system

Abstract

The system according to the embodiment aims to quickly detect and remove pirated content on the Internet. [Solution] A system according to an embodiment includes an issuing unit, a detection unit, and a deletion unit. The issuing unit issues an NFT for original content and grants copyright. The detection unit scans content on the Internet to detect pirated copies. The deletion unit promptly deletes pirated copies detected by the detection unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies make it difficult to quickly detect and remove pirated content on the Internet, making copyright infringement a serious problem.

[0005] The system according to the embodiment aims to quickly detect and remove pirated content on the Internet. [Means for solving the problem]

[0006] The system according to the embodiment includes an issuing unit, a detection unit, and a deletion unit. The issuing unit issues an NFT for original content and grants copyright. The detection unit scans content on the Internet and detects pirated copies. The deletion unit promptly deletes pirated copies detected by the detection unit. [Effects of the Invention]

[0007] A system according to an embodiment can quickly detect and remove pirated content on the Internet. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention utilizes NFT technology to grant copyright to original versions of Japanese manga and anime in order to prevent the distribution of pirated copies. This system issues an NFT for the original content, which serves as proof of copyright. Next, an online AI patrol is introduced to monitor the distribution of pirated copies in real time. The AI ​​patrol scans content on the internet and immediately removes any pirated copies it detects. This process is rapid and does not require confirmation from the provider. For example, an NFT is issued for original manga and anime to grant copyright. Next, an online AI patrol monitors the distribution of pirated copies in real time. If the AI ​​patrol detects a pirated copy, it immediately removes it. This system aims to protect the copyrights of manga and anime and improve the status of those involved in the industry. This system aims to protect the copyrights of manga and anime and improve the status of those involved in the industry.

[0029] The system according to the embodiment includes an issuing unit, a detection unit, and a deletion unit. The issuing unit issues an NFT for original content and grants copyright. The issuing unit issues an NFT using, for example, blockchain technology to certify copyright for a specific digital file. The blockchain technology may be implemented using, for example, Ethereum or Hyperledger. The specific digital file may include, for example, JPEG images or MP4 videos. The detection unit scans content on the Internet to detect pirated copies. The detection unit compares the original content with the pirated copy using, for example, image recognition technology or text analysis technology, and determines that the content is pirated if there is a match. The image recognition technology may be implemented using, for example, a convolutional neural network (CNN). The text analysis technology may be implemented using, for example, natural language processing (NLP). The deletion unit quickly deletes the pirated copy detected by the detection unit. For example, when the deletion unit detects a pirated copy, it quickly sends a deletion request and executes the deletion without confirming with the provider. The deletion request is sent by, for example, automatic deletion using an API, etc. This allows the system according to the embodiment to issue NFTs for original content and protect copyrights by detecting and immediately deleting pirated copies.

[0030] The issuing unit can issue NFTs using blockchain technology to prove copyright for specific digital files. Blockchain technology is implemented using technologies such as Ethereum and Hyperledger. Specific digital files include, but are not limited to, JPEG images and MP4 videos. The use of blockchain technology improves the reliability and transparency of NFTs. Some or all of the above-described processing by the issuing unit may be performed using, for example, AI, or may be performed without using AI. For example, the issuing unit can input a specific digital file into a generation AI and have the generation AI issue an NFT.

[0031] The detection unit can use image recognition technology or text analysis technology to compare the original content with the pirated copy and determine that it is a pirated copy if they match. Image recognition technology is realized, for example, by a technology such as CNN (convolutional neural network). Text analysis technology is realized, for example, by a technology such as natural language processing (NLP). As a result, the accuracy of pirated copy detection is improved by using image recognition technology or text analysis technology. Some or all of the above-mentioned processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input data of the original content and the pirated copy into a generation AI and have the generation AI detect the pirated copy.

[0032] When the deletion unit detects a pirated copy, it can quickly send a deletion request and execute the deletion without checking with the provider. The deletion request is sent, for example, by a method such as automatic deletion using an API. This allows for a rapid response by executing the deletion immediately without the need for checking with the provider. Some or all of the above-mentioned processing in the deletion unit may be performed, for example, using AI, or may be performed without using AI. For example, the deletion unit can input the data of the pirated copy into a generation AI and have the generation AI execute the transmission of the deletion request.

[0033] The deletion unit can execute deletion based on legal procedures. Legal procedures include, but are not limited to, procedures based on the DMCA (Digital Millennium Copyright Act). By executing deletion based on legal procedures, legal issues can be avoided. Some or all of the above-described processing in the deletion unit may be performed using, for example, AI, or may be performed without using AI. For example, the deletion unit can input a deletion request based on legal procedures to the generation AI and have the generation AI execute the deletion.

