System

Abstract

To automatize copyright confirmation and regeneration of generated content.SOLUTION: A system according to an embodiment of the present invention comprises a generation unit, a copyright confirmation unit, and a regeneration unit. The generation unit generates content by a generative AI. The copyright confirmation unit confirms whether or not the generated content infringes copyrights. The regeneration unit issues an instruction for regeneration when it is determined by the copyright confirmation unit that the copyright is infringed.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 a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional techniques have had the problem that it is time-consuming to check whether generated content infringes copyright, making it difficult to generate content efficiently.

[0005] The system according to the embodiment aims to automate the copyright check and regeneration of generated content. [Means for solving the problem]

[0006] The system according to the embodiment includes a generation unit, a copyright confirmation unit, and a regeneration unit. The generation unit generates content using a generation AI. The copyright confirmation unit confirms whether the content generated by the generation unit infringes copyright. The regeneration unit issues an instruction for regeneration when the copyright confirmation unit determines that the content infringes copyright. Effect of the Invention

[0007] A system according to an embodiment can automate copyright verification and regeneration of generated content. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or 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. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, 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).

[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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[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 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. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A content generation system according to an embodiment of the present invention is a system that automatically checks whether the generated content, such as an image, infringes copyright when the content is generated using a generation AI, and regenerates the content if it does. This system starts with a user inputting an instruction to generate content to the generation AI. For example, an instruction such as "I want you to generate a landscape painting" is input. This instruction is input to the generation AI. Next, the generation AI generates content based on the input instruction. The generated content is output in the form of an image, video, or the like. The generated content is sent to a copyright confirmation unit. The copyright confirmation unit checks whether the generated content matches an existing copyrighted work. For example, image recognition technology is used to check whether the generated image matches an existing copyrighted work. If the copyright confirmation unit determines that the generated content infringes copyright, an instruction is sent to a regeneration unit. The regeneration unit issues an instruction to the generation AI to regenerate the content, and the content is regenerated. The regenerated content is checked again by the copyright confirmation unit. By repeating this process, it is possible to confirm that the generated content does not infringe copyright. This allows the user to generate content that does not infringe copyright without much effort. For example, if a user inputs an instruction to "generate a landscape painting" and the generation AI generates a landscape painting, the copyright confirmation unit checks whether the landscape painting matches an existing copyrighted work. If it matches, the regeneration unit issues an instruction to regenerate it, and the generation AI generates a new landscape painting. By repeating this process, a landscape painting that does not infringe copyright is finally generated. This allows the content generation system to generate content that does not infringe copyrights without the user having to go through any effort.

[0029] The content generation system according to the embodiment includes a generation unit, a copyright confirmation unit, and a regeneration unit. The generation unit generates content using a generation AI. For example, a user inputs an instruction such as "I want a landscape painting to be generated," and the generation AI generates a landscape painting based on the instruction. The generation unit generates content based on the input instruction. The generated content is output in the form of an image, a video, or the like. The generation unit can generate content using, for example, a text generation AI (for example, LLM) or an image generation AI. The copyright confirmation unit checks whether the generated content infringes copyright. The copyright confirmation unit checks whether the generated image matches an existing work, for example, using image recognition technology. The copyright confirmation unit can also refer to a database that is regularly updated to check whether the generated content matches an existing work. The regeneration unit issues an instruction to regenerate when the copyright confirmation unit determines that the content infringes copyright. The regeneration unit issues an instruction to regenerate to the generation AI, and regenerates the content. The regeneration unit can issue an instruction to regenerate to the generation AI using different parameters, for example. The regeneration unit can also provide a feedback loop to prevent the same error from being repeated. As a result, the content generation system according to the embodiment automatically checks the copyright of the generated content, and regenerates it if it infringes. Some or all of the above-mentioned processing in the regeneration unit may be performed, for example, using AI, or may be performed without using AI. For example, the regeneration unit can input different parameters to the generation AI and issue an instruction for regeneration. This improves the diversity of regeneration. Furthermore, the regeneration unit can provide a feedback loop to prevent the same error from being repeated. For example, the regeneration unit evaluates the result of regeneration and updates the regeneration algorithm based on the evaluation result. This improves the accuracy of regeneration.

[0030] The generation unit generates content using a generation AI. For example, when a user inputs an instruction such as "I want a landscape painting to be generated," the generation AI generates a landscape painting based on the instruction. The generation unit generates content based on the input instruction. The generated content is output in the form of an image, video, or the like. The generation unit can generate content using, for example, a text generation AI (e.g., LLM) or an image generation AI. The generation unit has an interface for receiving instructions from a user, and the user can specify details of the content to be generated through text input or voice input. For example, if a user inputs "I want a landscape painting of a beach with a sunset to be generated," the generation AI analyzes the instruction and generates a landscape painting based on related image data and text data. The generation AI utilizes a large amount of data set that has been learned in advance to generate content that is most suitable for the user's instructions. The generated content is output as a high-resolution image or a high-quality video according to the user's request. In addition, the generation unit also has an evaluation function for evaluating the quality of the generated content, and checks whether the content output by the generation AI meets the user's expectations. Based on the evaluation results, the generation AI can self-learn and reflect the results in the next generation. This allows the generation unit to quickly generate high-quality content that meets the diverse needs of users.

[0031] The copyright confirmation unit checks whether the generated content infringes copyright. The copyright confirmation unit, for example, uses image recognition technology to check whether the generated image matches an existing copyrighted work. The copyright confirmation unit can also refer to a database that is regularly updated to check whether the generated content matches an existing copyrighted work. Specifically, the copyright confirmation unit uses an algorithm to compare the generated content with an existing copyrighted work database and calculate the similarity. For example, to evaluate how similar an image generated by an image generation AI is to an existing copyrighted work, feature extraction technology is used to extract image features and compare them with existing images in the database. If the similarity exceeds a certain threshold, it is determined that there is a possibility of copyright infringement. In addition, the text generated by a text generation AI is also compared with existing copyrighted works in the same way to evaluate the similarity. The copyright confirmation unit determines whether the generated content infringes copyright based on these comparison results. In addition, the copyright confirmation unit also has a function to evaluate whether the generated content has novelty and originality, and can provide users with advice to reduce the copyright risk of the generated content. In this way, the copyright confirmation unit checks that the generated content is legally acceptable, allowing users to use it with peace of mind.

[0032] The regeneration unit issues a regeneration instruction when the copyright confirmation unit determines that the content infringes copyright. The regeneration unit issues a regeneration instruction to the generation AI and regenerates the content. For example, the regeneration unit can issue a regeneration instruction to the generation AI using different parameters. The regeneration unit can also set a feedback loop to prevent the same error from being repeated. The regeneration unit has an interface for issuing specific regeneration instructions to the generation AI, and users and system administrators can set the conditions for regeneration in detail. For example, the regeneration unit can issue specific instructions to the generation AI, such as "regenerate with a different color tone" or "regenerate by excluding specific elements." The regeneration unit again sends the content regenerated by the generation AI to the copyright confirmation unit to check whether the regenerated content does not infringe copyright. By repeating the regeneration process, the regeneration unit can ultimately generate content that does not infringe copyright. Furthermore, the regeneration unit can improve the algorithm of the generation AI based on feedback obtained during the regeneration process and reflect it in the next generation. This allows the regeneration unit to ensure the quality and legal safety of the generated content, and provide the content that the user can use with peace of mind.

