system

Abstract

An object of the system according to the embodiment is to determine whether content is created by an AI or a person and to evaluate the value of the content.SOLUTION: A system includes a discrimination part, an evaluation part, and an NFT part. The determination unit determines a creator of the content by using the generation AI. The evaluation unit evaluates the result determined by the determination unit as a probability. The NFT part turns the content into the NFT on the basis of a result evaluated by the evaluation part.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult to determine whether content was created by AI or humans and to assess its value.

[0005] The system according to the embodiment aims to determine whether content has been created by AI or humans and to evaluate its value. [Means for solving the problem]

[0006] The system according to the embodiment includes a determination unit, an evaluation unit, and an NFT unit. The determination unit determines the creator of the content using a generation AI. The evaluation unit evaluates the result determined by the determination unit as a probability. The NFT unit converts the content into an NFT based on the result evaluated by the evaluation unit. [Effects of the Invention]

[0007] The system according to the embodiment can determine whether content was created by AI or humans and evaluate its value. [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) The content classification system according to an embodiment of the present invention is a system that distinguishes between content created by various AIs and content created by humans, evaluates the results of the discrimination as a probability, and provides them as value. As a result, the content classification system identifies the creator of the content and converts it into an NFT based on the results, making it possible to use it commercially.

[0029] A content discrimination system according to an embodiment includes a discrimination unit, an evaluation unit, and an NFT conversion unit. The discrimination unit discriminates the creator of content using a generation AI. For example, the generation AI analyzes the characteristics of each video frame to detect AI-specific patterns and human-specific patterns. The generation AI can also analyze musical waveforms, rhythms, and melody patterns to evaluate the likelihood that the content was generated by AI. The generation AI can also analyze sentence grammar and vocabulary usage patterns to determine whether the content was generated by AI. For example, when analyzing the characteristics of each video frame, techniques such as color histograms and edge detection are used. When analyzing musical waveforms, rhythms, and melody patterns, techniques such as frequency analysis and tempo analysis are used. When analyzing sentence grammar and vocabulary usage patterns, techniques such as grammar analysis and vocabulary frequency analysis are used. The evaluation unit evaluates the discrimination results obtained by the discrimination unit as a probability. For example, the evaluation unit calculates the value of the content based on the discrimination results. The evaluation unit can also provide the value of the content based on the discrimination results. For example, when calculating the value of the content based on the evaluation results, criteria such as market value and uniqueness are used. The NFT conversion unit converts the content into an NFT based on the evaluation results by the evaluation unit. For example, when converting the content into an NFT, the NFT conversion unit registers it as a unique digital asset using blockchain technology. As a result, the content identification system according to the embodiment identifies the creator of the content and converts it into an NFT based on the results, making it possible to use it commercially. For example, NFTed content can be sold through online marketplaces or social media and made available to a wide range of users. When using NFTed content commercially, transaction history can be tracked in detail using blockchain technology. Furthermore, usage status and user feedback of NFTed content can be collected in real time, allowing for optimization of commercial use.

[0030] The discrimination unit can analyze the features of each frame of video and detect patterns unique to AI and humans. The discrimination unit, for example, analyzes the features of each frame of video and detects patterns unique to AI and humans. For example, the generation AI is used to estimate the emotional state of the video creator and improve the accuracy of video discrimination based on that emotional state. For example, the color and movement patterns in the video are analyzed to estimate the creator's emotions. The generation AI also estimates the emotional state of the music creator and improves the accuracy of music discrimination based on that emotion. For example, the generation AI analyzes changes in the music's tempo and melody to estimate the creator's emotions. The generation AI also estimates the emotional state of the text creator and improves the accuracy of text discrimination based on that emotion. For example, the generation AI analyzes the writing style and vocabulary choice to estimate the creator's emotions. This makes it possible to distinguish between AI and human creation patterns in detail by analyzing the features of each frame of video.

[0031] The discrimination unit can analyze the waveform, rhythm, and melody patterns of music to evaluate the possibility that it was generated by AI. For example, the discrimination unit analyzes the waveform, rhythm, and melody patterns of music to evaluate the possibility that it was generated by AI. For example, the generation AI is used to estimate the emotional state of the music's creator and improve the accuracy of music discrimination based on that emotion. For example, it analyzes changes in the music's tempo and melody to estimate the creator's emotions. The generation AI also analyzes video metadata to distinguish between AI and human creation patterns based on the creation date and time and the tools used. For example, it determines that video created with specific software was created by AI. The generation AI also analyzes music metadata to distinguish between AI and human creation patterns based on the creation environment and the tools used. For example, it determines that music created with specific music production software was created by AI. The generation AI also analyzes text metadata to distinguish between AI and human creation patterns based on the creation date and time and the tools used. For example, it determines that text created with a specific text editor was created by AI. This allows us to evaluate the possibilities generated by AI by analyzing the waveform, rhythm, and melody patterns of the music.

[0032] The discrimination unit can analyze the grammar and vocabulary usage patterns of a sentence to determine whether it was generated by AI. For example, the discrimination unit analyzes the grammar and vocabulary usage patterns of a sentence to determine whether it was generated by AI. For example, when the generation AI makes its discrimination, it takes into account the genre and style of the content and applies a discrimination algorithm specialized for that particular genre. For example, it takes into account the video genre (e.g., anime, documentary) and applies a discrimination algorithm specialized for that particular genre. For example, it analyzes the drawing style unique to anime. The generation AI also takes into account the music genre (e.g., classical, pop) and applies a discrimination algorithm specialized for that particular genre. For example, it analyzes the instrument arrangement unique to classical music. The generation AI also takes into account the style of the writing (e.g., poetry, essay) and applies a discrimination algorithm specialized for that particular style. For example, it analyzes the rhythm and rhyme unique to poetry. In this way, it is possible to determine whether a sentence was generated by AI by analyzing the grammar and vocabulary usage patterns of the sentence.

