system

Abstract

The system according to this embodiment aims to enable artists to effectively exhibit and sell their works and to provide visitors with a personalized experience. [Solution] The system according to the embodiment comprises an upload unit, an exhibition optimization unit, a guided tour unit, an interaction unit, a transaction unit, and a data analysis unit. The upload unit uploads the artist's works. The exhibition optimization unit optimizes the placement of the works uploaded by the upload unit and the gallery design. The guided tour unit guides visitors to the most suitable works according to their interests. The interaction unit allows artists and visitors to interact in real time through avatars. The transaction unit allows for the purchase and transaction of works within the gallery. The data analysis unit collects and analyzes visitor behavior data.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult for an artist to effectively display and sell his / her works and provide a personalized experience for visitors.

[0005] The system according to the embodiment aims to enable an artist to effectively display and sell his / her works and provide a personalized experience for visitors.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an upload unit, an exhibition optimization unit, a guided tour unit, an interaction unit, a transaction unit, and a data analysis unit. The upload unit uploads artists' works. The exhibition optimization unit optimizes the placement of works uploaded by the upload unit and the gallery design. The guided tour unit guides visitors to the most suitable works according to their interests. The interaction unit allows artists and visitors to interact in real time through avatars. The transaction unit facilitates the purchase and transaction of works within the gallery. The data analysis unit collects and analyzes visitor behavior data. [Effects of the Invention]

[0007] The system according to this embodiment allows artists to effectively exhibit and sell their works and provide visitors with a personalized experience. [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 labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applicable to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

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

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

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

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

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

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

[0028] (Example of form 1) The MetaGallery system according to an embodiment of the present invention is a platform that allows artists to exhibit and sell their NFT artworks in a virtual gallery on the metaverse. The MetaGallery system allows artists to upload their NFT artworks, and an AI agent optimizes the placement of the artworks and the gallery design. When a visitor visits the gallery, the AI ​​agent provides a personalized tour that guides them to the most suitable artworks based on their interests. Furthermore, artists and visitors can interact in real time through avatars, and artworks can be purchased and traded within the gallery. In addition, visitor behavior data is collected and analyzed, and feedback on the popularity and trends of the artworks is provided to the artists. For example, an artist uploads their NFT artwork, inputting information such as the title, description, and price of the artwork. Next, the AI ​​agent optimizes the placement of the uploaded artworks and the gallery design. For example, it suggests the optimal placement and lighting based on the artwork's theme and color scheme. When a visitor visits the gallery, the AI ​​agent provides a personalized tour that guides them to the most suitable artworks based on their interests. For example, if a visitor is interested in a particular artist or theme, the AI ​​agent guides them to the most suitable artworks based on that information. Furthermore, by collecting visitor behavior data and analyzing visitors' interests and preferences, a more personalized experience will be provided. Artists and visitors can interact in real time through avatars. For example, artists can explain their work, and visitors can ask questions. This enables direct communication between artists and visitors, deepening understanding and appreciation of the work. Works can be purchased and traded within the gallery. Visitors can purchase their favorite works on the spot and own them as NFTs. They can also resell purchased works to other users. Visitor behavior data is collected and analyzed, and feedback on the popularity and trends of works is provided to artists. For example, data such as which works were viewed by the most visitors and which works resulted in the most purchases are analyzed.This allows artists to understand the reception and trends of their work and use that information to inform their future creative activities. The MetaGallery system enables efficient exhibition of artists' work, guided tours for visitors, real-time interaction, transactions, and data analysis.

[0029] The MetaGallery system according to this embodiment comprises an upload unit, an exhibition optimization unit, a guided tour unit, an interaction unit, a transaction unit, and a data analysis unit. The upload unit uploads artists' works. For example, when an artist uploads their NFT artwork, the upload unit can input information such as the title, description, and price of the artwork. The upload unit may also include AI processing and can use AI to optimize the upload procedure. The exhibition optimization unit optimizes the placement of the artworks uploaded by the upload unit and the gallery design. For example, the exhibition optimization unit can suggest the optimal placement and lighting based on the theme and color scheme of the artworks. The exhibition optimization unit includes AI processing and can use AI to adjust the placement and lighting of the artworks. The guided tour unit guides visitors to the most suitable artworks according to their interests. For example, if a visitor is interested in a particular artist or theme, the guided tour unit can guide them to the most suitable artworks based on that information. The guided tour unit includes AI processing and can use AI to analyze the visitor's interests and provide a personalized tour. The interaction unit allows artists and visitors to interact in real time through avatars. The interaction section allows artists to explain their work and visitors to ask questions. The interaction section may include AI processing and can use AI to optimize the interaction process. The trading section allows for the purchase and trading of artwork within the gallery. The trading section allows visitors to purchase artwork they like on the spot and own it as an NFT. The trading section may also include AI processing and can use AI to optimize the transaction process. The data analysis section collects and analyzes visitor behavior data. The data analysis section can analyze data such as which artwork was viewed by the most visitors and which artwork resulted in the most purchases. The data analysis section includes AI processing and can use AI to collect and analyze behavior data. As a result, the MetaGallery system according to this embodiment can efficiently perform artist artwork exhibitions, visitor guided tours, real-time interaction, trading, and data analysis.

[0030] The upload section allows artists to upload their works. When uploading their NFT artwork, artists can enter information such as the title, description, and price of their work. Specifically, artists select and upload image or video files of their work through a dedicated interface. Furthermore, they can also enter additional information such as the year of creation, the techniques used, and the source of inspiration. The upload section may also include AI processing, and can use AI to optimize the upload process. For example, the AI ​​can automatically analyze the information entered by the artist and display an alert prompting for appropriate input if necessary information is missing. The AI ​​can also analyze the artwork's images and automatically tag them to improve searchability. This allows artists to upload their works efficiently and visitors to easily find them. In addition, the upload section also has a function that uses blockchain technology to automatically generate NFTs that prove ownership of uploaded works. This ensures that artists' works are securely protected and visitors can verify the authenticity of the works.

[0031] The Exhibition Optimization Department optimizes the placement of artworks uploaded by the Upload Department and the gallery design. Specifically, the Exhibition Optimization Department proposes optimal placement and lighting based on information such as the artwork's theme, color scheme, size, and style. AI can be used to adjust the placement and lighting of artworks. For example, the AI ​​proposes placement that considers harmony with adjacent artworks based on the artwork's color scheme and theme. The AI ​​can also analyze the lighting conditions within the gallery and automatically adjust the optimal lighting settings for each artwork. This provides visitors with an environment in which they can appreciate the artworks in the most beautiful way. Furthermore, the Exhibition Optimization Department also has the function to dynamically change the gallery layout. For example, when new artworks are uploaded or when an exhibition event based on a specific theme is held, the AI ​​automatically calculates the optimal layout and updates the gallery's placement. This ensures that the latest exhibition content is always provided, allowing visitors to enjoy a fresh experience.

