system

Abstract

An object of a system according to an embodiment is to effectively monitor a user who performs a specific action from content posted to an SNS.SOLUTION: A system includes a post analysis unit and a specific act monitoring unit. A contribution analysis part is loaded with the generation AI and analyzes contribution contents. The specific act monitoring unit monitors a specific act based on the post content analyzed by the post analyzing 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 technologies have had the problem of making it difficult to effectively monitor users who engage in specific behavior based on the content they post on social networking sites.

[0005] The system according to the embodiment aims to effectively monitor users who engage in specific behaviors based on the content of their posts on SNS. [Means for solving the problem]

[0006] The system according to the embodiment includes a post analysis unit and a specific behavior monitoring unit. The post analysis unit is equipped with a generation AI and analyzes the content of posts. The specific behavior monitoring unit monitors specific behaviors based on the content of posts analyzed by the post analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can effectively monitor users who engage in specific behaviors based on the content of their posts on SNS. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) A privacy protection system according to an embodiment of the present invention is a system that aims to protect privacy and prevent specific acts in posts on social networking sites. This system uses a generation AI to strengthen privacy protection for posted content and prevent specific acts. As a result, the privacy protection system can strengthen privacy protection in posts on social networking sites and prevent specific acts.

[0029] A privacy protection system according to an embodiment includes a post analysis unit and a specific behavior monitoring unit. The post analysis unit is equipped with a generation AI and analyzes content posted by users on social media. For example, the generation AI analyzes the posted content using a text generation AI (e.g., LLM) to automatically detect information that may potentially identify individuals. The generation AI can also use a multimodal generation AI to detect backgrounds in photos, place names included in posted text, and specific events. For example, the generation AI detects place names and specific events included in posted text and suggests to the user to modify or delete the content. The specific behavior monitoring unit monitors specific behaviors based on the posted content analyzed by the post analysis unit. For example, the specific behavior monitoring unit monitors specific behaviors on social media and issues a warning when a specific behavior is committed. The generation AI detects specific behaviors based on data on posted content and comments on social media, records the behavior, and issues a warning. This enables the privacy protection system to protect privacy and prevent specific behaviors in posts on social media. For example, it can provide an environment where users can post on social media with peace of mind and prevent harm caused by specific behaviors. Furthermore, through education on privacy protection, users themselves can become more aware of privacy.

[0030] The post analysis unit can refer to a user's past posting history and provide consistent privacy protection. For example, the generation AI analyzes a user's past posting history and provides privacy protection to maintain consistency between past and current posting content. For example, the current posting content is modified so that it does not contradict information previously made public. This makes it possible to provide consistent privacy protection by referring to a user's past posting history.

[0031] The post analysis unit can understand the user's intention and propose privacy protection measures that are in line with the user's intention. For example, the post analysis unit uses natural language processing technology to enable the generative AI to analyze the content of the user's posts and understand the user's intention. For example, it analyzes the context of the post content and estimates the level of privacy protection that the user intends. This makes it possible to propose privacy protection measures that are in line with the user's intention.

[0032] The post analysis unit can use image recognition technology to automatically identify objects or people appearing in photos and protect privacy. For example, the post analysis unit can use image recognition technology to automatically identify objects or people appearing in posted photos and apply blurring or mosaic processing to protect privacy. For example, faces and car license plates can be automatically hidden. This allows the automatic identification of objects and people appearing in photos and enables privacy protection.

[0033] The post analysis unit analyzes the audio data, automatically detects personal information contained in the audio data, and can suggest correction or deletion. For example, the generative AI in the post analysis unit analyzes the audio data and automatically detects personal information contained in the audio. For example, it analyzes names and addresses contained in the audio and suggests correction or deletion to protect privacy. This makes it possible to automatically detect personal information contained in the audio data and correct or delete it.

