System

Abstract

An object of the system according to the embodiment is to efficiently identify tampered content and notify the original owner of the tampered content.SOLUTION: A system includes a social media cooperation unit, an image recognition unit, and a notification unit. The social media cooperation unit cooperates with a social media platform. The image recognizing unit identifies falsified content by using an image recognizing technique driven by a AI. The notification unit notifies the original owner when the falsified content is identified.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 techniques have had the problem of making it difficult to efficiently identify altered content on the Internet and notify the original owner.

[0005] The system according to the embodiment aims to efficiently identify tampered content and notify the original owner. [Means for solving the problem]

[0006] The system according to the embodiment includes a social media integration unit, an image recognition unit, and a notification unit. The social media integration unit integrates with a social media platform. The image recognition unit identifies tampered content using AI-driven image recognition technology. The notification unit notifies the original owner of tampered content when tampered content is identified. [Effects of the Invention]

[0007] An embodiment of the system can efficiently identify tampered content and notify the original owner. [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 removal request facilitation platform according to an embodiment of the present invention is a system for facilitating removal requests for images on the internet. The system works in conjunction with social media platforms and utilizes AI-driven image recognition technology to identify altered content and facilitate the removal of the content as requested by the original owner. This allows the removal request facilitation platform to quickly identify altered images on the internet, notify the original owner, and facilitate removal requests.

[0029] A removal request facilitation platform according to an embodiment includes a social media integration unit, an image recognition unit, and a notification unit. The social media integration unit interacts with social media platforms. For example, it interacts with major social media platforms such as Facebook, Instagram, and X (Twitter) to access image data shared on each platform. The image recognition unit identifies altered content using AI-driven image recognition technology. For example, the generation AI detects differences in color tone, shape, and text between the original image and the altered image. The generation AI also compares the characteristics of the original image and the altered image to determine whether or not the content has been altered. The notification unit notifies the original owner of altered content when it is identified. For example, it may send an email or an in-app notification to the original owner, guiding them through the process of requesting the deletion of the altered content. As a result, the removal request facilitation platform according to an embodiment can quickly identify altered images on the Internet, notify the original owner, and facilitate a deletion request.

[0030] The social media integration unit can acquire image data in real time using the API of a social media platform. For example, the social media integration unit acquires image data posted by users in real time using the API of a social media platform. For example, the social media integration unit uses Facebook's Graph API to instantly acquire images posted by users and analyze them for tampering. The social media integration unit also integrates the APIs of each social media platform to build a system for centrally acquiring image data. For example, it combines Instagram's Basic Display API and Twitter's API to simultaneously collect image data from multiple platforms. The social media integration unit also optimizes the frequency of API requests to acquire image data in real time. For example, it adjusts the frequency of API requests according to user activity to efficiently collect image data. This makes it possible to quickly identify tampered content by acquiring image data in real time.

[0031] The image recognition unit uses a generation AI to estimate the original state of a tampered image and compare it with the original image to determine whether it has been tampered with. For example, the image recognition unit uses a generation AI to develop an algorithm that estimates the original state of a tampered image. For example, it learns the features of the original image and builds a model that repairs the tampered parts. In addition, to estimate the original state of a tampered image, the image recognition unit provides the generation AI with pairs of original and tampered images as training data. For example, it compares the original image with the tampered image to identify the tampered parts. In addition, the image recognition unit uses a generation AI to build a system that estimates the original state of a tampered image and compares it with the original image to determine whether it has been tampered with. For example, it analyzes the differences between the original image and the tampered image to detect the tampered parts. This makes it possible to accurately determine whether the tampered part has been tampered with by estimating the original state of the tampered image.

[0032] The notification unit can send an email or an in-app notification to the original owner when tampered content is identified. For example, the notification unit sends an email or an in-app notification to the original owner when tampered content is identified. For example, the notification unit guides the original owner through a procedure to request the deletion of the tampered content. The notification unit also builds a system that quickly notifies the original owner when tampered content is identified. For example, the notification unit automatically sends a notification the moment tampered content is detected. This allows the original owner to be quickly notified and a deletion request can be made quickly.