[0032] The guided tour department guides visitors to the most suitable works based on their interests. Specifically, if a visitor is interested in a particular artist or theme, it can use that information to guide them to the most suitable works. The guided tour department incorporates AI processing, using AI to analyze visitors' interests and preferences and provide personalized tours. For example, when visitors enter the gallery, they are provided with an interface to select artists or themes they are interested in. The AI ​​analyzes the visitor's selections and past browsing history to generate a list of the most suitable works. Furthermore, the guided tour department can monitor visitors' movements and dwell times in real time and dynamically adjust the tour content according to changes in their interests. For example, if a visitor spends a long time at a particular work, it will prioritize guiding them to other works related to that piece. The guided tour department also has the function of providing audio guides and text guides, allowing visitors to view the works at their own pace while obtaining detailed information. This allows visitors to have a fulfilling viewing experience tailored to their interests.

[0033] The interaction section allows artists and visitors to interact in real time through avatars. Specifically, artists can explain their work, and visitors can ask questions. The interaction section may also include AI processing, and AI can be used to optimize the interaction method. For example, the AI ​​can analyze the conversation between the artist and the visitor and suggest relevant information or questions at the appropriate time. The AI ​​can also analyze the visitor's interests and suggest effective presentation methods to the artist. This ensures that interactions between artists and visitors proceed smoothly and that mutually beneficial information is exchanged. Furthermore, the interaction section offers multiple communication methods, such as text chat, voice chat, and video chat, allowing visitors to choose the method of interaction that suits their preference. This brings artists and visitors closer together, fostering deeper understanding and empathy.

[0034] The trading section allows users to buy and trade artwork within the gallery. Specifically, visitors can purchase artwork they like on the spot and own it as an NFT. The trading section may include AI processing and can use AI to optimize the transaction process. For example, the AI ​​can automatically suggest the price and payment method for artwork a visitor wishes to purchase, supporting a smooth transaction. The AI ​​can also analyze transaction history and recommend related artwork and artists to visitors. This allows visitors to efficiently find artwork that suits their preferences. Furthermore, the trading section utilizes blockchain technology to ensure the transparency and security of transactions. When a visitor purchases artwork, the transaction information is recorded on the blockchain and stored in an tamper-proof manner. This allows visitors to trade with peace of mind.

[0035] The Data Analysis Department collects and analyzes visitor behavior data. Specifically, it can analyze data such as which works were viewed by the most visitors and which works resulted in the most purchases. The Data Analysis Department also incorporates AI processing and can use AI to collect and analyze behavioral data. For example, AI can analyze visitors' movement routes and dwell times to identify popular works and areas. AI can also analyze visitors' interests and preferences to help improve the gallery's exhibits and layout. Furthermore, the Data Analysis Department provides valuable insights to artists and gallery operators based on the collected data. For example, if it is found that works on a particular theme or style are popular, this information can be used to plan new exhibitions. This can enhance the gallery's appeal and improve visitor satisfaction.

[0036] The exhibition optimization unit can propose the optimal placement and lighting based on the theme and color scheme of the artwork. For example, the exhibition optimization unit proposes the optimal placement based on the theme of the artwork. For example, the exhibition optimization unit proposes the optimal lighting based on the color scheme of the artwork. For example, the exhibition optimization unit can also propose the optimal placement and lighting based on both the theme and color scheme of the artwork. This makes it possible to create an optimal exhibition based on the theme and color scheme of the artwork. Some or all of the above processing in the exhibition optimization unit may be performed using AI or not. For example, the exhibition optimization unit can input data on the theme and color scheme of the artwork into a generating AI and have the generating AI propose the optimal placement and lighting.

[0037] The guided tour department can collect visitor behavior data and analyze visitors' interests and preferences. For example, the guided tour department can collect visitors' movement history and analyze their interests. For example, the guided tour department can collect visitors' browsing history and analyze their interests. For example, the guided tour department can collect both visitors' movement history and browsing history and comprehensively analyze visitors' interests and preferences. This allows for the provision of personalized tours based on visitors' interests and preferences. Some or all of the above processing in the guided tour department may be performed using AI or not. For example, the guided tour department can input visitor behavior data into a generating AI and have the generating AI perform an analysis of visitors' interests and preferences.

[0038] The interaction section allows artists to explain their work and visitors to ask questions. For example, the interaction section provides an interface for artists to explain their work. For example, the interaction section provides an interface for visitors to ask questions to artists. The interaction section can also provide a real-time chat function between artists and visitors. This enables direct communication between artists and visitors. Some or all of the above processes in the interaction section may be performed using AI or not. For example, the interaction section can input the artist's explanation and the visitor's questions into a generating AI and have the generating AI produce appropriate answers and explanations.

[0039] The trading unit allows visitors to purchase works they like on the spot and own them as NFTs. The trading unit provides, for example, an interface for visitors to select works they like and proceed with the purchase. The trading unit transfers the works as NFTs to the visitor's wallet after the purchase is completed. The trading unit can also provide, for example, an interface for reselling purchased works to other users. This allows visitors to purchase works they like on the spot and own them as NFTs. Some or all of the above processes in the trading unit may be performed using AI or not. For example, the trading unit can input purchase procedure data into a generating AI and have the generating AI execute the optimal transaction procedure.

[0040] The data analysis department can analyze data such as which works were viewed by the most visitors and which works resulted in the most purchases. For example, the data analysis department can collect visitors' browsing history and analyze which works were viewed by the most visitors. For example, the data analysis department can collect visitors' purchase history and analyze which works resulted in the most purchases. For example, the data analysis department can collect both browsing history and purchase history and comprehensively analyze the popularity and trends of works. This allows for an understanding of the popularity and trends of works and provides feedback to artists. Some or all of the above processing in the data analysis department may be performed using AI or not. For example, the data analysis department can input browsing history and purchase history data into a generating AI and have the generating AI perform an analysis of the popularity and trends of works.

[0041] The upload unit can analyze an artist's past upload history and select the optimal upload method. For example, the upload unit can analyze the time slots when an artist has successfully uploaded in the past and suggest similar time slots. For example, the upload unit can prioritize suggesting upload formats (images, videos, etc.) that the artist has used in the past. For example, the upload unit can also suggest an optimal upload schedule by referring to the artist's past upload frequency. This allows for the selection of the optimal upload method based on past upload history. Some or all of the above processing in the upload unit may be performed using AI or not. For example, the upload unit can input the artist's past upload history data into a generating AI and have the generating AI select the optimal upload method.

[0042] The upload unit can filter uploads based on the artist's current production status and areas of interest. For example, the upload unit can prioritize uploading themes related to works the artist is currently working on. For example, the upload unit can filter and upload relevant works based on the artist's areas of interest. For example, the upload unit can also prioritize uploading highly completed works depending on the artist's production status. This allows for the uploading of the most suitable works based on the artist's production status and areas of interest. Some or all of the above processing in the upload unit may be performed using AI or not. For example, the upload unit can input data on the artist's production status and areas of interest into a generating AI and have the generating AI perform the filtering.

