system

Abstract

An object of a system according to an embodiment is to efficiently detect malicious content or information manipulation on an SNS platform and take countermeasures.SOLUTION: A system according to an embodiment includes a collection unit, an analysis unit, a countermeasure unit, a feedback unit, and a provision unit. The collection unit collects content on the SNS platform. The analysis unit analyzes the content collected by the collection unit, and finds an unnatural linguistic expression or an alteration trace. The countermeasure unit executes a countermeasure against the malicious content detected by the analysis unit. The feedback unit collects feedback from the user and uses the feedback for machine learning. The providing unit provides an API or a dashboard.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have faced the challenge of making it difficult to efficiently detect malicious content and information manipulation on social media platforms and take countermeasures.

[0005] The system according to the embodiment aims to efficiently detect malicious content and information manipulation on SNS platforms and take countermeasures. [Means for solving the problem]

[0006] A system according to an embodiment includes a collection unit, an analysis unit, a countermeasure unit, a feedback unit, and a provision unit. The collection unit collects content on a social networking platform. The analysis unit analyzes the content collected by the collection unit to detect unnatural language expressions or traces of tampering. The countermeasure unit takes countermeasures against malicious content detected by the analysis unit. The feedback unit collects feedback from users and uses it for machine learning. The provision unit provides an API or dashboard. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently detect malicious content and information manipulation on social media platforms and take countermeasures. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention analyzes content such as text, images, and videos on social media platforms to automatically detect accounts and posts suspected of manipulating information. The system utilizes generative AI to detect unnatural language and signs of manipulation, helping to identify spam accounts. It also automates countermeasures against malicious content (e.g., displaying warnings, deleting content, and suspending accounts), reducing operational costs. Furthermore, accuracy can be continuously improved by utilizing user feedback in machine learning. For example, the system collects content such as text, images, and videos on social media platforms. Next, generative AI analyzes this content to detect unnatural language and signs of manipulation. Furthermore, it automates countermeasures against detected malicious content. For example, it implements specific procedures and conditions for displaying warnings, deleting content, and suspending accounts. Furthermore, user feedback is collected and utilized in machine learning to improve the accuracy of the system. This enables the system to efficiently collect, analyze, take countermeasures, and collect and provide feedback on content on social media platforms. For example, the system can use generative AI to accurately detect unnatural language and signs of manipulation, and automate countermeasures against malicious content. Furthermore, by incorporating user feedback into machine learning, the system's accuracy can be continuously improved.

[0029] An information manipulation detection system according to an embodiment includes a collection unit, an analysis unit, a countermeasure unit, a feedback unit, and a provision unit. The collection unit collects content on a social media platform. The collection unit collects data from the social media platform using, for example, an API. The collection unit can also estimate a user's emotions and adjust the timing of content collection based on the estimated user emotions. For example, if a user is feeling stressed, the collection timing can be delayed to reduce the user's burden. The analysis unit uses generative AI to analyze the content collected by the collection unit and detect unnatural language expressions and traces of tampering. For example, natural language processing technology or image analysis technology can be used to detect unnatural parts of text or images. The countermeasure unit takes countermeasures against malicious content detected by the analysis unit. For example, specific procedures and conditions can be implemented for displaying a warning, deleting the content, or suspending the account. The feedback unit collects user feedback and utilizes it for machine learning. For example, inappropriate content reported by users can be collected and used as training data for a machine learning model. The provision unit provides an API and a dashboard. For example, system functions can be provided through an API and the system status can be monitored through a dashboard. This enables the information manipulation detection system according to the embodiment to efficiently collect, analyze, take countermeasures, and collect and provide feedback on content on social media platforms.

[0030] The collection unit can collect data from the SNS platform using an API. The API provides, for example, a data acquisition endpoint and an authentication method. The collection unit can collect, for example, text data from the SNS platform using the API. The collection unit can also collect image data using the API. Furthermore, the collection unit can also collect video data using the API. This allows efficient data collection from the SNS platform by using the API. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the data collected using the API into a generation AI and have the generation AI analyze the data.

[0031] The analysis unit can detect unnatural parts of text or images using natural language processing technology or image analysis technology. Natural language processing technology includes, for example, morphological analysis, grammatical analysis, semantic analysis, and other technologies. The analysis unit can, for example, use morphological analysis to segment words in text and detect unnatural word usage. The analysis unit can also use grammatical analysis to analyze sentence structure and detect unnatural grammatical errors. The analysis unit can also use semantic analysis to analyze the meaning of sentences and detect out-of-context word usage. Image analysis technology includes, for example, facial recognition and object detection. The analysis unit can, for example, use facial recognition technology to detect faces in images and discover unnatural traces of facial tampering. The analysis unit can also use object detection technology to detect objects in images and discover unnatural object placement. As a result, unnatural parts of text or images can be detected with high accuracy using natural language processing technology or image analysis technology. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generative AI, or may be performed without using a generative AI. For example, the analysis unit can input text data or image data into the generation AI and have the generation AI detect unnatural parts.

[0032] The countermeasure unit can execute specific procedures or conditions for displaying a warning, deleting content, or suspending an account. Displaying a warning can be performed, for example, using a pop-up message or a notification. The countermeasure unit can warn the user, for example, by displaying a pop-up message. The countermeasure unit can also warn the user by sending a notification. Deletion can be performed, for example, by completely deleting or temporarily hiding content. The countermeasure unit can, for example, completely delete malicious content. The countermeasure unit can also temporarily hide malicious content. Suspending an account can be performed, for example, by temporarily suspending or permanently suspending the account. The countermeasure unit can, for example, temporarily suspend malicious accounts. The countermeasure unit can also permanently suspend malicious accounts. By executing specific procedures and conditions for displaying a warning, deleting content, or suspending an account, countermeasures against malicious content can be automated. Some or all of the above-described processing in the countermeasure unit may be performed, for example, using AI, or may be performed without using AI. For example, the countermeasure unit can input detected malicious content into the generation AI and have the generation AI execute countermeasures.

[0033] The feedback unit can collect inappropriate content reported by users and use it as training data for the machine learning model. Inappropriate content includes, for example, violent content and discriminatory content. For example, the feedback unit can collect violent content reported by users. The feedback unit can also collect discriminatory content reported by users. The feedback unit uses the collected inappropriate content as training data for the machine learning model. For example, the feedback unit can use the collected violent content as training data for the machine learning model. The feedback unit can also use the collected discriminatory content as training data for the machine learning model. In this way, by collecting feedback from users and using it as training data for the machine learning model, the accuracy of the system can be continuously improved. Some or all of the above-mentioned processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input inappropriate content reported by users to a generation AI and cause the generation AI to generate training data.

[0034] The providing unit can provide system functions through an API and monitor the system status through a dashboard. The API provides, for example, a data acquisition endpoint and an authentication method. The providing unit can provide system functions through an API. The providing unit can also monitor the system status through a dashboard. The dashboard provides, for example, real-time data display and a filtering function. The providing unit can provide, for example, a dashboard that displays real-time data. The providing unit can also provide a dashboard with a filtering function. This allows the platform operator to efficiently manage the system by providing system functions through an API and monitoring the system status through the dashboard. Some or all of the above-mentioned processing in the providing unit may be performed using, or without, AI, for example. For example, the providing unit can input data provided through the API to a generation AI and have the generation AI analyze the data.