[0033] The evaluation unit can calculate the value of the content based on the determination result. The evaluation unit calculates the value of the content based on, for example, the determination result. For example, the generation AI is used to determine whether the creator of the content has a specific emotion, and evaluate the value of the content based on that emotion. For example, the generation AI is used to determine whether the creator of a video has a specific emotion (e.g., joy, sadness), and evaluate the value of the video based on that emotion. For example, the generation AI is used to highly evaluate videos that express strong emotions. The generation AI is also used to determine whether the creator of music has a specific emotion (e.g., anger, peace), and evaluate the value of music based on that emotion. For example, the generation AI is used to highly evaluate music that expresses strong emotions. The generation AI is also used to determine whether the creator of a piece of text has a specific emotion (e.g., surprise, fear), and evaluate the value of the text based on that emotion. For example, the generation AI is used to highly evaluate text that expresses strong emotions. In this way, the value of the content can be calculated based on the determination result.

[0034] When converting content into an NFT, the NFT conversion unit can register it as a one-of-a-kind digital asset using blockchain technology. For example, when converting content into an NFT, the NFT conversion unit registers it as a one-of-a-kind digital asset using blockchain technology. For example, using a generation AI, the results of content classification may be shared on different platforms (e.g., social media or streaming services) to collect feedback from a wide range of users. For example, the results of video classification may be shared on social media to collect feedback from users. For example, the classification accuracy may be improved based on viewer comments and ratings. The generation AI may also share the results of music classification on streaming services to collect feedback from users. For example, the classification accuracy may be improved based on listeners' plays and ratings. The generation AI may also share the results of text classification on online platforms to collect feedback from users. For example, the classification accuracy may be improved based on readers' comments and ratings. In this way, by registering the content as a one-of-a-kind digital asset, it becomes possible to use it commercially.

[0035] The evaluation unit can provide the value of the content based on the determination results. The evaluation unit provides the value of the content based on the determination results, for example. For example, the generation AI can be used to determine whether the creator of the content is interested in a particular theme or topic, and evaluate the value of the content based on that theme. For example, the generation AI can determine whether the creator of a video is interested in a particular theme (e.g., environmental protection, social issues), and evaluate the value of the video based on that theme. For example, videos with high social significance can be highly rated. The generation AI can also determine whether the creator of music is interested in a particular topic (e.g., love, peace), and evaluate the value of music based on that topic. For example, music with emotional themes can be highly rated. The generation AI can also determine whether the creator of a piece of text is interested in a particular theme (e.g., history, the future), and evaluate the value of the text based on that theme. For example, text with high educational value can be highly rated. In this way, the value of the content can be provided based on the determination results.

[0036] The discrimination unit analyzes content metadata and can distinguish between AI and human creation patterns in more detail. The discrimination unit, for example, analyzes content metadata and distinguishes between AI and human creation patterns in more detail. For example, it analyzes video metadata and distinguishes between AI and human creation patterns based on the creation date and time and the tools used. For example, it determines that video created with specific software was created by AI. The generation AI also analyzes music metadata and distinguishes between AI and human creation patterns based on the creation environment and the tools used. For example, it determines that music created with specific music production software was created by AI. The generation AI also analyzes text metadata and distinguishes between AI and human creation patterns based on the creation date and time and the tools used. For example, it determines that text created with a specific text editor was created by AI. In this way, by analyzing metadata, it is possible to distinguish between AI and human creation patterns in more detail.

[0037] The discrimination unit can consider the genre and style of the content and apply a discrimination algorithm specialized for a specific genre. For example, when the generation AI makes a discrimination, the discrimination unit considers the genre and style of the content and applies a discrimination algorithm specialized for a specific genre. For example, it considers the video genre (e.g., anime, documentary) and applies a discrimination algorithm specialized for a specific genre. For example, it analyzes the drawing style unique to anime. The generation AI also considers the music genre (e.g., classical, pop) and applies a discrimination algorithm specialized for a specific genre. For example, it analyzes the instrument arrangement unique to classical music. The generation AI also considers the writing style (e.g., poetry, essay) and applies a discrimination algorithm specialized for a specific style. For example, it analyzes the rhythm and rhyme unique to poetry. In this way, by considering the genre and style, it becomes possible to make a discrimination specialized for a specific genre.

[0038] The evaluation unit can share the classification results on different platforms and collect feedback from a wide range of users. The evaluation unit, for example, shares the classification results on different platforms and collects feedback from a wide range of users. For example, the generation AI can use social media to share the video classification results and collect feedback from users. For example, the classification accuracy can be improved based on viewer comments and ratings. The generation AI can also share the music classification results on streaming services and collect user feedback. For example, the classification accuracy can be improved based on listeners' plays and ratings. The generation AI can also share the text classification results on online platforms and collect user feedback. For example, the classification accuracy can be improved based on readers' comments and ratings. In this way, the classification accuracy can be improved by collecting feedback on different platforms.

[0039] The evaluation unit can determine whether a content creator is interested in a particular theme or topic, and evaluate the value of the content based on that theme. For example, the evaluation unit uses a generation AI to determine whether a content creator is interested in a particular theme or topic, and evaluate the value of the content based on that theme. For example, the evaluation unit can determine whether a video creator is interested in a particular theme (e.g., environmental protection, social issues), and evaluate the value of the video based on that theme. For example, it can highly evaluate videos with high social significance. The generation AI can also determine whether a music creator is interested in a particular topic (e.g., love, peace), and evaluate the value of music based on that topic. For example, it can highly evaluate music with emotional themes. The generation AI can also determine whether a writing creator is interested in a particular theme (e.g., history, the future), and evaluate the value of writing based on that theme. For example, it can highly evaluate writing with high educational value. This allows the value of content to be evaluated based on the creator's interests.

[0040] The evaluation unit can perform a probability evaluation based on past data when evaluating the probability of the discrimination result by referring to historical data and trends of the content. For example, when evaluating the probability of the discrimination result, the evaluation unit performs a probability evaluation based on past data by referring to historical data and trends of the content. For example, when evaluating the probability of the video discrimination result, the evaluation refers to past video data and trends and performs a probability evaluation. For example, the evaluation is performed based on past successful examples and trends. Furthermore, when evaluating the probability of the music discrimination result, the generation AI performs a probability evaluation by referring to past music data and trends. For example, the evaluation is performed based on past hit songs and trends. Furthermore, when evaluating the probability of the text discrimination result, the generation AI performs a probability evaluation by referring to past text data and trends. For example, the evaluation is performed based on past bestsellers and trends. In this way, by referring to historical data and trends, the accuracy of the probability evaluation can be improved.

