system

Abstract

An object of a system according to an embodiment is to issue a warning when a content posted on an SNS or the like includes a dangerous element and propose an appropriate correction proposal.SOLUTION: A system includes a post analysis unit, a warning unit, and a proposal unit. The post analysis unit analyzes the post content using the generated AI. The warning unit issues a warning when the post content analyzed by the post analyzing unit includes a dangerous element. The proposal unit proposes an appropriate correction proposal for the content warned by the warning unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to respond appropriately when dangerous elements are included in posts on social media and other platforms.

[0005] The system according to the embodiment aims to warn users when dangerous elements are included in the content of posts on social media and to suggest appropriate corrections. [Means for solving the problem]

[0006] The system according to the embodiment includes a post analysis unit, a warning unit, and a suggestion unit. The post analysis unit analyzes the content of posts using a generation AI. The warning unit issues a warning if the content of posts analyzed by the post analysis unit contains dangerous elements. The suggestion unit proposes appropriate corrections to the content warned about by the warning unit. [Effects of the Invention]

[0007] The system according to the embodiment can warn users when dangerous elements are included in the content of posts on social media or the like, and can suggest appropriate corrections. [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) The call support system according to the embodiment of the present invention is a system that introduces a filter check function to the content that a user is about to post and issues a warning if dangerous content is included, thereby enabling the user to post safe and appropriate content.

[0029] The communication assistance system according to the embodiment includes a post analysis unit, a warning unit, and a suggestion unit. The post analysis unit analyzes the content of posts using a generation AI. For example, the generation AI analyzes the content of posts using a text generation AI such as GPT-3 or BERT. The generation AI can also handle content in the form of text, images, videos, and the like. The warning unit issues a warning if the content of posts analyzed by the post analysis unit contains dangerous elements. For example, the warning unit issues a warning if the content contains violent language, discriminatory remarks, or leaks of personal information. The warning unit can also issue a warning using methods such as a pop-up notification or email notification. The suggestion unit suggests appropriate corrections for the content warned by the warning unit. For example, the suggestion unit may suggest specific corrections such as, "This post contains violent language. Please try revising it as follows." The suggestion unit can also suggest alternative expressions and specific correction examples. This allows the communication assistance system according to the embodiment to enable users to post safe and appropriate content. For example, when a user enters content to post, the generation AI performs real-time analysis and immediately issues a warning if the content contains dangerous elements. Furthermore, when a user receives a warning, the suggestion unit presents a specific correction suggestion, allowing the user to quickly correct the content appropriately.

[0030] The post analysis unit can perform filter checks based on a user's past posting history and behavioral patterns. For example, the post analysis unit uses a generation AI to analyze a user's past posting history and learn specific patterns and trends. For example, it issues a warning if a post contains similar content based on content that has previously received a warning. The post analysis unit also analyzes a user's behavioral patterns and strengthens filter checks for content posted at specific times or situations. For example, it issues a warning to warn users about posts made late at night. The post analysis unit also improves the accuracy of filter checks based on the user's past feedback. For example, it learns how a user responded to warnings they received in the past and reflects this in the next check. This improves the accuracy of filter checks.

[0031] The post analysis unit can understand the user's intent by referring to the contextual information of the post content and related news articles. In the post analysis unit, for example, the generation AI analyzes the context of the post content and refers to related news articles and trend information. For example, it checks posts related to specific incidents or topics and issues appropriate warnings. In addition, in order to understand the intent of the post content, the generation AI analyzes the contextual information and extracts related keywords and phrases. For example, it issues a warning if a specific keyword is included. In addition, the post analysis unit allows the generation AI to collect background information of the post content to more accurately understand the user's intent. For example, it issues a warning if the post content is related to a specific event or campaign. This allows for a more accurate understanding of the user's intent.

[0032] The post analysis unit can simultaneously analyze not only text but also the content of images and videos. For example, the generation AI in the post analysis unit analyzes images and videos included in the post content to check whether they contain dangerous elements. For example, it issues a warning against violent images or inappropriate videos. The post analysis unit also comprehensively analyzes the content of text, images, and videos and issues a warning if they contain dangerous elements. For example, it issues a warning if the text and images match. The post analysis unit also uses image recognition technology to analyze the content of images and videos included in the post. For example, it issues a warning if certain symbols or gestures are included. This makes it possible to perform comprehensive filter checks.