[0035] The collection unit can analyze the usage status of the SNS platform and select an appropriate collection method. The collection unit can, for example, analyze peak hours of the SNS platform and intensively collect data during those hours. The collection unit can also analyze the user demographics of the SNS platform and preferentially collect data from specific user groups. Furthermore, the collection unit can analyze the frequency of posts on the SNS platform and collect data during hours with a high number of posts. In this way, by analyzing the usage status of the SNS platform, the optimal collection method can be selected and data can be collected efficiently. Some or all of the above-mentioned processing in the collection unit can be performed, for example, using AI or without AI. For example, the collection unit can input usage data of the SNS platform into a generation AI and have the generation AI select the optimal collection method.

[0036] When collecting content, the collection unit can filter based on specific keywords or hashtags. For example, the collection unit can preferentially collect posts that include specific keywords (e.g., fake news). The collection unit can also filter and collect posts that include specific hashtags (e.g., #propaganda). Furthermore, the collection unit can collect highly relevant posts using a combination of keywords and hashtags. This makes it possible to efficiently collect highly relevant posts by filtering based on specific keywords or hashtags. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input specific keywords or hashtags into a generation AI and have the generation AI perform filtering.

[0037] When collecting content, the collection unit can select an appropriate collection means depending on the user's input method. For example, if the user uses voice input, the collection unit can prioritize collecting voice data. Furthermore, if the user uses text input, the collection unit can also prioritize collecting text data. Furthermore, if the user posts an image, the collection unit can also prioritize collecting image data. This allows for efficient data collection by selecting the optimal collection means depending on the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input user input method data into a generation AI and cause the generation AI to select the optimal collection means.

[0038] When collecting content, the collection unit can prioritize collecting highly relevant content by taking into account the user's geographical location information. For example, the collection unit can prioritize collecting 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, the collection unit can collect information on nearby stores and services based on the user's location information. This makes it possible to efficiently collect highly relevant content by taking the user's geographical location information into consideration. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location information to the generation AI and cause the generation AI to collect highly relevant content.

[0039] When collecting content, the collection unit can analyze the user's social media activity and collect related content. For example, the collection unit can prioritize collecting posts on which the user frequently comments. The collection unit can also collect content that the user frequently shares. Furthermore, the collection unit can prioritize collecting posts from accounts the user follows. This allows for efficient collection of related content by analyzing the user's social media activity. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's social media activity data into the generation AI and cause the generation AI to collect related content.

[0040] When collecting content, the collection unit can customize the collection method by reflecting the user's past feedback. For example, the collection unit can prioritize collecting content that the user has previously rated highly. The collection unit can also avoid collecting inappropriate content that the user has previously reported. Furthermore, the collection unit can adjust the categories of content to be collected based on the user's past feedback. In this way, the collection method can be customized by reflecting the user's past feedback, and data can be collected efficiently. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the collection method.

[0041] During analysis, 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 content with high importance. The analysis unit can also perform a simplified analysis on content with low importance. Furthermore, the analysis unit can adjust the depth 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-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input content importance data into the generation AI and have the generation AI adjust the level of detail of the analysis.

[0042] During analysis, the analysis unit can apply different analysis algorithms depending on the content category. For example, the analysis unit can apply a natural language processing algorithm to text content. The analysis unit can also apply an image analysis algorithm to image content. Furthermore, the analysis unit can also apply a video analysis algorithm to video content. This allows for efficient analysis by applying different analysis algorithms depending on the content category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input text data, image data, and video data into the generation AI and have the generation AI perform an analysis according to each category.

[0043] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit can perform highly accurate analysis of similar content based on the user's past analysis results. The analysis unit can also adjust the analysis algorithm by referring to the user's past feedback. Furthermore, the analysis unit can use the user's past analysis results as learning data to improve the analysis model. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0044] During analysis, the analysis unit can determine the priority of analysis based on the time when the content was posted. For example, the analysis unit can prioritize the analysis of the most recent posts. The analysis unit can also prioritize the analysis of content posted within a specific period. Furthermore, when analyzing past posts, the analysis unit can determine the priority according to importance. This allows for efficient analysis by determining the priority of analysis based on the time when the content was posted. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the analysis unit can input data on the time when the content was posted into the generation AI and have the generation AI determine the priority of analysis.

[0045] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the content. For example, the analysis unit can prioritize analysis of highly relevant content. The analysis unit can also postpone analysis of less relevant content. Furthermore, the analysis unit can dynamically adjust the order of analysis according to 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-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input content relevance data into the generation AI and have the generation AI adjust the order of analysis.

[0046] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, the analysis unit can provide analysis results that use a lot of technical terminology to a user with high level of expertise. The analysis unit can also provide analysis results that are explained in simple terms to a user with low level of expertise. Furthermore, the analysis unit can adjust the way the analysis results are expressed according to the user's level of expertise. This makes it possible to provide analysis results that are easy for the user to understand by adjusting the use of technical terminology according to the user's level of expertise. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and have the generation AI adjust the use of technical terminology.

[0047] When taking countermeasures, the countermeasure unit can adjust the level of detail of the countermeasures based on the maliciousness of the content. For example, the countermeasure unit can implement strict countermeasures against highly malicious content. The countermeasure unit can also implement minor countermeasures against less malicious content. Furthermore, the countermeasure unit can adjust the level of detail of the countermeasures according to the maliciousness of the content. This allows countermeasures to be taken efficiently by adjusting the level of detail of the countermeasures based on the maliciousness of the content. Some or all of the above-mentioned processing in the countermeasure unit may be performed using, for example, AI, or may be performed without using AI. For example, the countermeasure unit can input maliciousness data of the content to a generation AI and cause the generation AI to adjust the level of detail of the countermeasures.

[0048] When taking countermeasures, the countermeasure unit can apply different countermeasure algorithms depending on the content category. For example, the countermeasure unit can display a warning if text content contains specific keywords. The countermeasure unit can also delete image content if there are signs of tampering. The countermeasure unit can also suspend an account if video content contains inappropriate content. This allows countermeasures to be taken efficiently by applying different countermeasure algorithms depending on the content category. Some or all of the above-mentioned processing in the countermeasure unit may be performed using, for example, AI, or may be performed without using AI. For example, the countermeasure unit can input text data, image data, and video data into a generation AI and have the generation AI execute countermeasures according to each category.

[0049] When taking a countermeasure, the countermeasure unit can improve the accuracy of the countermeasure by referring to the user's past countermeasure results. For example, the countermeasure unit can take highly accurate countermeasures against similar content based on the user's past countermeasure results. The countermeasure unit can also adjust the countermeasure algorithm by referring to the user's past feedback. Furthermore, the countermeasure unit can use the user's past countermeasure results as learning data to improve the countermeasure model. In this way, the accuracy of the countermeasure can be improved by referring to the user's past countermeasure results. Some or all of the above-mentioned processing in the countermeasure unit may be performed using, for example, AI, or may be performed without using AI. For example, the countermeasure unit can input the user's past countermeasure result data into the generation AI and cause the generation AI to improve the accuracy of the countermeasure.

