system

Abstract

The system according to the embodiment aims to automatically detect inappropriate expressions when posting or sending on social media or email, and to issue a warning. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, and a warning unit. The reception unit receives text from a user. The analysis unit analyzes the text received by the reception unit and checks whether it contains inappropriate language. The warning unit issues a warning when the analysis unit detects inappropriate language.

Description

[Technical Field]

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

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

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

[0004] Conventional technology does not have sufficient mechanisms in place to automatically check whether inappropriate language is included when posting or sending on social media or email, and there is room for improvement.

[0005] The system according to the embodiment aims to automatically detect inappropriate expressions when posting or sending on social media or email, and to issue a warning. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, and a warning unit. The reception unit receives text from a user. The analysis unit analyzes the text received by the reception unit and checks whether it contains inappropriate language. The warning unit issues a warning when the analysis unit detects inappropriate language. [Effects of the Invention]

[0007] The system according to the embodiment can automatically detect inappropriate expressions when posting or sending via social media or email, and issue warnings. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention uses a generative AI to check for inappropriate language that may offend others when posting on social media or sending emails, and issues a warning. In this system, a user inputs a text to be posted on social media or in an email, and the generative AI analyzes the text and checks whether it contains inappropriate language. If inappropriate language is found, the generative AI issues a warning, such as, "This text contains language that may offend others. Are you aware of this and would you like to post (send) it?" This warning allows the user time to reconsider the posting or sending. This allows the system to encourage the user to reconsider before sending a post or email that contains language that may offend others. For example, if a user is about to send an email in anger saying, "You are really incompetent," the generative AI can detect the expression and issue a warning, giving the user time to calm down. This prevents emotional posting or emails from being sent.

[0029] The system according to the embodiment includes a reception unit, an analysis unit, and a warning unit. The reception unit receives text from a user. The user inputs text to be posted on, for example, a social networking site or email. The reception unit transmits the text input by the user to, for example, a generation AI. The analysis unit uses the generation AI to analyze the text received by the reception unit and check whether it contains inappropriate language. For example, the generation AI has learned various sentence expressions, and the analysis unit checks whether the input text contains inappropriate language that may hurt others. For example, the generation AI detects expressions such as "You are really incompetent." The analysis unit uses a wide range of datasets and is regularly updated. For example, the analysis unit uses datasets such as news articles and social media posts and is regularly updated. The warning unit issues a warning when the analysis unit detects inappropriate language. The warning unit displays a message such as, "This text contains language that may hurt someone. Are you aware of this and would you like to post (send) it?" The warning unit also displays a message emphasizing the user to take time to calm down. For example, a message such as "Please wait 5 seconds and check again" is displayed. In this way, the system according to the embodiment accepts and analyzes the user's text and issues a warning if it contains inappropriate language, thereby supporting healthy communication.

[0030] The analysis unit learns multiple sentence expressions and can check whether the input text contains inappropriate expressions that may offend others. For example, the generation AI of the analysis unit learns multiple sentence expressions, such as everyday conversations and business documents. The analysis unit also checks for inappropriate expressions that may offend others, such as insulting or discriminatory language. For example, the generation AI detects insulting expressions such as "You're really incompetent." The analysis unit also uses a wide range of datasets and is regularly updated. For example, the analysis unit uses datasets such as news articles and social media posts and is regularly updated. This allows the analysis unit to learn various sentence expressions, thereby improving the accuracy of detecting inappropriate expressions.

[0031] The warning unit can display a message such as "This message contains language that may be hurtful to someone. Do you understand this and still want to post?" The warning unit displays a warning message such as "This message contains language that may be hurtful to someone. Do you understand this and still want to post?" The warning unit also displays a message emphasizing that the user should have time to calm down. For example, it displays a message such as "Please wait 5 seconds and check again." In this way, the warning unit can warn the user and give the user time to reconsider posting or sending.

[0032] The warning unit can display a message emphasizing that the user should take time to calm down. For example, the warning unit displays a message such as "Please wait 5 seconds and check again." In this way, the warning unit provides the user with time to calm down, thereby preventing emotional posting or transmission. The time to calm down is, for example, several seconds to several minutes. In this way, the warning unit provides the user with time to calm down, thereby preventing emotional posting or transmission.

[0033] The analysis unit may use a variety of datasets and may be updated regularly. The analysis unit may use a variety of datasets, such as news articles and social media posts. The analysis unit may also update the datasets regularly. For example, the analysis unit may add a new dataset every month to improve the accuracy of the analysis. This allows the analysis unit to use a wide range of datasets and update them regularly, thereby improving the accuracy of the analysis.

[0034] The warning unit can take measures to temporarily restrict posting or transmission if a user continues posting or transmission despite ignoring the warning. For example, if a user continues posting or transmission despite ignoring the warning, the warning unit takes measures such as pausing posting or delaying transmission. For example, if a user continues posting despite ignoring the warning, the warning unit temporarily stops posting. Furthermore, the warning unit can also delay transmission if a user continues transmitting despite ignoring the warning. In this way, the warning unit can restrict posting or transmission, thereby preventing the posting or transmission of inappropriate language.

[0035] The reception unit can analyze the user's past posting history and select the optimal reception method. For example, if the user has made many emotional posts in the past, the reception unit can display a message urging the user to reconfirm their decision before posting. The reception unit can also apply a normal reception method if the user has made many calm posts in the past. Furthermore, if the reception unit determines from the user's past posting history that there are many emotional posts during a specific time period, it can also delay posting during that time period. This allows the optimal reception method to be provided by analyzing the user's past posting history. The optimal reception method is selected based on, for example, priority settings and filtering criteria. For example, the reception unit can input the user's past posting history data into the generation AI and have the generation AI select the optimal reception method.