[0043] The upload unit can prioritize uploading highly relevant works by considering the artist's geographical location during the upload process. For example, if an artist is in a specific region, the upload unit will prioritize uploading works related to that region. For example, based on the artist's geographical location, the upload unit will upload works that match local trends. For example, if an artist is traveling, the upload unit can prioritize uploading works related to their travel destination. This allows for the uploading of the most suitable works based on the artist's geographical location. Some or all of the above processing in the upload unit may be performed using AI or not. For example, the upload unit can input the artist's geographical location data into a generating AI and have the generating AI select highly relevant works.

[0044] The upload unit can analyze the artist's social media activity during the upload process and upload relevant works. For example, the upload unit can upload works related to themes the artist is discussing on social media. For example, the upload unit can upload works based on themes the artist's followers are interested in. For example, the upload unit can also upload the most suitable works based on trend information obtained from the artist's social media activity. This allows for the uploading of the most suitable works based on the artist's social media activity. Some or all of the above processing in the upload unit may be performed using AI or not. For example, the upload unit can input the artist's social media activity data into a generating AI and have the generating AI select relevant works.

[0045] The exhibition optimization unit can adjust the level of detail in the placement of artworks based on their importance during the exhibition optimization process. For example, the unit can place important artworks in prominent locations and provide detailed explanations. For example, it can place less important artworks in sub-areas and provide concise explanations. The exhibition optimization unit can also adjust the size of the exhibition space according to the importance of each artwork. This allows for optimal placement based on the importance of each artwork. Some or all of the above-described processes in the exhibition optimization unit may be performed using AI or not. For example, the exhibition optimization unit can input artwork importance data into a generating AI and have the generating AI perform the adjustment of the level of detail in the placement.

[0046] The exhibition optimization unit can apply different optimization algorithms depending on the category of the artwork during exhibition optimization. For example, the exhibition optimization unit may apply an algorithm that emphasizes color to paintings. For example, the exhibition optimization unit may apply an algorithm that emphasizes three-dimensionality to sculptures. For example, the exhibition optimization unit may also apply dynamic exhibition methods to digital art. This allows for optimal exhibition according to the category of the artwork. Some or all of the above processing in the exhibition optimization unit may be performed using AI or not. For example, the exhibition optimization unit can input artwork category data into a generating AI and have the generating AI execute the application of the optimization algorithm.

[0047] The guided tour department can select the optimal tour content during a guided tour by referring to the visitor's past behavior data. For example, the guided tour department can select tour content based on works that the visitor has shown interest in in the past. For example, the guided tour department can suggest the optimal tour order based on the visitor's past behavior data. For example, the guided tour department can analyze the visitor's past behavior data and select tour content based on themes of interest. This allows for the selection of the optimal tour content based on the visitor's past behavior data. Some or all of the above processes in the guided tour department may be performed using AI or not. For example, the guided tour department can input the visitor's past behavior data into a generating AI and have the generating AI perform the selection of the optimal tour content.

[0048] The guided tour department can apply different tour algorithms during a guided tour according to the visitor's interests and preferences. For example, if a visitor is interested in a particular artist, the guided tour department can structure the tour around that artist's works. For example, if a visitor is interested in a particular theme, the guided tour department can structure the tour around works related to that theme. The guided tour department can also adjust the pace of the tour according to the visitor's interests. This allows for the provision of an optimal tour tailored to the visitor's interests and preferences. Some or all of the above processes in the guided tour department may be performed using AI or not. For example, the guided tour department can input data on the visitor's interests and preferences into a generating AI and have the generating AI execute the application of the tour algorithm.

[0049] The interaction unit can select the optimal interaction method by referring to the past interaction history of the artist and visitor during the interaction. For example, the interaction unit can select an interaction method based on topics that the artist and visitor have discussed in the past. For example, the interaction unit can suggest the optimal timing for interaction by referring to the frequency of past interactions between the artist and visitor. For example, the interaction unit can also suggest topics of interest based on the past interaction history of the artist and visitor. This allows for the selection of the optimal interaction method based on past interaction history. Some or all of the above processes in the interaction unit may be performed using AI or not. For example, the interaction unit can input past interaction history data of the artist and visitor into a generating AI and have the generating AI select the optimal interaction method.

[0050] The interaction department can customize the means of interaction based on the attribute information of the artist and visitor during the interaction. For example, the interaction department can suggest appropriate means of interaction according to the age group of the artist and visitor. For example, the interaction department can customize the topics of interaction based on the interests of the artist and visitor. For example, the interaction department can adjust the method of interaction according to the language and culture of the artist and visitor. This allows for the provision of the optimal means of interaction based on the attribute information of the artist and visitor. Some or all of the above processing in the interaction department may be performed using AI or not. For example, the interaction department can input attribute information data of the artist and visitor into a generating AI and have the generating AI perform the customization of the means of interaction.

[0051] The transaction department can select the optimal transaction method by referring to the visitor's past purchase history at the time of a transaction. For example, the transaction department may suggest the optimal transaction method based on the trends of works the visitor has purchased in the past. For example, the transaction department may prioritize suggesting works of interest based on the visitor's past purchase history. For example, the transaction department may analyze the visitor's past purchase history and suggest the most efficient transaction method. This allows the optimal transaction method to be selected based on past purchase history. Some or all of the above processes in the transaction department may be performed using AI or not. For example, the transaction department may input the visitor's past purchase history data into a generating AI and have the generating AI select the optimal transaction method.

[0052] The trading department can customize the transaction method based on the visitor's current financial situation at the time of the transaction. For example, the trading department can propose flexible transaction methods such as installment payments depending on the visitor's financial situation. For example, the trading department can offer discounts or benefits based on the visitor's financial situation. For example, the trading department can also propose the optimal transaction method considering the visitor's financial situation. This ensures that the optimal transaction method is provided according to the visitor's financial situation. Some or all of the above processing in the trading department may be performed using AI or not. For example, the trading department can input visitor financial situation data into a generating AI and have the generating AI perform the customization of the transaction method.

[0053] The data analysis unit can optimize its analysis algorithms by referring to past analysis data during data analysis. For example, the data analysis unit can select the optimal analysis algorithm based on past analysis data. For example, the data analysis unit can extract trends from past analysis data and optimize the analysis algorithm. For example, the data analysis unit can also improve the accuracy of the analysis by referring to past analysis data. This allows the optimal analysis algorithm to be applied based on past analysis data. Some or all of the above processes in the data analysis unit may be performed using AI or not. For example, the data analysis unit can input past analysis data into a generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0054] The data analysis department can customize the analysis methods based on visitor attribute information during data analysis. For example, the data analysis department can select appropriate analysis methods according to the visitor's age group. For example, the data analysis department can customize the analysis topics based on the visitor's interests and concerns. For example, the data analysis department can also adjust the analysis methods according to the visitor's language and culture. This allows the optimal analysis methods to be provided based on the visitor's attribute information. Some or all of the above processes in the data analysis department may be performed using AI or not. For example, the data analysis department can input visitor attribute information data into a generating AI and have the generating AI perform the customization of the analysis methods.

