system

Abstract

The system according to this embodiment aims to efficiently market the exhibitors' works to companies. [Solution] The system according to the embodiment comprises a registration unit, an analysis unit, a creation unit, a matching unit, and a proposal unit. The registration unit allows exhibitors to register their works. The analysis unit analyzes the characteristics of the works registered by the registration unit. The creation unit creates an AI visual summary based on the characteristics analyzed by the analysis unit. The matching unit matches the characteristics of the artwork with the requirements of the company based on the AI ​​visual summary created by the creation unit. The proposal unit proposes the artwork matched by the matching unit to the company.

Description

Technical Field

[0006] , ,

[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, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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 conventional technology, there is a problem that there is no route for a producer to sell a work to a company, and it is difficult for the company to efficiently select many works.

[0005] The system according to the embodiment aims to efficiently sell a producer's work to a company.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a registration unit, an analysis unit, a creation unit, a matching unit, and a proposal unit. The registration unit allows exhibitors to register their works. The analysis unit analyzes the characteristics of the works registered by the registration unit. The creation unit creates an AI visual summary based on the characteristics analyzed by the analysis unit. The matching unit matches the characteristics of the artwork with the requirements of the company based on the AI ​​visual summary created by the creation unit. The proposal unit proposes the artwork matched by the matching unit to the company. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently market the exhibitor's work to companies. [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, and the like. The communication I / F manages 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) An AI matching platform according to an embodiment of the present invention is a system for connecting paintings created by people with intellectual disabilities with companies that wish to utilize those paintings. In this system, exhibitors register their works on the platform, and the AI ​​analyzes the characteristics of each painting and creates an "AI Visual Summary." This visual summary includes visual indicators such as color tendencies, shapes, and themes, and is provided in a way that is easy for companies to understand intuitively. Companies request the artwork image they desire, and the AI ​​matches the company's requirements with the characteristics of the paintings to suggest the most suitable painting. Furthermore, companies can choose the best option from multiple licensing models. This mechanism allows paintings by people with intellectual disabilities to be efficiently marketed to companies, supporting their economic independence, while also allowing companies to easily find paintings that meet their needs. For example, exhibitors register their works on the platform. At this time, detailed information about the artwork (e.g., title of the painting, technique used, size, etc.) is also registered. Next, the AI ​​analyzes the characteristics of each painting. For example, characteristics such as color usage (warm colors, cool colors, neutral colors), painting type (landscape painting, portrait painting), and type of painting (oil painting, watercolor painting, crayon, CG) are analyzed. This process creates an "AI Visual Summary" for each artwork. Companies can request artwork that matches their desired image. For example, specific requests such as "warm-colored landscape paintings" or "crayon drawings of people" are possible. Based on the company's request, the AI ​​suggests the most suitable artwork. For example, if a company requests "warm-colored landscape paintings," the AI ​​will extract warm-colored landscape paintings from the registered artworks and suggest them to the company. Furthermore, companies can choose the most suitable from multiple licensing models. For example, there are models that allow them to purchase usage rights for a certain period, or models that limit usage to specific purposes. This allows companies to utilize the artwork in a way that best suits their needs. This system efficiently markets artwork by people with intellectual disabilities to companies and supports their economic independence. In addition, companies can easily find artwork that meets their needs. For example, companies looking for artwork to use in advertising or promotion can refer to the AI ​​Visual Summary to select the most suitable artwork.This will increase the value of artistic activities by people with intellectual disabilities and create a world where their talents are widely recognized. The AI ​​matching platform will efficiently market the artwork of people with intellectual disabilities to companies, supporting their economic independence, and allowing companies to easily find artwork that meets their needs.

[0029] The AI ​​matching platform according to this embodiment comprises a registration unit, an analysis unit, a creation unit, a matching unit, and a proposal unit. The registration unit allows exhibitors to register their works. When exhibitors register their works, detailed information such as the title of the work, the techniques used, and the size are also registered. The registration unit provides, for example, an interface for exhibitors to upload their works to the platform. The registration unit can also provide a form for exhibitors to input detailed information about their works. Furthermore, the registration unit has a function for exhibitors to upload images of their works. For example, the registration unit provides an interface that allows exhibitors to upload images of their works by dragging and dropping. The analysis unit analyzes the characteristics of the works registered by the registration unit. The analysis unit analyzes characteristics such as color usage, type of painting, and genre of painting. For example, the analysis unit uses AI to analyze the color usage of the works. For example, the analysis unit can analyze the distribution of colors and color combinations of the works. The analysis unit can also use AI to analyze the shape and theme of the works in order to analyze the type of painting. For example, the analysis unit analyzes which type of artwork it belongs to, such as landscape painting, portrait painting, or abstract painting. Furthermore, the analysis unit can also use AI to analyze the technique and materials of the artwork in order to analyze the type of painting. For example, the analysis unit can analyze which type of artwork it belongs to, such as oil painting, watercolor painting, or digital art. The creation unit creates an AI visual summary based on the characteristics analyzed by the analysis unit. The creation unit creates an AI visual summary that includes visual indicators such as color tendencies, shapes, and themes. For example, the creation unit can use AI to analyze the color tendencies of the artwork and reflect the results in the visual summary. For example, the creation unit can analyze the frequency and combinations of colors in the artwork and display the results in the visual summary. The creation unit can also analyze the shape of the artwork and reflect the results in the visual summary. For example, the creation unit can analyze the types and arrangement of shapes in the artwork and display the results in the visual summary. Furthermore, the creation unit can also analyze the theme of the artwork and reflect the results in the visual summary.For example, the creation unit analyzes the subject and message of a work and displays the results in a visual summary. The matching unit matches the characteristics of the painting with the company's requirements based on the AI ​​visual summary created by the creation unit. The matching unit proposes the most suitable painting based on the company's request. The matching unit uses AI to match the company's request with the characteristics of the painting. For example, if the company requests a "warm-colored landscape painting," the matching unit extracts warm-colored landscape paintings from the registered paintings and proposes them to the company. The proposal unit proposes the paintings matched by the matching unit to the company. The proposal unit allows the company to choose the most suitable from multiple licensing models. The proposal unit uses AI to propose the most suitable licensing model to the company. For example, the proposal unit proposes models such as one in which the company purchases usage rights for a certain period, or one in which the painting is used only for specific purposes. As a result, the AI ​​matching platform according to this embodiment efficiently sells paintings by people with intellectual disabilities to companies, supports their economic independence, and allows companies to easily find paintings that meet their needs.

[0030] The registration section allows exhibitors to register their works. When exhibitors register their works, they also register detailed information such as the title of the work, the techniques used, and the size. The registration section provides an interface for exhibitors to upload their works to the platform. Specifically, exhibitors can easily upload images and detailed information of their works through a dedicated webpage or application. The registration section can also provide a form for exhibitors to enter detailed information about their works. This form includes items such as the title of the work, the techniques used, the size, the year of creation, and the price, allowing exhibitors to convey the appeal of their works to the fullest by entering this information. Furthermore, the registration section has a function for exhibitors to upload images of their works. For example, the registration section provides an interface that allows exhibitors to upload images of their works using drag and drop. This allows exhibitors to easily upload high-quality images and visually convey the details of their works. The registration section also has a function to automatically check the quality of uploaded images and adjust or optimize them as needed. This ensures that the images of works displayed on the platform are always high quality and appealing to viewers. In addition, the registration section provides a dashboard for exhibitors to manage the registration status of their works. This dashboard allows sellers to check the registration status, views, and purchase requests of their works in real time. This enables sellers to understand the performance of their work and update information or conduct promotional activities as needed.

[0031] The analysis unit analyzes the characteristics of works registered by the registration unit. The analysis unit analyzes characteristics such as color usage, painting style, and type of painting. Specifically, it uses AI to analyze the color usage of a work. For example, the analysis unit can analyze the distribution of colors and color combinations in a work. The AI ​​uses image recognition technology to generate a color histogram from the image of the work and identifies the main colors and their combinations. The analysis unit can also use AI to analyze the shape and theme of a work in order to analyze the type of painting. For example, the analysis unit analyzes whether the work is classified as a landscape painting, portrait painting, abstract painting, etc. The AI ​​extracts image features and uses a pre-trained model to classify the type of work with high accuracy. Furthermore, the analysis unit can also use AI to analyze the technique and materials of a work in order to analyze the type of painting. For example, the analysis unit analyzes whether the work is classified as an oil painting, watercolor painting, digital art, etc. The AI ​​analyzes the texture and brushwork patterns of the image to identify the technique and materials of the work. This allows the analysis unit to analyze the characteristics of registered works in detail and accurately grasp their appeal and features. Furthermore, the analysis unit saves the analysis results to a database, making them accessible to other departments. This enables the analysis unit to efficiently analyze the characteristics of registered works and improve the overall system performance.

[0032] The creation unit creates an AI visual summary based on the characteristics analyzed by the analysis unit. The creation unit creates an AI visual summary that includes visual indicators such as color tendencies, shapes, and themes. Specifically, it uses AI to analyze the color tendencies of the artwork and reflects the results in the visual summary. For example, the creation unit analyzes the frequency and combinations of colors in the artwork and displays the results in the visual summary. The AI ​​generates color histograms and palettes and displays them in a visually easy-to-understand format. The creation unit can also analyze the shapes of the artwork and reflect the results in the visual summary. For example, the creation unit analyzes the types and arrangement of shapes in the artwork and displays the results in the visual summary. The AI ​​extracts shape features and identifies the main shapes and their arrangement. Furthermore, the creation unit can also analyze the themes of the artwork and reflect the results in the visual summary. For example, the creation unit analyzes the subject matter and message of the artwork and displays the results in the visual summary. The AI ​​analyzes the content and context of the artwork and identifies the main themes and messages. This allows the creation department to create visually easy-to-understand AI visual summaries based on the characteristics analyzed by the analysis department, effectively conveying the appeal of the work. Furthermore, the creation department saves the visual summaries to a database, making them accessible to other departments. This enables the creation department to efficiently utilize the analysis results and improve the overall system performance.

[0033] The matching unit matches the characteristics of a painting with the company's requirements based on the AI ​​visual summary created by the creation unit. For example, the matching unit proposes the most suitable painting based on the company's request. Specifically, it uses AI to match the company's request with the characteristics of the painting. For example, if a company requests a "warm-colored landscape painting," the matching unit extracts warm-colored landscape paintings from the registered paintings and proposes them to the company. The AI ​​analyzes the company's request and identifies the keywords and conditions included in the request. Next, the AI ​​analyzes the visual summary of the registered works and extracts works that match the request. This allows the matching unit to quickly and accurately propose the most suitable work for the company's request. Furthermore, the matching unit can also propose multiple candidate works according to the company's request. For example, if a company requests a "warm-colored landscape painting," the matching unit proposes multiple warm-colored landscape paintings, allowing the company to select the most suitable work. This allows the matching unit to respond to the diverse needs of companies and propose the most suitable work. In addition, the matching unit provides the company with detailed information and a visual summary of the proposed works, making it easier for the company to understand the characteristics of the works. This allows the matching department to propose the most suitable artwork in response to a company's request, making it easy to find paintings that meet the company's needs.