[0033] The copyright confirmation unit can confirm whether the content generated using image recognition technology matches an existing copyrighted work. The copyright confirmation unit, for example, confirms whether an image generated using a convolutional neural network (CNN) matches an existing copyrighted work. For example, the copyright confirmation unit inputs the generated image into a CNN and calculates the similarity with the existing copyrighted work. The copyright confirmation unit can also confirm whether the image generated using an object detection algorithm matches an existing copyrighted work. For example, the copyright confirmation unit inputs the generated image into an object detection algorithm and calculates the similarity with the existing copyrighted work. As a result, the accuracy of copyright confirmation is improved by using image recognition technology. A part or all of the above-mentioned processing in the copyright confirmation unit may be performed using, for example, AI, or may be performed without using AI. For example, the copyright confirmation unit can input the generated image into AI and calculate the similarity with the existing copyrighted work.

[0034] The copyright confirmation unit can perform copyright confirmation by referring to a database that is periodically updated. The copyright confirmation unit can perform copyright confirmation by referring to a database that is periodically updated, for example. For example, the copyright confirmation unit can compare the generated content with the database and calculate the degree of match with existing copyrighted works. The copyright confirmation unit can also perform copyright confirmation by referring to a database that is periodically updated. For example, the copyright confirmation unit can compare the generated content with the database and calculate the degree of match with existing copyrighted works. This makes it possible to perform confirmation based on the latest copyright information by referring to a database that is periodically updated. A part or all of the above-mentioned processing in the copyright confirmation unit can be performed, for example, using AI or can be performed without using AI. For example, the copyright confirmation unit can input the generated content into AI and compare it with the database.

[0035] The regeneration unit can issue a regeneration instruction to the generation AI using different parameters. The regeneration unit issues a regeneration instruction to the generation AI using different hyperparameters, for example. For example, the regeneration unit sets a different learning rate or batch size to the generation AI and issues a regeneration instruction. The regeneration unit can also issue a regeneration instruction to the generation AI using different input data. For example, the regeneration unit inputs different image data to the generation AI and issues a regeneration instruction. This improves the diversity of regeneration by using different parameters. Some or all of the above-mentioned processing in the regeneration unit may be performed, for example, using AI, or may be performed without using AI. For example, the regeneration unit can input different parameters to the generation AI and issue a regeneration instruction.

[0036] The regeneration unit can provide a feedback loop to prevent the same error from being repeated. The regeneration unit, for example, evaluates the result of the regeneration and updates the regeneration algorithm based on the evaluation result. For example, the regeneration unit inputs the result of the regeneration into a feedback loop to improve the regeneration algorithm. The regeneration unit can also improve the accuracy of the regeneration using an error detection method. For example, the regeneration unit inputs the result of the regeneration into an error detection algorithm to detect errors. This improves the accuracy of the regeneration by the feedback loop. Some or all of the above-mentioned processing in the regeneration unit may be performed, for example, using AI or may be performed without using AI. For example, the regeneration unit can input the result of the regeneration into AI to construct a feedback loop.

[0037] At the time of generation, the generation unit can analyze the user's past generation history and select the optimal generation method. The generation unit, for example, analyzes the style of a landscape painting generated by the user in the past and generates a new landscape painting in a similar style. For example, the generation unit inputs the past generation history into AI and selects the optimal generation method. The generation unit can also select the color of a new landscape painting by referring to the color pattern of content generated by the user in the past. For example, the generation unit inputs the past generation history into AI and analyzes the color pattern. Furthermore, the generation unit can analyze the composition of content generated by the user in the past and generate a new landscape painting using the optimal composition. For example, the generation unit inputs the past generation history into AI and analyzes the composition. This makes it possible to select the optimal generation method based on the user's past generation history. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input the past generation history into AI and select the optimal generation method.

[0038] The generation unit can customize the generated content based on the user's current field of interest at the time of generation. The generation unit generates a related landscape painting based on, for example, a keyword recently searched by the user. For example, the generation unit inputs the user's search history into the AI ​​and identifies the field of interest. The generation unit can also generate an interesting landscape painting by referring to the content of a website recently viewed by the user. For example, the generation unit inputs the user's browsing history into the AI ​​and identifies the field of interest. Furthermore, the generation unit can generate a related landscape painting based on the theme of an event in which the user recently participated. For example, the generation unit inputs the user's event participation history into the AI ​​and identifies the field of interest. This makes it possible to generate content according to the user's field of interest. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input the user's field of interest into the AI ​​and customize the generated content.

[0039] The generation unit can generate highly relevant content preferentially in consideration of the user's geographical location information at the time of generation. For example, when the user is at the seaside, the generation unit preferentially generates a seascape image. For example, the generation unit inputs the user's geographical location information to the AI ​​and identifies highly relevant content. In addition, when the user is in a mountainous area, the generation unit can also generate a mountain landscape image preferentially. For example, the generation unit inputs the user's geographical location information to the AI ​​and identifies highly relevant content. In addition, when the user is in an urban area, the generation unit can also generate a city landscape image preferentially. For example, the generation unit inputs the user's geographical location information to the AI ​​and identifies highly relevant content. This makes it possible to generate content based on the user's geographical location information. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input the user's geographical location information to the AI ​​and generate highly relevant content preferentially.

[0040] The generation unit can analyze the user's social media activity at the time of generation and generate related content. The generation unit generates a related landscape painting based on the theme of a photo recently posted by the user, for example. For example, the generation unit inputs the user's social media activity into AI and identifies related content. The generation unit can also generate an interesting landscape painting by referring to the posted content of an account followed by the user. For example, the generation unit inputs the user's social media activity into AI and identifies related content. Furthermore, the generation unit can generate a related landscape painting based on the theme of a group in which the user participates. For example, the generation unit inputs the user's social media activity into AI and identifies related content. This enables content generation based on the user's social media activity. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input the user's social media activity into AI and generate related content.

[0041] The copyright verification unit can optimize the verification algorithm by referring to past verification data when verifying the copyright. The copyright verification unit improves the verification algorithm, for example, based on cases where copyright infringement has occurred in the past. For example, the copyright verification unit inputs past verification data into an AI to optimize the verification algorithm. The copyright verification unit can also analyze past verification data to improve the verification accuracy. For example, the copyright verification unit inputs past verification data into an AI to improve the verification accuracy. Furthermore, the copyright verification unit can also optimize the verification speed by referring to past verification data. For example, the copyright verification unit inputs past verification data into an AI to optimize the verification speed. This allows the verification algorithm to be optimized based on the past verification data. Some or all of the above-mentioned processing in the copyright verification unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the copyright verification unit can input past verification data into an AI to optimize the verification algorithm.

[0042] The copyright confirmation unit can apply different confirmation methods depending on the category of the generated content when confirming the copyright. For example, in the case of image content, the copyright confirmation unit confirms using image recognition technology. For example, the copyright confirmation unit inputs the generated image into image recognition technology and calculates the degree of match with existing copyrighted works. In addition, in the case of video content, the copyright confirmation unit can also perform confirmation for each frame. For example, the copyright confirmation unit divides the generated video into frames and inputs each frame into image recognition technology to confirm. In addition, in the case of text content, the copyright confirmation unit can also confirm using text matching technology. For example, the copyright confirmation unit inputs the generated text into text matching technology and calculates the degree of match with existing copyrighted works. This makes it possible to apply a confirmation method according to the category of the content. Part or all of the above-mentioned processing in the copyright confirmation unit may be performed using AI, for example, or may be performed without using AI. For example, the copyright confirmation unit can input the generated content into AI and apply a confirmation method according to the category.

