyzes the user's facial expression and voice to estimate the emotion. The feedback providing unit provides appropriate feedback based on the user's emotional reaction to the misinformation. For example, the feedback providing unit provides feedback such as, "This information is incorrect. The correct information is as follows." The feedback providing unit can also provide feedback that encourages the user to express themselves calmly in accordance with the user's emotional reaction. In this way, calm communication can be promoted by analyzing the user's emotional reaction to the misinformation and providing appropriate feedback.

[0081] When the misinformation detection unit detects misinformation, the feedback providing unit can explain the background and historical background of the information. For example, the misinformation detection unit explains the background and historical background of the information when it detects misinformation. For example, the misinformation detection unit explains how the misinformation spread. For example, the feedback providing unit provides feedback explaining the background and historical background of the information. For example, the feedback providing unit provides feedback such as, "This information is incorrect. The correct information is as follows." The feedback providing unit can also present specific steps for explaining the background and historical background of the information. In this way, by explaining the background and historical background of the information when misinformation is detected, the user can understand the correct information.

[0082] The misinformation detection unit is configured to provide accurate information in advance for topics where misinformation is frequently posted, thereby preventing the spread of misinformation. The misinformation detection unit, for example, identifies topics where misinformation is frequently posted. For example, the misinformation detection unit analyzes past posting history and evaluates the frequency of misinformation. The feedback providing unit, for example, provides accurate information in advance. For example, the feedback providing unit provides feedback such as, "The accurate information regarding this topic is as follows." The feedback providing unit can also present specific steps for preventing the spread of misinformation. This makes it possible to provide accurate information in advance for topics where misinformation is frequently posted, thereby preventing the spread of misinformation and promoting more reliable communication.

[0083] The misinformation detection unit uses the emotion estimation function to monitor the user's emotional reaction to the misinformation in real time, and the feedback providing unit can provide appropriate feedback. The misinformation detection unit, for example, uses the emotion estimation function to monitor the user's emotional reaction to the misinformation in real time. For example, the misinformation detection unit analyzes the user's facial expression and voice to estimate the emotion. The feedback providing unit provides appropriate feedback based on the user's emotional reaction to the misinformation. For example, the feedback providing unit provides feedback such as, "This information is incorrect. The correct information is as follows." The feedback providing unit can also provide feedback that encourages the user to express themselves calmly in accordance with the user's emotional reaction. In this way, by monitoring the user's emotional reaction to the misinformation in real time and providing appropriate feedback, calm communication can be promoted.

[0084] The tone evaluation unit can take past posting history into consideration when evaluating tone and manner, and the feedback providing unit can provide feedback. The tone evaluation unit, for example, takes past posting history into consideration when evaluating tone and manner. For example, the tone evaluation unit analyzes the content, posting date and time, and frequency of past comments. The tone evaluation unit can also extract specific patterns and trends. The feedback providing unit provides feedback based on the past posting history, for example. For example, the feedback providing unit provides feedback encouraging calmer expression to a user who has made many aggressive comments in the past. The feedback providing unit can also present specific examples of improvement to the user based on the past posting history. In this way, by providing feedback taking past posting history into consideration when evaluating tone and manner, more effective communication can be promoted.

[0085] If the tone evaluation unit determines that the tone or manner is inappropriate, the feedback providing unit can present specific examples for improvement. The tone evaluation unit, for example, evaluates whether the tone or manner is inappropriate. For example, the tone evaluation unit evaluates whether the tone or manner is polite or aggressive. The feedback providing unit, for example, presents specific examples for improvement. For example, the feedback providing unit provides feedback such as, "This expression may be a little too strong. Why not try expressing it a little more softly?" The feedback providing unit can also present specific examples of sentences and advice for improvement. In this way, by presenting specific examples of improvement when the tone or manner is inappropriate, the user's communication skills can be improved.

[0086] The tone evaluation unit can use the emotion estimation function to provide feedback on tone and manner according to the user's emotional state. The tone evaluation unit, for example, uses the emotion estimation function to grasp the user's emotional state. For example, the tone evaluation unit analyzes the user's facial expressions and voice to estimate the emotion. The feedback providing unit, for example, provides feedback on tone and manner according to the user's emotional state. For example, when the user feels strong anger or sadness, the feedback providing unit provides feedback encouraging calm expression. The feedback providing unit can also present specific examples of improvement according to the user's emotional state. In this way, calm communication can be promoted by providing feedback on tone and manner according to the user's emotional state.