[0036] When receiving text, the reception unit can select the optimal reception means depending on the user's input method. For example, if the user uses voice input, the reception unit receives the text using voice recognition technology. Furthermore, if the user uses text input, the reception unit can also apply a normal text reception method. Furthermore, if the user uses images, the reception unit can also receive the text using image recognition technology. This enables efficient reception of text by selecting a reception means depending on the user's input method. Input methods include, for example, voice input, text input, and image input. For example, the reception unit can input the user's input data to a generation AI and have the generation AI select the optimal reception means.

[0037] When receiving a text, the reception unit can prioritize receiving highly relevant text by taking into account the user's geographical location information. For example, if the user is in a specific area, the reception unit can prioritize receiving text related to that area. Furthermore, if the user is traveling, the reception unit can prioritize receiving text related to the travel destination. Furthermore, if the user is at home, the reception unit can prioritize receiving text related to the user's home. This makes it possible to receive highly relevant text by receiving text based on the user's geographical location information. Geographical location information is obtained using, for example, GPS data or an IP address. For example, the reception unit can input the user's geographical location information data into the generation AI and cause the generation AI to select highly relevant text.

[0038] The reception unit can analyze the user's social media activity when receiving a text and receive related text. For example, the reception unit can prioritize receiving text related to content that the user frequently posts on social media. The reception unit can also receive related text by referring to the activity of the user's friends on social media. Furthermore, the reception unit can analyze the user's social media posting history and receive related text. In this way, by receiving text based on the user's social media activity, it is possible to receive highly relevant text. Social media activity includes, for example, the content of posts and comment history. For example, the reception unit can input the user's social media activity data into the generation AI and cause the generation AI to select related text.

[0039] The reception unit can customize the reception method by reflecting the user's past feedback when receiving a text. For example, the reception unit can suggest the optimal reception method based on feedback provided by the user in the past. The reception unit can also preferentially apply a specific reception method based on the user's past feedback. Furthermore, the reception unit can also periodically update the reception method by reflecting the user's feedback. This makes it possible to provide the optimal reception method by reflecting the user's past feedback. Past feedback includes, for example, the user's ratings and comments. For example, the reception unit can input the user's feedback data into the generation AI and have the generation AI customize the reception method.

[0040] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the text. For example, the analysis unit performs a detailed analysis for text of high importance. The analysis unit can also perform a simplified analysis for text of low importance. Furthermore, the analysis unit can perform an analysis at an appropriate level of detail for text of medium importance. This makes it possible to provide appropriate analysis results by adjusting the level of detail of the analysis according to the importance of the text. The importance of a text is evaluated based on, for example, the urgency of the content and the scope of impact. For example, the analysis unit can input text importance data to the generation AI and have the generation AI adjust the level of detail of the analysis.

[0041] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the text. For example, the analysis unit can apply a business analysis algorithm to business-related text. The analysis unit can also apply a personal analysis algorithm to personal text. The analysis unit can also apply an academic analysis algorithm to academic text. This makes it possible to provide appropriate analysis results by applying an analysis algorithm according to the text category. Text categories include, for example, technical documents and legal documents. For example, the analysis unit can input text category data into the generation AI and have the generation AI apply the analysis algorithm.

[0042] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, adjusts the analysis algorithm based on the user's past analysis results. The analysis unit can also learn specific patterns from the user's past analysis results and improve the analysis accuracy. Furthermore, the analysis unit can adjust the level of detail of the analysis by referring to the user's past analysis results. In this way, the accuracy of the analysis is improved by referring to the user's past analysis results. Past analysis results include, for example, past error rates and success rates. For example, the analysis unit can input the user's past analysis result data into the generation AI and have the generation AI improve the analysis accuracy.

[0043] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of the text. For example, the analysis unit prioritizes analysis of urgent text. The analysis unit can also analyze normal text with normal priority. Furthermore, the analysis unit can postpone analysis of low-priority text. In this way, by determining the priority of analysis based on the time of submission of the text, appropriate analysis results can be provided. The submission time is evaluated based on, for example, the submission date and time or the submission deadline. For example, the analysis unit can input text submission time data into the generation AI and have the generation AI determine the analysis priority.

[0044] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the sentences. For example, the analysis unit prioritizes analysis of highly relevant sentences. The analysis unit can also postpone analysis of less relevant sentences. Furthermore, the analysis unit can analyze sentences with moderate relevance in an appropriate order. In this way, by adjusting the order of analysis based on the relevance of the sentences, appropriate analysis results can be provided. Relevance is evaluated based on, for example, similarity of content or agreement of themes. For example, the analysis unit can input sentence relevance data into the generation AI and have the generation AI adjust the order of analysis.

[0045] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can provide analysis results that use a lot of technical terminology. Alternatively, if the user does not have technical expertise, the analysis unit can provide analysis results in simple language. Furthermore, the analysis unit can provide analysis results that use appropriate technical terminology according to the user's level of expertise. This allows appropriate analysis results to be provided by adjusting the use of technical terminology according to the user's level of expertise. The level of expertise is evaluated based on, for example, the presence or absence of qualifications, years of experience, etc. For example, the analysis unit can input the user's level of expertise data into the generation AI and have the generation AI adjust the use of technical terminology.

