system

Abstract

The system according to this embodiment aims to streamline collaborative production among artists and realize transparent and fair transactions. [Solution] The system according to the embodiment comprises a management unit, an efficiency unit, an authentication unit, and a distribution unit. The management unit uses AI to manage theme setting and role assignment. The efficiency unit streamlines the assignment of composition and color choices by artists based on the roles managed by the management unit. The authentication unit uses digital tools to create the artwork and authenticates it using blockchain. The distribution unit trades the artwork authenticated by the authentication unit in an online auction and automatically distributes the profits.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is 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 in 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 were problems such as difficulty in collaborative production by artists, transactions tending to be opaque, and a lack of diverse art production opportunities.

[0005] The system according to the embodiment aims to streamline the collaborative production of artists and realize transparent and fair transactions.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a management unit, an efficiency unit, an authentication unit, and a distribution unit. The management unit uses AI to manage theme setting and role assignment. The efficiency unit streamlines the assignment of composition and color choices by artists based on the roles managed by the management unit. The authentication unit creates the artwork using digital tools and authenticates it on the blockchain. The distribution unit trades the artwork authenticated by the authentication unit in an online auction and automatically distributes the profits. [Effects of the Invention]

[0007] The system according to this embodiment can streamline collaborative production among artists and realize transparent and fair transactions. [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 signed storage is one or more non-volatile storage devices that store various programs and various parameters. 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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applicable to the communication I / F include wireless communication standards such as 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 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 art collaboration system according to an embodiment of the present invention is a system that streamlines collaborative production among artists, improves transaction transparency, and provides diverse art production opportunities. In this art collaboration system, AI manages theme setting and role allocation, and uses project management tools to streamline the composition and color assignments of artists. Next, digital tools and blockchain are utilized, with production done using Adobe Creative Cloud and authentication on the blockchain. Authenticated works are securely stored, and transaction transparency is enhanced. Furthermore, online auctions and revenue sharing are conducted, with online auctions using OpenSea, and sales revenue is automatically distributed. This mechanism promotes international art collaboration, realizes transparent and fair transactions, and creates new value in art. For example, by automatically assigning roles based on each artist's skills and experience, the workload of artists is reduced and productivity is increased. Next, by registering the digital art works created by artists on the blockchain and transparently managing ownership and transaction history of the works, artists can trade their works with peace of mind. Furthermore, when works created by artists are put up for auction on OpenSea and are successfully bid on, the sales revenue is automatically distributed to the artists, allowing them to receive profits quickly and accurately. This allows artists from different countries and cultures to collaborate on artworks, which can then receive international acclaim. Furthermore, the increased transparency provided by blockchain technology allows artists to trade their work with confidence. For example, ownership and transaction history are recorded on the blockchain, preventing fraudulent activity. Additionally, the high prices fetched by digital artworks in online auctions recognize the value of new forms of art. Thus, art collaboration systems can streamline collaborative production, improve transaction transparency, and provide diverse opportunities for art creation.

[0029] The art collaboration system according to this embodiment comprises a management unit, an efficiency unit, an authentication unit, and a distribution unit. The management unit uses AI to manage theme setting and role assignment. For example, the management unit automatically assigns roles to each artist based on their skills and experience. The efficiency unit streamlines the assignment of composition and color to artists based on the roles managed by the management unit. For example, the efficiency unit uses project management tools to streamline the assignment of composition and color to artists. The authentication unit uses digital tools to create and authenticate works on the blockchain. For example, the authentication unit authenticates works created with Adobe Creative Cloud on the blockchain. The distribution unit trades works authenticated by the authentication unit in online auctions and automatically distributes the profits. For example, the distribution unit uses OpenSea to conduct online auctions and automatically distributes the sales revenue. As a result, the art collaboration system can streamline collaborative production among artists, improve transaction transparency, and provide diverse art production opportunities.

[0030] The management department uses AI to manage theme setting and role allocation. Specifically, the AI ​​automatically assigns roles based on each artist's skills and experience. For example, the AI ​​analyzes artists' past work data and portfolios to understand each artist's strengths and style. This allows the AI ​​to select the most suitable artist for the project theme and assign appropriate roles to each artist. Furthermore, the AI ​​monitors the project's progress in real time and reassigns or adjusts roles as needed. For example, if an artist completes their work ahead of schedule, the AI ​​can assign them additional tasks. The AI ​​also supports communication between artists and promotes efficient collaboration. For example, the AI ​​answers artists' questions via a chatbot and sends notifications about project progress. This allows the management department to improve the overall efficiency of the project and provide an environment where artists can focus on their creative work.

[0031] The Efficiency Department streamlines the composition and color work of artists based on roles managed by the Management Department. Specifically, it uses project management tools to streamline the composition and color work of artists. For example, the Efficiency Department monitors each artist's progress in real time and adjusts task priorities. This allows artists to concentrate more easily on their work and ensures smoother overall project progress. The Efficiency Department also uses AI to support artists' work. For example, AI suggests compositions and colors created by artists, enabling them to work more efficiently. Furthermore, the Efficiency Department provides tools and platforms to promote collaboration among artists. For example, it uses online whiteboards and collaborative editing tools to allow artists to share ideas in real time and work together. In this way, the Efficiency Department can improve the efficiency of artists' work and enhance the overall quality of the project.

[0032] The authentication department uses digital tools to create and authenticates works on the blockchain. Specifically, it authenticates works created with Adobe Creative Cloud on the blockchain. For example, when an artist creates digital art using Adobe Creative Cloud, the data of the creation process and the finished work are automatically recorded on the blockchain. This guarantees the originality and creation history of the work in a transparent and tamper-proof manner. The authentication department also utilizes blockchain technology to manage the ownership and transaction history of works. For example, when a work is sold, the transaction information is recorded on the blockchain, and ownership is transferred to the new owner. This ensures clear management of ownership and reduces the risk of forgery and fraudulent transactions. Furthermore, the authentication department provides artists and buyers with authentication information of their works via the blockchain. For example, artists can confirm that their works are legitimately authenticated, and buyers are assured that the works they purchased are authentic. In this way, the authentication department can improve the reliability and transparency of digital art and provide peace of mind to both artists and buyers.

[0033] The distribution unit trades works authenticated by the authentication unit in online auctions and automatically distributes the profits. Specifically, it uses OpenSea to conduct online auctions and automatically distributes the sales revenue. For example, when an authenticated work is listed on OpenSea, the auction starts and bids are placed. When the auction ends, the work is sold to the highest bidder, and the sales revenue is automatically distributed to the artist and related parties. The distribution unit utilizes blockchain technology to ensure transparent and accurate revenue distribution. For example, if the revenue distribution ratio is set in advance, the revenue is automatically distributed based on that ratio. This allows artists and related parties to conduct transactions with peace of mind without worrying about troubles or lack of transparency regarding revenue distribution. In addition, the distribution unit makes it possible to check the status of revenue distribution in real time. For example, artists and related parties can check online how their revenue is being distributed. This allows the distribution unit to manage the revenue distribution process transparently and efficiently, and to gain the trust of artists and related parties.