[0055] The data analysis department can select the optimal analysis method when analyzing data, taking into account the visitor's geographical location. For example, if a visitor is in a specific region, the data analysis department will prioritize the analysis of data related to that region. For example, based on the visitor's geographical location, the data analysis department will propose an analysis method that matches regional trends. For example, if a visitor is traveling, the data analysis department can also prioritize the analysis of data related to their travel destination. This allows the optimal analysis method to be provided based on the visitor's geographical location. Some or all of the above processing in the data analysis department may be performed using AI or not. For example, the data analysis department can input the visitor's geographical location data into a generating AI and have the generating AI select the optimal analysis method.

[0056] The data analysis department can analyze visitors' social media activity and propose methods of analysis during data analysis. For example, the data analysis department can propose data analysis related to themes that visitors are discussing on social media. For example, the data analysis department can propose methods of analysis based on themes that visitors' followers are interested in. For example, the data analysis department can also propose the optimal analysis method based on trend information obtained from visitors' social media activity. This allows the optimal analysis method to be provided based on visitors' social media activity. Some or all of the above processing in the data analysis department may be performed using AI or not. For example, the data analysis department can input visitors' social media activity data into a generating AI and have the generating AI execute the proposal of analysis methods.

[0057] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0058] The upload function can automatically generate metadata for artists' works when they upload them. For example, when an artist enters information such as the title, description, and price of their work, the upload function uses AI to automatically generate metadata. The upload function can also automatically generate tags from images and videos of works to improve searchability. The upload function can also automatically suggest relevant keywords based on the theme and style of the work. This allows artists to enrich the metadata of their works without any extra effort.

[0059] The exhibition optimization unit can consider visitor flow when optimizing the placement of artworks and lighting. For example, it can adjust the placement of artworks so that visitors can move smoothly through the gallery. For example, it can devise lighting arrangements to prevent visitor congestion. For example, it can also arrange artworks in an order that is likely to interest visitors. This allows visitors to comfortably enjoy the gallery.

[0060] The guided tour department can adjust the tour content in real time based on visitors' interests and preferences. For example, if a visitor shows interest in a particular artwork, the department will prioritize showing other related works. For example, if a visitor shows interest in a particular theme, the department will structure the tour around works related to that theme. The guided tour department can also analyze visitors' reactions in real time and adjust the tour's progress accordingly. This allows visitors to experience a more engaging tour.

[0061] The Community Interaction Department can host interactive events to promote interaction between artists and visitors. For example, the department could host events where artists give live presentations about their work. The department could also provide Q&A sessions where visitors can ask artists questions directly. The department could also host workshops where artists and visitors collaborate to create artwork. This deepens interaction between artists and visitors and enhances their understanding of the artwork.

[0062] The transaction department can offer visitors multiple payment methods when purchasing artwork. For example, it can offer payment methods such as credit cards, debit cards, and electronic money. It can also accept payments via cryptocurrency. Furthermore, it can offer flexible payment options such as installment payments. This allows visitors to choose a payment method that suits them and purchase artwork.

[0063] The data analytics department can provide evaluations and feedback on artwork to support artists' creative activities. For example, the data analytics department can collect visitor ratings and comments and provide feedback to artists. For example, the data analytics department can analyze the number of views and purchases of artwork to identify popular pieces. For example, the data analytics department can analyze visitor behavior data and provide artists with hints for their creative work. This allows artists to understand how their work is being received and use that information to improve their future creative activities.

[0064] The following briefly describes the processing flow for example form 1.

[0065] Step 1: The upload section allows artists to upload their artwork. When uploading their NFT artwork, artists can enter information such as the title, description, and price of their work. The upload section also includes AI processing, which can be used to optimize the upload process. Step 2: The exhibition optimization unit optimizes the placement of artworks uploaded by the upload unit and the gallery design. The exhibition optimization unit can suggest optimal placement and lighting based on the artwork's theme and color scheme. The exhibition optimization unit also includes AI processing and can use AI to adjust the placement and lighting of artworks. Step 3: The guided tour team guides visitors to the most suitable works based on their interests. If a visitor is interested in a particular artist or theme, the guided tour team can use that information to guide them to the most suitable works. The guided tour team also incorporates AI processing, using AI to analyze visitors' interests and preferences and provide personalized tours. Step 4: The interaction area allows artists and visitors to interact in real time through avatars. In the interaction area, artists can explain their work and visitors can ask questions. The interaction area also includes AI processing and can use AI to optimize the way interactions are conducted. Step 5: The trading section allows for the purchase and trading of artwork within the gallery. Visitors can purchase artwork they like on the spot and own it as an NFT. The trading section also incorporates AI processing and can use AI to optimize the trading process. Step 6: The Data Analysis Department collects and analyzes visitor behavior data. The Data Analysis Department can analyze data such as which works were viewed by the most visitors and which works resulted in the most purchases. The Data Analysis Department also includes AI processing and can use AI to collect and analyze behavioral data.

[0066] (Example of form 2) The MetaGallery system according to an embodiment of the present invention is a platform that allows artists to exhibit and sell their NFT artworks in a virtual gallery on the metaverse. The MetaGallery system allows artists to upload their NFT artworks, and an AI agent optimizes the placement of the artworks and the gallery design. When a visitor visits the gallery, the AI ​​agent provides a personalized tour that guides them to the most suitable artworks based on their interests. Furthermore, artists and visitors can interact in real time through avatars, and artworks can be purchased and traded within the gallery. In addition, visitor behavior data is collected and analyzed, and feedback on the popularity and trends of the artworks is provided to the artists. For example, an artist uploads their NFT artwork, inputting information such as the title, description, and price of the artwork. Next, the AI ​​agent optimizes the placement of the uploaded artworks and the gallery design. For example, it suggests the optimal placement and lighting based on the artwork's theme and color scheme. When a visitor visits the gallery, the AI ​​agent provides a personalized tour that guides them to the most suitable artworks based on their interests. For example, if a visitor is interested in a particular artist or theme, the AI ​​agent guides them to the most suitable artworks based on that information. Furthermore, by collecting visitor behavior data and analyzing visitors' interests and preferences, a more personalized experience will be provided. Artists and visitors can interact in real time through avatars. For example, artists can explain their work, and visitors can ask questions. This enables direct communication between artists and visitors, deepening understanding and appreciation of the work. Works can be purchased and traded within the gallery. Visitors can purchase their favorite works on the spot and own them as NFTs. They can also resell purchased works to other users. Visitor behavior data is collected and analyzed, and feedback on the popularity and trends of works is provided to artists. For example, data such as which works were viewed by the most visitors and which works resulted in the most purchases are analyzed.This allows artists to understand the reception and trends of their work and use that information to inform their future creative activities. The MetaGallery system enables efficient exhibition of artists' work, guided tours for visitors, real-time interaction, transactions, and data analysis.