[0033] The notification unit can track the status of removal requests for tampered content and notify the original owner. The notification unit, for example, develops a dashboard that tracks the progress of removal requests in real time and notifies the original owner. For example, the dashboard displays the status of the request and visualizes the progress. The notification unit also builds a system that sends notifications regarding the progress of removal requests to the original owner through the dashboard. For example, the notification unit sends notifications when the request is accepted or when the removal is completed. The notification unit also develops an algorithm that uses each platform's API to obtain the request status in order to track the progress of removal requests in real time. This makes it easier for the original owner to manage the requests by notifying them of the progress of the removal requests.

[0034] The social media integration unit can integrate with platforms other than social media to monitor a wide range of images on the Internet. For example, the social media integration unit will build a system to collect image data from platforms other than social media (e.g., blogs and forums). For example, it will use RSS feeds and web scraping technology to collect images from blogs and forums. The social media integration unit will also develop a system to integrate data from multiple platforms in order to monitor a wide range of images on the Internet. For example, it will collect image data from blogs, forums, news sites, etc. and manage it centrally. The social media integration unit will also strengthen integration with platforms other than social media to expand the detection range of manipulated images. For example, it will cooperate with administrators of specific blogs and forums to receive image data. This will allow the detection range of manipulated content to be expanded by monitoring platforms other than social media.

[0035] The image recognition unit can use the generative AI to not only detect tampered images but also analyze the tampering method and intent. For example, the image recognition unit uses the generative AI to develop algorithms that not only detect tampered images but also analyze the tampering method and intent. For example, it learns the characteristics of the tampering method and infers the purpose of the tampering. The image recognition unit also provides the generative AI with labeled data of the tampering method in order to analyze the tampering method and intent. For example, it teaches tampering methods such as applying filters, collages, and adding text. The image recognition unit also uses the generative AI to build a system that analyzes the method and intent of tampered images. For example, it develops a model that extracts the characteristics of the tampering method and infers the purpose of the tampering. This makes it possible to understand the background of the tampering and take appropriate action by analyzing the tampering method and intent.

[0036] The image recognition unit can use the generative AI to detect altered images and also detect altered text content. For example, the image recognition unit uses the generative AI to develop an algorithm that detects altered images and also detects altered text content. For example, it analyzes text in an image and determines whether it has been tampered with. The image recognition unit also provides the generative AI with a dataset of text tampering to detect altered text content. For example, it uses pairs of original text and altered text as training data. The image recognition unit also uses the generative AI to build a system that simultaneously detects altered images and text content. For example, it develops a model that extracts text in an image and analyzes whether it has been tampered with. This enables more comprehensive tamper detection by detecting tampering not only in images but also in text content.

[0037] The notification unit can automate the deletion request process, allowing the original owner to submit a request with one click. The notification unit, for example, automates the deletion request process and builds a system that allows the original owner to submit a request with one click. For example, a deletion request form may be automatically generated to enable easy submission. To automate deletion requests, the notification unit may register the original owner's information in advance so that it is automatically entered when the request is submitted. For example, user account information may be used. The notification unit may also develop an interface that allows the original owner to submit a deletion request with one click, allowing the original owner to quickly submit a request. For example, a deletion request button may be installed. By automating the deletion request process, the original owner may quickly submit a request.

[0038] The notification unit can provide a dashboard that tracks the progress of the deletion request in real time and notifies the original owner. The notification unit, for example, develops a dashboard that tracks the progress of the deletion request in real time and notifies the original owner. For example, the dashboard displays the status of the request and visualizes the progress. The notification unit also builds a system that sends notifications regarding the progress of the deletion request to the original owner through the dashboard. For example, the dashboard sends notifications when the request is accepted or when the deletion is completed. The notification unit also develops an algorithm that uses the API of each platform to obtain the status of the request in order to track the progress of the deletion request in real time. This makes it easier to manage the request by tracking the progress of the deletion request in real time and notifying the original owner.

[0039] The notification unit may store a history of deletion requests to prepare for future troubleshooting or legal action. For example, the notification unit may build a system to store a history of deletion requests to prepare for future troubleshooting or legal action. For example, detailed information about the requests may be stored in a database. The notification unit may also develop an interface that allows the original owner to view the history of deletion requests. For example, the interface may display the status and results of past requests. The notification unit may also develop an algorithm that automatically records metadata for each request (e.g., request date and time, request content, status) to store the history of deletion requests. This allows the history of deletion requests to be stored to prepare for future troubleshooting or legal action.