[0087] The tone evaluation unit can provide feedback by taking into account cultural and regional manners when evaluating tone and manners. The tone evaluation unit can provide feedback by taking into account cultural and regional manners, for example. For example, the tone evaluation unit can evaluate differences in etiquette in a specific culture and regional manners. The feedback providing unit can provide feedback by taking into account cultural and regional manners, for example. For example, the feedback providing unit can provide appropriate feedback to users from different cultural backgrounds. The feedback providing unit can also present specific examples of improvement based on cultural and regional manners. As a result, providing feedback by taking into account cultural and regional manners when evaluating tone and manners can promote more appropriate communication.

[0088] The tone evaluation unit can encourage improvement of tone and manners, and the feedback providing unit can periodically provide advice for improving communication skills. The tone evaluation unit, for example, performs evaluation to encourage improvement of tone and manners. For example, the tone evaluation unit evaluates whether or not there is politeness or aggression. The feedback providing unit, for example, periodically provides advice for improving communication skills. For example, the feedback providing unit provides advice such as "Learn the trick to expressing yourself calmly." The feedback providing unit can also present specific areas for improvement and practice methods. In this way, by periodically providing advice for improving communication skills to encourage improvement of tone and manners, the user's communication skills can be improved.

[0089] The tone evaluation unit uses the emotion estimation function to predict the tone and manner of the user before the user posts a message, and the feedback providing unit can provide feedback in advance. The tone evaluation unit, for example, predicts the user's emotional state using the emotion estimation function. For example, the tone evaluation unit estimates the user's emotion by analyzing the user's facial expression and voice. The feedback providing unit, for example, predicts the tone and manner of the user before the user posts a message and provides feedback in advance. For example, the feedback providing unit provides feedback such as, "This expression may be a little too strong. Why not try expressing it a little more softly?" The feedback providing unit can also provide feedback based on the timing when it predicts that emotions will become stronger. In this way, calm communication can be promoted by predicting the tone and manner of the user before the user posts a message and providing feedback in advance.

[0090] In order to protect the user's freedom of expression of opinion, the feedback providing unit can emphasize that the feedback is suggestive and give the user the final decision-making power. The feedback providing unit, for example, emphasizes that the feedback is suggestive. For example, the feedback providing unit displays a message such as, "This feedback is merely a suggestion. The final decision rests with you." The feedback providing unit can also present specific steps for giving the user the final decision-making power. In this way, by emphasizing that the feedback is suggestive and giving the user the final decision-making power, the user's freedom of expression of opinion can be protected.

[0091] In order to protect the user's freedom of expression, the feedback providing unit can make the content of the feedback transparent, allowing the user to confirm the basis for the feedback. The feedback providing unit, for example, makes the content of the feedback transparent. For example, the feedback providing unit displays data or information sources that are the basis for the feedback. The feedback providing unit can also present specific steps for allowing the user to confirm the basis for the feedback. In this way, the content of the feedback is made transparent, allowing the user to confirm the basis, thereby protecting the user's freedom of expression.

[0092] The feedback providing unit can use the emotion estimation function to consider the emotional state of the user and adjust the feedback so that it is not overly restrictive. The feedback providing unit, for example, uses the emotion estimation function to grasp the emotional state of the user. For example, the feedback providing unit analyzes the user's facial expressions and voice to estimate the emotion. The feedback providing unit can also provide feedback taking the emotional state of the user into consideration. For example, the feedback providing unit provides flexible feedback when the user's emotion is heightened. In this way, the user's freedom of expression can be protected by considering the emotional state of the user and adjusting the feedback so that it is not overly restrictive.

[0093] The feedback providing unit may provide the user with an option to customize the content of the feedback in order to protect the user's freedom of expression. The feedback providing unit, for example, provides the user with an option to customize the content of the feedback. For example, the feedback providing unit may allow the user to select the content of the feedback. The feedback providing unit may also adjust the content of the feedback to suit the user's preferences. Thus, by providing the user with an option to customize the content of the feedback, the user's freedom of expression can be protected.

[0094] The feedback providing unit can explicitly present to the user the right to choose to accept the feedback in order to protect the freedom of expression of opinions. The feedback providing unit, for example, explicitly presents the user the right to choose to accept the feedback. For example, the feedback providing unit displays a message such as "It is your choice whether to accept this feedback." The feedback providing unit can also present specific steps for allowing the user to choose to accept the feedback. In this way, by explicitly presenting the right to choose to accept the feedback, the freedom of expression of opinions of the user can be protected.

[0095] The feedback providing unit can use the emotion estimation function to analyze the emotional reaction of the user when accepting feedback and make appropriate adjustments. The feedback providing unit, for example, uses the emotion estimation function to analyze the emotional reaction of the user. For example, the feedback providing unit analyzes the user's facial expressions and voice to estimate emotions. The feedback providing unit can also make appropriate adjustments based on the emotional reaction of the user when accepting feedback. For example, the feedback providing unit provides flexible feedback when the user's emotions are heightened. In this way, by analyzing the emotional reaction of the user when accepting feedback and making appropriate adjustments, it is possible to protect the user's freedom of expression.