[0046] When issuing a warning, the warning unit can analyze the user's past behavioral history and select the optimal warning method. For example, if the user has posted many emotional messages in the past, the warning unit can display a message urging the user to stay calm. Furthermore, if the user has posted many calm messages in the past, the warning unit can also apply a normal warning method. Furthermore, if the user's past behavioral history shows that there are many emotional messages during a specific time period, the warning unit can strengthen the warning during that time period. This allows for appropriate warnings by selecting a warning method based on the user's past behavioral history. The behavioral history includes, for example, the content of past posts and click history. For example, the warning unit can input the user's behavioral history data into the generation AI and have the generation AI select the optimal warning method.

[0047] The warning unit can improve the warning method by reflecting user feedback when issuing a warning. The warning unit can adjust the warning method based on, for example, feedback provided by the user. The warning unit can also preferentially apply a specific warning method based on the user feedback. Furthermore, the warning unit can periodically update the warning method by reflecting user feedback. This makes it possible to provide an appropriate warning method by reflecting user feedback. The feedback includes, for example, user ratings and comments. For example, the warning unit can input user feedback data into the generation AI and cause the generation AI to improve the warning method.

[0048] When issuing a warning, the warning unit can select the optimal warning method taking into account the user's geographical location information. For example, if the user is in a specific area, the warning unit can issue a warning related to that area. Furthermore, if the user is traveling, the warning unit can issue a warning related to the user's travel destination. Furthermore, if the user is at home, the warning unit can issue a warning related to the user's home. This enables appropriate warnings by selecting a warning method based on the user's geographical location information. The geographical location information is obtained using, for example, GPS data or an IP address. For example, the warning unit can input the user's geographical location information data into the generation AI and cause the generation AI to select the optimal warning method.

[0049] When issuing a warning, the warning unit can analyze the user's social media activity and suggest warning methods. For example, the warning unit can issue warnings related to content that the user frequently posts on social media. The warning unit can also issue related warnings based on the activities of the user's friends on social media. Furthermore, the warning unit can analyze the user's social media posting history and issue related warnings. This makes it possible to issue appropriate warnings by suggesting warning methods based on the user's social media activity. Social media activity includes, for example, post content and comment history. For example, the warning unit can input the user's social media activity data into the generation AI and cause the generation AI to suggest warning methods.

[0050] The warning unit can customize the warning method by reflecting the user's past feedback when issuing a warning. For example, the warning unit can suggest an optimal warning method based on feedback provided by the user in the past. The warning unit can also preferentially apply a specific warning method based on the user's past feedback. Furthermore, the warning unit can also periodically update the warning method by reflecting the user's feedback. In this way, an appropriate warning method can be provided by reflecting the user's past feedback. The feedback includes, for example, the user's ratings and comments. For example, the warning unit can input the user's feedback data into the generation AI and cause the generation AI to customize the warning method.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The reception unit can analyze the user's input speed and temporarily delay the input if the input speed is fast. For example, if the user is typing text at an extremely fast speed, the reception unit can temporarily delay the input to give the user time to calm down. The reception unit can also accept the input at a normal speed if the user is typing slowly. Furthermore, the reception unit can also accept the input as is if the user's input speed is within a certain range. This makes it possible to prevent emotional posts and transmissions by providing a reception method that suits the user's input speed.

[0053] The analysis unit can learn the content of a user's past posts and, if certain keywords or phrases are frequently used, pay special attention to those keywords or phrases. For example, if a user has frequently used insulting words such as "incompetent" or "idiot" in the past, the analysis unit can pay special attention to those words, improving detection accuracy. The analysis unit can also learn specific phrases used by a user in the past and immediately issue a warning if those phrases are used again. Furthermore, based on the content of a user's past posts, the analysis unit can also pay special attention to specific contexts in which inappropriate language is likely to be used. In this way, by learning the content of a user's past posts, the accuracy of detecting inappropriate language is improved.

[0054] If a user continues posting or sending despite the warning, the warning unit can temporarily save the content of the post or send and prompt the user to recheck it later. For example, if a user continues posting despite the warning, the warning unit temporarily saves the content of the post and displays a message to the user such as "Please check again later." In addition, if a user continues sending despite the warning, the warning unit can temporarily save the content of the send and prompt the user to recheck it later. Furthermore, if a user continues posting or sending despite the warning, the warning unit can temporarily save the content and issue another warning after a certain period of time. In this way, by the warning unit temporarily saving the content of the post or send, it is possible to prevent the posting or sending of inappropriate language.

[0055] If a user continues posting or sending despite the warning, the warning unit can temporarily save the content of the post or send and prompt the user to recheck it later. For example, if a user continues posting despite the warning, the warning unit temporarily saves the content of the post and displays a message to the user such as "Please check again later." In addition, if a user continues sending despite the warning, the warning unit can temporarily save the content of the send and prompt the user to recheck it later. Furthermore, if a user continues posting or sending despite the warning, the warning unit can temporarily save the content and issue another warning after a certain period of time. In this way, by the warning unit temporarily saving the content of the post or send, it is possible to prevent the posting or sending of inappropriate language.

