system

Abstract

The system according to this embodiment aims to enable creators to generate new ideas and find the optimal style and sales channels. [Solution] The system according to the embodiment comprises an idea generation support unit, a style analysis unit, a style proposal unit, a market analysis unit, a sales channel proposal unit, an NFT deployment support unit, a fan base analysis unit, and a copyright verification unit. The idea generation support unit helps creators generate new ideas. The style analysis unit analyzes the style of the creator's work based on the ideas proposed by the idea generation support unit. The style proposal unit proposes the optimal style based on the style analyzed by the style analysis unit. The market analysis unit performs market analysis based on the style proposed by the style proposal unit. The sales channel proposal unit proposes the optimal sales channel based on the analysis results obtained by the market analysis unit. The NFT deployment support unit provides NFT deployment support based on the sales channels proposed by the sales channel proposal unit. The fan base analysis unit performs fan base analysis based on the NFTs deployed by the NFT deployment support unit. The copyright verification unit performs copyright verification and management based on the analysis results obtained by the fan base analysis unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that the process for a creator to come up with new ideas and find an optimal style and sales channel is complex and difficult to perform efficiently.

[0005] The system according to the embodiment aims to enable a creator to come up with new ideas and find an optimal style and sales channel.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an idea generation support unit, a style analysis unit, a style proposal unit, a market analysis unit, a sales channel proposal unit, an NFT deployment support unit, a fan base analysis unit, and a copyright verification unit. The idea generation support unit helps creators generate new ideas. The style analysis unit analyzes the style of the creator's work based on the ideas proposed by the idea generation support unit. The style proposal unit proposes the optimal style based on the style analyzed by the style analysis unit. The market analysis unit performs market analysis based on the style proposed by the style proposal unit. The sales channel proposal unit proposes the optimal sales channel based on the analysis results obtained by the market analysis unit. The NFT deployment support unit provides NFT deployment support based on the sales channels proposed by the sales channel proposal unit. The fan base analysis unit performs fan base analysis based on the NFTs deployed by the NFT deployment support unit. The copyright verification unit performs copyright verification and management based on the analysis results obtained by the fan base analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment allows creators to generate new ideas and find the optimal style and sales channels. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6]This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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. Also, the database 24 and the communication I / F 26 are 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by contact of an indicator (e.g., a pen or a finger, etc.) by detecting the contact of the indicator. The microphone 38B receives user input by voice by detecting the voice of the user. 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] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

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

[0025] Storage 32 stores the data generation model 58 and the 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 user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

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

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The AI ​​agent for artistic creation according to an embodiment of the present invention is a system that provides a comprehensive solution to solve various challenges faced by creators. This system provides idea generation support to help creators generate new ideas. For example, the AI ​​agent analyzes past works and trends and proposes new styles and themes. Next, the AI ​​agent analyzes the style of the creator's work and proposes the optimal style. For example, the AI ​​analyzes the creator's past works and the works of other artists and proposes a new style that suits the creator's style. Furthermore, the AI ​​agent performs market analysis and proposes the optimal sales channels. For example, the AI ​​analyzes current market trends and demand and proposes the platform and method that can most effectively sell the creator's work. The AI ​​agent also provides NFT deployment support. For example, the AI ​​proposes the optimal method for deploying the creator's work as an NFT. Furthermore, the AI ​​agent performs fan base analysis and understands the creator's fan base. For example, the AI ​​analyzes the behavior and preferences of the creator's fans and proposes ways for the creator to strengthen their relationship with their fans. Finally, the AI ​​agent performs copyright verification and management. For example, the AI ​​verifies the copyright of the creator's work and proposes a method for appropriate management. In this way, the AI ​​agent for supporting artistic creation provides a comprehensive solution to solve various challenges faced by creators and support their creative activities. This allows the AI ​​agent to comprehensively support creators from generating new ideas to managing copyrights.

[0029] The AI ​​agent supporting artistic creation according to this embodiment comprises an idea generation support unit, a style analysis unit, a style proposal unit, a market analysis unit, a sales channel proposal unit, an NFT deployment support unit, a fan base analysis unit, and a copyright verification unit. The idea generation support unit helps creators generate new ideas. For example, the idea generation support unit analyzes past works and trends and proposes new styles and themes. For example, the idea generation support unit retrieves past works from a database and extracts new styles and themes using a trend analysis algorithm. The idea generation support unit can also generate new ideas based on the creator's past works. The style analysis unit analyzes the style of the creator's work. For example, the style analysis unit analyzes the creator's past works and works of other artists and proposes new styles that match the creator's style. For example, the style analysis unit extracts the characteristics of a work using image recognition technology and evaluates the similarity of styles. The style analysis unit can also analyze the theme and expressive methods of a work using text analysis technology. The style proposal unit proposes the optimal style based on the style analyzed by the style analysis unit. The Style Proposal Department selects the optimal style based on, for example, the creator's goals and market demand. The Style Proposal Department uses AI to understand the creator's goals and propose styles accordingly. It can also analyze market demand and propose styles with high demand. The Market Analysis Department conducts market analysis. For example, the Market Analysis Department analyzes current market trends and demand and proposes the most effective platforms and methods for selling the creator's work. For example, the Market Analysis Department uses AI to collect market data and trend analysis algorithms to predict market trends. It can also use demand forecasting algorithms to predict demand for the creator's work. The Sales Channel Proposal Department proposes the optimal sales channels based on the analysis results of the Market Analysis Department. For example, the Sales Channel Proposal Department selects the optimal sales channels considering the target market and sales channels. For example, the Sales Channel Proposal Department uses AI to identify the target market and propose the optimal sales channels based on that.The Sales Channel Proposal Department can also select the optimal sales channel considering cost efficiency. The NFT Deployment Support Department proposes the best way to deploy a creator's work as an NFT. For example, the NFT Deployment Support Department selects the platform and deployment method to be used. For example, the NFT Deployment Support Department uses AI to analyze the characteristics of NFT platforms and proposes the optimal platform. The NFT Deployment Support Department can also propose the optimal deployment method considering marketing strategy. The Fan Base Analysis Department analyzes the behavior and preferences of a creator's fans and proposes ways for creators to strengthen their relationship with fans. For example, the Fan Base Analysis Department collects fan behavior data and preference data and uses analysis algorithms to understand the characteristics of fans. For example, the Fan Base Analysis Department uses AI to analyze fan behavior patterns and proposes ways to strengthen the relationship with fans based on that. The Fan Base Analysis Department can also analyze fan preferences and propose benefits and events for fans. The Copyright Verification Department verifies the copyright of a creator's work and proposes methods for appropriate management. For example, the Copyright Verification Department clarifies the scope of copyright and verification methods. The copyright verification unit, for example, uses AI to refer to copyright law and verify the copyright of a creator's work. Furthermore, the copyright verification unit can propose copyright management methods and provide creators with ways to avoid copyright issues. This allows the AI ​​agent supporting artistic creation according to this embodiment to comprehensively support creators from generating new ideas to managing copyrights.

[0030] The Idea Generation Support Department helps creators generate new ideas. For example, it analyzes past works and trends to propose new styles and themes. Specifically, the Idea Generation Support Department retrieves works previously created by creators from a database and applies a trend analysis algorithm to these works. The trend analysis algorithm analyzes the characteristics of past works and market trends, and extracts new styles and themes from them. For example, it analyzes elements such as the use of color, composition, and theme selection to identify styles and themes that are currently popular in the market. The Idea Generation Support Department can also use AI to generate new ideas based on the creator's past works. The AI ​​uses generative AI technology to learn the characteristics of the creator's past works and generate new ideas based on that. For example, it learns the style of paintings the creator has painted in the past and generates new painting ideas based on that style. These generated ideas are proposed to the creator, and the creator can use them as a reference to create new works. Furthermore, the Idea Generation Support Department can also provide information on themes and styles that the creator is interested in. For example, it can provide historical background and cultural information on a particular theme, allowing the creator to deepen their understanding of that theme. This allows the Idea Generation Support Department to provide diverse support to creators in generating new ideas and to promote their creative activities.

[0031] The Style Analysis Department analyzes the style of a creator's work. For example, it analyzes a creator's past works and the works of other artists to propose new styles that suit the creator's style. Specifically, the Style Analysis Department uses image recognition technology to extract the characteristics of a work and evaluate the similarity of styles. Image recognition technology analyzes the visual characteristics of a work, such as its color, shape, and composition, and classifies the style based on these characteristics. For example, it analyzes the color patterns and brushwork characteristics of a creator's past works and identifies works by other artists with similar styles. The Style Analysis Department can also analyze the theme and expressive techniques of a work using text analysis technology. Text analysis technology analyzes text data related to the work (for example, the title and description of the work) to extract characteristics of the theme and expressive techniques. In this way, the Style Analysis Department can analyze the style of a creator's work from multiple angles and propose new styles that suit the creator's style. Furthermore, the Style Analysis Department can also provide information that can serve as a reference when creators try out new styles. For example, it can provide information on techniques and materials related to new styles to help creators experiment with them. This allows the Style Analysis Department to provide support to creators in developing their own styles and to support their creative activities.

[0032] The Style Proposal Department proposes the most suitable style based on the styles analyzed by the Style Analysis Department. For example, the Style Proposal Department selects the optimal style based on the creator's goals and market demand. Specifically, the Style Proposal Department understands the creator's goals and proposes styles accordingly. AI understands the goals set by the creator (e.g., creating artwork on a specific theme or mastering new techniques) and proposes styles that match those goals. For example, if a creator wants to create an abstract painting, the AI ​​provides information on abstract painting styles and offers resources for the creator to experiment with that style. The Style Proposal Department can also analyze market demand and propose styles with high demand. The AI ​​collects market data and uses trend analysis algorithms to predict market trends. This identifies currently popular styles and themes in the market and proposes the most suitable styles to creators based on this. For example, it proposes new styles to creators based on currently popular colors and themes in the market. Furthermore, the Style Proposal Department can provide resources for creators to use when experimenting with new styles. For example, it provides information on techniques and materials related to new styles to help creators experiment with them. This allows the style proposal department to support creators' creative activities by enabling them to select the optimal style that suits their goals and market demands.

[0033] The Market Analysis Department conducts market analysis. For example, it analyzes current market trends and demand and proposes the most effective platforms and methods for selling creators' works. Specifically, the Market Analysis Department uses AI to collect market data and trend analysis algorithms to predict market trends. The AI ​​collects publicly available data and sales data from the internet and analyzes this data to understand current market trends and demand. For example, it identifies which platforms are popular for works in specific genres or styles and proposes the most suitable sales platform for creators based on that. The Market Analysis Department can also predict the demand for creators' works using demand forecasting algorithms. Based on past sales data and market trends, the demand forecasting algorithm predicts the level of demand for a creator's work. This allows creators to develop strategies for selling their works most effectively. Furthermore, the Market Analysis Department can provide information that can serve as a reference for creators when entering new markets. For example, it provides information on new markets and sales strategies for those markets, helping creators enter new markets. In this way, the Market Analysis Department can support creators' creative activities by providing assistance in selling their works most effectively.

[0034] The Sales Channel Proposal Department proposes the optimal sales channels based on the analysis results of the Market Analysis Department. For example, the Sales Channel Proposal Department selects the best sales channels by considering the target market and sales channels. Specifically, the Sales Channel Proposal Department uses AI to identify the target market and propose the optimal sales channels based on that. Based on market data provided by the Market Analysis Department, the AI ​​identifies the markets and channels where a creator's work will be sold most effectively. For example, if a particular genre or style of work is popular on a specific online platform, the AI ​​will propose that platform to the creator. The Sales Channel Proposal Department can also select the optimal sales channels by considering cost efficiency. The AI ​​considers factors such as sales costs and fees to propose the sales channels that will generate the most profit for the creator. For example, it will propose platforms with low fees and reduced sales costs. Furthermore, the Sales Channel Proposal Department can provide information that can serve as a reference when creators explore new sales channels. For example, it can provide information on new sales channels and sales strategies for those channels, helping creators develop new sales channels. In this way, the Sales Channel Proposal Department can support creators in selling their work most effectively and support their creative activities.

