system

Abstract

The system according to this embodiment aims to visually display the inclusion rate of AI-generated content within the content. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, an evaluation unit, and a display unit. The collection unit collects content. The analysis unit analyzes the content collected by the collection unit. The evaluation unit evaluates the inclusion rate of AI-generated content based on the results analyzed by the analysis unit. The display unit visually displays the results evaluated by the evaluation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document ① discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult to intuitively grasp whether AI-generated content is included in the content.

[0005] The system according to the embodiment aims to visually display the mixing rate of AI-generated content included in the content.

Means for Solving the Problems

[0006] Note: In the above translation, "①" in "Patent Document ①" should be replaced with the actual patent document number or other relevant identifier in the original text. Since it's not clear from the provided content, it's left as "①" for now.The system according to this embodiment comprises a collection unit, an analysis unit, an evaluation unit, and a display unit. The collection unit collects content. The analysis unit analyzes the content collected by the collection unit. The evaluation unit evaluates the inclusion rate of AI-generated content based on the results analyzed by the analysis unit. The display unit visually displays the results evaluated by the evaluation unit. [Effects of the Invention]

[0007] The system according to this embodiment can visually display the inclusion rate of AI-generated content within the content. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied

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

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

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

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

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

[0028] (Example of form 1) The AI-generated content detection system according to an embodiment of the present invention is a system that visually displays whether AI is used in news articles, images, and videos. The AI-generated content detection system evaluates the inclusion rate of AI-generated content on a scale of 0 to 100 points, indicating the likelihood of its use. This aims to create a world where users can easily select highly reliable content and products. For example, this system can be used to determine whether product images and videos on shopping and flea market sites are authentic. Furthermore, news articles, short videos, and sports videos created using generation AI are also evaluated in the same way. Users can select highly reliable content by visually confirming the evaluation results. This system is developed as an SDK for applications, enabling the provision of highly reliable information to a wide range of users. First, content such as news articles, images, and videos is collected. Next, the collected content is analyzed by AI, and the inclusion rate of AI-generated content is evaluated. The evaluation is performed on a scale of 0 to 100 points, with higher numbers indicating a higher probability of the content being AI-generated. For example, this system can be used to determine whether product images and videos on shopping and flea market sites are authentic. Furthermore, news articles, short videos, and sports videos created using generation AI are also evaluated in the same way. Users can select reliable content by visually confirming the evaluation results. This system, developed as an SDK for apps, enables the provision of reliable information to a wide range of users. As a result, the AI-generated content detection system can assist users in selecting reliable content.

[0029] The AI-generated content detection system according to this embodiment comprises a collection unit, an analysis unit, an evaluation unit, and a display unit. The collection unit collects content such as news articles, images, and videos. The collection unit collects content from, for example, publicly available databases on the internet. The collection unit can also collect content provided by users. Furthermore, the collection unit can obtain data from other systems via APIs. For example, the collection unit collects the latest articles from news sites. The collection unit can also collect images from image sharing sites. The collection unit can also collect videos from video streaming sites. The analysis unit analyzes the content collected by the collection unit. The analysis unit analyzes news articles using, for example, natural language processing technology. The analysis unit can also analyze images using image recognition technology. Furthermore, the analysis unit can analyze videos using video analysis technology. For example, the analysis unit analyzes the content of news articles and extracts features of AI-generated content. The analysis unit can also analyze image metadata and evaluate the possibility of AI generation. The analysis unit can also analyze video frames and detect traces of AI generation. The evaluation unit assesses the inclusion rate of AI-generated content based on the results analyzed by the analysis unit. For example, the evaluation unit scores the characteristics of the AI-generated content and evaluates the inclusion rate on a scale of 0 to 100 points. The evaluation unit can also perform a comprehensive evaluation by combining multiple evaluation criteria. Furthermore, the evaluation unit can update its evaluation model based on past data. For example, the evaluation unit learns the characteristics of the AI-generated content and improves its evaluation model. The evaluation unit can also adjust the evaluation criteria based on user feedback. The evaluation unit can also update the evaluation results in real time. The display unit visually displays the results evaluated by the evaluation unit. For example, the display unit displays the evaluation results using color coding. The display unit can also display the evaluation results as a graph. Furthermore, the display unit can display the evaluation results as icons. For example, the display unit displays the evaluation results in red, yellow, and green. The display unit can also display the evaluation results as a bar graph. The display unit can also display the evaluation results with a number of stars.This allows the AI-generated content detection system according to the embodiment to help users select reliable content.

[0030] The data collection unit collects content such as news articles, images, and videos. For example, it collects content from publicly available databases on the internet. It can also collect user-provided content. Furthermore, it can obtain data from other systems via APIs. For example, it can collect the latest articles from news sites, images from image-sharing sites, and videos from video streaming sites. Specifically, the data collection unit automatically collects content from publicly available databases on the internet using a web crawler. The web crawler traverses a specified list of URLs, analyzes the HTML structure, and extracts the necessary data. For user-provided content, it receives uploaded files through the user interface and stores them in the database. When obtaining data from other systems via APIs, the data collection unit communicates with external systems using protocols such as RESTful APIs and GraphQL to obtain the necessary data. For example, when collecting the latest articles from news sites, it uses RSS feeds to obtain information on new articles and collects the article text and metadata. When collecting images from image sharing sites, the system obtains the image URL and metadata, and then downloads the image file. When collecting videos from video streaming sites, it obtains the video URL, thumbnail image, and metadata, and then downloads the video file as needed. This allows the collection unit to efficiently collect content from diverse sources and provide it to the analysis and evaluation units.

[0031] The analysis unit analyzes the content collected by the collection unit. For example, the analysis unit analyzes news articles using natural language processing technology. The analysis unit can also analyze images using image recognition technology. Furthermore, the analysis unit can analyze videos using video analysis technology. Specifically, it analyzes the content of news articles using natural language processing technology and extracts features of AI-generated content. For example, it performs topic modeling and sentiment analysis to understand the characteristics of the article's content and writing style. When analyzing images using image recognition technology, it analyzes the image's metadata and pixel information to evaluate the possibility of AI generation. For example, it performs edge detection and pattern recognition of images to detect traces of artificial processing. When analyzing videos using video analysis technology, it analyzes the video frames to detect traces of AI generation. For example, it detects unnatural changes between frames or the use of specific effects. The analysis unit combines these technologies to comprehensively evaluate the possibility of AI generation of the collected content. Furthermore, the analysis unit can use machine learning models to learn from the collected data and automatically identify features of AI-generated content. For example, supervised learning is used to train a model based on known datasets of AI-generated and non-AI-generated content, and then classify newly collected content. This allows the analysis unit to quickly and accurately analyze the collected content and improve the accuracy of detecting AI-generated content.

[0032] The evaluation unit assesses the inclusion rate of AI-generated content based on the results analyzed by the analysis unit. For example, the evaluation unit scores the features of the AI-generated content and evaluates the inclusion rate on a scale of 0 to 100 points. The evaluation unit can also perform a comprehensive evaluation by combining multiple evaluation criteria. Specifically, the evaluation unit evaluates the importance of each feature based on the features provided by the analysis unit and calculates an overall score. For example, it may use the consistency of writing style and topic in news articles, metadata and pixel information in images, and changes between frames in videos as evaluation criteria. The evaluation unit combines these evaluation criteria to comprehensively evaluate the inclusion rate of AI-generated content. Furthermore, the evaluation unit can update its evaluation model based on past data. For example, it learns the features of AI-generated content detected in the past and reflects them in the evaluation of new content. The evaluation unit can also adjust the evaluation criteria based on user feedback. For example, it collects evaluation results and comments provided by users to improve the accuracy of the evaluation model. The evaluation unit can also update evaluation results in real time. For example, it immediately reflects the evaluation results each time newly collected content is analyzed, providing the latest information. This allows the evaluation unit to accurately assess the inclusion rate of AI-generated content and provide users with reliable information.

