Data processing device, data processing method and data processing program

Abstract

To provide a device, a method and a program capable of preventing an unjustifiable obtainment of a profit by contents generated by a generative model.SOLUTION: A data processing device includes: a monitoring unit that monitors a command to be input to a generative model to generate contents; a detecting unit that detects, based on the command, the plagiarism of the contents generated by the generative model; and an associating unit that associates the detected result with the contents.SELECTED DRAWING: Figure 4

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a data processing device, a data processing method, and a data processing program. [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] In recent years, remarkable advances in AI (Artificial Intelligence) have led to the emergence of various generative AIs that generate content using generative models. This allows creators who use generative AI to generate content (also known as "AI creators") to create content relatively easily. On the other hand, creators who create content without using generative AI (also known as "non-AI creators" or "handwritten creators") face the problem of their works being easily plagiarized by generative AI.

[0005] Therefore, the technology disclosed herein aims to provide an apparatus, method, and program that can prevent unfair profits from being made through content generated by a generative model. [Means for solving the problem]

[0006] A first aspect of the technology of the present disclosure is a data processing device comprising: a monitoring unit that monitors instructions input into a generative model to generate content; a detection unit that detects plagiarism of the content generated by the generative model based on the instructions; and an association unit that associates the detected results with the content.

[0007] The instructions and the content may be exchanged on a blockchain.

[0008] The data processing device may further include an interface unit that supplies the instructions to the generative model via an API and obtains the content from the generative model via the API.

[0009] The detection unit may detect that the content is plagiarized when one or more prohibited words registered in advance are extracted from the command.

[0010] The association unit may flag the content that is detected as plagiarized.

[0011] The association unit may assign a score to the content that quantifies the degree of plagiarism.

[0012] The data processing device may further include a determination unit that determines whether or not the content is available for auction based on the detection result.

[0013] A second aspect of the technology of the present disclosure is a data processing method in which a computer performs processing including monitoring instructions input into a generative model to generate content, detecting plagiarism in the content generated by the generative model based on the instructions, and associating the detected results with the content.

[0014] A third aspect of the technology of the present disclosure is a data processing program for causing a computer to perform processing including monitoring instructions input into a generative model to generate content, detecting plagiarism of the content generated by the generative model based on the instructions, and associating the detected results with the content. [Brief explanation of the drawings]

[0015] [Figure 1] FIG. 1 is a conceptual diagram illustrating an example of a configuration of a data processing system. [Figure 2] FIG. 2 is a conceptual diagram showing an example of main functions of a data processing device and a user terminal. [Figure 3] An overview of the specific processing is shown below. [Figure 4] 2 shows a schematic functional configuration of a specific processing unit of the data processing device. [Figure 5] 10 is a diagram illustrating an example of an operational flow of specific processing by a data processing device. DETAILED DESCRIPTION OF THE INVENTION

[0016] Hereinafter, an example of an embodiment of a data processing device, a data processing method, and a data processing program according to the technique of the present disclosure will be described with reference to the accompanying drawings.

[0017] First, the terms used in the following description will be explained.

[0018] 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).

[0019] 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.

[0020] 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.

[0021] 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), Bluetooth (registered trademark), etc.

[0022] 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."

[0023] FIG. 1 shows an example of the configuration of a data processing system 10 according to the embodiment.

[0024] 1, the data processing system 10 includes a data processing device 12 and a user terminal 14. An example of the data processing device 12 is a server. An example of the user terminal 14 is a personal computer, a smartphone, etc. The data processing device 12 is an example of a "data processing device" according to the technology of the present disclosure.

[0025] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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).

[0026] The user terminal 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.

[0027] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. 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 acquires the data indicating the user input.

[0028] The output device 40 is equipped with a display 40A, a speaker 40B, etc., and presents data to the user by outputting the data in a form of expression that the user can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The 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.

[0029] 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.

[0030] FIG. 2 shows an example of the main functions of the data processing device 12 and the user terminal 14.

[0031] As shown in FIG. 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 "data processing 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.

[0032] In the user terminal 14, the processor 46 performs the reception output processing. The storage 50 stores a reception output program 62. The reception output program 62 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 62 from the storage 50 and executes the read reception output program 62 on the RAM 48. The reception output processing is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 62 executed on the RAM 48.

[0033] Next, we will explain the processing of the identification processing unit 290 when the data processing device 12 performs identification processing to detect plagiarism of content. Note that the "content" referred to here refers to so-called "digital content" in digital format. From here on, we will explain the case where the content is an image as an example, but this is not limited to this. The content may be various content such as video, music, e-books, games, etc.

[0034] Figure 3 shows an overview of the process for detecting plagiarism of content. Generally, transactions take place as follows: (1) a creator generates content, (2) the creator puts the content up for sale, and (3) a purchasing user purchases the content. In such transactions, a known model is to trade content as a non-fungible token (NFT) on an NFT platform (also known as an "NFT marketplace").