[0034] The Proposal Department proposes images matched by the Matching Department to companies. For example, the Proposal Department enables companies to choose the most suitable license model from multiple options. Specifically, it uses AI to propose the most suitable license model for each company. For example, the Proposal Department proposes models such as one where the company purchases usage rights for a fixed period, or one where usage is limited to specific purposes. The AI ​​analyzes conditions such as the company's purpose of use, budget, and usage period to identify the most suitable license model. This allows the Proposal Department to propose the most suitable license model to companies, enabling them to select a license that meets their needs. Furthermore, the Proposal Department provides companies with detailed information on the proposed license model, making it easier for them to understand the license content. For example, the Proposal Department provides detailed information such as license usage conditions, fees, and scope of use, enabling companies to accurately grasp the license content. This allows the Proposal Department to propose the most suitable license model to companies, enabling them to select a license that meets their needs. In addition, the Proposal Department can collect feedback from companies and continuously improve the accuracy and effectiveness of its proposals. For example, by providing feedback on the proposed license model, the Proposal Department can revise its proposals based on that feedback and propose a more appropriate license model. This allows the proposal department to suggest the most suitable licensing model to companies, enabling them to select a license that meets their specific needs.

[0035] The registration section can register detailed information about the artwork. For example, the registration section can register detailed information such as the artwork's title, author, and year of creation. The registration section can provide a form for exhibitors to input detailed information about their artwork. The registration section also has functions for exhibitors to edit detailed information about their artwork. For example, the registration section can provide an interface that allows exhibitors to add, delete, and edit detailed information about their artwork. This makes it easier for companies to understand the background of the artwork by registering detailed information about it. Some or all of the above processes in the registration section may be performed using AI, for example, or not using AI. For example, the registration section can input the detailed information about the artwork entered by the exhibitor into an AI, which can then analyze and register that information.

[0036] The analysis unit can analyze characteristics such as color usage, painting style, and type of painting. For example, the analysis unit can analyze the characteristics of color usage. For example, the analysis unit can use AI to analyze the distribution of colors and color combinations in a work. For example, the analysis unit can analyze the frequency and color combinations of colors in a work and output the results. The analysis unit can also analyze the characteristics of the painting style. For example, the analysis unit can use AI to analyze the shape and theme of a work. For example, the analysis unit can analyze which type of painting a work is classified as, such as landscape painting, portrait painting, or abstract painting. Furthermore, the analysis unit can also analyze the characteristics of the type of painting. For example, the analysis unit can use AI to analyze the technique and materials of a work. For example, the analysis unit can analyze which type of painting a work is classified as, such as oil painting, watercolor painting, or digital art. By analyzing the characteristics of a work in detail, it becomes easier to propose paintings that meet the requirements of the company. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input image data of the artwork into the AI, which then analyzes the data and extracts its characteristics.

[0037] The creation unit can create an AI-powered visual summary that includes visual indicators such as color tendencies, shapes, and themes. For example, the creation unit can analyze color tendencies and reflect the results in the visual summary. For example, the creation unit can use AI to analyze the frequency and combinations of colors in an artwork and display the results in the visual summary. The creation unit can also analyze the characteristics of shapes and reflect the results in the visual summary. For example, the creation unit can use AI to analyze the types and arrangement of shapes in an artwork and display the results in the visual summary. Furthermore, the creation unit can analyze the characteristics of themes and reflect the results in the visual summary. For example, the creation unit can use AI to analyze the subject and message of an artwork and display the results in the visual summary. This makes it easier for companies to select artwork by providing them with an intuitively understandable visual summary. Some or all of the above-described processes in the creation unit may be performed using AI, or not. For example, the creation unit can input characteristic data extracted by the analysis unit into the AI, and the AI ​​can create a visual summary based on that data.

[0038] The matching unit can propose the most suitable image based on a company's request. For example, the matching unit matches a company's request with the characteristics of an image. For example, the matching unit uses AI to match a company's request with the characteristics of an image. For example, if a company requests a "warm-colored landscape painting," the matching unit will extract warm-colored landscape paintings from its registered collection and propose them to the company. Also, if a company requests a "crayon drawing of a person," the matching unit can extract crayon drawings of people from its registered collection and propose them to the company. Furthermore, if a company requests an "abstract painting," the matching unit can extract abstract paintings from its registered collection and propose them to the company. In this way, by proposing the most suitable image based on a company's request, it is possible to efficiently find an image that meets the company's needs. Some or all of the above processing in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input a company's request into AI, and the AI ​​can extract the most suitable image based on that request.

[0039] The proposal department can enable companies to choose the most suitable licensing model from multiple options. For example, the proposal department can suggest the most suitable licensing model for a company. For example, the proposal department can use AI to suggest the most suitable licensing model for a company. For example, the proposal department can suggest models in which companies purchase usage rights for a fixed period, or models in which usage is limited to specific purposes. The proposal department can also suggest models in which companies purchase exclusive licenses or non-exclusive licenses. Furthermore, the proposal department can suggest models in which companies can select the license period or the license usage. This allows companies to use the images in a way that best suits their needs. Some or all of the above processing in the proposal department may be performed using AI, or not using AI. For example, the proposal department can input the company's needs into AI, and the AI ​​can suggest the most suitable licensing model based on those needs.

[0040] The registration section can register background information of the exhibitor in addition to detailed information about the artwork during registration. For example, the registration section can add a field for entering the exhibitor's career information to provide a detailed explanation of the artwork's background. For example, the registration section can use AI to analyze the exhibitor's background information. For example, the registration section can provide a form for entering the exhibitor's career information. The registration section can also include a section for describing the exhibitor's creative intent to clarify the artwork's concept. For example, the registration section can provide a field for entering the exhibitor's creative intent. Furthermore, the registration section can register the exhibitor's past artwork history to show the evolution of the artwork and changes in style. For example, the registration section can provide a form for entering the exhibitor's past artwork history. This clarifies the artwork's concept and creative intent by registering the exhibitor's background information. Some or all of the above processes in the registration section may be performed using AI, or not. For example, the registration section can input the background information entered by the exhibitor into AI, which can then analyze and register that information.

[0041] The registration unit can record the physical condition of the artwork during registration. For example, the registration unit can add a field to describe the artwork's preservation status in detail, recording the preservation method and environment. For example, the registration unit can use AI to analyze the physical condition of the artwork. For example, the registration unit can provide a form for entering the artwork's preservation status. The registration unit can also include a section for entering the artwork's restoration history, recording details of the restoration and information about the restorer. For example, the registration unit can provide a field for entering the artwork's restoration history. Furthermore, the registration unit can record physical defects and damaged areas of the artwork along with photographs to understand its detailed condition. For example, the registration unit can provide a form for entering physical defects and damaged areas of the artwork. This allows for understanding the preservation status and restoration history by recording the physical condition of the artwork. Some or all of the above processes in the registration unit may be performed using AI, for example, or without AI. For example, the registration unit can input the physical condition of the artwork into AI, which can then analyze and record that information.

[0042] The registration unit can automatically generate a digital copy of the artwork during registration and optimize it for online exhibition. For example, the registration unit can automatically generate a high-resolution digital copy of the artwork and optimize it for online exhibition. The registration unit can generate a digital copy of the artwork using AI, for example. For example, the registration unit can scan the artwork's image and generate a high-resolution digital copy. The registration unit can also add metadata to the digital copy of the artwork to improve searchability. For example, the registration unit can add metadata such as the title and artist's name to the digital copy of the artwork. Furthermore, the registration unit can store the digital copy of the artwork in the cloud for easier access. For example, the registration unit can upload the digital copy of the artwork to cloud storage and optimize it for online exhibition. This makes online exhibition easier by automatically generating a digital copy of the artwork. Some or all of the above processes in the registration unit may be performed using AI, for example, or without AI. For example, the registration unit can input the artwork's image data into a generating AI, and the generating AI can generate a digital copy based on that data.

[0043] The registration unit can register information about events related to the artwork at the time of registration. For example, the registration unit can register information about exhibitions where the artwork is scheduled to be displayed and display related events. The registration unit can analyze the information about events related to the artwork using AI, for example. For example, the registration unit provides a form for entering information about exhibitions where the artwork is scheduled to be displayed. The registration unit can also register information about auctions where the artwork is scheduled to be offered and provide bidding information. For example, the registration unit provides a field for entering information about auctions where the artwork is scheduled to be offered. Furthermore, the registration unit can register information about art fairs in which the artwork is scheduled to participate and display the details of the events. For example, the registration unit provides a form for entering information about art fairs in which the artwork is scheduled to participate. In this way, by registering information about events related to the artwork, information about exhibitions and auctions is provided. Some or all of the above processing in the registration unit may be performed using AI, for example, or without AI. For example, the registration unit can input information about events related to the artwork into AI, and the AI ​​can analyze and register that information.

[0044] The analysis unit can analyze the production process of a work of art and identify the techniques and materials used during the analysis. For example, the analysis unit can analyze the production process of a work of art and identify the type of brush and technique used. The analysis unit can, for example, use AI to analyze the production process of a work of art. For example, the analysis unit can analyze the production process of a work of art and identify the type of brush and technique used. The analysis unit can also analyze the materials of a work of art and identify the type of paint and canvas used. For example, the analysis unit can use AI to analyze the materials of a work of art and identify the type of paint and canvas used. Furthermore, the analysis unit can analyze the production procedure of a work of art and identify the order in which it was painted. For example, the analysis unit can use AI to analyze the production procedure of a work of art and identify the order in which it was painted. In this way, by analyzing the production process of a work of art, the techniques and materials used are identified. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input image data of a work of art into a generating AI, and the generating AI can analyze that data to identify the production process.

[0045] The analysis unit can analyze the historical background and cultural significance of a work during the analysis process. For example, the analysis unit can analyze the time and place of creation of the work to identify its historical background. The analysis unit can also analyze the theme and motifs of the work to identify its cultural significance. For example, the analysis unit can use AI to analyze the theme and motifs of the work to identify its cultural significance. Furthermore, the analysis unit can analyze the art movements and schools that influenced the work to identify its cultural positioning. For example, the analysis unit can use AI to analyze the art movements and schools that influenced the work to identify its cultural positioning. In this way, the value of a work becomes clearer by analyzing its historical background and cultural significance. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input image data of the artwork into a generating AI, which can then analyze the data to identify its historical background and cultural significance.

[0046] The analysis unit can evaluate the market value of a work and estimate its price during the analysis process. For example, the analysis unit can analyze the past sales history of a work to evaluate its market value. For example, the analysis unit can use AI to evaluate the market value of a work. The analysis unit can also analyze the artist's reputation and popularity to estimate the price. For example, the analysis unit can use AI to analyze the artist's reputation and popularity to estimate the price. Furthermore, the analysis unit can analyze the market value of similar works to the work to estimate its price. For example, the analysis unit can use AI to analyze the market value of similar works to the work to estimate its price. By evaluating the market value of the work and estimating its price, an appropriate price is presented. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the sales history data of the work into a generating AI, which can then analyze the data to evaluate the market value and estimate the price.

[0047] The analysis unit can perform comparative analysis of a work with other related works during the analysis process. For example, the analysis unit can compare the style and theme of a work with other works to analyze its uniqueness. The analysis unit can, for example, use AI to perform comparative analysis of a work with other related works. For example, the analysis unit can compare the style and theme of a work with other works to analyze its uniqueness. The analysis unit can also compare the technique and materials of a work with other works to analyze its technical characteristics. For example, the analysis unit can use AI to compare the technique and materials of a work with other works to analyze its technical characteristics. Furthermore, the analysis unit can compare the market value of a work with other works to perform a relative evaluation. For example, the analysis unit can use AI to compare the market value of a work with other works to perform a relative evaluation. This makes the uniqueness and technical characteristics of a work clearer through comparative analysis with other related works. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input image data of the artwork into a generation AI, which can then analyze that data and compare it with other artworks.