[0043] The copyright confirmation unit can adjust the priority of confirmation based on the submission time of the generated content when confirming the copyright. For example, the copyright confirmation unit preferentially confirms content with a deadline approaching. For example, the copyright confirmation unit inputs the submission time to AI and determines the priority. The copyright confirmation unit can also postpone content with an early submission time. For example, the copyright confirmation unit inputs the submission time to AI and determines the priority. The copyright confirmation unit can also postpone content with a long submission time. For example, the copyright confirmation unit inputs the submission time to AI and determines the priority. This makes it possible to adjust the priority of confirmation based on the submission time. Part or all of the above-mentioned processing in the copyright confirmation unit may be performed, for example, using AI, or may be performed without using AI. For example, the copyright confirmation unit can input the submission time to AI and adjust the priority.

[0044] The copyright confirmation unit can improve the accuracy of the confirmation by referring to related literature of the generated content at the time of copyright confirmation. The copyright confirmation unit, for example, refers to a related image database to confirm whether the generated image matches an existing work. For example, the copyright confirmation unit inputs the generated image into an image database and calculates the degree of match. The copyright confirmation unit can also refer to a related video database to confirm whether the generated video matches an existing work. For example, the copyright confirmation unit inputs the generated video into a video database and calculates the degree of match. Furthermore, the copyright confirmation unit can also refer to a related text database to confirm whether the generated text matches an existing work. For example, the copyright confirmation unit inputs the generated text into a text database and calculates the degree of match. This improves the accuracy of the confirmation by referring to related literature. A part or all of the above-mentioned processing in the copyright confirmation unit may be performed, for example, using AI, or may be performed without using AI. For example, the copyright confirmation unit can input the generated content into AI and improve the accuracy of the confirmation by referring to related literature.

[0045] The regeneration unit can optimize the regeneration algorithm by referring to past regeneration data at the time of regeneration. The regeneration unit improves the regeneration algorithm, for example, based on cases where regeneration was successful in the past. For example, the regeneration unit inputs past regeneration data into AI and optimizes the regeneration algorithm. The regeneration unit can also analyze past regeneration data and improve regeneration accuracy. For example, the regeneration unit inputs past regeneration data into AI and improves regeneration accuracy. Furthermore, the regeneration unit can also optimize the regeneration speed by referring to past regeneration data. For example, the regeneration unit inputs past regeneration data into AI and optimizes the regeneration speed. This allows the regeneration algorithm to be optimized based on the past regeneration data. Part or all of the above-mentioned processing in the regeneration unit may be performed, for example, using AI, or may be performed without using AI. For example, the regeneration unit can input past regeneration data into AI and optimize the regeneration algorithm.

[0046] The regeneration unit can apply different regeneration methods according to the category of the generated content at the time of regeneration. For example, in the case of image content, the regeneration unit regenerates using different image recognition techniques. For example, the regeneration unit inputs the generated image to different image recognition techniques and issues an instruction for regeneration. In addition, in the case of video content, the regeneration unit can also perform regeneration for each different frame. For example, the regeneration unit divides the generated video into frames and inputs each frame to a different image recognition technique to regenerate. Furthermore, in the case of text content, the regeneration unit can also regenerate using different text matching techniques. For example, the regeneration unit inputs the generated text to a different text matching technique and issues an instruction for regeneration. This makes it possible to apply a regeneration method according to the category of the content. Part or all of the above-mentioned processing in the regeneration unit may be performed using, for example, AI, or may be performed without using AI. For example, the regeneration unit can input the generated content to AI and apply a regeneration method according to the category.

[0047] The regeneration unit can perform regeneration in consideration of the geographical distribution of the generated content at the time of regeneration. The regeneration unit performs regeneration, for example, by referring to a style popular in a specific region. For example, the regeneration unit inputs geographical distribution data to an AI and issues an instruction for regeneration. The regeneration unit can also preferentially regenerate content with high geographical relevance. For example, the regeneration unit inputs geographical distribution data to an AI and identifies highly relevant content. Furthermore, the regeneration unit can perform regeneration in consideration of geographical trends. For example, the regeneration unit inputs geographical distribution data to an AI and identifies trends. This enables regeneration in consideration of geographical distribution. A part or all of the above-mentioned processing in the regeneration unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the regeneration unit can input geographical distribution data to an AI and perform regeneration.

[0048] The regeneration unit can improve the accuracy of the regeneration by referring to related literature of the generated content at the time of regeneration. For example, the regeneration unit refers to a related image database to check whether the generated image matches an existing work. For example, the regeneration unit inputs the generated image into an image database and calculates the degree of match. The regeneration unit can also refer to a related video database to check whether the generated video matches an existing work. For example, the regeneration unit inputs the generated video into a video database and calculates the degree of match. Furthermore, the regeneration unit can also refer to a related text database to check whether the generated text matches an existing work. For example, the regeneration unit inputs the generated text into a text database and calculates the degree of match. As a result, the accuracy of the regeneration is improved by referring to related literature. Part or all of the above-mentioned processing in the regeneration unit may be performed, for example, using AI, or may be performed without using AI. For example, the regeneration unit can input the generated content into AI and improve the accuracy of the regeneration by referring to related literature.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] The generation unit can analyze the user's generation history and learn the user's preferences and tendencies. For example, the generation unit can analyze the style and theme of content previously generated by the user and generate new content with a similar style and theme. The generation unit can also analyze the color and composition of content previously generated by the user and generate new content incorporating these elements. Furthermore, the generation unit can generate new content incorporating highly rated elements based on the evaluation of content previously generated by the user. This makes it possible to generate content according to the user's preferences and tendencies.

[0051] The copyright verification unit can analyze metadata of the generated content to improve the accuracy of copyright verification. For example, the copyright verification unit can analyze metadata such as the shooting date and location of the generated image and information on the camera used, and calculate the degree of match with existing copyrighted works. The copyright verification unit can also analyze metadata such as the frame rate and resolution of the generated video and information on the editing software used to improve the accuracy of copyright verification. Furthermore, the copyright verification unit can analyze metadata such as the creation date and time of the generated text, the font used, and stylistic features to improve the accuracy of copyright verification. This makes it possible to perform copyright verification using metadata.

[0052] The regeneration unit can evaluate the quality of the generated content and issue a regeneration instruction if the quality is low. For example, the regeneration unit can evaluate the resolution and color balance of the generated image and issue a regeneration instruction if the quality is low. The regeneration unit can also evaluate the frame rate and audio quality of the generated video and issue a regeneration instruction if the quality is low. Furthermore, the regeneration unit can evaluate the grammar and naturalness of expression of the generated text and issue a regeneration instruction if the quality is low. This makes it possible to ensure the quality of the generated content.

[0053] The generation unit can collect evaluations of the generated content and improve the generation algorithm based on the evaluations. For example, the generation unit collects evaluations made by users on the generated content and generates new content incorporating highly rated elements. The generation unit can also generate new content that excludes low rated elements based on the evaluations made by users on the generated content. Furthermore, the generation unit can dynamically adjust the generation algorithm based on the user's evaluations to improve generation accuracy. This makes it possible to generate high-quality content that reflects user evaluations.

[0054] The generator can adjust the generation algorithm taking into account the intended use of the generated content. For example, if the generated image is for advertising, the generator can emphasize visually attractive elements. If the generated video is for education, the generator can emphasize elements that emphasize information transmission. If the generated text is for a report, the generator can emphasize elements that emphasize accuracy and clarity. This makes it possible to generate optimal content according to the intended use.

[0055] The generator can adjust the generation algorithm taking into account the distribution destination of the generated content. For example, if the generated image is to be distributed on social media, the generator can emphasize elements with visual impact. If the generated video is to be distributed on an educational platform, the generator can emphasize elements that emphasize information transmission. If the generated text is to be distributed as a business report, the generator can emphasize elements that emphasize accuracy and clarity. This makes it possible to generate optimal content according to the distribution destination.