[0040] The social media integration unit analyzes user behavior data from each platform and can detect altered images before they are spread. For example, the social media integration unit collects user behavior data from each platform and develops algorithms to detect altered images before they are spread. For example, it analyzes users' posting and sharing histories to detect abnormal patterns. The social media integration unit also builds a system that evaluates the risk of altered images being spread based on user behavior data. For example, if a specific user frequently shares altered images, it will prioritize analyzing that user's posts. The social media integration unit also develops a system that monitors user behavior data in real time to detect altered images before they are spread. For example, it will analyze the timing of users' posting and sharing to immediately detect abnormal behavior. This allows the analysis of user behavior data to detect altered images before they are spread.

[0041] The social media integration unit can also identify altered video content through collaboration with social media platforms. For example, the social media integration unit uses the API of a social media platform to acquire not only images but also video data and build a system to identify altered video content. For example, it uses YouTube's API to detect video tampering. The social media integration unit also uses generative AI to develop algorithms to identify altered video content. For example, it analyzes each frame of a video to detect differences between the original video and altered videos. The social media integration unit also strengthens collaboration with social media platforms to expand the scope of identification of altered video content. For example, it collects and analyzes video data from platforms such as TikTok and Vimeo. This will enable more comprehensive tamper detection by identifying altered video content.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The removal request facilitation platform may further include a behavioral analysis unit that analyzes a user's behavioral history. The behavioral analysis unit may, for example, analyze what images a user has posted in the past and what reactions they have received. This may allow the unit to predict the impact of altered images and determine the priority of removal requests. The behavioral analysis unit may also learn user posting patterns and detect abnormal behavior, thereby contributing to the early detection of altered images. Furthermore, the behavioral analysis unit may evaluate the risk of altered images being spread based on user behavioral data and prioritize analysis of high-risk images.

[0044] The removal request facilitation platform may further include an impact assessment unit that assesses the impact of the altered image. The impact assessment unit, for example, assesses the impact of the altered image on a specific community or event. This allows the extent of impact of the altered image to be predicted and the priority of the removal request to be determined. The impact assessment unit may also assess the impact of the altered image on a specific target group and make a customized removal request for each target group. Furthermore, the impact assessment unit may monitor the impact of the altered image on a specific event or campaign in real time and propose appropriate countermeasures.

[0045] The removal request facilitation platform may further include a diffusion prevention unit that prevents the diffusion of altered images. The diffusion prevention unit, for example, detects altered images before they are diffused and takes measures to prevent their diffusion. This reduces the risk of the altered images being diffused widely. The diffusion prevention unit may also evaluate the risk of the altered images being diffused based on user behavior data and prioritize analysis of images with a high risk. Furthermore, the diffusion prevention unit may build a system that monitors user behavior data in real time to detect altered images before they are diffused.

[0046] The removal request facilitation platform can further include a method analysis unit that analyzes the method and intent of altered images. The method analysis unit, for example, learns the characteristics of alteration methods and develops an algorithm that infers the purpose of the alteration. This allows for an understanding of the background of the alteration and enables appropriate responses by analyzing the alteration method and intent. The method analysis unit can also extract the characteristics of the alteration method by providing labeled data of the alteration method and having the generative AI learn from it. Furthermore, the method analysis unit can build a system that analyzes the method and intent of altered images and develop a model that infers the purpose of the alteration.

[0047] The removal request facilitation platform can further include a text analysis unit that detects altered text content while simultaneously detecting altered images. The text analysis unit, for example, analyzes text within images and develops an algorithm to identify whether or not the text content has been altered. This enables more comprehensive tamper detection by detecting alterations not only in images but also in text content. The text analysis unit can also provide a dataset of text tampering to the generative AI, allowing pairs of original text and altered text to be used as training data. Furthermore, the text analysis unit can build a system that simultaneously detects altered images and text content, extracting text within images and developing a model that analyzes whether or not the text has been altered.