[0048] The creation unit can include audio and textual explanations in addition to the visual features of the artwork during the creation process. For example, the creation unit can add an audio guide that explains the visual features of the artwork. For example, the creation unit can use AI to generate the audio guide. For example, the creation unit can add an audio guide that explains the visual features of the artwork. The creation unit can also add text that explains the background and intentions behind the artwork. For example, the creation unit can use AI to generate text that explains the background and intentions behind the artwork. Furthermore, the creation unit can add video clips that explain the techniques and materials used in the artwork. For example, the creation unit can use AI to generate video clips that explain the techniques and materials used in the artwork. This deepens the understanding of the artwork by including audio and textual explanations in addition to the visual features. Some or all of the above processes in the creation unit may be performed using AI, for example, or not using AI. For example, the creation unit can input data to explain the visual features of the artwork into a generating AI, and the generating AI can generate an audio guide or text based on that data.

[0049] The creation unit can add interactive elements to the visual summary of the work during creation. For example, the creation unit can add augmented reality (AR) elements to the visual summary of the work, allowing users to visualize the work in 360 degrees. For example, the creation unit can generate augmented reality elements using AI. For example, the creation unit can add augmented reality elements to the visual summary of the work, allowing users to visualize the work in 360 degrees. The creation unit can also add virtual reality (VR) elements to the visual summary of the work, providing users with an immersive experience of being inside the work. For example, the creation unit can generate virtual reality elements using AI. Furthermore, the creation unit can add interactive animations to the visual summary of the work, allowing users to explore the details of the work. For example, the creation unit can generate interactive animations using AI. This allows users to explore the work more deeply by adding interactive elements to the visual summary. Some or all of the above processes in the creation unit may be performed using AI, for example, or without AI. For example, the creation unit can input data for a work into a generation AI, which can then generate interactive elements based on that data.

[0050] The creation unit can include relevant artist information for the artwork in the visual summary during creation. For example, the creation unit can add the artist's biography to the artwork's visual summary. For example, the creation unit can generate the artist's biography using AI. The creation unit can also add information about the artist's other works to the artwork's visual summary. For example, the creation unit can generate information about the artist's other works using AI. Furthermore, the creation unit can add an interview video of the artist to the artwork's visual summary. For example, the creation unit can generate an interview video of the artist using AI. This provides background information about the artwork and the artist by including artist information in the visual summary. Some or all of the above processing in the creation unit may be performed using AI, for example, or without AI. For example, the creation unit can input artist information into a generating AI, and the generating AI can generate artist information based on that information.

[0051] The creation unit can include the exhibition location and duration of the artwork in the visual summary during creation. For example, the creation unit can add information about the exhibition location to the artwork's visual summary. For example, the creation unit can generate information about the exhibition location using AI. For example, the creation unit can add information about the exhibition duration to the artwork's visual summary. For example, the creation unit can generate information about the exhibition duration using AI. Furthermore, the creation unit can add detailed information about the exhibition to the artwork's visual summary. For example, the creation unit can generate detailed information about the exhibition using AI. This provides exhibition information by including the exhibition location and duration in the visual summary. Some or all of the above processing in the creation unit may be performed using AI, for example, or without AI. For example, the creation unit can input information about the exhibition location and duration into a generating AI, and the generating AI can generate exhibition information based on that information.

[0052] The matching unit can improve matching accuracy by considering a company's past purchase history and ratings during the matching process. For example, the matching unit can analyze a company's past purchase history and suggest similar works. The matching unit can, for example, use AI to analyze a company's past purchase history. For example, the matching unit can analyze a company's past purchase history and suggest similar works. The matching unit can also analyze a company's rating history and prioritize suggesting highly-rated works. For example, the matching unit can use AI to analyze a company's rating history and prioritize suggesting highly-rated works. Furthermore, the matching unit can analyze a company's purchase patterns and suggest the most suitable works. For example, the matching unit can use AI to analyze a company's purchase patterns and suggest the most suitable works. This improves matching accuracy by considering a company's past purchase history and ratings. Some or all of the above processing in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input a company's purchase history data into a generating AI, which can then analyze the data and suggest similar works.

[0053] The matching unit can propose the optimal picture during the matching process, taking into account the industry characteristics and market trends of the companies. For example, the matching unit can analyze the industry characteristics of the companies and propose a picture suitable for the industry. The matching unit can, for example, use AI to analyze the industry characteristics of the companies. For example, the matching unit can analyze the industry characteristics of the companies and propose a picture suitable for the industry. The matching unit can also analyze market trends and propose a picture that aligns with the trends. For example, the matching unit can use AI to analyze market trends and propose a picture that aligns with the trends. Furthermore, the matching unit can analyze the trends of the companies' competitors and propose a picture that allows for differentiation. For example, the matching unit can use AI to analyze the trends of the companies' competitors and propose a picture that allows for differentiation. In this way, the optimal picture can be proposed by taking into account the industry characteristics and market trends of the companies. Some or all of the above processing in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input company industry characteristic data into a generating AI, and the generating AI can analyze that data and propose the optimal picture.

[0054] The matching unit can propose the most suitable image when matching, taking into account the geographical location information of the company. For example, the matching unit can analyze the company's location and propose an image relevant to the region. The matching unit can analyze the company's geographical location information using AI, for example. For example, the matching unit can analyze the company's location and propose an image relevant to the region. The matching unit can also analyze the company's geographical market and propose an image suitable for that market. For example, the matching unit can use AI to analyze the company's geographical market and propose an image suitable for that market. Furthermore, the matching unit can analyze the company's geographical competitors and propose an image that allows for differentiation. For example, the matching unit can use AI to analyze the company's geographical competitors and propose an image that allows for differentiation. In this way, by taking into account the company's geographical location information, the matching unit can propose the most suitable image relevant to the region. Some or all of the above processing in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input the company's geographical location information into a generating AI, which can then analyze the data and propose the most suitable image.

[0055] The matching unit can propose the optimal project plan by considering the company's relevant project information during the matching process. For example, the matching unit can analyze a company's currently ongoing projects and propose a project plan suitable for those projects. The matching unit can also analyze a company's past projects and propose a project plan suitable for similar projects. For example, the matching unit can analyze a company's past projects and propose a project plan suitable for similar projects. Furthermore, the matching unit can analyze a company's future project plans and propose a project plan suitable for those plans. For example, the matching unit can analyze a company's future project plans and propose a project plan suitable for those plans. This allows the matching unit to propose the optimal project plan by considering the company's relevant project information. Some or all of the above processing in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input a company's project information into a generating AI, which can then analyze the data and propose the optimal project plan.

[0056] The proposal department can customize the proposal content according to the company's needs at the time of proposal. For example, the proposal department can customize the proposal content according to the company's specific needs. The proposal department can analyze the company's needs using AI, for example. For example, the proposal department can customize the proposal content according to the company's specific needs. The proposal department can also customize the proposal content according to the company's industry characteristics. For example, the proposal department can analyze the company's industry characteristics using AI and customize the proposal content. Furthermore, the proposal department can also customize the proposal content according to the company's market trends. For example, the proposal department can analyze the company's market trends using AI and customize the proposal content. In this way, by customizing the proposal content according to the company's needs, the optimal proposal is provided. Some or all of the above processes in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can input company needs data into a generating AI, and the generating AI can analyze that data and customize the proposal content.

[0057] The proposal department can reflect company feedback in real time and update the proposal content. For example, the proposal department can collect company feedback in real time and update the proposal content. The proposal department can analyze company feedback using AI, for example. For example, the proposal department can collect company feedback in real time and update the proposal content. The proposal department can also analyze company feedback and optimize the proposal content. For example, the proposal department can use AI to analyze company feedback and optimize the proposal content. Furthermore, the proposal department can generate new proposals based on company feedback. For example, the proposal department can use AI to generate new proposals based on company feedback. This optimizes the proposal content by reflecting company feedback in real time. Some or all of the above processes in the proposal department may be performed using AI, or not using AI. For example, the proposal department can input company feedback data into a generating AI, and the generating AI can analyze that data and update the proposal content.

[0058] The proposal department can propose the optimal license model by considering the company's budget information during the proposal process. For example, the proposal department can analyze the company's budget information and propose the optimal license model. For example, the proposal department can use AI to analyze the company's budget information. For example, the proposal department can analyze the company's budget information and propose the optimal license model. The proposal department can also propose multiple license models depending on the company's budget. For example, the proposal department can use AI to analyze the company's budget information and propose multiple license models. Furthermore, the proposal department can propose a cost-effective license model by considering the company's budget constraints. For example, the proposal department can use AI to analyze the company's budget information and propose a cost-effective license model. In this way, the optimal license model is proposed by considering the company's budget information. Some or all of the above processing in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can input the company's budget information into a generating AI, and the generating AI can analyze that data and propose the optimal license model.

[0059] The proposal department can adjust the optimal timing of a proposal by considering the company's project schedule. For example, the proposal department can analyze the company's project schedule and adjust the optimal timing. For example, the proposal department can use AI to analyze the company's project schedule. For example, the proposal department can analyze the company's project schedule and adjust the optimal timing. The proposal department can also adjust the timing of a proposal according to the progress of the company's project. For example, the proposal department can use AI to analyze the progress of the company's project and adjust the timing. Furthermore, the proposal department can also adjust the timing of a proposal by considering the deadlines of the company's project. For example, the proposal department can use AI to analyze the deadlines of the company's project and adjust the timing. This ensures that the optimal timing of a proposal is provided by considering the company's project schedule. Some or all of the above processes in the proposal department may be performed using AI, or not using AI. For example, the proposal department can input the company's project schedule into a generating AI, which can then analyze the data and adjust the optimal timing of the proposal.

[0060] The proposal department can provide optimal proposal content by referring to the company's past proposal history when making a proposal. For example, the proposal department can analyze the company's past proposal history and provide optimal proposal content. For example, the proposal department can use AI to analyze the company's past proposal history. For example, the proposal department can analyze the company's past proposal history and provide optimal proposal content. The proposal department can also extract and provide successful proposal content from the company's past proposal history. For example, the proposal department can use AI to analyze the company's past proposal history, extract and provide successful proposal content. Furthermore, the proposal department can generate new proposal content based on the company's past proposal history. For example, the proposal department can use AI to generate new proposal content based on the company's past proposal history. In this way, optimal proposal content is provided by referring to the company's past proposal history. Some or all of the above processes in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can input the company's past proposal history data into a generating AI, and the generating AI can analyze that data to provide optimal proposal content.

[0061] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0062] The registration system can record not only detailed information about the artwork but also its production process. For example, it can register information such as how the artwork was created, the techniques and materials used, and the time it took to create it. This allows companies to gain a deeper understanding of the artwork's background. Furthermore, the registration system can also record the production process as a video and provide it to companies. For example, it can create a time-lapse video of the production process, allowing companies to visually understand the artwork's creation process. This further enhances the value of the artwork and attracts the interest of companies.

[0063] The analysis unit can add audio and text descriptions to the characteristics of a work when analyzing its properties. For example, when analyzing characteristics such as the use of color, shape, and theme of a work, audio guides and text descriptions can be added. This allows companies to understand the characteristics of the work more intuitively. Furthermore, the analysis unit can also add interactive elements when analyzing the characteristics of a work. For example, when a company clicks on a characteristic of a work, detailed information can be displayed. This allows companies to understand the characteristics of the work more deeply.

[0064] The creation team can include relevant artist information for a work in the AI ​​visual summary. For example, they can add the artist's biography to the visual summary of a work. This allows companies to understand the background of the work and information about the artist. Furthermore, the creation team can also add information about the artist's other works to the visual summary. For example, by introducing the artist's past and current works, companies can understand the artist's style and techniques. This can increase companies' interest in the artist's work.

[0065] The matching function can improve matching accuracy by considering a company's past purchase history and ratings. For example, it can analyze a company's past purchase history and suggest similar works. This allows companies to find works similar to those they have purchased in the past. It can also analyze a company's rating history and prioritize suggesting highly-rated works. This allows companies to efficiently find highly-rated works. Furthermore, it can analyze a company's purchase patterns and suggest the most suitable works. This allows companies to easily find works that meet their needs.