[0041] The evaluation unit can perform a more accurate probability evaluation by analyzing the visual and auditory features of the content in detail when performing a probability evaluation. For example, the evaluation unit can perform a more accurate probability evaluation by analyzing the visual and auditory features of the content in detail when performing a probability evaluation. For example, the evaluation unit can perform a more accurate probability evaluation by analyzing the visual features of the video (e.g., color, movement) in detail. For example, the evaluation unit can perform a more accurate probability evaluation by analyzing the features of each frame of the video. The generation AI can also perform a more accurate probability evaluation by analyzing the auditory features of the music (e.g., sound waveform, rhythm). For example, the generation AI can perform a more accurate probability evaluation by analyzing changes in the tempo and melody of the music. The generation AI can also perform a more accurate probability evaluation by analyzing the visual features of the text (e.g., grammar, vocabulary) in detail. For example, the generation AI can perform a more accurate probability evaluation by analyzing the style of the text and vocabulary choice. In this way, the accuracy of the probability evaluation can be improved by analyzing the visual and auditory features in detail.

[0042] The evaluation unit can set industry-specific evaluation criteria to evaluate the probability evaluation results for usability in different industries or fields. The evaluation unit, for example, sets industry-specific evaluation criteria to evaluate the probability evaluation results for usability in different industries or fields. For example, based on the probability evaluation results for video, criteria for evaluating usability in different industries (e.g., advertising, movies) are set. For example, evaluation criteria for the advertising industry are set. Furthermore, the generation AI sets criteria for evaluating usability in different fields (e.g., entertainment, education) based on the probability evaluation results for music. For example, evaluation criteria for the education field are set. Furthermore, based on the probability evaluation results for text, criteria for evaluating usability in different industries (e.g., publishing, journalism). For example, evaluation criteria for the publishing industry are set. In this way, by setting industry-specific evaluation criteria, usability in different industries or fields can be evaluated.

[0043] The evaluation unit can set evaluation criteria from an international perspective to evaluate the results of the probability evaluation for usability in different languages ​​and cultural spheres. The evaluation unit, for example, sets evaluation criteria from an international perspective to evaluate the results of the probability evaluation for usability in different languages ​​and cultural spheres. For example, criteria for evaluating usability in different languages ​​and cultural spheres are set based on the probability evaluation results of video. For example, evaluation criteria for the international market are set. Furthermore, the generation AI sets criteria for evaluating usability in different languages ​​and cultural spheres based on the probability evaluation results of music. For example, evaluation criteria for the global market are set. Furthermore, the generation AI sets criteria for evaluating usability in different languages ​​and cultural spheres based on the probability evaluation results of text. For example, evaluation criteria for multilingual compatibility are set. In this way, usability in different languages ​​and cultural spheres can be evaluated by setting evaluation criteria from an international perspective.

[0044] The NFT unit can utilize blockchain technology to track the transaction history of the content in detail when using NFTed content commercially. For example, the NFT unit utilizes blockchain technology to track the transaction history of the content in detail when using NFTed content commercially. For example, when using NFTed video commercially, it utilizes blockchain technology to track the transaction history of the video in detail. For example, it records the transfer history of ownership of the video. Furthermore, the generation AI utilizes blockchain technology to track the transaction history of the music in detail when using NFTed music commercially. For example, it records the licensing history of the song. Furthermore, when using NFTed text commercially, it utilizes blockchain technology to track the transaction history of the text in detail. For example, it records the publication history of an article. In this way, by utilizing blockchain technology, the transaction history of the content can be tracked in detail.

[0045] The NFT unit can optimize the commercial use of NFTed content by collecting content usage status and user feedback in real time. For example, when using NFTed content commercially, the NFT unit can optimize the commercial use by collecting content usage status and user feedback in real time. For example, when using NFTed video commercially, the NFT unit can optimize the commercial use by collecting content usage status and user feedback in real time. For example, optimization is performed based on viewers' viewing time and ratings. Furthermore, when using NFTed music commercially, the generation AI can optimize the commercial use by collecting content usage status and user feedback in real time. For example, optimization is performed based on listeners' play counts and ratings. Furthermore, when using NFTed text commercially, the generation AI can optimize the commercial use by collecting content usage status and user feedback in real time. For example, optimization is performed based on readers' viewing time and ratings. This allows for optimization of commercial use by collecting usage status and feedback in real time.

[0046] The NFT unit can sell NFTed content on different platforms and provide it to a wide range of users. For example, the NFT unit can sell NFTed content on different platforms and provide it to a wide range of users. For example, NFTed video can be sold on an online marketplace and provided to a wide range of users. For example, purchasers of video can use the video for advertising and promotions. The generation AI can also sell NFTed music on social media and provide it to a wide range of users. For example, purchasers of a song can use the song as background music for videos. The generation AI can also sell NFTed text on online platforms and provide it to a wide range of users. For example, purchasers of an article can post the article on blogs and news sites. This allows the content to be sold on different platforms and provided to a wide range of users.

[0047] The NFT Department can develop industry-specific marketing strategies to promote the commercial use of NFTed content in different industries and applications. For example, the NFT Department develops industry-specific marketing strategies to promote the commercial use of NFTed content in different industries and applications. For example, to promote the commercial use of NFTed video in the education industry, it develops a marketing strategy for education. For example, it could sell it as educational video materials. The generation AI also develops an entertainment marketing strategy to promote the commercial use of NFTed music in the entertainment industry. For example, it could sell it as a soundtrack for movies or games. The generation AI also develops an advertising marketing strategy to promote the commercial use of NFTed text in the advertising industry. For example, it could sell it as advertising copy or promotional articles. In this way, by developing industry-specific marketing strategies, commercial use in different industries and applications can be promoted.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] The acquisition unit can also analyze the user's past browsing history and search history to infer the user's interests. For example, it can analyze the genres and keywords of content the user has previously viewed to identify the user's interests. It can also infer the user's interests based on keywords the user frequently searches for. It can also analyze the user's social media activity to infer the user's interests. This makes it possible to provide content based on the user's interests.

[0050] The acquisition unit can also analyze the user's device information and provide content optimized for the user's usage environment. For example, if the user is using a smartphone, content optimized for the mobile device can be provided. If the user is using a tablet, content optimized for the tablet can be provided. Furthermore, if the user is using a desktop, content optimized for the desktop can be provided. This makes it possible to provide optimal content according to the user's usage environment.