[0056] The reception unit can analyze the user's past posting history and select the optimal reception method. For example, if the user has made many emotional posts in the past, it can display a message urging the user to reconfirm their post before posting. The reception unit can also apply the normal reception method if the user has made many calm posts in the past. Furthermore, if the reception unit determines from the user's past posting history that there are many emotional posts during a specific time period, it can delay posting during that time period. In this way, the optimal reception method can be provided by analyzing the user's past posting history. The optimal reception method is selected based on, for example, priority settings and filtering criteria. For example, the reception unit can input the user's past posting history data into the generation AI and have the generation AI select the optimal reception method.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The reception unit receives text from the user. For example, the user inputs text to post on social media or via email. The reception unit sends the text entered by the user to the generation AI. Step 2: The analysis unit uses the generation AI to analyze the text received by the reception unit and check whether it contains inappropriate language. The generation AI in the analysis unit has learned various sentence expressions, and checks whether the input text contains inappropriate language that could hurt others. For example, it detects expressions such as "You are really incompetent." The analysis unit also uses a wide range of datasets, which are updated regularly. For example, it uses datasets such as news articles and social media posts, and updates them regularly. Step 3: The warning unit issues a warning when the analysis unit detects inappropriate language. The warning unit displays a message such as, "This message contains language that may offend someone. Do you understand this and want to post (send) it?". It also displays a message emphasizing that the user should take some time to calm down. For example, it displays a message such as, "Please wait 5 seconds and check again."

[0059] (Example 2) A system according to an embodiment of the present invention uses a generative AI to check for inappropriate language that may offend others when posting on social media or sending emails, and issues a warning. In this system, a user inputs a text to be posted on social media or in an email, and the generative AI analyzes the text and checks whether it contains inappropriate language. If inappropriate language is found, the generative AI issues a warning, such as, "This text contains language that may offend others. Are you aware of this and would you like to post (send) it?" This warning allows the user time to reconsider the posting or sending. This allows the system to encourage the user to reconsider before sending a post or email that contains language that may offend others. For example, if a user is about to send an email in anger saying, "You are really incompetent," the generative AI can detect the expression and issue a warning, giving the user time to calm down. This prevents emotional posting or emails from being sent.

[0060] The system according to the embodiment includes a reception unit, an analysis unit, and a warning unit. The reception unit receives text from a user. The user inputs text to be posted on, for example, a social networking site or email. The reception unit transmits the text input by the user to, for example, a generation AI. The analysis unit uses the generation AI to analyze the text received by the reception unit and check whether it contains inappropriate language. For example, the generation AI has learned various sentence expressions, and the analysis unit checks whether the input text contains inappropriate language that may hurt others. For example, the generation AI detects expressions such as "You are really incompetent." The analysis unit uses a wide range of datasets and is regularly updated. For example, the analysis unit uses datasets such as news articles and social media posts and is regularly updated. The warning unit issues a warning when the analysis unit detects inappropriate language. The warning unit displays a message such as, "This text contains language that may hurt someone. Are you aware of this and would you like to post (send) it?" The warning unit also displays a message emphasizing the user to take time to calm down. For example, a message such as "Please wait 5 seconds and check again" is displayed. In this way, the system according to the embodiment accepts and analyzes the user's text and issues a warning if it contains inappropriate language, thereby supporting healthy communication.

[0061] The analysis unit learns multiple sentence expressions and can check whether the input text contains inappropriate expressions that may offend others. For example, the generation AI of the analysis unit learns multiple sentence expressions, such as everyday conversations and business documents. The analysis unit also checks for inappropriate expressions that may offend others, such as insulting or discriminatory language. For example, the generation AI detects insulting expressions such as "You're really incompetent." The analysis unit also uses a wide range of datasets and is regularly updated. For example, the analysis unit uses datasets such as news articles and social media posts and is regularly updated. This allows the analysis unit to learn various sentence expressions, thereby improving the accuracy of detecting inappropriate expressions.

[0062] The warning unit can display a message such as "This message contains language that may be hurtful to someone. Do you understand this and still want to post?" The warning unit displays a warning message such as "This message contains language that may be hurtful to someone. Do you understand this and still want to post?" The warning unit also displays a message emphasizing that the user should have time to calm down. For example, it displays a message such as "Please wait 5 seconds and check again." In this way, the warning unit can warn the user and give the user time to reconsider posting or sending.

[0063] The warning unit can display a message emphasizing that the user should take time to calm down. For example, the warning unit displays a message such as "Please wait 5 seconds and check again." In this way, the warning unit provides the user with time to calm down, thereby preventing emotional posting or transmission. The time to calm down is, for example, several seconds to several minutes. In this way, the warning unit provides the user with time to calm down, thereby preventing emotional posting or transmission.

[0064] The analysis unit may use a variety of datasets and may be updated regularly. The analysis unit may use a variety of datasets, such as news articles and social media posts. The analysis unit may also update the datasets regularly. For example, the analysis unit may add a new dataset every month to improve the accuracy of the analysis. This allows the analysis unit to use a wide range of datasets and update them regularly, thereby improving the accuracy of the analysis.

[0065] The warning unit can take measures to temporarily restrict posting or transmission if a user continues posting or transmission despite ignoring the warning. For example, if a user continues posting or transmission despite ignoring the warning, the warning unit takes measures such as pausing posting or delaying transmission. For example, if a user continues posting despite ignoring the warning, the warning unit temporarily stops posting. Furthermore, the warning unit can also delay transmission if a user continues transmitting despite ignoring the warning. In this way, the warning unit can restrict posting or transmission, thereby preventing the posting or transmission of inappropriate language.