[0066] The proposal department can propose the most suitable licensing model when making a proposal, taking into account the company's budget information. For example, it can analyze the company's budget information and propose the most suitable licensing model. This allows the company to choose a licensing model that fits their budget. Furthermore, the proposal department can propose multiple licensing models depending on the company's budget. For example, it can analyze the company's budget information and propose multiple licensing models. In addition, the proposal department can propose a cost-effective licensing model, taking into account the company's budget constraints. This allows the company to use the images in a way that best suits their budget.

[0067] The following briefly describes the processing flow for example form 1.

[0068] Step 1: The registration section allows exhibitors to register their works. When exhibitors register their works, they also register detailed information such as the title of the work, the techniques used, and the size. The registration section provides, for example, an interface for exhibitors to upload their works to the platform. The registration section can also provide a form for exhibitors to enter detailed information about their works. Furthermore, the registration section has a function for exhibitors to upload images of their works. For example, the registration section provides an interface that allows exhibitors to upload images of their works using drag and drop. Step 2: The analysis unit analyzes the characteristics of the artwork registered by the registration unit. The analysis unit analyzes characteristics such as color usage, painting style, and type of painting. For example, the analysis unit uses AI to analyze the color usage of the artwork. For example, the analysis unit can analyze the distribution of colors and color combinations in the artwork. The analysis unit can also use AI to analyze the shape and theme of the artwork in order to analyze the type of painting. For example, the analysis unit analyzes whether the artwork is classified as a landscape painting, portrait painting, abstract painting, etc. Furthermore, the analysis unit can use AI to analyze the technique and materials of the artwork in order to analyze the type of painting. For example, the analysis unit analyzes whether the artwork is classified as an oil painting, watercolor painting, digital art, etc. Step 3: The creation unit creates an AI visual summary based on the characteristics analyzed by the analysis unit. The creation unit creates an AI visual summary that includes visual indicators such as color tendencies, shapes, and themes. For example, the creation unit uses AI to analyze the color tendencies of the artwork and reflects the results in the visual summary. For example, the creation unit analyzes the frequency and combinations of colors in the artwork and displays the results in the visual summary. The creation unit can also analyze the shapes of the artwork and reflect the results in the visual summary. For example, the creation unit analyzes the types and arrangement of shapes in the artwork and displays the results in the visual summary. Furthermore, the creation unit can also analyze the themes of the artwork and reflect the results in the visual summary. For example, the creation unit analyzes the subject matter and message of the artwork and displays the results in the visual summary. Step 4: The matching unit matches the company's requirements with the characteristics of the images based on the AI ​​visual summary created by the creation unit. The matching unit proposes the most suitable image based on the company's request, for example. The matching unit uses AI to match the company's request with the characteristics of the images. For example, if the company requests a "warm-colored landscape painting," the matching unit extracts warm-colored landscape paintings from the registered images and proposes them to the company. Step 5: The proposal department proposes the images matched by the matching department to the company. The proposal department enables the company to choose the best option from multiple licensing models. For example, the proposal department uses AI to suggest the best licensing model for the company. For example, the proposal department may suggest models where the company purchases usage rights for a certain period, or models where usage is limited to specific purposes.

[0069] (Example of form 2) An AI matching platform according to an embodiment of the present invention is a system for connecting paintings created by people with intellectual disabilities with companies that wish to utilize those paintings. In this system, exhibitors register their works on the platform, and the AI ​​analyzes the characteristics of each painting and creates an "AI Visual Summary." This visual summary includes visual indicators such as color tendencies, shapes, and themes, and is provided in a way that is easy for companies to understand intuitively. Companies request the artwork image they desire, and the AI ​​matches the company's requirements with the characteristics of the paintings to suggest the most suitable painting. Furthermore, companies can choose the best option from multiple licensing models. This mechanism allows paintings by people with intellectual disabilities to be efficiently marketed to companies, supporting their economic independence, while also allowing companies to easily find paintings that meet their needs. For example, exhibitors register their works on the platform. At this time, detailed information about the artwork (e.g., title of the painting, technique used, size, etc.) is also registered. Next, the AI ​​analyzes the characteristics of each painting. For example, characteristics such as color usage (warm colors, cool colors, neutral colors), painting type (landscape painting, portrait painting), and type of painting (oil painting, watercolor painting, crayon, CG) are analyzed. This process creates an "AI Visual Summary" for each artwork. Companies can request artwork that matches their desired image. For example, specific requests such as "warm-colored landscape paintings" or "crayon drawings of people" are possible. Based on the company's request, the AI ​​suggests the most suitable artwork. For example, if a company requests "warm-colored landscape paintings," the AI ​​will extract warm-colored landscape paintings from the registered artworks and suggest them to the company. Furthermore, companies can choose the most suitable from multiple licensing models. For example, there are models that allow them to purchase usage rights for a certain period, or models that limit usage to specific purposes. This allows companies to utilize the artwork in a way that best suits their needs. This system efficiently markets artwork by people with intellectual disabilities to companies and supports their economic independence. In addition, companies can easily find artwork that meets their needs. For example, companies looking for artwork to use in advertising or promotion can refer to the AI ​​Visual Summary to select the most suitable artwork.This will increase the value of artistic activities by people with intellectual disabilities and create a world where their talents are widely recognized. The AI ​​matching platform will efficiently market the artwork of people with intellectual disabilities to companies, supporting their economic independence, and allowing companies to easily find artwork that meets their needs.

[0070] The AI ​​matching platform according to this embodiment comprises a registration unit, an analysis unit, a creation unit, a matching unit, and a proposal unit. The registration unit allows exhibitors to register their works. When exhibitors register their works, detailed information such as the title of the work, the techniques used, and the size are also registered. The registration unit provides, for example, an interface for exhibitors to upload their works to the platform. The registration unit can also provide a form for exhibitors to input detailed information about their works. Furthermore, the registration unit has a function for exhibitors to upload images of their works. For example, the registration unit provides an interface that allows exhibitors to upload images of their works by dragging and dropping. The analysis unit analyzes the characteristics of the works registered by the registration unit. The analysis unit analyzes characteristics such as color usage, type of painting, and genre of painting. For example, the analysis unit uses AI to analyze the color usage of the works. For example, the analysis unit can analyze the distribution of colors and color combinations of the works. The analysis unit can also use AI to analyze the shape and theme of the works in order to analyze the type of painting. For example, the analysis unit analyzes which type of artwork it belongs to, such as landscape painting, portrait painting, or abstract painting. Furthermore, the analysis unit can also use AI to analyze the technique and materials of the artwork in order to analyze the type of painting. For example, the analysis unit can analyze which type of artwork it belongs to, such as oil painting, watercolor painting, or digital art. The creation unit creates an AI visual summary based on the characteristics analyzed by the analysis unit. The creation unit creates an AI visual summary that includes visual indicators such as color tendencies, shapes, and themes. For example, the creation unit can use AI to analyze the color tendencies of the artwork and reflect the results in the visual summary. For example, the creation unit can analyze the frequency and combinations of colors in the artwork and display the results in the visual summary. The creation unit can also analyze the shape of the artwork and reflect the results in the visual summary. For example, the creation unit can analyze the types and arrangement of shapes in the artwork and display the results in the visual summary. Furthermore, the creation unit can also analyze the theme of the artwork and reflect the results in the visual summary.For example, the creation unit analyzes the subject and message of a work and displays the results in a visual summary. The matching unit matches the characteristics of the painting with the company's requirements based on the AI ​​visual summary created by the creation unit. The matching unit proposes the most suitable painting based on the company's request. The matching unit uses AI to match the company's request with the characteristics of the painting. For example, if the company requests a "warm-colored landscape painting," the matching unit extracts warm-colored landscape paintings from the registered paintings and proposes them to the company. The proposal unit proposes the paintings matched by the matching unit to the company. The proposal unit allows the company to choose the most suitable from multiple licensing models. The proposal unit uses AI to propose the most suitable licensing model to the company. For example, the proposal unit proposes models such as one in which the company purchases usage rights for a certain period, or one in which the painting is used only for specific purposes. As a result, the AI ​​matching platform according to this embodiment efficiently sells paintings by people with intellectual disabilities to companies, supports their economic independence, and allows companies to easily find paintings that meet their needs.

[0071] The registration section allows exhibitors to register their works. When exhibitors register their works, they also register detailed information such as the title of the work, the techniques used, and the size. The registration section provides an interface for exhibitors to upload their works to the platform. Specifically, exhibitors can easily upload images and detailed information of their works through a dedicated webpage or application. The registration section can also provide a form for exhibitors to enter detailed information about their works. This form includes items such as the title of the work, the techniques used, the size, the year of creation, and the price, allowing exhibitors to convey the appeal of their works to the fullest by entering this information. Furthermore, the registration section has a function for exhibitors to upload images of their works. For example, the registration section provides an interface that allows exhibitors to upload images of their works using drag and drop. This allows exhibitors to easily upload high-quality images and visually convey the details of their works. The registration section also has a function to automatically check the quality of uploaded images and adjust or optimize them as needed. This ensures that the images of works displayed on the platform are always high quality and appealing to viewers. In addition, the registration section provides a dashboard for exhibitors to manage the registration status of their works. This dashboard allows sellers to check the registration status, views, and purchase requests of their works in real time. This enables sellers to understand the performance of their work and update information or conduct promotional activities as needed.

[0072] The analysis unit analyzes the characteristics of works registered by the registration unit. The analysis unit analyzes characteristics such as color usage, painting style, and type of painting. Specifically, it uses AI to analyze the color usage of a work. For example, the analysis unit can analyze the distribution of colors and color combinations in a work. The AI ​​uses image recognition technology to generate a color histogram from the image of the work and identifies the main colors and their combinations. The analysis unit can also use AI to analyze the shape and theme of a work in order to analyze the type of painting. For example, the analysis unit analyzes whether the work is classified as a landscape painting, portrait painting, abstract painting, etc. The AI ​​extracts image features and uses a pre-trained model to classify the type of work with high accuracy. Furthermore, the analysis unit can also use AI to analyze the technique and materials of a work in order to analyze the type of painting. For example, the analysis unit analyzes whether the work is classified as an oil painting, watercolor painting, digital art, etc. The AI ​​analyzes the texture and brushwork patterns of the image to identify the technique and materials of the work. This allows the analysis unit to analyze the characteristics of registered works in detail and accurately grasp their appeal and features. Furthermore, the analysis unit saves the analysis results to a database, making them accessible to other departments. This enables the analysis unit to efficiently analyze the characteristics of registered works and improve the overall system performance.

[0073] The creation unit creates an AI visual summary based on the characteristics analyzed by the analysis unit. The creation unit creates an AI visual summary that includes visual indicators such as color tendencies, shapes, and themes. Specifically, it uses AI to analyze the color tendencies of the artwork and reflects the results in the visual summary. For example, the creation unit analyzes the frequency and combinations of colors in the artwork and displays the results in the visual summary. The AI ​​generates color histograms and palettes and displays them in a visually easy-to-understand format. The creation unit can also analyze the shapes of the artwork and reflect the results in the visual summary. For example, the creation unit analyzes the types and arrangement of shapes in the artwork and displays the results in the visual summary. The AI ​​extracts shape features and identifies the main shapes and their arrangement. Furthermore, the creation unit can also analyze the themes of the artwork and reflect the results in the visual summary. For example, the creation unit analyzes the subject matter and message of the artwork and displays the results in the visual summary. The AI ​​analyzes the content and context of the artwork and identifies the main themes and messages. This allows the creation department to create visually easy-to-understand AI visual summaries based on the characteristics analyzed by the analysis department, effectively conveying the appeal of the work. Furthermore, the creation department saves the visual summaries to a database, making them accessible to other departments. This enables the creation department to efficiently utilize the analysis results and improve the overall system performance.