[0051] The providing unit can also analyze the user's geographical location information and provide content based on the user's location. For example, if the user is in a specific area, content related to that area can be provided preferentially. Also, if the user is traveling, content related to the user's travel destination can be provided. Furthermore, it is possible to provide local events and news based on the user's location information. This allows for personalized content provision based on the user's location.

[0052] The determination unit can also analyze a user's social media activity to infer the user's interests. For example, it can identify the user's interests by analyzing the content the user frequently posts and the accounts the user follows. It can also infer the user's interests based on the content the user "likes" or shares. It can also analyze the user's comments and replies to infer the user's interests. This makes it possible to provide content based on the user's social media activity.

[0053] The providing unit can also analyze the user's purchasing history and provide content based on the user's purchasing tendencies. For example, it can analyze products and services purchased by the user in the past to identify the user's purchasing tendencies. It can also estimate the user's purchasing tendencies based on the categories and brands that the user frequently purchases. It can also recommend related products and services based on the user's purchasing history. This makes it possible to provide personalized content based on the user's purchasing tendencies.

[0054] The determination unit can also analyze a user's browsing history to infer the user's interests. For example, it can analyze the websites the user frequently visits and the pages the user views to identify the user's interests. It can also infer the user's interests based on the pages the user bookmarks and the content the user saves. It can also analyze the user's search queries to infer the user's interests. This makes it possible to provide content based on the user's browsing history.

[0055] The processing flow of the first embodiment will be briefly explained below.

[0056] Step 1: The discrimination unit uses the generative AI to determine the creator of the content. For example, the generative AI analyzes the characteristics of each frame of video to detect patterns that are unique to AI and patterns that are unique to humans. The generative AI can also analyze musical waveforms, rhythms, and melody patterns to evaluate the possibility that the content was generated by AI. Furthermore, the generative AI can analyze the grammar and vocabulary usage patterns of text to determine whether it was generated by AI. Techniques such as color histograms and edge detection are used to analyze video, frequency analysis and tempo analysis to analyze music, and grammar analysis and vocabulary frequency analysis to analyze text. Step 2: The evaluation unit evaluates the results of the discrimination by the discrimination unit as a probability. For example, the evaluation unit calculates the value of the content based on the discrimination results. The evaluation unit can also provide the value of the content based on the discrimination results using criteria such as market value and uniqueness. Step 3: The NFT conversion unit converts the content into an NFT based on the evaluation results. For example, the NFT conversion unit uses blockchain technology to register the content as a unique digital asset. This allows the content to be used commercially and sold through online marketplaces and social media. Furthermore, blockchain technology can be used to track transaction history in detail and collect usage and user feedback in real time, optimizing commercial use.

[0057] (Example 2) The content classification system according to an embodiment of the present invention is a system that distinguishes between content created by various AIs and content created by humans, evaluates the results of the discrimination as a probability, and provides them as value. As a result, the content classification system identifies the creator of the content and converts it into an NFT based on the results, making it possible to use it commercially.

[0058] A content discrimination system according to an embodiment includes a discrimination unit, an evaluation unit, and an NFT conversion unit. The discrimination unit discriminates the creator of content using a generation AI. For example, the generation AI analyzes the characteristics of each video frame to detect AI-specific patterns and human-specific patterns. The generation AI can also analyze musical waveforms, rhythms, and melody patterns to evaluate the likelihood that the content was generated by AI. The generation AI can also analyze sentence grammar and vocabulary usage patterns to determine whether the content was generated by AI. For example, when analyzing the characteristics of each video frame, techniques such as color histograms and edge detection are used. When analyzing musical waveforms, rhythms, and melody patterns, techniques such as frequency analysis and tempo analysis are used. When analyzing sentence grammar and vocabulary usage patterns, techniques such as grammar analysis and vocabulary frequency analysis are used. The evaluation unit evaluates the discrimination results obtained by the discrimination unit as a probability. For example, the evaluation unit calculates the value of the content based on the discrimination results. The evaluation unit can also provide the value of the content based on the discrimination results. For example, when calculating the value of the content based on the evaluation results, criteria such as market value and uniqueness are used. The NFT conversion unit converts the content into an NFT based on the evaluation results by the evaluation unit. For example, when converting the content into an NFT, the NFT conversion unit registers it as a unique digital asset using blockchain technology. As a result, the content identification system according to the embodiment identifies the creator of the content and converts it into an NFT based on the results, making it possible to use it commercially. For example, NFTed content can be sold through online marketplaces or social media and made available to a wide range of users. When using NFTed content commercially, transaction history can be tracked in detail using blockchain technology. Furthermore, usage status and user feedback of NFTed content can be collected in real time, allowing for optimization of commercial use.

[0059] The discrimination unit can analyze the features of each frame of video and detect patterns unique to AI and humans. The discrimination unit, for example, analyzes the features of each frame of video and detects patterns unique to AI and humans. For example, the generation AI is used to estimate the emotional state of the video creator and improve the accuracy of video discrimination based on that emotional state. For example, the color and movement patterns in the video are analyzed to estimate the creator's emotions. The generation AI also estimates the emotional state of the music creator and improves the accuracy of music discrimination based on that emotion. For example, the generation AI analyzes changes in the music's tempo and melody to estimate the creator's emotions. The generation AI also estimates the emotional state of the text creator and improves the accuracy of text discrimination based on that emotion. For example, the generation AI analyzes the writing style and vocabulary choice to estimate the creator's emotions. This makes it possible to distinguish between AI and human creation patterns in detail by analyzing the features of each frame of video.

[0060] The discrimination unit can analyze the waveform, rhythm, and melody patterns of music to evaluate the possibility that it was generated by AI. For example, the discrimination unit analyzes the waveform, rhythm, and melody patterns of music to evaluate the possibility that it was generated by AI. For example, the generation AI is used to estimate the emotional state of the music's creator and improve the accuracy of music discrimination based on that emotion. For example, it analyzes changes in the music's tempo and melody to estimate the creator's emotions. The generation AI also analyzes video metadata to distinguish between AI and human creation patterns based on the creation date and time and the tools used. For example, it determines that video created with specific software was created by AI. The generation AI also analyzes music metadata to distinguish between AI and human creation patterns based on the creation environment and the tools used. For example, it determines that music created with specific music production software was created by AI. The generation AI also analyzes text metadata to distinguish between AI and human creation patterns based on the creation date and time and the tools used. For example, it determines that text created with a specific text editor was created by AI. This allows us to evaluate the possibilities generated by AI by analyzing the waveform, rhythm, and melody patterns of the music.