[0067] The MetaGallery system according to this embodiment comprises an upload unit, an exhibition optimization unit, a guided tour unit, an interaction unit, a transaction unit, and a data analysis unit. The upload unit uploads artists' works. For example, when an artist uploads their NFT artwork, the upload unit can input information such as the title, description, and price of the artwork. The upload unit may also include AI processing and can use AI to optimize the upload procedure. The exhibition optimization unit optimizes the placement of the artworks uploaded by the upload unit and the gallery design. For example, the exhibition optimization unit can suggest the optimal placement and lighting based on the theme and color scheme of the artworks. The exhibition optimization unit includes AI processing and can use AI to adjust the placement and lighting of the artworks. The guided tour unit guides visitors to the most suitable artworks according to their interests. For example, if a visitor is interested in a particular artist or theme, the guided tour unit can guide them to the most suitable artworks based on that information. The guided tour unit includes AI processing and can use AI to analyze the visitor's interests and provide a personalized tour. The interaction unit allows artists and visitors to interact in real time through avatars. The interaction section allows artists to explain their work and visitors to ask questions. The interaction section may include AI processing and can use AI to optimize the interaction process. The trading section allows for the purchase and trading of artwork within the gallery. The trading section allows visitors to purchase artwork they like on the spot and own it as an NFT. The trading section may also include AI processing and can use AI to optimize the transaction process. The data analysis section collects and analyzes visitor behavior data. The data analysis section can analyze data such as which artwork was viewed by the most visitors and which artwork resulted in the most purchases. The data analysis section includes AI processing and can use AI to collect and analyze behavior data. As a result, the MetaGallery system according to this embodiment can efficiently perform artist artwork exhibitions, visitor guided tours, real-time interaction, trading, and data analysis.

[0068] The upload section allows artists to upload their works. When uploading their NFT artwork, artists can enter information such as the title, description, and price of their work. Specifically, artists select and upload image or video files of their work through a dedicated interface. Furthermore, they can also enter additional information such as the year of creation, the techniques used, and the source of inspiration. The upload section may also include AI processing, and can use AI to optimize the upload process. For example, the AI ​​can automatically analyze the information entered by the artist and display an alert prompting for appropriate input if necessary information is missing. The AI ​​can also analyze the artwork's images and automatically tag them to improve searchability. This allows artists to upload their works efficiently and visitors to easily find them. In addition, the upload section also has a function that uses blockchain technology to automatically generate NFTs that prove ownership of uploaded works. This ensures that artists' works are securely protected and visitors can verify the authenticity of the works.

[0069] The Exhibition Optimization Department optimizes the placement of artworks uploaded by the Upload Department and the gallery design. Specifically, the Exhibition Optimization Department proposes optimal placement and lighting based on information such as the artwork's theme, color scheme, size, and style. AI can be used to adjust the placement and lighting of artworks. For example, the AI ​​proposes placement that considers harmony with adjacent artworks based on the artwork's color scheme and theme. The AI ​​can also analyze the lighting conditions within the gallery and automatically adjust the optimal lighting settings for each artwork. This provides visitors with an environment in which they can appreciate the artworks in the most beautiful way. Furthermore, the Exhibition Optimization Department also has the function to dynamically change the gallery layout. For example, when new artworks are uploaded or when an exhibition event based on a specific theme is held, the AI ​​automatically calculates the optimal layout and updates the gallery's placement. This ensures that the latest exhibition content is always provided, allowing visitors to enjoy a fresh experience.

[0070] The guided tour department guides visitors to the most suitable works based on their interests. Specifically, if a visitor is interested in a particular artist or theme, it can use that information to guide them to the most suitable works. The guided tour department incorporates AI processing, using AI to analyze visitors' interests and preferences and provide personalized tours. For example, when visitors enter the gallery, they are provided with an interface to select artists or themes they are interested in. The AI ​​analyzes the visitor's selections and past browsing history to generate a list of the most suitable works. Furthermore, the guided tour department can monitor visitors' movements and dwell times in real time and dynamically adjust the tour content according to changes in their interests. For example, if a visitor spends a long time at a particular work, it will prioritize guiding them to other works related to that piece. The guided tour department also has the function of providing audio guides and text guides, allowing visitors to view the works at their own pace while obtaining detailed information. This allows visitors to have a fulfilling viewing experience tailored to their interests.

[0071] The interaction section allows artists and visitors to interact in real time through avatars. Specifically, artists can explain their work, and visitors can ask questions. The interaction section may also include AI processing, and AI can be used to optimize the interaction method. For example, the AI ​​can analyze the conversation between the artist and the visitor and suggest relevant information or questions at the appropriate time. The AI ​​can also analyze the visitor's interests and suggest effective presentation methods to the artist. This ensures that interactions between artists and visitors proceed smoothly and that mutually beneficial information is exchanged. Furthermore, the interaction section offers multiple communication methods, such as text chat, voice chat, and video chat, allowing visitors to choose the method of interaction that suits their preference. This brings artists and visitors closer together, fostering deeper understanding and empathy.

[0072] The trading section allows users to buy and trade artwork within the gallery. Specifically, visitors can purchase artwork they like on the spot and own it as an NFT. The trading section may include AI processing and can use AI to optimize the transaction process. For example, the AI ​​can automatically suggest the price and payment method for artwork a visitor wishes to purchase, supporting a smooth transaction. The AI ​​can also analyze transaction history and recommend related artwork and artists to visitors. This allows visitors to efficiently find artwork that suits their preferences. Furthermore, the trading section utilizes blockchain technology to ensure the transparency and security of transactions. When a visitor purchases artwork, the transaction information is recorded on the blockchain and stored in an tamper-proof manner. This allows visitors to trade with peace of mind.

[0073] The Data Analysis Department collects and analyzes visitor behavior data. Specifically, it can analyze data such as which works were viewed by the most visitors and which works resulted in the most purchases. The Data Analysis Department also incorporates AI processing and can use AI to collect and analyze behavioral data. For example, AI can analyze visitors' movement routes and dwell times to identify popular works and areas. AI can also analyze visitors' interests and preferences to help improve the gallery's exhibits and layout. Furthermore, the Data Analysis Department provides valuable insights to artists and gallery operators based on the collected data. For example, if it is found that works on a particular theme or style are popular, this information can be used to plan new exhibitions. This can enhance the gallery's appeal and improve visitor satisfaction.

[0074] The exhibition optimization unit can propose the optimal placement and lighting based on the theme and color scheme of the artwork. For example, the exhibition optimization unit proposes the optimal placement based on the theme of the artwork. For example, the exhibition optimization unit proposes the optimal lighting based on the color scheme of the artwork. For example, the exhibition optimization unit can also propose the optimal placement and lighting based on both the theme and color scheme of the artwork. This makes it possible to create an optimal exhibition based on the theme and color scheme of the artwork. Some or all of the above processing in the exhibition optimization unit may be performed using AI or not. For example, the exhibition optimization unit can input data on the theme and color scheme of the artwork into a generating AI and have the generating AI propose the optimal placement and lighting.