[0034] The Specific Action Monitoring Unit can learn patterns of specific actions and be able to respond to new methods of specific actions. For example, the Specific Action Monitoring Unit develops algorithms that enable the generation AI to learn patterns of specific actions and respond to new methods of specific actions. For example, it analyzes patterns based on data on past specific actions. This makes it possible to respond to new methods of specific actions.

[0035] The specific behavior monitoring unit can analyze the behavioral history of users who perform specific behaviors and detect signs of specific behaviors. For example, the specific behavior monitoring unit constructs a system in which a generation AI analyzes the behavioral history of users who perform specific behaviors and detects signs of specific behaviors. For example, signs are identified based on past behavioral patterns. This makes it possible to detect signs of specific behaviors.

[0036] The Specific Behavior Monitoring Unit can analyze comments or messages on social media and detect signs of specific behavior at an early stage. For example, the Specific Behavior Monitoring Unit will build a system in which a generative AI analyzes comments and messages on social media and detects signs of specific behavior at an early stage. For example, it will analyze specific keywords and phrases. This will enable early detection of signs of specific behavior.

[0037] The specific conduct monitoring unit can integrate data between different SNS platforms and prevent specific cross-platform conduct. The specific conduct monitoring unit, for example, integrates data between different SNS platforms and builds a system to prevent specific cross-platform conduct. For example, it centrally manages data from multiple SNSs. This makes it possible to prevent specific cross-platform conduct.

[0038] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0039] The privacy protection system can also evaluate the legal risks of users' posts and issue a warning to users if the risk is high. For example, a warning can be issued if the posted content is likely to constitute defamation or a violation of privacy. It can also issue a warning if there is a risk of copyright infringement. This allows users to understand legal risks in advance and take measures to avoid them.

[0040] The privacy protection system can also evaluate the social impact of a user's posts and issue a warning to the user if the impact is significant. For example, a warning can be issued if the post deals with a socially sensitive topic. It can also issue a warning if the post has a significant impact on a specific group or community. This allows users to understand the social impact of their posts in advance and modify their posts as necessary.

[0041] The privacy protection system can also monitor third-party reactions to users' posts in real time and issue a warning to users if there are a large number of negative reactions. For example, it can analyze comments and reactions to posts and issue a warning if the number of negative reactions exceeds a certain number. It can also issue a warning if there are a large number of comments containing specific keywords or phrases. This allows users to understand how their posts are being received by others and to modify their posts if necessary.

[0042] The privacy protection system can also evaluate the legal risks of users' posts and issue a warning to users if the risk is high. For example, a warning can be issued if the posted content is likely to constitute defamation or a violation of privacy. It can also issue a warning if there is a risk of copyright infringement. This allows users to understand legal risks in advance and take measures to avoid them.

[0043] The privacy protection system can also evaluate the social impact of a user's posts and issue a warning to the user if the impact is significant. For example, a warning can be issued if the post deals with a socially sensitive topic. It can also issue a warning if the post has a significant impact on a specific group or community. This allows users to understand the social impact of their posts in advance and modify their posts as necessary.

[0044] The processing flow of the first embodiment will be briefly explained below.

[0045] Step 1: The post analysis unit is equipped with a generation AI and analyzes the content posted by users on social media. For example, the generation AI uses a text generation AI (e.g., LLM) to analyze the content of the post and automatically detect information that may potentially identify individuals. The generation AI can also use multimodal generation AI to detect the background of a photo, place names included in the post, and specific events. For example, the generation AI detects place names and specific events included in the post and suggests that the user modify or delete them. Step 2: The specific behavior monitoring unit monitors specific behaviors based on the post content analyzed by the post analysis unit. For example, the specific behavior monitoring unit monitors specific behaviors on social media and issues a warning if a specific behavior is committed. The generation AI detects specific behaviors based on the post content and comment data on social media, records the behavior, and issues a warning.

[0046] (Example 2) A privacy protection system according to an embodiment of the present invention is a system that aims to protect privacy and prevent specific acts in posts on social networking sites. This system uses a generation AI to strengthen privacy protection for posted content and prevent specific acts. As a result, the privacy protection system can strengthen privacy protection in posts on social networking sites and prevent specific acts.