[0035] NFTs are non-fungible digital data created based on blockchain. Until now, digital content has been easily copied and tampered with, making it difficult to give it asset value. In contrast, NFTs that utilize blockchain have attracted attention because they make it possible for digital content to have unique asset value, just like analog content.

[0036] In recent years, remarkable advances in AI have led to the emergence of various generation tools 16 that generate such content using AI. The generation tools 16 generate various pieces of content using a generation model 58. The generation model 58 is an example of a "generative model" according to the technology of the present disclosure.

[0037] If the content is an image, an example of a generative model 58 is Stable Diffusion (Internet search)<URL: https: / / stablediffusionweb.com / > ), DALL-E2 (Internet search<URL: https: / / openai.com / dall-e-2> ), Midjourney (Internet search<URL:https: / / www.midjourney.com / > In such a generative AI, when a command called a prompt is input to the generative model 58, the generative model 58 generates content according to the command and outputs it.

[0038] The identification process in this embodiment monitors the commands input to the generative model 58 in this manner to detect plagiarism of the content generated by the generative model 58, thereby preventing unfair profits from being gained from the content generated by the generative model 58.

[0039] To achieve this, the NFT platform may be API-linked with the generation tool 16. API (Application Programming Interface) linkage refers to data linkage between external applications and systems using APIs.

[0040] In this case, the AI ​​creator can connect their wallet to the NFT platform (i.e., log in) and select the generation tool 16, which is linked to the data as an extension, to use the generation AI as if they were accessing it directly. Meanwhile, because all interactions between the AI ​​creator and the generation model 58 are via the API, the data processing device 12 implemented in the NFT platform can monitor such interactions. Therefore, the data processing device 12 according to this embodiment detects plagiarism of generated content by tracking the generation route of content created by the generation model 58. This will be described in detail.

[0041] 4 shows a schematic functional configuration of a specific processing unit of the data processing device. The specific processing unit 290 includes an interface unit 291, a monitoring unit 292, a detection unit 293, an association unit 294, and a determination unit 295.

[0042] The interface unit 291 supplies commands for generating content to the generative model 58 via the API, and acquires content from the generative model 58 via the API.

[0043] The monitor 292 monitors the instructions input to the generative model 58 to generate content.

[0044] The detection unit 293 detects plagiarism of the content generated by the generative model 58 based on the instruction.

[0045] The association unit 294 associates the detected results with the content.

[0046] The determination unit 295 determines whether or not the content is available for auction based on the detected result.

[0047] Next, the operation of the data processing system 10 will be described.

[0048] 5 shows an example of the operational flow of specific processing by a data processing device. The flow of the specific processing will be explained with reference to this figure. Note that the flow of the specific processing shown in this figure is an example of a "data processing method" according to the technology of the present disclosure.

[0049] This operational flow may begin when the AI ​​creator connects their wallet to the NFT platform and selects the generation tool 16.

[0050] In step S301, the computer 22, functioning as the interface unit 291, supplies a command for generating content via the API to the generative model 58. In response, the generative model 58 generates and outputs content according to the input command. Then, the computer 22, functioning as the interface unit 291, acquires the content from the generative model 58 via the API.

[0051] Here, if the content is an image, the instruction may be a prompt such as: <prompt 1> "Please create an image that Mr. A (the original author) would likely create." <Prompt 2> "Please create an image that imitates X (work title)." <Prompt 3> "Please create a cartoon image of a 3-year-old child playing in the snow with his parents in the garden."

[0052] When prompt 1 is input by the AI ​​creator, computer 22 may provide it to generative model 58 via the API. Then, computer 22 may obtain image 1 generated by generative model 58 in response to prompt 1 via the API. Similarly, when prompt 2 is input by the AI ​​creator, computer 22 may provide it to generative model 58 via the API. Then, computer 22 may obtain image 2 generated by generative model 58 in response to prompt 2 via the API. Similarly, when prompt 3 is input by the AI ​​creator, computer 22 may provide it to generative model 58 via the API. Then, computer 22 may obtain image 3 generated by generative model 58 in response to prompt 3 via the API.

[0053] As mentioned above, the generation tool 16 is API-linked to the blockchain-based NFT platform, so all commands and content exchanged between the AI ​​creator and the generation model 58 are exchanged on the blockchain. In this way, when content is generated under the blockchain, it is virtually impossible to tamper with the commands used to generate the content.

[0054] In step S302, the computer 22, functioning as the monitoring unit 292, monitors instructions to be input to the generative model 58 for generating content.