[0033] The display unit visually displays the results evaluated by the evaluation unit. For example, the display unit can display evaluation results using color coding. The display unit can also display evaluation results in graphs. Furthermore, the display unit can display evaluation results using icons. Specifically, evaluation results can be displayed in red, yellow, and green so that users can judge reliability at a glance. For example, red indicates high risk, yellow indicates medium risk, and green indicates low risk. When evaluation results are displayed in a bar graph, the scores for each evaluation criterion can be visually compared. For example, the writing style of news articles, image metadata, and video frame analysis results can be displayed in bar graphs, showing the scores for each criterion. When evaluation results are displayed with a star rating, the overall rating is expressed with a number of stars so that users can understand it intuitively. For example, five stars indicate high reliability, and one star indicates low reliability. Furthermore, the display unit also has a function to display evaluation results in detail. For example, detailed scores and analysis results for each evaluation criterion can be displayed in a pop-up window so that users can check detailed information. In this way, the display unit can visually display evaluation results in an easy-to-understand manner and help users select reliable content.

[0034] The evaluation unit can assess the inclusion rate of AI-generated content on a scale of 0 to 100 points. For example, the evaluation unit can score the features of the AI-generated content and evaluate the inclusion rate on a scale of 0 to 100 points. The evaluation unit can also perform a comprehensive evaluation by combining multiple evaluation criteria. Furthermore, the evaluation unit can update its evaluation model based on past data. For example, the evaluation unit can learn the features of the AI-generated content and improve its evaluation model. The evaluation unit can also adjust the evaluation criteria based on user feedback. The evaluation unit can also update the evaluation results in real time. This allows users to intuitively understand the inclusion rate of AI-generated content by evaluating it numerically. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can perform the evaluation using an AI model that takes the features of the AI-generated content as input and outputs the inclusion rate.

[0035] The display unit can visually display evaluation results using color coding or graphs. For example, the display unit can display evaluation results in red, yellow, or green. It can also display evaluation results as a bar graph. Furthermore, the display unit can display evaluation results using a star rating. For example, the display unit can display evaluation results in red to indicate a high inclusion rate of AI-generated content. It can also display evaluation results in yellow to indicate a moderate inclusion rate of AI-generated content. It can also display evaluation results in green to indicate a low inclusion rate of AI-generated content. This visual display of evaluation results makes them easily understandable to the user. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can use an AI model that takes evaluation results as input and outputs a visual display.

[0036] The display unit may have a function to filter content based on evaluation results. For example, the display unit may exclude unreliable content based on evaluation results. The display unit may also prioritize the display of highly reliable content based on evaluation results. Furthermore, the display unit may adjust the display order of content based on evaluation results. For example, the display unit may hide unreliable content based on evaluation results. The display unit may also display highly reliable content at the top based on evaluation results. The display unit may also dynamically change the display order of content based on evaluation results. This makes it easier for users to select highly reliable content by filtering content based on evaluation results. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit may perform filtering using an AI model that takes evaluation results as input and outputs filtered content.

[0037] The data collection unit can collect product images and videos from shopping and flea market websites. For example, the data collection unit can collect product images from shopping websites. It can also collect product videos from flea market websites. Furthermore, the data collection unit can obtain data from shopping and flea market websites via APIs. For example, the data collection unit can automatically collect product images from shopping websites. The data collection unit can also periodically collect product videos from flea market websites. The data collection unit can also obtain data from shopping and flea market websites in real time via APIs. This allows for the evaluation of product reliability by collecting product images and videos from shopping and flea market websites. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input product images and videos obtained from shopping and flea market websites into a generating AI and have the generating AI perform analysis of the collected data.

[0038] The analysis unit can analyze news articles, short videos, and sports videos created using generative AI. For example, the analysis unit can analyze news articles created using generative AI. It can also analyze short videos created using generative AI. Furthermore, it can analyze sports videos created using generative AI. For example, the analysis unit can analyze the content of news articles created using generative AI and extract AI-generated features. The analysis unit can also analyze frames of short videos created using generative AI and detect traces of AI generation. The analysis unit can also analyze the metadata of sports videos created using generative AI and evaluate the possibility of AI generation. This allows for the evaluation of the inclusion rate of AI-generated content by analyzing content created using generative AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input news articles, short videos, and sports videos created using generative AI into the generative AI and have the generative AI perform the analysis.

[0039] The display unit can be developed as an SDK for apps and made available to a wide range of users. For example, the display unit can provide an SDK for apps, allowing developers to integrate it into their own apps. The display unit can also provide an API for displaying evaluation results through the SDK. Furthermore, the display unit can provide tools for customizing and displaying evaluation results through the SDK. For example, the display unit can provide an SDK for apps, allowing developers to integrate it into their own apps. The display unit can also provide an API for displaying evaluation results through the SDK. The display unit can also provide tools for customizing and displaying evaluation results through the SDK. This makes it possible to provide reliable information to a wide range of users by developing it as an SDK for apps. Some or all of the above processing in the display unit may be performed using AI, for example, or not using AI. For example, the display unit can perform the display using an AI model that takes evaluation results as input and outputs a visual display.

[0040] The data collection unit can analyze the user's past browsing history and select the optimal collection method. For example, the data collection unit can prioritize collecting similar content based on the types of content the user has frequently viewed in the past. The data collection unit can also select content to collect at specific time periods based on the user's past browsing history. Furthermore, the data collection unit can collect related content based on content that the user has previously given high ratings to. For example, the data collection unit can prioritize collecting similar content based on the types of content the user has frequently viewed in the past. The data collection unit can also select content to collect at specific time periods based on the user's past browsing history. The data collection unit can also collect related content based on content that the user has previously given high ratings to. This allows the optimal collection method to be selected by analyzing the user's past browsing history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past browsing history data into a generating AI and have the generating AI select the optimal collection method.

[0041] The collection unit can filter content based on the user's current areas of interest when collecting it. For example, the collection unit can prioritize collecting content related to topics the user is currently interested in. The collection unit can also filter out unnecessary content based on the user's current areas of interest. Furthermore, the collection unit can dynamically adjust the types of content it collects if the user's areas of interest change. For example, the collection unit can prioritize collecting content related to topics the user is currently interested in. The collection unit can also filter out unnecessary content based on the user's current areas of interest. The collection unit can also dynamically adjust the types of content it collects if the user's areas of interest change. This allows for the collection of highly relevant content by filtering based on the user's current areas of interest. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's current areas of interest data into a generating AI and have the generating AI perform the filtering.

[0042] The collection unit can prioritize the collection of highly relevant content by considering the user's geographical location when collecting content. For example, the collection unit can prioritize the collection of news articles related to the user's current location. The collection unit can also collect local event information based on the user's geographical location. Furthermore, if the user is traveling, the collection unit can also collect tourist information related to their travel destination. For example, the collection unit can prioritize the collection of news articles related to the user's current location. The collection unit can also collect local event information based on the user's geographical location. If the user is traveling, the collection unit can also collect tourist information related to their travel destination. This allows for the priority collection of highly relevant content by considering the user's geographical location. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's geographical location data into a generating AI and have the generating AI collect highly relevant content.

[0043] The collection unit can analyze a user's social media activity and collect relevant content when collecting content. For example, the collection unit can collect information related to content that a user has shared on social media. The collection unit can also collect content related to topics that a user's followers are interested in. Furthermore, the collection unit can also collect information related to content that a user has "liked" on social media. For example, the collection unit can collect information related to content that a user has shared on social media. The collection unit can also collect content related to topics that a user's followers are interested in. The collection unit can also collect information related to content that a user has "liked" on social media. This allows the collection of relevant content by analyzing a user's social media activity. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's social media activity data into a generating AI and have the generating AI collect relevant content.

[0044] The analysis unit can adjust the level of detail of the analysis based on the importance of the content. For example, the analysis unit can perform a detailed analysis on high-importance content. It can also perform a concise analysis on low-importance content. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the content. For example, the analysis unit can perform a detailed analysis on high-importance content. It can also perform a concise analysis on low-importance content. It can also determine the priority of the analysis according to the importance of the content. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the content. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input content importance data into a generating AI and have the generating AI adjust the level of detail of the analysis.