[0034] The management department can streamline the composition and color assignments of artists by using project management tools. For example, the management department can streamline the composition and color assignments of artists by using project management tools. For example, the management department can streamline the artists' work by using project management tools such as Trello or Asana. For example, the management department can use project management tools to track and efficiently manage the artists' work progress in real time. Thus, by using project management tools, the composition and color assignments of artists can be streamlined.

[0035] The authentication unit can authenticate works created with Adobe Creative Cloud using blockchain technology. For example, the authentication unit authenticates works created with Adobe Creative Cloud using blockchain technology. For example, the authentication unit authenticates works created using Adobe Creative Cloud applications such as Photoshop and Illustrator using blockchain technology. For example, the authentication unit can use blockchain technology to transparently manage the ownership and transaction history of works. This means that authenticating works created with Adobe Creative Cloud using blockchain technology enhances the secure storage of works and the transparency of transactions.

[0036] The distribution unit can conduct online auctions using OpenSea and automatically distribute the sales revenue. For example, the distribution unit can use OpenSea's auction function to list artwork created by artists, and if an artwork is successfully bid on, the sales revenue can be automatically distributed to the artist. The distribution unit can use blockchain technology to ensure transparency in revenue distribution. This allows artists to receive their earnings quickly and accurately by conducting online auctions using OpenSea and automatically distributing the sales revenue.

[0037] The management department can automatically assign roles to each artist based on their skills and experience. For example, the management department can automatically assign roles based on each artist's skills and experience. For example, the management department can evaluate an artist's past project history and technical skills to assign the most suitable role. For example, the management department can use AI to analyze an artist's skills and experience and suggest the most suitable role. This reduces the workload on artists and improves productivity by automatically assigning roles based on each artist's skills and experience.

[0038] The authentication system can transparently manage the ownership and transaction history of artworks. For example, the authentication system can transparently manage the ownership and transaction history of artworks. For example, the authentication system can use blockchain technology to record and transparently manage the ownership and transaction history of artworks. For example, the authentication system can use a blockchain platform to securely store the ownership and transaction history of artworks. This allows artists to trade their works with confidence by transparently managing the ownership and transaction history of their artworks.

[0039] The management department can analyze each artist's past project history and automatically propose the optimal division of roles. For example, the management department can analyze each artist's past project history and automatically propose the optimal division of roles. For example, the management department can propose similar roles based on the roles an artist has played in past successful projects. For example, the management department can suggest avoiding roles that an artist has struggled with in the past. For example, the management department can propose the most efficient division of roles based on an artist's past project history. In this way, by analyzing each artist's past project history, the optimal division of roles is proposed, improving project efficiency. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input the artist's past project history data into a generating AI and have the generating AI propose the optimal division of roles.

[0040] The management department can readjust roles and responsibilities in real time according to the project's progress. For example, if the project is behind schedule, the management department can readjust roles and responsibilities to improve efficiency. For example, if the project is progressing smoothly, the management department can maintain the same roles and responsibilities. For example, the management department can change roles and responsibilities as needed, depending on the project's progress. This maximizes project efficiency by readjusting roles and responsibilities according to the project's progress. Some or all of the above processes in the management department may be performed using AI, for example, or without AI. For example, the management department can input project progress data into a generating AI and have the generating AI perform real-time readjustments of roles and responsibilities.

[0041] The management department can propose the most suitable collaboration partners by considering the artist's geographical location. For example, the management department may prioritize proposing artists who are nearby. For example, the management department may propose artists who are geographically distant but whose skills complement each other. For example, the management department may propose the most suitable collaboration partners while considering geographical constraints. This improves project efficiency by proposing the most suitable collaboration partners by considering the artist's geographical location. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input the artist's geographical location data into a generating AI and have the generating AI propose the most suitable collaboration partners.

[0042] The management department can analyze an artist's social media activity and propose relevant projects. For example, the management department can propose projects based on themes the artist has shown interest in on social media. For example, the management department can propose projects based on themes the artist's followers are interested in. For example, the management department can analyze an artist's past social media activity and propose the most suitable project. In this way, by analyzing an artist's social media activity, relevant projects are proposed and projects based on the artist's interests are provided. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input the artist's social media activity data into a generating AI and have the generating AI generate proposals for relevant projects.

[0043] The efficiency unit can suggest the optimal tools and technologies based on each artist's skill set. For example, the efficiency unit can suggest the optimal digital tools based on each artist's skill set. For example, the efficiency unit can suggest the optimal technologies based on each artist's skill set. For example, the efficiency unit can suggest the optimal work procedures based on each artist's skill set. This improves the artists' work efficiency by suggesting the optimal tools and technologies based on each artist's skill set. Some or all of the above processes in the efficiency unit may be performed using AI, for example, or without AI. For example, the efficiency unit can input artist skill set data into a generating AI and have the generating AI suggest the optimal tools and technologies.

[0044] The efficiency unit can optimize work procedures in real time according to the progress of the work. For example, if the work is behind schedule, the efficiency unit can simplify the procedures to improve efficiency. For example, if the work is progressing smoothly, the efficiency unit can maintain the procedures. For example, the efficiency unit can change the procedures as needed according to the progress of the work. In this way, the efficiency of the work is maximized by optimizing the work procedures according to the progress of the work. Some or all of the above processing in the efficiency unit may be performed using AI, for example, or without AI. For example, the efficiency unit can input work progress data into a generating AI and have the generating AI perform real-time optimization of the work procedures.

[0045] The efficiency unit can propose the optimal work environment by considering the artist's geographical location. For example, the efficiency unit can prioritize proposing a nearby work environment. For example, the efficiency unit can propose an optimal work environment even if it is geographically distant. For example, the efficiency unit can propose an optimal work environment while considering geographical constraints. In this way, by considering the artist's geographical location, the efficiency unit proposes an optimal work environment and improves work efficiency. Some or all of the above processing in the efficiency unit may be performed using AI, for example, or without AI. For example, the efficiency unit can input the artist's geographical location data into a generating AI and have the generating AI propose an optimal work environment.

[0046] The efficiency unit can analyze an artist's social media activity and propose relevant work procedures. For example, the efficiency unit can propose work procedures that the artist has shown interest in on social media. For example, the efficiency unit can propose work procedures that the artist's followers are interested in. For example, the efficiency unit can analyze an artist's past social media activity and propose the optimal work procedures. In this way, by analyzing an artist's social media activity, it proposes relevant work procedures and provides work procedures based on the artist's interests. Some or all of the above processing in the efficiency unit may be performed using AI, for example, or without AI. For example, the efficiency unit can input the artist's social media activity data into a generating AI and have the generating AI execute the proposal of relevant work procedures.

[0047] The efficiency unit can analyze an artist's social media activity and propose relevant work procedures. For example, the efficiency unit can propose work procedures that the artist has shown interest in on social media. For example, the efficiency unit can propose work procedures that the artist's followers are interested in. For example, the efficiency unit can analyze an artist's past social media activity and propose the optimal work procedures. In this way, by analyzing an artist's social media activity, it proposes relevant work procedures and provides work procedures based on the artist's interests. Some or all of the above processing in the efficiency unit may be performed using AI, for example, or without AI. For example, the efficiency unit can input the artist's social media activity data into a generating AI and have the generating AI execute the proposal of relevant work procedures.