[0074] The matching unit matches the characteristics of a painting with the company's requirements based on the AI ​​visual summary created by the creation unit. For example, the matching unit proposes the most suitable painting based on the company's request. Specifically, it uses AI to match the company's request with the characteristics of the painting. For example, if a company requests a "warm-colored landscape painting," the matching unit extracts warm-colored landscape paintings from the registered paintings and proposes them to the company. The AI ​​analyzes the company's request and identifies the keywords and conditions included in the request. Next, the AI ​​analyzes the visual summary of the registered works and extracts works that match the request. This allows the matching unit to quickly and accurately propose the most suitable work for the company's request. Furthermore, the matching unit can also propose multiple candidate works according to the company's request. For example, if a company requests a "warm-colored landscape painting," the matching unit proposes multiple warm-colored landscape paintings, allowing the company to select the most suitable work. This allows the matching unit to respond to the diverse needs of companies and propose the most suitable work. In addition, the matching unit provides the company with detailed information and a visual summary of the proposed works, making it easier for the company to understand the characteristics of the works. This allows the matching department to propose the most suitable artwork for each company's request, making it easy to find paintings that meet the company's needs.

[0075] The Proposal Department proposes images matched by the Matching Department to companies. For example, the Proposal Department enables companies to choose the most suitable license model from multiple options. Specifically, it uses AI to propose the most suitable license model for each company. For example, the Proposal Department proposes models such as one where the company purchases usage rights for a fixed period, or one where usage is limited to specific purposes. The AI ​​analyzes conditions such as the company's purpose of use, budget, and usage period to identify the most suitable license model. This allows the Proposal Department to propose the most suitable license model to companies, enabling them to select a license that meets their needs. Furthermore, the Proposal Department provides companies with detailed information on the proposed license model, making it easier for them to understand the license content. For example, the Proposal Department provides detailed information such as license usage conditions, fees, and scope of use, enabling companies to accurately grasp the license content. This allows the Proposal Department to propose the most suitable license model to companies, enabling them to select a license that meets their needs. In addition, the Proposal Department can collect feedback from companies and continuously improve the accuracy and effectiveness of its proposals. For example, by providing feedback on the proposed license model, the Proposal Department can revise its proposals based on that feedback and propose a more appropriate license model. This allows the proposal department to suggest the most suitable licensing model to companies, enabling them to select a license that meets their specific needs.

[0076] The registration section can register detailed information about the artwork. For example, the registration section can register detailed information such as the artwork's title, author, and year of creation. The registration section can provide a form for exhibitors to input detailed information about their artwork. The registration section also has functions for exhibitors to edit detailed information about their artwork. For example, the registration section can provide an interface that allows exhibitors to add, delete, and edit detailed information about their artwork. This makes it easier for companies to understand the background of the artwork by registering detailed information about it. Some or all of the above processes in the registration section may be performed using AI, for example, or not using AI. For example, the registration section can input the detailed information about the artwork entered by the exhibitor into an AI, which can then analyze and register that information.

[0077] The analysis unit can analyze characteristics such as color usage, painting style, and type of painting. For example, the analysis unit can analyze the characteristics of color usage. For example, the analysis unit can use AI to analyze the distribution of colors and color combinations in a work. For example, the analysis unit can analyze the frequency and color combinations of colors in a work and output the results. The analysis unit can also analyze the characteristics of the painting style. For example, the analysis unit can use AI to analyze the shape and theme of a work. For example, the analysis unit can analyze which type of painting a work is classified as, such as landscape painting, portrait painting, or abstract painting. Furthermore, the analysis unit can also analyze the characteristics of the type of painting. For example, the analysis unit can use AI to analyze the technique and materials of a work. For example, the analysis unit can analyze which type of painting a work is classified as, such as oil painting, watercolor painting, or digital art. By analyzing the characteristics of a work in detail, it becomes easier to propose paintings that meet the requirements of the company. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input image data of the artwork into the AI, which then analyzes the data and extracts its characteristics.

[0078] The creation unit can create an AI-powered visual summary that includes visual indicators such as color tendencies, shapes, and themes. For example, the creation unit can analyze color tendencies and reflect the results in the visual summary. For example, the creation unit can use AI to analyze the frequency and combinations of colors in an artwork and display the results in the visual summary. The creation unit can also analyze the characteristics of shapes and reflect the results in the visual summary. For example, the creation unit can use AI to analyze the types and arrangement of shapes in an artwork and display the results in the visual summary. Furthermore, the creation unit can analyze the characteristics of themes and reflect the results in the visual summary. For example, the creation unit can use AI to analyze the subject and message of an artwork and display the results in the visual summary. This makes it easier for companies to select artwork by providing them with an intuitively understandable visual summary. Some or all of the above-described processes in the creation unit may be performed using AI, or not. For example, the creation unit can input characteristic data extracted by the analysis unit into the AI, and the AI ​​can create a visual summary based on that data.

[0079] The matching unit can propose the most suitable image based on a company's request. For example, the matching unit matches a company's request with the characteristics of an image. For example, the matching unit uses AI to match a company's request with the characteristics of an image. For example, if a company requests a "warm-colored landscape painting," the matching unit will extract warm-colored landscape paintings from its registered collection and propose them to the company. Also, if a company requests a "crayon drawing of a person," the matching unit can extract crayon drawings of people from its registered collection and propose them to the company. Furthermore, if a company requests an "abstract painting," the matching unit can extract abstract paintings from its registered collection and propose them to the company. In this way, by proposing the most suitable image based on a company's request, it is possible to efficiently find an image that meets the company's needs. Some or all of the above processing in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input a company's request into AI, and the AI ​​can extract the most suitable image based on that request.

[0080] The proposal department can enable companies to choose the most suitable licensing model from multiple options. For example, the proposal department can suggest the most suitable licensing model for a company. For example, the proposal department can use AI to suggest the most suitable licensing model for a company. For example, the proposal department can suggest models in which companies purchase usage rights for a fixed period, or models in which usage is limited to specific purposes. The proposal department can also suggest models in which companies purchase exclusive licenses or non-exclusive licenses. Furthermore, the proposal department can suggest models in which companies can select the license period or the license usage. This allows companies to use the images in a way that best suits their needs. Some or all of the above processing in the proposal department may be performed using AI, or not using AI. For example, the proposal department can input the company's needs into AI, and the AI ​​can suggest the most suitable licensing model based on those needs.

[0081] The registration unit can estimate the user's emotions and adjust the timing of the artwork registration based on the estimated emotions. For example, if the user is relaxed, the registration unit will ensure a smooth registration process. The registration unit estimates the user's emotions using AI, for example. For example, the registration unit uses facial expression analysis technology to analyze the user's facial expressions and determine if they are relaxed. The registration unit can also simplify the registration process and minimize the required input fields if the user is stressed. For example, the registration unit uses voice analysis technology to analyze the user's voice tone and determine if they are stressed. Furthermore, if the user is excited, the registration unit can make the registration process interactive and add elements to engage the user. For example, the registration unit uses biometric data analysis technology to analyze the user's heart rate and determine if they are excited. This allows the registration process to proceed smoothly by adjusting the registration timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the registration unit may be performed using AI, for example, or without AI. For example, the registration unit can input user facial expression data into a generating AI, which can then analyze the data to estimate emotions.

[0082] The registration section can register background information of the exhibitor in addition to detailed information about the artwork during registration. For example, the registration section can add a field for entering the exhibitor's career information to provide a detailed explanation of the artwork's background. For example, the registration section can use AI to analyze the exhibitor's background information. For example, the registration section can provide a form for entering the exhibitor's career information. The registration section can also include a section for describing the exhibitor's creative intent to clarify the artwork's concept. For example, the registration section can provide a field for entering the exhibitor's creative intent. Furthermore, the registration section can register the exhibitor's past artwork history to show the evolution of the artwork and changes in style. For example, the registration section can provide a form for entering the exhibitor's past artwork history. This clarifies the artwork's concept and creative intent by registering the exhibitor's background information. Some or all of the above processes in the registration section may be performed using AI, or not. For example, the registration section can input the background information entered by the exhibitor into AI, which can then analyze and register that information.

[0083] The registration unit can record the physical condition of the artwork during registration. For example, the registration unit can add a field to describe the artwork's preservation status in detail, recording the preservation method and environment. For example, the registration unit can use AI to analyze the physical condition of the artwork. For example, the registration unit can provide a form for entering the artwork's preservation status. The registration unit can also include a section for entering the artwork's restoration history, recording details of the restoration and information about the restorer. For example, the registration unit can provide a field for entering the artwork's restoration history. Furthermore, the registration unit can record physical defects and damaged areas of the artwork along with photographs to understand its detailed condition. For example, the registration unit can provide a form for entering physical defects and damaged areas of the artwork. This allows for understanding the preservation status and restoration history by recording the physical condition of the artwork. Some or all of the above processes in the registration unit may be performed using AI, for example, or without AI. For example, the registration unit can input the physical condition of the artwork into AI, which can then analyze and record that information.

[0084] The registration unit can estimate the user's emotions and determine the priority of works to register based on the estimated emotions. For example, if the user is relaxed, the registration unit will set a higher priority for works to register. The registration unit estimates the user's emotions using AI, for example. For example, the registration unit analyzes the user's facial expressions using facial expression analysis technology to determine whether they are relaxed. The registration unit can also postpone less important works if the user is stressed. For example, the registration unit analyzes the user's voice tone using voice analysis technology to determine whether they are stressed. Furthermore, if the user is excited, the registration unit can prioritize the registration of highly noteworthy works. For example, the registration unit analyzes the user's heart rate using biometric data analysis technology to determine whether they are excited. In this way, by determining the priority of works according to the user's emotions, important works are registered preferentially. 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-described processes in the registration unit may be performed using AI, for example, or without AI. For example, the registration unit can input user facial expression data into a generating AI, which can then analyze the data to estimate emotions.

[0085] The registration unit can automatically generate a digital copy of the artwork during registration and optimize it for online exhibition. For example, the registration unit can automatically generate a high-resolution digital copy of the artwork and optimize it for online exhibition. The registration unit can generate a digital copy of the artwork using AI, for example. For example, the registration unit can scan the artwork's image and generate a high-resolution digital copy. The registration unit can also add metadata to the digital copy of the artwork to improve searchability. For example, the registration unit can add metadata such as the title and artist's name to the digital copy of the artwork. Furthermore, the registration unit can store the digital copy of the artwork in the cloud for easier access. For example, the registration unit can upload the digital copy of the artwork to cloud storage and optimize it for online exhibition. This makes online exhibition easier by automatically generating a digital copy of the artwork. Some or all of the above processes in the registration unit may be performed using AI, for example, or without AI. For example, the registration unit can input the artwork's image data into a generating AI, and the generating AI can generate a digital copy based on that data.

[0086] The registration unit can register information about events related to the artwork at the time of registration. For example, the registration unit can register information about exhibitions where the artwork is scheduled to be displayed and display related events. The registration unit can analyze the information about events related to the artwork using AI, for example. For example, the registration unit provides a form for entering information about exhibitions where the artwork is scheduled to be displayed. The registration unit can also register information about auctions where the artwork is scheduled to be offered and provide bidding information. For example, the registration unit provides a field for entering information about auctions where the artwork is scheduled to be offered. Furthermore, the registration unit can register information about art fairs in which the artwork is scheduled to participate and display the details of the events. For example, the registration unit provides a form for entering information about art fairs in which the artwork is scheduled to participate. In this way, by registering information about events related to the artwork, information about exhibitions and auctions is provided. Some or all of the above processing in the registration unit may be performed using AI, for example, or without AI. For example, the registration unit can input information about events related to the artwork into AI, and the AI ​​can analyze and register that information.