[0096] The feedback providing unit can make effective suggestions by taking into account the user's past feedback acceptance history when suggesting feedback. The feedback providing unit, for example, analyzes the user's past feedback acceptance history. For example, the feedback providing unit analyzes the content of past feedback and the frequency of acceptance. The feedback providing unit can also extract specific patterns and trends. The feedback providing unit makes effective suggestions based on, for example, patterns of feedback that have been accepted in the past. For example, the feedback providing unit presents examples of feedback that have been successful in the past. The feedback providing unit can also present specific examples of improvement based on the user's past feedback acceptance history. In this way, the user's acceptance rate can be improved by making more effective suggestions by taking into account the user's past feedback acceptance history when suggesting feedback.

[0097] The feedback providing unit can present specific examples and success stories to show that the feedback proposal is beneficial to the user. The feedback providing unit, for example, presents specific examples and success stories to show that the feedback proposal is beneficial to the user. For example, the feedback providing unit displays past success stories. The feedback providing unit can also present specific examples of improvement. In this way, by presenting specific examples and success stories to show that the feedback proposal is beneficial to the user, it is possible to improve the acceptance rate of users.

[0098] The feedback providing unit can use the emotion estimation function to suggest feedback according to the user's emotional state and promote positive acceptance. The feedback providing unit, for example, uses the emotion estimation function to grasp the user's emotional state. For example, the feedback providing unit analyzes the user's facial expressions and voice to estimate the user's emotion. The feedback providing unit can also suggest feedback according to the user's emotional state. For example, the feedback providing unit provides positive feedback when the user's emotion is positive. In this way, the user acceptance rate can be improved by suggesting feedback according to the user's emotional state and promoting positive acceptance.

[0099] The feedback providing unit may provide different perspectives or approaches to diversify the feedback suggestions. For example, the feedback providing unit may present multiple feedback options. The feedback providing unit may also provide perspectives from different cultures or backgrounds. This may improve user acceptance rates by providing different perspectives or approaches to diversify the feedback suggestions.

[0100] The feedback providing unit can refer to the feedback acceptance histories of other users to show that the feedback proposal is beneficial to the user. The feedback providing unit, for example, analyzes the feedback acceptance histories of other users. For example, the feedback providing unit analyzes success stories and acceptance frequencies of other users. The feedback providing unit can also extract specific patterns and trends. The feedback providing unit makes effective suggestions based on the feedback acceptance histories of other users. For example, the feedback providing unit presents examples of feedback that have been successful in the past. The feedback providing unit can also present specific examples of improvement based on the feedback acceptance histories of other users. In this way, by referring to the feedback acceptance histories of other users to show that the feedback proposal is beneficial to the user, the user acceptance rate can be improved.

[0101] The feedback providing unit can use the emotion estimation function to monitor the emotional reaction of the user when accepting feedback in real time and make appropriate adjustments. The feedback providing unit, for example, uses the emotion estimation function to monitor the emotional reaction of the user in real time. For example, the feedback providing unit analyzes the user's facial expressions and voice to estimate the emotion. The feedback providing unit can also make appropriate adjustments based on the emotional reaction of the user when accepting feedback. For example, the feedback providing unit provides flexible feedback when the user's emotion is heightened. In this way, the user acceptance rate can be improved by monitoring the emotional reaction of the user when accepting feedback in real time and making appropriate adjustments.

[0102] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0103] The comment quality improvement system may further include a learning tracking unit that tracks the user's learning progress. The learning tracking unit records the feedback the user has received in the past and subsequent changes in the quality of their comments, thereby visualizing the user's growth. For example, the learning tracking unit may record what kind of feedback the user has received and display, in a graph or chart, how the quality of their comments has improved since then. The learning tracking unit may also evaluate how much the user has improved in response to specific feedback and provide advice for further learning. This allows the user to realize their own growth and increase their motivation.

[0104] The comment quality improvement system may further include a learning resource providing unit that provides relevant learning resources based on the content of a user's comment. For example, when a user posts a comment on a specific topic, the learning resource providing unit may suggest online courses, articles, or videos related to the topic. The learning resource providing unit may also provide customized learning resources based on the user's interests. This allows users to gain new knowledge through their comments and promote deeper discussions.

[0105] The comment quality improvement system may further include a community suggestion unit that suggests related communities and forums based on the content of a user's comment. For example, when a user posts a comment on a specific topic, the community suggestion unit suggests online communities and forums related to the topic. The community suggestion unit may also suggest customized communities based on the user's interests. This allows the user to connect with other users who share the same interests and engage in deeper discussions.