[0048] The authentication unit can analyze each artist's past work history and propose the optimal authentication method. For example, the authentication unit can analyze each artist's past work history and propose the optimal authentication method. For example, the authentication unit can propose the optimal authentication method based on the artist's past work history. For example, the authentication unit can optimize the authentication process from the artist's past work history. For example, the authentication unit can analyze the artist's past work history and propose the most efficient authentication method. By analyzing each artist's past work history, the authentication unit can propose the optimal authentication method and improve the efficiency of authentication. Some or all of the above processing in the authentication unit may be performed using AI, for example, or without AI. For example, the authentication unit can input the artist's past work history data into a generating AI and have the generating AI propose the optimal authentication method.

[0049] The authentication unit can optimize the authentication procedure in real time according to the progress of the authentication process. For example, if the authentication process is behind schedule, the authentication unit can simplify the procedure to improve efficiency. For example, if the authentication process is progressing smoothly, the authentication unit can maintain the procedure. For example, the authentication unit can change the procedure as needed according to the progress of the authentication process. This maximizes the efficiency of authentication by optimizing the authentication procedure according to the progress of the authentication process. Some or all of the above-described processes in the authentication unit may be performed using AI, for example, or without AI. For example, the authentication unit can input authentication process progress data into a generating AI and have the generating AI perform real-time optimization of the authentication procedure.

[0050] The authentication unit can propose the optimal authentication method by considering the artist's geographical location information. For example, the authentication unit may prioritize authentication for artists who are nearby. For example, the authentication unit may propose the optimal authentication method even for artists who are geographically distant. For example, the authentication unit may propose the optimal authentication method while considering geographical constraints. This improves the efficiency of authentication by proposing the optimal authentication method while considering the artist's geographical location information. Some or all of the above processing in the authentication unit may be performed using AI, for example, or without AI. For example, the authentication unit may input the artist's geographical location information data into a generating AI and have the generating AI execute the proposal of the optimal authentication method.

[0051] The authentication unit can analyze an artist's social media activity and propose relevant authentication procedures. For example, the authentication unit can propose authentication procedures that the artist has shown interest in on social media. For example, the authentication unit can propose authentication procedures that the artist's followers are interested in. For example, the authentication unit can analyze an artist's past social media activity and propose the optimal authentication procedure. In this way, by analyzing an artist's social media activity, it proposes relevant authentication procedures and provides authentication procedures based on the artist's interests. Some or all of the above processing in the authentication unit may be performed using AI, for example, or without AI. For example, the authentication unit can input the artist's social media activity data into a generating AI and have the generating AI execute the proposal of relevant authentication procedures.

[0052] The distribution unit can analyze each artist's past earnings history and propose the optimal distribution method. For example, the distribution unit can analyze each artist's past earnings history and propose the optimal distribution method. For example, the distribution unit can propose the optimal distribution method based on the artist's past earnings history. For example, the distribution unit can optimize the distribution process from the artist's past earnings history. For example, the distribution unit can analyze the artist's past earnings history and propose the most efficient distribution method. By analyzing each artist's past earnings history, the distribution unit can propose the optimal distribution method and improve the efficiency of earnings distribution. Some or all of the above processing in the distribution unit may be performed using AI, for example, or without AI. For example, the distribution unit can input the artist's past earnings history data into a generating AI and have the generating AI propose the optimal distribution method.

[0053] The distribution unit can optimize the distribution procedure in real time according to the progress of revenue distribution. For example, if the progress of revenue distribution is behind schedule, the distribution unit can simplify the procedure to improve efficiency. For example, if the progress of revenue distribution is on track, the distribution unit can maintain the procedure. For example, the distribution unit can change the procedure as needed according to the progress of revenue distribution. This maximizes the efficiency of revenue distribution by optimizing the distribution procedure according to the progress of revenue distribution. Some or all of the above processing in the distribution unit may be performed using AI, for example, or without AI. For example, the distribution unit can input revenue distribution progress data into a generating AI and have the generating AI perform real-time optimization of the distribution procedure.

[0054] The distribution unit can propose the optimal revenue distribution method by considering the geographical location information of the artists. For example, the distribution unit may prioritize distribution to artists who are nearby. For example, the distribution unit may propose the optimal revenue distribution method even for artists who are geographically distant. For example, the distribution unit may propose the optimal revenue distribution method while considering geographical constraints. In this way, by considering the geographical location information of the artists, the optimal revenue distribution method is proposed, improving the efficiency of revenue distribution. Some or all of the above processing in the distribution unit may be performed using AI, for example, or without AI. For example, the distribution unit may input the geographical location information data of the artists into a generating AI and have the generating AI execute a proposal for the optimal revenue distribution method.

[0055] The distribution unit can analyze an artist's social media activity and propose relevant revenue distribution procedures. For example, the distribution unit can propose revenue distribution procedures that the artist has shown interest in on social media. For example, the distribution unit can propose revenue distribution procedures that the artist's followers are interested in. For example, the distribution unit can analyze an artist's past social media activity and propose the optimal revenue distribution procedure. In this way, by analyzing an artist's social media activity, it proposes relevant revenue distribution procedures and provides revenue distribution procedures based on the artist's interests. Some or all of the above processing in the distribution unit may be performed using AI, for example, or without AI. For example, the distribution unit can input the artist's social media activity data into a generating AI and have the generating AI execute the proposal of relevant revenue distribution procedures.

[0056] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0057] The efficiency unit can monitor the artist's work progress in real time and optimize the work procedures according to the progress. For example, if work is behind schedule, the procedures can be simplified to improve efficiency. If work is progressing smoothly, the procedures can be maintained. If work is progressing too quickly, the procedures can be made more complex to improve quality. In this way, the system can provide the optimal procedures according to the work progress and maximize work efficiency.

[0058] The management department can propose the most suitable collaboration partners by considering the artist's geographical location. For example, it can prioritize suggesting artists who are nearby. It can also suggest artists who are geographically distant but whose skills complement each other. It can propose the most suitable collaboration partners while considering geographical constraints. In this way, by considering the artist's geographical location, the optimal collaboration partners can be suggested, improving the efficiency of the project.

[0059] The efficiency department can suggest the optimal tools and techniques based on each artist's skill set. For example, it can suggest the optimal digital tools, techniques, and workflows based on each artist's skill set. This allows for improved artist efficiency by suggesting the most suitable tools and techniques based on each artist's skill set.

[0060] The authentication unit can analyze each artist's past work history and propose the optimal authentication method. For example, it can analyze each artist's past work history and propose the optimal authentication method. Based on the artist's past work history, it can propose the optimal authentication method. The authentication process can be optimized from the artist's past work history. In this way, by analyzing each artist's past work history, the optimal authentication method can be proposed, and the efficiency of authentication can be improved.