[0050] When taking countermeasures, the countermeasure unit can determine the priority of the countermeasures based on the time when the content was posted. The countermeasure unit can, for example, quickly implement countermeasures against the most recent posts. The countermeasure unit can also prioritize countermeasures against content posted within a specific period of time. Furthermore, the countermeasure unit can determine the priority of countermeasures against older posts according to their importance. This allows countermeasures to be taken efficiently by determining the priority of countermeasures based on the time when the content was posted. Some or all of the above-described processing in the countermeasure unit may be performed using, for example, AI, or may be performed without using AI. For example, the countermeasure unit can input data on the time when the content was posted to a generation AI and have the generation AI determine the priority of countermeasures.

[0051] When taking countermeasures, the countermeasure unit can adjust the order of countermeasures based on the relevance of the content. For example, the countermeasure unit can prioritize the execution of countermeasures against highly relevant content. The countermeasure unit can also postpone the execution of countermeasures against less relevant content. Furthermore, the countermeasure unit can dynamically adjust the order of countermeasures according to the relevance of the content. This allows countermeasures to be taken efficiently by adjusting the order of countermeasures based on the relevance of the content. Some or all of the above-described processing in the countermeasure unit may be performed using, for example, AI, or may be performed without using AI. For example, the countermeasure unit can input content relevance data to a generation AI and cause the generation AI to adjust the order of countermeasures.

[0052] When taking a countermeasure, the countermeasure unit can adjust the use of technical terminology in the countermeasure according to the user's level of expertise. For example, the countermeasure unit can provide a countermeasure that uses a lot of technical terminology to a user with high technical expertise. The countermeasure unit can also provide a countermeasure explained in simple language to a user with low technical expertise. Furthermore, the countermeasure unit can adjust the way the countermeasure is expressed according to the user's level of expertise. This makes it possible to provide a countermeasure that is easy for the user to understand by adjusting the use of technical terminology according to the user's level of expertise. Some or all of the above-mentioned processing in the countermeasure unit may be performed using, for example, AI, or may be performed without using AI. For example, the countermeasure unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0053] When collecting feedback, the feedback unit can select an appropriate collection method by referring to the user's past feedback history. For example, the feedback unit can preferentially provide feedback formats that the user has used favorably in the past. The feedback unit can also suggest optimal question content based on the user's past feedback history. Furthermore, the feedback unit can also customize the collection method by referring to the user's past feedback history. In this way, by referring to the user's past feedback history, the optimal collection method can be selected and feedback can be collected efficiently. Some or all of the above-mentioned processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the user's past feedback history data into a generation AI and cause the generation AI to select a collection method.

[0054] The feedback unit can customize the feedback means based on the user's current situation when collecting feedback. For example, if the user is on the move, the feedback unit can collect feedback using voice input. If the user is using a desktop, the feedback unit can also collect feedback using text input. Furthermore, if the user is using a smartphone, the feedback unit can also collect feedback using touch operations. This allows for efficient feedback collection by customizing the feedback means based on the user's current situation. Some or all of the above-described processing in the feedback unit can be performed using AI, for example, or without AI. For example, the feedback unit can input the user's current situation data to the generation AI and cause the generation AI to customize the feedback means.

[0055] When collecting feedback, the feedback unit can select an appropriate collection method by taking into account the user's geographical location information. For example, if the user is in a specific area, the feedback unit can prioritize collecting questions related to that area. The feedback unit can also collect feedback regarding issues specific to the area based on the user's location information. Furthermore, the feedback unit can select an optimal collection method by referring to the user's geographical location information. In this way, by taking the user's geographical location information into consideration, the optimal collection method can be selected and feedback can be collected efficiently. Some or all of the above-mentioned processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the user's geographical location information data into the generation AI and cause the generation AI to select a collection method.

[0056] When collecting feedback, the feedback unit can analyze the user's social media activity and suggest a means of feedback. For example, the feedback unit can suggest feedback questions based on words frequently used by the user on social media. The feedback unit can also analyze the user's social media activity and suggest an optimal feedback format. Furthermore, the feedback unit can customize the means of feedback by referring to the content of the user's social media posts. In this way, by analyzing the user's social media activity, the optimal means of feedback can be suggested and feedback can be collected efficiently. Some or all of the above-mentioned processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the user's social media activity data into a generation AI and have the generation AI suggest a means of feedback.

[0057] When providing an API or dashboard, the providing unit can select the optimal delivery method by referring to the user's past usage history. For example, the providing unit can preferentially provide a dashboard format that the user has used favorably in the past. The providing unit can also suggest the optimal API endpoint based on the user's past usage history. Furthermore, the providing unit can also customize the delivery method by referring to the user's past usage history. This makes it possible to select the optimal delivery method and provide information efficiently by referring to the user's past usage history. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past usage history data into the generation AI and have the generation AI select the delivery method.

[0058] The providing unit can customize the content to be provided based on the user's current needs when providing an API or dashboard. For example, the providing unit can prioritize providing information currently required by the user. The providing unit can also adjust the dashboard layout according to the user's current needs. Furthermore, the providing unit can provide an optimal API endpoint based on the user's current needs. This allows for efficient information provision by customizing the content to be provided based on the user's current needs. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's current needs data into a generation AI and cause the generation AI to customize the content to be provided.

[0059] The providing unit can select an appropriate delivery method by taking into account the user's device information when providing an API or dashboard. For example, if the user is using a smartphone, the providing unit can provide a dashboard that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can provide a dashboard optimized for a large screen. Furthermore, if the user is using a desktop, the providing unit can provide a dashboard that includes detailed information. This allows the optimal delivery method to be selected by taking into account the user's device information, and information to be provided efficiently. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's device information data into the generation AI and cause the generation AI to select a delivery method.

[0060] The providing unit can select the optimal delivery method by taking into account the user's device information when providing an API or dashboard. For example, if the user is using a smartphone, the providing unit can provide an API endpoint that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can provide an API endpoint that is optimized for a large screen. Furthermore, if the user is using a desktop, the providing unit can provide an API endpoint that includes detailed information. This allows the optimal delivery method to be selected and information to be provided efficiently by taking into account the user's device information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's device information data into the generation AI and cause the generation AI to select the delivery method.

[0061] The providing unit can provide the API or dashboard in multiple languages ​​according to the user's language settings when providing the API or dashboard. The providing unit can automatically set the language of the API or dashboard based on, for example, the language setting of the user's device. The providing unit can also provide a language switching function when the user uses multiple languages. Furthermore, when the user selects a specific language, the providing unit can provide the API or dashboard in that language. This allows for efficient information provision by providing the content in multiple languages ​​according to the user's language setting. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's language setting data into a generation AI and cause the generation AI to provide multilingual support.

[0062] The providing unit can analyze the user's social media activity and provide related information when providing an API or dashboard. For example, the providing unit can provide information about places where the user has checked in on social media. The providing unit can also analyze the content of the user's social media posts and provide information about related tourist spots and stores. Furthermore, the providing unit can provide information about related places and events by referring to the activities of the user's friends on social media. This allows for efficient provision of related information by analyzing the user's social media activity. Some or all of the above-described processing by the providing unit may be performed using, or without, AI. For example, the providing unit can input the user's social media activity data into a generation AI and cause the generation AI to provide related information.

[0063] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0064] The collection unit can analyze the user's past behavior history and determine the priority of content to be collected. For example, it can prioritize collection of content that the user has frequently viewed in the past. It can also prioritize collection of content that the user has given high ratings in the past. It can also prioritize collection of content that the user has shared in the past. In this way, by determining the priority of content to be collected based on the user's past behavior history, it is possible to efficiently collect content that is highly relevant to the user.