[0035] The NFT Deployment Support Department proposes the optimal method for deploying creators' works as NFTs. For example, it selects the platform and deployment method to be used. Specifically, the NFT Deployment Support Department uses AI to analyze the characteristics of NFT platforms and propose the most suitable platform. The AI ​​analyzes factors such as fees, number of users, and transaction volume of each NFT platform to identify the platform best suited to the creator's work. For example, it might propose a platform with low fees and a large number of users. Furthermore, the NFT Deployment Support Department can also propose the optimal deployment method considering marketing strategies. Based on the characteristics of the creator's work and market demand, the AI ​​develops the optimal marketing strategy. For example, it might propose promotions targeting specific demographics or marketing methods utilizing social media. In addition, the NFT Deployment Support Department provides technical support when creators issue NFTs. For example, it provides information on NFT issuance procedures and blockchain technology to ensure creators can issue NFTs smoothly. In this way, the NFT Deployment Support Department can provide comprehensive support to creators in deploying their works as NFTs, thereby supporting their creative activities.

[0036] The Fan Base Analysis Department analyzes the behavior and preferences of creators' fans and proposes ways for creators to strengthen their relationships with fans. For example, the Fan Base Analysis Department collects fan behavior data and preference data and uses analysis algorithms to understand fan characteristics. Specifically, the Fan Base Analysis Department collects fan behavior data from social media and fan sites and analyzes this data. AI analyzes fan posts, reactions, and behavior patterns to identify fan preferences and interests. For example, it analyzes fan reactions to specific works or themes and uses that to understand fan preferences. The Fan Base Analysis Department also analyzes fan behavior patterns and proposes ways to strengthen relationships with fans based on that. For example, it identifies what kinds of events and perks fans are interested in and plans events and perks based on that. Furthermore, the Fan Base Analysis Department can also propose perks and events for fans. Based on fan preferences, the AI ​​proposes perks and events that fans will enjoy. For example, it proposes merchandise related to specific works or fan-only events. This allows the fan base analysis department to provide support to creators in strengthening their relationships with fans and increasing fan satisfaction, thereby supporting their creative activities.

[0037] The Copyright Verification Department verifies the copyright of creators' works and proposes methods for proper management. For example, the Copyright Verification Department clarifies the scope of copyright and verification methods. Specifically, the Copyright Verification Department uses AI to refer to copyright law and verify the copyright of creators' works. The AI ​​analyzes articles and precedents of copyright law to identify what kind of copyright protection the creator's work receives. For example, it verifies the scope of copyright based on factors such as the date of creation, publication date, and content of the work. The Copyright Verification Department can also propose methods for managing copyright and provide creators with ways to avoid copyright issues. Based on best practices for copyright management, the AI ​​proposes methods for creators to properly manage their copyrights. For example, it proposes copyright registration procedures and methods for monitoring copyright infringement. Furthermore, the Copyright Verification Department also assists creators in verifying copyright when using other works. For example, it proposes copyright verification procedures and licensing agreement methods when quoting other works. The AI ​​analyzes the copyright information of the source material and assists creators in properly quoting. In this way, the Copyright Verification Department can support creators' creative activities by providing comprehensive support to help them properly manage the copyright of their own works and avoid copyright issues when using other works.

[0038] The Idea Generation Support Department can analyze past works and trends to propose new styles and themes. For example, it can retrieve past works from a database and extract new styles and themes using trend analysis algorithms. For example, it can input past works into an AI, which then analyzes trends and proposes new styles and themes. The Idea Generation Support Department can also have an AI generate new ideas based on a creator's past works. For example, it can input a creator's past works into an AI, which then generates new ideas. This makes it easier for creators to find new styles and themes.

[0039] The Style Analysis Department can analyze a creator's past works and the works of other artists to suggest new styles that suit the creator's style. For example, the Style Analysis Department can retrieve a creator's past works from a database, and the AI ​​will analyze the characteristics of the works. For example, the Style Analysis Department can use image recognition technology to extract the characteristics of the works and evaluate the similarity of styles. The Style Analysis Department can also analyze the works of other artists and suggest new styles that suit the creator's style. For example, the Style Analysis Department can input the works of other artists into the AI, and the AI ​​will analyze the characteristics of the works and suggest new styles. This makes it easier for creators to find new styles that suit their own style.

[0040] The Market Analysis Department can analyze current market trends and demand and propose the most effective platforms and methods for selling creators' works. For example, the Market Analysis Department can obtain current market trends from a database and use AI to analyze market trends. The Market Analysis Department can predict market trends using, for example, trend analysis algorithms. The Market Analysis Department can also predict the demand for creators' works using demand forecasting algorithms. For example, the Market Analysis Department can predict the demand for creators' works using demand forecasting algorithms and propose the optimal sales platform and method based on that. This enables creators to sell their works effectively.

[0041] The Sales Channel Proposal Department can propose the optimal sales channels based on the analysis results of the Market Analysis Department. For example, the Sales Channel Proposal Department selects the optimal sales channels by considering the target market and sales channels. For example, the Sales Channel Proposal Department can use AI to identify the target market and propose the optimal sales channels based on that. The Sales Channel Proposal Department can also select the optimal sales channels by considering cost efficiency. For example, the Sales Channel Proposal Department can use AI to evaluate cost efficiency and propose the optimal sales channels based on that. This makes it easier for creators to find the optimal sales channels.

[0042] The NFT Deployment Support Department can propose the optimal method for deploying creators' works as NFTs. For example, the NFT Deployment Support Department can select the platform and deployment method to be used. For example, the NFT Deployment Support Department uses AI to analyze the characteristics of NFT platforms and propose the most suitable platform. The NFT Deployment Support Department can also propose the optimal deployment method considering the marketing strategy. For example, the NFT Deployment Support Department uses AI to evaluate the marketing strategy and propose the optimal deployment method based on that evaluation. This makes it easier for creators to deploy their works as NFTs.

[0043] The Fan Base Analysis Department can analyze the behavior and preferences of a creator's fans and propose ways for creators to strengthen their relationships with their fans. For example, the Fan Base Analysis Department collects fan behavior and preference data and uses analytical algorithms to understand fan characteristics. For example, the Fan Base Analysis Department uses AI to analyze fan behavior patterns and proposes ways to strengthen relationships with fans based on that analysis. The Fan Base Analysis Department can also analyze fan preferences and propose perks and events for fans. This makes it easier for creators to strengthen their relationships with their fans.

[0044] The Copyright Verification Department can verify the copyright of a creator's work and propose appropriate management methods. For example, the Copyright Verification Department can clarify the scope of copyright and verification methods. For example, the Copyright Verification Department can use AI to refer to copyright law and verify the copyright of a creator's work. The Copyright Verification Department can also propose copyright management methods and provide creators with ways to avoid copyright issues. This makes it easier for creators to avoid copyright problems.

[0045] The Idea Generation Support Department can analyze a creator's past creative history and select the optimal timing for providing inspiration. For example, it can analyze the time periods in which a creator has engaged in creative activities in the past and provide inspiration during those times. It can also analyze the frequency with which a creator has engaged in creative activities in the past and provide inspiration at appropriate intervals. Furthermore, it can analyze the locations where a creator has engaged in creative activities in the past and provide inspiration when the creator is in those locations. This allows for the provision of inspiration based on a creator's past creative history.

[0046] The Idea Generation Support Department can filter inspiration based on the creator's current projects and areas of interest. For example, it can provide inspiration related to the creator's current projects. It can also provide relevant inspiration based on the creator's areas of interest. Furthermore, it can provide inspiration based on themes the creator has shown interest in in the past. This allows the department to provide inspiration tailored to the creator's current projects and areas of interest.

[0047] The Idea Generation Support Department can prioritize providing highly relevant inspiration by considering the creator's geographical location. For example, the Idea Generation Support Department can provide inspiration related to the culture and history of the creator's location. It can also provide inspiration related to the natural environment of the creator's location. Furthermore, the Idea Generation Support Department can provide inspiration related to events and activities in the creator's location. This allows for the provision of inspiration based on the creator's geographical location.

[0048] The Idea Generation Support Department can analyze a creator's social media activity and provide relevant inspiration when offering it. For example, the Idea Generation Support Department can provide inspiration based on themes the creator has shown interest in on social media. For example, the Idea Generation Support Department can also provide inspiration related to the works of artists the creator follows on social media. Furthermore, the Idea Generation Support Department can provide inspiration based on the trends of communities the creator participates in on social media. This allows the department to provide inspiration based on the creator's social media activity.

[0049] The Style Analysis Department can improve the accuracy of its style analysis by referring to evaluation data of the creator's past works. For example, the Style Analysis Department can improve the accuracy of its style analysis based on evaluation data of the creator's past works. For example, the Style Analysis Department can input evaluation data of the creator's past works into an AI, which then analyzes the data to improve the accuracy of the style analysis. Furthermore, the Style Analysis Department can also suggest the optimal style based on evaluation data of the creator's past works. For example, the Style Analysis Department can input evaluation data of the creator's past works into an AI, which then suggests the optimal style. This allows for improved accuracy of style analysis based on evaluation data of the creator's past works.

[0050] The style analysis unit can apply different analysis algorithms depending on the category of the creator's work during style analysis. For example, if the creator's work is a painting, the style analysis unit will apply an analysis algorithm specialized for painting. If the creator's work is music, the style analysis unit can also apply an analysis algorithm specialized for music. Furthermore, if the creator's work is a video, the style analysis unit can also apply an analysis algorithm specialized for video. This allows for style analysis tailored to the category of the creator's work.

[0051] The Style Analysis Department can perform style analysis while considering the geographical distribution of creators. For example, the Style Analysis Department can perform style analysis while considering the culture and history of the region where the creator is located. For example, the Style Analysis Department can also perform style analysis while considering the trends of the region where the creator is located. Furthermore, the Style Analysis Department can perform style analysis by referencing the works of artists in the region where the creator is located. This allows for style analysis that takes into account the geographical distribution of creators.

[0052] The style analysis unit can improve the accuracy of its analysis by referring to the creator's related literature during style analysis. For example, the style analysis unit can improve the accuracy of its style analysis by referring to the creator's related literature. For example, the style analysis unit can input the creator's related literature into the AI, and the AI ​​will analyze the literature to improve the accuracy of its style analysis. In addition, the style analysis unit can also suggest the optimal style by referring to the creator's related literature. For example, the style analysis unit can input the creator's related literature into the AI, and the AI ​​will suggest the optimal style. In this way, the accuracy of style analysis can be improved by referring to the creator's related literature.

[0053] The style suggestion department can improve the accuracy of its suggestions by referring to evaluation data of the creator's past works when making style suggestions. For example, the style suggestion department can improve the accuracy of style suggestions based on evaluation data of the creator's past works. For example, the style suggestion department can input evaluation data of the creator's past works into an AI, which then analyzes the data to improve the accuracy of style suggestions. In addition, the style suggestion department can also propose the optimal style based on evaluation data of the creator's past works. For example, the style suggestion department can input evaluation data of the creator's past works into an AI, which then proposes the optimal style. This allows for improved accuracy of style suggestions based on evaluation data of the creator's past works.

[0054] The style suggestion unit can apply different suggestion algorithms depending on the category of the creator's work when suggesting styles. For example, if the creator's work is a painting, the style suggestion unit will apply a suggestion algorithm specialized for paintings. If the creator's work is music, the style suggestion unit can also apply a suggestion algorithm specialized for music. Furthermore, if the creator's work is a video, the style suggestion unit can also apply a suggestion algorithm specialized for video. This allows the system to provide style suggestions tailored to the category of the creator's work.

[0055] The Style Proposal Department can prioritize proposals based on the timing of creator submissions. For example, the Style Proposal Department can provide the most suitable style proposals based on the timing of the creator's submitted work. The Style Proposal Department can also adjust the order of proposals based on the timing of the creator's submitted work. This allows for style proposals to be presented in a priority order based on the creator's submission timing.

[0056] The style suggestion department can adjust the order of suggestions based on the creator's relevance when making style suggestions. For example, the style suggestion department can make optimal style suggestions based on the relevance of the creator's past works. The style suggestion department can also adjust the order of suggestions based on the relevance of the creator's past works. Furthermore, the style suggestion department can determine the priority of suggestions based on the relevance of the creator's past works. This allows style suggestions to be made in an order based on the creator's relevance.

[0057] The market analysis department can predict current market trends by referring to historical market data during market analysis. For example, the market analysis department predicts current market trends based on historical market data. For example, the market analysis department inputs historical market data into AI, and the AI ​​predicts market trends. Furthermore, the market analysis department can also propose optimal sales strategies based on historical market data. For example, the market analysis department inputs historical market data into AI, and the AI ​​proposes optimal sales strategies. This allows for the prediction of current market trends based on historical market data.