[0061] The distribution unit can optimize the distribution procedure in real time according to the progress of revenue distribution. For example, if the progress of revenue distribution is behind schedule, the procedure can be simplified to improve efficiency. If the progress of revenue distribution is on track, the procedure can be maintained. The procedure can be changed as needed according to the progress of revenue distribution. In this way, the efficiency of revenue distribution can be maximized by optimizing the distribution procedure according to the progress of revenue distribution.

[0062] The management department can analyze an artist's social media activity and propose relevant projects. For example, they can propose projects based on themes the artist has shown interest in on social media. They can also propose projects based on themes the artist's followers are interested in. By analyzing an artist's past social media activity, they can propose relevant projects and provide projects that are tailored to the artist's interests.

[0063] The following briefly describes the processing flow for example form 1.

[0064] Step 1: The management department uses AI to manage theme setting and role allocation. For example, the AI ​​automatically assigns roles based on each artist's skills and experience. Step 2: The Efficiency Department streamlines the composition and color assignments of artists based on the roles managed by the Management Department. For example, they might use project management tools to streamline the composition and color assignments of artists. Step 3: The authentication section is created using digital tools and authenticated on the blockchain. For example, works created with Adobe Creative Cloud are authenticated on the blockchain. Step 4: The distribution unit trades the works authenticated by the authentication unit in an online auction and automatically distributes the profits. For example, it uses OpenSea to conduct an online auction and automatically distributes the sales revenue.

[0065] (Example of form 2) The art collaboration system according to an embodiment of the present invention is a system that streamlines collaborative production among artists, improves transaction transparency, and provides diverse art production opportunities. In this art collaboration system, AI manages theme setting and role allocation, and uses project management tools to streamline the composition and color assignments of artists. Next, digital tools and blockchain are utilized, with production done using Adobe Creative Cloud and authentication on the blockchain. Authenticated works are securely stored, and transaction transparency is enhanced. Furthermore, online auctions and revenue sharing are conducted, with online auctions using OpenSea, and sales revenue is automatically distributed. This mechanism promotes international art collaboration, realizes transparent and fair transactions, and creates new value in art. For example, by automatically assigning roles based on each artist's skills and experience, the workload of artists is reduced and productivity is increased. Next, by registering the digital art works created by artists on the blockchain and transparently managing ownership and transaction history of the works, artists can trade their works with peace of mind. Furthermore, when works created by artists are put up for auction on OpenSea and are successfully bid on, the sales revenue is automatically distributed to the artists, allowing them to receive profits quickly and accurately. This allows artists from different countries and cultures to collaborate on artworks, which can then receive international acclaim. Furthermore, the increased transparency provided by blockchain technology allows artists to trade their work with confidence. For example, ownership and transaction history are recorded on the blockchain, preventing fraudulent activity. Additionally, the high prices fetched by digital artworks in online auctions recognize the value of new forms of art. Thus, art collaboration systems can streamline collaborative production, improve transaction transparency, and provide diverse opportunities for art creation.

[0066] The art collaboration system according to this embodiment comprises a management unit, an efficiency unit, an authentication unit, and a distribution unit. The management unit uses AI to manage theme setting and role assignment. For example, the management unit automatically assigns roles to each artist based on their skills and experience. The efficiency unit streamlines the assignment of composition and color to artists based on the roles managed by the management unit. For example, the efficiency unit uses project management tools to streamline the assignment of composition and color to artists. The authentication unit uses digital tools to create and authenticate works on the blockchain. For example, the authentication unit authenticates works created with Adobe Creative Cloud on the blockchain. The distribution unit trades works authenticated by the authentication unit in online auctions and automatically distributes the profits. For example, the distribution unit uses OpenSea to conduct online auctions and automatically distributes the sales revenue. As a result, the art collaboration system can streamline collaborative production among artists, improve transaction transparency, and provide diverse art production opportunities.

[0067] The management department uses AI to manage theme setting and role allocation. Specifically, the AI ​​automatically assigns roles based on each artist's skills and experience. For example, the AI ​​analyzes artists' past work data and portfolios to understand each artist's strengths and style. This allows the AI ​​to select the most suitable artist for the project theme and assign appropriate roles to each artist. Furthermore, the AI ​​monitors the project's progress in real time and reassigns or adjusts roles as needed. For example, if an artist completes their work ahead of schedule, the AI ​​can assign them additional tasks. The AI ​​also supports communication between artists and promotes efficient collaboration. For example, the AI ​​answers artists' questions via a chatbot and sends notifications about project progress. This allows the management department to improve the overall efficiency of the project and provide an environment where artists can focus on their creative work.

[0068] The Efficiency Department streamlines the composition and color work of artists based on roles managed by the Management Department. Specifically, it uses project management tools to streamline the composition and color work of artists. For example, the Efficiency Department monitors each artist's progress in real time and adjusts task priorities. This allows artists to concentrate more easily on their work and ensures smoother overall project progress. The Efficiency Department also uses AI to support artists' work. For example, AI suggests compositions and colors created by artists, enabling them to work more efficiently. Furthermore, the Efficiency Department provides tools and platforms to promote collaboration among artists. For example, it uses online whiteboards and collaborative editing tools to allow artists to share ideas in real time and work together. In this way, the Efficiency Department can improve the efficiency of artists' work and enhance the overall quality of the project.

[0069] The authentication department uses digital tools to create and authenticates works on the blockchain. Specifically, it authenticates works created with Adobe Creative Cloud on the blockchain. For example, when an artist creates digital art using Adobe Creative Cloud, the data of the creation process and the finished work are automatically recorded on the blockchain. This guarantees the originality and creation history of the work in a transparent and tamper-proof manner. The authentication department also utilizes blockchain technology to manage the ownership and transaction history of works. For example, when a work is sold, the transaction information is recorded on the blockchain, and ownership is transferred to the new owner. This ensures clear management of ownership and reduces the risk of forgery and fraudulent transactions. Furthermore, the authentication department provides artists and buyers with authentication information of their works via the blockchain. For example, artists can confirm that their works are legitimately authenticated, and buyers are assured that the works they purchased are authentic. In this way, the authentication department can improve the reliability and transparency of digital art and provide peace of mind to both artists and buyers.

[0070] The distribution unit trades works authenticated by the authentication unit in online auctions and automatically distributes the profits. Specifically, it uses OpenSea to conduct online auctions and automatically distributes the sales revenue. For example, when an authenticated work is listed on OpenSea, the auction starts and bids are placed. When the auction ends, the work is sold to the highest bidder, and the sales revenue is automatically distributed to the artist and related parties. The distribution unit utilizes blockchain technology to ensure transparent and accurate revenue distribution. For example, if the revenue distribution ratio is set in advance, the revenue is automatically distributed based on that ratio. This allows artists and related parties to conduct transactions with peace of mind without worrying about troubles or lack of transparency regarding revenue distribution. In addition, the distribution unit makes it possible to check the status of revenue distribution in real time. For example, artists and related parties can check online how their revenue is being distributed. This allows the distribution unit to manage the revenue distribution process transparently and efficiently, and to gain the trust of artists and related parties.