[0065] The analysis unit can refer to an external reliability evaluation database to evaluate the reliability of content. For example, the analysis unit can evaluate the reliability of content by referring to a database of reliable news sources. The analysis unit can also evaluate the reliability of scientific content by referring to a database of academic papers. The analysis unit can also evaluate the reliability of content by referring to user ratings and reviews. In this way, by referring to an external reliability evaluation database, the reliability of content can be evaluated with high accuracy.

[0066] The countermeasure unit can analyze the user's behavioral patterns and optimize the timing of the countermeasure. For example, the countermeasure can be executed during the time period when the user is most active. The countermeasure unit can also analyze the user's reactions when countermeasures were applied in the past to determine the optimal timing. Furthermore, the countermeasure timing can be dynamically adjusted based on the user's behavioral patterns. In this way, by analyzing the user's behavioral patterns, the timing of the countermeasure can be optimized and effective countermeasures can be executed.

[0067] The feedback unit can analyze user feedback in real time and immediately reflect it in system improvements. For example, it can instantly analyze a defect reported by a user and correct the system. It can also instantly add new features or improve existing features based on user feedback. Furthermore, analyzing user feedback in real time can also continuously improve system performance. This allows user feedback to be analyzed in real time and immediately reflected in system improvements, thereby increasing user satisfaction.

[0068] The providing unit can adjust the format of the content to be provided taking into account the remaining battery level of the user's device. For example, when the remaining battery level is low, lightweight text-format content can be provided preferentially. Also, when the remaining battery level is sufficient, rich media-format content can be provided. Furthermore, the content download speed can be adjusted according to the remaining battery level. In this way, the optimal content format can be provided by taking into account the remaining battery level of the user's device, thereby improving user convenience.

[0069] The processing flow of the first embodiment will be briefly explained below.

[0070] Step 1: The collection unit collects content on the SNS platform. The collection unit collects data from the SNS platform using, for example, an API. The collection unit can also estimate the user's emotions and adjust the timing of content collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection timing can be delayed to reduce the burden on the user. Step 2: The analysis unit uses generative AI to analyze the content collected by the collection unit and detect unnatural language expressions and traces of tampering. For example, natural language processing technology and image analysis technology can be used to detect unnatural parts of text and images. Step 3: The countermeasures department takes countermeasures against malicious content detected by the analysis department. For example, it can implement specific procedures and conditions for displaying a warning, deleting the content, or suspending the account. Step 4: The feedback unit collects user feedback and uses it for machine learning. For example, inappropriate content reported by users can be collected and used as training data for machine learning models. Step 5: The provider provides APIs and dashboards. For example, the system functions can be provided through APIs and the system status can be monitored through dashboards.

[0071] (Example 2) A system according to an embodiment of the present invention analyzes content such as text, images, and videos on social media platforms to automatically detect accounts and posts suspected of manipulating information. The system utilizes generative AI to detect unnatural language and signs of manipulation, helping to identify spam accounts. It also automates countermeasures against malicious content (e.g., displaying warnings, deleting content, and suspending accounts), reducing operational costs. Furthermore, accuracy can be continuously improved by utilizing user feedback in machine learning. For example, the system collects content such as text, images, and videos on social media platforms. Next, generative AI analyzes this content to detect unnatural language and signs of manipulation. Furthermore, it automates countermeasures against detected malicious content. For example, it implements specific procedures and conditions for displaying warnings, deleting content, and suspending accounts. Furthermore, user feedback is collected and utilized in machine learning to improve the accuracy of the system. This enables the system to efficiently collect, analyze, take countermeasures, and collect and provide feedback on content on social media platforms. For example, the system can use generative AI to accurately detect unnatural language and signs of manipulation, and automate countermeasures against malicious content. Furthermore, by incorporating user feedback into machine learning, the system's accuracy can be continuously improved.

[0072] An information manipulation detection system according to an embodiment includes a collection unit, an analysis unit, a countermeasure unit, a feedback unit, and a provision unit. The collection unit collects content on a social media platform. The collection unit collects data from the social media platform using, for example, an API. The collection unit can also estimate a user's emotions and adjust the timing of content collection based on the estimated user emotions. For example, if a user is feeling stressed, the collection timing can be delayed to reduce the user's burden. The analysis unit uses generative AI to analyze the content collected by the collection unit and detect unnatural language expressions and traces of tampering. For example, natural language processing technology or image analysis technology can be used to detect unnatural parts of text or images. The countermeasure unit takes countermeasures against malicious content detected by the analysis unit. For example, specific procedures and conditions can be implemented for displaying a warning, deleting the content, or suspending the account. The feedback unit collects user feedback and utilizes it for machine learning. For example, inappropriate content reported by users can be collected and used as training data for a machine learning model. The provision unit provides an API and a dashboard. For example, system functions can be provided through an API and the system status can be monitored through a dashboard. This enables the information manipulation detection system according to the embodiment to efficiently collect, analyze, take countermeasures, and collect and provide feedback on content on social media platforms.

[0073] The collection unit can collect data from the SNS platform using an API. The API provides, for example, a data acquisition endpoint and an authentication method. The collection unit can collect, for example, text data from the SNS platform using the API. The collection unit can also collect image data using the API. Furthermore, the collection unit can also collect video data using the API. This allows efficient data collection from the SNS platform by using the API. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the data collected using the API into a generation AI and have the generation AI analyze the data.

[0074] The analysis unit can detect unnatural parts of text or images using natural language processing technology or image analysis technology. Natural language processing technology includes, for example, morphological analysis, grammatical analysis, semantic analysis, and other technologies. The analysis unit can, for example, use morphological analysis to segment words in text and detect unnatural word usage. The analysis unit can also use grammatical analysis to analyze sentence structure and detect unnatural grammatical errors. The analysis unit can also use semantic analysis to analyze the meaning of sentences and detect out-of-context word usage. Image analysis technology includes, for example, facial recognition and object detection. The analysis unit can, for example, use facial recognition technology to detect faces in images and discover unnatural traces of facial tampering. The analysis unit can also use object detection technology to detect objects in images and discover unnatural object placement. As a result, unnatural parts of text or images can be detected with high accuracy using natural language processing technology or image analysis technology. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generative AI, or may be performed without using a generative AI. For example, the analysis unit can input text data or image data into the generation AI and have the generation AI detect unnatural parts.

[0075] The countermeasure unit can execute specific procedures or conditions for displaying a warning, deleting content, or suspending an account. Displaying a warning can be performed, for example, using a pop-up message or a notification. The countermeasure unit can warn the user, for example, by displaying a pop-up message. The countermeasure unit can also warn the user by sending a notification. Deletion can be performed, for example, by completely deleting or temporarily hiding content. The countermeasure unit can, for example, completely delete malicious content. The countermeasure unit can also temporarily hide malicious content. Suspending an account can be performed, for example, by temporarily suspending or permanently suspending the account. The countermeasure unit can, for example, temporarily suspend malicious accounts. The countermeasure unit can also permanently suspend malicious accounts. By executing specific procedures and conditions for displaying a warning, deleting content, or suspending an account, countermeasures against malicious content can be automated. Some or all of the above-described processing in the countermeasure unit may be performed, for example, using AI, or may be performed without using AI. For example, the countermeasure unit can input detected malicious content into the generation AI and have the generation AI execute countermeasures.