[0075] The guided tour department can collect visitor behavior data and analyze visitors' interests and preferences. For example, the guided tour department can collect visitors' movement history and analyze their interests. For example, the guided tour department can collect visitors' browsing history and analyze their interests. For example, the guided tour department can collect both visitors' movement history and browsing history and comprehensively analyze visitors' interests and preferences. This allows for the provision of personalized tours based on visitors' interests and preferences. Some or all of the above processing in the guided tour department may be performed using AI or not. For example, the guided tour department can input visitor behavior data into a generating AI and have the generating AI perform an analysis of visitors' interests and preferences.

[0076] The interaction section allows artists to explain their work and visitors to ask questions. For example, the interaction section provides an interface for artists to explain their work. For example, the interaction section provides an interface for visitors to ask questions to artists. The interaction section can also provide a real-time chat function between artists and visitors. This enables direct communication between artists and visitors. Some or all of the above processes in the interaction section may be performed using AI or not. For example, the interaction section can input the artist's explanation and the visitor's questions into a generating AI and have the generating AI produce appropriate answers and explanations.

[0077] The trading unit allows visitors to purchase works they like on the spot and own them as NFTs. The trading unit provides, for example, an interface for visitors to select works they like and proceed with the purchase. The trading unit transfers the works as NFTs to the visitor's wallet after the purchase is completed. The trading unit can also provide, for example, an interface for reselling purchased works to other users. This allows visitors to purchase works they like on the spot and own them as NFTs. Some or all of the above processes in the trading unit may be performed using AI or not. For example, the trading unit can input purchase procedure data into a generating AI and have the generating AI execute the optimal transaction procedure.

[0078] The data analysis department can analyze data such as which works were viewed by the most visitors and which works resulted in the most purchases. For example, the data analysis department can collect visitors' browsing history and analyze which works were viewed by the most visitors. For example, the data analysis department can collect visitors' purchase history and analyze which works resulted in the most purchases. For example, the data analysis department can collect both browsing history and purchase history and comprehensively analyze the popularity and trends of works. This allows for an understanding of the popularity and trends of works and provides feedback to artists. Some or all of the above processing in the data analysis department may be performed using AI or not. For example, the data analysis department can input browsing history and purchase history data into a generating AI and have the generating AI perform an analysis of the popularity and trends of works.

[0079] The upload unit can estimate the artist's emotions and adjust the upload timing based on the estimated emotions. For example, if the artist is feeling stressed, the upload unit may encourage them to upload during a time when they can relax. If the artist is feeling creative, the upload unit may suggest uploading immediately. If the artist is tired, the upload unit may also adjust the schedule to upload after they have rested. This ensures that uploads are performed at the optimal time according to the artist's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the upload unit may be performed using AI or not. For example, the upload unit can input the artist's emotion data into a generative AI and have the generative AI adjust the upload timing.

[0080] The upload unit can analyze an artist's past upload history and select the optimal upload method. For example, the upload unit can analyze the time slots when an artist has successfully uploaded in the past and suggest similar time slots. For example, the upload unit can prioritize suggesting upload formats (images, videos, etc.) that the artist has used in the past. For example, the upload unit can also suggest an optimal upload schedule by referring to the artist's past upload frequency. This allows for the selection of the optimal upload method based on past upload history. Some or all of the above processing in the upload unit may be performed using AI or not. For example, the upload unit can input the artist's past upload history data into a generating AI and have the generating AI select the optimal upload method.

[0081] The upload unit can filter uploads based on the artist's current production status and areas of interest. For example, the upload unit can prioritize uploading themes related to works the artist is currently working on. For example, the upload unit can filter and upload relevant works based on the artist's areas of interest. For example, the upload unit can also prioritize uploading highly completed works depending on the artist's production status. This allows for the uploading of the most suitable works based on the artist's production status and areas of interest. Some or all of the above processing in the upload unit may be performed using AI or not. For example, the upload unit can input data on the artist's production status and areas of interest into a generating AI and have the generating AI perform the filtering.

[0082] The upload unit can estimate the artist's emotions and determine the priority of works to upload based on the estimated emotions. For example, if the artist is excited, the upload unit will prioritize uploading the newest works. If the artist is calm, the upload unit will suggest re-uploading older works. If the artist is feeling anxious, the upload unit can also prioritize uploading the highest-rated works. This allows for the uploading of the most suitable works according to the artist's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the upload unit may be performed using AI or not. For example, the upload unit can input the artist's emotion data into a generative AI and have the generative AI determine the priority of works to upload.

[0083] The upload unit can prioritize uploading highly relevant works by considering the artist's geographical location during the upload process. For example, if an artist is in a specific region, the upload unit will prioritize uploading works related to that region. For example, based on the artist's geographical location, the upload unit will upload works that match local trends. For example, if an artist is traveling, the upload unit can prioritize uploading works related to their travel destination. This allows for the uploading of the most suitable works based on the artist's geographical location. Some or all of the above processing in the upload unit may be performed using AI or not. For example, the upload unit can input the artist's geographical location data into a generating AI and have the generating AI select highly relevant works.

[0084] The upload unit can analyze the artist's social media activity during the upload process and upload relevant works. For example, the upload unit can upload works related to themes the artist is discussing on social media. For example, the upload unit can upload works based on themes the artist's followers are interested in. For example, the upload unit can also upload the most suitable works based on trend information obtained from the artist's social media activity. This allows for the uploading of the most suitable works based on the artist's social media activity. Some or all of the above processing in the upload unit may be performed using AI or not. For example, the upload unit can input the artist's social media activity data into a generating AI and have the generating AI select relevant works.

[0085] The exhibition optimization unit can estimate the artist's emotions and adjust the exhibition layout and lighting based on the estimated emotions. For example, if the artist is relaxed, the exhibition optimization unit will use soft lighting for the exhibition. If the artist is excited, the exhibition optimization unit will use bright lighting for the exhibition. If the artist is calm, the exhibition optimization unit can also use a simple layout for the exhibition. This allows for optimal adjustment of the exhibition layout and lighting according to the artist's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the exhibition optimization unit may be performed using AI or not. For example, the exhibition optimization unit can input artist emotion data into a generative AI and have the generative AI perform adjustments to the exhibition layout and lighting.

[0086] The exhibition optimization unit can adjust the level of detail in the placement of artworks based on their importance during the exhibition optimization process. For example, the unit can place important artworks in prominent locations and provide detailed explanations. For example, it can place less important artworks in sub-areas and provide concise explanations. The exhibition optimization unit can also adjust the size of the exhibition space according to the importance of each artwork. This allows for optimal placement based on the importance of each artwork. Some or all of the above-described processes in the exhibition optimization unit may be performed using AI or not. For example, the exhibition optimization unit can input artwork importance data into a generating AI and have the generating AI perform the adjustment of the level of detail in the placement.