[0056] The process flow of the first embodiment will be briefly described below.

[0057] Step 1: The generation unit generates content using a generation AI. When a user inputs an instruction such as "I want a landscape painting to be generated," the generation AI generates a landscape painting based on the instruction. The generated content is output in the form of an image, video, or the like. The generation unit can generate content using a text generation AI (e.g., LLM) or an image generation AI. Step 2: The copyright verification unit checks whether the generated content infringes a copyright. For example, it uses image recognition technology to check whether the generated image matches an existing copyrighted work. The copyright verification unit can also refer to a regularly updated database to check whether the generated content matches an existing copyrighted work. Step 3: The regeneration unit issues a regeneration instruction if the copyright verification unit determines that a copyright has been infringed. The regeneration unit issues a regeneration instruction to the generation AI, which regenerates the content. For example, the regeneration unit can instruct the generation AI to regenerate using different parameters. The regeneration unit can also set up a feedback loop to prevent the same error from being repeated. The accuracy of the regeneration is improved by evaluating the results of the regeneration and updating the regeneration algorithm based on the evaluation results.

[0058] (Example 2) A content generation system according to an embodiment of the present invention is a system that automatically checks whether the generated content, such as an image, infringes copyright when the content is generated using a generation AI, and regenerates the content if it does. This system starts with a user inputting an instruction to generate content to the generation AI. For example, an instruction such as "I want you to generate a landscape painting" is input. This instruction is input to the generation AI. Next, the generation AI generates content based on the input instruction. The generated content is output in the form of an image, video, or the like. The generated content is sent to a copyright confirmation unit. The copyright confirmation unit checks whether the generated content matches an existing copyrighted work. For example, image recognition technology is used to check whether the generated image matches an existing copyrighted work. If the copyright confirmation unit determines that the generated content infringes copyright, an instruction is sent to a regeneration unit. The regeneration unit issues an instruction to the generation AI to regenerate the content, and the content is regenerated. The regenerated content is checked again by the copyright confirmation unit. By repeating this process, it is possible to confirm that the generated content does not infringe copyright. This allows the user to generate content that does not infringe copyright without much effort. For example, if a user inputs an instruction to "generate a landscape painting" and the generation AI generates a landscape painting, the copyright confirmation unit checks whether the landscape painting matches an existing copyrighted work. If it matches, the regeneration unit issues an instruction to regenerate it, and the generation AI generates a new landscape painting. By repeating this process, a landscape painting that does not infringe copyright is finally generated. This allows the content generation system to generate content that does not infringe copyrights without the user having to go through any effort.

[0059] The content generation system according to the embodiment includes a generation unit, a copyright confirmation unit, and a regeneration unit. The generation unit generates content using a generation AI. For example, a user inputs an instruction such as "I want a landscape painting to be generated," and the generation AI generates a landscape painting based on the instruction. The generation unit generates content based on the input instruction. The generated content is output in the form of an image, a video, or the like. The generation unit can generate content using, for example, a text generation AI (for example, LLM) or an image generation AI. The copyright confirmation unit checks whether the generated content infringes copyright. The copyright confirmation unit checks whether the generated image matches an existing work, for example, using image recognition technology. The copyright confirmation unit can also refer to a database that is regularly updated to check whether the generated content matches an existing work. The regeneration unit issues an instruction to regenerate when the copyright confirmation unit determines that the content infringes copyright. The regeneration unit issues an instruction to regenerate to the generation AI, and regenerates the content. The regeneration unit can issue an instruction to regenerate to the generation AI using different parameters, for example. The regeneration unit can also provide a feedback loop to prevent the same error from being repeated. As a result, the content generation system according to the embodiment automatically checks the copyright of the generated content, and regenerates it if it infringes. Some or all of the above-mentioned processing in the regeneration unit may be performed, for example, using AI, or may be performed without using AI. For example, the regeneration unit can input different parameters to the generation AI and issue an instruction for regeneration. This improves the diversity of regeneration. Furthermore, the regeneration unit can provide a feedback loop to prevent the same error from being repeated. For example, the regeneration unit evaluates the result of regeneration and updates the regeneration algorithm based on the evaluation result. This improves the accuracy of regeneration.

[0060] The generation unit generates content using a generation AI. For example, when a user inputs an instruction such as "I want a landscape painting to be generated," the generation AI generates a landscape painting based on the instruction. The generation unit generates content based on the input instruction. The generated content is output in the form of an image, video, or the like. The generation unit can generate content using, for example, a text generation AI (e.g., LLM) or an image generation AI. The generation unit has an interface for receiving instructions from a user, and the user can specify details of the content to be generated through text input or voice input. For example, if a user inputs "I want a landscape painting of a beach with a sunset to be generated," the generation AI analyzes the instruction and generates a landscape painting based on related image data and text data. The generation AI utilizes a large amount of data set that has been learned in advance to generate content that is most suitable for the user's instructions. The generated content is output as a high-resolution image or a high-quality video according to the user's request. In addition, the generation unit also has an evaluation function for evaluating the quality of the generated content, and checks whether the content output by the generation AI meets the user's expectations. Based on the evaluation results, the generation AI can self-learn and reflect the results in the next generation. This allows the generation unit to quickly generate high-quality content that meets the diverse needs of users.

[0061] The copyright confirmation unit checks whether the generated content infringes copyright. The copyright confirmation unit, for example, uses image recognition technology to check whether the generated image matches an existing copyrighted work. The copyright confirmation unit can also refer to a database that is regularly updated to check whether the generated content matches an existing copyrighted work. Specifically, the copyright confirmation unit uses an algorithm to compare the generated content with an existing copyrighted work database and calculate the similarity. For example, to evaluate how similar an image generated by an image generation AI is to an existing copyrighted work, feature extraction technology is used to extract image features and compare them with existing images in the database. If the similarity exceeds a certain threshold, it is determined that there is a possibility of copyright infringement. In addition, the text generated by a text generation AI is also compared with existing copyrighted works in the same way to evaluate the similarity. The copyright confirmation unit determines whether the generated content infringes copyright based on these comparison results. In addition, the copyright confirmation unit also has a function to evaluate whether the generated content has novelty and originality, and can provide users with advice to reduce the copyright risk of the generated content. In this way, the copyright confirmation unit checks that the generated content is legally acceptable, allowing users to use it with peace of mind.

[0062] The regeneration unit issues a regeneration instruction when the copyright confirmation unit determines that the content infringes copyright. The regeneration unit issues a regeneration instruction to the generation AI and regenerates the content. For example, the regeneration unit can issue a regeneration instruction to the generation AI using different parameters. The regeneration unit can also set a feedback loop to prevent the same error from being repeated. The regeneration unit has an interface for issuing specific regeneration instructions to the generation AI, and users and system administrators can set the conditions for regeneration in detail. For example, the regeneration unit can issue specific instructions to the generation AI, such as "regenerate with a different color tone" or "regenerate by excluding specific elements." The regeneration unit again sends the content regenerated by the generation AI to the copyright confirmation unit to check whether the regenerated content does not infringe copyright. By repeating the regeneration process, the regeneration unit can ultimately generate content that does not infringe copyright. Furthermore, the regeneration unit can improve the algorithm of the generation AI based on feedback obtained during the regeneration process and reflect it in the next generation. This allows the regeneration unit to ensure the quality and legal safety of the generated content, and provide the content that the user can use with peace of mind.