[0034] The issuing unit can determine the priority of NFT issuance based on the popularity and rating of the content. For example, the issuing unit prioritizes issuing NFTs for highly rated content. The issuing unit can also issue NFTs for popular content earlier than other content. The issuing unit can also issue special NFTs for content that is highly rated by users. In this way, important content is prioritized by determining the priority of NFT issuance based on popularity and rating. Some or all of the above-described processing in the issuing unit may be performed using, for example, AI, or may be performed without using AI. For example, the issuing unit can input content popularity and rating data into the generation AI and have the generation AI determine the issuance priority.

[0035] The issuing unit can apply different NFT issuance algorithms depending on the type of content. For example, for manga, the issuing unit can apply an algorithm that issues an NFT for each page. For anime, the issuing unit can also apply an algorithm that issues an NFT for each episode. For music, the issuing unit can also apply an algorithm that issues an NFT for each track. This makes it possible to issue optimal NFTs by applying an algorithm depending on the type of content. Some or all of the above-mentioned processing by the issuing unit may be performed using, or without, AI, for example. For example, the issuing unit can input content type data into a generation AI and have the generation AI apply the issuance algorithm.

[0036] The issuing unit can issue NFTs taking into account the geographical popularity of content. For example, the issuing unit can prioritize issuing NFTs for content that is popular in a specific region. The issuing unit can also issue NFTs in accordance with demand in a specific region, for example, based on geographical data. The issuing unit can also analyze popularity by region and determine the optimal issuance timing. This makes it possible to issue NFTs in accordance with demand in each region by taking geographical popularity into consideration. Some or all of the above-mentioned processing by the issuing unit can be performed using, or without, AI, for example. For example, the issuing unit can input geographical popularity data into a generation AI and have the generation AI determine the issuance timing.

[0037] The issuing unit can analyze the sharing status of content on social media and determine the optimal timing for issuing NFTs. For example, the issuing unit prioritizes issuing NFTs for content with a large number of shares on social media. The issuing unit can also issue NFTs at peak sharing times, for example, to maximize the effect. The issuing unit can also analyze reactions on social media in real time and determine the optimal timing for issuance. This makes it possible to issue NFTs with the aim of maximizing the effect by analyzing sharing status on social media. Some or all of the above-mentioned processing by the issuing unit may be performed using, or without, AI, for example. For example, the issuing unit can input social media sharing data into a generation AI and have the generation AI determine the timing of issuance.

[0038] The detection unit can improve the accuracy of detecting pirated copies by taking into account the interrelationships between content. The detection unit detects pirated copies by taking into account, for example, the relationship between original content and its derivative works. The detection unit can also detect pirated copies by taking into account, for example, the relationship between different episodes of the same series. The detection unit can also detect pirated copies by taking into account, for example, similarities in the genre or theme of the content. In this way, by taking into account the interrelationships between content, the accuracy of detecting pirated copies is improved. Some or all of the above-mentioned processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input content interrelationship data into a generation AI and cause the generation AI to improve detection accuracy.

[0039] The detection unit can detect pirated copies by taking into account the attribute information of the content submitter. The detection unit can, for example, analyze the submitter's past behavioral history to evaluate the possibility of pirated copies. The detection unit can also, for example, detect pirated copies by taking into account the submitter's geographical location information. The detection unit can also, for example, analyze the submitter's social media activities to evaluate the possibility of pirated copies. By taking the submitter's attribute information into account, the accuracy of pirated copies detection is improved. Some or all of the above-described processing in the detection unit can be performed, for example, using AI, or can be performed without using AI. For example, the detection unit can input the submitter's attribute information data into the generation AI and cause the generation AI to detect pirated copies.

[0040] The detection unit can detect pirated copies by taking into account the geographical distribution of content. For example, the detection unit can prioritize the detection of distribution of pirated copies in a specific region. The detection unit can also evaluate the possibility of pirated copies in a specific region based on geographical data. The detection unit can also analyze the distribution patterns of pirated copies in each region and apply the optimal detection method. This makes it possible to detect pirated copies in each region by taking geographical distribution into account. Some or all of the above-mentioned processing in the detection unit can be performed using, for example, AI, or without AI. For example, the detection unit can input geographical distribution data into a generation AI and cause the generation AI to apply a detection method.

[0041] The detection unit can improve the accuracy of piracy detection by referring to literature related to the content. The detection unit can improve the accuracy of piracy detection by referring to, for example, related academic papers and articles. The detection unit can also improve the accuracy of piracy detection by referring to, for example, related patent information. The detection unit can also improve the accuracy of piracy detection by referring to, for example, related industry reports. In this way, by referring to related literature, the accuracy of piracy detection is improved. Some or all of the above-mentioned processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input related literature data into the generation AI and cause the generation AI to improve the detection accuracy.