[0087] The exhibition optimization unit can apply different optimization algorithms depending on the category of the artwork during exhibition optimization. For example, the exhibition optimization unit may apply an algorithm that emphasizes color to paintings. For example, the exhibition optimization unit may apply an algorithm that emphasizes three-dimensionality to sculptures. For example, the exhibition optimization unit may also apply dynamic exhibition methods to digital art. This allows for optimal exhibition according to the category of the artwork. Some or all of the above processing in the exhibition optimization unit may be performed using AI or not. For example, the exhibition optimization unit can input artwork category data into a generating AI and have the generating AI execute the application of the optimization algorithm.

[0088] The guided tour unit can estimate the visitor's emotions and adjust the content of the guided tour based on the estimated emotions. For example, if the visitor is excited, the guided tour unit will prioritize showing stimulating works. For example, if the visitor is relaxed, the guided tour unit will prioritize showing calming works. The guided tour unit can also adjust the tour content based on themes that the visitor is interested in. This allows for the provision of an optimal guided tour according to the visitor's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the guided tour unit may be performed using AI or not. For example, the guided tour unit can input visitor emotion data into a generative AI and have the generative AI adjust the content of the guided tour.

[0089] The guided tour department can select the optimal tour content during a guided tour by referring to the visitor's past behavior data. For example, the guided tour department can select tour content based on works that the visitor has shown interest in in the past. For example, the guided tour department can suggest the optimal tour order based on the visitor's past behavior data. For example, the guided tour department can analyze the visitor's past behavior data and select tour content based on themes of interest. This allows for the selection of the optimal tour content based on the visitor's past behavior data. Some or all of the above processes in the guided tour department may be performed using AI or not. For example, the guided tour department can input the visitor's past behavior data into a generating AI and have the generating AI perform the selection of the optimal tour content.

[0090] The guided tour department can apply different tour algorithms during a guided tour according to the visitor's interests and preferences. For example, if a visitor is interested in a particular artist, the guided tour department can structure the tour around that artist's works. For example, if a visitor is interested in a particular theme, the guided tour department can structure the tour around works related to that theme. The guided tour department can also adjust the pace of the tour according to the visitor's interests. This allows for the provision of an optimal tour tailored to the visitor's interests and preferences. Some or all of the above processes in the guided tour department may be performed using AI or not. For example, the guided tour department can input data on the visitor's interests and preferences into a generating AI and have the generating AI execute the application of the tour algorithm.

[0091] The interaction unit can estimate the emotions of the artist and the visitor and adjust the method of interaction based on the estimated emotions. For example, if the artist is relaxed, the interaction unit will suggest a casual method of interaction. For example, if the visitor is excited, the interaction unit will suggest an interactive method of interaction. The interaction unit can also adjust the topic of interaction according to the emotions of the artist and the visitor. This ensures that the optimal method of interaction is provided according to the emotions of the artist and the visitor. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the interaction unit may be performed using AI or not. For example, the interaction unit can input the emotion data of the artist and the visitor into the generative AI and have the generative AI adjust the method of interaction.

[0092] The interaction unit can select the optimal interaction method by referring to the past interaction history of the artist and visitor during the interaction. For example, the interaction unit can select an interaction method based on topics that the artist and visitor have discussed in the past. For example, the interaction unit can suggest the optimal timing for interaction by referring to the frequency of past interactions between the artist and visitor. For example, the interaction unit can also suggest topics of interest based on the past interaction history of the artist and visitor. This allows for the selection of the optimal interaction method based on past interaction history. Some or all of the above processes in the interaction unit may be performed using AI or not. For example, the interaction unit can input past interaction history data of the artist and visitor into a generating AI and have the generating AI select the optimal interaction method.

[0093] The interaction department can customize the means of interaction based on the attribute information of the artist and visitor during the interaction. For example, the interaction department can suggest appropriate means of interaction according to the age group of the artist and visitor. For example, the interaction department can customize the topics of interaction based on the interests of the artist and visitor. For example, the interaction department can adjust the method of interaction according to the language and culture of the artist and visitor. This allows for the provision of the optimal means of interaction based on the attribute information of the artist and visitor. Some or all of the above processing in the interaction department may be performed using AI or not. For example, the interaction department can input attribute information data of the artist and visitor into a generating AI and have the generating AI perform the customization of the means of interaction.

[0094] The trading unit can estimate the visitor's emotions and adjust the trading method based on the estimated visitor's emotions. For example, if the visitor is excited, the trading unit may suggest a quick trading method. For example, if the visitor is relaxed, the trading unit may suggest a trading method that includes detailed explanations. For example, if the visitor is feeling anxious, the trading unit may also suggest a trading method that provides reassurance. This allows the optimal trading method to be provided according to the visitor's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the trading unit may be performed using AI or not. For example, the trading unit can input visitor emotion data into a generative AI and have the generative AI adjust the trading method.

[0095] The transaction department can select the optimal transaction method by referring to the visitor's past purchase history at the time of a transaction. For example, the transaction department may suggest the optimal transaction method based on the trends of works the visitor has purchased in the past. For example, the transaction department may prioritize suggesting works of interest based on the visitor's past purchase history. For example, the transaction department may analyze the visitor's past purchase history and suggest the most efficient transaction method. This allows the optimal transaction method to be selected based on past purchase history. Some or all of the above processes in the transaction department may be performed using AI or not. For example, the transaction department may input the visitor's past purchase history data into a generating AI and have the generating AI select the optimal transaction method.

[0096] The trading department can customize the transaction method based on the visitor's current financial situation at the time of the transaction. For example, the trading department can propose flexible transaction methods such as installment payments depending on the visitor's financial situation. For example, the trading department can offer discounts or benefits based on the visitor's financial situation. For example, the trading department can also propose the optimal transaction method considering the visitor's financial situation. This ensures that the optimal transaction method is provided according to the visitor's financial situation. Some or all of the above processing in the trading department may be performed using AI or not. For example, the trading department can input visitor financial situation data into a generating AI and have the generating AI perform the customization of the transaction method.

[0097] The data analysis unit can estimate the visitor's emotions and adjust the data analysis method based on the estimated visitor's emotions. For example, if the visitor is excited, the data analysis unit performs real-time data analysis. For example, if the visitor is relaxed, the data analysis unit performs detailed data analysis. The data analysis unit can also adjust the priority of data analysis according to the visitor's emotions. This allows for the provision of the optimal data analysis method according to the visitor's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data analysis unit may be performed using AI or not. For example, the data analysis unit can input visitor emotion data into a generative AI and have the generative AI adjust the data analysis method.