[0071] The management department can streamline the composition and color assignments of artists by using project management tools. For example, the management department can streamline the composition and color assignments of artists by using project management tools. For example, the management department can streamline the artists' work by using project management tools such as Trello or Asana. For example, the management department can use project management tools to track and efficiently manage the artists' work progress in real time. Thus, by using project management tools, the composition and color assignments of artists can be streamlined.

[0072] The authentication unit can authenticate works created with Adobe Creative Cloud using blockchain technology. For example, the authentication unit authenticates works created with Adobe Creative Cloud using blockchain technology. For example, the authentication unit authenticates works created using Adobe Creative Cloud applications such as Photoshop and Illustrator using blockchain technology. For example, the authentication unit can use blockchain technology to transparently manage the ownership and transaction history of works. This means that authenticating works created with Adobe Creative Cloud using blockchain technology enhances the secure storage of works and the transparency of transactions.

[0073] The distribution unit can conduct online auctions using OpenSea and automatically distribute the sales revenue. For example, the distribution unit can use OpenSea's auction function to list artwork created by artists, and if an artwork is successfully bid on, the sales revenue can be automatically distributed to the artist. The distribution unit can use blockchain technology to ensure transparency in revenue distribution. This allows artists to receive their earnings quickly and accurately by conducting online auctions using OpenSea and automatically distributing the sales revenue.

[0074] The management department can automatically assign roles to each artist based on their skills and experience. For example, the management department can automatically assign roles based on each artist's skills and experience. For example, the management department can evaluate an artist's past project history and technical skills to assign the most suitable role. For example, the management department can use AI to analyze an artist's skills and experience and suggest the most suitable role. This reduces the workload on artists and improves productivity by automatically assigning roles based on each artist's skills and experience.

[0075] The authentication system can transparently manage the ownership and transaction history of artworks. For example, the authentication system can transparently manage the ownership and transaction history of artworks. For example, the authentication system can use blockchain technology to record and transparently manage the ownership and transaction history of artworks. For example, the authentication system can use a blockchain platform to securely store the ownership and transaction history of artworks. This allows artists to trade their works with confidence by transparently managing the ownership and transaction history of their artworks.

[0076] The management department can estimate the artist's emotions and adjust themes and role assignments based on the estimated emotions. For example, if an artist is stressed, the management department can assign them a less burdensome role. For example, if an artist is relaxed, the management department can assign them a role that allows them to be creative. For example, if an artist is in a hurry, the management department can assign them a role that can be completed quickly. In this way, by adjusting themes and role assignments based on the artist's emotions, the management department can reduce the artist's burden and provide an environment in which they can be creative. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the management department may be performed using AI, for example, or not using AI. For example, the management department can input artist emotion data into a generative AI and have the generative AI perform emotion estimation.

[0077] The management department can analyze each artist's past project history and automatically propose the optimal division of roles. For example, the management department can analyze each artist's past project history and automatically propose the optimal division of roles. For example, the management department can propose similar roles based on the roles an artist has played in past successful projects. For example, the management department can suggest avoiding roles that an artist has struggled with in the past. For example, the management department can propose the most efficient division of roles based on an artist's past project history. In this way, by analyzing each artist's past project history, the optimal division of roles is proposed, improving project efficiency. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input the artist's past project history data into a generating AI and have the generating AI propose the optimal division of roles.

[0078] The management department can readjust roles and responsibilities in real time according to the project's progress. For example, if the project is behind schedule, the management department can readjust roles and responsibilities to improve efficiency. For example, if the project is progressing smoothly, the management department can maintain the same roles and responsibilities. For example, the management department can change roles and responsibilities as needed, depending on the project's progress. This maximizes project efficiency by readjusting roles and responsibilities according to the project's progress. Some or all of the above processes in the management department may be performed using AI, for example, or without AI. For example, the management department can input project progress data into a generating AI and have the generating AI perform real-time readjustments of roles and responsibilities.

[0079] The management department can estimate the artist's emotions and adjust the project's progress based on those emotions. For example, if the artist is stressed, the management department can slow down the progress. For example, if the artist is relaxed, the management department can maintain the progress speed. For example, if the artist is in a hurry, the management department can speed up the progress. By adjusting the project's progress based on the artist's emotions, the management department can reduce the artist's burden and improve project efficiency. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the management department may be performed using AI or not. For example, the management department can input artist emotion data into a generative AI and have the generative AI adjust the project's progress speed.

[0080] The management department can propose the most suitable collaboration partners by considering the artist's geographical location. For example, the management department may prioritize proposing artists who are nearby. For example, the management department may propose artists who are geographically distant but whose skills complement each other. For example, the management department may propose the most suitable collaboration partners while considering geographical constraints. This improves project efficiency by proposing the most suitable collaboration partners by considering the artist's geographical location. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input the artist's geographical location data into a generating AI and have the generating AI propose the most suitable collaboration partners.

[0081] The management department can analyze an artist's social media activity and propose relevant projects. For example, the management department can propose projects based on themes the artist has shown interest in on social media. For example, the management department can propose projects based on themes the artist's followers are interested in. For example, the management department can analyze an artist's past social media activity and propose the most suitable project. In this way, by analyzing an artist's social media activity, relevant projects are proposed and projects based on the artist's interests are provided. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input the artist's social media activity data into a generating AI and have the generating AI generate proposals for relevant projects.

[0082] The efficiency unit can estimate the artist's emotions and optimize the work environment based on the estimated emotions. For example, if the artist is stressed, the efficiency unit can provide a relaxing work environment. For example, if the artist is relaxed, the efficiency unit can provide a work environment that allows for concentration. For example, if the artist is in a hurry, the efficiency unit can provide an environment that allows for efficient work. In this way, the artist's work efficiency is improved by optimizing the work environment based on the artist's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the efficiency unit may be performed using AI or not using AI. For example, the efficiency unit can input the artist's emotion data into the generative AI and have the generative AI perform the optimization of the work environment.

[0083] The efficiency unit can suggest the optimal tools and technologies based on each artist's skill set. For example, the efficiency unit can suggest the optimal digital tools based on each artist's skill set. For example, the efficiency unit can suggest the optimal technologies based on each artist's skill set. For example, the efficiency unit can suggest the optimal work procedures based on each artist's skill set. This improves the artists' work efficiency by suggesting the optimal tools and technologies based on each artist's skill set. Some or all of the above processes in the efficiency unit may be performed using AI, for example, or without AI. For example, the efficiency unit can input artist skill set data into a generating AI and have the generating AI suggest the optimal tools and technologies.

[0084] The efficiency unit can optimize work procedures in real time according to the progress of the work. For example, if the work is behind schedule, the efficiency unit can simplify the procedures to improve efficiency. For example, if the work is progressing smoothly, the efficiency unit can maintain the procedures. For example, the efficiency unit can change the procedures as needed according to the progress of the work. In this way, the efficiency of the work is maximized by optimizing the work procedures according to the progress of the work. Some or all of the above processing in the efficiency unit may be performed using AI, for example, or without AI. For example, the efficiency unit can input work progress data into a generating AI and have the generating AI perform real-time optimization of the work procedures.