[0047] A privacy protection system according to an embodiment includes a post analysis unit and a specific behavior monitoring unit. The post analysis unit is equipped with a generation AI and analyzes content posted by users on social media. For example, the generation AI analyzes the posted content using a text generation AI (e.g., LLM) to automatically detect information that may potentially identify individuals. The generation AI can also use a multimodal generation AI to detect backgrounds in photos, place names included in posted text, and specific events. For example, the generation AI detects place names and specific events included in posted text and suggests to the user to modify or delete the content. The specific behavior monitoring unit monitors specific behaviors based on the posted content analyzed by the post analysis unit. For example, the specific behavior monitoring unit monitors specific behaviors on social media and issues a warning when a specific behavior is committed. The generation AI detects specific behaviors based on data on posted content and comments on social media, records the behavior, and issues a warning. This enables the privacy protection system to protect privacy and prevent specific behaviors in posts on social media. For example, it can provide an environment where users can post on social media with peace of mind and prevent harm caused by specific behaviors. Furthermore, through education on privacy protection, users themselves can become more aware of privacy.

[0048] The post analysis unit can refer to a user's past posting history and provide consistent privacy protection. For example, the generation AI analyzes a user's past posting history and provides privacy protection to maintain consistency between past and current posting content. For example, the current posting content is modified so that it does not contradict information previously made public. This makes it possible to provide consistent privacy protection by referring to a user's past posting history.

[0049] The post analysis unit can understand the user's intention and propose privacy protection measures that are in line with the user's intention. For example, the post analysis unit uses natural language processing technology to enable the generative AI to analyze the content of the user's posts and understand the user's intention. For example, it analyzes the context of the post content and estimates the level of privacy protection that the user intends. This makes it possible to propose privacy protection measures that are in line with the user's intention.

[0050] The post analysis unit uses the emotion estimation function to analyze the emotion of the content the user is about to post, and if negative emotion is included, can make a suggestion to modify the content. The post analysis unit, for example, uses the emotion estimation function to analyze the emotion of the content the user is about to post in real time. For example, it detects negative emotion included in the post content and makes a suggestion to modify the content. This makes it possible to make a suggestion to modify the post content that includes negative emotion.

[0051] The post analysis unit can use image recognition technology to automatically identify objects or people appearing in photos and protect privacy. For example, the post analysis unit can use image recognition technology to automatically identify objects or people appearing in posted photos and apply blurring or mosaic processing to protect privacy. For example, faces and car license plates can be automatically hidden. This allows the automatic identification of objects and people appearing in photos and enables privacy protection.

[0052] The post analysis unit analyzes the audio data, automatically detects personal information contained in the audio data, and can suggest correction or deletion. For example, the generative AI in the post analysis unit analyzes the audio data and automatically detects personal information contained in the audio. For example, it analyzes names and addresses contained in the audio and suggests correction or deletion to protect privacy. This makes it possible to automatically detect personal information contained in the audio data and correct or delete it.

[0053] The post analysis unit can use the emotion estimation function to predict other users' emotional reactions to content that a user is about to post and make suggestions for revisions to elicit a positive reaction. The post analysis unit, for example, uses the emotion estimation function to predict other users' emotional reactions to content that a user is about to post. For example, it analyzes whether the content of the post will elicit a positive reaction. This makes it possible to predict other users' emotional reactions and make suggestions for revisions to elicit a positive reaction.

[0054] The Specific Action Monitoring Unit can learn patterns of specific actions and be able to respond to new methods of specific actions. For example, the Specific Action Monitoring Unit develops algorithms that enable the generation AI to learn patterns of specific actions and respond to new methods of specific actions. For example, it analyzes patterns based on data on past specific actions. This makes it possible to respond to new methods of specific actions.