[0058] The Market Analysis Department can apply different analytical methods to each category of a creator's work during market analysis. For example, if a creator's work is painting, the Market Analysis Department can conduct a market analysis specifically for painting. If a creator's work is music, the Market Analysis Department can also conduct a market analysis specifically for music. Furthermore, if a creator's work is video, the Market Analysis Department can conduct a market analysis specifically for video. This allows for market analysis tailored to the category of the creator's work.

[0059] The Market Analysis Department can analyze market changes based on the timing of creator submissions during market analysis. For example, the Market Analysis Department can analyze market changes based on the timing of works submitted by creators. For example, the Market Analysis Department can input the timing of creator submissions into an AI, which then analyzes market changes. The Market Analysis Department can also propose optimal sales strategies based on the timing of creator submissions. For example, the Market Analysis Department can input the timing of creator submissions into an AI, which then proposes optimal sales strategies. This allows for analysis of market changes based on the timing of creator submissions.

[0060] The Market Analysis Department can perform market analysis by referring to market data related to creators. For example, the Market Analysis Department can propose optimal sales strategies based on market data related to creators. For example, the Market Analysis Department can input market data related to creators into an AI, which then proposes the optimal sales strategy. Furthermore, the Market Analysis Department can also predict changes in demand based on market data related to creators. For example, the Market Analysis Department can input market data related to creators into an AI, which then predicts changes in demand. This enables market analysis based on market data related to creators.

[0061] The sales channel proposal department can select the optimal sales channel by referring to the creator's past sales history when proposing sales channels. For example, the sales channel proposal department can propose the optimal sales channel based on the creator's past sales history. For example, the sales channel proposal department can input the creator's past sales history into an AI, and the AI ​​will propose the optimal sales channel. In addition, the sales channel proposal department can also propose the optimal sales strategy based on the creator's past sales history. For example, the sales channel proposal department can input the creator's past sales history into an AI, and the AI ​​will propose the optimal sales strategy. This allows for the selection of the optimal sales channel based on the creator's past sales history.

[0062] The sales channel proposal department can apply different proposal algorithms depending on the category of the creator's work when proposing sales channels. For example, if the creator's work is painting, the sales channel proposal department will provide sales channel proposals specifically for painting. If the creator's work is music, the sales channel proposal department can also provide sales channel proposals specifically for music. Furthermore, if the creator's work is video, the sales channel proposal department can provide sales channel proposals specifically for video. This allows for sales channel proposals tailored to the category of the creator's work.

[0063] The sales channel proposal department can select the most suitable sales channels by considering the creator's geographical location when proposing sales channels. For example, the sales channel proposal department can propose sales channels considering the market characteristics of the region where the creator is located. For example, the sales channel proposal department can also prioritize proposing sales platforms in the region where the creator is located. Furthermore, the sales channel proposal department can propose sales channels considering the consumer preferences of the region where the creator is located. This allows for the selection of the most suitable sales channels based on the creator's geographical location.

[0064] The sales channel proposal department can analyze a creator's social media activity when proposing sales channels. For example, the sales channel proposal department can propose the optimal sales channel based on the creator's social media activity. For example, the sales channel proposal department can input the creator's social media activity into an AI, which will then propose the optimal sales channel. Furthermore, the sales channel proposal department can also propose the optimal sales strategy based on the creator's social media activity. For example, the sales channel proposal department can input the creator's social media activity into an AI, which will then propose the optimal sales strategy. This allows for sales channel proposals based on the creator's social media activity.

[0065] The NFT Deployment Support Department can select the optimal deployment method by referring to the creator's past NFT deployment history during NFT deployment. For example, the NFT Deployment Support Department can propose the optimal deployment method based on the creator's past NFT deployment history. For example, the NFT Deployment Support Department can input the creator's past NFT deployment history into an AI, which will then propose the optimal deployment method. Furthermore, the NFT Deployment Support Department can also propose the optimal sales strategy based on the creator's past NFT deployment history. For example, the NFT Deployment Support Department can input the creator's past NFT deployment history into an AI, which will then propose the optimal sales strategy. This allows for the selection of the optimal deployment method based on the creator's past NFT deployment history.

[0066] The NFT Deployment Support Department can apply different deployment methods to NFTs depending on the category of the creator's work. For example, if a creator's work is a painting, the NFT Deployment Support Department can propose an NFT deployment method specialized for paintings. If a creator's work is music, the NFT Deployment Support Department can also propose an NFT deployment method specialized for music. Furthermore, if a creator's work is a video, the NFT Deployment Support Department can propose an NFT deployment method specialized for video. This allows for NFT deployment tailored to the category of the creator's work.

[0067] The NFT Deployment Support Department can select the optimal deployment method when deploying NFTs, taking into account the creator's geographical location. For example, the NFT Deployment Support Department can deploy NFTs considering the market characteristics of the region where the creator is located. For example, the NFT Deployment Support Department can also prioritize proposing sales platforms in the region where the creator is located. Furthermore, the NFT Deployment Support Department can deploy NFTs considering the consumer preferences of the region where the creator is located. This allows for the selection of the optimal NFT deployment method based on the creator's geographical location.

[0068] The NFT Deployment Support Department can analyze a creator's social media activity and propose deployment methods during NFT deployment. For example, the NFT Deployment Support Department can propose the optimal NFT deployment method based on the creator's social media activity. For example, the NFT Deployment Support Department can input the creator's social media activity into an AI, which then proposes the optimal NFT deployment method. Furthermore, the NFT Deployment Support Department can also propose the optimal sales strategy based on the creator's social media activity. For example, the NFT Deployment Support Department can input the creator's social media activity into an AI, which then proposes the optimal sales strategy. This allows for the proposal of NFT deployment methods based on the creator's social media activity.

[0069] The fan base analysis unit can improve the accuracy of its analysis by referring to the creator's past fan base data during the analysis process. For example, the fan base analysis unit can improve the accuracy of the fan base analysis based on the creator's past fan base data. For example, the fan base analysis unit can input the creator's past fan base data into an AI, which then analyzes the data to improve the accuracy of the fan base analysis. Furthermore, the fan base analysis unit can also perform optimal fan base analysis based on the creator's past fan base data. For example, the fan base analysis unit can input the creator's past fan base data into an AI, which then performs optimal fan base analysis. This enables highly accurate fan base analysis based on the creator's past fan base data.

[0070] The fan base analysis department can apply different analysis methods depending on the category of the creator's work. For example, if the creator's work is painting, the fan base analysis department can perform a fan base analysis specifically for painting. If the creator's work is music, the fan base analysis department can also perform a fan base analysis specifically for music. Furthermore, if the creator's work is video, the fan base analysis department can also perform a fan base analysis specifically for video. This allows for fan base analysis tailored to the category of the creator's work.

[0071] The fan base analysis unit can perform fan base analysis while considering the geographical distribution of creators. For example, the fan base analysis unit can perform fan base analysis while considering the characteristics of the fan base in the region where the creator is located. For example, the fan base analysis unit can also perform fan base analysis while considering the preferences of the fan base in the region where the creator is located. Furthermore, the fan base analysis unit can perform fan base analysis while considering the behavioral patterns of the fan base in the region where the creator is located. This allows for fan base analysis that takes into account the geographical distribution of creators.

[0072] The fan base analysis unit can improve the accuracy of its analysis by referring to the creator's related literature during fan base analysis. For example, the fan base analysis unit can improve the accuracy of its fan base analysis by referring to the creator's related literature. For example, the fan base analysis unit can input the creator's related literature into an AI, which then analyzes the literature to improve the accuracy of its fan base analysis. Furthermore, the fan base analysis unit can perform optimal fan base analysis by referring to the creator's related literature. For example, the fan base analysis unit can input the creator's related literature into an AI, which then performs optimal fan base analysis. This allows for improved accuracy of fan base analysis by referring to the creator's related literature.

[0073] The copyright verification unit can improve the accuracy of copyright verification by referring to the creator's past copyright history during the verification process. For example, the copyright verification unit can improve the accuracy of copyright verification based on the creator's past copyright history. For example, the copyright verification unit can input the creator's past copyright history into an AI, which then analyzes the history to improve the accuracy of copyright verification. Furthermore, the copyright verification unit can also perform optimal copyright verification based on the creator's past copyright history. For example, the copyright verification unit can input the creator's past copyright history into an AI, which then performs optimal copyright verification. This enables highly accurate copyright verification based on the creator's past copyright history.

[0074] The copyright verification unit can apply different verification methods depending on the category of the creator's work during copyright verification. For example, if the creator's work is a painting, the copyright verification unit will perform a copyright verification specifically for paintings. If the creator's work is music, the copyright verification unit can also perform a copyright verification specifically for music. Furthermore, if the creator's work is a video, the copyright verification unit can also perform a copyright verification specifically for video. This allows for copyright verification tailored to the category of the creator's work.

[0075] The copyright verification unit can perform copyright verification while considering the creator's geographical location. For example, the copyright verification unit can perform copyright verification considering the copyright laws of the region where the creator is located. For example, the copyright verification unit can also perform copyright verification by referring to the copyright protection authority in the region where the creator is located. Furthermore, the copyright verification unit can also perform copyright verification by referring to copyright-related case law in the region where the creator is located. This enables copyright verification based on the creator's geographical location.

[0076] The copyright verification unit can improve the accuracy of copyright verification by referring to the creator's related literature during the verification process. For example, the copyright verification unit can improve the accuracy of copyright verification by referring to the creator's related literature. For example, the copyright verification unit can input the creator's related literature into an AI, which then analyzes the literature to improve the accuracy of copyright verification. Furthermore, the copyright verification unit can perform optimal copyright verification by referring to the creator's related literature. For example, the copyright verification unit can input the creator's related literature into an AI, which then performs optimal copyright verification. This allows for improved accuracy of copyright verification by referring to the creator's related literature.

[0077] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0078] The Idea Support Department can analyze a creator's past creative history and select the optimal timing for providing inspiration. For example, it can analyze the time periods when a creator has engaged in creative activities in the past and provide inspiration during those times. It can also analyze the frequency of a creator's past creative activities and provide inspiration at appropriate intervals. Furthermore, it can analyze the locations where a creator has engaged in creative activities in the past and provide inspiration when they are in those locations. In this way, inspiration can be provided based on the creator's past creative history.

[0079] The style analysis department can improve the accuracy of its analysis by referring to evaluation data of the creator's past works during the style analysis process. For example, it can improve the accuracy of style analysis based on evaluation data of the creator's past works. It can also input evaluation data of the creator's past works into an AI, which then analyzes the data to improve the accuracy of style analysis. Furthermore, it can suggest the optimal style based on the evaluation data of the creator's past works. In this way, the accuracy of style analysis can be improved based on evaluation data of the creator's past works.

[0080] The market analysis department can predict current market trends by referring to historical market data during market analysis. For example, it can predict current market trends based on historical market data. It can also input historical market data into AI, which can then predict market trends. Furthermore, it can propose optimal sales strategies based on historical market data. In this way, it is possible to predict current market trends based on historical market data.

[0081] The sales channel proposal department can select the optimal sales channel by referring to the creator's past sales history when proposing sales channels. For example, it can propose the optimal sales channel based on the creator's past sales history. It can also input the creator's past sales history into AI, which can then propose the optimal sales channel. Furthermore, it can propose the optimal sales strategy based on the creator's past sales history. This allows for the selection of the optimal sales channel based on the creator's past sales history.

[0082] The NFT Deployment Support Department can select the optimal deployment method by referring to the creator's past NFT deployment history during NFT deployment. For example, it can propose the optimal deployment method based on the creator's past NFT deployment history. It can also input the creator's past NFT deployment history into AI, which can then propose the optimal deployment method. Furthermore, it can propose the optimal sales strategy based on the creator's past NFT deployment history. This allows for the selection of the optimal deployment method based on the creator's past NFT deployment history.

[0083] The following briefly describes the processing flow for example form 1.