[0085] The efficiency unit can estimate the artist's emotions and adjust the priority of tasks based on the estimated emotions. For example, if the artist is stressed, the efficiency unit can lower the priority of a task. For example, if the artist is relaxed, the efficiency unit can maintain the priority. For example, if the artist is in a hurry, the efficiency unit can raise the priority of a task. This reduces the burden on the artist and improves work efficiency by adjusting the priority of tasks based on the artist's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the efficiency unit may be performed using AI or not using AI. For example, the efficiency unit can input the artist's emotion data into the generative AI and have the generative AI adjust the priority of tasks.

[0086] The efficiency unit can propose the optimal work environment by considering the artist's geographical location. For example, the efficiency unit can prioritize proposing a nearby work environment. For example, the efficiency unit can propose an optimal work environment even if it is geographically distant. For example, the efficiency unit can propose an optimal work environment while considering geographical constraints. In this way, by considering the artist's geographical location, the efficiency unit proposes an optimal work environment and improves work efficiency. Some or all of the above processing in the efficiency unit may be performed using AI, for example, or without AI. For example, the efficiency unit can input the artist's geographical location data into a generating AI and have the generating AI propose an optimal work environment.

[0087] The efficiency unit can analyze an artist's social media activity and propose relevant work procedures. For example, the efficiency unit can propose work procedures that the artist has shown interest in on social media. For example, the efficiency unit can propose work procedures that the artist's followers are interested in. For example, the efficiency unit can analyze an artist's past social media activity and propose the optimal work procedures. In this way, by analyzing an artist's social media activity, it proposes relevant work procedures and provides work procedures based on the artist's interests. Some or all of the above processing in the efficiency unit may be performed using AI, for example, or without AI. For example, the efficiency unit can input the artist's social media activity data into a generating AI and have the generating AI execute the proposal of relevant work procedures.

[0088] The efficiency unit can analyze an artist's social media activity and propose relevant work procedures. For example, the efficiency unit can propose work procedures that the artist has shown interest in on social media. For example, the efficiency unit can propose work procedures that the artist's followers are interested in. For example, the efficiency unit can analyze an artist's past social media activity and propose the optimal work procedures. In this way, by analyzing an artist's social media activity, it proposes relevant work procedures and provides work procedures based on the artist's interests. Some or all of the above processing in the efficiency unit may be performed using AI, for example, or without AI. For example, the efficiency unit can input the artist's social media activity data into a generating AI and have the generating AI execute the proposal of relevant work procedures.

[0089] The authentication unit can estimate the artist's emotions and adjust the authentication process based on the estimated emotions. For example, if the artist is stressed, the authentication unit can simplify the authentication process. For example, if the artist is relaxed, the authentication unit can provide a detailed authentication process. For example, if the artist is in a hurry, the authentication unit can complete the authentication process quickly. This reduces the burden on the artist and improves the efficiency of authentication by adjusting the authentication process based on the artist's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the authentication unit may be performed using AI or not using AI. For example, the authentication unit can input the artist's emotion data into the generative AI and have the generative AI perform the adjustment of the authentication process.

[0090] The authentication unit can analyze each artist's past work history and propose the optimal authentication method. For example, the authentication unit can analyze each artist's past work history and propose the optimal authentication method. For example, the authentication unit can propose the optimal authentication method based on the artist's past work history. For example, the authentication unit can optimize the authentication process from the artist's past work history. For example, the authentication unit can analyze the artist's past work history and propose the most efficient authentication method. By analyzing each artist's past work history, the authentication unit can propose the optimal authentication method and improve the efficiency of authentication. Some or all of the above processing in the authentication unit may be performed using AI, for example, or without AI. For example, the authentication unit can input the artist's past work history data into a generating AI and have the generating AI propose the optimal authentication method.

[0091] The authentication unit can optimize the authentication procedure in real time according to the progress of the authentication process. For example, if the authentication process is behind schedule, the authentication unit can simplify the procedure to improve efficiency. For example, if the authentication process is progressing smoothly, the authentication unit can maintain the procedure. For example, the authentication unit can change the procedure as needed according to the progress of the authentication process. This maximizes the efficiency of authentication by optimizing the authentication procedure according to the progress of the authentication process. Some or all of the above-described processes in the authentication unit may be performed using AI, for example, or without AI. For example, the authentication unit can input authentication process progress data into a generating AI and have the generating AI perform real-time optimization of the authentication procedure.

[0092] The authentication unit can estimate the artist's emotions and adjust the authentication priority based on the estimated emotions. For example, if the artist is stressed, the authentication unit can lower the priority. For example, if the artist is relaxed, the authentication unit can maintain the priority. For example, if the artist is in a hurry, the authentication unit can raise the priority. This reduces the burden on the artist and improves the efficiency of authentication by adjusting the authentication priority based on the artist's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the authentication unit may be performed using AI or not using AI. For example, the authentication unit can input the artist's emotion data into the generative AI and have the generative AI perform the adjustment of authentication priorities.

[0093] The authentication unit can propose the optimal authentication method by considering the artist's geographical location information. For example, the authentication unit may prioritize authentication for artists who are nearby. For example, the authentication unit may propose the optimal authentication method even for artists who are geographically distant. For example, the authentication unit may propose the optimal authentication method while considering geographical constraints. This improves the efficiency of authentication by proposing the optimal authentication method while considering the artist's geographical location information. Some or all of the above processing in the authentication unit may be performed using AI, for example, or without AI. For example, the authentication unit may input the artist's geographical location information data into a generating AI and have the generating AI execute the proposal of the optimal authentication method.

[0094] The authentication unit can analyze an artist's social media activity and propose relevant authentication procedures. For example, the authentication unit can propose authentication procedures that the artist has shown interest in on social media. For example, the authentication unit can propose authentication procedures that the artist's followers are interested in. For example, the authentication unit can analyze an artist's past social media activity and propose the optimal authentication procedure. In this way, by analyzing an artist's social media activity, it proposes relevant authentication procedures and provides authentication procedures based on the artist's interests. Some or all of the above processing in the authentication unit may be performed using AI, for example, or without AI. For example, the authentication unit can input the artist's social media activity data into a generating AI and have the generating AI execute the proposal of relevant authentication procedures.

[0095] The distribution unit can estimate the artist's emotions and adjust the revenue distribution method based on the estimated emotions. For example, if the artist is stressed, the distribution unit can distribute revenue quickly. For example, if the artist is relaxed, the distribution unit can distribute revenue normally. For example, if the artist is in a hurry, the distribution unit can prioritize revenue distribution. This reduces the burden on the artist and improves the efficiency of revenue distribution by adjusting the revenue distribution method based on the artist's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the distribution unit may be performed using AI or not using AI. For example, the distribution unit can input artist emotion data into a generative AI and have the generative AI adjust the revenue distribution method.

[0096] The distribution unit can analyze each artist's past earnings history and propose the optimal distribution method. For example, the distribution unit can analyze each artist's past earnings history and propose the optimal distribution method. For example, the distribution unit can propose the optimal distribution method based on the artist's past earnings history. For example, the distribution unit can optimize the distribution process from the artist's past earnings history. For example, the distribution unit can analyze the artist's past earnings history and propose the most efficient distribution method. By analyzing each artist's past earnings history, the distribution unit can propose the optimal distribution method and improve the efficiency of earnings distribution. Some or all of the above processing in the distribution unit may be performed using AI, for example, or without AI. For example, the distribution unit can input the artist's past earnings history data into a generating AI and have the generating AI propose the optimal distribution method.