[0042] The deletion unit can improve the accuracy of deletion by taking into account the interrelationships between content. The deletion unit performs deletion by taking into account, for example, the relationship between original content and its derivative works. The deletion unit can also perform deletion by taking into account, for example, the relationship between different episodes of the same series. The deletion unit can also perform deletion by taking into account, for example, the similarity in the genre or theme of the content. In this way, the accuracy of deletion is improved by taking into account the interrelationships between content. Some or all of the above-described processing in the deletion unit may be performed using, for example, AI, or may be performed without using AI. For example, the deletion unit can input content interrelationship data into a generation AI and cause the generation AI to improve the accuracy of deletion.

[0043] The deletion unit can perform deletion taking into account attribute information of the submitter of the content. The deletion unit can, for example, analyze the submitter's past behavioral history and determine the deletion priority. The deletion unit can also, for example, perform deletion taking into account geographical location information of the submitter. The deletion unit can also, for example, analyze the submitter's social media activities and determine the deletion priority. By taking the submitter's attribute information into account, the accuracy of deletion is improved. Some or all of the above-described processing in the deletion unit can be performed using, for example, AI, or can be performed without using AI. For example, the deletion unit can input the submitter's attribute information data into the generation AI and cause the generation AI to execute deletion.

[0044] The deletion unit can perform deletion taking into account the geographic distribution of content. For example, the deletion unit prioritizes deletion of pirated copies distributed in a specific region. The deletion unit can also evaluate the possibility of pirated copies in a specific region based on geographical data and perform deletion. The deletion unit can also analyze the distribution patterns of pirated copies in each region and apply the optimal deletion method. This makes it possible to delete pirated copies in each region by taking geographical distribution into consideration. Some or all of the above-mentioned processing in the deletion unit can be performed using, for example, AI, or without AI. For example, the deletion unit can input geographical distribution data into a generation AI and cause the generation AI to apply a deletion method.

[0045] The deletion unit can improve the accuracy of deletion by referring to literature related to the content. The deletion unit can improve the accuracy of deletion by referring to, for example, related academic papers and articles. The deletion unit can also improve the accuracy of deletion by referring to, for example, related patent information. The deletion unit can also improve the accuracy of deletion by referring to, for example, related industry reports. In this way, the accuracy of deletion is improved by referring to related literature. Some or all of the above-described processing in the deletion unit can be performed using, for example, AI, or can be performed without using AI. For example, the deletion unit can input related literature data into the generation AI and cause the generation AI to improve the accuracy of deletion.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The issuer can analyze users' purchasing history and issue special NFTs to specific users. For example, a limited-edition NFT can be issued to a user who has purchased a lot of content in the past. The issuer can also issue NFTs containing special rewards to users who have shown a high interest in a particular genre or series. Furthermore, the issuer can issue NFTs that provide incentives to encourage future purchases based on the user's purchasing history. This can improve user satisfaction by utilizing the user's purchasing history.

[0048] The deletion unit can analyze the history of deletion requests and learn the optimal deletion method. For example, it can analyze the success rate of past deletion requests and identify the most effective deletion method. The deletion unit can also apply the optimal deletion method to a specific provider based on the history of deletion requests. Furthermore, the deletion unit can also optimize the timing of deletion based on the history of deletion requests. In this way, the accuracy and efficiency of deletion can be improved by utilizing the history of deletion requests.

[0049] The detection unit can analyze the viewing history of content and improve the accuracy of pirated copy detection based on the viewing history. For example, stricter detection criteria can be applied to content that a particular user frequently views. It can also prioritize detection of specific genres or series based on the viewing history. Furthermore, it can prioritize detection of pirated copies that are likely to interest the user based on the viewing history. Thus, utilizing the viewing history improves the accuracy of pirated copy detection.

[0050] Issuers can issue NFTs taking into account the content production process. For example, NFTs can be issued at each stage of the production process to show the progress of production. Special NFTs can also be issued at specific milestones in the production process. Furthermore, NFTs containing documentaries or behind-the-scenes footage of the production process can be issued. This will increase transparency in production and interest among fans by taking the content production process into consideration.

[0051] The deletion unit can evaluate the influence of content to determine the priority of deletion requests. For example, deletion of content with a high influence can be prioritized. Content involving influential users can also be prioritized for deletion. Furthermore, content related to influential events or campaigns can also be prioritized for deletion. This allows for effective deletion by taking the influence of content into consideration.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The issuer issues an NFT for the original content and grants copyright. The issuer issues an NFT using, for example, blockchain technology to certify copyright for a specific digital file. Blockchain technology is implemented using technologies such as Ethereum and Hyperledger. Examples of specific digital files include, but are not limited to, JPEG images and MP4 videos. Step 2: The detection unit scans content on the Internet and detects pirated copies. The detection unit uses, for example, image recognition technology or text analysis technology to compare the original content with the pirated copy and determine that it is pirated if they match. Image recognition technology is realized, for example, by CNN (Convolutional Neural Network). Text analysis technology is realized, for example, by natural language processing (NLP). Step 3: The deletion unit promptly deletes the pirated copy detected by the detection unit. For example, when the deletion unit detects a pirated copy, it promptly sends a deletion request and executes the deletion without confirmation from the provider. The deletion request is sent, for example, by a method such as automatic deletion using an API.