[0048] The removal request facilitation platform can further automate the removal request process, allowing the original owner to submit a request with one click. The notification unit, for example, automatically generates a removal request form to enable easy submission. To automate the removal request, the notification unit also registers the original owner's information in advance so that it is automatically entered when the request is submitted. For example, the notification unit uses user account information. The notification unit also develops an interface that allows the original owner to submit a removal request with one click, allowing the original owner to submit the request quickly. This automation of the removal request process allows the original owner to submit the request quickly.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The social media integration unit integrates with social media platforms, such as Facebook, Instagram, and Twitter, to access image data shared on each platform. Step 2: The image recognition unit uses AI-powered image recognition technology to identify manipulated content. For example, the generation AI detects differences in color tone, shape, and text between the original and manipulated images. The generation AI also compares the features of the original and manipulated images to identify whether or not they have been manipulated. Step 3: If tampered content is identified, the notification unit notifies the original owner, for example by sending an email or in-app notification to the original owner, guiding them through the process of requesting the removal of the tampered content.

[0051] (Example 2) A removal request facilitation platform according to an embodiment of the present invention is a system for facilitating removal requests for images on the internet. The system works in conjunction with social media platforms and utilizes AI-driven image recognition technology to identify altered content and facilitate the removal of the content as requested by the original owner. This allows the removal request facilitation platform to quickly identify altered images on the internet, notify the original owner, and facilitate removal requests.

[0052] A removal request facilitation platform according to an embodiment includes a social media integration unit, an image recognition unit, and a notification unit. The social media integration unit interacts with social media platforms. For example, it interacts with major social media platforms such as Facebook, Instagram, and X (Twitter) to access image data shared on each platform. The image recognition unit identifies altered content using AI-driven image recognition technology. For example, the generation AI detects differences in color tone, shape, and text between the original image and the altered image. The generation AI also compares the characteristics of the original image and the altered image to determine whether or not the content has been altered. The notification unit notifies the original owner of altered content when it is identified. For example, it may send an email or an in-app notification to the original owner, guiding them through the process of requesting the deletion of the altered content. As a result, the removal request facilitation platform according to an embodiment can quickly identify altered images on the Internet, notify the original owner, and facilitate a deletion request.

[0053] The social media integration unit can acquire image data in real time using the API of a social media platform. For example, the social media integration unit acquires image data posted by users in real time using the API of a social media platform. For example, the social media integration unit uses Facebook's Graph API to instantly acquire images posted by users and analyze them for tampering. The social media integration unit also integrates the APIs of each social media platform to build a system for centrally acquiring image data. For example, it combines Instagram's Basic Display API and Twitter's API to simultaneously collect image data from multiple platforms. The social media integration unit also optimizes the frequency of API requests to acquire image data in real time. For example, it adjusts the frequency of API requests according to user activity to efficiently collect image data. This makes it possible to quickly identify tampered content by acquiring image data in real time.

[0054] The image recognition unit uses a generation AI to estimate the original state of a tampered image and compare it with the original image to determine whether it has been tampered with. For example, the image recognition unit uses a generation AI to develop an algorithm that estimates the original state of a tampered image. For example, it learns the features of the original image and builds a model that repairs the tampered parts. In addition, to estimate the original state of a tampered image, the image recognition unit provides the generation AI with pairs of original and tampered images as training data. For example, it compares the original image with the tampered image to identify the tampered parts. In addition, the image recognition unit uses a generation AI to build a system that estimates the original state of a tampered image and compares it with the original image to determine whether it has been tampered with. For example, it analyzes the differences between the original image and the tampered image to detect the tampered parts. This makes it possible to accurately determine whether the tampered part has been tampered with by estimating the original state of the tampered image.

[0055] The notification unit can send an email or an in-app notification to the original owner when tampered content is identified. For example, the notification unit sends an email or an in-app notification to the original owner when tampered content is identified. For example, the notification unit guides the original owner through a procedure to request the deletion of the tampered content. The notification unit also builds a system that quickly notifies the original owner when tampered content is identified. For example, the notification unit automatically sends a notification the moment tampered content is detected. This allows the original owner to be quickly notified and a deletion request can be made quickly.