[0055] The specific behavior monitoring unit can analyze the behavioral history of users who perform specific behaviors and detect signs of specific behaviors. For example, the specific behavior monitoring unit constructs a system in which a generation AI analyzes the behavioral history of users who perform specific behaviors and detects signs of specific behaviors. For example, signs are identified based on past behavioral patterns. This makes it possible to detect signs of specific behaviors.

[0056] The specific behavior monitoring unit uses the emotion estimation function to analyze the emotions of a user performing a specific behavior and can issue a warning when negative emotions are increasing. The specific behavior monitoring unit, for example, uses the emotion estimation function to analyze the emotions of a user performing a specific behavior in real time. For example, a system is constructed that issues a warning when negative emotions are increasing. This makes it possible to issue a warning when negative emotions are increasing.

[0057] The Specific Behavior Monitoring Unit can analyze comments or messages on social media and detect signs of specific behavior at an early stage. For example, the Specific Behavior Monitoring Unit will build a system in which a generative AI analyzes comments and messages on social media and detects signs of specific behavior at an early stage. For example, it will analyze specific keywords and phrases. This will enable early detection of signs of specific behavior.

[0058] The specific conduct monitoring unit can integrate data between different SNS platforms and prevent specific cross-platform conduct. The specific conduct monitoring unit, for example, integrates data between different SNS platforms and builds a system to prevent specific cross-platform conduct. For example, it centrally manages data from multiple SNSs. This makes it possible to prevent specific cross-platform conduct.

[0059] The specific conduct monitoring unit can use the emotion estimation function to analyze the emotions of victims of specific conduct and automatically generate support messages to reduce the stress and anxiety felt by the victims. The specific conduct monitoring unit, for example, uses the emotion estimation function to analyze the emotions of victims of specific conduct in real time and build a system that automatically generates support messages to reduce stress and anxiety. For example, it sends encouraging messages. This makes it possible to automatically generate support messages to reduce the stress and anxiety felt by victims of specific conduct.

[0060] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0061] The privacy protection system can also monitor third-party reactions to users' posts in real time and issue a warning to users if there are a large number of negative reactions. For example, it can analyze comments and reactions to posts and issue a warning if the number of negative reactions exceeds a certain number. It can also issue a warning if there are a large number of comments containing specific keywords or phrases. This allows users to understand how their posts are being received by others and to modify their posts if necessary.

[0062] The privacy protection system can also evaluate the legal risks of users' posts and issue a warning to users if the risk is high. For example, a warning can be issued if the posted content is likely to constitute defamation or a violation of privacy. It can also issue a warning if there is a risk of copyright infringement. This allows users to understand legal risks in advance and take measures to avoid them.

[0063] The privacy protection system can also evaluate the social impact of a user's posts and issue a warning to the user if the impact is significant. For example, a warning can be issued if the post deals with a socially sensitive topic. It can also issue a warning if the post has a significant impact on a specific group or community. This allows users to understand the social impact of their posts in advance and modify their posts as necessary.

[0064] The privacy protection system also uses an emotion estimation function for the user's posted content to predict what emotions the content the user is about to post will evoke in others, and can suggest revisions if it is likely to evoke negative emotions. For example, if the content of the post is likely to evoke anger or sadness in others, the system can suggest revisions. It can also suggest revisions if the content of the post is likely to evoke anxiety or fear in others. This allows users to post with peace of mind, without inciting negative emotions in others.

[0065] The privacy protection system also uses an emotion estimation function for the user's posted content to make suggestions to emphasize the content if the content the user is about to post is likely to evoke positive emotions in others. For example, if the content of the post is likely to evoke feelings of joy or gratitude in others, the system makes suggestions to emphasize the content. Also, if the content of the post is likely to evoke feelings of security or trust in others, the system can make suggestions to emphasize the content. This allows users to evoke positive emotions in others, facilitating smooth communication on SNS.

[0066] The privacy protection system also uses an emotion estimation function for the user's posted content to predict what emotions the content the user is about to post will evoke in others, and can suggest revisions if it is likely to evoke negative emotions. For example, if the content of the post is likely to evoke anger or sadness in others, the system can suggest revisions. It can also suggest revisions if the content of the post is likely to evoke anxiety or fear in others. This allows users to post with peace of mind, without inciting negative emotions in others.