[0033] The post analysis unit can check the consistency of posted content across different social media platforms. For example, the generation AI in the post analysis unit analyzes posted content across different social media platforms and checks for consistency. For example, it issues a warning if the same content is posted on multiple platforms. The post analysis unit also detects content that may be problematic on other platforms and issues a warning. For example, it issues a warning about content that is prohibited on a specific platform. The post analysis unit also allows the generation AI to refer to the policies of different platforms and check the posted content based on those policies. For example, it issues a warning about content that violates the guidelines of each platform. This makes it possible to warn about content that may be problematic on other platforms.

[0034] When the warning unit detects dangerous content, it can provide the user with a specific suggested correction and the reason for the suggestion. For example, when the generation AI detects dangerous content, the warning unit provides the user with a specific suggested correction and the reason for the suggestion. For example, the warning unit presents a specific suggested correction and the reason for the suggestion in the form of, "This expression is violent. Please correct it as follows. Reason: Because it may offend other users." In addition, when presenting a suggested correction, the warning unit provides a detailed explanation of why the suggested correction is appropriate. For example, the warning unit presents a specific suggested correction and the reason for the suggestion in the form of, "This expression is discriminatory. Please correct it as follows. Reason: Because it may offend a specific group." In addition, when the generation AI presents a suggested correction, the warning unit provides an explanation to help the user understand why the suggested correction is appropriate. For example, the warning unit presents a specific suggested correction and the reason for the suggestion in the form of, "This expression contains personal information. Please correct it as follows. Reason: To protect privacy." This allows the user to understand the appropriateness of the suggested correction.

[0035] In addition to warning the user about dangerous content, the warning unit can also present examples of alternative expressions and positive expressions to the user. For example, when the generation AI detects dangerous content, the warning unit presents examples of alternative expressions and positive expressions to the user. For example, the warning unit presents examples of alternative expressions and positive expressions in the form of, "This expression is inappropriate. Please correct it as follows. Example: Use '△△' instead of '○○'." The warning unit also presents specific examples to improve the user's expressiveness along with the warning about dangerous content. For example, the warning unit presents examples of alternative expressions and positive expressions in the form of, "This expression is offensive. Please correct it as follows. Example: Use '□□' instead of 'XX'." The warning unit also provides examples of alternative expressions and positive expressions to the user, improving the user's expressiveness. For example, the warning unit presents examples of alternative expressions and positive expressions in the form of, "This expression is negative. Please correct it as follows. Example: Use '○○' instead of '△△'." This can improve the user's expressiveness.

[0036] The warning unit can provide warnings and suggested corrections that correspond to different languages ​​and cultural spheres. For example, the generation AI of the warning unit provides warnings and suggested corrections that correspond to different languages ​​and cultural spheres. For example, warnings and suggested corrections are provided in multiple languages, such as English, Japanese, and Spanish. The warning unit also provides warnings and suggested corrections that correspond to different cultural spheres. For example, it suggests expressions that take into consideration specific cultures and religions. The warning unit also provides warnings and suggested corrections in multiple languages, so that appropriate advice can be given to global users. For example, it suggests expressions that are appropriate for each language. This makes it possible to provide appropriate advice to global users.

[0037] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0038] The communication support system can also be equipped with a trend analysis function for the content posted by users. For example, the system can analyze current trends and topics and check whether the content posted by users matches those trends. Specifically, when a user enters content to post, if the content is related to a current trend, the system will provide feedback such as "This post matches the current trend." The trend analysis function can also compare content posted by users in the past with current trends and provide appropriate advice. This allows users to post content that matches the trend.

[0039] The communication assistance system can also be equipped with a multilingual translation function for user posts. For example, the system can translate user-entered posts into multiple languages ​​in real time, providing appropriate content for users who speak different languages. Specifically, when a user enters a post in Japanese, the system translates the content into English, Spanish, or other languages ​​and displays it. The multilingual translation function can also provide appropriate translations based on content previously posted by the user. This allows users to share their posts from a global perspective.