[0045] The analysis unit can apply different analysis algorithms depending on the content category during analysis. For example, the analysis unit can apply a text analysis algorithm to news articles. It can also apply an image analysis algorithm to images. Furthermore, it can apply a video analysis algorithm to videos. For example, the analysis unit can apply a text analysis algorithm to news articles. It can also apply an image analysis algorithm to images. It can also apply a video analysis algorithm to videos. This allows for highly accurate analysis by applying different analysis algorithms depending on the content category. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input content category data into a generating AI and have the generating AI select the analysis algorithm to apply.

[0046] The analysis unit can determine the priority of analysis based on the content creation date during analysis. For example, the analysis unit prioritizes the analysis of the most recent content. The analysis unit can also determine the priority of analysis of older content according to its importance. Furthermore, the analysis unit can adjust the analysis schedule based on the content creation date. For example, the analysis unit prioritizes the analysis of the most recent content. The analysis unit can also determine the priority of analysis of older content according to its importance. The analysis unit can also adjust the analysis schedule based on the content creation date. This enables efficient analysis by determining the priority of analysis based on the content creation date. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input content creation date data into a generating AI and have the generating AI determine the analysis priority.

[0047] The analysis unit can adjust the order of analysis based on the relevance of the content during analysis. For example, the analysis unit can prioritize the analysis of highly relevant content. It can also postpone the analysis of less relevant content. Furthermore, the analysis unit can adjust the analysis schedule based on the relevance of the content. For example, the analysis unit can prioritize the analysis of highly relevant content. It can also postpone the analysis of less relevant content. The analysis unit can also adjust the analysis schedule based on the relevance of the content. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the content. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input content relevance data into a generating AI and have the generating AI adjust the order of analysis.

[0048] The evaluation unit can improve the accuracy of its evaluation by considering the interrelationships of content during the evaluation process. For example, the evaluation unit can evaluate related content together and consider their interrelationships. The evaluation unit can also improve the accuracy of its evaluation based on the interrelationships of content. Furthermore, the evaluation unit can analyze the interrelationships of content and adjust the evaluation criteria. For example, the evaluation unit can evaluate related content together and consider their interrelationships. The evaluation unit can also improve the accuracy of its evaluation based on the interrelationships of content. The evaluation unit can also analyze the interrelationships of content and adjust the evaluation criteria. This improves the accuracy of the evaluation by considering the interrelationships of content. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input content interrelationship data into a generating AI and have the generating AI perform the evaluation accuracy improvement.

[0049] The evaluation unit can perform evaluations while considering the attribute information of the content creator. For example, the evaluation unit may give a high rating if the content creator is an expert. Conversely, the evaluation unit may also give a low rating if the content creator is unreliable. Furthermore, the evaluation unit may adjust the evaluation criteria based on the attribute information of the content creator. For example, the evaluation unit may give a high rating if the content creator is an expert. Conversely, the evaluation unit may also give a low rating if the content creator is unreliable. The evaluation unit may also adjust the evaluation criteria based on the attribute information of the content creator. This improves the reliability of the evaluation by considering the attribute information of the content creator. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or not using AI. For example, the evaluation unit may input content creator attribute information data into a generating AI and have the generating AI perform the adjustment of the evaluation criteria.

[0050] The evaluation unit can perform evaluations while considering the geographical distribution of the content. For example, the evaluation unit can prioritize evaluating content that is highly geographically relevant. It can also postpone the evaluation of content that is less geographically relevant. Furthermore, the evaluation unit can adjust the evaluation criteria based on the geographical distribution of the content. For example, the evaluation unit can prioritize evaluating content that is highly geographically relevant. It can also postpone the evaluation of content that is less geographically relevant. It can also adjust the evaluation criteria based on the geographical distribution of the content. This improves the accuracy of the evaluation by considering the geographical distribution of the content. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or not using AI. For example, the evaluation unit can input geographical distribution data of the content into a generating AI and have the generating AI perform the adjustment of the evaluation criteria.

[0051] The evaluation unit can improve the accuracy of its evaluation by referring to relevant literature for the content during the evaluation process. For example, the evaluation unit can improve the accuracy of its evaluation by referring to relevant literature. The evaluation unit can also adjust the evaluation criteria based on the relevant literature for the content. Furthermore, the evaluation unit can improve the accuracy of its evaluation by analyzing the relevant literature. For example, the evaluation unit can improve the accuracy of its evaluation by referring to relevant literature. The evaluation unit can also adjust the evaluation criteria based on the relevant literature for the content. The evaluation unit can also improve the accuracy of its evaluation by analyzing the relevant literature. As a result, the accuracy of the evaluation is improved by referring to relevant literature. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input relevant literature data into a generating AI and have the generating AI perform the evaluation accuracy improvement.

[0052] The display unit can select the optimal display method by referring to the user's past operation history when displaying information. For example, the display unit may prioritize display methods that the user has previously preferred to use. The display unit can also select the optimal display method based on the user's past operation history. Furthermore, the display unit can analyze the user's operation history and dynamically adjust the display method. For example, the display unit may prioritize display methods that the user has previously preferred to use. The display unit can also select the optimal display method based on the user's past operation history. The display unit can also analyze the user's operation history and dynamically adjust the display method. This allows the display unit to provide the optimal display method by referring to the user's past operation history. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the user's past operation history data into a generating AI and have the generating AI select the optimal display method.

[0053] The display unit can apply different display algorithms depending on the content category during display. For example, the display unit can apply a text-based display algorithm to news articles. It can also apply an image-based display algorithm to images. Furthermore, it can apply a video-based display algorithm to videos. For example, the display unit can apply a text-based display algorithm to news articles. It can also apply an image-based display algorithm to images. It can also apply a video-based display algorithm to videos. This enables optimal display by applying different display algorithms depending on the content category. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input content category data into a generating AI and have the generating AI select the display algorithm to apply.

[0054] The display unit can select the optimal display method when displaying information, taking into account the user's device information. For example, if the user is using a smartphone, the display unit provides a display method that matches the screen size. Furthermore, if the user is using a tablet, the display unit can provide a display method optimized for a larger screen. Additionally, if the user is using a smartwatch, the display unit can provide a concise and highly visible display method. This allows the display unit to provide the optimal display method by considering the user's device information. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input user device information data into a generating AI and have the generating AI select the optimal display method.

[0055] The display unit can analyze the user's social media activity and suggest display methods when displaying information. For example, the display unit can display information related to content the user has shared on social media. The display unit can also display information related to topics of interest to the user's followers. Furthermore, the display unit can display information related to content the user has "liked" on social media. For example, the display unit can display information related to content the user has shared on social media. The display unit can also display information related to topics of interest to the user's followers. The display unit can also display information related to content the user has "liked" on social media. This allows the display unit to suggest the optimal display method by analyzing the user's social media activity. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the user's social media activity data into a generating AI and have the generating AI suggest display methods.

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

[0057] The data collection unit can analyze the user's past browsing history and select the optimal data collection method. For example, it can prioritize collecting similar content based on the types of content the user has frequently viewed in the past. It can also select content to collect at specific times based on the user's past browsing history. Furthermore, it can collect related content based on content that the user has previously given high ratings to. In this way, the optimal data collection method can be selected by analyzing the user's past browsing history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past browsing history data into a generating AI and have the generating AI select the optimal data collection method.

[0058] The analysis unit can adjust the level of detail of the analysis based on the importance of the content. For example, it can perform a detailed analysis on high-importance content and a concise analysis on low-importance content. Furthermore, it can determine the priority of the analysis according to the importance of the content. This allows for efficient analysis by adjusting the level of detail based on the importance of the content. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input content importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0059] The evaluation unit can perform evaluations while considering the attribute information of the content creator. For example, if the content creator is an expert, it can be given a high rating. Conversely, if the content creator is unreliable, it can be given a low rating. Furthermore, the evaluation criteria can be adjusted based on the attribute information of the content creator. This improves the reliability of the evaluation by considering the attribute information of the content creator. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the attribute information data of the content creator into a generating AI and have the generating AI perform adjustments to the evaluation criteria.