[0087] The analysis unit can estimate the user's emotions and adjust the level of detail of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit provides detailed analysis results. The analysis unit estimates the user's emotions using AI, for example. For example, the analysis unit analyzes the user's facial expressions using facial expression analysis technology to determine if they are relaxed. The analysis unit can also provide concise analysis results if the user is stressed. For example, the analysis unit analyzes the tone of the user's voice using voice analysis technology to determine if they are stressed. Furthermore, if the user is excited, the analysis unit can provide visually appealing analysis results. For example, the analysis unit analyzes the user's heart rate using biometric data analysis technology to determine if they are excited. By adjusting the level of detail of the analysis according to the user's emotions, appropriate analysis results are provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user facial expression data into a generating AI, which can then analyze the data to estimate emotions.

[0088] The analysis unit can analyze the production process of a work of art and identify the techniques and materials used during the analysis. For example, the analysis unit can analyze the production process of a work of art and identify the type of brush and technique used. The analysis unit can, for example, use AI to analyze the production process of a work of art. For example, the analysis unit can analyze the production process of a work of art and identify the type of brush and technique used. The analysis unit can also analyze the materials of a work of art and identify the type of paint and canvas used. For example, the analysis unit can use AI to analyze the materials of a work of art and identify the type of paint and canvas used. Furthermore, the analysis unit can analyze the production procedure of a work of art and identify the order in which it was painted. For example, the analysis unit can use AI to analyze the production procedure of a work of art and identify the order in which it was painted. In this way, by analyzing the production process of a work of art, the techniques and materials used are identified. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input image data of a work of art into a generating AI, and the generating AI can analyze that data to identify the production process.

[0089] The analysis unit can analyze the historical background and cultural significance of a work during the analysis process. For example, the analysis unit can analyze the time and place of creation of the work to identify its historical background. The analysis unit can also analyze the theme and motifs of the work to identify its cultural significance. For example, the analysis unit can use AI to analyze the theme and motifs of the work to identify its cultural significance. Furthermore, the analysis unit can analyze the art movements and schools that influenced the work to identify its cultural positioning. For example, the analysis unit can use AI to analyze the art movements and schools that influenced the work to identify its cultural positioning. In this way, the value of a work becomes clearer by analyzing its historical background and cultural significance. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input image data of the artwork into a generating AI, which can then analyze the data to identify its historical background and cultural significance.

[0090] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is relaxed, the analysis unit can display detailed analysis results in a visually appealing way. The analysis unit estimates the user's emotions using AI, for example. For example, the analysis unit uses facial expression analysis technology to analyze the user's facial expressions and determine whether they are relaxed. The analysis unit can also display concise and to-the-point analysis results if the user is stressed. For example, the analysis unit uses voice analysis technology to analyze the tone of the user's voice and determine whether they are stressed. Furthermore, the analysis unit can display interactive analysis results if the user is agitated. For example, the analysis unit uses biometric data analysis technology to analyze the user's heart rate and determine whether they are agitated. This allows for appropriate display by adjusting the display method of the analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user facial expression data into a generating AI, which can then analyze the data to estimate emotions.

[0091] The analysis unit can evaluate the market value of a work and estimate its price during the analysis process. For example, the analysis unit can analyze the past sales history of a work to evaluate its market value. For example, the analysis unit can use AI to evaluate the market value of a work. The analysis unit can also analyze the artist's reputation and popularity to estimate the price. For example, the analysis unit can use AI to analyze the artist's reputation and popularity to estimate the price. Furthermore, the analysis unit can analyze the market value of similar works to the work to estimate its price. For example, the analysis unit can use AI to analyze the market value of similar works to the work to estimate its price. By evaluating the market value of the work and estimating its price, an appropriate price is presented. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the sales history data of the work into a generating AI, which can then analyze the data to evaluate the market value and estimate the price.

[0092] The analysis unit can perform comparative analysis of a work with other related works during the analysis process. For example, the analysis unit can compare the style and theme of a work with other works to analyze its uniqueness. The analysis unit can, for example, use AI to perform comparative analysis of a work with other related works. For example, the analysis unit can compare the style and theme of a work with other works to analyze its uniqueness. The analysis unit can also compare the technique and materials of a work with other works to analyze its technical characteristics. For example, the analysis unit can use AI to compare the technique and materials of a work with other works to analyze its technical characteristics. Furthermore, the analysis unit can compare the market value of a work with other works to perform a relative evaluation. For example, the analysis unit can use AI to compare the market value of a work with other works to perform a relative evaluation. This makes the uniqueness and technical characteristics of a work clearer through comparative analysis with other related works. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input image data of the artwork into a generation AI, which can then analyze that data and compare it with other artworks.

[0093] The creation unit can estimate the user's emotions and adjust the presentation of the visual summary based on the estimated emotions. For example, if the user is relaxed, the creation unit can provide a visually appealing visual summary. The creation unit can estimate the user's emotions using, for example, AI. For example, the creation unit can analyze the user's facial expressions using facial expression analysis technology to determine if they are relaxed. The creation unit can also provide a concise and to-the-point visual summary if the user is stressed. For example, the creation unit can analyze the user's voice tone using voice analysis technology to determine if they are stressed. Furthermore, the creation unit can provide an interactive visual summary if the user is excited. For example, the creation unit can analyze the user's heart rate using biometric data analysis technology to determine if they are excited. This allows for appropriate presentation by adjusting the presentation of the visual summary according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the creation unit may be performed using AI, for example, or without AI. For example, the creation unit can input user facial expression data into a generating AI, which can then analyze the data to estimate emotions.

[0094] The creation unit can include audio and textual explanations in addition to the visual features of the artwork during the creation process. For example, the creation unit can add an audio guide that explains the visual features of the artwork. For example, the creation unit can use AI to generate the audio guide. For example, the creation unit can add an audio guide that explains the visual features of the artwork. The creation unit can also add text that explains the background and intentions behind the artwork. For example, the creation unit can use AI to generate text that explains the background and intentions behind the artwork. Furthermore, the creation unit can add video clips that explain the techniques and materials used in the artwork. For example, the creation unit can use AI to generate video clips that explain the techniques and materials used in the artwork. This deepens the understanding of the artwork by including audio and textual explanations in addition to the visual features. Some or all of the above processes in the creation unit may be performed using AI, for example, or not using AI. For example, the creation unit can input data to explain the visual features of the artwork into a generating AI, and the generating AI can generate an audio guide or text based on that data.

[0095] The creation unit can add interactive elements to the visual summary of the work during creation. For example, the creation unit can add augmented reality (AR) elements to the visual summary of the work, allowing users to visualize the work in 360 degrees. For example, the creation unit can generate augmented reality elements using AI. For example, the creation unit can add augmented reality elements to the visual summary of the work, allowing users to visualize the work in 360 degrees. The creation unit can also add virtual reality (VR) elements to the visual summary of the work, providing users with an immersive experience of being inside the work. For example, the creation unit can generate virtual reality elements using AI. Furthermore, the creation unit can add interactive animations to the visual summary of the work, allowing users to explore the details of the work. For example, the creation unit can generate interactive animations using AI. This allows users to explore the work more deeply by adding interactive elements to the visual summary. Some or all of the above processes in the creation unit may be performed using AI, for example, or without AI. For example, the creation unit can input data for a work into a generation AI, which can then generate interactive elements based on that data.

[0096] The creation unit can estimate the user's emotions and adjust the length of the visual summary based on the estimated emotions. For example, if the user is relaxed, the creation unit can provide a detailed visual summary. The creation unit can estimate the user's emotions using AI, for example. For example, the creation unit can analyze the user's facial expressions using facial expression analysis technology to determine if they are relaxed. The creation unit can also provide a concise visual summary if the user is stressed. For example, the creation unit can analyze the tone of the user's voice using voice analysis technology to determine if they are stressed. Furthermore, if the user is excited, the creation unit can provide a visually stimulating visual summary. For example, the creation unit can analyze the user's heart rate using biometric data analysis technology to determine if they are excited. By adjusting the length of the visual summary according to the user's emotions, a summary of appropriate length is provided. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the creation unit may be performed using AI, for example, or without AI. For example, the creation unit can input user facial expression data into a generating AI, which can then analyze the data to estimate emotions.

[0097] The creation unit can include relevant artist information for the artwork in the visual summary during creation. For example, the creation unit can add the artist's biography to the artwork's visual summary. For example, the creation unit can generate the artist's biography using AI. The creation unit can also add information about the artist's other works to the artwork's visual summary. For example, the creation unit can generate information about the artist's other works using AI. Furthermore, the creation unit can add an interview video of the artist to the artwork's visual summary. For example, the creation unit can generate an interview video of the artist using AI. This provides background information about the artwork and the artist by including artist information in the visual summary. Some or all of the above processing in the creation unit may be performed using AI, for example, or without AI. For example, the creation unit can input artist information into a generating AI, and the generating AI can generate artist information based on that information.

[0098] The creation unit can include the exhibition location and duration of the artwork in the visual summary during creation. For example, the creation unit can add information about the exhibition location to the artwork's visual summary. For example, the creation unit can generate information about the exhibition location using AI. For example, the creation unit can add information about the exhibition duration to the artwork's visual summary. For example, the creation unit can generate information about the exhibition duration using AI. Furthermore, the creation unit can add detailed information about the exhibition to the artwork's visual summary. For example, the creation unit can generate detailed information about the exhibition using AI. This provides exhibition information by including the exhibition location and duration in the visual summary. Some or all of the above processing in the creation unit may be performed using AI, for example, or without AI. For example, the creation unit can input information about the exhibition location and duration into a generating AI, and the generating AI can generate exhibition information based on that information.

[0099] The matching unit can estimate the user's emotions and adjust the matching criteria based on the estimated emotions. For example, if the user is relaxed, the matching unit uses detailed matching criteria. The matching unit estimates the user's emotions using AI, for example. For example, the matching unit analyzes the user's facial expressions using facial expression analysis technology to determine if they are relaxed. The matching unit can also use concise matching criteria if the user is stressed. For example, the matching unit analyzes the user's voice tone using voice analysis technology to determine if they are stressed. Furthermore, if the user is excited, the matching unit can use visually appealing matching criteria. For example, the matching unit analyzes the user's heart rate using biometric data analysis technology to determine if they are excited. This allows for appropriate matching by adjusting the matching criteria according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input user facial expression data into a generating AI, which can then analyze the data to estimate emotions.

[0100] The matching unit can improve matching accuracy by considering a company's past purchase history and ratings during the matching process. For example, the matching unit can analyze a company's past purchase history and suggest similar works. The matching unit can, for example, use AI to analyze a company's past purchase history. For example, the matching unit can analyze a company's past purchase history and suggest similar works. The matching unit can also analyze a company's rating history and prioritize suggesting highly-rated works. For example, the matching unit can use AI to analyze a company's rating history and prioritize suggesting highly-rated works. Furthermore, the matching unit can analyze a company's purchase patterns and suggest the most suitable works. For example, the matching unit can use AI to analyze a company's purchase patterns and suggest the most suitable works. This improves matching accuracy by considering a company's past purchase history and ratings. Some or all of the above processing in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input a company's purchase history data into a generating AI, which can then analyze the data and suggest similar works.

[0101] The matching unit can propose the optimal picture during the matching process, taking into account the industry characteristics and market trends of the companies. For example, the matching unit can analyze the industry characteristics of the companies and propose a picture suitable for the industry. The matching unit can, for example, use AI to analyze the industry characteristics of the companies. For example, the matching unit can analyze the industry characteristics of the companies and propose a picture suitable for the industry. The matching unit can also analyze market trends and propose a picture that aligns with the trends. For example, the matching unit can use AI to analyze market trends and propose a picture that aligns with the trends. Furthermore, the matching unit can analyze the trends of the companies' competitors and propose a picture that allows for differentiation. For example, the matching unit can use AI to analyze the trends of the companies' competitors and propose a picture that allows for differentiation. In this way, the optimal picture can be proposed by taking into account the industry characteristics and market trends of the companies. Some or all of the above processing in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input company industry characteristic data into a generating AI, and the generating AI can analyze that data and propose the optimal picture.