[0063] The copyright confirmation unit can confirm whether the content generated using image recognition technology matches an existing copyrighted work. The copyright confirmation unit, for example, confirms whether an image generated using a convolutional neural network (CNN) matches an existing copyrighted work. For example, the copyright confirmation unit inputs the generated image into a CNN and calculates the similarity with the existing copyrighted work. The copyright confirmation unit can also confirm whether the image generated using an object detection algorithm matches an existing copyrighted work. For example, the copyright confirmation unit inputs the generated image into an object detection algorithm and calculates the similarity with the existing copyrighted work. As a result, the accuracy of copyright confirmation is improved by using image recognition technology. A part or all of the above-mentioned processing in the copyright confirmation unit may be performed using, for example, AI, or may be performed without using AI. For example, the copyright confirmation unit can input the generated image into AI and calculate the similarity with the existing copyrighted work.

[0064] The copyright confirmation unit can perform copyright confirmation by referring to a database that is periodically updated. The copyright confirmation unit can perform copyright confirmation by referring to a database that is periodically updated, for example. For example, the copyright confirmation unit can compare the generated content with the database and calculate the degree of match with existing copyrighted works. The copyright confirmation unit can also perform copyright confirmation by referring to a database that is periodically updated. For example, the copyright confirmation unit can compare the generated content with the database and calculate the degree of match with existing copyrighted works. This makes it possible to perform confirmation based on the latest copyright information by referring to a database that is periodically updated. A part or all of the above-mentioned processing in the copyright confirmation unit can be performed, for example, using AI or can be performed without using AI. For example, the copyright confirmation unit can input the generated content into AI and compare it with the database.

[0065] The regeneration unit can issue a regeneration instruction to the generation AI using different parameters. The regeneration unit issues a regeneration instruction to the generation AI using different hyperparameters, for example. For example, the regeneration unit sets a different learning rate or batch size to the generation AI and issues a regeneration instruction. The regeneration unit can also issue a regeneration instruction to the generation AI using different input data. For example, the regeneration unit inputs different image data to the generation AI and issues a regeneration instruction. This improves the diversity of regeneration by using different parameters. Some or all of the above-mentioned processing in the regeneration unit may be performed, for example, using AI, or may be performed without using AI. For example, the regeneration unit can input different parameters to the generation AI and issue a regeneration instruction.

[0066] The regeneration unit can provide a feedback loop to prevent the same error from being repeated. The regeneration unit, for example, evaluates the result of the regeneration and updates the regeneration algorithm based on the evaluation result. For example, the regeneration unit inputs the result of the regeneration into a feedback loop to improve the regeneration algorithm. The regeneration unit can also improve the accuracy of the regeneration using an error detection method. For example, the regeneration unit inputs the result of the regeneration into an error detection algorithm to detect errors. This improves the accuracy of the regeneration by the feedback loop. Some or all of the above-mentioned processing in the regeneration unit may be performed, for example, using AI or may be performed without using AI. For example, the regeneration unit can input the result of the regeneration into AI to construct a feedback loop.

[0067] The generation unit can estimate the user's emotion and adjust the style of the content to be generated based on the estimated user's emotion. For example, the generation unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the generation unit calculates an emotion score based on the change in facial expression. The generation unit can also record the user's voice and estimate the emotion using a voice analysis technique. For example, the generation unit analyzes the tone and speed of the voice and calculates an emotion score. Furthermore, the generation unit can analyze the user's text input and estimate the emotion using a text analysis technique. For example, the generation unit analyzes the contents of the text and calculates an emotion score. This makes it possible to adjust the style of the content according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generator can input a user's emotional data into the AI ​​and adjust the style of the content based on the emotion.

[0068] At the time of generation, the generation unit can analyze the user's past generation history and select the optimal generation method. The generation unit, for example, analyzes the style of a landscape painting generated by the user in the past and generates a new landscape painting in a similar style. For example, the generation unit inputs the past generation history into AI and selects the optimal generation method. The generation unit can also select the color of a new landscape painting by referring to the color pattern of content generated by the user in the past. For example, the generation unit inputs the past generation history into AI and analyzes the color pattern. Furthermore, the generation unit can analyze the composition of content generated by the user in the past and generate a new landscape painting using the optimal composition. For example, the generation unit inputs the past generation history into AI and analyzes the composition. This makes it possible to select the optimal generation method based on the user's past generation history. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input the past generation history into AI and select the optimal generation method.

[0069] The generation unit can customize the generated content based on the user's current field of interest at the time of generation. The generation unit generates a related landscape painting based on, for example, a keyword recently searched by the user. For example, the generation unit inputs the user's search history into the AI ​​and identifies the field of interest. The generation unit can also generate an interesting landscape painting by referring to the content of a website recently viewed by the user. For example, the generation unit inputs the user's browsing history into the AI ​​and identifies the field of interest. Furthermore, the generation unit can generate a related landscape painting based on the theme of an event in which the user recently participated. For example, the generation unit inputs the user's event participation history into the AI ​​and identifies the field of interest. This makes it possible to generate content according to the user's field of interest. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input the user's field of interest into the AI ​​and customize the generated content.

[0070] The generation unit can estimate the user's emotion and determine the priority of the content to be generated based on the estimated user's emotion. For example, the generation unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the generation unit calculates an emotion score based on the change in facial expression. The generation unit can also record the user's voice and estimate the emotion using a voice analysis technique. For example, the generation unit analyzes the tone and speed of the voice and calculates an emotion score. Furthermore, the generation unit can analyze the user's text input and estimate the emotion using a text analysis technique. For example, the generation unit analyzes the contents of the text and calculates an emotion score. This makes it possible to determine the priority of the content according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input a user's emotional data into the AI ​​and prioritize content based on emotions.

[0071] The generation unit can generate highly relevant content preferentially in consideration of the user's geographical location information at the time of generation. For example, when the user is at the seaside, the generation unit preferentially generates a seascape image. For example, the generation unit inputs the user's geographical location information to the AI ​​and identifies highly relevant content. In addition, when the user is in a mountainous area, the generation unit can also generate a mountain landscape image preferentially. For example, the generation unit inputs the user's geographical location information to the AI ​​and identifies highly relevant content. In addition, when the user is in an urban area, the generation unit can also generate a city landscape image preferentially. For example, the generation unit inputs the user's geographical location information to the AI ​​and identifies highly relevant content. This makes it possible to generate content based on the user's geographical location information. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input the user's geographical location information to the AI ​​and generate highly relevant content preferentially.

[0072] The generation unit can analyze the user's social media activity at the time of generation and generate related content. The generation unit generates a related landscape painting based on the theme of a photo recently posted by the user, for example. For example, the generation unit inputs the user's social media activity into AI and identifies related content. The generation unit can also generate an interesting landscape painting by referring to the posted content of an account followed by the user. For example, the generation unit inputs the user's social media activity into AI and identifies related content. Furthermore, the generation unit can generate a related landscape painting based on the theme of a group in which the user participates. For example, the generation unit inputs the user's social media activity into AI and identifies related content. This enables content generation based on the user's social media activity. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input the user's social media activity into AI and generate related content.

[0073] The copyright confirmation unit can estimate the user's emotion and adjust the copyright confirmation criteria based on the estimated user's emotion. For example, the copyright confirmation unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the copyright confirmation unit calculates an emotion score based on the change in facial expression. The copyright confirmation unit can also record the user's voice and estimate the emotion using a voice analysis technique. For example, the copyright confirmation unit analyzes the tone and speed of the voice and calculates an emotion score. Furthermore, the copyright confirmation unit can analyze the user's text input and estimate the emotion using a text analysis technique. For example, the copyright confirmation unit analyzes the contents of the text and calculates an emotion score. This makes it possible to adjust the copyright confirmation criteria according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the copyright confirmation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the copyright verification unit can input user emotion data into the AI ​​and adjust the criteria for copyright verification based on the emotion.