[0054] (Example 2) A system according to an embodiment of the present invention utilizes NFT technology to grant copyright to original versions of Japanese manga and anime in order to prevent the distribution of pirated copies. This system issues an NFT for the original content, which serves as proof of copyright. Next, an online AI patrol is introduced to monitor the distribution of pirated copies in real time. The AI ​​patrol scans content on the internet and immediately removes any pirated copies it detects. This process is rapid and does not require confirmation from the provider. For example, an NFT is issued for original manga and anime to grant copyright. Next, an online AI patrol monitors the distribution of pirated copies in real time. If the AI ​​patrol detects a pirated copy, it immediately removes it. This system aims to protect the copyrights of manga and anime and improve the status of those involved in the industry. This system aims to protect the copyrights of manga and anime and improve the status of those involved in the industry.

[0055] The system according to the embodiment includes an issuing unit, a detection unit, and a deletion unit. The issuing unit issues an NFT for original content and grants copyright. The issuing unit issues an NFT using, for example, blockchain technology to certify copyright for a specific digital file. The blockchain technology may be implemented using, for example, Ethereum or Hyperledger. The specific digital file may include, for example, JPEG images or MP4 videos. The detection unit scans content on the Internet to detect pirated copies. The detection unit compares the original content with the pirated copy using, for example, image recognition technology or text analysis technology, and determines that the content is pirated if there is a match. The image recognition technology may be implemented using, for example, a convolutional neural network (CNN). The text analysis technology may be implemented using, for example, natural language processing (NLP). The deletion unit quickly deletes the pirated copy detected by the detection unit. For example, when the deletion unit detects a pirated copy, it quickly sends a deletion request and executes the deletion without confirming with the provider. The deletion request is sent by, for example, automatic deletion using an API, etc. This allows the system according to the embodiment to issue NFTs for original content and protect copyrights by detecting and immediately deleting pirated copies.

[0056] The issuing unit can issue NFTs using blockchain technology to prove copyright for specific digital files. Blockchain technology is implemented using technologies such as Ethereum and Hyperledger. Specific digital files include, but are not limited to, JPEG images and MP4 videos. The use of blockchain technology improves the reliability and transparency of NFTs. Some or all of the above-described processing by the issuing unit may be performed using, for example, AI, or may be performed without using AI. For example, the issuing unit can input a specific digital file into a generation AI and have the generation AI issue an NFT.

[0057] The detection unit can use image recognition technology or text analysis technology to compare the original content with the pirated copy and determine that it is a pirated copy if they match. Image recognition technology is realized, for example, by a technology such as CNN (convolutional neural network). Text analysis technology is realized, for example, by a technology such as natural language processing (NLP). As a result, the accuracy of pirated copy detection is improved by using image recognition technology or text analysis technology. Some or all of the above-mentioned processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input data of the original content and the pirated copy into a generation AI and have the generation AI detect the pirated copy.

[0058] When the deletion unit detects a pirated copy, it can quickly send a deletion request and execute the deletion without checking with the provider. The deletion request is sent, for example, by a method such as automatic deletion using an API. This allows for a rapid response by executing the deletion immediately without the need for checking with the provider. Some or all of the above-mentioned processing in the deletion unit may be performed, for example, using AI, or may be performed without using AI. For example, the deletion unit can input the data of the pirated copy into a generation AI and have the generation AI execute the transmission of the deletion request.

[0059] The deletion unit can execute deletion based on legal procedures. Legal procedures include, but are not limited to, procedures based on the DMCA (Digital Millennium Copyright Act). By executing deletion based on legal procedures, legal issues can be avoided. Some or all of the above-described processing in the deletion unit may be performed using, for example, AI, or may be performed without using AI. For example, the deletion unit can input a deletion request based on legal procedures to the generation AI and have the generation AI execute the deletion.

[0060] The issuing unit can estimate the user's emotions and adjust the timing of NFT issuance based on the estimated user's emotions. For example, if the user is excited, the issuing unit can issue NFTs immediately to maintain the user's excitement. Alternatively, if the user is relaxed, the issuing unit can issue NFTs at a slower pace to maintain the user's sense of relaxation. Alternatively, if the user is stressed, the issuing unit can provide a simple and quick issuance process to relieve stress. This improves the user experience by adjusting the timing of NFT issuance according to the user's emotions. The user's emotions are estimated using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the issuing unit can be performed using AI, or without AI. For example, the issuing unit can input the user's emotion data into the generation AI and have the generation AI adjust the issuance timing.