[0097] The distribution unit can optimize the distribution procedure in real time according to the progress of revenue distribution. For example, if the progress of revenue distribution is behind schedule, the distribution unit can simplify the procedure to improve efficiency. For example, if the progress of revenue distribution is on track, the distribution unit can maintain the procedure. For example, the distribution unit can change the procedure as needed according to the progress of revenue distribution. This maximizes the efficiency of revenue distribution by optimizing the distribution procedure according to the progress of revenue distribution. Some or all of the above processing in the distribution unit may be performed using AI, for example, or without AI. For example, the distribution unit can input revenue distribution progress data into a generating AI and have the generating AI perform real-time optimization of the distribution procedure.

[0098] The distribution unit can estimate the artist's emotions and adjust the priority of revenue distribution based on the estimated emotions. For example, if the artist is stressed, the distribution unit will give them a higher priority. If the artist is relaxed, the distribution unit can distribute revenue at the normal priority. If the artist is in a hurry, the distribution unit can give them the highest priority. By adjusting the priority of revenue distribution based on the artist's emotions, the distribution unit reduces the burden on the artist and improves the efficiency of revenue distribution. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the distribution unit may be performed using AI or not using AI. For example, the distribution unit can input artist emotion data into a generative AI and have the generative AI adjust the priority of revenue distribution.

[0099] The distribution unit can propose the optimal revenue distribution method by considering the geographical location information of the artists. For example, the distribution unit may prioritize distribution to artists who are nearby. For example, the distribution unit may propose the optimal revenue distribution method even for artists who are geographically distant. For example, the distribution unit may propose the optimal revenue distribution method while considering geographical constraints. In this way, by considering the geographical location information of the artists, the optimal revenue distribution method is proposed, improving the efficiency of revenue distribution. Some or all of the above processing in the distribution unit may be performed using AI, for example, or without AI. For example, the distribution unit may input the geographical location information data of the artists into a generating AI and have the generating AI execute a proposal for the optimal revenue distribution method.

[0100] The distribution unit can analyze an artist's social media activity and propose relevant revenue distribution procedures. For example, the distribution unit can propose revenue distribution procedures that the artist has shown interest in on social media. For example, the distribution unit can propose revenue distribution procedures that the artist's followers are interested in. For example, the distribution unit can analyze an artist's past social media activity and propose the optimal revenue distribution procedure. In this way, by analyzing an artist's social media activity, it proposes relevant revenue distribution procedures and provides revenue distribution procedures based on the artist's interests. Some or all of the above processing in the distribution unit may be performed using AI, for example, or without AI. For example, the distribution unit can input the artist's social media activity data into a generating AI and have the generating AI execute the proposal of relevant revenue distribution procedures.

[0101] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0102] The management team can estimate the artist's emotions and adjust the project theme based on those estimates. For example, if the artist is stressed, a relaxing theme can be set. If the artist is excited, a challenging theme can be set. If the artist is calm, a theme requiring concentration can be set. This allows for theme setting tailored to the artist's emotions, maximizing their creativity. Emotion estimation can be performed using an emotion engine or generative AI.

[0103] The efficiency unit can monitor the artist's work progress in real time and optimize the work procedures according to the progress. For example, if work is behind schedule, the procedures can be simplified to improve efficiency. If work is progressing smoothly, the procedures can be maintained. If work is progressing too quickly, the procedures can be made more complex to improve quality. In this way, the system can provide the optimal procedures according to the work progress and maximize work efficiency.

[0104] The authentication unit can estimate the artist's emotions and adjust the authentication process based on those emotions. For example, if the artist is stressed, the authentication process can be simplified. If the artist is relaxed, a more detailed authentication process can be provided. If the artist is in a hurry, the authentication process can be completed quickly. This allows for an authentication process tailored to the artist's emotions, improving the efficiency of authentication. Emotion estimation can be performed using an emotion engine or generative AI.

[0105] The distribution unit can estimate the artist's emotions and adjust the revenue distribution method based on the estimated emotions. For example, if the artist is stressed, revenue can be distributed quickly. If the artist is relaxed, revenue can be distributed normally. If the artist is in a hurry, revenue can be distributed preferentially. This allows for revenue distribution tailored to the artist's emotions and improves the efficiency of revenue distribution. Emotion estimation can be performed using an emotion engine or generative AI.

[0106] The management department can estimate the artist's emotions and adjust the project's pace based on those estimates. For example, if the artist is stressed, the pace can be slowed down. If the artist is relaxed, the pace can be maintained. If the artist is in a hurry, the pace can be sped up. This allows for a pace that matches the artist's emotions, improving project efficiency. Emotion estimation can be performed using an emotion engine or generative AI.

[0107] The management department can propose the most suitable collaboration partners by considering the artist's geographical location. For example, it can prioritize suggesting artists who are nearby. It can also suggest artists who are geographically distant but whose skills complement each other. It can propose the most suitable collaboration partners while considering geographical constraints. In this way, by considering the artist's geographical location, the optimal collaboration partners can be suggested, improving the efficiency of the project.

[0108] The efficiency department can suggest the optimal tools and techniques based on each artist's skill set. For example, it can suggest the optimal digital tools, techniques, and workflows based on each artist's skill set. This allows for improved artist efficiency by suggesting the most suitable tools and techniques based on each artist's skill set.

[0109] The authentication unit can analyze each artist's past work history and propose the optimal authentication method. For example, it can analyze each artist's past work history and propose the optimal authentication method. Based on the artist's past work history, it can propose the optimal authentication method. The authentication process can be optimized from the artist's past work history. In this way, by analyzing each artist's past work history, the optimal authentication method can be proposed, and the efficiency of authentication can be improved.

[0110] The distribution unit can optimize the distribution procedure in real time according to the progress of revenue distribution. For example, if the progress of revenue distribution is behind schedule, the procedure can be simplified to improve efficiency. If the progress of revenue distribution is on track, the procedure can be maintained. The procedure can be changed as needed according to the progress of revenue distribution. In this way, the efficiency of revenue distribution can be maximized by optimizing the distribution procedure according to the progress of revenue distribution.

[0111] The management department can analyze an artist's social media activity and propose relevant projects. For example, they can propose projects based on themes the artist has shown interest in on social media. They can also propose projects based on themes the artist's followers are interested in. By analyzing an artist's past social media activity, they can propose relevant projects and provide projects that are tailored to the artist's interests.

[0112] The following briefly describes the processing flow for example form 2.