[0056] The notification unit can track the status of removal requests for tampered content and notify the original owner. The notification unit, for example, develops a dashboard that tracks the progress of removal requests in real time and notifies the original owner. For example, the dashboard displays the status of the request and visualizes the progress. The notification unit also builds a system that sends notifications regarding the progress of removal requests to the original owner through the dashboard. For example, the notification unit sends notifications when the request is accepted or when the removal is completed. The notification unit also develops an algorithm that uses each platform's API to obtain the request status in order to track the progress of removal requests in real time. This makes it easier for the original owner to manage the requests by notifying them of the progress of the removal requests.

[0057] The image recognition unit can use the emotion estimation function to evaluate the emotional impact of a tampered image on a user and determine the priority of deletion requests. The image recognition unit, for example, uses the emotion estimation function to build a system that evaluates the emotional impact of a tampered image on a user. For example, the image recognition unit analyzes user comments and reactions and calculates an emotion score. The image recognition unit also develops an algorithm that determines the priority of deletion requests based on the emotional impact of a tampered image on a user. For example, images with a high emotion score are given priority in deletion requests. The image recognition unit also uses the emotion estimation function to evaluate the emotional impact of a tampered image on a specific community and build a system that makes deletion requests customized for each community. This makes it possible to appropriately determine the priority of deletion requests by taking the emotional impact into consideration.

[0058] The social media integration unit can integrate with platforms other than social media to monitor a wide range of images on the Internet. For example, the social media integration unit will build a system to collect image data from platforms other than social media (e.g., blogs and forums). For example, it will use RSS feeds and web scraping technology to collect images from blogs and forums. The social media integration unit will also develop a system to integrate data from multiple platforms in order to monitor a wide range of images on the Internet. For example, it will collect image data from blogs, forums, news sites, etc. and manage it centrally. The social media integration unit will also strengthen integration with platforms other than social media to expand the detection range of manipulated images. For example, it will cooperate with administrators of specific blogs and forums to receive image data. This will allow the detection range of manipulated content to be expanded by monitoring platforms other than social media.

[0059] The image recognition unit can use the generative AI to not only detect tampered images but also analyze the tampering method and intent. For example, the image recognition unit uses the generative AI to develop algorithms that not only detect tampered images but also analyze the tampering method and intent. For example, it learns the characteristics of the tampering method and infers the purpose of the tampering. The image recognition unit also provides the generative AI with labeled data of the tampering method in order to analyze the tampering method and intent. For example, it teaches tampering methods such as applying filters, collages, and adding text. The image recognition unit also uses the generative AI to build a system that analyzes the method and intent of tampered images. For example, it develops a model that extracts the characteristics of the tampering method and infers the purpose of the tampering. This makes it possible to understand the background of the tampering and take appropriate action by analyzing the tampering method and intent.

[0060] The image recognition unit can use the generative AI to detect altered images and also detect altered text content. For example, the image recognition unit uses the generative AI to develop an algorithm that detects altered images and also detects altered text content. For example, it analyzes text in an image and determines whether it has been tampered with. The image recognition unit also provides the generative AI with a dataset of text tampering to detect altered text content. For example, it uses pairs of original text and altered text as training data. The image recognition unit also uses the generative AI to build a system that simultaneously detects altered images and text content. For example, it develops a model that extracts text in an image and analyzes whether it has been tampered with. This enables more comprehensive tamper detection by detecting tampering not only in images but also in text content.

[0061] The notification unit can automate the deletion request process, allowing the original owner to submit a request with one click. The notification unit, for example, automates the deletion request process and builds a system that allows the original owner to submit a request with one click. For example, a deletion request form may be automatically generated to enable easy submission. To automate deletion requests, the notification unit may register the original owner's information in advance so that it is automatically entered when the request is submitted. For example, user account information may be used. The notification unit may also develop an interface that allows the original owner to submit a deletion request with one click, allowing the original owner to quickly submit a request. For example, a deletion request button may be installed. By automating the deletion request process, the original owner may quickly submit a request.

[0062] The notification unit can use the emotion estimation function to consider the emotions of the original owner when a deletion request is made and provide an appropriate support message. The notification unit, for example, uses the emotion estimation function to analyze the emotions of the original owner when a deletion request is made and build a system that provides an appropriate support message. For example, a message based on the emotion score is displayed. The notification unit also develops an algorithm that considers the emotions of the original owner when a deletion request is made and customizes the support message. For example, if the negative emotion is strong, an encouraging message is provided. The notification unit also uses the emotion estimation function to monitor the emotions of the original owner in real time when a deletion request is made and build a system that provides an appropriate support message. This makes it possible to provide appropriate support by taking the emotions of the original owner into consideration.