[0084] Step 1: The Idea Generation Support Department helps creators generate new ideas. For example, it analyzes past works and trends to propose new styles and themes. The Idea Generation Support Department retrieves past works from a database and extracts new styles and themes using trend analysis algorithms. AI can also generate new ideas based on the creator's past works. Step 2: The Style Analysis Department analyzes the style of the creator's work. For example, it analyzes the creator's past works and the works of other artists to propose a new style that suits the creator's style. The Style Analysis Department uses image recognition technology to extract the characteristics of the work and evaluate the similarity of styles. It can also use text analysis technology to analyze the theme and expressive techniques of the work. Step 3: The Style Proposal Department proposes the optimal style based on the styles analyzed by the Style Analysis Department. For example, it selects the optimal style based on the creator's goals and market demand. The Style Proposal Department uses AI to understand the creator's goals and propose styles based on them. It can also analyze market demand and propose styles that are in high demand. Step 4: The Market Analysis Department conducts market analysis. For example, it analyzes current market trends and demand and proposes the most effective platforms and methods for selling creators' works. The Market Analysis Department uses AI to collect market data and predicts market trends using trend analysis algorithms. It can also predict the demand for creators' works using demand forecasting algorithms. Step 5: The Sales Channel Proposal Department proposes the optimal sales channels based on the analysis results of the Market Analysis Department. For example, they select the optimal sales channels by considering the target market and sales channels. The Sales Channel Proposal Department uses AI to identify the target market and proposes the optimal sales channels based on that. They can also select the optimal sales channels by considering cost efficiency. Step 6: The NFT Deployment Support Department proposes the optimal method for deploying the creator's work as an NFT. For example, it selects the platform and deployment method to be used. The NFT Deployment Support Department uses AI to analyze the characteristics of NFT platforms and propose the most suitable platform. It can also propose the optimal deployment method considering the marketing strategy. Step 7: The Fan Base Analysis Department analyzes the behavior and preferences of the creator's fans and proposes ways for the creator to strengthen their relationship with their fans. For example, it collects fan behavior and preference data and uses analytical algorithms to understand the characteristics of the fans. The Fan Base Analysis Department uses AI to analyze fan behavior patterns and proposes ways to strengthen the relationship with fans based on that analysis. It can also analyze fan preferences and propose perks and events for fans. Step 8: The Copyright Verification Unit verifies the copyright of the creator's work and proposes appropriate management methods. For example, it clarifies the scope of copyright and verification methods. The Copyright Verification Unit uses AI to refer to copyright law and verify the copyright of the creator's work. It can also propose copyright management methods and provide creators with ways to avoid copyright issues.

[0085] (Example of form 2) The AI ​​agent for artistic creation according to an embodiment of the present invention is a system that provides a comprehensive solution to solve various challenges faced by creators. This system provides idea generation support to help creators generate new ideas. For example, the AI ​​agent analyzes past works and trends and proposes new styles and themes. Next, the AI ​​agent analyzes the style of the creator's work and proposes the optimal style. For example, the AI ​​analyzes the creator's past works and the works of other artists and proposes a new style that suits the creator's style. Furthermore, the AI ​​agent performs market analysis and proposes the optimal sales channels. For example, the AI ​​analyzes current market trends and demand and proposes the platform and method that can most effectively sell the creator's work. The AI ​​agent also provides NFT deployment support. For example, the AI ​​proposes the optimal method for deploying the creator's work as an NFT. Furthermore, the AI ​​agent performs fan base analysis and understands the creator's fan base. For example, the AI ​​analyzes the behavior and preferences of the creator's fans and proposes ways for the creator to strengthen their relationship with their fans. Finally, the AI ​​agent performs copyright verification and management. For example, the AI ​​verifies the copyright of the creator's work and proposes a method for appropriate management. In this way, the AI ​​agent for supporting artistic creation provides a comprehensive solution to solve various challenges faced by creators and support their creative activities. This allows the AI ​​agent to comprehensively support creators from generating new ideas to managing copyrights.

[0086] The AI ​​agent supporting artistic creation according to this embodiment comprises an idea generation support unit, a style analysis unit, a style proposal unit, a market analysis unit, a sales channel proposal unit, an NFT deployment support unit, a fan base analysis unit, and a copyright verification unit. The idea generation support unit helps creators generate new ideas. For example, the idea generation support unit analyzes past works and trends and proposes new styles and themes. For example, the idea generation support unit retrieves past works from a database and extracts new styles and themes using a trend analysis algorithm. The idea generation support unit can also generate new ideas based on the creator's past works. The style analysis unit analyzes the style of the creator's work. For example, the style analysis unit analyzes the creator's past works and works of other artists and proposes new styles that match the creator's style. For example, the style analysis unit extracts the characteristics of a work using image recognition technology and evaluates the similarity of styles. The style analysis unit can also analyze the theme and expressive methods of a work using text analysis technology. The style proposal unit proposes the optimal style based on the style analyzed by the style analysis unit. The Style Proposal Department selects the optimal style based on, for example, the creator's goals and market demand. The Style Proposal Department uses AI to understand the creator's goals and propose styles accordingly. It can also analyze market demand and propose styles with high demand. The Market Analysis Department conducts market analysis. For example, the Market Analysis Department analyzes current market trends and demand and proposes the most effective platforms and methods for selling the creator's work. For example, the Market Analysis Department uses AI to collect market data and trend analysis algorithms to predict market trends. It can also use demand forecasting algorithms to predict demand for the creator's work. The Sales Channel Proposal Department proposes the optimal sales channels based on the analysis results of the Market Analysis Department. For example, the Sales Channel Proposal Department selects the optimal sales channels considering the target market and sales channels. For example, the Sales Channel Proposal Department uses AI to identify the target market and propose the optimal sales channels based on that.The Sales Channel Proposal Department can also select the optimal sales channel considering cost efficiency. The NFT Deployment Support Department proposes the best way to deploy a creator's work as an NFT. For example, the NFT Deployment Support Department selects the platform and deployment method to be used. For example, the NFT Deployment Support Department uses AI to analyze the characteristics of NFT platforms and proposes the optimal platform. The NFT Deployment Support Department can also propose the optimal deployment method considering marketing strategy. The Fan Base Analysis Department analyzes the behavior and preferences of a creator's fans and proposes ways for creators to strengthen their relationship with fans. For example, the Fan Base Analysis Department collects fan behavior data and preference data and uses analysis algorithms to understand the characteristics of fans. For example, the Fan Base Analysis Department uses AI to analyze fan behavior patterns and proposes ways to strengthen the relationship with fans based on that. The Fan Base Analysis Department can also analyze fan preferences and propose benefits and events for fans. The Copyright Verification Department verifies the copyright of a creator's work and proposes methods for appropriate management. For example, the Copyright Verification Department clarifies the scope of copyright and verification methods. The copyright verification unit, for example, uses AI to refer to copyright law and verify the copyright of a creator's work. Furthermore, the copyright verification unit can propose copyright management methods and provide creators with ways to avoid copyright issues. This allows the AI ​​agent supporting artistic creation according to this embodiment to comprehensively support creators from generating new ideas to managing copyrights.

[0087] The Idea Generation Support Department helps creators generate new ideas. For example, it analyzes past works and trends to propose new styles and themes. Specifically, the Idea Generation Support Department retrieves works previously created by creators from a database and applies a trend analysis algorithm to these works. The trend analysis algorithm analyzes the characteristics of past works and market trends, and extracts new styles and themes from them. For example, it analyzes elements such as the use of color, composition, and theme selection to identify styles and themes that are currently popular in the market. The Idea Generation Support Department can also use AI to generate new ideas based on the creator's past works. The AI ​​uses generative AI technology to learn the characteristics of the creator's past works and generate new ideas based on that. For example, it learns the style of paintings the creator has painted in the past and generates new painting ideas based on that style. These generated ideas are proposed to the creator, and the creator can use them as a reference to create new works. Furthermore, the Idea Generation Support Department can also provide information on themes and styles that the creator is interested in. For example, it can provide historical background and cultural information on a particular theme, allowing the creator to deepen their understanding of that theme. This allows the Idea Generation Support Department to provide diverse support to creators in generating new ideas and to promote their creative activities.

[0088] The Style Analysis Department analyzes the style of a creator's work. For example, it analyzes a creator's past works and the works of other artists to propose new styles that suit the creator's style. Specifically, the Style Analysis Department uses image recognition technology to extract the characteristics of a work and evaluate the similarity of styles. Image recognition technology analyzes the visual characteristics of a work, such as its color, shape, and composition, and classifies the style based on these characteristics. For example, it analyzes the color patterns and brushwork characteristics of a creator's past works and identifies works by other artists with similar styles. The Style Analysis Department can also analyze the theme and expressive techniques of a work using text analysis technology. Text analysis technology analyzes text data related to the work (for example, the title and description of the work) to extract characteristics of the theme and expressive techniques. In this way, the Style Analysis Department can analyze the style of a creator's work from multiple angles and propose new styles that suit the creator's style. Furthermore, the Style Analysis Department can also provide information that can serve as a reference when creators try out new styles. For example, it can provide information on techniques and materials related to new styles to help creators experiment with them. This allows the Style Analysis Department to provide support to creators in developing their own styles and to support their creative activities.

[0089] The Style Proposal Department proposes the most suitable style based on the styles analyzed by the Style Analysis Department. For example, the Style Proposal Department selects the optimal style based on the creator's goals and market demand. Specifically, the Style Proposal Department understands the creator's goals and proposes styles accordingly. AI understands the goals set by the creator (e.g., creating artwork on a specific theme or mastering new techniques) and proposes styles that match those goals. For example, if a creator wants to create an abstract painting, the AI ​​provides information on abstract painting styles and offers resources for the creator to experiment with that style. The Style Proposal Department can also analyze market demand and propose styles with high demand. The AI ​​collects market data and uses trend analysis algorithms to predict market trends. This identifies currently popular styles and themes in the market and proposes the most suitable styles to creators based on this. For example, it proposes new styles to creators based on currently popular colors and themes in the market. Furthermore, the Style Proposal Department can provide resources for creators to use when experimenting with new styles. For example, it provides information on techniques and materials related to new styles to help creators experiment with them. This allows the style proposal department to support creators' creative activities by enabling them to select the optimal style that suits their goals and market demands.

[0090] The Market Analysis Department conducts market analysis. For example, it analyzes current market trends and demand and proposes the most effective platforms and methods for selling creators' works. Specifically, the Market Analysis Department uses AI to collect market data and trend analysis algorithms to predict market trends. The AI ​​collects publicly available data and sales data from the internet and analyzes this data to understand current market trends and demand. For example, it identifies which platforms are popular for works in specific genres or styles and proposes the most suitable sales platform for creators based on that. The Market Analysis Department can also predict the demand for creators' works using demand forecasting algorithms. Based on past sales data and market trends, the demand forecasting algorithm predicts the level of demand for a creator's work. This allows creators to develop strategies for selling their works most effectively. Furthermore, the Market Analysis Department can provide information that can serve as a reference for creators when entering new markets. For example, it provides information on new markets and sales strategies for those markets, helping creators enter new markets. In this way, the Market Analysis Department can support creators' creative activities by providing assistance in selling their works most effectively.

[0091] The Sales Channel Proposal Department proposes the optimal sales channels based on the analysis results of the Market Analysis Department. For example, the Sales Channel Proposal Department selects the best sales channels by considering the target market and sales channels. Specifically, the Sales Channel Proposal Department uses AI to identify the target market and propose the optimal sales channels based on that. Based on market data provided by the Market Analysis Department, the AI ​​identifies the markets and channels where a creator's work will be sold most effectively. For example, if a particular genre or style of work is popular on a specific online platform, the AI ​​will propose that platform to the creator. The Sales Channel Proposal Department can also select the optimal sales channels by considering cost efficiency. The AI ​​considers factors such as sales costs and fees to propose the sales channels that will generate the most profit for the creator. For example, it will propose platforms with low fees and reduced sales costs. Furthermore, the Sales Channel Proposal Department can provide information that can serve as a reference when creators explore new sales channels. For example, it can provide information on new sales channels and sales strategies for those channels, helping creators develop new sales channels. In this way, the Sales Channel Proposal Department can support creators in selling their work most effectively and support their creative activities.

[0092] The NFT Deployment Support Department proposes the optimal method for deploying creators' works as NFTs. For example, it selects the platform and deployment method to be used. Specifically, the NFT Deployment Support Department uses AI to analyze the characteristics of NFT platforms and propose the most suitable platform. The AI ​​analyzes factors such as fees, number of users, and transaction volume of each NFT platform to identify the platform best suited to the creator's work. For example, it might propose a platform with low fees and a large number of users. Furthermore, the NFT Deployment Support Department can also propose the optimal deployment method considering marketing strategies. Based on the characteristics of the creator's work and market demand, the AI ​​develops the optimal marketing strategy. For example, it might propose promotions targeting specific demographics or marketing methods utilizing social media. In addition, the NFT Deployment Support Department provides technical support when creators issue NFTs. For example, it provides information on NFT issuance procedures and blockchain technology to ensure creators can issue NFTs smoothly. In this way, the NFT Deployment Support Department can provide comprehensive support to creators in deploying their works as NFTs, thereby supporting their creative activities.