[0113] Step 1: The management department uses AI to manage theme setting and role allocation. For example, the AI ​​automatically assigns roles based on each artist's skills and experience. Step 2: The Efficiency Department streamlines the composition and color assignments of artists based on the roles managed by the Management Department. For example, they might use project management tools to streamline the composition and color assignments of artists. Step 3: The authentication section is created using digital tools and authenticated on the blockchain. For example, works created with Adobe Creative Cloud are authenticated on the blockchain. Step 4: The distribution unit trades the works authenticated by the authentication unit in an online auction and automatically distributes the profits. For example, it uses OpenSea to conduct an online auction and automatically distributes the sales revenue.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] Each of the multiple elements described above, including the management unit, efficiency unit, authentication unit, and distribution unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the management unit is implemented by the control unit 46A of the smart device 14, where AI automatically assigns roles based on each artist's skills and experience. The efficiency unit is implemented by the specific processing unit 290 of the data processing unit 12, where project management tools are used to streamline the assignment of artists to composition and color. The authentication unit is implemented by the control unit 46A of the smart device 14, where works created with Adobe Creative Cloud are authenticated using blockchain. The distribution unit is implemented by the specific processing unit 290 of the data processing unit 12, where OpenSea is used to conduct online auctions and automatically distributes the sales revenue. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0118] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] Each of the multiple elements described above, including the management unit, efficiency unit, authentication unit, and distribution unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the management unit is implemented by the control unit 46A of the smart glasses 214, where AI automatically assigns roles based on each artist's skills and experience. The efficiency unit is implemented by the specific processing unit 290 of the data processing unit 12, where project management tools are used to streamline the assignment of artists to composition and color. The authentication unit is implemented by the control unit 46A of the smart glasses 214, where works created with Adobe Creative Cloud are authenticated using blockchain. The distribution unit is implemented by the specific processing unit 290 of the data processing unit 12, where OpenSea is used to conduct online auctions and automatically distributes the sales revenue. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0134] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] Each of the multiple elements described above, including the management unit, efficiency unit, authentication unit, and distribution unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the management unit is implemented by the control unit 46A of the headset terminal 314, where AI automatically assigns roles based on each artist's skills and experience. The efficiency unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, where project management tools are used to streamline the assignment of artists to composition and color. The authentication unit is implemented by, for example, the control unit 46A of the headset terminal 314, where works created with Adobe Creative Cloud are authenticated using blockchain. The distribution unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, where online auctions are conducted using OpenSea, and sales revenue is automatically distributed. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0150] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] Each of the multiple elements described above, including the management unit, efficiency unit, authentication unit, and distribution unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the management unit is implemented by the control unit 46A of the robot 414, where AI automatically assigns roles based on each artist's skills and experience. The efficiency unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, where project management tools are used to streamline the assignment of artists to composition and color. The authentication unit is implemented by, for example, the control unit 46A of the robot 414, where works created with Adobe Creative Cloud are authenticated using blockchain. The distribution unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, where online auctions are conducted using OpenSea, and sales revenue is automatically distributed. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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."

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] (Note 1) The management department, where AI manages theme setting and role assignment, Based on the roles managed by the aforementioned management department, an efficiency department is established to streamline the composition and color assignments of artists. The authentication unit is created using digital tools and authenticated using blockchain, The system includes a distribution unit that trades works authenticated by the authentication unit in an online auction and automatically distributes the profits. A system characterized by the following features. (Note 2) The aforementioned management department, Use project management tools to streamline the process of artists handling composition and color. The system described in Appendix 1, characterized by the features described herein. (Note 3) The authentication unit, Authenticate works created with Adobe Creative Cloud using blockchain technology. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned distribution unit is We use OpenSea to conduct online auctions and automatically distribute the revenue. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned management department, Roles are automatically assigned based on each artist's skills and experience. The system described in Appendix 1, characterized by the features described herein. (Note 6) The authentication unit, Transparent management of artwork ownership and transaction history. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned management department, We estimate the artist's emotions and adjust the theme setting and role assignments based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned management department, The system analyzes each artist's past project history and automatically suggests the optimal division of labor. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned management department, Roles and responsibilities are readjusted in real time according to the project's progress. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned management department, It estimates the artist's emotions and adjusts the project's progress based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned management department, We propose the most suitable collaboration partner, taking into account the artist's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned management department, Analyze artists' social media activities and propose related projects. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned efficiency improvement unit is It estimates the artist's emotions and optimizes the work environment based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned efficiency improvement unit is We propose the most suitable tools and techniques based on each artist's skill set. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned efficiency improvement unit is Optimize work procedures in real time according to the progress of the work. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned efficiency improvement unit is Estimate the artist's emotions and adjust the priority of tasks based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned efficiency improvement unit is We propose the optimal working environment, taking into account the artist's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned efficiency improvement unit is Analyze artists' social media activity and propose relevant work procedures. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned efficiency improvement unit is Analyze artists' social media activity and propose relevant work procedures. The system described in Appendix 1, characterized by the features described herein. (Note 20) The authentication unit, It estimates the artist's emotions and adjusts the authentication process based on the estimated emotions of the artist. The system described in Appendix 1, characterized by the features described herein. (Note 21) The authentication unit, We analyze each artist's past work history and propose the most suitable authentication method. The system described in Appendix 1, characterized by the features described herein. (Note 22) The authentication unit, The authentication process is optimized in real time based on its progress. The system described in Appendix 1, characterized by the features described herein. (Note 23) The authentication unit, It estimates the artist's emotions and adjusts authentication priorities based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The authentication unit, We will propose the optimal authentication method, taking into account the artist's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 25) The authentication unit, Analyze artists' social media activity and propose relevant verification procedures. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned distribution unit is We estimate the artist's emotions and adjust the revenue sharing method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned distribution unit is We analyze each artist's past earnings history and propose the optimal distribution method. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned distribution unit is The distribution process is optimized in real time according to the progress of revenue distribution. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned distribution unit is It estimates the artist's emotions and adjusts the revenue sharing priority based on the estimated artist's emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned distribution unit is We propose the optimal revenue sharing method, taking into account the artist's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned distribution unit is We analyze artists' social media activities and propose relevant revenue sharing procedures. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0186] 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 management department, where AI manages theme setting and role allocation, Based on the roles managed by the aforementioned management department, an efficiency department is established to streamline the composition and color assignments of artists. The authentication unit is created using digital tools and authenticated using blockchain, The system includes a distribution unit that trades works authenticated by the authentication unit in an online auction and automatically distributes the profits. A system characterized by the following features.

2. The aforementioned management department, Use project management tools to streamline the process of artists handling composition and color. The system according to feature 1.

3. The authentication unit, Authenticate works created with Adobe Creative Cloud using blockchain technology. The system according to feature 1.

4. The aforementioned distribution unit is We use OpenSea to conduct online auctions and automatically distribute the revenue. The system according to feature 1.

5. The aforementioned management department, Roles are automatically assigned based on each artist's skills and experience. The system according to feature 1.

6. The authentication unit, Transparent management of artwork ownership and transaction history. The system according to feature 1.

7. The aforementioned management department, We estimate the artist's emotions and adjust the theme setting and role assignments based on those estimated emotions. The system according to feature 1.

8. The aforementioned management department, The system analyzes each artist's past project history and automatically suggests the optimal division of labor. The system according to feature 1.

Images