[0063] The notification unit can provide a dashboard that tracks the progress of the deletion request in real time and notifies the original owner. The notification unit, for example, develops a dashboard that tracks the progress of the deletion request in real time and notifies the original owner. For example, the dashboard displays the status of the request and visualizes the progress. The notification unit also builds a system that sends notifications regarding the progress of the deletion request to the original owner through the dashboard. For example, the dashboard sends notifications when the request is accepted or when the deletion is completed. The notification unit also develops an algorithm that uses the API of each platform to obtain the status of the request in order to track the progress of the deletion request in real time. This makes it easier to manage the request by tracking the progress of the deletion request in real time and notifying the original owner.

[0064] The notification unit may store a history of deletion requests to prepare for future troubleshooting or legal action. For example, the notification unit may build a system to store a history of deletion requests to prepare for future troubleshooting or legal action. For example, detailed information about the requests may be stored in a database. The notification unit may also develop an interface that allows the original owner to view the history of deletion requests. For example, the interface may display the status and results of past requests. The notification unit may also develop an algorithm that automatically records metadata for each request (e.g., request date and time, request content, status) to store the history of deletion requests. This allows the history of deletion requests to be stored to prepare for future troubleshooting or legal action.

[0065] The notification unit can use the emotion estimation function to monitor the original owner's emotions regarding the progress of the deletion request and provide appropriate support. For example, the notification unit uses the emotion estimation function to build a system that monitors the original owner's emotions regarding the progress of the deletion request. For example, it calculates an emotion score according to the progress of the request. The notification unit also develops an algorithm that takes into account the original owner's emotions regarding the progress of the deletion request and provides an appropriate support message. For example, it provides an encouraging message if the request is delayed. The notification unit also uses the emotion estimation function to build a system that monitors the original owner's emotions regarding the progress of the deletion request in real time and provides appropriate support. In this way, appropriate support can be provided by monitoring the original owner's emotions.

[0066] The image recognition unit can use the emotion estimation function to evaluate the emotional impact that a tampered image has on a specific event or campaign and propose appropriate countermeasures. The image recognition unit, for example, uses the emotion estimation function to build a system that evaluates the emotional impact that a tampered image has on a specific event or campaign. For example, it analyzes the comments and reactions of event participants and calculates an emotion score. The image recognition unit also develops an algorithm that proposes appropriate countermeasures based on the emotional impact that a tampered image has on the event or campaign. For example, if the emotion score is high, it suggests sending a prompt deletion request or an apology message. The image recognition unit also uses the emotion estimation function to build a system that monitors the emotional impact that a tampered image has on a specific event or campaign in real time and proposes appropriate countermeasures. This enables a prompt response by evaluating the emotional impact on an event or campaign and proposing appropriate countermeasures.

[0067] The social media integration unit analyzes user behavior data from each platform and can detect altered images before they are spread. For example, the social media integration unit collects user behavior data from each platform and develops algorithms to detect altered images before they are spread. For example, it analyzes users' posting and sharing histories to detect abnormal patterns. The social media integration unit also builds a system that evaluates the risk of altered images being spread based on user behavior data. For example, if a specific user frequently shares altered images, it will prioritize analyzing that user's posts. The social media integration unit also develops a system that monitors user behavior data in real time to detect altered images before they are spread. For example, it will analyze the timing of users' posting and sharing to immediately detect abnormal behavior. This allows the analysis of user behavior data to detect altered images before they are spread.

[0068] The social media integration unit can also identify altered video content through collaboration with social media platforms. For example, the social media integration unit uses the API of a social media platform to acquire not only images but also video data and build a system to identify altered video content. For example, it uses YouTube's API to detect video tampering. The social media integration unit also uses generative AI to develop algorithms to identify altered video content. For example, it analyzes each frame of a video to detect differences between the original video and altered videos. The social media integration unit also strengthens collaboration with social media platforms to expand the scope of identification of altered video content. For example, it collects and analyzes video data from platforms such as TikTok and Vimeo. This will enable more comprehensive tamper detection by identifying altered video content.