[0074] The copyright verification unit can optimize the verification algorithm by referring to past verification data when verifying the copyright. The copyright verification unit improves the verification algorithm, for example, based on cases where copyright infringement has occurred in the past. For example, the copyright verification unit inputs past verification data into an AI to optimize the verification algorithm. The copyright verification unit can also analyze past verification data to improve the verification accuracy. For example, the copyright verification unit inputs past verification data into an AI to improve the verification accuracy. Furthermore, the copyright verification unit can also optimize the verification speed by referring to past verification data. For example, the copyright verification unit inputs past verification data into an AI to optimize the verification speed. This allows the verification algorithm to be optimized based on the past verification data. Some or all of the above-mentioned processing in the copyright verification unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the copyright verification unit can input past verification data into an AI to optimize the verification algorithm.

[0075] The copyright confirmation unit can apply different confirmation methods depending on the category of the generated content when confirming the copyright. For example, in the case of image content, the copyright confirmation unit confirms using image recognition technology. For example, the copyright confirmation unit inputs the generated image into image recognition technology and calculates the degree of match with existing copyrighted works. In addition, in the case of video content, the copyright confirmation unit can also perform confirmation for each frame. For example, the copyright confirmation unit divides the generated video into frames and inputs each frame into image recognition technology to confirm. In addition, in the case of text content, the copyright confirmation unit can also confirm using text matching technology. For example, the copyright confirmation unit inputs the generated text into text matching technology and calculates the degree of match with existing copyrighted works. This makes it possible to apply a confirmation method according to the category of the content. Part or all of the above-mentioned processing in the copyright confirmation unit may be performed using AI, for example, or may be performed without using AI. For example, the copyright confirmation unit can input the generated content into AI and apply a confirmation method according to the category.

[0076] The copyright confirmation unit can estimate the user's emotion and determine the priority of copyright confirmation based on the estimated user's emotion. For example, the copyright confirmation unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the copyright confirmation unit calculates an emotion score based on the change in facial expression. The copyright confirmation unit can also record the user's voice and estimate the emotion using a voice analysis technique. For example, the copyright confirmation unit analyzes the tone and speed of the voice and calculates an emotion score. Furthermore, the copyright confirmation unit can analyze the user's text input and estimate the emotion using a text analysis technique. For example, the copyright confirmation unit analyzes the content of the text and calculates an emotion score. This makes it possible to determine the priority of copyright confirmation according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the copyright confirmation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the copyright verification unit can input a user's emotional data into the AI ​​and determine the priority of copyright verification based on the emotion.

[0077] The copyright confirmation unit can adjust the priority of confirmation based on the submission time of the generated content when confirming the copyright. For example, the copyright confirmation unit preferentially confirms content with a deadline approaching. For example, the copyright confirmation unit inputs the submission time to AI and determines the priority. The copyright confirmation unit can also postpone content with an early submission time. For example, the copyright confirmation unit inputs the submission time to AI and determines the priority. The copyright confirmation unit can also postpone content with a long submission time. For example, the copyright confirmation unit inputs the submission time to AI and determines the priority. This makes it possible to adjust the priority of confirmation based on the submission time. Part or all of the above-mentioned processing in the copyright confirmation unit may be performed, for example, using AI, or may be performed without using AI. For example, the copyright confirmation unit can input the submission time to AI and adjust the priority.

[0078] The copyright confirmation unit can improve the accuracy of the confirmation by referring to related literature of the generated content at the time of copyright confirmation. The copyright confirmation unit, for example, refers to a related image database to confirm whether the generated image matches an existing work. For example, the copyright confirmation unit inputs the generated image into an image database and calculates the degree of match. The copyright confirmation unit can also refer to a related video database to confirm whether the generated video matches an existing work. For example, the copyright confirmation unit inputs the generated video into a video database and calculates the degree of match. Furthermore, the copyright confirmation unit can also refer to a related text database to confirm whether the generated text matches an existing work. For example, the copyright confirmation unit inputs the generated text into a text database and calculates the degree of match. This improves the accuracy of the confirmation by referring to related literature. A part or all of the above-mentioned processing in the copyright confirmation unit may be performed, for example, using AI, or may be performed without using AI. For example, the copyright confirmation unit can input the generated content into AI and improve the accuracy of the confirmation by referring to related literature.

[0079] The regeneration unit can estimate the user's emotion and adjust the regeneration method based on the estimated user's emotion. For example, the regeneration unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the regeneration unit calculates an emotion score based on the change in facial expression. The regeneration unit can also record the user's voice and estimate the emotion using a voice analysis technique. For example, the regeneration unit analyzes the tone and speed of the voice and calculates an emotion score. Furthermore, the regeneration unit can analyze the user's text input and estimate the emotion using a text analysis technique. For example, the regeneration unit analyzes the contents of the text and calculates an emotion score. This makes it possible to adjust the regeneration method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the regeneration unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the regeneration unit can input a user's emotional data into the AI ​​and adjust the regeneration method based on the emotion.

[0080] The regeneration unit can optimize the regeneration algorithm by referring to past regeneration data at the time of regeneration. The regeneration unit improves the regeneration algorithm, for example, based on cases where regeneration was successful in the past. For example, the regeneration unit inputs past regeneration data into AI and optimizes the regeneration algorithm. The regeneration unit can also analyze past regeneration data and improve regeneration accuracy. For example, the regeneration unit inputs past regeneration data into AI and improves regeneration accuracy. Furthermore, the regeneration unit can also optimize the regeneration speed by referring to past regeneration data. For example, the regeneration unit inputs past regeneration data into AI and optimizes the regeneration speed. This allows the regeneration algorithm to be optimized based on the past regeneration data. Part or all of the above-mentioned processing in the regeneration unit may be performed, for example, using AI, or may be performed without using AI. For example, the regeneration unit can input past regeneration data into AI and optimize the regeneration algorithm.

[0081] The regeneration unit can apply different regeneration methods according to the category of the generated content at the time of regeneration. For example, in the case of image content, the regeneration unit regenerates using different image recognition techniques. For example, the regeneration unit inputs the generated image to different image recognition techniques and issues an instruction for regeneration. In addition, in the case of video content, the regeneration unit can also perform regeneration for each different frame. For example, the regeneration unit divides the generated video into frames and inputs each frame to a different image recognition technique to regenerate. Furthermore, in the case of text content, the regeneration unit can also regenerate using different text matching techniques. For example, the regeneration unit inputs the generated text to a different text matching technique and issues an instruction for regeneration. This makes it possible to apply a regeneration method according to the category of the content. Part or all of the above-mentioned processing in the regeneration unit may be performed using, for example, AI, or may be performed without using AI. For example, the regeneration unit can input the generated content to AI and apply a regeneration method according to the category.

[0082] The regeneration unit can estimate the user's emotion and determine the priority of regeneration based on the estimated user's emotion. The regeneration unit, for example, captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the regeneration unit calculates an emotion score based on the change in facial expression. The regeneration unit can also record the user's voice and estimate the emotion using a voice analysis technique. For example, the regeneration unit analyzes the tone and speed of the voice and calculates an emotion score. Furthermore, the regeneration unit can analyze the user's text input and estimate the emotion using a text analysis technique. For example, the regeneration unit analyzes the contents of the text and calculates an emotion score. This makes it possible to determine the priority of regeneration according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the regeneration unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the regeneration unit can input a user's emotion data into the AI ​​and determine the priority of regeneration based on the emotion.