[0066] The reception unit can estimate the user's emotions and adjust the timing of message reception based on the estimated user emotions. For example, if the user is feeling angry, the reception unit can temporarily delay the reception of messages to give the user time to calm down. Furthermore, if the user is feeling sad, the reception unit can also slightly delay the reception of messages to give the user time to reconsider. Furthermore, if the user is excited, the reception unit can temporarily suspend the reception of messages and wait until the user has calmed down. This can prevent emotional posts and transmissions by adjusting the timing of message reception according to the user's emotions. Emotions can be estimated using technologies such as facial expression recognition and text analysis. For example, the reception unit can input the user's facial expression data into a generation AI and have the generation AI perform emotion estimation.

[0067] The reception unit can analyze the user's past posting history and select the optimal reception method. For example, if the user has made many emotional posts in the past, the reception unit can display a message urging the user to reconfirm their decision before posting. The reception unit can also apply a normal reception method if the user has made many calm posts in the past. Furthermore, if the reception unit determines from the user's past posting history that there are many emotional posts during a specific time period, it can also delay posting during that time period. This allows the optimal reception method to be provided by analyzing the user's past posting history. The optimal reception method is selected based on, for example, priority settings and filtering criteria. For example, the reception unit can input the user's past posting history data into the generation AI and have the generation AI select the optimal reception method.

[0068] The reception unit can filter posts based on the user's current psychological state and areas of interest when receiving the posts. For example, if the user is feeling stressed, the reception unit can filter posts to reduce stress. Furthermore, if the user is concentrating on a particular area of ​​interest, the reception unit can preferentially receive posts related to that area. Furthermore, if the user is relaxed, the reception unit can perform normal filtering. This makes it possible to receive appropriate posts by filtering according to the user's psychological state and areas of interest. The psychological state can be identified using, for example, survey results or behavioral history. For example, the reception unit can input the user's psychological state data into the generation AI and have the generation AI perform filtering.

[0069] When receiving text, the reception unit can select the optimal reception means depending on the user's input method. For example, if the user uses voice input, the reception unit receives the text using voice recognition technology. Furthermore, if the user uses text input, the reception unit can also apply a normal text reception method. Furthermore, if the user uses images, the reception unit can also receive the text using image recognition technology. This enables efficient reception of text by selecting a reception means depending on the user's input method. Input methods include, for example, voice input, text input, and image input. For example, the reception unit can input the user's input data to a generation AI and have the generation AI select the optimal reception means.

[0070] The reception unit can estimate the user's emotions and determine the priority of sentences to be received based on the estimated user emotions. For example, if the user is feeling angry, the reception unit temporarily delays the reception of the sentence. Furthermore, if the user is feeling relaxed, the reception unit can also preferentially receive the sentence. Furthermore, if the user is feeling sad, the reception unit can slightly delay the reception of the sentence. This allows appropriate sentences to be received by determining the priority according to the user's emotions. The priority can be set based on, for example, urgency or importance. For example, the reception unit can input the user's emotion data into the generation AI and have the generation AI determine the priority.

[0071] When receiving a text, the reception unit can prioritize receiving highly relevant text by taking into account the user's geographical location information. For example, if the user is in a specific area, the reception unit can prioritize receiving text related to that area. Furthermore, if the user is traveling, the reception unit can prioritize receiving text related to the travel destination. Furthermore, if the user is at home, the reception unit can prioritize receiving text related to the user's home. This makes it possible to receive highly relevant text by receiving text based on the user's geographical location information. Geographical location information is obtained using, for example, GPS data or an IP address. For example, the reception unit can input the user's geographical location information data into the generation AI and cause the generation AI to select highly relevant text.

[0072] The reception unit can analyze the user's social media activity when receiving a text and receive related text. For example, the reception unit can prioritize receiving text related to content that the user frequently posts on social media. The reception unit can also receive related text by referring to the activity of the user's friends on social media. Furthermore, the reception unit can analyze the user's social media posting history and receive related text. In this way, by receiving text based on the user's social media activity, it is possible to receive highly relevant text. Social media activity includes, for example, the content of posts and comment history. For example, the reception unit can input the user's social media activity data into the generation AI and cause the generation AI to select related text.

[0073] The reception unit can customize the reception method by reflecting the user's past feedback when receiving a text. For example, the reception unit can suggest the optimal reception method based on feedback provided by the user in the past. The reception unit can also preferentially apply a specific reception method based on the user's past feedback. Furthermore, the reception unit can also periodically update the reception method by reflecting the user's feedback. This makes it possible to provide the optimal reception method by reflecting the user's past feedback. Past feedback includes, for example, the user's ratings and comments. For example, the reception unit can input the user's feedback data into the generation AI and have the generation AI customize the reception method.

[0074] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, if the user is feeling angry, the analysis unit presents the analysis results in a calm manner. The analysis unit can also provide detailed analysis results if the user is relaxed. Furthermore, the analysis unit can present the analysis results in a gentle manner if the user is feeling sad. In this way, by adjusting the way the analysis is presented according to the user's emotions, appropriate analysis results can be provided. The way the analysis is presented is adjusted based on, for example, the use of technical terms and the level of detail in the explanation. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the way the analysis is presented.

[0075] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the text. For example, the analysis unit performs a detailed analysis for text of high importance. The analysis unit can also perform a simplified analysis for text of low importance. Furthermore, the analysis unit can perform an analysis at an appropriate level of detail for text of medium importance. This makes it possible to provide appropriate analysis results by adjusting the level of detail of the analysis according to the importance of the text. The importance of a text is evaluated based on, for example, the urgency of the content and the scope of impact. For example, the analysis unit can input text importance data to the generation AI and have the generation AI adjust the level of detail of the analysis.