[0069] The image recognition unit uses the emotion estimation function to analyze the emotional impact that a manipulated image has on a specific community and can make a customized deletion request for each community. The image recognition unit, for example, uses the emotion estimation function to build a system that analyzes the emotional impact that a manipulated image has on a specific community. For example, it analyzes comments and reactions within a community and calculates an emotion score. The image recognition unit also develops an algorithm that determines the priority of deletion requests based on the emotion estimation data in order to make a customized deletion request for each community. For example, it prioritizes deletion requests for images from communities with high emotion scores. The image recognition unit also uses the emotion estimation function to build a system that monitors the emotional impact that a manipulated image has on a specific community in real time and immediately makes a customized deletion request. This enables a more effective response by making a deletion request customized for each community.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The removal request facilitation platform may further include a behavioral analysis unit that analyzes a user's behavioral history. The behavioral analysis unit may, for example, analyze what images a user has posted in the past and what reactions they have received. This may allow the unit to predict the impact of altered images and determine the priority of removal requests. The behavioral analysis unit may also learn user posting patterns and detect abnormal behavior, thereby contributing to the early detection of altered images. Furthermore, the behavioral analysis unit may evaluate the risk of altered images being spread based on user behavioral data and prioritize analysis of high-risk images.

[0072] The removal request facilitation platform may further include an emotion estimation unit that estimates the user's emotions. The emotion estimation unit, for example, analyzes the user's comments and reactions and calculates an emotion score. This allows the emotional impact of the altered image on the user to be evaluated and the priority of the removal request to be determined. The emotion estimation unit can also provide an appropriate support message based on the user's emotion data when a removal request is made. For example, an encouraging message can be provided if the user has strong negative emotions. The emotion estimation unit can also monitor the user's emotions in real time and suggest appropriate countermeasures.

[0073] The removal request facilitation platform may further include an impact assessment unit that assesses the impact of the altered image. The impact assessment unit, for example, assesses the impact of the altered image on a specific community or event. This allows the extent of impact of the altered image to be predicted and the priority of the removal request to be determined. The impact assessment unit may also assess the impact of the altered image on a specific target group and make a customized removal request for each target group. Furthermore, the impact assessment unit may monitor the impact of the altered image on a specific event or campaign in real time and propose appropriate countermeasures.

[0074] The removal request facilitation platform may further include a diffusion prevention unit that prevents the diffusion of altered images. The diffusion prevention unit, for example, detects altered images before they are diffused and takes measures to prevent their diffusion. This reduces the risk of the altered images being diffused widely. The diffusion prevention unit may also evaluate the risk of the altered images being diffused based on user behavior data and prioritize analysis of images with a high risk. Furthermore, the diffusion prevention unit may build a system that monitors user behavior data in real time to detect altered images before they are diffused.

[0075] The removal request facilitation platform can further include a method analysis unit that analyzes the method and intent of altered images. The method analysis unit, for example, learns the characteristics of alteration methods and develops an algorithm that infers the purpose of the alteration. This allows for an understanding of the background of the alteration and enables appropriate responses by analyzing the alteration method and intent. The method analysis unit can also extract the characteristics of the alteration method by providing labeled data of the alteration method and having the generative AI learn from it. Furthermore, the method analysis unit can build a system that analyzes the method and intent of altered images and develop a model that infers the purpose of the alteration.

[0076] The removal request facilitation platform can further include a text analysis unit that detects altered text content while simultaneously detecting altered images. The text analysis unit, for example, analyzes text within images and develops an algorithm to identify whether or not the text content has been altered. This enables more comprehensive tamper detection by detecting alterations not only in images but also in text content. The text analysis unit can also provide a dataset of text tampering to the generative AI, allowing pairs of original text and altered text to be used as training data. Furthermore, the text analysis unit can build a system that simultaneously detects altered images and text content, extracting text within images and developing a model that analyzes whether or not the text has been altered.