[0061] The issuing unit can determine the priority of NFT issuance based on the popularity and rating of the content. For example, the issuing unit prioritizes issuing NFTs for highly rated content. The issuing unit can also issue NFTs for popular content earlier than other content. The issuing unit can also issue special NFTs for content that is highly rated by users. In this way, important content is prioritized by determining the priority of NFT issuance based on popularity and rating. Some or all of the above-described processing in the issuing unit may be performed using, for example, AI, or may be performed without using AI. For example, the issuing unit can input content popularity and rating data into the generation AI and have the generation AI determine the issuance priority.

[0062] The issuing unit can apply different NFT issuance algorithms depending on the type of content. For example, for manga, the issuing unit can apply an algorithm that issues an NFT for each page. For anime, the issuing unit can also apply an algorithm that issues an NFT for each episode. For music, the issuing unit can also apply an algorithm that issues an NFT for each track. This makes it possible to issue optimal NFTs by applying an algorithm depending on the type of content. Some or all of the above-mentioned processing by the issuing unit may be performed using, or without, AI, for example. For example, the issuing unit can input content type data into a generation AI and have the generation AI apply the issuance algorithm.

[0063] The issuing unit can estimate a user's emotions and customize the NFT issuance method based on the estimated user's emotions. For example, if the user is excited, the issuing unit can issue an NFT with a visually appealing effect. For example, if the user is relaxed, the issuing unit can issue an NFT with a simple, calming design. For example, if the user is stressed, the issuing unit can provide a simple and quick issuance process to relieve stress. This improves the user experience by customizing the issuance method according to the user's emotions. The user's emotions are estimated using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the issuing unit can be performed using AI, for example, or without AI. For example, the issuing unit can input the user's emotion data into the generation AI and have the generation AI customize the issuance method.

[0064] The issuing unit can issue NFTs taking into account the geographical popularity of content. For example, the issuing unit can prioritize issuing NFTs for content that is popular in a specific region. The issuing unit can also issue NFTs in accordance with demand in a specific region, for example, based on geographical data. The issuing unit can also analyze popularity by region and determine the optimal issuance timing. This makes it possible to issue NFTs in accordance with demand in each region by taking geographical popularity into consideration. Some or all of the above-mentioned processing by the issuing unit can be performed using, or without, AI, for example. For example, the issuing unit can input geographical popularity data into a generation AI and have the generation AI determine the issuance timing.

[0065] The issuing unit can analyze the sharing status of content on social media and determine the optimal timing for issuing NFTs. For example, the issuing unit prioritizes issuing NFTs for content with a large number of shares on social media. The issuing unit can also issue NFTs at peak sharing times, for example, to maximize the effect. The issuing unit can also analyze reactions on social media in real time and determine the optimal timing for issuance. This makes it possible to issue NFTs with the aim of maximizing the effect by analyzing sharing status on social media. Some or all of the above-mentioned processing by the issuing unit may be performed using, or without, AI, for example. For example, the issuing unit can input social media sharing data into a generation AI and have the generation AI determine the timing of issuance.

[0066] The detection unit can estimate the user's emotions and adjust the piracy detection criteria based on the estimated user's emotions. For example, if the user is excited, the detection unit can detect piracy using strict criteria. Alternatively, if the user is relaxed, the detection unit can detect piracy using flexible criteria. Alternatively, if the user is stressed, the detection unit can apply criteria for quick and easy piracy detection. This enables more appropriate piracy detection by adjusting the detection criteria according to the user's emotions. The estimation of the user's emotions is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the detection unit can be performed using AI, or without AI. For example, the detection unit can input the user's emotion data into the generation AI and have the generation AI adjust the detection criteria.

[0067] The detection unit can improve the accuracy of detecting pirated copies by taking into account the interrelationships between content. The detection unit detects pirated copies by taking into account, for example, the relationship between original content and its derivative works. The detection unit can also detect pirated copies by taking into account, for example, the relationship between different episodes of the same series. The detection unit can also detect pirated copies by taking into account, for example, similarities in the genre or theme of the content. In this way, by taking into account the interrelationships between content, the accuracy of detecting pirated copies is improved. Some or all of the above-mentioned processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input content interrelationship data into a generation AI and cause the generation AI to improve detection accuracy.