[0060] The display unit can select the optimal display method when displaying information, taking into account the user's device information. For example, if the user is using a smartphone, it can provide a display method that matches the screen size. If the user is using a tablet, it can also provide a display method optimized for a larger screen. Furthermore, if the user is using a smartwatch, it can provide a concise and highly visible display method. In this way, the optimal display method can be provided by taking into account the user's device information. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input user device information data into a generating AI and have the generating AI select the optimal display method.

[0061] The display unit can analyze the user's social media activity and suggest display methods when displaying information. For example, it can display information related to content the user has shared on social media. It can also display information related to topics that the user's followers are interested in. Furthermore, it can display information related to content the user has "liked" on social media. In this way, by analyzing the user's social media activity, the optimal display method can be suggested. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the user's social media activity data into a generating AI and have the generating AI perform the task of suggesting display methods.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The collection unit collects content such as news articles, images, and videos. The collection unit collects content from publicly available databases on the internet, and can also obtain data from other systems through user-provided content and APIs. For example, it collects the latest articles from news sites, images from image-sharing sites, and videos from video-sharing sites. Step 2: The analysis unit analyzes the content collected by the collection unit. The analysis unit analyzes news articles using natural language processing technology, images using image recognition technology, and videos using video analysis technology. For example, it analyzes the content of news articles, extracts features of AI-generated content, analyzes image metadata, evaluates the possibility of AI generation, analyzes video frames, and detects traces of AI generation. Step 3: The evaluation unit evaluates the inclusion rate of AI-generated content based on the results analyzed by the analysis unit. The evaluation unit scores the characteristics of the AI-generated content and evaluates the inclusion rate on a scale of 0 to 100 points. It can also perform a comprehensive evaluation by combining multiple evaluation criteria and update the evaluation model based on past data. For example, it can learn the characteristics of AI-generated content, improve the evaluation model, adjust the evaluation criteria based on user feedback, and update the evaluation results in real time. Step 4: The display unit visually displays the results evaluated by the evaluation unit. The display unit can display the evaluation results using color coding, graphs, and icons. For example, the evaluation results can be displayed in red, yellow, and green, as a bar graph, and as a number of stars.

[0064] (Example of form 2) The AI-generated content detection system according to an embodiment of the present invention is a system that visually displays whether AI is used in news articles, images, and videos. The AI-generated content detection system evaluates the inclusion rate of AI-generated content on a scale of 0 to 100 points, indicating the likelihood of its use. This aims to create a world where users can easily select highly reliable content and products. For example, this system can be used to determine whether product images and videos on shopping and flea market sites are authentic. Furthermore, news articles, short videos, and sports videos created using generation AI are also evaluated in the same way. Users can select highly reliable content by visually confirming the evaluation results. This system is developed as an SDK for applications, enabling the provision of highly reliable information to a wide range of users. First, content such as news articles, images, and videos is collected. Next, the collected content is analyzed by AI, and the inclusion rate of AI-generated content is evaluated. The evaluation is performed on a scale of 0 to 100 points, with higher numbers indicating a higher probability of the content being AI-generated. For example, this system can be used to determine whether product images and videos on shopping and flea market sites are authentic. Furthermore, news articles, short videos, and sports videos created using generation AI are also evaluated in the same way. Users can select reliable content by visually confirming the evaluation results. This system, developed as an SDK for apps, enables the provision of reliable information to a wide range of users. As a result, the AI-generated content detection system can assist users in selecting reliable content.

[0065] The AI-generated content detection system according to this embodiment comprises a collection unit, an analysis unit, an evaluation unit, and a display unit. The collection unit collects content such as news articles, images, and videos. The collection unit collects content from, for example, publicly available databases on the internet. The collection unit can also collect content provided by users. Furthermore, the collection unit can obtain data from other systems via APIs. For example, the collection unit collects the latest articles from news sites. The collection unit can also collect images from image sharing sites. The collection unit can also collect videos from video streaming sites. The analysis unit analyzes the content collected by the collection unit. The analysis unit analyzes news articles using, for example, natural language processing technology. The analysis unit can also analyze images using image recognition technology. Furthermore, the analysis unit can analyze videos using video analysis technology. For example, the analysis unit analyzes the content of news articles and extracts features of AI-generated content. The analysis unit can also analyze image metadata and evaluate the possibility of AI generation. The analysis unit can also analyze video frames and detect traces of AI generation. The evaluation unit assesses the inclusion rate of AI-generated content based on the results analyzed by the analysis unit. For example, the evaluation unit scores the characteristics of the AI-generated content and evaluates the inclusion rate on a scale of 0 to 100 points. The evaluation unit can also perform a comprehensive evaluation by combining multiple evaluation criteria. Furthermore, the evaluation unit can update its evaluation model based on past data. For example, the evaluation unit learns the characteristics of the AI-generated content and improves its evaluation model. The evaluation unit can also adjust the evaluation criteria based on user feedback. The evaluation unit can also update the evaluation results in real time. The display unit visually displays the results evaluated by the evaluation unit. For example, the display unit displays the evaluation results using color coding. The display unit can also display the evaluation results as a graph. Furthermore, the display unit can display the evaluation results as icons. For example, the display unit displays the evaluation results in red, yellow, and green. The display unit can also display the evaluation results as a bar graph. The display unit can also display the evaluation results with a number of stars.This allows the AI-generated content detection system according to the embodiment to help users select reliable content.

[0066] The data collection unit collects content such as news articles, images, and videos. For example, it collects content from publicly available databases on the internet. It can also collect user-provided content. Furthermore, it can obtain data from other systems via APIs. For example, it can collect the latest articles from news sites, images from image-sharing sites, and videos from video streaming sites. Specifically, the data collection unit automatically collects content from publicly available databases on the internet using a web crawler. The web crawler traverses a specified list of URLs, analyzes the HTML structure, and extracts the necessary data. For user-provided content, it receives uploaded files through the user interface and stores them in the database. When obtaining data from other systems via APIs, the data collection unit communicates with external systems using protocols such as RESTful APIs and GraphQL to obtain the necessary data. For example, when collecting the latest articles from news sites, it uses RSS feeds to obtain information on new articles and collects the article text and metadata. When collecting images from image sharing sites, the system obtains the image URL and metadata, and then downloads the image file. When collecting videos from video streaming sites, it obtains the video URL, thumbnail image, and metadata, and then downloads the video file as needed. This allows the collection unit to efficiently collect content from diverse sources and provide it to the analysis and evaluation units.

[0067] The analysis unit analyzes the content collected by the collection unit. For example, the analysis unit analyzes news articles using natural language processing technology. The analysis unit can also analyze images using image recognition technology. Furthermore, the analysis unit can analyze videos using video analysis technology. Specifically, it analyzes the content of news articles using natural language processing technology and extracts features of AI-generated content. For example, it performs topic modeling and sentiment analysis to understand the characteristics of the article's content and writing style. When analyzing images using image recognition technology, it analyzes the image's metadata and pixel information to evaluate the possibility of AI generation. For example, it performs edge detection and pattern recognition of images to detect traces of artificial processing. When analyzing videos using video analysis technology, it analyzes the video frames to detect traces of AI generation. For example, it detects unnatural changes between frames or the use of specific effects. The analysis unit combines these technologies to comprehensively evaluate the possibility of AI generation of the collected content. Furthermore, the analysis unit can use machine learning models to learn from the collected data and automatically identify features of AI-generated content. For example, supervised learning is used to train a model based on known datasets of AI-generated and non-AI-generated content, and then classify newly collected content. This allows the analysis unit to quickly and accurately analyze the collected content and improve the accuracy of detecting AI-generated content.