[0055] In step S303, the computer 22, functioning as the monitoring unit 292, determines whether or not one or more prohibited words registered in advance have been extracted from the command. Then, the computer 22, functioning as the detection unit 293, may detect that the content is plagiarized if it is determined that one or more prohibited words registered in advance have been extracted from the command (Yes). If it is detected that the content is plagiarized, the computer 22 proceeds to the process at step S304. On the other hand, if it is determined that no prohibited words have been extracted (No), the computer 22 proceeds to the process at step S305.

[0056] In this case, prohibited words may include the name of the original author of the content (including the name of an individual or an organization) or the name of an artist, such as "Mr. A" or "Group B." Prohibited words may also include the name of the work of the content, such as "X" or "Y." Prohibited words may also include terms that suggest plagiarism, such as "imitation" or "mock." The computer 22 may extract prohibited words from the command, for example, by searching the command using such prohibited words (including fuzzy search).

[0057] Here, suppose that a fuzzy search of prompt 1 results in the extraction of the prohibited word "Mr. A." In this case, the computer 22 may detect that image 1 is plagiarized. Similarly, suppose that a fuzzy search of prompt 2 results in the extraction of the prohibited words "X" and "imitation." In this case, the computer 22 may detect that image 2 is plagiarized. In the case where one or more prohibited words registered in advance are extracted from the command in this way, the computer 22 may detect that the content is plagiarized.

[0058] Then, in step S304, the computer 22, as the association unit 294, assigns a flag indicating plagiarism to image 1 and image 2. The computer 22 may assign a flag to content detected as plagiarism in this manner, for example. This allows the computer 22 to associate the detection result with the content.

[0059] On the other hand, suppose that no prohibited words are extracted as a result of the fuzzy search of prompt 3. In this case, the computer 22 omits the process of step S304.

[0060] In step S305, the computer 22 stores the content. For example, the computer 22 stores the flagged image 1 and image 2 and the unflagged image 3 in the collection box of the NFT platform.

[0061] This allows the AI ​​creator to select the content they wish to put up for sale from the content stored in the collection box. In other words, content not stored in the collection box cannot be put up for sale. Therefore, content traded on the NFT platform is limited to content generated using the generation AI under conditions where the data processing device 12 tracks the generation route.

[0062] In step S306, the computer 22 receives a listing instruction from the user terminal 14. Here, it is assumed that the computer 22 has received an instruction to list image 1, image 2, and image 3 stored in a collection box on the NFT platform.

[0063] In step S307, the computer 22, functioning as the determination unit 295, determines whether or not a flag has been assigned to the content desired to be put up for sale. For example, the computer 22 determines that images 1 and 2 cannot be put up for sale because flags have been assigned to them. If it has been determined that images 1 and 2 cannot be put up for sale, the computer 22 proceeds with the process to step S308. On the other hand, the computer 22 determines that image 3 can be put up for sale because no flag has been assigned to image 3. If it has been determined that image 3 can be put up for sale, the computer 22 proceeds with the process to step S309. In this way, for example, the computer 22 can determine whether or not content can be put up for sale based on the results of detection.

[0064] In step S308, the computer 22 rejects the listing of the content. Here, the computer 22 rejects the listing of image 1 and image 2, and sends a message to the user terminal 14 indicating that the listing of image 1 and image 2 has been rejected.

[0065] Meanwhile, in step S309, the computer 22 permits the listing of content. Here, the computer 22 permits the listing of image 3 and sends a message to the user terminal 14 indicating that image 3 has been listed. This allows the purchasing user to purchase image 3 on the NFT platform. The computer 22 then ends the identification process.

[0066] In the above description, the case where the computer 22 executes the monitoring process after acquiring the content in step S301 has been described as an example. However, if it takes time to generate the content, the computer 22 may execute the monitoring process before acquiring the content.

[0067] In the above description, a case where the computer 22 assigns a flag to content detected as plagiarism has been described as an example. However, this is not limiting. The computer 22 may assign a score of 1 to image 1, indicating that one prohibited word has been extracted. Similarly, the computer 22 may assign a score of 2 to image 2, indicating that two prohibited words have been extracted. In this way, for example, the computer 22 may assign a score to the content that quantifies the degree of plagiarism.

[0068] In this case, in step S307, the computer 22 may determine whether the score exceeds a predetermined threshold, and may determine that content whose score exceeds the predetermined threshold cannot be put up for sale, and that content whose score is equal to or less than the predetermined threshold can be put up for sale.

[0069] In the above explanation, a flag or score is assigned only to content that is detected as plagiarism, while nothing is assigned to content that is not detected as plagiarism, i.e., an example is given in which the content is implicitly indicated as not being plagiarized. However, it is also possible to explicitly indicate that content is not plagiarized by assigning a flag indicating that it is not plagiarized to content that is not detected as plagiarized, or by assigning a score of 0 indicating that the number of extracted prohibited words is 0.