[0068] The detection unit can detect pirated copies by taking into account the attribute information of the content submitter. The detection unit can, for example, analyze the submitter's past behavioral history to evaluate the possibility of pirated copies. The detection unit can also, for example, detect pirated copies by taking into account the submitter's geographical location information. The detection unit can also, for example, analyze the submitter's social media activities to evaluate the possibility of pirated copies. By taking the submitter's attribute information into account, the accuracy of pirated copies detection is improved. Some or all of the above-described processing in the detection unit can be performed, for example, using AI, or can be performed without using AI. For example, the detection unit can input the submitter's attribute information data into the generation AI and cause the generation AI to detect pirated copies.

[0069] The detection unit can estimate the user's emotions and adjust the display order of piracy detection results based on the estimated user's emotions. For example, when the user is excited, the detection unit can prioritize displaying the most important detection results. Furthermore, for example, when the user is relaxed, the detection unit can sequentially display detailed detection results. Furthermore, for example, when the user is stressed, the detection unit can display concise, to-the-point detection results. This improves the user experience by adjusting the display order according to the user's emotions. The estimation of the user's emotions is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the detection unit can be performed using, for example, an AI, or without an AI. For example, the detection unit can input the user's emotion data into the generation AI and have the generation AI adjust the display order.

[0070] The detection unit can detect pirated copies by taking into account the geographical distribution of content. For example, the detection unit can prioritize the detection of distribution of pirated copies in a specific region. The detection unit can also evaluate the possibility of pirated copies in a specific region based on geographical data. The detection unit can also analyze the distribution patterns of pirated copies in each region and apply the optimal detection method. This makes it possible to detect pirated copies in each region by taking geographical distribution into account. Some or all of the above-mentioned processing in the detection unit can be performed using, for example, AI, or without AI. For example, the detection unit can input geographical distribution data into a generation AI and cause the generation AI to apply a detection method.

[0071] The detection unit can improve the accuracy of piracy detection by referring to literature related to the content. The detection unit can improve the accuracy of piracy detection by referring to, for example, related academic papers and articles. The detection unit can also improve the accuracy of piracy detection by referring to, for example, related patent information. The detection unit can also improve the accuracy of piracy detection by referring to, for example, related industry reports. In this way, by referring to related literature, the accuracy of piracy detection is improved. Some or all of the above-mentioned processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input related literature data into the generation AI and cause the generation AI to improve the detection accuracy.

[0072] The deletion unit can estimate the user's emotions and determine the deletion priority based on the estimated user's emotions. For example, if the user is excited, the deletion unit can immediately delete the content. For example, if the user is relaxed, the deletion unit can also adjust the timing of deletion. For example, if the user is stressed, the deletion unit can quickly and easily delete the content. This improves the user experience by determining the deletion priority according to the user's emotions. The estimation of the user's emotions is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the deletion unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the deletion unit can input the user's emotion data into the generation AI and have the generation AI determine the deletion priority.

[0073] The deletion unit can improve the accuracy of deletion by taking into account the interrelationships between content. The deletion unit performs deletion by taking into account, for example, the relationship between original content and its derivative works. The deletion unit can also perform deletion by taking into account, for example, the relationship between different episodes of the same series. The deletion unit can also perform deletion by taking into account, for example, the similarity in the genre or theme of the content. In this way, the accuracy of deletion is improved by taking into account the interrelationships between content. Some or all of the above-described processing in the deletion unit may be performed using, for example, AI, or may be performed without using AI. For example, the deletion unit can input content interrelationship data into a generation AI and cause the generation AI to improve the accuracy of deletion.

[0074] The deletion unit can perform deletion taking into account attribute information of the submitter of the content. The deletion unit can, for example, analyze the submitter's past behavioral history and determine the deletion priority. The deletion unit can also, for example, perform deletion taking into account geographical location information of the submitter. The deletion unit can also, for example, analyze the submitter's social media activities and determine the deletion priority. By taking the submitter's attribute information into account, the accuracy of deletion is improved. Some or all of the above-described processing in the deletion unit can be performed using, for example, AI, or can be performed without using AI. For example, the deletion unit can input the submitter's attribute information data into the generation AI and cause the generation AI to execute deletion.

[0075] The deletion unit can estimate the user's emotions and adjust the deletion display method based on the estimated user's emotions. For example, if the user is excited, the deletion unit can display a deletion notification with a visually appealing effect. For example, if the user is relaxed, the deletion unit can display a deletion notification with a simple and calm design. For example, if the user is stressed, the deletion unit can display a concise and quick deletion notification. This improves the user experience by adjusting the display method according to the user's emotions. The estimation of the user's emotions is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the deletion unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the deletion unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the display method.

[0076] The deletion unit can perform deletion taking into account the geographic distribution of content. For example, the deletion unit prioritizes deletion of pirated copies distributed in a specific region. The deletion unit can also evaluate the possibility of pirated copies in a specific region based on geographical data and perform deletion. The deletion unit can also analyze the distribution patterns of pirated copies in each region and apply the optimal deletion method. This makes it possible to delete pirated copies in each region by taking geographical distribution into consideration. Some or all of the above-mentioned processing in the deletion unit can be performed using, for example, AI, or without AI. For example, the deletion unit can input geographical distribution data into a generation AI and cause the generation AI to apply a deletion method.