[0076] The feedback unit can collect inappropriate content reported by users and use it as training data for the machine learning model. Inappropriate content includes, for example, violent content and discriminatory content. For example, the feedback unit can collect violent content reported by users. The feedback unit can also collect discriminatory content reported by users. The feedback unit uses the collected inappropriate content as training data for the machine learning model. For example, the feedback unit can use the collected violent content as training data for the machine learning model. The feedback unit can also use the collected discriminatory content as training data for the machine learning model. In this way, by collecting feedback from users and using it as training data for the machine learning model, the accuracy of the system can be continuously improved. Some or all of the above-mentioned processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input inappropriate content reported by users to a generation AI and cause the generation AI to generate training data.

[0077] The providing unit can provide system functions through an API and monitor the system status through a dashboard. The API provides, for example, a data acquisition endpoint and an authentication method. The providing unit can provide system functions through an API. The providing unit can also monitor the system status through a dashboard. The dashboard provides, for example, real-time data display and a filtering function. The providing unit can provide, for example, a dashboard that displays real-time data. The providing unit can also provide a dashboard with a filtering function. This allows the platform operator to efficiently manage the system by providing system functions through an API and monitoring the system status through the dashboard. Some or all of the above-mentioned processing in the providing unit may be performed using, or without, AI, for example. For example, the providing unit can input data provided through the API to a generation AI and have the generation AI analyze the data.

[0078] The collection unit can estimate the user's emotions and adjust the timing of content collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit can delay the collection timing to reduce the user's burden. Furthermore, if the user is relaxed, the collection unit can also accelerate the collection timing to efficiently collect data. Furthermore, if the user is in a hurry, the collection unit can optimize the collection timing to quickly collect data. This reduces the user's burden and enables efficient data collection by adjusting the collection timing based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI adjust the collection timing.

[0079] The collection unit can analyze the usage status of the SNS platform and select an appropriate collection method. The collection unit can, for example, analyze peak hours of the SNS platform and intensively collect data during those hours. The collection unit can also analyze the user demographics of the SNS platform and preferentially collect data from specific user groups. Furthermore, the collection unit can analyze the frequency of posts on the SNS platform and collect data during hours with a high number of posts. In this way, by analyzing the usage status of the SNS platform, the optimal collection method can be selected and data can be collected efficiently. Some or all of the above-mentioned processing in the collection unit can be performed, for example, using AI or without AI. For example, the collection unit can input usage data of the SNS platform into a generation AI and have the generation AI select the optimal collection method.

[0080] When collecting content, the collection unit can filter based on specific keywords or hashtags. For example, the collection unit can preferentially collect posts that include specific keywords (e.g., fake news). The collection unit can also filter and collect posts that include specific hashtags (e.g., #propaganda). Furthermore, the collection unit can collect highly relevant posts using a combination of keywords and hashtags. This makes it possible to efficiently collect highly relevant posts by filtering based on specific keywords or hashtags. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input specific keywords or hashtags into a generation AI and have the generation AI perform filtering.

[0081] When collecting content, the collection unit can select an appropriate collection means depending on the user's input method. For example, if the user uses voice input, the collection unit can prioritize collecting voice data. Furthermore, if the user uses text input, the collection unit can also prioritize collecting text data. Furthermore, if the user posts an image, the collection unit can also prioritize collecting image data. This allows for efficient data collection by selecting the optimal collection means depending on the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input user input method data into a generation AI and cause the generation AI to select the optimal collection means.

[0082] The collection unit can estimate the user's emotions and prioritize the content to be collected based on the estimated user emotions. For example, if the user is feeling anxious, the collection unit can prioritize collecting content that alleviates anxiety. Furthermore, if the user is excited, the collection unit can prioritize collecting content that reduces excitement. Furthermore, if the user is relaxed, the collection unit can prioritize collecting content that maintains relaxation. By prioritizing the content to be collected based on the user's emotions, optimal content for the user can be collected. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of the content to be collected.

[0083] When collecting content, the collection unit can prioritize collecting highly relevant content by taking into account the user's geographical location information. For example, the collection unit can prioritize collecting 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, the collection unit can collect information on nearby stores and services based on the user's location information. This makes it possible to efficiently collect highly relevant content by taking the user's geographical location information into consideration. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location information to the generation AI and cause the generation AI to collect highly relevant content.

[0084] When collecting content, the collection unit can analyze the user's social media activity and collect related content. For example, the collection unit can prioritize collecting posts on which the user frequently comments. The collection unit can also collect content that the user frequently shares. Furthermore, the collection unit can prioritize collecting posts from accounts the user follows. This allows for efficient collection of related content by analyzing the user's social media activity. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's social media activity data into the generation AI and cause the generation AI to collect related content.

[0085] When collecting content, the collection unit can customize the collection method by reflecting the user's past feedback. For example, the collection unit can prioritize collecting content that the user has previously rated highly. The collection unit can also avoid collecting inappropriate content that the user has previously reported. Furthermore, the collection unit can adjust the categories of content to be collected based on the user's past feedback. In this way, the collection method can be customized by reflecting the user's past feedback, and data can be collected efficiently. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the collection method.

[0086] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user's emotions. For example, if the user is nervous, the analysis unit can provide simple, highly visible analysis results. Furthermore, if the user is relaxed, the analysis unit can provide detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can provide concise analysis results that focus on the main points. By adjusting the presentation method of the analysis based on the user's emotions, optimal analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the presentation method of the analysis.

[0087] During analysis, 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 content with high importance. The analysis unit can also perform a simplified analysis on content with low importance. Furthermore, the analysis unit can adjust the depth 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-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input content importance data into the generation AI and have the generation AI adjust the level of detail of the analysis.

[0088] During analysis, the analysis unit can apply different analysis algorithms depending on the content category. For example, the analysis unit can apply a natural language processing algorithm to text content. The analysis unit can also apply an image analysis algorithm to image content. Furthermore, the analysis unit can also apply a video analysis algorithm to video content. This allows for efficient analysis by applying different analysis algorithms depending on the content category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input text data, image data, and video data into the generation AI and have the generation AI perform an analysis according to each category.

[0089] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit can perform highly accurate analysis of similar content based on the user's past analysis results. The analysis unit can also adjust the analysis algorithm by referring to the user's past feedback. Furthermore, the analysis unit can use the user's past analysis results as learning data to improve the analysis model. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0090] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis result. If the user is relaxed, the analysis unit can provide a longer analysis result with detailed explanations. If the user is excited, the analysis unit can provide an analysis result with visually stimulating effects. By adjusting the length of the analysis based on the user's emotions, optimal analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, the generation AI, or without the generation AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the length of the analysis.

[0091] During analysis, the analysis unit can determine the priority of analysis based on the time when the content was posted. For example, the analysis unit can prioritize the analysis of the most recent posts. The analysis unit can also prioritize the analysis of content posted within a specific period. Furthermore, when analyzing past posts, the analysis unit can determine the priority according to importance. This allows for efficient analysis by determining the priority of analysis based on the time when the content was posted. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the analysis unit can input data on the time when the content was posted into the generation AI and have the generation AI determine the priority of analysis.