[0102] The matching unit can estimate the user's emotions and adjust the display order of the matching results based on the estimated emotions. For example, if the user is relaxed, the matching unit can display detailed matching results. The matching unit estimates the user's emotions using AI, for example. For example, the matching unit analyzes the user's facial expressions using facial expression analysis technology to determine if they are relaxed. The matching unit can also display concise matching results if the user is stressed. For example, the matching unit analyzes the user's voice tone using voice analysis technology to determine if they are stressed. Furthermore, if the user is excited, the matching unit can display visually appealing matching results. For example, the matching unit analyzes the user's heart rate using biometric data analysis technology to determine if they are excited. This allows the display order of the matching results to be adjusted according to the user's emotions, ensuring that the results are displayed in an appropriate order. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input user facial expression data into a generating AI, which can then analyze the data to estimate emotions.

[0103] The matching unit can propose the most suitable image when matching, taking into account the geographical location information of the company. For example, the matching unit can analyze the company's location and propose an image relevant to the region. The matching unit can analyze the company's geographical location information using AI, for example. For example, the matching unit can analyze the company's location and propose an image relevant to the region. The matching unit can also analyze the company's geographical market and propose an image suitable for that market. For example, the matching unit can use AI to analyze the company's geographical market and propose an image suitable for that market. Furthermore, the matching unit can analyze the company's geographical competitors and propose an image that allows for differentiation. For example, the matching unit can use AI to analyze the company's geographical competitors and propose an image that allows for differentiation. In this way, by taking into account the company's geographical location information, the matching unit can propose the most suitable image relevant to the region. Some or all of the above processing in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input the company's geographical location information into a generating AI, which can then analyze the data and propose the most suitable image.

[0104] The matching unit can propose the optimal project plan by considering the company's relevant project information during the matching process. For example, the matching unit can analyze a company's currently ongoing projects and propose a project plan suitable for those projects. The matching unit can also analyze a company's past projects and propose a project plan suitable for similar projects. For example, the matching unit can analyze a company's past projects and propose a project plan suitable for similar projects. Furthermore, the matching unit can analyze a company's future project plans and propose a project plan suitable for those plans. For example, the matching unit can analyze a company's future project plans and propose a project plan suitable for those plans. This allows the matching unit to propose the optimal project plan by considering the company's relevant project information. Some or all of the above processing in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input a company's project information into a generating AI, which can then analyze the data and propose the optimal project plan.

[0105] The suggestion unit can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is relaxed, the suggestion unit can provide detailed suggestions. The suggestion unit can estimate the user's emotions using AI, for example. For example, the suggestion unit can analyze the user's facial expressions using facial expression analysis technology to determine if they are relaxed. The suggestion unit can also provide concise suggestions if the user is stressed. For example, the suggestion unit can analyze the user's voice tone using voice analysis technology to determine if they are stressed. Furthermore, if the user is excited, the suggestion unit can provide visually appealing suggestions. For example, the suggestion unit can analyze the user's heart rate using biometric data analysis technology to determine if they are excited. This allows the system to adjust the way it presents suggestions according to the user's emotions, thereby providing appropriate suggestions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input user facial expression data into a generating AI, which can then analyze the data to estimate emotions.

[0106] The proposal department can customize the proposal content according to the company's needs at the time of proposal. For example, the proposal department can customize the proposal content according to the company's specific needs. The proposal department can analyze the company's needs using AI, for example. For example, the proposal department can customize the proposal content according to the company's specific needs. The proposal department can also customize the proposal content according to the company's industry characteristics. For example, the proposal department can analyze the company's industry characteristics using AI and customize the proposal content. Furthermore, the proposal department can also customize the proposal content according to the company's market trends. For example, the proposal department can analyze the company's market trends using AI and customize the proposal content. In this way, by customizing the proposal content according to the company's needs, the optimal proposal is provided. Some or all of the above processes in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can input company needs data into a generating AI, and the generating AI can analyze that data and customize the proposal content.

[0107] The proposal department can reflect company feedback in real time and update the proposal content. For example, the proposal department can collect company feedback in real time and update the proposal content. The proposal department can analyze company feedback using AI, for example. For example, the proposal department can collect company feedback in real time and update the proposal content. The proposal department can also analyze company feedback and optimize the proposal content. For example, the proposal department can use AI to analyze company feedback and optimize the proposal content. Furthermore, the proposal department can generate new proposals based on company feedback. For example, the proposal department can use AI to generate new proposals based on company feedback. This optimizes the proposal content by reflecting company feedback in real time. Some or all of the above processes in the proposal department may be performed using AI, or not using AI. For example, the proposal department can input company feedback data into a generating AI, and the generating AI can analyze that data and update the proposal content.

[0108] The suggestion unit can estimate the user's emotions and prioritize suggestions based on those emotions. For example, if the user is relaxed, the suggestion unit will prioritize providing detailed suggestions. The suggestion unit can estimate the user's emotions using AI, for example. For example, the suggestion unit can analyze the user's facial expressions using facial expression analysis technology to determine if they are relaxed. The suggestion unit can also prioritize providing concise suggestions if the user is stressed. For example, the suggestion unit can analyze the user's voice tone using voice analysis technology to determine if they are stressed. Furthermore, the suggestion unit can prioritize providing visually appealing suggestions if the user is excited. For example, the suggestion unit can analyze the user's heart rate using biometric data analysis technology to determine if they are excited. By prioritizing suggestions according to the user's emotions, appropriate suggestions are provided preferentially. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input user facial expression data into a generating AI, which can then analyze the data to estimate emotions.

[0109] The proposal department can propose the optimal license model by considering the company's budget information during the proposal process. For example, the proposal department can analyze the company's budget information and propose the optimal license model. For example, the proposal department can use AI to analyze the company's budget information. For example, the proposal department can analyze the company's budget information and propose the optimal license model. The proposal department can also propose multiple license models depending on the company's budget. For example, the proposal department can use AI to analyze the company's budget information and propose multiple license models. Furthermore, the proposal department can propose a cost-effective license model by considering the company's budget constraints. For example, the proposal department can use AI to analyze the company's budget information and propose a cost-effective license model. In this way, the optimal license model is proposed by considering the company's budget information. Some or all of the above processing in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can input the company's budget information into a generating AI, and the generating AI can analyze that data and propose the optimal license model.

[0110] The proposal department can adjust the optimal timing of a proposal by considering the company's project schedule. For example, the proposal department can analyze the company's project schedule and adjust the optimal timing. For example, the proposal department can use AI to analyze the company's project schedule. For example, the proposal department can analyze the company's project schedule and adjust the optimal timing. The proposal department can also adjust the timing of a proposal according to the progress of the company's project. For example, the proposal department can use AI to analyze the progress of the company's project and adjust the timing. Furthermore, the proposal department can also adjust the timing of a proposal by considering the deadlines of the company's project. For example, the proposal department can use AI to analyze the deadlines of the company's project and adjust the timing. This ensures that the optimal timing of a proposal is provided by considering the company's project schedule. Some or all of the above processes in the proposal department may be performed using AI, or not using AI. For example, the proposal department can input the company's project schedule into a generating AI, which can then analyze the data and adjust the optimal timing of the proposal.

[0111] The proposal department can provide optimal proposal content by referring to the company's past proposal history when making a proposal. For example, the proposal department can analyze the company's past proposal history and provide optimal proposal content. For example, the proposal department can use AI to analyze the company's past proposal history. For example, the proposal department can analyze the company's past proposal history and provide optimal proposal content. The proposal department can also extract and provide successful proposal content from the company's past proposal history. For example, the proposal department can use AI to analyze the company's past proposal history, extract and provide successful proposal content. Furthermore, the proposal department can generate new proposal content based on the company's past proposal history. For example, the proposal department can use AI to generate new proposal content based on the company's past proposal history. In this way, optimal proposal content is provided by referring to the company's past proposal history. Some or all of the above processes in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can input the company's past proposal history data into a generating AI, and the generating AI can analyze that data to provide optimal proposal content.

[0112] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0113] An AI matching platform can estimate a user's emotions and recommend content based on those emotions. For example, if a user is relaxed, the AI ​​will recommend content that matches that relaxed mood. Specifically, the AI ​​analyzes the user's facial expressions to determine if they are relaxed. If a user is stressed, the AI ​​can also recommend content that will reduce stress. For example, the AI ​​analyzes the user's tone of voice to determine if they are stressed. Furthermore, if a user is excited, the AI ​​can recommend content that will further enhance their excitement. For example, the AI ​​analyzes the user's heart rate to determine if they are excited. This enables the recommendation of content that matches the user's emotions, providing a more personalized experience.

[0114] The registration system can record not only detailed information about the artwork but also its production process. For example, it can register information such as how the artwork was created, the techniques and materials used, and the time it took to create it. This allows companies to gain a deeper understanding of the artwork's background. Furthermore, the registration system can also record the production process as a video and provide it to companies. For example, it can create a time-lapse video of the production process, allowing companies to visually understand the artwork's creation process. This further enhances the value of the artwork and attracts the interest of companies.

[0115] The analysis unit can add audio and text descriptions to the characteristics of a work when analyzing its properties. For example, when analyzing characteristics such as the use of color, shape, and theme of a work, audio guides and text descriptions can be added. This allows companies to understand the characteristics of the work more intuitively. Furthermore, the analysis unit can also add interactive elements when analyzing the characteristics of a work. For example, when a company clicks on a characteristic of a work, detailed information can be displayed. This allows companies to understand the characteristics of the work more deeply.

[0116] The creation team can include relevant artist information for a work in the AI ​​visual summary. For example, they can add the artist's biography to the visual summary of a work. This allows companies to understand the background of the work and information about the artist. Furthermore, the creation team can also add information about the artist's other works to the visual summary. For example, by introducing the artist's past and current works, companies can understand the artist's style and techniques. This can increase companies' interest in the artist's work.

[0117] The matching function can improve matching accuracy by considering a company's past purchase history and ratings. For example, it can analyze a company's past purchase history and suggest similar works. This allows companies to find works similar to those they have purchased in the past. It can also analyze a company's rating history and prioritize suggesting highly-rated works. This allows companies to efficiently find highly-rated works. Furthermore, it can analyze a company's purchase patterns and suggest the most suitable works. This allows companies to easily find works that meet their needs.

[0118] The suggestion unit can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is relaxed, it can provide detailed suggestions. Specifically, the suggestion unit analyzes the user's facial expressions to determine if they are relaxed. If the user is stressed, it can provide concise suggestions. For example, it analyzes the user's tone of voice to determine if they are stressed. Furthermore, if the user is excited, it can provide visually appealing suggestions. For example, it analyzes the user's heart rate to determine if they are excited. This enables suggestions tailored to the user's emotions, providing a more personalized experience.

[0119] The registration unit can estimate the user's emotions and adjust the timing of the submission based on those emotions. For example, if the user is relaxed, the registration process can proceed smoothly. Specifically, the registration unit analyzes the user's facial expressions to determine if they are relaxed. Also, if the user is stressed, the registration process can be simplified, minimizing the number of required input fields. For example, the registration unit analyzes the user's tone of voice to determine if they are stressed. Furthermore, if the user is excited, the registration process can be made interactive, adding elements to engage the user. For example, the registration unit analyzes the user's heart rate to determine if they are excited. This allows for adjustments to the registration timing according to the user's emotions, resulting in a smoother registration process.