[0068] The evaluation unit assesses the inclusion rate of AI-generated content based on the results analyzed by the analysis unit. For example, the evaluation unit scores the features of the AI-generated content and evaluates the inclusion rate on a scale of 0 to 100 points. The evaluation unit can also perform a comprehensive evaluation by combining multiple evaluation criteria. Specifically, the evaluation unit evaluates the importance of each feature based on the features provided by the analysis unit and calculates an overall score. For example, it may use the consistency of writing style and topic in news articles, metadata and pixel information in images, and changes between frames in videos as evaluation criteria. The evaluation unit combines these evaluation criteria to comprehensively evaluate the inclusion rate of AI-generated content. Furthermore, the evaluation unit can update its evaluation model based on past data. For example, it learns the features of AI-generated content detected in the past and reflects them in the evaluation of new content. The evaluation unit can also adjust the evaluation criteria based on user feedback. For example, it collects evaluation results and comments provided by users to improve the accuracy of the evaluation model. The evaluation unit can also update evaluation results in real time. For example, it immediately reflects the evaluation results each time newly collected content is analyzed, providing the latest information. This allows the evaluation unit to accurately assess the inclusion rate of AI-generated content and provide users with reliable information.

[0069] The display unit visually displays the results evaluated by the evaluation unit. For example, the display unit can display evaluation results using color coding. The display unit can also display evaluation results in graphs. Furthermore, the display unit can display evaluation results using icons. Specifically, evaluation results can be displayed in red, yellow, and green so that users can judge reliability at a glance. For example, red indicates high risk, yellow indicates medium risk, and green indicates low risk. When evaluation results are displayed in a bar graph, the scores for each evaluation criterion can be visually compared. For example, the writing style of news articles, image metadata, and video frame analysis results can be displayed in bar graphs, showing the scores for each criterion. When evaluation results are displayed with a star rating, the overall rating is expressed with a number of stars so that users can understand it intuitively. For example, five stars indicate high reliability, and one star indicates low reliability. Furthermore, the display unit also has a function to display evaluation results in detail. For example, detailed scores and analysis results for each evaluation criterion can be displayed in a pop-up window so that users can check detailed information. In this way, the display unit can visually display evaluation results in an easy-to-understand manner and help users select reliable content.

[0070] The evaluation unit can assess the inclusion rate of AI-generated content on a scale of 0 to 100 points. For example, the evaluation unit can score the features of the AI-generated content and evaluate the inclusion rate on a scale of 0 to 100 points. The evaluation unit can also perform a comprehensive evaluation by combining multiple evaluation criteria. Furthermore, the evaluation unit can update its evaluation model based on past data. For example, the evaluation unit can learn the features of the AI-generated content and improve its evaluation model. The evaluation unit can also adjust the evaluation criteria based on user feedback. The evaluation unit can also update the evaluation results in real time. This allows users to intuitively understand the inclusion rate of AI-generated content by evaluating it numerically. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can perform the evaluation using an AI model that takes the features of the AI-generated content as input and outputs the inclusion rate.

[0071] The display unit can visually display evaluation results using color coding or graphs. For example, the display unit can display evaluation results in red, yellow, or green. It can also display evaluation results as a bar graph. Furthermore, the display unit can display evaluation results using a star rating. For example, the display unit can display evaluation results in red to indicate a high inclusion rate of AI-generated content. It can also display evaluation results in yellow to indicate a moderate inclusion rate of AI-generated content. It can also display evaluation results in green to indicate a low inclusion rate of AI-generated content. This visual display of evaluation results makes them easily understandable to the user. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can use an AI model that takes evaluation results as input and outputs a visual display.

[0072] The display unit may have a function to filter content based on evaluation results. For example, the display unit may exclude unreliable content based on evaluation results. The display unit may also prioritize the display of highly reliable content based on evaluation results. Furthermore, the display unit may adjust the display order of content based on evaluation results. For example, the display unit may hide unreliable content based on evaluation results. The display unit may also display highly reliable content at the top based on evaluation results. The display unit may also dynamically change the display order of content based on evaluation results. This makes it easier for users to select highly reliable content by filtering content based on evaluation results. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit may perform filtering using an AI model that takes evaluation results as input and outputs filtered content.

[0073] The data collection unit can collect product images and videos from shopping and flea market websites. For example, the data collection unit can collect product images from shopping websites. It can also collect product videos from flea market websites. Furthermore, the data collection unit can obtain data from shopping and flea market websites via APIs. For example, the data collection unit can automatically collect product images from shopping websites. The data collection unit can also periodically collect product videos from flea market websites. The data collection unit can also obtain data from shopping and flea market websites in real time via APIs. This allows for the evaluation of product reliability by collecting product images and videos from shopping and flea market websites. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input product images and videos obtained from shopping and flea market websites into a generating AI and have the generating AI perform analysis of the collected data.

[0074] The analysis unit can analyze news articles, short videos, and sports videos created using generative AI. For example, the analysis unit can analyze news articles created using generative AI. It can also analyze short videos created using generative AI. Furthermore, it can analyze sports videos created using generative AI. For example, the analysis unit can analyze the content of news articles created using generative AI and extract AI-generated features. The analysis unit can also analyze frames of short videos created using generative AI and detect traces of AI generation. The analysis unit can also analyze the metadata of sports videos created using generative AI and evaluate the possibility of AI generation. This allows for the evaluation of the inclusion rate of AI-generated content by analyzing content created using generative AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input news articles, short videos, and sports videos created using generative AI into the generative AI and have the generative AI perform the analysis.

[0075] The display unit can be developed as an SDK for apps and made available to a wide range of users. For example, the display unit can provide an SDK for apps, allowing developers to integrate it into their own apps. The display unit can also provide an API for displaying evaluation results through the SDK. Furthermore, the display unit can provide tools for customizing and displaying evaluation results through the SDK. For example, the display unit can provide an SDK for apps, allowing developers to integrate it into their own apps. The display unit can also provide an API for displaying evaluation results through the SDK. The display unit can also provide tools for customizing and displaying evaluation results through the SDK. This makes it possible to provide reliable information to a wide range of users by developing it as an SDK for apps. Some or all of the above processing in the display unit may be performed using AI, for example, or not using AI. For example, the display unit can perform the display using an AI model that takes evaluation results as input and outputs a visual display.

[0076] The data collection unit can estimate the user's emotions and adjust the timing of content collection based on those emotions. For example, if the user is stressed, the data collection unit can delay collection until the user is relaxed. Alternatively, if the user is excited, the data collection unit can immediately collect content to capture the user's interest. Furthermore, if the user is tired, the data collection unit can adjust the timing of collection until the user has rested. This allows for content collection at the optimal time for the user by adjusting the timing based on their emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into a generating AI and have the generating AI adjust the timing of data collection.

[0077] The data collection unit can analyze the user's past browsing history and select the optimal collection method. For example, the data collection unit can prioritize collecting similar content based on the types of content the user has frequently viewed in the past. The data collection unit can also select content to collect at specific time periods based on the user's past browsing history. Furthermore, the data collection unit can collect related content based on content that the user has previously given high ratings to. For example, the data collection unit can prioritize collecting similar content based on the types of content the user has frequently viewed in the past. The data collection unit can also select content to collect at specific time periods based on the user's past browsing history. The data collection unit can also collect related content based on content that the user has previously given high ratings to. This allows the optimal collection method to be selected by analyzing the user's past browsing history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past browsing history data into a generating AI and have the generating AI select the optimal collection method.

[0078] The collection unit can filter content based on the user's current areas of interest when collecting it. For example, the collection unit can prioritize collecting content related to topics the user is currently interested in. The collection unit can also filter out unnecessary content based on the user's current areas of interest. Furthermore, the collection unit can dynamically adjust the types of content it collects if the user's areas of interest change. For example, the collection unit can prioritize collecting content related to topics the user is currently interested in. The collection unit can also filter out unnecessary content based on the user's current areas of interest. The collection unit can also dynamically adjust the types of content it collects if the user's areas of interest change. This allows for the collection of highly relevant content by filtering based on the user's current areas of interest. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's current areas of interest data into a generating AI and have the generating AI perform the filtering.