[0077] The removal request facilitation platform can further automate the removal request process, allowing the original owner to submit a request with one click. The notification unit, for example, automatically generates a removal request form to enable easy submission. To automate the removal request, the notification unit also registers the original owner's information in advance so that it is automatically entered when the request is submitted. For example, the notification unit uses user account information. The notification unit also develops an interface that allows the original owner to submit a removal request with one click, allowing the original owner to submit the request quickly. This automation of the removal request process allows the original owner to submit the request quickly.

[0078] The deletion request facilitation platform can further use an emotion estimation function to consider the emotions of the original owner when a deletion request is made and provide an appropriate support message. The notification unit, for example, uses the emotion estimation function to analyze the emotions of the original owner when a deletion request is made and builds a system to provide an appropriate support message. For example, a message based on the emotion score is displayed. The notification unit also develops an algorithm to consider the emotions of the original owner when a deletion request is made and customize the support message. For example, if the emotion is strong, an encouraging message is provided. The notification unit also uses the emotion estimation function to monitor the emotions of the original owner in real time when a deletion request is made and builds a system to provide an appropriate support message. This makes it possible to provide appropriate support by taking the emotions of the original owner into consideration.

[0079] The deletion request facilitation platform can further use an emotion estimation function to monitor the original owner's emotions regarding the progress of the deletion request and provide appropriate support. The notification unit, for example, uses the emotion estimation function to build a system that monitors the original owner's emotions regarding the progress of the deletion request. For example, it calculates an emotion score according to the progress of the request. The notification unit also develops an algorithm that takes into account the original owner's emotions regarding the progress of the deletion request and provides an appropriate support message. For example, it provides an encouraging message if the request is delayed. The notification unit also uses the emotion estimation function to build a system that monitors the original owner's emotions regarding the progress of the deletion request in real time and provides appropriate support. In this way, appropriate support can be provided by monitoring the original owner's emotions.

[0080] The removal request promotion platform can further use an emotion estimation function to evaluate the emotional impact that a manipulated image has on a specific event or campaign and propose appropriate countermeasures. The image recognition unit, for example, uses the emotion estimation function to build a system that evaluates the emotional impact that a manipulated image has on a specific event or campaign. For example, it analyzes the comments and reactions of event participants and calculates an emotion score. The image recognition unit also develops an algorithm that proposes appropriate countermeasures based on the emotional impact that a manipulated image has on an event or campaign. For example, if the emotion score is high, it suggests sending a prompt removal request or an apology message. The image recognition unit also uses the emotion estimation function to build a system that monitors the emotional impact that a manipulated image has on a specific event or campaign in real time and proposes appropriate countermeasures. This enables a prompt response by evaluating the emotional impact on an event or campaign and proposing appropriate countermeasures.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The social media integration unit integrates with social media platforms, such as Facebook, Instagram, and Twitter, to access image data shared on each platform. Step 2: The image recognition unit uses AI-powered image recognition technology to identify manipulated content. For example, the generation AI detects differences in color tone, shape, and text between the original and manipulated images. The generation AI also compares the features of the original and manipulated images to identify whether or not they have been manipulated. Step 3: If tampered content is identified, the notification unit notifies the original owner, for example by sending an email or in-app notification to the original owner, guiding them through the process of requesting the removal of the tampered content.

[0083] 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.

[0084] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0085] 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.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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).

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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 AI 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.

[0100] 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.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0112] 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.

[0113] 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.

[0114] 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 AI 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.

[0115] 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.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 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 processing similar to that of the specific processing unit 290 using these models.

[0128] 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.

[0129] 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.

[0130] 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 AI 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] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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."

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0150] 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. Social Media Collaboration Department, which collaborates with social media platforms; an image recognition unit that uses AI-powered image recognition technology to identify altered content; a notification unit that notifies the original owner when the tampered content is identified. A system characterized by:

2. The social media linking unit Use the API of the social media platform to obtain image data in real time 2. The system of claim 1.

3. The image recognition unit Generative AI is used to estimate the original state of the tampered image, and by comparing it with the original image, it is possible to determine whether or not the image has been tampered with.

2. The system of claim 1.

4. The notification unit Send an email or in-app notification to the original owner if the altered content is identified.

2. The system of claim 1.

5. The notification unit Track the status of requests to remove said altered content and notify said original owners; 2. The system of claim 1.

Images