[0093] The Fan Base Analysis Department analyzes the behavior and preferences of creators' fans and proposes ways for creators to strengthen their relationships with fans. For example, the Fan Base Analysis Department collects fan behavior data and preference data and uses analysis algorithms to understand fan characteristics. Specifically, the Fan Base Analysis Department collects fan behavior data from social media and fan sites and analyzes this data. AI analyzes fan posts, reactions, and behavior patterns to identify fan preferences and interests. For example, it analyzes fan reactions to specific works or themes and uses that to understand fan preferences. The Fan Base Analysis Department also analyzes fan behavior patterns and proposes ways to strengthen relationships with fans based on that. For example, it identifies what kinds of events and perks fans are interested in and plans events and perks based on that. Furthermore, the Fan Base Analysis Department can also propose perks and events for fans. Based on fan preferences, the AI ​​proposes perks and events that fans will enjoy. For example, it proposes merchandise related to specific works or fan-only events. This allows the fan base analysis department to provide support to creators in strengthening their relationships with fans and increasing fan satisfaction, thereby supporting their creative activities.

[0094] The Copyright Verification Department verifies the copyright of creators' works and proposes methods for proper management. For example, the Copyright Verification Department clarifies the scope of copyright and verification methods. Specifically, the Copyright Verification Department uses AI to refer to copyright law and verify the copyright of creators' works. The AI ​​analyzes articles and precedents of copyright law to identify what kind of copyright protection the creator's work receives. For example, it verifies the scope of copyright based on factors such as the date of creation, publication date, and content of the work. The Copyright Verification Department can also propose methods for managing copyright and provide creators with ways to avoid copyright issues. Based on best practices for copyright management, the AI ​​proposes methods for creators to properly manage their copyrights. For example, it proposes copyright registration procedures and methods for monitoring copyright infringement. Furthermore, the Copyright Verification Department also assists creators in verifying copyright when using other works. For example, it proposes copyright verification procedures and licensing agreement methods when quoting other works. The AI ​​analyzes the copyright information of the source material and assists creators in properly quoting. In this way, the Copyright Verification Department can support creators' creative activities by providing comprehensive support to help them properly manage the copyright of their own works and avoid copyright issues when using other works.

[0095] The Idea Generation Support Department can analyze past works and trends to propose new styles and themes. For example, it can retrieve past works from a database and extract new styles and themes using trend analysis algorithms. For example, it can input past works into an AI, which then analyzes trends and proposes new styles and themes. The Idea Generation Support Department can also have an AI generate new ideas based on a creator's past works. For example, it can input a creator's past works into an AI, which then generates new ideas. This makes it easier for creators to find new styles and themes.

[0096] The Style Analysis Department can analyze a creator's past works and the works of other artists to suggest new styles that suit the creator's style. For example, the Style Analysis Department can retrieve a creator's past works from a database, and the AI ​​will analyze the characteristics of the works. For example, the Style Analysis Department can use image recognition technology to extract the characteristics of the works and evaluate the similarity of styles. The Style Analysis Department can also analyze the works of other artists and suggest new styles that suit the creator's style. For example, the Style Analysis Department can input the works of other artists into the AI, and the AI ​​will analyze the characteristics of the works and suggest new styles. This makes it easier for creators to find new styles that suit their own style.

[0097] The Market Analysis Department can analyze current market trends and demand and propose the most effective platforms and methods for selling creators' works. For example, the Market Analysis Department can obtain current market trends from a database and use AI to analyze market trends. The Market Analysis Department can predict market trends using, for example, trend analysis algorithms. The Market Analysis Department can also predict the demand for creators' works using demand forecasting algorithms. For example, the Market Analysis Department can predict the demand for creators' works using demand forecasting algorithms and propose the optimal sales platform and method based on that. This enables creators to sell their works effectively.

[0098] The Sales Channel Proposal Department can propose the optimal sales channels based on the analysis results of the Market Analysis Department. For example, the Sales Channel Proposal Department selects the optimal sales channels by considering the target market and sales channels. For example, the Sales Channel Proposal Department can use AI to identify the target market and propose the optimal sales channels based on that. The Sales Channel Proposal Department can also select the optimal sales channels by considering cost efficiency. For example, the Sales Channel Proposal Department can use AI to evaluate cost efficiency and propose the optimal sales channels based on that. This makes it easier for creators to find the optimal sales channels.

[0099] The NFT Deployment Support Department can propose the optimal method for deploying creators' works as NFTs. For example, the NFT Deployment Support Department can select the platform and deployment method to be used. For example, the NFT Deployment Support Department uses AI to analyze the characteristics of NFT platforms and propose the most suitable platform. The NFT Deployment Support Department can also propose the optimal deployment method considering the marketing strategy. For example, the NFT Deployment Support Department uses AI to evaluate the marketing strategy and propose the optimal deployment method based on that evaluation. This makes it easier for creators to deploy their works as NFTs.

[0100] The Fan Base Analysis Department can analyze the behavior and preferences of a creator's fans and propose ways for creators to strengthen their relationships with their fans. For example, the Fan Base Analysis Department collects fan behavior and preference data and uses analytical algorithms to understand fan characteristics. For example, the Fan Base Analysis Department uses AI to analyze fan behavior patterns and proposes ways to strengthen relationships with fans based on that analysis. The Fan Base Analysis Department can also analyze fan preferences and propose perks and events for fans. This makes it easier for creators to strengthen their relationships with their fans.

[0101] The Copyright Verification Department can verify the copyright of a creator's work and propose appropriate management methods. For example, the Copyright Verification Department can clarify the scope of copyright and verification methods. For example, the Copyright Verification Department can use AI to refer to copyright law and verify the copyright of a creator's work. The Copyright Verification Department can also propose copyright management methods and provide creators with ways to avoid copyright issues. This makes it easier for creators to avoid copyright problems.

[0102] The Idea Generation Support Unit can estimate a creator's emotions and adjust how inspiration is provided based on those emotions. For example, if a creator is feeling stressed, the Idea Generation Support Unit can provide relaxing inspiration. If a creator is excited, the Idea Generation Support Unit can also provide challenging ideas. Furthermore, if a creator is feeling down, the Idea Generation Support Unit can provide inspiration along with encouraging messages. This allows for the provision of inspiration tailored to the creator's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0103] The Idea Generation Support Department can analyze a creator's past creative history and select the optimal timing for providing inspiration. For example, it can analyze the time periods in which a creator has engaged in creative activities in the past and provide inspiration during those times. It can also analyze the frequency with which a creator has engaged in creative activities in the past and provide inspiration at appropriate intervals. Furthermore, it can analyze the locations where a creator has engaged in creative activities in the past and provide inspiration when the creator is in those locations. This allows for the provision of inspiration based on a creator's past creative history.

[0104] The Idea Generation Support Department can filter inspiration based on the creator's current projects and areas of interest. For example, it can provide inspiration related to the creator's current projects. It can also provide relevant inspiration based on the creator's areas of interest. Furthermore, it can provide inspiration based on themes the creator has shown interest in in the past. This allows the department to provide inspiration tailored to the creator's current projects and areas of interest.

[0105] The Idea Generation Support Unit can estimate a creator's emotions and prioritize the inspiration it provides based on those emotions. For example, if a creator is stressed, the Idea Generation Support Unit will prioritize providing relaxing inspiration. If a creator is excited, the Idea Generation Support Unit can also prioritize providing challenging inspiration. Furthermore, if a creator is depressed, the Idea Generation Support Unit can prioritize providing inspiration along with encouraging messages. This allows for the provision of inspiration tailored to the creator's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0106] The Idea Generation Support Department can prioritize providing highly relevant inspiration by considering the creator's geographical location. For example, the Idea Generation Support Department can provide inspiration related to the culture and history of the creator's location. It can also provide inspiration related to the natural environment of the creator's location. Furthermore, the Idea Generation Support Department can provide inspiration related to events and activities in the creator's location. This allows for the provision of inspiration based on the creator's geographical location.

[0107] The Idea Generation Support Department can analyze a creator's social media activity and provide relevant inspiration when offering it. For example, the Idea Generation Support Department can provide inspiration based on themes the creator has shown interest in on social media. For example, the Idea Generation Support Department can also provide inspiration related to the works of artists the creator follows on social media. Furthermore, the Idea Generation Support Department can provide inspiration based on the trends of communities the creator participates in on social media. This allows the department to provide inspiration based on the creator's social media activity.

[0108] The style analysis unit can estimate the creator's emotions and adjust the style analysis method based on the estimated emotions. For example, if the creator is relaxed, the style analysis unit can perform a detailed style analysis. If the creator is in a hurry, the style analysis unit can perform a concise style analysis. Furthermore, if the creator is excited, the style analysis unit can perform a visually stimulating style analysis. This allows for style analysis tailored to the creator's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0109] The Style Analysis Department can improve the accuracy of its style analysis by referring to evaluation data of the creator's past works. For example, the Style Analysis Department can improve the accuracy of its style analysis based on evaluation data of the creator's past works. For example, the Style Analysis Department can input evaluation data of the creator's past works into an AI, which then analyzes the data to improve the accuracy of the style analysis. Furthermore, the Style Analysis Department can also suggest the optimal style based on evaluation data of the creator's past works. For example, the Style Analysis Department can input evaluation data of the creator's past works into an AI, which then suggests the optimal style. This allows for improved accuracy of style analysis based on evaluation data of the creator's past works.

[0110] The style analysis unit can apply different analysis algorithms depending on the category of the creator's work during style analysis. For example, if the creator's work is a painting, the style analysis unit will apply an analysis algorithm specialized for painting. If the creator's work is music, the style analysis unit can also apply an analysis algorithm specialized for music. Furthermore, if the creator's work is a video, the style analysis unit can also apply an analysis algorithm specialized for video. This allows for style analysis tailored to the category of the creator's work.

[0111] The style analysis unit can estimate the creator's emotions and adjust the order in which the style analysis results are displayed based on the estimated emotions. For example, if the creator is relaxed, the style analysis unit may prioritize displaying detailed style analysis results. For example, if the creator is in a hurry, the style analysis unit may prioritize displaying concise style analysis results. Furthermore, if the creator is excited, the style analysis unit may prioritize displaying visually stimulating style analysis results. This allows the style analysis results to be displayed in an order that corresponds to the creator's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0112] The Style Analysis Department can perform style analysis while considering the geographical distribution of creators. For example, the Style Analysis Department can perform style analysis while considering the culture and history of the region where the creator is located. For example, the Style Analysis Department can also perform style analysis while considering the trends of the region where the creator is located. Furthermore, the Style Analysis Department can perform style analysis by referencing the works of artists in the region where the creator is located. This allows for style analysis that takes into account the geographical distribution of creators.

[0113] The style analysis unit can improve the accuracy of its analysis by referring to the creator's related literature during style analysis. For example, the style analysis unit can improve the accuracy of its style analysis by referring to the creator's related literature. For example, the style analysis unit can input the creator's related literature into the AI, and the AI ​​will analyze the literature to improve the accuracy of its style analysis. In addition, the style analysis unit can also suggest the optimal style by referring to the creator's related literature. For example, the style analysis unit can input the creator's related literature into the AI, and the AI ​​will suggest the optimal style. In this way, the accuracy of style analysis can be improved by referring to the creator's related literature.

[0114] The style suggestion unit can estimate the creator's emotions and adjust the expression of style suggestions based on the estimated emotions. For example, if the creator is relaxed, the style suggestion unit can provide detailed style suggestions. If the creator is in a hurry, for example, the style suggestion unit can provide concise style suggestions. Furthermore, if the creator is excited, the style suggestion unit can provide visually stimulating style suggestions. This allows style suggestions to be expressed in a way that suits the creator's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0115] The style suggestion department can improve the accuracy of its suggestions by referring to evaluation data of the creator's past works when making style suggestions. For example, the style suggestion department can improve the accuracy of style suggestions based on evaluation data of the creator's past works. For example, the style suggestion department can input evaluation data of the creator's past works into an AI, which then analyzes the data to improve the accuracy of style suggestions. In addition, the style suggestion department can also propose the optimal style based on evaluation data of the creator's past works. For example, the style suggestion department can input evaluation data of the creator's past works into an AI, which then proposes the optimal style. This allows for improved accuracy of style suggestions based on evaluation data of the creator's past works.