[0079] The data collection unit can estimate the user's emotions and determine the priority of content to collect based on the estimated emotions. For example, if the user is relaxed, the data collection unit will prioritize collecting entertainment content. It can also prioritize collecting academic content if the user is focused. Furthermore, if the user is tired, the data collection unit can prioritize collecting relaxing content. This allows for the collection of optimal content for the user by prioritizing content based on their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input user emotion data into a generating AI, which can then determine the priority of the content to be collected.

[0080] The collection unit can prioritize the collection of highly relevant content by considering the user's geographical location when collecting content. For example, the collection unit can prioritize the collection of news articles related to the user's current location. The collection unit can also collect local event information based on the user's geographical location. Furthermore, if the user is traveling, the collection unit can also collect tourist information related to their travel destination. For example, the collection unit can prioritize the collection of news articles related to the user's current location. The collection unit can also collect local event information based on the user's geographical location. If the user is traveling, the collection unit can also collect tourist information related to their travel destination. This allows for the priority collection of highly relevant content by considering the user's geographical location. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's geographical location data into a generating AI and have the generating AI collect highly relevant content.

[0081] The collection unit can analyze a user's social media activity and collect relevant content when collecting content. For example, the collection unit can collect information related to content that a user has shared on social media. The collection unit can also collect content related to topics that a user's followers are interested in. Furthermore, the collection unit can also collect information related to content that a user has "liked" on social media. For example, the collection unit can collect information related to content that a user has shared on social media. The collection unit can also collect content related to topics that a user's followers are interested in. The collection unit can also collect information related to content that a user has "liked" on social media. This allows the collection of relevant content by analyzing a user's social media activity. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's social media activity data into a generating AI and have the generating AI collect relevant content.

[0082] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. If the user is in a hurry, the analysis unit can also provide concise analysis results that get straight to the point. Furthermore, if the user is excited, the analysis unit can provide visually stimulating analysis results. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. If the user is in a hurry, the analysis unit can also provide concise analysis results that get straight to the point. If the user is excited, the analysis unit can also provide visually stimulating analysis results. By adjusting the presentation of the analysis based on the user's emotions, the system can provide the user with the most optimal analysis results. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generating AI and have the generating AI adjust the way the analysis is expressed.

[0083] The analysis unit can adjust the level of detail of the analysis based on the importance of the content. For example, the analysis unit can perform a detailed analysis on high-importance content. It can also perform a concise analysis on low-importance content. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the content. For example, the analysis unit can perform a detailed analysis on high-importance content. It can also perform a concise analysis on low-importance content. It can also determine the priority of the analysis according to the importance of the content. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the content. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input content importance data into a generating AI and have the generating AI adjust the level of detail of the analysis.

[0084] The analysis unit can apply different analysis algorithms depending on the content category during analysis. For example, the analysis unit can apply a text analysis algorithm to news articles. It can also apply an image analysis algorithm to images. Furthermore, it can apply a video analysis algorithm to videos. For example, the analysis unit can apply a text analysis algorithm to news articles. It can also apply an image analysis algorithm to images. It can also apply a video analysis algorithm to videos. This allows for highly accurate analysis by applying different analysis algorithms depending on the content category. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input content category data into a generating AI and have the generating AI select the analysis algorithm to apply.

[0085] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis result. It can also provide a detailed analysis result if the user is relaxed. Furthermore, if the user is excited, the analysis unit can provide a visually stimulating analysis result. This allows the system to provide the user with the most optimal analysis result by adjusting the length of the analysis based on their emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using AI, or not. For example, the analysis unit can input user emotion data into the generating AI and have the generating AI adjust the length of the analysis.

[0086] The analysis unit can determine the priority of analysis based on the content creation date during analysis. For example, the analysis unit prioritizes the analysis of the most recent content. The analysis unit can also determine the priority of analysis of older content according to its importance. Furthermore, the analysis unit can adjust the analysis schedule based on the content creation date. For example, the analysis unit prioritizes the analysis of the most recent content. The analysis unit can also determine the priority of analysis of older content according to its importance. The analysis unit can also adjust the analysis schedule based on the content creation date. This enables efficient analysis by determining the priority of analysis based on the content creation date. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input content creation date data into a generating AI and have the generating AI determine the analysis priority.

[0087] The analysis unit can adjust the order of analysis based on the relevance of the content during analysis. For example, the analysis unit can prioritize the analysis of highly relevant content. It can also postpone the analysis of less relevant content. Furthermore, the analysis unit can adjust the analysis schedule based on the relevance of the content. For example, the analysis unit can prioritize the analysis of highly relevant content. It can also postpone the analysis of less relevant content. The analysis unit can also adjust the analysis schedule based on the relevance of the content. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the content. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input content relevance data into a generating AI and have the generating AI adjust the order of analysis.

[0088] The evaluation unit can estimate the user's emotions and adjust the evaluation criteria based on the estimated emotions. For example, if the user is relaxed, the evaluation unit can apply detailed evaluation criteria. If the user is in a hurry, the evaluation unit can also apply concise evaluation criteria. Furthermore, if the user is excited, the evaluation unit can also apply visually stimulating evaluation criteria. For example, if the user is relaxed, the evaluation unit can apply detailed evaluation criteria. If the user is in a hurry, the evaluation unit can also apply concise evaluation criteria. If the user is excited, the evaluation unit can also apply visually stimulating evaluation criteria. By adjusting the evaluation criteria based on the user's emotions, the system can provide the user with the most optimal evaluation result. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input user emotion data into a generating AI and have the generating AI adjust the evaluation criteria.

[0089] The evaluation unit can improve the accuracy of its evaluation by considering the interrelationships of content during the evaluation process. For example, the evaluation unit can evaluate related content together and consider their interrelationships. The evaluation unit can also improve the accuracy of its evaluation based on the interrelationships of content. Furthermore, the evaluation unit can analyze the interrelationships of content and adjust the evaluation criteria. For example, the evaluation unit can evaluate related content together and consider their interrelationships. The evaluation unit can also improve the accuracy of its evaluation based on the interrelationships of content. The evaluation unit can also analyze the interrelationships of content and adjust the evaluation criteria. This improves the accuracy of the evaluation by considering the interrelationships of content. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input content interrelationship data into a generating AI and have the generating AI perform the evaluation accuracy improvement.

[0090] The evaluation unit can perform evaluations while considering the attribute information of the content creator. For example, the evaluation unit may give a high rating if the content creator is an expert. Conversely, the evaluation unit may also give a low rating if the content creator is unreliable. Furthermore, the evaluation unit may adjust the evaluation criteria based on the attribute information of the content creator. For example, the evaluation unit may give a high rating if the content creator is an expert. Conversely, the evaluation unit may also give a low rating if the content creator is unreliable. The evaluation unit may also adjust the evaluation criteria based on the attribute information of the content creator. This improves the reliability of the evaluation by considering the attribute information of the content creator. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or not using AI. For example, the evaluation unit may input content creator attribute information data into a generating AI and have the generating AI perform the adjustment of the evaluation criteria.

[0091] The evaluation unit can estimate the user's emotions and adjust the order in which evaluation results are displayed based on the estimated emotions. For example, if the user is relaxed, the evaluation unit may prioritize displaying detailed evaluation results. It can also prioritize displaying concise evaluation results if the user is in a hurry. Furthermore, if the user is excited, the evaluation unit may prioritize displaying visually stimulating evaluation results. This allows the system to provide the user with optimal information by adjusting the display order of evaluation results based on their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the evaluation unit may be performed using AI, or not. For example, the evaluation unit can input user emotion data into the generating AI and have the generating AI adjust the display order of the evaluation results.

[0092] The evaluation unit can perform evaluations while considering the geographical distribution of the content. For example, the evaluation unit can prioritize evaluating content that is highly geographically relevant. It can also postpone the evaluation of content that is less geographically relevant. Furthermore, the evaluation unit can adjust the evaluation criteria based on the geographical distribution of the content. For example, the evaluation unit can prioritize evaluating content that is highly geographically relevant. It can also postpone the evaluation of content that is less geographically relevant. It can also adjust the evaluation criteria based on the geographical distribution of the content. This improves the accuracy of the evaluation by considering the geographical distribution of the content. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or not using AI. For example, the evaluation unit can input geographical distribution data of the content into a generating AI and have the generating AI perform the adjustment of the evaluation criteria.