[0061] The discrimination unit can analyze the grammar and vocabulary usage patterns of a sentence to determine whether it was generated by AI. For example, the discrimination unit analyzes the grammar and vocabulary usage patterns of a sentence to determine whether it was generated by AI. For example, when the generation AI makes its discrimination, it takes into account the genre and style of the content and applies a discrimination algorithm specialized for that particular genre. For example, it takes into account the video genre (e.g., anime, documentary) and applies a discrimination algorithm specialized for that particular genre. For example, it analyzes the drawing style unique to anime. The generation AI also takes into account the music genre (e.g., classical, pop) and applies a discrimination algorithm specialized for that particular genre. For example, it analyzes the instrument arrangement unique to classical music. The generation AI also takes into account the style of the writing (e.g., poetry, essay) and applies a discrimination algorithm specialized for that particular style. For example, it analyzes the rhythm and rhyme unique to poetry. In this way, it is possible to determine whether a sentence was generated by AI by analyzing the grammar and vocabulary usage patterns of the sentence.

[0062] The evaluation unit can calculate the value of the content based on the determination result. The evaluation unit calculates the value of the content based on, for example, the determination result. For example, the generation AI is used to determine whether the creator of the content has a specific emotion, and evaluate the value of the content based on that emotion. For example, the generation AI is used to determine whether the creator of a video has a specific emotion (e.g., joy, sadness), and evaluate the value of the video based on that emotion. For example, the generation AI is used to highly evaluate videos that express strong emotions. The generation AI is also used to determine whether the creator of music has a specific emotion (e.g., anger, peace), and evaluate the value of music based on that emotion. For example, the generation AI is used to highly evaluate music that expresses strong emotions. The generation AI is also used to determine whether the creator of a piece of text has a specific emotion (e.g., surprise, fear), and evaluate the value of the text based on that emotion. For example, the generation AI is used to highly evaluate text that expresses strong emotions. In this way, the value of the content can be calculated based on the determination result.

[0063] When converting content into an NFT, the NFT conversion unit can register it as a one-of-a-kind digital asset using blockchain technology. For example, when converting content into an NFT, the NFT conversion unit registers it as a one-of-a-kind digital asset using blockchain technology. For example, using a generation AI, the results of content classification may be shared on different platforms (e.g., social media or streaming services) to collect feedback from a wide range of users. For example, the results of video classification may be shared on social media to collect feedback from users. For example, the classification accuracy may be improved based on viewer comments and ratings. The generation AI may also share the results of music classification on streaming services to collect feedback from users. For example, the classification accuracy may be improved based on listeners' plays and ratings. The generation AI may also share the results of text classification on online platforms to collect feedback from users. For example, the classification accuracy may be improved based on readers' comments and ratings. In this way, by registering the content as a one-of-a-kind digital asset, it becomes possible to use it commercially.

[0064] The evaluation unit can provide the value of the content based on the determination results. The evaluation unit provides the value of the content based on the determination results, for example. For example, the generation AI can be used to determine whether the creator of the content is interested in a particular theme or topic, and evaluate the value of the content based on that theme. For example, the generation AI can determine whether the creator of a video is interested in a particular theme (e.g., environmental protection, social issues), and evaluate the value of the video based on that theme. For example, videos with high social significance can be highly rated. The generation AI can also determine whether the creator of music is interested in a particular topic (e.g., love, peace), and evaluate the value of music based on that topic. For example, music with emotional themes can be highly rated. The generation AI can also determine whether the creator of a piece of text is interested in a particular theme (e.g., history, the future), and evaluate the value of the text based on that theme. For example, text with high educational value can be highly rated. In this way, the value of the content can be provided based on the determination results.

[0065] The discrimination unit can estimate the emotional state of the content creator and improve the accuracy of discrimination based on that emotional state. The discrimination unit, for example, uses a generation AI to estimate the emotional state of the content creator and improve the accuracy of discrimination based on that emotional state. For example, the discrimination unit uses a generation AI to estimate the emotional state of the video creator and improve the accuracy of video discrimination based on that emotional state. For example, the discrimination unit analyzes color and movement patterns in the video to estimate the creator's emotions. The generation AI also estimates the emotional state of the music creator and improves the accuracy of music discrimination based on that emotion. For example, the generation AI analyzes changes in the music's tempo and melody to estimate the creator's emotions. The generation AI also estimates the emotional state of the text creator and improves the accuracy of text discrimination based on that emotion. For example, the generation AI analyzes the text's writing style and vocabulary choice to estimate the creator's emotions. In this way, the accuracy of discrimination can be improved by taking the creator's emotional state into consideration.

[0066] The discrimination unit analyzes content metadata and can distinguish between AI and human creation patterns in more detail. The discrimination unit, for example, analyzes content metadata and distinguishes between AI and human creation patterns in more detail. For example, it analyzes video metadata and distinguishes between AI and human creation patterns based on the creation date and time and the tools used. For example, it determines that video created with specific software was created by AI. The generation AI also analyzes music metadata and distinguishes between AI and human creation patterns based on the creation environment and the tools used. For example, it determines that music created with specific music production software was created by AI. The generation AI also analyzes text metadata and distinguishes between AI and human creation patterns based on the creation date and time and the tools used. For example, it determines that text created with a specific text editor was created by AI. In this way, by analyzing metadata, it is possible to distinguish between AI and human creation patterns in more detail.

[0067] The discrimination unit can consider the genre and style of the content and apply a discrimination algorithm specialized for a specific genre. For example, when the generation AI makes a discrimination, the discrimination unit considers the genre and style of the content and applies a discrimination algorithm specialized for a specific genre. For example, it considers the video genre (e.g., anime, documentary) and applies a discrimination algorithm specialized for a specific genre. For example, it analyzes the drawing style unique to anime. The generation AI also considers the music genre (e.g., classical, pop) and applies a discrimination algorithm specialized for a specific genre. For example, it analyzes the instrument arrangement unique to classical music. The generation AI also considers the writing style (e.g., poetry, essay) and applies a discrimination algorithm specialized for a specific style. For example, it analyzes the rhythm and rhyme unique to poetry. In this way, by considering the genre and style, it becomes possible to make a discrimination specialized for a specific genre.