[0116] The style suggestion unit can apply different suggestion algorithms depending on the category of the creator's work when suggesting styles. For example, if the creator's work is a painting, the style suggestion unit will apply a suggestion algorithm specialized for paintings. If the creator's work is music, the style suggestion unit can also apply a suggestion algorithm specialized for music. Furthermore, if the creator's work is a video, the style suggestion unit can also apply a suggestion algorithm specialized for video. This allows the system to provide style suggestions tailored to the category of the creator's work.

[0117] The style suggestion unit can estimate the creator's emotions and adjust the length of the style suggestion based on the estimated emotions. For example, if the creator is relaxed, the style suggestion unit can provide a detailed style suggestion. If the creator is in a hurry, for example, the style suggestion unit can provide a concise style suggestion. Furthermore, if the creator is excited, the style suggestion unit can provide a visually stimulating style suggestion. This allows for style suggestions to be provided at a length appropriate to the creator's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0118] The Style Proposal Department can prioritize proposals based on the timing of creator submissions. For example, the Style Proposal Department can provide the most suitable style proposals based on the timing of the creator's submitted work. The Style Proposal Department can also adjust the order of proposals based on the timing of the creator's submitted work. This allows for style proposals to be presented in a priority order based on the creator's submission timing.

[0119] The style suggestion department can adjust the order of suggestions based on the creator's relevance when making style suggestions. For example, the style suggestion department can make optimal style suggestions based on the relevance of the creator's past works. The style suggestion department can also adjust the order of suggestions based on the relevance of the creator's past works. Furthermore, the style suggestion department can determine the priority of suggestions based on the relevance of the creator's past works. This allows style suggestions to be made in an order based on the creator's relevance.

[0120] The market analysis department can estimate the creator's emotions and adjust the market analysis method based on the estimated emotions. For example, if the creator is relaxed, the market analysis department can perform a detailed market analysis. If the creator is in a hurry, the market analysis department can perform a concise market analysis. Furthermore, if the creator is excited, the market analysis department can perform a visually stimulating market analysis. This allows market analysis to be performed in a way that is appropriate to the creator's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0121] The market analysis department can predict current market trends by referring to historical market data during market analysis. For example, the market analysis department predicts current market trends based on historical market data. For example, the market analysis department inputs historical market data into AI, and the AI ​​predicts market trends. Furthermore, the market analysis department can also propose optimal sales strategies based on historical market data. For example, the market analysis department inputs historical market data into AI, and the AI ​​proposes optimal sales strategies. This allows for the prediction of current market trends based on historical market data.

[0122] The Market Analysis Department can apply different analytical methods to each category of a creator's work during market analysis. For example, if a creator's work is painting, the Market Analysis Department can conduct a market analysis specifically for painting. If a creator's work is music, the Market Analysis Department can also conduct a market analysis specifically for music. Furthermore, if a creator's work is video, the Market Analysis Department can conduct a market analysis specifically for video. This allows for market analysis tailored to the category of the creator's work.

[0123] The market analysis department can estimate the creator's emotions and adjust the importance of the market analysis based on the estimated emotions. For example, if the creator is relaxed, the market analysis department can perform a detailed market analysis. If the creator is in a hurry, for example, the market analysis department can perform a concise market analysis. Furthermore, if the creator is excited, the market analysis department can perform a visually stimulating market analysis. This allows the market analysis to be performed with importance levels that correspond to the creator's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0124] The Market Analysis Department can analyze market changes based on the timing of creator submissions during market analysis. For example, the Market Analysis Department can analyze market changes based on the timing of works submitted by creators. For example, the Market Analysis Department can input the timing of creator submissions into an AI, which then analyzes market changes. The Market Analysis Department can also propose optimal sales strategies based on the timing of creator submissions. For example, the Market Analysis Department can input the timing of creator submissions into an AI, which then proposes optimal sales strategies. This allows for analysis of market changes based on the timing of creator submissions.

[0125] The Market Analysis Department can perform market analysis by referring to market data related to creators. For example, the Market Analysis Department can propose optimal sales strategies based on market data related to creators. For example, the Market Analysis Department can input market data related to creators into an AI, which then proposes the optimal sales strategy. Furthermore, the Market Analysis Department can also predict changes in demand based on market data related to creators. For example, the Market Analysis Department can input market data related to creators into an AI, which then predicts changes in demand. This enables market analysis based on market data related to creators.

[0126] The sales channel proposal department can estimate the creator's emotions and adjust its sales channel proposal methods based on those emotions. For example, if the creator is relaxed, the sales channel proposal department can provide detailed sales channel proposals. If the creator is in a hurry, for example, the sales channel proposal department can provide concise sales channel proposals. Furthermore, if the creator is excited, the sales channel proposal department can provide visually stimulating sales channel proposals. This allows sales channel proposals to be made in a way that is appropriate to the creator's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0127] The sales channel proposal department can select the optimal sales channel by referring to the creator's past sales history when proposing sales channels. For example, the sales channel proposal department can propose the optimal sales channel based on the creator's past sales history. For example, the sales channel proposal department can input the creator's past sales history into an AI, and the AI ​​will propose the optimal sales channel. In addition, the sales channel proposal department can also propose the optimal sales strategy based on the creator's past sales history. For example, the sales channel proposal department can input the creator's past sales history into an AI, and the AI ​​will propose the optimal sales strategy. This allows for the selection of the optimal sales channel based on the creator's past sales history.

[0128] The sales channel proposal department can apply different proposal algorithms depending on the category of the creator's work when proposing sales channels. For example, if the creator's work is painting, the sales channel proposal department will provide sales channel proposals specifically for painting. If the creator's work is music, the sales channel proposal department can also provide sales channel proposals specifically for music. Furthermore, if the creator's work is video, the sales channel proposal department can provide sales channel proposals specifically for video. This allows for sales channel proposals tailored to the category of the creator's work.

[0129] The sales channel proposal department can estimate the creator's emotions and prioritize sales channel proposals based on those emotions. For example, if the creator is relaxed, the sales channel proposal department may prioritize detailed sales channel proposals. If the creator is in a hurry, for example, the sales channel proposal department may prioritize concise sales channel proposals. Furthermore, if the creator is excited, the sales channel proposal department may prioritize visually stimulating sales channel proposals. This allows for sales channel proposals to be prioritized according to the creator's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0130] The sales channel proposal department can select the most suitable sales channels by considering the creator's geographical location when proposing sales channels. For example, the sales channel proposal department can propose sales channels considering the market characteristics of the region where the creator is located. For example, the sales channel proposal department can also prioritize proposing sales platforms in the region where the creator is located. Furthermore, the sales channel proposal department can propose sales channels considering the consumer preferences of the region where the creator is located. This allows for the selection of the most suitable sales channels based on the creator's geographical location.

[0131] The sales channel proposal department can analyze a creator's social media activity when proposing sales channels. For example, the sales channel proposal department can propose the optimal sales channel based on the creator's social media activity. For example, the sales channel proposal department can input the creator's social media activity into an AI, which will then propose the optimal sales channel. Furthermore, the sales channel proposal department can also propose the optimal sales strategy based on the creator's social media activity. For example, the sales channel proposal department can input the creator's social media activity into an AI, which will then propose the optimal sales strategy. This allows for sales channel proposals based on the creator's social media activity.

[0132] The NFT deployment support unit can estimate the creator's emotions and adjust the NFT deployment method based on the estimated emotions. For example, if the creator is relaxed, the NFT deployment support unit can suggest a detailed NFT deployment method. If the creator is in a hurry, for example, the NFT deployment support unit can suggest a concise NFT deployment method. Furthermore, if the creator is excited, the NFT deployment support unit can suggest a visually stimulating NFT deployment method. This allows for NFT deployment in a way that suits the creator's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0133] The NFT Deployment Support Department can select the optimal deployment method by referring to the creator's past NFT deployment history during NFT deployment. For example, the NFT Deployment Support Department can propose the optimal deployment method based on the creator's past NFT deployment history. For example, the NFT Deployment Support Department can input the creator's past NFT deployment history into an AI, which will then propose the optimal deployment method. Furthermore, the NFT Deployment Support Department can also propose the optimal sales strategy based on the creator's past NFT deployment history. For example, the NFT Deployment Support Department can input the creator's past NFT deployment history into an AI, which will then propose the optimal sales strategy. This allows for the selection of the optimal deployment method based on the creator's past NFT deployment history.

[0134] The NFT Deployment Support Department can apply different deployment methods to NFTs depending on the category of the creator's work. For example, if a creator's work is a painting, the NFT Deployment Support Department can propose an NFT deployment method specialized for paintings. If a creator's work is music, the NFT Deployment Support Department can also propose an NFT deployment method specialized for music. Furthermore, if a creator's work is a video, the NFT Deployment Support Department can propose an NFT deployment method specialized for video. This allows for NFT deployment tailored to the category of the creator's work.

[0135] The NFT deployment support unit can estimate the creator's emotions and determine the priority of NFT deployment based on the estimated emotions. For example, if the creator is relaxed, the NFT deployment support unit may prioritize detailed NFT deployments. If the creator is in a hurry, for example, the NFT deployment support unit may also prioritize concise NFT deployments. Furthermore, if the creator is excited, the NFT deployment support unit may also prioritize visually stimulating NFT deployments. This allows for NFT deployments to be prioritized according to the creator's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0136] The NFT Deployment Support Department can select the optimal deployment method when deploying NFTs, taking into account the creator's geographical location. For example, the NFT Deployment Support Department can deploy NFTs considering the market characteristics of the region where the creator is located. For example, the NFT Deployment Support Department can also prioritize proposing sales platforms in the region where the creator is located. Furthermore, the NFT Deployment Support Department can deploy NFTs considering the consumer preferences of the region where the creator is located. This allows for the selection of the optimal NFT deployment method based on the creator's geographical location.

[0137] The NFT Deployment Support Department can analyze a creator's social media activity and propose deployment methods during NFT deployment. For example, the NFT Deployment Support Department can propose the optimal NFT deployment method based on the creator's social media activity. For example, the NFT Deployment Support Department can input the creator's social media activity into an AI, which then proposes the optimal NFT deployment method. Furthermore, the NFT Deployment Support Department can also propose the optimal sales strategy based on the creator's social media activity. For example, the NFT Deployment Support Department can input the creator's social media activity into an AI, which then proposes the optimal sales strategy. This allows for the proposal of NFT deployment methods based on the creator's social media activity.

[0138] The fan base analysis unit can estimate the creator's emotions and adjust the fan base analysis method based on the estimated emotions. For example, if the creator is relaxed, the fan base analysis unit can perform a detailed fan base analysis. If the creator is in a hurry, for example, the fan base analysis unit can perform a concise fan base analysis. Furthermore, if the creator is excited, the fan base analysis unit can perform a visually stimulating fan base analysis. This allows for fan base analysis to be performed in a way that is appropriate to the creator's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0139] The fan base analysis unit can improve the accuracy of its analysis by referring to the creator's past fan base data during the analysis process. For example, the fan base analysis unit can improve the accuracy of the fan base analysis based on the creator's past fan base data. For example, the fan base analysis unit can input the creator's past fan base data into an AI, which then analyzes the data to improve the accuracy of the fan base analysis. Furthermore, the fan base analysis unit can also perform optimal fan base analysis based on the creator's past fan base data. For example, the fan base analysis unit can input the creator's past fan base data into an AI, which then performs optimal fan base analysis. This enables highly accurate fan base analysis based on the creator's past fan base data.

[0140] The fan base analysis department can apply different analysis methods depending on the category of the creator's work. For example, if the creator's work is painting, the fan base analysis department can perform a fan base analysis specifically for painting. If the creator's work is music, the fan base analysis department can also perform a fan base analysis specifically for music. Furthermore, if the creator's work is video, the fan base analysis department can also perform a fan base analysis specifically for video. This allows for fan base analysis tailored to the category of the creator's work.

[0141] The fan base analysis unit can estimate the creator's emotions and adjust the order in which the results of the fan base analysis are displayed based on the estimated emotions of the creator. For example, if the creator is relaxed, the fan base analysis unit can prioritize displaying detailed fan base analysis results. For example, if the creator is in a hurry, the fan base analysis unit can also prioritize displaying concise fan base analysis results. Furthermore, if the creator is excited, the fan base analysis unit can prioritize displaying visually stimulating fan base analysis results. This allows the results of the fan base analysis to be displayed in an order that corresponds to the creator's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0142] The fan base analysis unit can perform fan base analysis while considering the geographical distribution of creators. For example, the fan base analysis unit can perform fan base analysis while considering the characteristics of the fan base in the region where the creator is located. For example, the fan base analysis unit can also perform fan base analysis while considering the preferences of the fan base in the region where the creator is located. Furthermore, the fan base analysis unit can perform fan base analysis while considering the behavioral patterns of the fan base in the region where the creator is located. This allows for fan base analysis that takes into account the geographical distribution of creators.