[0106] The comment quality improvement system may further include an event suggestion unit that suggests related events and seminars based on the content of a user's comment. For example, when a user posts a comment on a specific topic, the event suggestion unit suggests online events and seminars related to the topic. The event suggestion unit may also suggest customized events based on the user's interests. This increases the user's opportunities to gain new knowledge and promotes deeper discussions.

[0107] The comment quality improvement system may further include a book suggestion unit that suggests related books and papers based on the content of a user's comment. For example, when a user posts a comment on a specific topic, the book suggestion unit may suggest books and academic papers related to the topic. The book suggestion unit may also suggest books customized based on the user's interests. This increases the user's opportunities to gain new knowledge and promotes deeper discussions.

[0108] The feedback providing unit can estimate the user's emotions and suggest relaxing music or natural sounds based on the estimated user's emotions. For example, if the user is feeling stressed, the feedback providing unit can suggest relaxing music. If the user is feeling angry, the feedback providing unit can suggest natural sounds or meditative music. This can promote calm communication by providing a relaxation method that suits the user's emotional state.

[0109] The feedback providing unit can estimate the user's emotions and suggest art or creative activities to express emotions based on the estimated user emotions. For example, if the user is feeling sad, the feedback providing unit can suggest drawing a picture or writing a poem. If the user is feeling happy, the feedback providing unit can suggest making music or dancing. In this way, by providing creative activities according to the user's emotional state, it is possible to encourage emotional expression.

[0110] The feedback providing unit can estimate the user's emotions and suggest psychological techniques for controlling emotions based on the estimated user's emotions. For example, if the user is feeling anxious, it can suggest cognitive behavioral therapy techniques. Also, if the user is feeling angry, it can suggest methods for controlling emotions. In this way, by providing psychological techniques according to the user's emotional state, it is possible to promote calm communication.

[0111] The feedback providing unit can estimate the user's emotions and suggest a safe platform for sharing emotions based on the estimated user emotions. For example, if the user feels lonely, the feedback providing unit can suggest an online support group where the user can share their emotions. Also, if the user feels stressed, the feedback providing unit can suggest an anonymous forum where the user can share their emotions. This can promote calm communication by providing a place for sharing emotions according to the user's emotional state.

[0112] The feedback providing unit can estimate the user's emotions and suggest educational resources for understanding emotions based on the estimated user emotions. For example, if the user is feeling angry, the feedback providing unit can suggest videos or articles for understanding the emotion of anger. Also, if the user is feeling sad, the feedback providing unit can suggest resources for understanding the emotion of sadness. In this way, by providing educational resources according to the user's emotional state, it is possible to deepen understanding of emotions and promote calm communication.

[0113] The processing flow of the second embodiment will be briefly explained below.

[0114] Step 1: The emotion detection unit detects emotional language in comments, such as offensive or insulting words and emotional expressions. Step 2: The misinformation detector detects misinformation in comments, such as information that is factually incorrect or misleading. Step 3: The tone evaluator evaluates the tone and manner of the comments, for example, whether they are polite, aggressive, or respectful. Step 4: The feedback providing unit provides feedback based on the information detected by the emotion detection unit, misinformation detection unit, and tone evaluation unit. For example, feedback encouraging cooling down based on emotional language, feedback providing accurate data or information based on misinformation, and feedback encouraging improvement based on tone and manner are provided.

[0115] 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.

[0116] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<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.

[0117] 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.

[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 a 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0134] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0141] 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.

[0142] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0143] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

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

[0149] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0150] The data processing device 12 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0159] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0160] 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.

[0161] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the 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.

[0162] 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.

[0163] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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).

[0168] 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.

[0169] 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."

[0170] 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.

[0171] 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.

[0172] 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.

[0173] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0174] 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.

[0175] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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. [Explanation of symbols]

[0182] 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. an emotion detection unit that detects emotional language in the comments; a feedback providing unit that provides feedback encouraging the user to cool down based on the emotional language detected by the emotion detecting unit; a misinformation detection unit that detects misinformation in comments; a feedback providing unit that provides accurate data or information based on the false information detected by the false information detecting unit; A tone evaluation section evaluates the tone and manner of comments; a feedback providing unit that provides feedback to encourage improvement based on the tone and manner evaluated by the tone evaluating unit. A system characterized by:

2. The false information detection unit Detecting false information, the feedback providing unit Provide accurate data and information based on the misinformation 2. The system of claim 1.

3. The feedback providing unit: When posting comments, voice input is supported and converted to text using voice recognition technology.

2. The system of claim 1.

4. The false information detection unit When detecting misinformation, the data is automatically collected from reliable sources, and the feedback providing unit: Present to the user 2. The system of claim 1.

5. The feedback providing unit: Using emotion estimation functionality, feedback is provided according to the user's emotional state, promoting positive communication.

2. The system of claim 1.

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