[0068] The evaluation unit can determine whether the creator of content has a specific emotion and evaluate the value of the content based on that emotion. The evaluation unit, for example, uses a generation AI to determine whether the creator of content has a specific emotion and evaluate the value of the content based on that emotion. For example, the evaluation unit determines whether the creator of a video has a specific emotion (e.g., joy, sadness) and evaluates the value of the video based on that emotion. For example, it highly rates videos that express emotions. The generation AI can also determine whether the creator of music has a specific emotion (e.g., anger, peace) and evaluate the value of music based on that emotion. For example, it highly rates music that expresses emotions. The generation AI can also determine whether the creator of a piece of text has a specific emotion (e.g., surprise, fear) and evaluate the value of the text based on that emotion. For example, it highly rates text that expresses emotions. In this way, the value of content can be evaluated based on the creator's emotions.

[0069] The evaluation unit can share the classification results on different platforms and collect feedback from a wide range of users. The evaluation unit, for example, shares the classification results on different platforms and collects feedback from a wide range of users. For example, the generation AI can use social media to share the video classification results and collect feedback from users. For example, the classification accuracy can be improved based on viewer comments and ratings. The generation AI can also share the music classification results on streaming services and collect user feedback. For example, the classification accuracy can be improved based on listeners' plays and ratings. The generation AI can also share the text classification results on online platforms and collect user feedback. For example, the classification accuracy can be improved based on readers' comments and ratings. In this way, the classification accuracy can be improved by collecting feedback on different platforms.

[0070] The evaluation unit can determine whether a content creator is interested in a particular theme or topic, and evaluate the value of the content based on that theme. For example, the evaluation unit uses a generation AI to determine whether a content creator is interested in a particular theme or topic, and evaluate the value of the content based on that theme. For example, the evaluation unit can determine whether a video creator is interested in a particular theme (e.g., environmental protection, social issues), and evaluate the value of the video based on that theme. For example, it can highly evaluate videos with high social significance. The generation AI can also determine whether a music creator is interested in a particular topic (e.g., love, peace), and evaluate the value of music based on that topic. For example, it can highly evaluate music with emotional themes. The generation AI can also determine whether a writing creator is interested in a particular theme (e.g., history, the future), and evaluate the value of writing based on that theme. For example, it can highly evaluate writing with high educational value. This allows the value of content to be evaluated based on the creator's interests.

[0071] The evaluation unit can perform a probability evaluation based on emotion, taking into account the emotional state of the creator, when evaluating the probability of the discrimination result. The evaluation unit, for example, uses a generation AI to perform a probability evaluation based on emotion, taking into account the emotional state of the creator, when evaluating the probability of the discrimination result. For example, when evaluating the probability of a video discrimination result, the evaluation unit performs a probability evaluation based on emotion, taking into account the emotional state of the creator. For example, the generation AI increases the probability evaluation of a video that expresses strong emotion. Furthermore, when evaluating the probability of a music discrimination result, the generation AI performs a probability evaluation based on emotion, taking into account the emotional state of the creator. For example, the generation AI increases the probability evaluation of music that expresses strong emotion. Furthermore, when evaluating the probability of a text discrimination result, the generation AI performs a probability evaluation based on emotion, taking into account the emotional state of the creator. For example, the generation AI increases the probability evaluation of a text that expresses strong emotion. In this way, by taking into account the emotional state of the creator, the accuracy of the probability evaluation can be improved.

[0072] The evaluation unit can perform a probability evaluation based on past data when evaluating the probability of the discrimination result by referring to historical data and trends of the content. For example, when evaluating the probability of the discrimination result, the evaluation unit performs a probability evaluation based on past data by referring to historical data and trends of the content. For example, when evaluating the probability of the video discrimination result, the evaluation refers to past video data and trends and performs a probability evaluation. For example, the evaluation is performed based on past successful examples and trends. Furthermore, when evaluating the probability of the music discrimination result, the generation AI performs a probability evaluation by referring to past music data and trends. For example, the evaluation is performed based on past hit songs and trends. Furthermore, when evaluating the probability of the text discrimination result, the generation AI performs a probability evaluation by referring to past text data and trends. For example, the evaluation is performed based on past bestsellers and trends. In this way, by referring to historical data and trends, the accuracy of the probability evaluation can be improved.

[0073] The evaluation unit can perform a more accurate probability evaluation by analyzing the visual and auditory features of the content in detail when performing a probability evaluation. For example, the evaluation unit can perform a more accurate probability evaluation by analyzing the visual and auditory features of the content in detail when performing a probability evaluation. For example, the evaluation unit can perform a more accurate probability evaluation by analyzing the visual features of the video (e.g., color, movement) in detail. For example, the evaluation unit can perform a more accurate probability evaluation by analyzing the features of each frame of the video. The generation AI can also perform a more accurate probability evaluation by analyzing the auditory features of the music (e.g., sound waveform, rhythm). For example, the generation AI can perform a more accurate probability evaluation by analyzing changes in the tempo and melody of the music. The generation AI can also perform a more accurate probability evaluation by analyzing the visual features of the text (e.g., grammar, vocabulary) in detail. For example, the generation AI can perform a more accurate probability evaluation by analyzing the style of the text and vocabulary choice. In this way, the accuracy of the probability evaluation can be improved by analyzing the visual and auditory features in detail.

[0074] The evaluation unit can set industry-specific evaluation criteria to evaluate the probability evaluation results for usability in different industries or fields. The evaluation unit, for example, sets industry-specific evaluation criteria to evaluate the probability evaluation results for usability in different industries or fields. For example, based on the probability evaluation results for video, criteria for evaluating usability in different industries (e.g., advertising, movies) are set. For example, evaluation criteria for the advertising industry are set. Furthermore, the generation AI sets criteria for evaluating usability in different fields (e.g., entertainment, education) based on the probability evaluation results for music. For example, evaluation criteria for the education field are set. Furthermore, based on the probability evaluation results for text, criteria for evaluating usability in different industries (e.g., publishing, journalism). For example, evaluation criteria for the publishing industry are set. In this way, by setting industry-specific evaluation criteria, usability in different industries or fields can be evaluated.