[0076] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the text. For example, the analysis unit can apply a business analysis algorithm to business-related text. The analysis unit can also apply a personal analysis algorithm to personal text. The analysis unit can also apply an academic analysis algorithm to academic text. This makes it possible to provide appropriate analysis results by applying an analysis algorithm according to the text category. Text categories include, for example, technical documents and legal documents. For example, the analysis unit can input text category data into the generation AI and have the generation AI apply the analysis algorithm.

[0077] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, adjusts the analysis algorithm based on the user's past analysis results. The analysis unit can also learn specific patterns from the user's past analysis results and improve the analysis accuracy. Furthermore, the analysis unit can adjust the level of detail of the analysis by referring to the user's past analysis results. In this way, the accuracy of the analysis is improved by referring to the user's past analysis results. Past analysis results include, for example, past error rates and success rates. For example, the analysis unit can input the user's past analysis result data into the generation AI and have the generation AI improve the analysis accuracy.

[0078] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user's emotions. For example, if the user is in a hurry, the analysis unit can provide a short and to-the-point analysis result. The analysis unit can also provide a detailed analysis result if the user is relaxed. Furthermore, the analysis unit can provide a visually stimulating analysis result if the user is excited. This allows appropriate analysis results to be provided by adjusting the length of the analysis according to the user's emotions. The length of the analysis is adjusted based on, for example, a detailed analysis or a simple analysis. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the length of the analysis.

[0079] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of the text. For example, the analysis unit prioritizes analysis of urgent text. The analysis unit can also analyze normal text with normal priority. Furthermore, the analysis unit can postpone analysis of low-priority text. In this way, by determining the priority of analysis based on the time of submission of the text, appropriate analysis results can be provided. The submission time is evaluated based on, for example, the submission date and time or the submission deadline. For example, the analysis unit can input text submission time data into the generation AI and have the generation AI determine the analysis priority.

[0080] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the sentences. For example, the analysis unit prioritizes analysis of highly relevant sentences. The analysis unit can also postpone analysis of less relevant sentences. Furthermore, the analysis unit can analyze sentences with moderate relevance in an appropriate order. In this way, by adjusting the order of analysis based on the relevance of the sentences, appropriate analysis results can be provided. Relevance is evaluated based on, for example, similarity of content or agreement of themes. For example, the analysis unit can input sentence relevance data into the generation AI and have the generation AI adjust the order of analysis.

[0081] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can provide analysis results that use a lot of technical terminology. Alternatively, if the user does not have technical expertise, the analysis unit can provide analysis results in simple language. Furthermore, the analysis unit can provide analysis results that use appropriate technical terminology according to the user's level of expertise. This allows appropriate analysis results to be provided by adjusting the use of technical terminology according to the user's level of expertise. The level of expertise is evaluated based on, for example, the presence or absence of qualifications, years of experience, etc. For example, the analysis unit can input the user's level of expertise data into the generation AI and have the generation AI adjust the use of technical terminology.

[0082] The warning unit can estimate the user's emotions and adjust the warning method based on the estimated user's emotions. For example, if the user is feeling angry, the warning unit can display a message urging the user to stay calm. Furthermore, if the user is feeling sad, the warning unit can also warn the user in gentle words. Furthermore, if the user is excited, the warning unit can also display a message urging the user to stay calm. This makes it possible to provide an appropriate warning by adjusting the warning method according to the user's emotions. The warning method is adjusted based on, for example, a pop-up message or an audio alert. For example, the warning unit can input the user's emotional data into the generation AI and cause the generation AI to adjust the warning method.

[0083] When issuing a warning, the warning unit can analyze the user's past behavioral history and select the optimal warning method. For example, if the user has posted many emotional messages in the past, the warning unit can display a message urging the user to stay calm. Furthermore, if the user has posted many calm messages in the past, the warning unit can also apply a normal warning method. Furthermore, if the user's past behavioral history shows that there are many emotional messages during a specific time period, the warning unit can strengthen the warning during that time period. This allows for appropriate warnings by selecting a warning method based on the user's past behavioral history. The behavioral history includes, for example, the content of past posts and click history. For example, the warning unit can input the user's behavioral history data into the generation AI and have the generation AI select the optimal warning method.

[0084] The alerting unit can customize the alerting means based on the user's current psychological state when issuing an alert. For example, if the user is feeling stressed, the alerting unit issues an alert to reduce stress. Furthermore, if the user is relaxed, the alerting unit can issue a normal alert. Furthermore, if the user is excited, the alerting unit can issue an alert urging the user to stay calm. This makes it possible to issue an appropriate alert by customizing the alerting means according to the user's current psychological state. The psychological state is identified using, for example, questionnaire results or behavioral history. For example, the alerting unit can input the user's psychological state data into the generation AI and cause the generation AI to customize the alerting means.

[0085] The warning unit can improve the warning method by reflecting user feedback when issuing a warning. The warning unit can adjust the warning method based on, for example, feedback provided by the user. The warning unit can also preferentially apply a specific warning method based on the user feedback. Furthermore, the warning unit can periodically update the warning method by reflecting user feedback. This makes it possible to provide an appropriate warning method by reflecting user feedback. The feedback includes, for example, user ratings and comments. For example, the warning unit can input user feedback data into the generation AI and cause the generation AI to improve the warning method.