[0093] The evaluation unit can improve the accuracy of its evaluation by referring to relevant literature for the content during the evaluation process. For example, the evaluation unit can improve the accuracy of its evaluation by referring to relevant literature. The evaluation unit can also adjust the evaluation criteria based on the relevant literature for the content. Furthermore, the evaluation unit can improve the accuracy of its evaluation by analyzing the relevant literature. For example, the evaluation unit can improve the accuracy of its evaluation by referring to relevant literature. The evaluation unit can also adjust the evaluation criteria based on the relevant literature for the content. The evaluation unit can also improve the accuracy of its evaluation by analyzing the relevant literature. As a result, the accuracy of the evaluation is improved by referring to relevant literature. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input relevant literature data into a generating AI and have the generating AI perform the evaluation accuracy improvement.

[0094] The display unit can estimate the user's emotions and adjust the display method based on the estimated emotions. For example, if the user is relaxed, the display unit can provide a display method that includes detailed information. If the user is in a hurry, the display unit can also provide a concise display method that gets straight to the point. Furthermore, if the user is excited, the display unit can also provide a visually stimulating display method. For example, if the user is relaxed, the display unit can provide a display method that includes detailed information. If the user is in a hurry, the display unit can also provide a concise display method that gets straight to the point. If the user is excited, the display unit can also provide a visually stimulating display method. By adjusting the display method based on the user's emotions, the system can provide the user with the most relevant information. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input user emotion data into a generating AI and have the AI ​​adjust the display method.

[0095] The display unit can select the optimal display method by referring to the user's past operation history when displaying information. For example, the display unit may prioritize display methods that the user has previously preferred to use. The display unit can also select the optimal display method based on the user's past operation history. Furthermore, the display unit can analyze the user's operation history and dynamically adjust the display method. For example, the display unit may prioritize display methods that the user has previously preferred to use. The display unit can also select the optimal display method based on the user's past operation history. The display unit can also analyze the user's operation history and dynamically adjust the display method. This allows the display unit to provide the optimal display method by referring to the user's past operation history. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the user's past operation history data into a generating AI and have the generating AI select the optimal display method.

[0096] The display unit can apply different display algorithms depending on the content category during display. For example, the display unit can apply a text-based display algorithm to news articles. It can also apply an image-based display algorithm to images. Furthermore, it can apply a video-based display algorithm to videos. For example, the display unit can apply a text-based display algorithm to news articles. It can also apply an image-based display algorithm to images. It can also apply a video-based display algorithm to videos. This enables optimal display by applying different display algorithms depending on the content category. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input content category data into a generating AI and have the generating AI select the display algorithm to apply.

[0097] The display unit can estimate the user's emotions and determine the display priority based on the estimated emotions. For example, if the user is relaxed, the display unit will prioritize displaying detailed information. If the user is in a hurry, the display unit can also prioritize displaying concise information. Furthermore, if the user is excited, the display unit can prioritize displaying visually stimulating information. This allows the display unit to provide the user with optimal information by prioritizing the display based on their emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input user emotion data into a generating AI, which can then determine the display priority.

[0098] The display unit can select the optimal display method when displaying information, taking into account the user's device information. For example, if the user is using a smartphone, the display unit provides a display method that matches the screen size. Furthermore, if the user is using a tablet, the display unit can provide a display method optimized for a larger screen. Additionally, if the user is using a smartwatch, the display unit can provide a concise and highly visible display method. This allows the display unit to provide the optimal display method by considering the user's device information. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input user device information data into a generating AI and have the generating AI select the optimal display method.

[0099] The display unit can analyze the user's social media activity and suggest display methods when displaying information. For example, the display unit can display information related to content the user has shared on social media. The display unit can also display information related to topics of interest to the user's followers. Furthermore, the display unit can display information related to content the user has "liked" on social media. For example, the display unit can display information related to content the user has shared on social media. The display unit can also display information related to topics of interest to the user's followers. The display unit can also display information related to content the user has "liked" on social media. This allows the display unit to suggest the optimal display method by analyzing the user's social media activity. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the user's social media activity data into a generating AI and have the generating AI suggest display methods.

[0100] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0101] The data collection unit can estimate the user's emotions and adjust the type of content collected based on the estimated emotions. For example, if the user is relaxed, it can prioritize collecting entertainment content. If the user is focused, it can prioritize collecting academic content. Furthermore, if the user is tired, it can prioritize collecting relaxing content. In this way, by adjusting the type of content collected based on the user's emotions, the system can provide the user with the most suitable content. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI adjust the type of content to be collected.

[0102] The analysis unit can estimate the user's emotions and adjust the level of detail of the analysis based on the estimated emotions. For example, if the user is relaxed, it can provide detailed analysis results. If the user is in a hurry, it can provide concise analysis results that get straight to the point. Furthermore, if the user is excited, it can provide visually stimulating analysis results. In this way, by adjusting the level of detail of the analysis based on the user's emotions, the system can provide the user with the most optimal analysis results. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the level of detail of the analysis.

[0103] The evaluation unit can estimate the user's emotions and adjust the evaluation criteria based on the estimated emotions. For example, if the user is relaxed, detailed evaluation criteria can be applied. If the user is in a hurry, concise evaluation criteria can be applied. Furthermore, if the user is excited, visually stimulating evaluation criteria can be applied. By adjusting the evaluation criteria based on the user's emotions, the system can provide the user with the most optimal evaluation result. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using AI, or not using AI. For example, the evaluation unit can input user emotion data into a generative AI and have the generative AI adjust the evaluation criteria.

[0104] The display unit can estimate the user's emotions and adjust the display method based on the estimated emotions. For example, if the user is relaxed, it can provide a display method that includes detailed information. If the user is in a hurry, it can provide a concise display method that gets straight to the point. Furthermore, if the user is excited, it can provide a visually stimulating display method. In this way, by adjusting the display method based on the user's emotions, the display unit can provide the user with the most appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the display unit may be performed using AI, for example, or not using AI. For example, the display unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the display method.

[0105] The display unit can estimate the user's emotions and determine the display priority based on the estimated emotions. For example, if the user is relaxed, detailed information can be displayed preferentially. If the user is in a hurry, concise information can be displayed preferentially. Furthermore, if the user is excited, visually stimulating information can be displayed preferentially. In this way, by determining the display priority based on the user's emotions, the system can provide the user with the most relevant information. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the display unit may be performed using AI, or not using AI. For example, the display unit can input user emotion data into a generative AI and have the generative AI determine the display priority.

[0106] The data collection unit can analyze the user's past browsing history and select the optimal data collection method. For example, it can prioritize collecting similar content based on the types of content the user has frequently viewed in the past. It can also select content to collect at specific times based on the user's past browsing history. Furthermore, it can collect related content based on content that the user has previously given high ratings to. In this way, the optimal data collection method can be selected by analyzing the user's past browsing history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past browsing history data into a generating AI and have the generating AI select the optimal data collection method.

[0107] The analysis unit can adjust the level of detail of the analysis based on the importance of the content. For example, it can perform a detailed analysis on high-importance content and a concise analysis on low-importance content. Furthermore, it can determine the priority of the analysis according to the importance of the content. This allows for efficient analysis by adjusting the level of detail based on the importance of the content. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input content importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0108] The evaluation unit can perform evaluations while considering the attribute information of the content creator. For example, if the content creator is an expert, it can be given a high rating. Conversely, if the content creator is unreliable, it can be given a low rating. Furthermore, the evaluation criteria can be adjusted based on the attribute information of the content creator. This improves the reliability of the evaluation by considering the attribute information of the content creator. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the attribute information data of the content creator into a generating AI and have the generating AI perform adjustments to the evaluation criteria.