[0075] The evaluation unit can set evaluation criteria from an international perspective to evaluate the results of the probability evaluation for usability in different languages ​​and cultural spheres. The evaluation unit, for example, sets evaluation criteria from an international perspective to evaluate the results of the probability evaluation for usability in different languages ​​and cultural spheres. For example, criteria for evaluating usability in different languages ​​and cultural spheres are set based on the probability evaluation results of video. For example, evaluation criteria for the international market are set. Furthermore, the generation AI sets criteria for evaluating usability in different languages ​​and cultural spheres based on the probability evaluation results of music. For example, evaluation criteria for the global market are set. Furthermore, the generation AI sets criteria for evaluating usability in different languages ​​and cultural spheres based on the probability evaluation results of text. For example, evaluation criteria for multilingual compatibility are set. In this way, usability in different languages ​​and cultural spheres can be evaluated by setting evaluation criteria from an international perspective.

[0076] The evaluation unit can use the generation AI to collect users' emotional responses in the probability evaluation of the discrimination results and perform a probability evaluation based on those emotions. The evaluation unit, for example, uses the generation AI to collect users' emotional responses in the probability evaluation of the discrimination results and perform a probability evaluation based on those emotions. For example, in the probability evaluation of the video discrimination results, the evaluation unit collects users' emotional responses and performs a probability evaluation based on those emotions. For example, the evaluation is performed based on the viewer's emotional score. Furthermore, in the probability evaluation of the music discrimination results, the generation AI collects users' emotional responses and performs a probability evaluation based on those emotions. For example, the evaluation is performed based on the listener's emotional score. Furthermore, in the probability evaluation of the text discrimination results, the generation AI collects users' emotional responses and performs a probability evaluation based on those emotions. For example, the evaluation is performed based on the reader's emotional score. In this way, by collecting users' emotional responses, the accuracy of the probability evaluation can be improved.

[0077] The NFT unit can use the generation AI to analyze users' emotional responses when using NFTed content commercially and develop a marketing strategy based on their emotions. The NFT unit, for example, uses the generation AI to analyze users' emotional responses when using NFTed content commercially and develop a marketing strategy based on their emotions. For example, when using NFTed video commercially, the NFT unit analyzes users' emotional responses and develops a marketing strategy based on their emotions. For example, an advertising strategy is developed based on viewers' emotional scores. The generation AI also analyzes users' emotional responses when using NFTed music commercially and develops a marketing strategy based on their emotions. For example, a promotion strategy is developed based on listeners' emotional scores. The generation AI also analyzes users' emotional responses when using NFTed text commercially and develops a marketing strategy based on their emotions. For example, a sales strategy is developed based on readers' emotional scores. In this way, by analyzing users' emotional responses, a marketing strategy based on their emotions can be developed.

[0078] The NFT unit can utilize blockchain technology to track the transaction history of the content in detail when using NFTed content commercially. For example, the NFT unit utilizes blockchain technology to track the transaction history of the content in detail when using NFTed content commercially. For example, when using NFTed video commercially, it utilizes blockchain technology to track the transaction history of the video in detail. For example, it records the transfer history of ownership of the video. Furthermore, the generation AI utilizes blockchain technology to track the transaction history of the music in detail when using NFTed music commercially. For example, it records the licensing history of the song. Furthermore, when using NFTed text commercially, it utilizes blockchain technology to track the transaction history of the text in detail. For example, it records the publication history of an article. In this way, by utilizing blockchain technology, the transaction history of the content can be tracked in detail.

[0079] The NFT unit can optimize the commercial use of NFTed content by collecting content usage status and user feedback in real time. For example, when using NFTed content commercially, the NFT unit can optimize the commercial use by collecting content usage status and user feedback in real time. For example, when using NFTed video commercially, the NFT unit can optimize the commercial use by collecting content usage status and user feedback in real time. For example, optimization is performed based on viewers' viewing time and ratings. Furthermore, when using NFTed music commercially, the generation AI can optimize the commercial use by collecting content usage status and user feedback in real time. For example, optimization is performed based on listeners' play counts and ratings. Furthermore, when using NFTed text commercially, the generation AI can optimize the commercial use by collecting content usage status and user feedback in real time. For example, optimization is performed based on readers' viewing time and ratings. This allows for optimization of commercial use by collecting usage status and feedback in real time.

[0080] The NFT unit can sell NFTed content on different platforms and provide it to a wide range of users. For example, the NFT unit can sell NFTed content on different platforms and provide it to a wide range of users. For example, NFTed video can be sold on an online marketplace and provided to a wide range of users. For example, purchasers of video can use the video for advertising and promotions. The generation AI can also sell NFTed music on social media and provide it to a wide range of users. For example, purchasers of a song can use the song as background music for videos. The generation AI can also sell NFTed text on online platforms and provide it to a wide range of users. For example, purchasers of an article can post the article on blogs and news sites. This allows the content to be sold on different platforms and provided to a wide range of users.

[0081] The NFT Department can develop industry-specific marketing strategies to promote the commercial use of NFTed content in different industries and applications. For example, the NFT Department develops industry-specific marketing strategies to promote the commercial use of NFTed content in different industries and applications. For example, to promote the commercial use of NFTed video in the education industry, it develops a marketing strategy for education. For example, it could sell it as educational video materials. The generation AI also develops an entertainment marketing strategy to promote the commercial use of NFTed music in the entertainment industry. For example, it could sell it as a soundtrack for movies or games. The generation AI also develops an advertising marketing strategy to promote the commercial use of NFTed text in the advertising industry. For example, it could sell it as advertising copy or promotional articles. In this way, by developing industry-specific marketing strategies, commercial use in different industries and applications can be promoted.

[0082] The NFT unit can use the generation AI to monitor users' emotional responses in real time when using NFTed content commercially, thereby optimizing the commercial use based on their emotions. The NFT unit, for example, can use the generation AI to monitor users' emotional responses in real time when using NFTed content commercially, thereby optimizing the commercial use based on their emotions. For example, when using NFTed video commercially, the NFT unit can monitor users' emotional responses in real time and optimize the commercial use based on their emotions. For example, the NFT unit can adjust an advertising strategy based on viewers' emotional scores. The generation AI can also monitor users' emotional responses in real time when using NFTed music commercially, thereby optimizing the commercial use based on their emotions. For example, the generation AI can adjust a promotional strategy based on listeners' emotional scores. The generation AI can also monitor users' emotional responses in real time when using NFTed text commercially, thereby optimizing the commercial use based on their emotions. For example, the generation AI can adjust a sales strategy based on readers' emotional scores. In this way, by monitoring users' emotional responses in real time, it is possible to optimize the commercial use based on their emotions.