[0143] The fan base analysis unit can improve the accuracy of its analysis by referring to the creator's related literature during fan base analysis. For example, the fan base analysis unit can improve the accuracy of its fan base analysis by referring to the creator's related literature. For example, the fan base analysis unit can input the creator's related literature into an AI, which then analyzes the literature to improve the accuracy of its fan base analysis. Furthermore, the fan base analysis unit can perform optimal fan base analysis by referring to the creator's related literature. For example, the fan base analysis unit can input the creator's related literature into an AI, which then performs optimal fan base analysis. This allows for improved accuracy of fan base analysis by referring to the creator's related literature.

[0144] The copyright verification unit can estimate the creator's emotions and adjust the copyright verification method based on the estimated emotions. For example, if the creator is relaxed, the copyright verification unit can perform a detailed copyright verification. If the creator is in a hurry, for example, the copyright verification unit can perform a concise copyright verification. Furthermore, if the creator is excited, the copyright verification unit can perform a visually stimulating copyright verification. This allows copyright verification to be performed in a way that is appropriate to the creator's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0145] The copyright verification unit can improve the accuracy of copyright verification by referring to the creator's past copyright history during the verification process. For example, the copyright verification unit can improve the accuracy of copyright verification based on the creator's past copyright history. For example, the copyright verification unit can input the creator's past copyright history into an AI, which then analyzes the history to improve the accuracy of copyright verification. Furthermore, the copyright verification unit can also perform optimal copyright verification based on the creator's past copyright history. For example, the copyright verification unit can input the creator's past copyright history into an AI, which then performs optimal copyright verification. This enables highly accurate copyright verification based on the creator's past copyright history.

[0146] The copyright verification unit can apply different verification methods depending on the category of the creator's work during copyright verification. For example, if the creator's work is a painting, the copyright verification unit will perform a copyright verification specifically for paintings. If the creator's work is music, the copyright verification unit can also perform a copyright verification specifically for music. Furthermore, if the creator's work is a video, the copyright verification unit can also perform a copyright verification specifically for video. This allows for copyright verification tailored to the category of the creator's work.

[0147] The copyright verification unit can estimate the creator's emotions and determine the priority of copyright verification based on the estimated emotions. For example, if the creator is relaxed, the copyright verification unit may prioritize detailed copyright verification. If the creator is in a hurry, for example, the copyright verification unit may prioritize concise copyright verification. Furthermore, if the creator is excited, the copyright verification unit may prioritize visually stimulating copyright verification. This allows copyright verification to be performed with priorities according to the creator's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0148] The copyright verification unit can perform copyright verification while considering the creator's geographical location. For example, the copyright verification unit can perform copyright verification considering the copyright laws of the region where the creator is located. For example, the copyright verification unit can also perform copyright verification by referring to the copyright protection authority in the region where the creator is located. Furthermore, the copyright verification unit can also perform copyright verification by referring to copyright-related case law in the region where the creator is located. This enables copyright verification based on the creator's geographical location.

[0149] The copyright verification unit can improve the accuracy of copyright verification by referring to the creator's related literature during the verification process. For example, the copyright verification unit can improve the accuracy of copyright verification by referring to the creator's related literature. For example, the copyright verification unit can input the creator's related literature into an AI, which then analyzes the literature to improve the accuracy of copyright verification. Furthermore, the copyright verification unit can perform optimal copyright verification by referring to the creator's related literature. For example, the copyright verification unit can input the creator's related literature into an AI, which then performs optimal copyright verification. This allows for improved accuracy of copyright verification by referring to the creator's related literature.

[0150] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0151] The Idea Support Department can estimate a creator's emotions and adjust how inspiration is provided based on those estimates. For example, if a creator is feeling stressed, it can provide relaxing inspiration. If a creator is excited, it can provide challenging ideas. Furthermore, if a creator is feeling down, it can provide inspiration along with encouraging messages. This allows for the provision of inspiration tailored to the creator's emotions.

[0152] The style analysis department can estimate the creator's emotions and adjust the style analysis method based on those emotions. For example, if the creator is relaxed, a detailed style analysis can be performed. If the creator is in a hurry, a concise style analysis can be performed. Furthermore, if the creator is excited, a visually stimulating style analysis can be performed. This allows for style analysis tailored to the creator's emotions.

[0153] The market analysis department can estimate the creator's emotions and adjust the market analysis method based on those estimates. For example, if the creator is relaxed, a detailed market analysis can be performed. If the creator is in a hurry, a concise market analysis can be performed. Furthermore, if the creator is excited, a visually stimulating market analysis can be performed. This allows for market analysis tailored to the creator's emotions.

[0154] The sales channel proposal department can estimate the creator's emotions and adjust the sales channel proposal method based on those estimates. For example, if the creator is relaxed, a detailed sales channel proposal can be made. If the creator is in a hurry, a concise sales channel proposal can be made. Furthermore, if the creator is excited, a visually stimulating sales channel proposal can be made. In this way, sales channel proposals can be tailored to the creator's emotions.

[0155] The NFT deployment support unit can estimate the creator's emotions and adjust the NFT deployment method based on those emotions. For example, if the creator is relaxed, it can suggest a detailed NFT deployment method. If the creator is in a hurry, it can suggest a concise NFT deployment method. Furthermore, if the creator is excited, it can suggest a visually stimulating NFT deployment method. This allows for NFT deployment tailored to the creator's emotions.

[0156] The Idea Support Department can analyze a creator's past creative history and select the optimal timing for providing inspiration. For example, it can analyze the time periods when a creator has engaged in creative activities in the past and provide inspiration during those times. It can also analyze the frequency of a creator's past creative activities and provide inspiration at appropriate intervals. Furthermore, it can analyze the locations where a creator has engaged in creative activities in the past and provide inspiration when they are in those locations. In this way, inspiration can be provided based on the creator's past creative history.

[0157] The style analysis department can improve the accuracy of its analysis by referring to evaluation data of the creator's past works during the style analysis process. For example, it can improve the accuracy of style analysis based on evaluation data of the creator's past works. It can also input evaluation data of the creator's past works into an AI, which then analyzes the data to improve the accuracy of style analysis. Furthermore, it can suggest the optimal style based on the evaluation data of the creator's past works. In this way, the accuracy of style analysis can be improved based on evaluation data of the creator's past works.

[0158] The market analysis department can predict current market trends by referring to historical market data during market analysis. For example, it can predict current market trends based on historical market data. It can also input historical market data into AI, which can then predict market trends. Furthermore, it can propose optimal sales strategies based on historical market data. In this way, it is possible to predict current market trends based on historical market data.

[0159] The sales channel proposal department can select the optimal sales channel by referring to the creator's past sales history when proposing sales channels. For example, it can propose the optimal sales channel based on the creator's past sales history. It can also input the creator's past sales history into AI, which can then propose the optimal sales channel. Furthermore, it can propose the optimal sales strategy based on the creator's past sales history. This allows for the selection of the optimal sales channel based on the creator's past sales history.

[0160] The NFT Deployment Support Department can select the optimal deployment method by referring to the creator's past NFT deployment history during NFT deployment. For example, it can propose the optimal deployment method based on the creator's past NFT deployment history. It can also input the creator's past NFT deployment history into AI, which can then propose the optimal deployment method. Furthermore, it can propose the optimal sales strategy based on the creator's past NFT deployment history. This allows for the selection of the optimal deployment method based on the creator's past NFT deployment history.

[0161] The following briefly describes the processing flow for example form 2.

[0162] Step 1: The Idea Generation Support Department helps creators generate new ideas. For example, it analyzes past works and trends to propose new styles and themes. The Idea Generation Support Department retrieves past works from a database and extracts new styles and themes using trend analysis algorithms. AI can also generate new ideas based on the creator's past works. Step 2: The Style Analysis Department analyzes the style of the creator's work. For example, it analyzes the creator's past works and the works of other artists to propose a new style that suits the creator's style. The Style Analysis Department uses image recognition technology to extract the characteristics of the work and evaluate the similarity of styles. It can also use text analysis technology to analyze the theme and expressive techniques of the work. Step 3: The Style Proposal Department proposes the optimal style based on the styles analyzed by the Style Analysis Department. For example, it selects the optimal style based on the creator's goals and market demand. The Style Proposal Department uses AI to understand the creator's goals and propose styles based on them. It can also analyze market demand and propose styles that are in high demand. Step 4: The Market Analysis Department conducts market analysis. For example, it analyzes current market trends and demand and proposes the most effective platforms and methods for selling creators' works. The Market Analysis Department uses AI to collect market data and predicts market trends using trend analysis algorithms. It can also predict the demand for creators' works using demand forecasting algorithms. Step 5: The Sales Channel Proposal Department proposes the optimal sales channels based on the analysis results of the Market Analysis Department. For example, they select the optimal sales channels by considering the target market and sales channels. The Sales Channel Proposal Department uses AI to identify the target market and proposes the optimal sales channels based on that. They can also select the optimal sales channels by considering cost efficiency. Step 6: The NFT Deployment Support Department proposes the optimal method for deploying the creator's work as an NFT. For example, it selects the platform and deployment method to be used. The NFT Deployment Support Department uses AI to analyze the characteristics of NFT platforms and propose the most suitable platform. It can also propose the optimal deployment method considering the marketing strategy. Step 7: The Fan Base Analysis Department analyzes the behavior and preferences of the creator's fans and proposes ways for the creator to strengthen their relationship with their fans. For example, it collects fan behavior and preference data and uses analytical algorithms to understand the characteristics of the fans. The Fan Base Analysis Department uses AI to analyze fan behavior patterns and proposes ways to strengthen the relationship with fans based on that analysis. It can also analyze fan preferences and propose perks and events for fans. Step 8: The Copyright Verification Unit verifies the copyright of the creator's work and proposes appropriate management methods. For example, it clarifies the scope of copyright and verification methods. The Copyright Verification Unit uses AI to refer to copyright law and verify the copyright of the creator's work. It can also propose copyright management methods and provide creators with ways to avoid copyright issues.