[0092] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the content. For example, the analysis unit can prioritize analysis of highly relevant content. The analysis unit can also postpone analysis of less relevant content. Furthermore, the analysis unit can dynamically adjust the order of analysis according to 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-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input content relevance data into the generation AI and have the generation AI adjust the order of analysis.

[0093] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, the analysis unit can provide analysis results that use a lot of technical terminology to a user with high level of expertise. The analysis unit can also provide analysis results that are explained in simple terms to a user with low level of expertise. Furthermore, the analysis unit can adjust the way the analysis results are expressed according to the user's level of expertise. This makes it possible to provide analysis results that are easy for the user to understand by adjusting the use of technical terminology according to the user's level of expertise. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and have the generation AI adjust the use of technical terminology.

[0094] The countermeasure unit can estimate the user's emotions and adjust the countermeasure method based on the estimated user's emotions. For example, if the user is angry, the countermeasure unit can suggest a mild countermeasure. Furthermore, if the user is relaxed, the countermeasure unit can suggest an aggressive countermeasure. Furthermore, if the user is feeling anxious, the countermeasure unit can suggest a countermeasure that provides a sense of security. By adjusting the countermeasure method based on the user's emotions, the optimal countermeasure can be provided for the user. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the countermeasure unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the countermeasure unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the countermeasure method.

[0095] When taking countermeasures, the countermeasure unit can adjust the level of detail of the countermeasures based on the maliciousness of the content. For example, the countermeasure unit can implement strict countermeasures against highly malicious content. The countermeasure unit can also implement minor countermeasures against less malicious content. Furthermore, the countermeasure unit can adjust the level of detail of the countermeasures according to the maliciousness of the content. This allows countermeasures to be taken efficiently by adjusting the level of detail of the countermeasures based on the maliciousness of the content. Some or all of the above-mentioned processing in the countermeasure unit may be performed using, for example, AI, or may be performed without using AI. For example, the countermeasure unit can input maliciousness data of the content to a generation AI and cause the generation AI to adjust the level of detail of the countermeasures.

[0096] When taking countermeasures, the countermeasure unit can apply different countermeasure algorithms depending on the content category. For example, the countermeasure unit can display a warning if text content contains specific keywords. The countermeasure unit can also delete image content if there are signs of tampering. The countermeasure unit can also suspend an account if video content contains inappropriate content. This allows countermeasures to be taken efficiently by applying different countermeasure algorithms depending on the content category. Some or all of the above-mentioned processing in the countermeasure unit may be performed using, for example, AI, or may be performed without using AI. For example, the countermeasure unit can input text data, image data, and video data into a generation AI and have the generation AI execute countermeasures according to each category.

[0097] When taking a countermeasure, the countermeasure unit can improve the accuracy of the countermeasure by referring to the user's past countermeasure results. For example, the countermeasure unit can take highly accurate countermeasures against similar content based on the user's past countermeasure results. The countermeasure unit can also adjust the countermeasure algorithm by referring to the user's past feedback. Furthermore, the countermeasure unit can use the user's past countermeasure results as learning data to improve the countermeasure model. In this way, the accuracy of the countermeasure can be improved by referring to the user's past countermeasure results. Some or all of the above-mentioned processing in the countermeasure unit may be performed using, for example, AI, or may be performed without using AI. For example, the countermeasure unit can input the user's past countermeasure result data into the generation AI and cause the generation AI to improve the accuracy of the countermeasure.

[0098] The countermeasure unit can estimate the user's emotions and determine the priority of countermeasures based on the estimated user emotions. For example, if the user is angry, the countermeasure unit can quickly execute countermeasures. Furthermore, if the user is relaxed, the countermeasure unit can also set a low priority for countermeasures. Furthermore, if the user is feeling anxious, the countermeasure unit can prioritize countermeasures that provide a sense of security. By determining the priority of countermeasures based on the user's emotions, optimal countermeasures can be provided for the user. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the countermeasure unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the countermeasure unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of countermeasures.

[0099] When taking countermeasures, the countermeasure unit can determine the priority of the countermeasures based on the time when the content was posted. The countermeasure unit can, for example, quickly implement countermeasures against the most recent posts. The countermeasure unit can also prioritize countermeasures against content posted within a specific period of time. Furthermore, the countermeasure unit can determine the priority of countermeasures against older posts according to their importance. This allows countermeasures to be taken efficiently by determining the priority of countermeasures based on the time when the content was posted. Some or all of the above-described processing in the countermeasure unit may be performed using, for example, AI, or may be performed without using AI. For example, the countermeasure unit can input data on the time when the content was posted to a generation AI and have the generation AI determine the priority of countermeasures.

[0100] When taking countermeasures, the countermeasure unit can adjust the order of countermeasures based on the relevance of the content. For example, the countermeasure unit can prioritize the execution of countermeasures against highly relevant content. The countermeasure unit can also postpone the execution of countermeasures against less relevant content. Furthermore, the countermeasure unit can dynamically adjust the order of countermeasures according to the relevance of the content. This allows countermeasures to be taken efficiently by adjusting the order of countermeasures based on the relevance of the content. Some or all of the above-described processing in the countermeasure unit may be performed using, for example, AI, or may be performed without using AI. For example, the countermeasure unit can input content relevance data to a generation AI and cause the generation AI to adjust the order of countermeasures.

[0101] When taking a countermeasure, the countermeasure unit can adjust the use of technical terminology in the countermeasure according to the user's level of expertise. For example, the countermeasure unit can provide a countermeasure that uses a lot of technical terminology to a user with high technical expertise. The countermeasure unit can also provide a countermeasure explained in simple language to a user with low technical expertise. Furthermore, the countermeasure unit can adjust the way the countermeasure is expressed according to the user's level of expertise. This makes it possible to provide a countermeasure that is easy for the user to understand by adjusting the use of technical terminology according to the user's level of expertise. Some or all of the above-mentioned processing in the countermeasure unit may be performed using, for example, AI, or may be performed without using AI. For example, the countermeasure unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0102] The feedback unit can estimate the user's emotions and adjust the feedback collection method based on the estimated user emotions. For example, if the user is nervous, the feedback unit can collect feedback in the form of a simple question. Furthermore, if the user is relaxed, the feedback unit can also request detailed feedback. Furthermore, if the user is in a hurry, the feedback unit can provide a feedback format that can be completed in a short time. By adjusting the feedback collection method based on the user's emotions, optimal feedback can be collected for the user. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the feedback unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the feedback unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the feedback collection method.

[0103] When collecting feedback, the feedback unit can select an appropriate collection method by referring to the user's past feedback history. For example, the feedback unit can preferentially provide feedback formats that the user has used favorably in the past. The feedback unit can also suggest optimal question content based on the user's past feedback history. Furthermore, the feedback unit can also customize the collection method by referring to the user's past feedback history. In this way, by referring to the user's past feedback history, the optimal collection method can be selected and feedback can be collected efficiently. Some or all of the above-mentioned processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the user's past feedback history data into a generation AI and cause the generation AI to select a collection method.

[0104] The feedback unit can customize the feedback means based on the user's current situation when collecting feedback. For example, if the user is on the move, the feedback unit can collect feedback using voice input. If the user is using a desktop, the feedback unit can also collect feedback using text input. Furthermore, if the user is using a smartphone, the feedback unit can also collect feedback using touch operations. This allows for efficient feedback collection by customizing the feedback means based on the user's current situation. Some or all of the above-described processing in the feedback unit can be performed using AI, for example, or without AI. For example, the feedback unit can input the user's current situation data to the generation AI and cause the generation AI to customize the feedback means.