[0083] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0084] The determination unit can also estimate the user's emotions and determine the suitability of content based on the estimated user emotions. For example, if the user has a specific emotion (e.g., joy or sadness), content that matches that emotion can be provided preferentially. The display order of content can also be dynamically changed based on the user's emotions. Furthermore, it is also possible to adjust the recommendation level of content according to the user's emotions. This makes it possible to provide personalized content based on the user's emotions.

[0085] The acquisition unit can also analyze the user's past browsing history and search history to infer the user's interests. For example, it can analyze the genres and keywords of content the user has previously viewed to identify the user's interests. It can also infer the user's interests based on keywords the user frequently searches for. It can also analyze the user's social media activity to infer the user's interests. This makes it possible to provide content based on the user's interests.

[0086] The providing unit can also estimate the user's emotions and adjust the content display method based on the estimated user's emotions. For example, if the user feels like relaxing, content with a visually calming design can be provided. Alternatively, if the user is excited, content with a visually stimulating design can be provided. Furthermore, it is possible to adjust the content display speed and animation effects according to the user's emotions. This makes it possible to display content optimally according to the user's emotions.

[0087] The determination unit can also estimate the user's emotions and dynamically change the content evaluation criteria based on the estimated user emotions. For example, if the user has positive emotions, evaluation criteria that match those emotions can be applied. Conversely, if the user has negative emotions, evaluation criteria that match those emotions can also be applied. Furthermore, it is possible to adjust the content evaluation score according to the user's emotions. This allows for flexible content evaluation based on the user's emotions.

[0088] The providing unit can also estimate the user's emotion and dynamically adjust the recommendation level of content based on the estimated user's emotion. For example, if the user has a specific emotion (e.g., surprise or fear), content that matches that emotion is preferentially recommended. The recommendation level of content can also be adjusted in real time based on the user's emotion. Furthermore, it is also possible to customize the recommended content list according to the user's emotion. This makes it possible to implement personalized content recommendations based on the user's emotion.

[0089] The acquisition unit can also analyze the user's device information and provide content optimized for the user's usage environment. For example, if the user is using a smartphone, content optimized for the mobile device can be provided. If the user is using a tablet, content optimized for the tablet can be provided. Furthermore, if the user is using a desktop, content optimized for the desktop can be provided. This makes it possible to provide optimal content according to the user's usage environment.

[0090] The providing unit can also analyze the user's geographical location information and provide content based on the user's location. For example, if the user is in a specific area, content related to that area can be provided preferentially. Also, if the user is traveling, content related to the user's travel destination can be provided. Furthermore, it is possible to provide local events and news based on the user's location information. This allows for personalized content provision based on the user's location.

[0091] The determination unit can also analyze a user's social media activity to infer the user's interests. For example, it can identify the user's interests by analyzing the content the user frequently posts and the accounts the user follows. It can also infer the user's interests based on the content the user "likes" or shares. It can also analyze the user's comments and replies to infer the user's interests. This makes it possible to provide content based on the user's social media activity.

[0092] The providing unit can also analyze the user's purchasing history and provide content based on the user's purchasing tendencies. For example, it can analyze products and services purchased by the user in the past to identify the user's purchasing tendencies. It can also estimate the user's purchasing tendencies based on the categories and brands that the user frequently purchases. It can also recommend related products and services based on the user's purchasing history. This makes it possible to provide personalized content based on the user's purchasing tendencies.

[0093] The determination unit can also analyze a user's browsing history to infer the user's interests. For example, it can analyze the websites the user frequently visits and the pages the user views to identify the user's interests. It can also infer the user's interests based on the pages the user bookmarks and the content the user saves. It can also analyze the user's search queries to infer the user's interests. This makes it possible to provide content based on the user's browsing history.

[0094] The processing flow of the second embodiment will be briefly explained below.

[0095] Step 1: The discrimination unit uses the generative AI to determine the creator of the content. For example, the generative AI analyzes the characteristics of each frame of video to detect patterns that are unique to AI and patterns that are unique to humans. The generative AI can also analyze musical waveforms, rhythms, and melody patterns to evaluate the possibility that the content was generated by AI. Furthermore, the generative AI can analyze the grammar and vocabulary usage patterns of text to determine whether it was generated by AI. Techniques such as color histograms and edge detection are used to analyze video, frequency analysis and tempo analysis to analyze music, and grammar analysis and vocabulary frequency analysis to analyze text. Step 2: The evaluation unit evaluates the results of the discrimination by the discrimination unit as a probability. For example, the evaluation unit calculates the value of the content based on the discrimination results. The evaluation unit can also provide the value of the content based on the discrimination results using criteria such as market value and uniqueness. Step 3: The NFT conversion unit converts the content into an NFT based on the evaluation results. For example, the NFT conversion unit uses blockchain technology to register the content as a unique digital asset. This allows the content to be used commercially and sold through online marketplaces and social media. Furthermore, blockchain technology can be used to track transaction history in detail and collect usage and user feedback in real time, optimizing commercial use.

[0096] 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.

[0097] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0098] 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.

[0099] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0100] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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).

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0113] 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.

[0114] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0115] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0125] 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.

[0126] 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.

[0127] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0128] 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.

[0129] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0130] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 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.

[0141] 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.

[0142] 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.

[0143] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0150] 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."

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[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 determination unit that determines the creator of the content using a generation AI; an evaluation unit that evaluates the result of the determination by the determination unit as a probability; An NFT conversion unit that converts content into NFT based on the results of evaluation by the evaluation unit. A system characterized by:

2. The determination unit Analyzes the characteristics of each video frame and detects AI-specific and human-specific patterns 2. The system of claim 1.

3. The determination unit Analyze the waveform, rhythm, and melody patterns of the music to evaluate the possibilities of AI-generated music 2. The system of claim 1.

4. The determination unit Analyzes the grammar and vocabulary patterns of a sentence to determine whether it was generated by AI 2. The system of claim 1.

5. The evaluation unit Calculating the value of the content based on the determination result 2. The system of claim 1.

Images