[0120] The analysis unit can estimate the user's emotions and adjust the level of detail of the analysis based on the estimated emotions. For example, if the user is relaxed, it can provide detailed analysis results. Specifically, the analysis unit analyzes the user's facial expressions to determine if they are relaxed. Conversely, if the user is stressed, it can provide concise analysis results. For example, the analysis unit analyzes the user's tone of voice to determine if they are stressed. Furthermore, if the user is excited, it can provide visually appealing analysis results. For example, the analysis unit analyzes the user's heart rate to determine if they are excited. This allows for adjustment of the level of detail of the analysis according to the user's emotions, providing appropriate analysis results.

[0121] The creation unit can estimate the user's emotions and adjust the presentation of the visual summary based on those emotions. For example, if the user is relaxed, it can provide a visually appealing visual summary. Specifically, the creation unit analyzes the user's facial expressions to determine if they are relaxed. If the user is stressed, it can provide a concise and to-the-point visual summary. For example, the creation unit analyzes the user's tone of voice to determine if they are stressed. Furthermore, if the user is excited, it can provide an interactive visual summary. For example, the creation unit analyzes the user's heart rate to determine if they are excited. This allows for adjustments to the presentation of the visual summary according to the user's emotions, providing an appropriate representation.

[0122] The proposal department can propose the most suitable licensing model when making a proposal, taking into account the company's budget information. For example, it can analyze the company's budget information and propose the most suitable licensing model. This allows the company to choose a licensing model that fits their budget. Furthermore, the proposal department can propose multiple licensing models depending on the company's budget. For example, it can analyze the company's budget information and propose multiple licensing models. In addition, the proposal department can propose a cost-effective licensing model, taking into account the company's budget constraints. This allows the company to use the images in a way that best suits their budget.

[0123] The following briefly describes the processing flow for example form 2.

[0124] Step 1: The registration section allows exhibitors to register their works. When exhibitors register their works, they also register detailed information such as the title of the work, the techniques used, and the size. The registration section provides, for example, an interface for exhibitors to upload their works to the platform. The registration section can also provide a form for exhibitors to enter detailed information about their works. Furthermore, the registration section has a function for exhibitors to upload images of their works. For example, the registration section provides an interface that allows exhibitors to upload images of their works using drag and drop. Step 2: The analysis unit analyzes the characteristics of the artwork registered by the registration unit. The analysis unit analyzes characteristics such as color usage, painting style, and type of painting. For example, the analysis unit uses AI to analyze the color usage of the artwork. For example, the analysis unit can analyze the distribution of colors and color combinations in the artwork. The analysis unit can also use AI to analyze the shape and theme of the artwork in order to analyze the type of painting. For example, the analysis unit analyzes whether the artwork is classified as a landscape painting, portrait painting, abstract painting, etc. Furthermore, the analysis unit can use AI to analyze the technique and materials of the artwork in order to analyze the type of painting. For example, the analysis unit analyzes whether the artwork is classified as an oil painting, watercolor painting, digital art, etc. Step 3: The creation unit creates an AI visual summary based on the characteristics analyzed by the analysis unit. The creation unit creates an AI visual summary that includes visual indicators such as color tendencies, shapes, and themes. For example, the creation unit uses AI to analyze the color tendencies of the artwork and reflects the results in the visual summary. For example, the creation unit analyzes the frequency and combinations of colors in the artwork and displays the results in the visual summary. The creation unit can also analyze the shapes of the artwork and reflect the results in the visual summary. For example, the creation unit analyzes the types and arrangement of shapes in the artwork and displays the results in the visual summary. Furthermore, the creation unit can also analyze the themes of the artwork and reflect the results in the visual summary. For example, the creation unit analyzes the subject matter and message of the artwork and displays the results in the visual summary. Step 4: The matching unit matches the company's requirements with the characteristics of the images based on the AI ​​visual summary created by the creation unit. The matching unit proposes the most suitable image based on the company's request, for example. The matching unit uses AI to match the company's request with the characteristics of the images. For example, if the company requests a "warm-colored landscape painting," the matching unit extracts warm-colored landscape paintings from the registered images and proposes them to the company. Step 5: The proposal department proposes the images matched by the matching department to the company. The proposal department enables the company to choose the best option from multiple licensing models. For example, the proposal department uses AI to suggest the best licensing model for the company. For example, the proposal department may suggest models where the company purchases usage rights for a certain period, or models where usage is limited to specific purposes.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] Each of the multiple elements described above, including the registration unit, analysis unit, creation unit, matching unit, and proposal unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the registration unit is implemented by the control unit 46A of the smart device 14 and provides an interface for exhibitors to upload their works. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the characteristics of the registered works. The creation unit is implemented by the specific processing unit 290 of the data processing unit 12 and creates an AI visual summary based on the analyzed characteristics. The matching unit is implemented by the specific processing unit 290 of the data processing unit 12 and matches the characteristics of the paintings with the requirements of the companies. The proposal unit is implemented by the control unit 46A of the smart device 14 and proposes the most suitable paintings for the companies. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0129] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] Each of the multiple elements described above, including the registration unit, analysis unit, creation unit, matching unit, and proposal unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the registration unit is implemented by the control unit 46A of the smart glasses 214 and provides an interface for exhibitors to upload their works. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the characteristics of the registered works. The creation unit is implemented by the specific processing unit 290 of the data processing unit 12 and creates an AI visual summary based on the analyzed characteristics. The matching unit is implemented by the specific processing unit 290 of the data processing unit 12 and matches the characteristics of the images with the requirements of the companies. The proposal unit is implemented by the control unit 46A of the smart glasses 214 and proposes the most suitable images for the companies. 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.

[0145] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] Each of the multiple elements described above, including the registration unit, analysis unit, creation unit, matching unit, and proposal unit, is implemented by at least one of the headset terminal 314 and the data processing unit 12. For example, the registration unit is implemented by the control unit 46A of the headset terminal 314 and provides an interface for exhibitors to upload their works. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the characteristics of the registered works. The creation unit is implemented by the specific processing unit 290 of the data processing unit 12 and creates an AI visual summary based on the analyzed characteristics. The matching unit is implemented by the specific processing unit 290 of the data processing unit 12 and matches the characteristics of the paintings with the requirements of the companies. The proposal unit is implemented by the control unit 46A of the headset terminal 314 and proposes the most suitable paintings for the companies. 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.

[0161] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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).

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.).

[0174] 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.

[0175] 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.

[0176] 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.

[0177] Each of the multiple elements described above, including the registration unit, analysis unit, creation unit, matching unit, and proposal unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the registration unit is implemented by the control unit 46A of the robot 414 and provides an interface for exhibitors to upload their works. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the characteristics of the registered works. The creation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and creates an AI visual summary based on the analyzed characteristics. The matching unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and matches the characteristics of the paintings with the requirements of the companies. The proposal unit is implemented by, for example, the control unit 46A of the robot 414 and proposes the most suitable paintings for the companies. 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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."

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] (Note 1) The registration section where exhibitors register their works, An analysis unit that analyzes the characteristics of the works registered by the registration unit, A creation unit that creates an AI visual summary based on the characteristics analyzed by the aforementioned analysis unit, A matching unit matches the company's requirements with the characteristics of the image based on the AI ​​visual summary created by the creation unit, The system comprises a proposal unit that proposes images matched by the matching unit to companies. A system characterized by the following features. (Note 2) The aforementioned registration unit is Register detailed information about your work. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Analyze characteristics such as color usage, picture type, and type of picture. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned creation unit, Create an AI-powered visual summary that includes visual indicators such as color trends, shapes, and themes. The system described in Appendix 1, characterized by the features described herein. (Note 5) The matching unit is We propose the most suitable artwork based on the company's request. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, To allow companies to choose the best licensing model from multiple options. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned registration unit is The system estimates the user's emotions and adjusts the timing of artwork registration based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned registration unit is When registering, in addition to detailed information about the artwork, the seller's background information will also be registered. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned registration unit is The physical condition of the artwork is recorded during registration. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned registration unit is It estimates the user's emotions and determines the priority of registered works based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned registration unit is Upon registration, a digital copy of the artwork is automatically generated and optimized for online exhibition. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned registration unit is When registering, register information about events related to the work. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, It estimates the user's emotions and adjusts the level of detail in the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During the analysis, the production process of the artwork is examined, and the techniques and materials used are identified. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During the analysis, we will analyze the historical background and cultural significance of the work. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During the analysis, the market value of the artwork is evaluated, and a price estimate is performed. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During the analysis, a comparative analysis will be performed with other related works. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned creation unit, It estimates the user's emotions and adjusts how the visual summary is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned creation unit, When creating the work, include audio and text descriptions in addition to the visual features of the work. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned creation unit, When creating the project, add interactive elements to the visual summary. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned creation unit, It estimates the user's emotions and adjusts the length of the visual summary based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned creation unit, When creating the visual summary, include relevant artist information for the work. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned creation unit, When creating the visual summary, include the exhibition location and duration of the artwork. The system described in Appendix 1, characterized by the features described herein. (Note 25) The matching unit is It estimates the user's emotions and adjusts the matching criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The matching unit is During the matching process, we improve matching accuracy by considering companies' past purchase history and ratings. The system described in Appendix 1, characterized by the features described herein. (Note 27) The matching unit is During the matching process, we propose the most suitable image, taking into account the company's industry characteristics and market trends. The system described in Appendix 1, characterized by the features described herein. (Note 28) The matching unit is It estimates the user's emotions and adjusts the display order of matching results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The matching unit is During the matching process, we propose the most suitable image considering the company's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The matching unit is During the matching process, we propose the most suitable image, taking into account the company's relevant project information. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned proposal section is, When making a proposal, we customize the proposal content according to the company's needs. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned proposal section is, During the proposal stage, we incorporate company feedback in real time and update the proposal accordingly. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned proposal section is, It estimates the user's emotions and determines the priority of suggestions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned proposal section is, When making a proposal, we will consider the company's budget information to propose the most suitable licensing model. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned proposal section is, When making a proposal, we adjust the timing to the optimal level, taking into account the company's project schedule. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned proposal section is, When making a proposal, we refer to the company's past proposal history to provide the most suitable proposal content. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0197] 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 registration section where exhibitors register their works, An analysis unit that analyzes the characteristics of the works registered by the registration unit, A creation unit that creates an AI visual summary based on the characteristics analyzed by the aforementioned analysis unit, A matching unit matches the company's requirements with the characteristics of the image based on the AI ​​visual summary created by the creation unit, The system comprises a proposal unit that proposes images matched by the matching unit to companies. A system characterized by the following features.

2. The aforementioned registration unit is Register detailed information about your work. The system according to feature 1.

3. The aforementioned analysis unit, Analyze characteristics such as color usage, picture type, and type of picture. The system according to feature 1.

4. The aforementioned creation unit, Create an AI visual summary that includes visual indicators such as color trends, shapes, and themes. The system according to feature 1.

5. The matching unit is We propose the most suitable artwork based on the company's request. The system according to feature 1.

6. The aforementioned proposal section is, To allow companies to choose the best licensing model from multiple options. The system according to feature 1.

7. The aforementioned registration unit is The system estimates the user's emotions and adjusts the timing of artwork registration based on those estimated emotions. The system according to feature 1.

8. The aforementioned registration unit is When registering, in addition to detailed information about the artwork, the seller's background information will also be registered. The system according to feature 1.

9. The aforementioned registration unit is The physical condition of the artwork is recorded during registration. The system according to feature 1.

10. The aforementioned registration unit is It estimates the user's emotions and determines the priority of registered works based on the estimated user emotions. The system according to feature 1.

Images