[0105] The feedback unit can estimate the user's emotions and determine the priority of feedback based on the estimated user emotions. For example, the feedback unit can quickly collect feedback when the user is dissatisfied. Furthermore, the feedback unit can also set a low priority for feedback when the user is satisfied. Furthermore, the feedback unit can preferentially collect detailed feedback when the user is excited. This allows optimal feedback to be collected by determining the priority of feedback based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the feedback unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the feedback unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of feedback.

[0106] When collecting feedback, the feedback unit can select an appropriate collection method by taking into account the user's geographical location information. For example, if the user is in a specific area, the feedback unit can prioritize collecting questions related to that area. The feedback unit can also collect feedback regarding issues specific to the area based on the user's location information. Furthermore, the feedback unit can select an optimal collection method by referring to the user's geographical location information. In this way, by taking the user's geographical location information into consideration, the optimal collection method can be selected and feedback can be collected efficiently. Some or all of the above-mentioned processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the user's geographical location information data into the generation AI and cause the generation AI to select a collection method.

[0107] When collecting feedback, the feedback unit can analyze the user's social media activity and suggest a means of feedback. For example, the feedback unit can suggest feedback questions based on words frequently used by the user on social media. The feedback unit can also analyze the user's social media activity and suggest an optimal feedback format. Furthermore, the feedback unit can customize the means of feedback by referring to the content of the user's social media posts. In this way, by analyzing the user's social media activity, the optimal means of feedback can be suggested and feedback can be collected efficiently. Some or all of the above-mentioned processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the user's social media activity data into a generation AI and have the generation AI suggest a means of feedback.

[0108] The providing unit can estimate the user's emotions and adjust the method of providing the API or dashboard based on the estimated user emotions. For example, if the user is nervous, the providing unit can provide a simple, highly visible dashboard. Furthermore, if the user is relaxed, the providing unit can provide a dashboard containing detailed information. Furthermore, if the user is in a hurry, the providing unit can provide a concise dashboard that focuses on the main points. By adjusting the method of providing the API or dashboard based on the user's emotions, optimal information can be provided to the user. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data into the generation AI and have the generation AI adjust the method of providing the information.

[0109] When providing an API or dashboard, the providing unit can select the optimal delivery method by referring to the user's past usage history. For example, the providing unit can preferentially provide a dashboard format that the user has used favorably in the past. The providing unit can also suggest the optimal API endpoint based on the user's past usage history. Furthermore, the providing unit can also customize the delivery method by referring to the user's past usage history. This makes it possible to select the optimal delivery method and provide information efficiently by referring to the user's past usage history. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past usage history data into the generation AI and have the generation AI select the delivery method.

[0110] The providing unit can customize the content to be provided based on the user's current needs when providing an API or dashboard. For example, the providing unit can prioritize providing information currently required by the user. The providing unit can also adjust the dashboard layout according to the user's current needs. Furthermore, the providing unit can provide an optimal API endpoint based on the user's current needs. This allows for efficient information provision by customizing the content to be provided based on the user's current needs. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's current needs data into a generation AI and cause the generation AI to customize the content to be provided.

[0111] The providing unit can select an appropriate delivery method by taking into account the user's device information when providing an API or dashboard. For example, if the user is using a smartphone, the providing unit can provide a dashboard that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can provide a dashboard optimized for a large screen. Furthermore, if the user is using a desktop, the providing unit can provide a dashboard that includes detailed information. This allows the optimal delivery method to be selected by taking into account the user's device information, and information to be provided efficiently. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's device information data into the generation AI and cause the generation AI to select a delivery method.

[0112] The providing unit can estimate the user's emotions and determine the priority of APIs and dashboards based on the estimated user emotions. For example, if the user is nervous, the providing unit can prioritize displaying important information. Furthermore, if the user is relaxed, the providing unit can prioritize displaying detailed information. Furthermore, if the user is in a hurry, the providing unit can prioritize displaying information that covers the main points. This allows the optimal information to be provided to the user by determining the priority of APIs and dashboards based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data into the generation AI and have the generation AI determine the priority.

[0113] The providing unit can select the optimal delivery method by taking into account the user's device information when providing an API or dashboard. For example, if the user is using a smartphone, the providing unit can provide an API endpoint that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can provide an API endpoint that is optimized for a large screen. Furthermore, if the user is using a desktop, the providing unit can provide an API endpoint that includes detailed information. This allows the optimal delivery method to be selected and information to be provided efficiently by taking into account the user's device information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's device information data into the generation AI and cause the generation AI to select the delivery method.

[0114] The providing unit can provide the API or dashboard in multiple languages ​​according to the user's language settings when providing the API or dashboard. The providing unit can automatically set the language of the API or dashboard based on, for example, the language setting of the user's device. The providing unit can also provide a language switching function when the user uses multiple languages. Furthermore, when the user selects a specific language, the providing unit can provide the API or dashboard in that language. This allows for efficient information provision by providing the content in multiple languages ​​according to the user's language setting. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's language setting data into a generation AI and cause the generation AI to provide multilingual support.

[0115] The providing unit can analyze the user's social media activity and provide related information when providing an API or dashboard. For example, the providing unit can provide information about places where the user has checked in on social media. The providing unit can also analyze the content of the user's social media posts and provide information about related tourist spots and stores. Furthermore, the providing unit can provide information about related places and events by referring to the activities of the user's friends on social media. This allows for efficient provision of related information by analyzing the user's social media activity. Some or all of the above-described processing by the providing unit may be performed using, or without, AI. For example, the providing unit can input the user's social media activity data into a generation AI and cause the generation AI to provide related information. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, countermeasure unit, feedback unit, and provision unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects data from an SNS platform using the communication I / F 44 of the smart device 14. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected content using a generation AI. The countermeasure unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and executes procedures for displaying a warning about malicious content, deleting the content, and suspending the account. The feedback unit is realized, for example, by the control unit 46A of the smart device 14 and collects feedback from users and uses it for machine learning. The provision unit is realized, for example, by the control unit 46A of the smart device 14 and provides an API and a dashboard. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, countermeasure unit, feedback unit, and provision unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects data from an SNS platform using the communication I / F 44 of the smart glasses 214. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected content using a generation AI. The countermeasure unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and executes procedures for displaying a warning about malicious content, deleting the content, and suspending the account. The feedback unit is realized, for example, by the control unit 46A of the smart glasses 214 and collects feedback from users and utilizes it for machine learning. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214 and provides an API and a dashboard. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, countermeasure unit, feedback unit, and provision unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit collects data from an SNS platform using the communication I / F 44 of the headset type terminal 314. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected content using a generation AI. The countermeasure unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and executes procedures for displaying a warning about malicious content, deleting the content, and suspending the account. The feedback unit is realized, for example, by the control unit 46A of the headset type terminal 314 and collects feedback from users and uses it for machine learning. The provision unit is realized, for example, by the control unit 46A of the headset type terminal 314 and provides an API and a dashboard. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, countermeasure unit, feedback unit, and provision unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects data from SNS platforms using the communication I / F 44 of the robot 414. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected content using a generation AI. The countermeasure unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and executes procedures for displaying a warning about malicious content, deleting the content, and suspending the account. The feedback unit is realized, for example, by the control unit 46A of the robot 414 and collects feedback from users and uses it for machine learning. The provision unit is realized, for example, by the control unit 46A of the robot 414 and provides an API and a dashboard.