[0040] The communication assistance system can also be equipped with an image generation function for the content posted by the user. For example, the system can automatically generate a related image based on the text content entered by the user and attach it to the post. Specifically, if the user enters "beautiful scenery," the system will generate and display a landscape image that matches the content. The image generation function can also generate appropriate images based on content posted by the user in the past. This allows users to create visually appealing posts.

[0041] The communication assistance system can also be equipped with a voice input function for users' posting content. For example, the system converts what the user inputs by voice into text and displays it as the posting content. Specifically, if the user inputs by voice, "I want to post this sentence," the system converts that content into text and displays it. The voice input function can also perform appropriate text conversion based on what the user has previously input by voice. This allows users to easily input posting content by voice.

[0042] The processing flow of the first embodiment will be briefly explained below.

[0043] Step 1: The post analysis unit uses the generation AI to analyze the post content. For example, the generation AI analyzes the post content using text generation AI such as GPT-3 or BERT. The generation AI can also handle post content in the form of text, images, videos, etc. Step 2: The warning unit issues a warning if the post content analyzed by the post analysis unit contains dangerous elements. For example, the warning unit issues a warning if the post contains violent language, discriminatory remarks, or the leakage of personal information. The warning unit can also issue a warning by using methods such as a pop-up notification or email notification. Step 3: The suggestion unit proposes appropriate corrections for the content warned by the warning unit. For example, the suggestion unit may suggest specific corrections in the form of, "This post contains violent language. Please try correcting it as follows." The suggestion unit may also suggest alternative expressions or provide specific examples of corrections.

[0044] (Example 2) The call support system according to the embodiment of the present invention is a system that introduces a filter check function to the content that a user is about to post and issues a warning if dangerous content is included, thereby enabling the user to post safe and appropriate content.

[0045] The communication assistance system according to the embodiment includes a post analysis unit, a warning unit, and a suggestion unit. The post analysis unit analyzes the content of posts using a generation AI. For example, the generation AI analyzes the content of posts using a text generation AI such as GPT-3 or BERT. The generation AI can also handle content in the form of text, images, videos, and the like. The warning unit issues a warning if the content of posts analyzed by the post analysis unit contains dangerous elements. For example, the warning unit issues a warning if the content contains violent language, discriminatory remarks, or leaks of personal information. The warning unit can also issue a warning using methods such as a pop-up notification or email notification. The suggestion unit suggests appropriate corrections for the content warned by the warning unit. For example, the suggestion unit may suggest specific corrections such as, "This post contains violent language. Please try revising it as follows." The suggestion unit can also suggest alternative expressions and specific correction examples. This allows the communication assistance system according to the embodiment to enable users to post safe and appropriate content. For example, when a user enters content to post, the generation AI performs real-time analysis and immediately issues a warning if the content contains dangerous elements. Furthermore, when a user receives a warning, the suggestion unit presents a specific correction suggestion, allowing the user to quickly correct the content appropriately.

[0046] The post analysis unit can perform filter checks based on a user's past posting history and behavioral patterns. For example, the post analysis unit uses a generation AI to analyze a user's past posting history and learn specific patterns and trends. For example, it issues a warning if a post contains similar content based on content that has previously received a warning. The post analysis unit also analyzes a user's behavioral patterns and strengthens filter checks for content posted at specific times or situations. For example, it issues a warning to warn users about posts made late at night. The post analysis unit also improves the accuracy of filter checks based on the user's past feedback. For example, it learns how a user responded to warnings they received in the past and reflects this in the next check. This improves the accuracy of filter checks.

[0047] The post analysis unit can understand the user's intent by referring to the contextual information of the post content and related news articles. In the post analysis unit, for example, the generation AI analyzes the context of the post content and refers to related news articles and trend information. For example, it checks posts related to specific incidents or topics and issues appropriate warnings. In addition, in order to understand the intent of the post content, the generation AI analyzes the contextual information and extracts related keywords and phrases. For example, it issues a warning if a specific keyword is included. In addition, the post analysis unit allows the generation AI to collect background information of the post content to more accurately understand the user's intent. For example, it issues a warning if the post content is related to a specific event or campaign. This allows for a more accurate understanding of the user's intent.