[0086] The alerting unit can estimate the user's emotions and determine the priority of alerts based on the estimated user's emotions. For example, if the user is feeling angry, the alerting unit can prioritize the alert. Furthermore, if the user is feeling relaxed, the alerting unit can also issue an alert with normal priority. Furthermore, if the user is feeling sad, the alerting unit can slightly delay the alert. In this way, by determining the priority of alerts according to the user's emotions, appropriate alerts can be issued. The priority can be set based on, for example, urgency or importance. For example, the alerting unit can input the user's emotion data into the generation AI and cause the generation AI to determine the priority of alerts.

[0087] When issuing a warning, the warning unit can select the optimal warning method taking into account the user's geographical location information. For example, if the user is in a specific area, the warning unit can issue a warning related to that area. Furthermore, if the user is traveling, the warning unit can issue a warning related to the user's travel destination. Furthermore, if the user is at home, the warning unit can issue a warning related to the user's home. This enables appropriate warnings by selecting a warning method based on the user's geographical location information. The geographical location information is obtained using, for example, GPS data or an IP address. For example, the warning unit can input the user's geographical location information data into the generation AI and cause the generation AI to select the optimal warning method.

[0088] When issuing a warning, the warning unit can analyze the user's social media activity and suggest warning methods. For example, the warning unit can issue warnings related to content that the user frequently posts on social media. The warning unit can also issue related warnings based on the activities of the user's friends on social media. Furthermore, the warning unit can analyze the user's social media posting history and issue related warnings. This makes it possible to issue appropriate warnings by suggesting warning methods based on the user's social media activity. Social media activity includes, for example, post content and comment history. For example, the warning unit can input the user's social media activity data into the generation AI and cause the generation AI to suggest warning methods.

[0089] The warning unit can customize the warning method by reflecting the user's past feedback when issuing a warning. For example, the warning unit can suggest an optimal warning method based on feedback provided by the user in the past. The warning unit can also preferentially apply a specific warning method based on the user's past feedback. Furthermore, the warning unit can also periodically update the warning method by reflecting the user's feedback. In this way, an appropriate warning method can be provided by reflecting the user's past feedback. The feedback includes, for example, the user's ratings and comments. For example, the warning unit can input the user's feedback data into the generation AI and cause the generation AI to customize the warning method. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, and warning unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14, and inputs text that a user will post on social media or by email. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the input text using a generation AI to check whether it contains inappropriate language. The warning unit is realized by the output device 40 of the smart device 14, and displays a warning message when inappropriate language is detected. === Hard Collateral 1-2 === Each of the multiple elements including the above-described reception unit, analysis unit, and warning unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214, and inputs by voice a sentence that the user will post on SNS or by email. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the input sentence using a generation AI to check whether it contains inappropriate language. The warning unit is realized by the speaker 240 of the smart glasses 214, and outputs a voice warning message if inappropriate language is detected. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, and warning unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset-type terminal 314, and inputs by voice the text that the user will post on SNS or by email. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the input text using a generation AI to check whether it contains inappropriate language. The warning unit is realized by the display 343 of the headset-type terminal 314, and displays a warning message when inappropriate language is detected. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, and warning unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414, and inputs by voice the text that the user will post on SNS or by email. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the input text using a generation AI to check whether it contains inappropriate language. The warning unit is realized by the speaker 240 of the robot 414, and outputs a warning message by voice when inappropriate language is detected.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The reception unit can analyze the user's input speed and temporarily delay the input if the input speed is fast. For example, if the user is typing text at an extremely fast speed, the reception unit can temporarily delay the input to give the user time to calm down. The reception unit can also accept the input at a normal speed if the user is typing slowly. Furthermore, the reception unit can also accept the input as is if the user's input speed is within a certain range. This makes it possible to prevent emotional posts and transmissions by providing a reception method that suits the user's input speed.

[0092] The analysis unit can learn the content of a user's past posts and, if certain keywords or phrases are frequently used, pay special attention to those keywords or phrases. For example, if a user has frequently used insulting words such as "incompetent" or "idiot" in the past, the analysis unit can pay special attention to those words, improving detection accuracy. The analysis unit can also learn specific phrases used by a user in the past and immediately issue a warning if those phrases are used again. Furthermore, based on the content of a user's past posts, the analysis unit can also pay special attention to specific contexts in which inappropriate language is likely to be used. In this way, by learning the content of a user's past posts, the accuracy of detecting inappropriate language is improved.

[0093] If a user continues posting or sending despite the warning, the warning unit can temporarily save the content of the post or send and prompt the user to recheck it later. For example, if a user continues posting despite the warning, the warning unit temporarily saves the content of the post and displays a message to the user such as "Please check again later." In addition, if a user continues sending despite the warning, the warning unit can temporarily save the content of the send and prompt the user to recheck it later. Furthermore, if a user continues posting or sending despite the warning, the warning unit can temporarily save the content and issue another warning after a certain period of time. In this way, by the warning unit temporarily saving the content of the post or send, it is possible to prevent the posting or sending of inappropriate language.

[0094] If a user continues posting or sending despite the warning, the warning unit can temporarily save the content of the post or send and prompt the user to recheck it later. For example, if a user continues posting despite the warning, the warning unit temporarily saves the content of the post and displays a message to the user such as "Please check again later." In addition, if a user continues sending despite the warning, the warning unit can temporarily save the content of the send and prompt the user to recheck it later. Furthermore, if a user continues posting or sending despite the warning, the warning unit can temporarily save the content and issue another warning after a certain period of time. In this way, by the warning unit temporarily saving the content of the post or send, it is possible to prevent the posting or sending of inappropriate language.