[0067] The privacy protection system also uses an emotion estimation function for the user's posted content to make suggestions to emphasize the content if the content the user is about to post is likely to evoke positive emotions in others. For example, if the content of the post is likely to evoke feelings of joy or gratitude in others, the system makes suggestions to emphasize the content. Also, if the content of the post is likely to evoke feelings of security or trust in others, the system can make suggestions to emphasize the content. This allows users to evoke positive emotions in others, facilitating smooth communication on SNS.

[0068] The privacy protection system can also monitor third-party reactions to users' posts in real time and issue a warning to users if there are a large number of negative reactions. For example, it can analyze comments and reactions to posts and issue a warning if the number of negative reactions exceeds a certain number. It can also issue a warning if there are a large number of comments containing specific keywords or phrases. This allows users to understand how their posts are being received by others and to modify their posts if necessary.

[0069] The privacy protection system can also evaluate the legal risks of users' posts and issue a warning to users if the risk is high. For example, a warning can be issued if the posted content is likely to constitute defamation or a violation of privacy. It can also issue a warning if there is a risk of copyright infringement. This allows users to understand legal risks in advance and take measures to avoid them.

[0070] The privacy protection system can also evaluate the social impact of a user's posts and issue a warning to the user if the impact is significant. For example, a warning can be issued if the post deals with a socially sensitive topic. It can also issue a warning if the post has a significant impact on a specific group or community. This allows users to understand the social impact of their posts in advance and modify their posts as necessary.

[0071] The processing flow of the second embodiment will be briefly explained below.

[0072] Step 1: The post analysis unit is equipped with a generation AI and analyzes the content posted by users on social media. For example, the generation AI uses a text generation AI (e.g., LLM) to analyze the content of the post and automatically detect information that may potentially identify individuals. The generation AI can also use multimodal generation AI to detect the background of a photo, place names included in the post, and specific events. For example, the generation AI detects place names and specific events included in the post and suggests that the user modify or delete them. Step 2: The specific behavior monitoring unit monitors specific behaviors based on the post content analyzed by the post analysis unit. For example, the specific behavior monitoring unit monitors specific behaviors on social media and issues a warning if a specific behavior is committed. The generation AI detects specific behaviors based on the post content and comment data on social media, records the behavior, and issues a warning.

[0073] 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.

[0074] 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.

[0075] 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.

[0076] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0077] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0078] 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.

[0079] 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.

[0080] 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.

[0081] 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).

[0082] 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.

[0083] 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.

[0084] 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.

[0085] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0086] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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).

[0097] 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.

[0098] 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.

[0099] 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.

[0100] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0101] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

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

[0117] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 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.

[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 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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."

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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]

[0140] 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 equipped with generative AI, a specific action monitoring unit that monitors specific actions based on the posted content analyzed by the post analysis unit. A system characterized by:

2. The post analysis unit Refer to users' past posting history to ensure consistent privacy protection 2. The system of claim 1.

3. The post analysis unit Uses image recognition technology to automatically identify objects or people in photos and protect privacy 2. The system of claim 1.

4. The specific conduct monitoring unit Learn the patterns of the specific actions and be able to respond to new specific action methods 2. The system of claim 1.

5. The post analysis unit Using the emotion estimation function, the system analyzes the emotions of the content the user is about to post, and if negative emotions are present, suggests modifying the content.

2. The system of claim 1.

6. The specific conduct monitoring unit Using an emotion estimation function, the emotions of the user performing the specific behavior are analyzed, and a warning is issued if negative emotions are increasing.

2. The system of claim 1.

7. The specific conduct monitoring unit Using an emotion estimation function, the emotions of the victim of the specific act are analyzed, and a support message is automatically generated to reduce the stress and anxiety felt by the victim.

2. The system of claim 1.

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