[0048] The post analysis unit can use the emotion estimation function to evaluate the emotional impact that the posted content has on other users. For example, the post analysis unit uses a generation AI to perform emotion analysis of the posted content and issue a warning if the content contains strong negative emotions. For example, a warning is issued for posted content that includes emotions such as anger or sadness. The post analysis unit also uses the emotion estimation function to evaluate the emotional impact that the posted content has on other users. For example, a warning is issued if the posted content is likely to cause discomfort to other users. The post analysis unit also issues a warning if the posted content is likely to have a negative impact based on the emotion score of the posted content. For example, a warning is issued for posted content with a low emotion score. This makes it possible to issue a warning if the posted content is likely to have a negative impact.

[0049] The post analysis unit can simultaneously analyze not only text but also the content of images and videos. For example, the generation AI in the post analysis unit analyzes images and videos included in the post content to check whether they contain dangerous elements. For example, it issues a warning against violent images or inappropriate videos. The post analysis unit also comprehensively analyzes the content of text, images, and videos and issues a warning if they contain dangerous elements. For example, it issues a warning if the text and images match. The post analysis unit also uses image recognition technology to analyze the content of images and videos included in the post. For example, it issues a warning if certain symbols or gestures are included. This makes it possible to perform comprehensive filter checks.

[0050] The post analysis unit can check the consistency of posted content across different social media platforms. For example, the generation AI in the post analysis unit analyzes posted content across different social media platforms and checks for consistency. For example, it issues a warning if the same content is posted on multiple platforms. The post analysis unit also detects content that may be problematic on other platforms and issues a warning. For example, it issues a warning about content that is prohibited on a specific platform. The post analysis unit also allows the generation AI to refer to the policies of different platforms and check the posted content based on those policies. For example, it issues a warning about content that violates the guidelines of each platform. This makes it possible to warn about content that may be problematic on other platforms.

[0051] The post analysis unit can analyze the emotions of users when they enter post content in real time and make suggestions that will elicit positive emotions. For example, the post analysis unit uses a generation AI to analyze the emotions of users when they enter content in real time and make suggestions that will elicit positive emotions. For example, it displays an encouraging message if the user is feeling negative. The post analysis unit also uses an emotion estimation function to make suggestions that will make the user feel positive. For example, it presents success stories or positive news. The post analysis unit also analyzes the emotions of users in real time and provides an interface for eliciting positive emotions. For example, it provides positive feedback when the user reviews their input. This makes it possible to elicit positive emotions from the user.

[0052] When the warning unit detects dangerous content, it can provide the user with a specific suggested correction and the reason for the suggestion. For example, when the generation AI detects dangerous content, the warning unit provides the user with a specific suggested correction and the reason for the suggestion. For example, the warning unit presents a specific suggested correction and the reason for the suggestion in the form of, "This expression is violent. Please correct it as follows. Reason: Because it may offend other users." In addition, when presenting a suggested correction, the warning unit provides a detailed explanation of why the suggested correction is appropriate. For example, the warning unit presents a specific suggested correction and the reason for the suggestion in the form of, "This expression is discriminatory. Please correct it as follows. Reason: Because it may offend a specific group." In addition, when the generation AI presents a suggested correction, the warning unit provides an explanation to help the user understand why the suggested correction is appropriate. For example, the warning unit presents a specific suggested correction and the reason for the suggestion in the form of, "This expression contains personal information. Please correct it as follows. Reason: To protect privacy." This allows the user to understand the appropriateness of the suggested correction.

[0053] In addition to warning the user about dangerous content, the warning unit can also present examples of alternative expressions and positive expressions to the user. For example, when the generation AI detects dangerous content, the warning unit presents examples of alternative expressions and positive expressions to the user. For example, the warning unit presents examples of alternative expressions and positive expressions in the form of, "This expression is inappropriate. Please correct it as follows. Example: Use '△△' instead of '○○'." The warning unit also presents specific examples to improve the user's expressiveness along with the warning about dangerous content. For example, the warning unit presents examples of alternative expressions and positive expressions in the form of, "This expression is offensive. Please correct it as follows. Example: Use '□□' instead of 'XX'." The warning unit also provides examples of alternative expressions and positive expressions to the user, improving the user's expressiveness. For example, the warning unit presents examples of alternative expressions and positive expressions in the form of, "This expression is negative. Please correct it as follows. Example: Use '○○' instead of '△△'." This can improve the user's expressiveness.