[0077] The deletion unit can improve the accuracy of deletion by referring to literature related to the content. The deletion unit can improve the accuracy of deletion by referring to, for example, related academic papers and articles. The deletion unit can also improve the accuracy of deletion by referring to, for example, related patent information. The deletion unit can also improve the accuracy of deletion by referring to, for example, related industry reports. In this way, the accuracy of deletion is improved by referring to related literature. Some or all of the above-described processing in the deletion unit can be performed using, for example, AI, or can be performed without using AI. For example, the deletion unit can input related literature data into the generation AI and cause the generation AI to improve the accuracy of deletion. === Hard Collateral 1-1 === Each of the multiple elements, including the issuing unit, detection unit, and deletion unit, described above, is implemented, for example, in at least one of the smart device 14 and the data processing device 12. For example, the issuing unit is implemented by the specific processing unit 290 of the data processing device 12 and issues an NFT using blockchain technology to certify copyright for a specific digital file. The detection unit is implemented, for example, by the control unit 46A of the smart device 14 and scans content on the Internet and detects pirated copies using image recognition technology and text analysis technology. The deletion unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and promptly deletes detected pirated copies. === Hard Collateral 1-2 === Each of the multiple elements, including the issuing unit, the detecting unit, and the deleting unit, described above, is implemented, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the issuing unit is implemented by the specific processing unit 290 of the data processing device 12 and issues an NFT using blockchain technology to certify copyright for a specific digital file. The detecting unit is implemented, for example, by the control unit 46A of the smart glasses 214 and scans content on the Internet and detects pirated copies using image recognition technology and text analysis technology. The deleting unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and promptly deletes detected pirated copies. === Hard Collateral 1-3 === Each of the multiple elements including the issuing unit, detecting unit, and deleting unit described above is implemented, for example, in at least one of the headset-type terminal 314 and the data processing device 12. For example, the issuing unit is implemented by the specific processing unit 290 of the data processing device 12 and issues an NFT using blockchain technology to certify copyright for a specific digital file. The detection unit is implemented, for example, by the control unit 46A of the headset-type terminal 314 and scans content on the Internet and detects pirated copies using image recognition technology and text analysis technology. The deletion unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and promptly deletes any detected pirated copies. === Hard Collateral 1-4 === Each of the multiple elements, including the issuing unit, detection unit, and deletion unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the issuing unit is realized by the specific processing unit 290 of the data processing device 12 and issues an NFT using blockchain technology to certify copyright for a specific digital file. The detection unit is realized, for example, by the control unit 46A of the robot 414 and scans content on the Internet and detects pirated copies using image recognition technology and text analysis technology. The deletion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and promptly deletes detected pirated copies.

[0078] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0079] The issuer can analyze users' purchasing history and issue special NFTs to specific users. For example, a limited-edition NFT can be issued to a user who has purchased a lot of content in the past. The issuer can also issue NFTs containing special rewards to users who have shown a high interest in a particular genre or series. Furthermore, the issuer can issue NFTs that provide incentives to encourage future purchases based on the user's purchasing history. This can improve user satisfaction by utilizing the user's purchasing history.

[0080] The detection unit can estimate the user's emotions and customize the piracy detection alert based on the estimated user emotions. For example, if the user is excited, the alert can be highlighted to prompt an immediate response. If the user is relaxed, the alert can be displayed subtly to avoid disrupting the user's sense of relaxation. Furthermore, if the user is stressed, the alert can be displayed succinctly to prompt an immediate response. This allows the alert to be displayed in accordance with the user's emotions, improving the user experience.

[0081] The deletion unit can analyze the history of deletion requests and learn the optimal deletion method. For example, it can analyze the success rate of past deletion requests and identify the most effective deletion method. The deletion unit can also apply the optimal deletion method to a specific provider based on the history of deletion requests. Furthermore, the deletion unit can also optimize the timing of deletion based on the history of deletion requests. In this way, the accuracy and efficiency of deletion can be improved by utilizing the history of deletion requests.

[0082] The issuing unit can estimate the user's emotions and customize the design of the NFT based on the estimated user's emotions. For example, if the user is excited, an NFT with a vivid and dynamic design can be issued. Alternatively, if the user is relaxed, an NFT with calm colors and a simple design can be issued. Furthermore, if the user is feeling stressed, an NFT with a relaxing design to reduce stress can be issued. This improves the user experience by providing NFTs with designs that correspond to the user's emotions.