[0109] The display unit can select the optimal display method when displaying information, taking into account the user's device information. For example, if the user is using a smartphone, it can provide a display method that matches the screen size. If the user is using a tablet, it can also provide a display method optimized for a larger screen. Furthermore, if the user is using a smartwatch, it can provide a concise and highly visible display method. In this way, the optimal display method can be provided by taking into account the user's device information. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input user device information data into a generating AI and have the generating AI select the optimal display method.

[0110] The display unit can analyze the user's social media activity and suggest display methods when displaying information. For example, it can display information related to content the user has shared on social media. It can also display information related to topics that the user's followers are interested in. Furthermore, it can display information related to content the user has "liked" on social media. In this way, by analyzing the user's social media activity, the optimal display method can be suggested. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the user's social media activity data into a generating AI and have the generating AI perform the task of suggesting display methods.

[0111] The following briefly describes the processing flow for example form 2.

[0112] Step 1: The collection unit collects content such as news articles, images, and videos. The collection unit collects content from publicly available databases on the internet, and can also obtain data from other systems through user-provided content and APIs. For example, it collects the latest articles from news sites, images from image-sharing sites, and videos from video-sharing sites. Step 2: The analysis unit analyzes the content collected by the collection unit. The analysis unit analyzes news articles using natural language processing technology, images using image recognition technology, and videos using video analysis technology. For example, it analyzes the content of news articles, extracts features of AI-generated content, analyzes image metadata, evaluates the possibility of AI generation, analyzes video frames, and detects traces of AI generation. Step 3: The evaluation unit evaluates the inclusion rate of AI-generated content based on the results analyzed by the analysis unit. The evaluation unit scores the characteristics of the AI-generated content and evaluates the inclusion rate on a scale of 0 to 100 points. It can also perform a comprehensive evaluation by combining multiple evaluation criteria and update the evaluation model based on past data. For example, it can learn the characteristics of AI-generated content, improve the evaluation model, adjust the evaluation criteria based on user feedback, and update the evaluation results in real time. Step 4: The display unit visually displays the results evaluated by the evaluation unit. The display unit can display the evaluation results using color coding, graphs, and icons. For example, the evaluation results can be displayed in red, yellow, and green, as a bar graph, and as a number of stars.

[0113] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0114] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0115] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0116] Each of the multiple elements described above, including the collection unit, analysis unit, evaluation unit, and display unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the collection unit is implemented by the control unit 46A of the smart device 14 and collects content such as news articles, images, and videos. The analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the collected content. The evaluation unit is implemented by the specific processing unit 290 of the data processing device 12 and evaluates the inclusion rate of AI-generated content based on the analysis results. The display unit is implemented by the control unit 46A of the smart device 14 and visually displays the evaluation results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0117] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0118] As shown in Figure 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.

[0119] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0120] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0121] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0123] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0124] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0125] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0126] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0127] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0128] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0129] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0130] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0131] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0132] Each of the multiple elements described above, including the collection unit, analysis unit, evaluation unit, and display unit, is implemented in at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit is implemented by the control unit 46A of the smart glasses 214 and collects content such as news articles, images, and videos. The analysis unit is implemented by the identification processing unit 290 of the data processing device 12 and analyzes the collected content. The evaluation unit is implemented by the identification processing unit 290 of the data processing device 12 and evaluates the inclusion rate of AI-generated content based on the analysis results. The display unit is implemented by the control unit 46A of the smart glasses 214 and visually displays the evaluation results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0133] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

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

[0135] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0136] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0137] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0139] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0140] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0141] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0142] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0143] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0144] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0145] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0146] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0147] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0148] Each of the multiple elements described above, including the collection unit, analysis unit, evaluation unit, and display unit, is implemented in at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit is implemented by the control unit 46A of the headset terminal 314 and collects content such as news articles, images, and videos. The analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the collected content. The evaluation unit is implemented by the specific processing unit 290 of the data processing device 12 and evaluates the inclusion rate of AI-generated content based on the analysis results. The display unit is implemented by the control unit 46A of the headset terminal 314 and visually displays the evaluation results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0149] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0150] As shown in Figure 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.

[0151] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0152] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0153] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0154] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0155] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0156] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0157] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0158] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0159] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0160] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0161] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0162] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0163] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0164] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0165] Each of the multiple elements described above, including the collection unit, analysis unit, evaluation unit, and display unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the robot 414 and collects content such as news articles, images, and videos. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected content. The evaluation unit is implemented by the specific processing unit 290 of the data processing unit 12 and evaluates the inclusion rate of AI-generated content based on the analysis results. The display unit is implemented by the control unit 46A of the robot 414 and visually displays the evaluation results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0166] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0167] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0168] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0169] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0170] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0171] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0172] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0173] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0174] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0175] 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.

[0176] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0177] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0178] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0179] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0180] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0181] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0182] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0183] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0184] (Note 1) The collection department collects content, An analysis unit analyzes the content collected by the aforementioned collection unit, An evaluation unit evaluates the inclusion rate of AI-generated content based on the results of the analysis performed by the aforementioned analysis unit, The system includes a display unit that visually displays the results evaluated by the evaluation unit. A system characterized by the following features. (Note 2) The evaluation unit, The inclusion rate of AI-generated content is evaluated on a scale of 0 to 100 points. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned display unit is The evaluation results are visually displayed using color coding and graphs. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned display unit is It has a function to filter content based on evaluation results. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is Collect product images and videos from shopping and flea market websites. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit, Analyze news articles, short videos, and sports videos created using generation AI. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned display unit is Developed as an SDK for applications, making it available to a wide range of users. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is It estimates user sentiment and adjusts the timing of content collection based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is Analyze the user's past browsing history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting content, filter it based on the user's current areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is It estimates user sentiment and determines the priority of content to collect based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting content, the system prioritizes collecting highly relevant content by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is When collecting content, we analyze users' social media activity and collect relevant content. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the content. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the content category. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the analysis priority is determined based on when the content was created. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the content. The system described in Appendix 1, characterized by the features described herein. (Note 20) The evaluation unit, It estimates the user's emotions and adjusts the evaluation criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The evaluation unit, When evaluating, consider the interrelationships between content to improve the accuracy of the evaluation. The system described in Appendix 1, characterized by the features described herein. (Note 22) The evaluation unit, During the evaluation process, the attribute information of the content creator will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 23) The evaluation unit, It estimates the user's emotions and adjusts the order in which evaluation results are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The evaluation unit, During the evaluation process, the geographical distribution of the content will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 25) The evaluation unit, During evaluation, we refer to relevant literature related to the content to improve the accuracy of the evaluation. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned display unit is It estimates the user's emotions and adjusts the display method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned display unit is When displaying information, the system selects the optimal display method by referring to the user's past operation history. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned display unit is When displaying content, different display algorithms are applied depending on the content category. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned display unit is It estimates the user's emotions and determines the display priority based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned display unit is When displaying content, the system selects the optimal display method by considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned display unit is When displaying content, the system analyzes the user's social media activity and suggests display methods accordingly. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0185] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The collection department collects content, An analysis unit analyzes the content collected by the aforementioned collection unit, An evaluation unit that evaluates the inclusion rate of AI-generated content based on the results analyzed by the aforementioned analysis unit, The system includes a display unit that visually displays the results evaluated by the evaluation unit. A system characterized by the following features.

2. The evaluation unit, The inclusion rate of AI-generated content is evaluated on a scale of 0 to 100 points. The system according to feature 1.

3. The aforementioned display unit is The evaluation results are visually displayed using color coding and graphs. The system according to feature 1.

4. The aforementioned display unit is It has a function to filter content based on evaluation results. The system according to feature 1.

5. The aforementioned collection unit is Collect product images and videos from shopping and flea market websites. The system according to feature 1.

6. The aforementioned analysis unit, Analyze news articles, short videos, and sports videos created using generative AI. The system according to feature 1.

7. The aforementioned display unit is Developed as an SDK for applications, making it available to a wide range of users. The system according to feature 1.

8. The aforementioned collection unit is It estimates user sentiment and adjusts the timing of content collection based on the estimated user sentiment. The system according to feature 1.

Images