[0070] In this way, the data processing device 12 according to this embodiment monitors the instructions input to the generative model 58 to generate content, detects plagiarism in the content generated by the generative model 58, and associates the detection result with the content. As a result, the data processing device 12 according to this embodiment can detect plagiarism by a generative AI and associate it with the content, thereby preventing unfair profits from being made from the generated content. Therefore, without legal restrictions halting the evolution of AI, handwritten creators can continue to devote themselves to creating works as before without worrying about the rights of their works being infringed.

[0071] In addition, both the instructions input to the generative model and the content generated by the generative model may be exchanged on a blockchain. This makes it virtually impossible to tamper with the instructions that generated the content. Therefore, the data processing device 12 can prevent tampering with information that is monitored to detect plagiarism.

[0072] Additionally, the data processing device 12 may provide instructions to the generative model via an API and obtain content from the generative model via the API. This allows the data processing device 12 to track the generation route of content by the generative model 58 and provides an environment in which the AI ​​creator can use the generative AI as if they were directly accessing the generation tool 16. In particular, when using an existing generative AI, the existing generative AI itself cannot be implemented in the blockchain. In contrast, the data processing device 12 uses API integration to incorporate the existing generative AI into the blockchain without having to design a new generative AI for the blockchain.

[0073] Furthermore, the data processing device 12 may detect that the content is plagiarized if prohibited words are extracted from the command, thereby making it possible to detect plagiarism of the content through the relatively simple process of searching for prohibited words.

[0074] Furthermore, the data processing device 12 may assign a flag to content that is detected as plagiarized, thereby enabling the data processing device 12 to identify whether or not the content is plagiarized based solely on the presence or absence of the flag.

[0075] The data processing device 12 may also assign a score to the content that quantifies the degree of plagiarism, thereby enabling the data processing device 12 to associate an objective numerical value with the content even when it is unclear whether the content is plagiarized or not.

[0076] Furthermore, the data processing device 12 may determine whether or not to allow content to be exhibited based on the detection results. This allows the data processing device 12 to provide an integrated service that combines a plagiarism detection function with an exhibiting function. Therefore, the data processing device 12 can prevent the risk of being sued for infringing the rights of a handwritten creator's work with content generated by a generation AI.

[0077] Furthermore, as described above, content traded on the NFT platform may be limited to content generated using a generation AI under conditions where the data processing device 12 tracks the generation route. This allows the data processing device 12 to create an NFT platform where only content created by a generation AI exists. This makes it possible to separate the works of hand-drawn creators, whose main job is creating, from content created by AI creators using a generation AI. This makes it possible to realize a world in which the value of the works of hand-drawn creators is guaranteed as original works, while content created by AI creators can be enjoyed as content created by a generation AI.

[0078] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[0079] 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.

[0080] 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.

[0081] 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.

[0082] 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.

[0083] The hardware resource for executing a specific process can be any of the following processors: An example of a processor 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. Another example of a processor is 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.

[0084] The hardware resource that executes the specific processing 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 processing may be a single processor.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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]

[0089] 10 Data Processing System 12 Data Processing Device 14 User terminal 58 Generative Models 290 Special Processing Department 291 Interface Section 292 Monitoring Department 293 Detection Unit 294 Related Departments 295 Judgment Department

Claims

1. An interface unit that supplies instructions to be input into a generative model to generate content to the generative model via an API, and obtains content generated by the generative model based on the instructions from the generative model via the API; a monitoring unit that monitors the command; a detection unit that detects that the content generated by the generative model is plagiarized when one or more prohibited words registered in advance are extracted from the command; an association unit that associates the detected results with content generated by the generative model; Equipped with A data processing device wherein the instructions and the content generated by the generative model are exchanged on a blockchain.

2. The association unit flags the content that is detected as plagiarized.

2. The data processing device according to claim 1.

3. the association unit assigns a score to the content that quantifies the degree of plagiarism; 3. A data processing device according to claim 1 or 2.

4. a determination unit that determines whether or not the content is available for auction based on the detection result; 3. A data processing device according to claim 1 or 2.

5. The computer Supplying instructions to be input to a generative model to generate content to the generative model via an API, and obtaining content generated by the generative model based on the instructions from the generative model via the API; monitoring said instructions; Detecting that the content generated by the generative model is plagiarized when one or more prohibited words registered in advance are extracted from the command; Associating the detected results with content generated by the generative model; and Perform a process including A data processing method in which the instructions and the content generated by the generative model are exchanged on a blockchain.

6. On the computer, Supplying instructions to be input to a generative model to generate content to the generative model via an API, and obtaining content generated by the generative model based on the instructions from the generative model via the API; monitoring said instructions; Detecting that the content generated by the generative model is plagiarized when one or more prohibited words registered in advance are extracted from the command; Associating the detected results with content generated by the generative model; and Execute a process including A data processing program, wherein the instructions and the content generated by the generative model are exchanged on a blockchain.

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