[0116] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0117] The collection unit can analyze the user's past behavior history and determine the priority of content to be collected. For example, it can prioritize collection of content that the user has frequently viewed in the past. It can also prioritize collection of content that the user has given high ratings in the past. It can also prioritize collection of content that the user has shared in the past. In this way, by determining the priority of content to be collected based on the user's past behavior history, it is possible to efficiently collect content that is highly relevant to the user.

[0118] The analysis unit can refer to an external reliability evaluation database to evaluate the reliability of content. For example, the analysis unit can evaluate the reliability of content by referring to a database of reliable news sources. The analysis unit can also evaluate the reliability of scientific content by referring to a database of academic papers. The analysis unit can also evaluate the reliability of content by referring to user ratings and reviews. In this way, by referring to an external reliability evaluation database, the reliability of content can be evaluated with high accuracy.

[0119] The countermeasure unit can analyze the user's behavioral patterns and optimize the timing of the countermeasure. For example, the countermeasure can be executed during the time period when the user is most active. The countermeasure unit can also analyze the user's reactions when countermeasures were applied in the past to determine the optimal timing. Furthermore, the countermeasure timing can be dynamically adjusted based on the user's behavioral patterns. In this way, by analyzing the user's behavioral patterns, the timing of the countermeasure can be optimized and effective countermeasures can be executed.

[0120] The feedback unit can analyze user feedback in real time and immediately reflect it in system improvements. For example, it can instantly analyze a defect reported by a user and correct the system. It can also instantly add new features or improve existing features based on user feedback. Furthermore, analyzing user feedback in real time can also continuously improve system performance. This allows user feedback to be analyzed in real time and immediately reflected in system improvements, thereby increasing user satisfaction.

[0121] The providing unit can adjust the format of the content to be provided taking into account the remaining battery level of the user's device. For example, when the remaining battery level is low, lightweight text-format content can be provided preferentially. Also, when the remaining battery level is sufficient, rich media-format content can be provided. Furthermore, the content download speed can be adjusted according to the remaining battery level. In this way, the optimal content format can be provided by taking into account the remaining battery level of the user's device, thereby improving user convenience.

[0122] The collection unit can estimate the user's emotions and determine the type of content to collect based on the estimated user's emotions. For example, if the user is sad, encouraging messages and positive content can be preferentially collected. Also, if the user is excited, relaxing content can be preferentially collected. Furthermore, if the user is tired, light reading material and short videos can be preferentially collected. In this way, by determining the type of content to collect based on the user's emotions, it is possible to provide the user with the most suitable content.

[0123] The analysis unit can estimate the user's emotions and adjust the way in which the analysis results are presented based on the estimated user emotions. For example, if the user is feeling stressed, simple and visually easy-to-understand analysis results can be provided. If the user is relaxed, detailed analysis results can be provided. Furthermore, if the user is in a hurry, concise analysis results that focus on the main points can be provided. In this way, by adjusting the way in which the analysis results are presented based on the user's emotions, it is possible to provide the user with optimal information.

[0124] The countermeasure unit can estimate the user's emotions and adjust the content of the countermeasure based on the estimated user's emotions. For example, if the user is angry, a mild countermeasure can be suggested. Also, if the user is relaxed, an aggressive countermeasure can be suggested. Furthermore, if the user is feeling anxious, a countermeasure that gives a sense of security can be suggested. In this way, by adjusting the content of the countermeasure based on the user's emotions, it is possible to provide the optimal countermeasure for the user.

[0125] The feedback unit can estimate the user's emotions and adjust the feedback collection method based on the estimated user's emotions. For example, if the user is nervous, feedback can be collected in the form of simple questions. If the user is relaxed, detailed feedback can be requested. Furthermore, if the user is in a hurry, a feedback format that can be completed in a short time can be provided. In this way, by adjusting the feedback collection method based on the user's emotions, it is possible to collect optimal feedback for the user.

[0126] The providing unit can estimate the user's emotions and adjust the method of providing the API or dashboard based on the estimated user's emotions. For example, if the user is nervous, a simple and highly visible dashboard can be provided. If the user is relaxed, a dashboard including detailed information can be provided. Furthermore, if the user is in a hurry, a concise dashboard that focuses on the main points can be provided. In this way, by adjusting the method of providing the API or dashboard based on the user's emotions, it is possible to provide the user with optimal information.

[0127] The processing flow of the second embodiment will be briefly explained below.

[0128] Step 1: The collection unit collects content on the SNS platform. The collection unit collects data from the SNS platform using, for example, an API. The collection unit can also estimate the user's emotions and adjust the timing of content collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection timing can be delayed to reduce the burden on the user. Step 2: The analysis unit uses generative AI to analyze the content collected by the collection unit and detect unnatural language expressions and traces of tampering. For example, natural language processing technology and image analysis technology can be used to detect unnatural parts of text and images. Step 3: The countermeasures department takes countermeasures against malicious content detected by the analysis department. For example, it can implement specific procedures and conditions for displaying a warning, deleting the content, or suspending the account. Step 4: The feedback unit collects user feedback and uses it for machine learning. For example, inappropriate content reported by users can be collected and used as training data for machine learning models. Step 5: The provider provides APIs and dashboards. For example, the system functions can be provided through APIs and the system status can be monitored through dashboards.

[0129] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0130] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0131] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0132] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0133] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0134] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

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

[0136] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

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

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

[0139] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0140] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0141] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0142] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0143] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0144] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0145] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0146] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0147] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0148] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0149] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0150] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

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

[0152] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

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

[0154] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0155] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0156] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0157] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0158] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0159] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0160] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0161] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0162] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0163] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0164] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0165] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0166] 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.

[0167] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0168] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0169] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0170] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0171] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0172] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0173] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0174] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0175] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0176] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0177] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0178] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0179] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0180] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0181] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0182] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0183] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0184] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0185] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0186] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0187] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0188] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0189] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0190] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0191] 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.

[0192] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0193] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0194] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0195] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0196] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0197] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0198] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0199] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0200] [Explanation of symbols]

[0201] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects content on SNS platforms; an analysis unit that analyzes the content collected by the collection unit and detects unnatural language expressions or traces of tampering; a countermeasure unit that executes countermeasures against malicious content detected by the analysis unit; A feedback department that collects user feedback and uses it for machine learning; a providing unit that provides an API or a dashboard; A system characterized by:

2. The collecting unit Collect data from social media platforms using APIs 2. The system of claim 1.

3. The analysis unit Use natural language processing or image analysis techniques to detect unnatural parts of text or images 2. The system of claim 1.

4. The countermeasures department Implementing specific procedures or conditions for warning, removal, or account suspension 2. The system of claim 1.

5. The feedback unit Collecting inappropriate content reported by users and using it as training data for machine learning models 2. The system of claim 1.

6. The providing unit Provide system functions through API and monitor system status through dashboard 2. The system of claim 1.

7. The collecting unit Estimates user emotions and adjusts content collection timing based on the estimated user emotions.

2. The system of claim 1.

8. The collecting unit Analyze the usage of social media platforms and select appropriate collection methods 2. The system of claim 1.

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