[0083] The regeneration unit can perform regeneration in consideration of the geographical distribution of the generated content at the time of regeneration. The regeneration unit performs regeneration, for example, by referring to a style popular in a specific region. For example, the regeneration unit inputs geographical distribution data to an AI and issues an instruction for regeneration. The regeneration unit can also preferentially regenerate content with high geographical relevance. For example, the regeneration unit inputs geographical distribution data to an AI and identifies highly relevant content. Furthermore, the regeneration unit can perform regeneration in consideration of geographical trends. For example, the regeneration unit inputs geographical distribution data to an AI and identifies trends. This enables regeneration in consideration of geographical distribution. A part or all of the above-mentioned processing in the regeneration unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the regeneration unit can input geographical distribution data to an AI and perform regeneration.

[0084] The regeneration unit can improve the accuracy of the regeneration by referring to related literature of the generated content at the time of regeneration. For example, the regeneration unit refers to a related image database to check whether the generated image matches an existing work. For example, the regeneration unit inputs the generated image into an image database and calculates the degree of match. The regeneration unit can also refer to a related video database to check whether the generated video matches an existing work. For example, the regeneration unit inputs the generated video into a video database and calculates the degree of match. Furthermore, the regeneration unit can also refer to a related text database to check whether the generated text matches an existing work. For example, the regeneration unit inputs the generated text into a text database and calculates the degree of match. As a result, the accuracy of the regeneration is improved by referring to related literature. Part or all of the above-mentioned processing in the regeneration unit may be performed, for example, using AI, or may be performed without using AI. For example, the regeneration unit can input the generated content into AI and improve the accuracy of the regeneration by referring to related literature.

[0085] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0086] The generation unit can analyze the user's generation history and learn the user's preferences and tendencies. For example, the generation unit can analyze the style and theme of content previously generated by the user and generate new content with a similar style and theme. The generation unit can also analyze the color and composition of content previously generated by the user and generate new content incorporating these elements. Furthermore, the generation unit can generate new content incorporating highly rated elements based on the evaluation of content previously generated by the user. This makes it possible to generate content according to the user's preferences and tendencies.

[0087] The copyright verification unit can analyze metadata of the generated content to improve the accuracy of copyright verification. For example, the copyright verification unit can analyze metadata such as the shooting date and location of the generated image and information on the camera used, and calculate the degree of match with existing copyrighted works. The copyright verification unit can also analyze metadata such as the frame rate and resolution of the generated video and information on the editing software used to improve the accuracy of copyright verification. Furthermore, the copyright verification unit can analyze metadata such as the creation date and time of the generated text, the font used, and stylistic features to improve the accuracy of copyright verification. This makes it possible to perform copyright verification using metadata.

[0088] The copyright confirmation unit can take the user's emotions into account when calculating the similarity of the generated content. For example, if the user has positive emotions toward the generated content, the copyright confirmation unit can strictly calculate the similarity of the content. Also, if the user has negative emotions toward the generated content, the copyright confirmation unit can loosely calculate the similarity of the content. Furthermore, the copyright confirmation standard can be dynamically adjusted based on the user's emotions. This enables flexible copyright confirmation that takes the user's emotions into account.

[0089] The regeneration unit can evaluate the quality of the generated content and issue a regeneration instruction if the quality is low. For example, the regeneration unit can evaluate the resolution and color balance of the generated image and issue a regeneration instruction if the quality is low. The regeneration unit can also evaluate the frame rate and audio quality of the generated video and issue a regeneration instruction if the quality is low. Furthermore, the regeneration unit can evaluate the grammar and naturalness of expression of the generated text and issue a regeneration instruction if the quality is low. This makes it possible to ensure the quality of the generated content.

[0090] The regeneration unit can estimate the user's emotions and adjust the regeneration parameters based on the estimated user's emotions. For example, the regeneration unit can gently adjust the regeneration parameters when the user has positive emotions toward the generated content. Also, the regeneration unit can drastically adjust the regeneration parameters when the user has negative emotions toward the generated content. Furthermore, the number and timing of regeneration can be dynamically adjusted based on the user's emotions. This enables flexible regeneration that takes the user's emotions into account.

[0091] The generation unit can collect evaluations of the generated content and improve the generation algorithm based on the evaluations. For example, the generation unit collects evaluations made by users on the generated content and generates new content incorporating highly rated elements. The generation unit can also generate new content that excludes low rated elements based on the evaluations made by users on the generated content. Furthermore, the generation unit can dynamically adjust the generation algorithm based on the user's evaluations to improve generation accuracy. This makes it possible to generate high-quality content that reflects user evaluations.

[0092] The generation unit can estimate the user's emotion and select a theme for the content to be generated based on the estimated user's emotion. For example, when the user is feeling relaxed, the generation unit can generate a relaxing landscape image. When the user is feeling excited, the generation unit can also generate an energetic landscape image. Furthermore, the color and composition of the content to be generated can be dynamically adjusted based on the user's emotion. This makes it possible to select a theme according to the user's emotion.

[0093] The generator can adjust the generation algorithm taking into account the intended use of the generated content. For example, if the generated image is for advertising, the generator can emphasize visually attractive elements. If the generated video is for education, the generator can emphasize elements that emphasize information transmission. If the generated text is for a report, the generator can emphasize elements that emphasize accuracy and clarity. This makes it possible to generate optimal content according to the intended use.

[0094] The generation unit can estimate the user's emotion and adjust the difficulty of the generated content based on the estimated user's emotion. For example, the generation unit can generate easy puzzles and games when the user has a relaxed emotion. Also, the generation unit can generate puzzles and games with high difficulty when the user has a challenging emotion. Furthermore, the generation unit can dynamically adjust the complexity and volume of the generated content based on the user's emotion. This makes it possible to adjust the difficulty level according to the user's emotion.

[0095] The generator can adjust the generation algorithm taking into account the distribution destination of the generated content. For example, if the generated image is to be distributed on social media, the generator can emphasize elements with visual impact. If the generated video is to be distributed on an educational platform, the generator can emphasize elements that emphasize information transmission. If the generated text is to be distributed as a business report, the generator can emphasize elements that emphasize accuracy and clarity. This makes it possible to generate optimal content according to the distribution destination.

[0096] The process flow of the second embodiment will be briefly described below.

[0097] Step 1: The generation unit generates content using a generation AI. When a user inputs an instruction such as "I want a landscape painting to be generated," the generation AI generates a landscape painting based on the instruction. The generated content is output in the form of an image, video, or the like. The generation unit can generate content using a text generation AI (e.g., LLM) or an image generation AI. Step 2: The copyright verification unit checks whether the generated content infringes a copyright. For example, it uses image recognition technology to check whether the generated image matches an existing copyrighted work. The copyright verification unit can also refer to a regularly updated database to check whether the generated content matches an existing copyrighted work. Step 3: The regeneration unit issues a regeneration instruction if the copyright verification unit determines that a copyright has been infringed. The regeneration unit issues a regeneration instruction to the generation AI, which regenerates the content. For example, the regeneration unit can instruct the generation AI to regenerate using different parameters. The regeneration unit can also set up a feedback loop to prevent the same error from being repeated. The accuracy of the regeneration is improved by evaluating the results of the regeneration and updating the regeneration algorithm based on the evaluation results.

[0098] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0099] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. 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 a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0100] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0101] Each of the multiple elements including the above-mentioned generating unit, copyright confirmation unit, and regeneration unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the generating unit is realized by the control unit 46A of the smart device 14, and the generation AI generates content based on an instruction input by the user. The copyright confirmation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and checks whether the generated content matches an existing copyrighted work. The regeneration unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and issues an instruction to regenerate when it is determined that the copyright is infringed. The regeneration unit is also realized, for example, by the control unit 46A of the smart device 14, and can issue an instruction to regenerate to the generation AI using different parameters. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0102] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0103] 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.