[0163] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0164] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0165] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0166] Each of the multiple elements mentioned above, including the Idea Generation Support Unit, Style Analysis Unit, Style Proposal Unit, Market Analysis Unit, Sales Channel Proposal Unit, NFT Deployment Support Unit, Fan Base Analysis Unit, and Copyright Verification Unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the Idea Generation Support Unit is implemented by the control unit 46A of the smart device 14, which analyzes past works and trends and proposes new styles and themes. The Style Analysis Unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes the style of a creator's work. The Style Proposal Unit is implemented, for example, by the control unit 46A of the smart device 14, which proposes the optimal style. The Market Analysis Unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes market trends and demand. The Sales Channel Proposal Unit is implemented, for example, by the control unit 46A of the smart device 14, which proposes the optimal sales channels. The NFT Deployment Support Unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which proposes the optimal method for deployment as an NFT. The fan base analysis unit is implemented, for example, by the control unit 46A of the smart device 14, and analyzes the behavior and preferences of fans. The copyright verification unit is implemented, for example, by the identification processing unit 290 of the data processing device 12, and proposes a method for verifying and managing copyrights. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0167] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0168] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0169] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0170] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0171] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0172] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0173] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0174] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0175] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0176] Storage 32 stores the data generation model 58 and the 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 user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0177] In the smart glasses 214, 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0178] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0179] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0180] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0181] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed 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 necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0182] Each of the multiple elements mentioned above, including the idea generation support unit, style analysis unit, style proposal unit, market analysis unit, sales channel proposal unit, NFT deployment support unit, fan base analysis unit, and copyright verification unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the idea generation support unit is implemented by the control unit 46A of the smart glasses 214, which analyzes past works and trends and proposes new styles and themes. The style analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the style of a creator's work. The style proposal unit is implemented by the control unit 46A of the smart glasses 214, which proposes the optimal style. The market analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes market trends and demand. The sales channel proposal unit is implemented by the control unit 46A of the smart glasses 214, which proposes the optimal sales channels. The NFT deployment support unit is implemented by the specific processing unit 290 of the data processing unit 12, which proposes the optimal method for deployment as an NFT. The fan base analysis unit is implemented, for example, by the control unit 46A of the smart glasses 214, and analyzes the behavior and preferences of fans. The copyright verification unit is implemented, for example, by the identification processing unit 290 of the data processing device 12, and proposes a method for verifying and managing copyright. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0183] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0184] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0185] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0186] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0187] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0188] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0189] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0190] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0191] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0192] Storage 32 stores the data generation model 58 and the 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 user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0193] In the headset terminal 314, 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 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0194] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0195] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0196] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0197] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0198] Each of the multiple elements mentioned above, including the Idea Generation Support Unit, Style Analysis Unit, Style Proposal Unit, Market Analysis Unit, Sales Channel Proposal Unit, NFT Deployment Support Unit, Fan Base Analysis Unit, and Copyright Verification Unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the Idea Generation Support Unit is implemented by the control unit 46A of the headset terminal 314, which analyzes past works and trends and proposes new styles and themes. The Style Analysis Unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes the style of a creator's work. The Style Proposal Unit is implemented, for example, by the control unit 46A of the headset terminal 314, which proposes the optimal style. The Market Analysis Unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes market trends and demand. The Sales Channel Proposal Unit is implemented, for example, by the control unit 46A of the headset terminal 314, which proposes the optimal sales channels. The NFT Deployment Support Unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which proposes the optimal method for deployment as an NFT. The fan base analysis unit is implemented, for example, by the control unit 46A of the headset terminal 314, and analyzes the behavior and preferences of fans. The copyright verification unit is implemented, for example, by the identification processing unit 290 of the data processing device 12, and proposes a method for verifying and managing copyrights. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0199] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0200] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0201] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0202] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0203] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0204] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0205] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0206] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0207] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0208] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0209] Storage 32 stores the data generation model 58 and the 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 user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0210] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0211] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0212] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0213] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0214] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed 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 necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0215] Each of the multiple elements mentioned above, including the idea generation support unit, style analysis unit, style proposal unit, market analysis unit, sales channel proposal unit, NFT deployment support unit, fan base analysis unit, and copyright verification unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the idea generation support unit is implemented by the control unit 46A of the robot 414, which analyzes past works and trends and proposes new styles and themes. The style analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which analyzes the style of a creator's work. The style proposal unit is implemented by, for example, the control unit 46A of the robot 414, which proposes the optimal style. The market analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which analyzes market trends and demand. The sales channel proposal unit is implemented by, for example, the control unit 46A of the robot 414, which proposes the optimal sales channels. The NFT deployment support unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which proposes the optimal method for deployment as an NFT. The fan base analysis unit is implemented, for example, by the control unit 46A of the robot 414, and analyzes the behavior and preferences of fans. The copyright verification unit is implemented, for example, by the identification processing unit 290 of the data processing device 12, and proposes a method for verifying and managing copyrights. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0216] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0217] Figure 9 shows the emotion map 400, in 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 the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0218] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0219] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0220] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0221] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it 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 emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0222] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0223] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0224] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0225] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0226] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0227] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0228] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0229] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0230] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0231] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0232] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0233] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0234] (Note 1) The Idea Generation Support Department helps creators generate new ideas, Based on the ideas proposed by the aforementioned Idea Generation Support Department, the Style Analysis Department analyzes the style of the creator's work. A style proposal unit proposes the optimal style based on the style analyzed by the aforementioned style analysis unit, The Market Analysis Department conducts market analysis based on the styles proposed by the Style Proposal Department, Based on the analysis results obtained by the aforementioned Market Analysis Department, the Sales Channel Proposal Department proposes the optimal sales channels, The NFT Deployment Support Department provides NFT deployment support based on the sales channels proposed by the aforementioned Sales Channel Proposal Department, A fan base analysis unit performs fan base analysis based on the NFTs deployed by the aforementioned NFT deployment support unit, The system includes a copyright verification unit that performs copyright verification and management based on the analysis results obtained by the aforementioned fan base analysis unit. A system characterized by the following features. (Note 2) The aforementioned idea generation support department, We analyze past works and trends to propose new styles and themes. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned style analysis unit, We analyze the creator's past works and the works of other artists, and propose a new style that suits the creator's style. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned Market Analysis Department We analyze current market trends and demand, and propose the most effective platforms and methods for selling creators' works. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned sales channel proposal department, Based on the analysis results from the market analysis department, we propose the optimal sales channels. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned NFT deployment support unit is: We propose the optimal method for developing creators' works as NFTs. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned fan base analysis unit, We analyze the behavior and preferences of creators' fans and propose ways for creators to strengthen their relationship with their fans. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned copyright verification unit, We propose methods for verifying and properly managing the copyrights of creators' works. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned idea generation support department, We estimate the creator's emotions and adjust how we provide inspiration based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned idea generation support department, We analyze the creator's past creative history and select the optimal timing for providing inspiration. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned idea generation support department, When providing inspiration, filter based on the creator's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned idea generation support department, It estimates the creator's emotions and prioritizes the inspiration it provides based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned idea generation support department, When providing inspiration, we prioritize providing highly relevant inspiration by taking into account the creator's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned idea generation support department, When providing inspiration, we analyze the creator's social media activity and provide relevant inspiration. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned style analysis unit, We estimate the creator's emotions and adjust the style analysis method based on the estimated emotions of the creator. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned style analysis unit, During style analysis, we improve the accuracy of the analysis by referring to evaluation data of the creator's past works. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned style analysis unit, When performing style analysis, different analysis algorithms are applied depending on the category of the creator's work. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned style analysis unit, It estimates the creator's emotions and adjusts the order in which the style analysis results are displayed based on the estimated creator's emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned style analysis unit, When conducting style analysis, the geographical distribution of creators should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned style analysis unit, When performing style analysis, referencing relevant literature related to the creator improves the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned style proposal unit, It estimates the creator's emotions and adjusts the expression of style suggestions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned style proposal unit, When proposing styles, we refer to evaluation data of the creator's past works to improve the accuracy of the suggestions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned style proposal unit, When suggesting styles, different suggestion algorithms are applied depending on the category of the creator's work. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned style proposal unit, It estimates the creator's emotions and adjusts the length of the style suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned style proposal unit, When proposing styles, we prioritize proposals based on the submission timing of the creators. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned style proposal unit, When suggesting styles, adjust the order of suggestions based on the creator's relevance. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned Market Analysis Department We estimate the creator's emotions and adjust the market analysis method based on the estimated creator's emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned Market Analysis Department When conducting market analysis, historical market data is used to predict current market trends. The system according to Appendix 1, characterized in that... (Appendix 29) The market analysis unit applies different analysis methods for each category of the creator's works during market analysis The system according to Appendix 1, characterized in that... (Appendix 30) The market analysis unit estimates the creator's emotions and adjusts the importance of market analysis based on the estimated creator's emotions The system according to Appendix 1, characterized in that... (Appendix 31) The market analysis unit analyzes market changes based on the submission time of the creator during market analysis The system according to Appendix 1, characterized in that... (Appendix 32) The market analysis unit performs analysis by referring to the creator's related market data during market analysis The system according to Appendix 1, characterized in that... (Appendix 33) The distribution channel proposal unit estimates the creator's emotions and adjusts the method of distribution channel proposal based on the estimated creator's emotions The system according to Appendix 1, characterized in that... (Appendix 34) The distribution channel proposal unit selects the optimal distribution channel by referring to the creator's past sales history during distribution channel proposal The system according to Appendix 1, characterized in that... (Appendix 35) The distribution channel proposal unit applies different proposal algorithms according to the category of the creator's works during distribution channel proposal The system according to Appendix 1, characterized in that... (Appendix 36) The distribution channel proposal unit The system estimates the creator's emotions and prioritizes sales channel proposals based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned sales channel proposal department, When proposing sales channels, the creator's geographical location information is taken into consideration to select the most suitable channel. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned sales channel proposal department, When proposing sales channels, we analyze the creator's social media activities and propose suitable channels. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned NFT deployment support unit is: We estimate the creator's emotions and adjust the NFT deployment method based on the estimated emotions of the creator. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned NFT deployment support unit is: When deploying NFTs, the system selects the optimal deployment method by referring to the creator's past NFT deployment history. The system described in Appendix 1, characterized by the features described herein. (Note 41) The aforementioned NFT deployment support unit is: When deploying NFTs, different deployment methods are applied depending on the category of the creator's work. The system described in Appendix 1, characterized by the features described herein. (Note 42) The aforementioned NFT deployment support unit is: The system estimates the creator's emotions and determines the priority of NFT deployment based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 43) The aforementioned NFT deployment support unit is: When deploying NFTs, the optimal deployment method is selected by considering the creator's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Supplementary Note 44) The NFT deployment support department Analyzes the social media activities of the creator during NFT deployment and proposes deployment methods The system according to Supplementary Note 1, characterized in that it does so (Supplementary Note 45) The fan layer analysis department Estimates the emotions of the creator and adjusts the method of fan layer analysis based on the estimated emotions of the creator The system according to Supplementary Note 1, characterized in that it does so (Supplementary Note 46) The fan layer analysis department Refers to the past fan layer data of the creator during fan layer analysis to improve the accuracy of analysis The system according to Supplementary Note 1, characterized in that it does so (Supplementary Note 47) The fan layer analysis department Applies different analysis methods according to the category of the creator's works during fan layer analysis The system according to Supplementary Note 1, characterized in that it does so (Supplementary Note 48) The fan layer analysis department Estimates the emotions of the creator and adjusts the order of displaying the results of fan layer analysis based on the estimated emotions of the creator The system according to Supplementary Note 1, characterized in that it does so (Supplementary Note 49) The fan layer analysis department Considers the geographical distribution of the creator during fan layer analysis to conduct the analysis The system according to Supplementary Note 1, characterized in that it does so (Supplementary Note 50) The fan layer analysis department Refers to the relevant literature of the creator during fan layer analysis to improve the accuracy of analysis The system according to Supplementary Note 1, characterized in that it does so (Supplementary Note 51) The copyright confirmation department Estimates the emotions of the creator and adjusts the method of copyright confirmation based on the estimated emotions of the creator The system described in Appendix 1, characterized by the features described herein. (Note 52) The aforementioned copyright verification unit, When verifying copyright, we improve the accuracy of the verification by referring to the creator's past copyright history. The system described in Appendix 1, characterized by the features described herein. (Note 53) The aforementioned copyright verification unit, When verifying copyright, different verification methods are applied depending on the category of the creator's work. The system described in Appendix 1, characterized by the features described herein. (Note 54) The aforementioned copyright verification unit, We estimate the creator's emotions and determine the priority of copyright verification based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 55) The aforementioned copyright verification unit, When verifying copyright, the creator's geographical location information will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 56) The aforementioned copyright verification unit, When verifying copyright, we improve the accuracy of the verification by referring to the creator's relevant literature. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0235] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The Idea Generation Support Department helps creators generate new ideas, Based on the ideas proposed by the aforementioned Idea Generation Support Department, the Style Analysis Department analyzes the style of the creator's work. A style proposal unit proposes the optimal style based on the style analyzed by the aforementioned style analysis unit, The Market Analysis Department conducts market analysis based on the styles proposed by the Style Proposal Department, Based on the analysis results obtained by the aforementioned Market Analysis Department, the Sales Channel Proposal Department proposes the optimal sales channels, The NFT Deployment Support Department provides NFT deployment support based on the sales channels proposed by the aforementioned Sales Channel Proposal Department, A fan base analysis unit performs fan base analysis based on the NFTs deployed by the aforementioned NFT deployment support unit, The system includes a copyright verification unit that performs copyright verification and management based on the analysis results obtained by the aforementioned fan base analysis unit. A system characterized by the following features.

2. The aforementioned idea generation support department, We analyze past works and trends to propose new styles and themes. The system according to feature 1.

3. The aforementioned style analysis unit, We analyze the creator's past works and the works of other artists, and propose a new style that suits the creator's style. The system according to feature 1.

4. The aforementioned Market Analysis Department We analyze current market trends and demand, and propose the most effective platforms and methods for selling creators' works. The system according to feature 1.

5. The aforementioned sales channel proposal department, Based on the analysis results from the market analysis department, we propose the optimal sales channels. The system according to feature 1.

6. The aforementioned NFT deployment support unit is: We propose the optimal method for developing creators' works as NFTs. The system according to feature 1.

7. The aforementioned fan base analysis unit, We analyze the behavior and preferences of creators' fans and propose ways for creators to strengthen their relationship with their fans. The system according to feature 1.

8. The aforementioned copyright verification unit, We propose methods for verifying and properly managing the copyrights of creators' works. The system according to feature 1.

Images