[0083] The detection unit can analyze the viewing history of content and improve the accuracy of pirated copy detection based on the viewing history. For example, stricter detection criteria can be applied to content that a particular user frequently views. It can also prioritize detection of specific genres or series based on the viewing history. Furthermore, it can prioritize detection of pirated copies that are likely to interest the user based on the viewing history. Thus, utilizing the viewing history improves the accuracy of pirated copy detection.

[0084] The deletion unit can estimate the user's emotions and customize the content of the deletion notification based on the estimated user's emotions. For example, if the user is excited, the deletion unit can send a deletion notification with a detailed explanation. If the user is relaxed, the deletion unit can send a concise and to-the-point deletion notification. Furthermore, if the user is stressed, the deletion unit can send a deletion notification with an encouraging message to reduce stress. This improves the user experience by providing a deletion notification that suits the user's emotions.

[0085] Issuers can issue NFTs taking into account the content production process. For example, NFTs can be issued at each stage of the production process to show the progress of production. Special NFTs can also be issued at specific milestones in the production process. Furthermore, NFTs containing documentaries or behind-the-scenes footage of the production process can be issued. This will increase transparency in production and interest among fans by taking the content production process into consideration.

[0086] The detection unit can estimate the user's emotions and customize the piracy detection method based on the estimated user emotions. For example, if the user is excited, a fast and detailed detection method can be applied. If the user is relaxed, a flexible and relaxed detection method can be applied. Furthermore, if the user is stressed, a simple and fast detection method can be applied. This improves the user experience by providing a detection method that suits the user's emotions.

[0087] The deletion unit can evaluate the influence of content to determine the priority of deletion requests. For example, deletion of content with a high influence can be prioritized. Content involving influential users can also be prioritized for deletion. Furthermore, content related to influential events or campaigns can also be prioritized for deletion. This allows for effective deletion by taking the influence of content into consideration.

[0088] The issuing unit can estimate the user's emotions and guide them through the NFT issuance process based on the estimated user emotions. For example, if the user is excited, a step-by-step guide can be provided to maintain the user's excitement. Alternatively, if the user is relaxed, a simple and intuitive guide can be provided to maintain the user's sense of relaxation. Furthermore, if the user is stressed, a quick and easy guide can be provided to reduce stress. This improves the user experience by providing guidance tailored to the user's emotions.

[0089] The processing flow of the second embodiment will be briefly explained below.

[0090] Step 1: The issuer issues an NFT for the original content and grants copyright. The issuer issues an NFT using, for example, blockchain technology to certify copyright for a specific digital file. Blockchain technology is implemented using technologies such as Ethereum and Hyperledger. Examples of specific digital files include, but are not limited to, JPEG images and MP4 videos. Step 2: The detection unit scans content on the Internet and detects pirated copies. The detection unit uses, for example, image recognition technology or text analysis technology to compare the original content with the pirated copy and determine that it is pirated if they match. Image recognition technology is realized, for example, by CNN (Convolutional Neural Network). Text analysis technology is realized, for example, by natural language processing (NLP). Step 3: The deletion unit promptly deletes the pirated copy detected by the detection unit. For example, when the deletion unit detects a pirated copy, it promptly sends a deletion request and executes the deletion without confirmation from the provider. The deletion request is sent, for example, by a method such as automatic deletion using an API.

[0091] 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.

[0092] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0093] 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.

[0094] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0095] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0096] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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).

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0109] 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.

[0110] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0111] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0122] 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.

[0123] 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.

[0124] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0125] 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.

[0126] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0127] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0128] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0139] 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.

[0140] 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.

[0141] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0142] 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.

[0143] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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 indicated, and when they approach the ideal, a state of pleasure is indicated. 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 indicated, and when they approach the ideal, a state of pleasure is indicated. 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.

[0149] 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."

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] [Explanation of symbols]

[0163] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A publishing department issues NFTs for original content and grants copyrights; a detection unit that scans content on the Internet to detect pirated copies; a deletion unit that promptly deletes the pirated copy detected by the detection unit. A system characterized by:

2. The issuing department: Using blockchain technology to issue NFTs and prove copyright to specific digital files 2. The system of claim 1.

3. The detection unit Using image recognition or text analysis technology, the original content is compared with the pirated version, and if there is a match, it is determined to be pirated.

2. The system of claim 1.

4. The deletion unit When piracy is detected, we will promptly send a removal request and execute the removal without confirmation from the provider.

2. The system of claim 1.

5. The deletion unit Implementing legal process for removal 2. The system of claim 1.

6. The issuing department: Estimate user emotions and adjust the timing of NFT issuance based on the estimated user emotions.

2. The system of claim 1.

7. The issuing department: Determine the priority of NFT issuance based on content popularity and ratings 2. The system of claim 1.

8. The issuing department: Apply different NFT issuance algorithms depending on the type of content 2. The system of claim 1.

Images