[0095] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, if the user is feeling angry, the analysis results can be presented in a calm manner. The analysis unit can also provide detailed analysis results if the user is relaxed. Furthermore, the analysis unit can also present analysis results in a gentle manner if the user is feeling sad. This makes it possible to provide appropriate analysis results by adjusting the way the analysis is presented according to the user's emotions. The way the analysis is presented can be adjusted based on, for example, the use of technical terms and the level of detail in the explanation. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the way the analysis is presented.

[0096] The reception unit can estimate the user's emotions and adjust the timing of message reception based on the estimated user emotions. For example, if the user is feeling angry, the reception unit can temporarily delay the reception of messages to give the user time to calm down. Also, if the user is feeling sad, the reception unit can slightly delay the reception of messages to give the user time to reconsider. Furthermore, if the user is excited, the reception unit can temporarily suspend the reception of messages and wait until the user has calmed down. This allows for preventing emotional posts and transmissions by adjusting the timing of message reception according to the user's emotions. Emotions can be estimated using technologies such as facial expression recognition and text analysis. For example, the reception unit can input the user's facial expression data into a generation AI and have the generation AI perform emotion estimation.

[0097] The warning unit can estimate the user's emotions and adjust the warning method based on the estimated user's emotions. For example, if the user is feeling angry, the warning unit can display a message urging the user to stay calm. Furthermore, if the user is feeling sad, the warning unit can warn the user in gentle words. Furthermore, if the user is excited, the warning unit can display a message urging the user to stay calm. This makes it possible to provide an appropriate warning by adjusting the warning method according to the user's emotions. The warning method is adjusted based on, for example, a pop-up message or a voice alert. For example, the warning unit can input the user's emotional data into the generation AI and have the generation AI adjust the warning method.

[0098] The alerting unit can estimate the user's emotions and determine the priority of alerts based on the estimated user's emotions. For example, if the user is feeling angry, the alert is given priority. The alerting unit can also issue an alert with normal priority if the user is relaxed. Furthermore, the alerting unit can slightly delay issuing an alert if the user is feeling sad. This enables appropriate alerts to be issued by determining the priority of alerts according to the user's emotions. The priority can be set based on, for example, urgency or importance. For example, the alerting unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of alerts.

[0099] When receiving text, the reception unit can filter the text based on the user's current psychological state and areas of interest. For example, if the user is feeling stressed, the reception unit can perform filtering to reduce stress. In addition, if the user is concentrating on a particular area of ​​interest, the reception unit can also preferentially receive posts related to that area. Furthermore, if the user is relaxed, the reception unit can perform normal filtering. This makes it possible to receive appropriate text by filtering according to the user's psychological state and areas of interest. The psychological state can be identified using, for example, survey results or behavioral history. For example, the reception unit can input the user's psychological state data into the generation AI and have the generation AI perform filtering.

[0100] The reception unit can analyze the user's past posting history and select the optimal reception method. For example, if the user has made many emotional posts in the past, it can display a message urging the user to reconfirm their post before posting. The reception unit can also apply the normal reception method if the user has made many calm posts in the past. Furthermore, if the reception unit determines from the user's past posting history that there are many emotional posts during a specific time period, it can delay posting during that time period. In this way, the optimal reception method can be provided by analyzing the user's past posting history. The optimal reception method is selected based on, for example, priority settings and filtering criteria. For example, the reception unit can input the user's past posting history data into the generation AI and have the generation AI select the optimal reception method.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The reception unit receives text from the user. For example, the user inputs text to post on social media or via email. The reception unit sends the text entered by the user to the generation AI. Step 2: The analysis unit uses the generation AI to analyze the text received by the reception unit and check whether it contains inappropriate language. The generation AI in the analysis unit has learned various sentence expressions, and checks whether the input text contains inappropriate language that could hurt others. For example, it detects expressions such as "You are really incompetent." The analysis unit also uses a wide range of datasets, which are updated regularly. For example, it uses datasets such as news articles and social media posts, and updates them regularly. Step 3: The warning unit issues a warning when the analysis unit detects inappropriate language. The warning unit displays a message such as, "This message contains language that may offend someone. Do you understand this and want to post (send) it?". It also displays a message emphasizing that the user should take some time to calm down. For example, it displays a message such as, "Please wait 5 seconds and check again."

[0103] 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.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0105] 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.

[0106] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0117] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0131] 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.

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0133] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0140] 7, a 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0150] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] 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.

[0161] 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."

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] [Explanation of symbols]

[0175] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives text from a user; an analysis unit that analyzes the text received by the reception unit and checks whether it contains inappropriate expressions; and a warning unit that issues a warning when an inappropriate expression is detected by the analysis unit. A system characterized by:

2. The analysis unit It learns multiple sentence expressions and checks whether the input text contains inappropriate language that could offend others.

2. The system of claim 1.

3. The attention drawing unit Display a message that emphasizes that the user should take time to calm down 2. The system of claim 1.

4. The analysis unit Uses diverse datasets and is regularly updated 2. The system of claim 1.

5. The attention drawing unit If a user ignores the warning and continues to post or send, measures will be taken to temporarily restrict posting or sending.

2. The system of claim 1.

6. The reception unit Estimate the user's emotions and adjust the timing of receiving text based on the estimated user emotions 2. The system of claim 1.

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

8. The reception unit When receiving text, it filters it based on the user's current state of mind and areas of interest.

2. The system of claim 1.

9. The reception unit When accepting text, select the most appropriate acceptance method depending on the user's input method.

2. The system of claim 1.

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