[0104] 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, 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 and / or a LAN.

[0105] 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.

[0106] 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 the voice according to instructions from the processor 46.

[0107] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0108] 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 performed in a secure state.

[0109] Fig. 4 shows an example of 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.

[0110] 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.

[0111] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0112] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 glasses 214 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 also perform processing similar to that of the specific processing unit 290 using these models.

[0113] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0114] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0115] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0116] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, 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, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0117] Each of the multiple elements including the above-mentioned generation unit, copyright confirmation unit, and regeneration unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the generation unit is realized by the control unit 46A of the smart glasses 214, and the generation AI generates content based on an instruction input by a user. The copyright confirmation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and checks whether the generated content matches an existing copyrighted work. The regeneration unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and issues an instruction to regenerate when it is determined that the content infringes copyright. The regeneration unit is also realized, for example, by the control unit 46A of the smart glasses 214, and can issue an instruction to regenerate to the generation AI using different parameters. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0118] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0119] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0120] 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, 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 and / or a LAN.

[0121] 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.

[0122] 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 the voice according to instructions from the processor 46.

[0123] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0124] 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 performed in a secure state.

[0125] 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.

[0126] 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.

[0127] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0128] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific 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 program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0129] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0130] 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 a voice indicating a user input for 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.

[0131] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0132] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0133] Each of the multiple elements including the above-mentioned generating unit, copyright confirmation unit, and regeneration unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the generating unit is realized by the control unit 46A of the headset type terminal 314, and the generation AI generates content based on an instruction input by the user. The copyright confirmation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and checks whether the generated content matches an existing copyrighted work. The regeneration unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and issues an instruction to regenerate when it is determined that the content infringes copyright. The regeneration unit is also realized, for example, by the control unit 46A of the headset type terminal 314, and can issue an instruction to regenerate to the generation AI using different parameters. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0134] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0135] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0136] 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, 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 and / or a LAN.

[0137] 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. In addition, the microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0138] 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 the voice according to instructions from the processor 46.

[0139] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0140] 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 performed in a secure state.

[0141] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0142] 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.

[0143] 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.

[0144] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0145] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 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.

[0146] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0147] 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 a voice indicating a user input for 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.

[0148] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0149] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, 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.

[0150] Each of the multiple elements including the above-mentioned generating unit, copyright confirmation unit, and regeneration unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the generating unit is realized by the control unit 46A of the robot 414, and the generation AI generates content based on instructions input by the user. The copyright confirmation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and checks whether the generated content matches an existing copyrighted work. The regeneration unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and issues an instruction to regenerate when it is determined that the copyright is infringed. The regeneration unit is also realized, for example, by the control unit 46A of the robot 414, and can issue an instruction to regenerate to the generation AI using different parameters. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0151] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0152] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0153] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually 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.

[0154] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0155] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0156] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is 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 the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0157] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0158] 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.

[0159] 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 Universal Serial Bus (USB) 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.

[0160] In addition, 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 upon request from the data processing device 12.

[0161] 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.

[0162] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0163] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0164] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[0165] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0166] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0167] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0168] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0169] (Appendix 1) A generation unit that generates content using a generation AI; a copyright confirmation unit that confirms whether the content generated by the generation unit infringes a copyright; a regeneration unit that issues an instruction for regeneration when the copyright confirmation unit determines that the copyright is infringed. A system characterized by: (Appendix 2) The copyright confirmation unit: Check whether content generated using image recognition technology matches existing copyrighted work 2. The system of claim 1, (Appendix 3) The copyright confirmation unit: Check copyright by referencing a regularly updated database 2. The system of claim 1, (Appendix 4) The regeneration unit is Give the generation AI instructions to regenerate using different parameters 2. The system of claim 1, (Appendix 5) The regeneration unit is Create a feedback loop to prevent the same error from being repeated 2. The system of claim 1, (Appendix 6) The generation unit is Estimating user emotions and adjusting the style of generated content based on the estimated user emotions 2. The system of claim 1, (Appendix 7) The generation unit is When generating, the system analyzes the user's past generation history and selects the optimal generation method. 2. The system of claim 1, (Appendix 8) The generation unit is At generation time, customize the content based on the user's current interests 2. The system of claim 1, (Appendix 9) The generation unit is Estimating user emotions and determining the priority of content to be generated based on the estimated user emotions 2. The system of claim 1, (Appendix 10) The generation unit is When generating content, the system takes into account the user's geographic location information and prioritizes the generation of content that is highly relevant. 2. The system of claim 1, (Appendix 11) The generation unit is At the time of generation, the user's social media activity is analyzed to generate relevant content. 2. The system of claim 1, (Appendix 12) The copyright confirmation unit: To estimate a user's emotion and adjust a copyright confirmation criterion based on the estimated user's emotion. 2. The system of claim 1, (Appendix 13) The copyright confirmation unit: When checking copyright, the algorithm is optimized by referencing past verification data. 2. The system of claim 1, (Appendix 14) The copyright confirmation unit: During copyright checks, different check methods are applied depending on the category of generated content. 2. The system of claim 1, (Appendix 15) The copyright confirmation unit: Estimating user sentiment and determining priority of copyright confirmation based on the estimated user sentiment 2. The system of claim 1, (Appendix 16) The copyright confirmation unit: During copyright checks, reviews will be prioritized based on when generated content was submitted 2. The system of claim 1, (Appendix 17) The copyright confirmation unit: When checking copyright, improve the accuracy of the check by referring to related literature of the generated content. 2. The system of claim 1, (Appendix 18) The regeneration unit is Estimate the user's emotion and adjust the reproduction method based on the estimated user's emotion. 2. The system of claim 1, (Appendix 19) The regeneration unit is During regeneration, the regeneration algorithm is optimized by referencing past regeneration data. 2. The system of claim 1, (Appendix 20) The regeneration unit is During regeneration, apply different regeneration techniques depending on the category of generated content 2. The system of claim 1, (Appendix 21) The regeneration unit is Estimate the user's emotions and determine the priority of regeneration based on the estimated user's emotions. 2. The system of claim 1, (Appendix 22) The regeneration unit is During regeneration, the regeneration takes into account the geographic distribution of generated content. 2. The system of claim 1, (Appendix 23) The regeneration unit is During regeneration, the accuracy of the regeneration is improved by referencing related literature of the generated content. 2. The system of claim 1, [Explanation of symbols]

[0170] 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 generation unit that generates content using a generation AI; a copyright confirmation unit that confirms whether the content generated by the generation unit infringes a copyright; a regeneration unit that issues an instruction for regeneration when the copyright confirmation unit determines that the copyright is infringed. A system characterized by:

2. The copyright confirmation unit: Check whether content generated using image recognition technology matches existing copyrighted work 2. The system of claim 1 .

3. The copyright confirmation unit: Check copyright by referencing a regularly updated database 2. The system of claim 1 .

4. The regeneration unit is Instruct the generation AI to regenerate using different parameters 2. The system of claim 1 .

5. The regeneration unit is Create a feedback loop to prevent the same error from being repeated 2. The system of claim 1 .

6. The generation unit is Estimating user emotions and adjusting the style of generated content based on the estimated user emotions 2. The system of claim 1 .

7. The generation unit is When generating, the system analyzes the user's past generation history and selects the optimal generation method.

2. The system of claim 1 .

8. The generation unit is At generation time, customize the content based on the user's current interests 2. The system of claim 1 .

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