[0054] The warning unit uses the emotion estimation function to evaluate the emotional impact that the warned content will have on other users, and can propose revisions that are likely to resonate with them emotionally. For example, the warning unit uses the emotion estimation function to evaluate the emotional impact that the warned content will have on other users. For example, it issues a warning if there is a possibility that the content will evoke negative emotions. The warning unit also uses the emotion estimation function to propose revisions that are likely to resonate with them emotionally. For example, it proposes expressions that elicit positive emotions. The warning unit also uses the emotion estimation data to evaluate the emotional impact that the warned content will have on other users, and proposes revisions that are likely to resonate with them. For example, it proposes expressions with a high emotion score. This makes it possible to propose revisions that are likely to resonate with them.

[0055] The warning unit can provide warnings and suggested corrections that correspond to different languages ​​and cultural spheres. For example, the generation AI of the warning unit provides warnings and suggested corrections that correspond to different languages ​​and cultural spheres. For example, warnings and suggested corrections are provided in multiple languages, such as English, Japanese, and Spanish. The warning unit also provides warnings and suggested corrections that correspond to different cultural spheres. For example, it suggests expressions that take into consideration specific cultures and religions. The warning unit also provides warnings and suggested corrections in multiple languages, so that appropriate advice can be given to global users. For example, it suggests expressions that are appropriate for each language. This makes it possible to provide appropriate advice to global users.

[0056] The warning unit can analyze the user's emotional reaction when receiving the warning and provide a support message to reduce stress. For example, the generation AI in the warning unit uses an emotion estimation function to analyze the user's emotional reaction when receiving the warning. For example, a support message is provided if the user is feeling stressed. The warning unit also provides a support message to reduce the user's stress based on the emotion estimation data. For example, an encouraging message or a suggestion for relaxation methods. The warning unit also analyzes the user's emotional reaction using the generation AI and provides a specific support message to reduce stress. For example, a specific support message is provided in the form of "Please relax. Try the following methods." This can reduce the user's stress.

[0057] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0058] The communication assistance system can also be equipped with a real-time feedback function for the content of user posts. For example, when a user is entering content to post, the system analyzes the content in real time and provides appropriate feedback. Specifically, if the content of the post is positive, the system will display positive feedback such as "Great post!", and if it is negative, it will prompt the user to make corrections such as "This expression may be a little too strong." The feedback function can also learn from feedback the user has received in the past and provide more appropriate advice the next time the user posts. This allows the user to make more appropriate corrections to the content of their post.

[0059] The communication support system can also be equipped with a trend analysis function for the content posted by users. For example, the system can analyze current trends and topics and check whether the content posted by users matches those trends. Specifically, when a user enters content to post, if the content is related to a current trend, the system will provide feedback such as "This post matches the current trend." The trend analysis function can also compare content posted by users in the past with current trends and provide appropriate advice. This allows users to post content that matches the trend.

[0060] The communication assistance system can also be equipped with a multilingual translation function for user posts. For example, the system can translate user-entered posts into multiple languages ​​in real time, providing appropriate content for users who speak different languages. Specifically, when a user enters a post in Japanese, the system translates the content into English, Spanish, or other languages ​​and displays it. The multilingual translation function can also provide appropriate translations based on content previously posted by the user. This allows users to share their posts from a global perspective.

[0061] The communication assistance system can also be equipped with an image generation function for the content posted by the user. For example, the system can automatically generate a related image based on the text content entered by the user and attach it to the post. Specifically, if the user enters "beautiful scenery," the system will generate and display a landscape image that matches the content. The image generation function can also generate appropriate images based on content posted by the user in the past. This allows users to create visually appealing posts.

[0062] The communication assistance system can also be equipped with a voice input function for users' posting content. For example, the system converts what the user inputs by voice into text and displays it as the posting content. Specifically, if the user inputs by voice, "I want to post this sentence," the system converts that content into text and displays it. The voice input function can also perform appropriate text conversion based on what the user has previously input by voice. This allows users to easily input posting content by voice.

[0063] The communication assistance system can also use its emotion estimation function to provide customized feedback based on the user's emotions. For example, the system analyzes the content entered by the user and displays a supportive message such as "Relax" if the user is feeling stressed. On the other hand, if the user is feeling positive, the system provides positive feedback such as "Great post!" This allows users to receive appropriate feedback based on their emotions.

[0064] The communication assistance system can also use its emotion estimation function to evaluate the emotional impact of a user's posts on other users and suggest appropriate revisions. For example, the system analyzes the content entered by the user and suggests revisions such as "This expression may be a little too strong" if it is likely to have a negative impact on other users. On the other hand, if it is likely to have a positive impact, it provides feedback such as "This expression is great." This allows users to make posts that take into account the emotional impact they will have on other users.

[0065] The communication assistance system can also use its emotion estimation function to provide customized revision suggestions based on the user's emotions. For example, the system analyzes the content entered by the user and, if the user expresses anger or sadness, suggests a revision such as "Try toning down this expression a bit." Alternatively, if the user expresses joy or excitement, the system provides feedback such as "This expression is great." This allows users to receive appropriate revision suggestions based on their emotions.

[0066] The communication assistance system can also use its emotion estimation function to evaluate the emotional impact of a user's posts on other users and suggest revisions that are more likely to resonate with them emotionally. For example, the system analyzes the content entered by the user and suggests expressions that are more likely to resonate with other users. Specifically, if a post is likely to evoke negative emotions, the system will suggest revisions such as "Try toning down this expression a bit" and suggest expressions that elicit positive emotions. This allows users to make posts that are more likely to resonate with other users.

[0067] The communication assistance system can also use its emotion estimation function for the user's posted content to provide customized support messages based on the user's emotions. For example, the system analyzes the content entered by the user and displays a support message such as "Relax" if the user is feeling stressed. Also, if the user has positive emotions, the system provides positive feedback such as "Great post!" This allows users to receive appropriate support messages according to their emotions.

[0068] The processing flow of the second embodiment will be briefly explained below.

[0069] Step 1: The post analysis unit uses the generation AI to analyze the post content. For example, the generation AI analyzes the post content using text generation AI such as GPT-3 or BERT. The generation AI can also handle post content in the form of text, images, videos, etc. Step 2: The warning unit issues a warning if the post content analyzed by the post analysis unit contains dangerous elements. For example, the warning unit issues a warning if the post contains violent language, discriminatory remarks, or the leakage of personal information. The warning unit can also issue a warning by using methods such as a pop-up notification or email notification. Step 3: The suggestion unit proposes appropriate corrections for the content warned by the warning unit. For example, the suggestion unit may suggest specific corrections in the form of, "This post contains violent language. Please try correcting it as follows." The suggestion unit may also suggest alternative expressions or provide specific examples of corrections.

[0070] 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.

[0071] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0072] 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.

[0073] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0074] 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.

[0075] 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.

[0076] 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.

[0077] 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.

[0078] 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).

[0079] 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.

[0080] 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.

[0081] 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.

[0082] 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.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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).

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the 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 specific processing unit 290 using these models.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0104] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] In the robot 414, 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 robot 414 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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."

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0137] 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 post analysis unit that analyzes post content using generation AI; a warning unit that issues a warning when the posted content analyzed by the post analysis unit includes a dangerous element; a suggestion unit that proposes an appropriate correction plan for the content warned by the warning unit. A system characterized by:

2. The post analysis unit Filter checks are performed based on the user's past posting history and behavioral patterns.

2. The system of claim 1.

3. The post analysis unit Understand user intent by referencing contextual information about the post and related news articles 2. The system of claim 1.

4. The post analysis unit Evaluating the emotional impact of the post on other users 2. The system of claim 1.

5. The post analysis unit Analyze not only text but also images and video content simultaneously 2. The system of claim 1.

6. The post analysis unit Check the consistency of said posts across different social media platforms 2. The system of claim 1.

Images