[0098] The data analysis unit can optimize its analysis algorithms by referring to past analysis data during data analysis. For example, the data analysis unit can select the optimal analysis algorithm based on past analysis data. For example, the data analysis unit can extract trends from past analysis data and optimize the analysis algorithm. For example, the data analysis unit can also improve the accuracy of the analysis by referring to past analysis data. This allows the optimal analysis algorithm to be applied based on past analysis data. Some or all of the above processes in the data analysis unit may be performed using AI or not. For example, the data analysis unit can input past analysis data into a generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0099] The data analysis department can customize the analysis methods based on visitor attribute information during data analysis. For example, the data analysis department can select appropriate analysis methods according to the visitor's age group. For example, the data analysis department can customize the analysis topics based on the visitor's interests and concerns. For example, the data analysis department can also adjust the analysis methods according to the visitor's language and culture. This allows the optimal analysis methods to be provided based on the visitor's attribute information. Some or all of the above processes in the data analysis department may be performed using AI or not. For example, the data analysis department can input visitor attribute information data into a generating AI and have the generating AI perform the customization of the analysis methods.

[0100] The data analysis unit can estimate the visitor's emotions and determine the priority of analysis based on the estimated visitor's emotions. For example, if the visitor is excited, the data analysis unit will prioritize the analysis of the data that the visitor is most interested in. For example, if the visitor is relaxed, the data analysis unit will prioritize the analysis of detailed data. The data analysis unit can also adjust the order of analysis according to the visitor's emotions. This allows for the determination of the optimal analysis priority according to the visitor's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data analysis unit may be performed using AI or not. For example, the data analysis unit can input visitor emotion data into a generative AI and have the generative AI determine the analysis priority.

[0101] The data analysis department can select the optimal analysis method when analyzing data, taking into account the visitor's geographical location. For example, if a visitor is in a specific region, the data analysis department will prioritize the analysis of data related to that region. For example, based on the visitor's geographical location, the data analysis department will propose an analysis method that matches regional trends. For example, if a visitor is traveling, the data analysis department can also prioritize the analysis of data related to their travel destination. This allows the optimal analysis method to be provided based on the visitor's geographical location. Some or all of the above processing in the data analysis department may be performed using AI or not. For example, the data analysis department can input the visitor's geographical location data into a generating AI and have the generating AI select the optimal analysis method.

[0102] The data analysis department can analyze visitors' social media activity and propose methods of analysis during data analysis. For example, the data analysis department can propose data analysis related to themes that visitors are discussing on social media. For example, the data analysis department can propose methods of analysis based on themes that visitors' followers are interested in. For example, the data analysis department can also propose the optimal analysis method based on trend information obtained from visitors' social media activity. This allows the optimal analysis method to be provided based on visitors' social media activity. Some or all of the above processing in the data analysis department may be performed using AI or not. For example, the data analysis department can input visitors' social media activity data into a generating AI and have the generating AI execute the proposal of analysis methods.

[0103] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0104] The upload function can automatically generate metadata for artists' works when they upload them. For example, when an artist enters information such as the title, description, and price of their work, the upload function uses AI to automatically generate metadata. The upload function can also automatically generate tags from images and videos of works to improve searchability. The upload function can also automatically suggest relevant keywords based on the theme and style of the work. This allows artists to enrich the metadata of their works without any extra effort.

[0105] The exhibition optimization unit can consider visitor flow when optimizing the placement of artworks and lighting. For example, it can adjust the placement of artworks so that visitors can move smoothly through the gallery. For example, it can devise lighting arrangements to prevent visitor congestion. For example, it can also arrange artworks in an order that is likely to interest visitors. This allows visitors to comfortably enjoy the gallery.

[0106] The guided tour department can adjust the tour content in real time based on visitors' interests and preferences. For example, if a visitor shows interest in a particular artwork, the department will prioritize showing other related works. For example, if a visitor shows interest in a particular theme, the department will structure the tour around works related to that theme. The guided tour department can also analyze visitors' reactions in real time and adjust the tour's progress accordingly. This allows visitors to experience a more engaging tour.

[0107] The Community Interaction Department can host interactive events to promote interaction between artists and visitors. For example, the department could host events where artists give live presentations about their work. The department could also provide Q&A sessions where visitors can ask artists questions directly. The department could also host workshops where artists and visitors collaborate to create artwork. This deepens interaction between artists and visitors and enhances their understanding of the artwork.

[0108] The transaction department can offer visitors multiple payment methods when purchasing artwork. For example, it can offer payment methods such as credit cards, debit cards, and electronic money. It can also accept payments via cryptocurrency. Furthermore, it can offer flexible payment options such as installment payments. This allows visitors to choose a payment method that suits them and purchase artwork.

[0109] The data analytics department can provide evaluations and feedback on artwork to support artists' creative activities. For example, the data analytics department can collect visitor ratings and comments and provide feedback to artists. For example, the data analytics department can analyze the number of views and purchases of artwork to identify popular pieces. For example, the data analytics department can analyze visitor behavior data and provide artists with hints for their creative work. This allows artists to understand how their work is being received and use that information to improve their future creative activities.

[0110] The upload function can estimate the artist's emotions and adjust the upload timing based on those estimates. For example, if the artist is feeling stressed, the upload function will encourage them to upload during a time when they can relax. If the artist is feeling creative, the upload function will suggest uploading immediately. If the artist is tired, the upload function can also adjust the schedule to upload after they have rested. This ensures that uploads are performed at the optimal time according to the artist's emotions.

[0111] The exhibition optimization unit can estimate the artist's emotions and adjust the exhibition layout and lighting based on those estimates. For example, if the artist is relaxed, the unit will use soft lighting for the exhibition. If the artist is excited, the unit will use bright lighting for the exhibition. If the artist is calm, the unit can also use a simple layout for the exhibition. This allows for optimal exhibition layout and lighting adjustments according to the artist's emotions.

[0112] The guided tour department can estimate visitors' emotions and adjust the content of the guided tour based on those estimates. For example, if a visitor is excited, the guided tour department will prioritize showing them stimulating works. For example, if a visitor is relaxed, the guided tour department will prioritize showing them calming works. The guided tour department can also adjust the tour content based on themes that visitors are interested in. This allows for the provision of the most suitable guided tour according to the visitor's emotions.

[0113] The interaction department can estimate the emotions of the artist and the visitor and adjust the method of interaction based on the estimated emotions. For example, if the artist is relaxed, the interaction department will suggest a casual method of interaction. For example, if the visitor is excited, the interaction department will suggest an interactive method of interaction. The interaction department can also adjust the topic of interaction according to the emotions of the artist and the visitor. This ensures that the most appropriate method of interaction is provided according to the emotions of the artist and the visitor.

[0114] The following briefly describes the processing flow for example form 2.

[0115] Step 1: The upload section allows artists to upload their artwork. When uploading their NFT artwork, artists can enter information such as the title, description, and price of their work. The upload section also includes AI processing, which can be used to optimize the upload process. Step 2: The exhibition optimization unit optimizes the placement of artworks uploaded by the upload unit and the gallery design. The exhibition optimization unit can suggest optimal placement and lighting based on the artwork's theme and color scheme. The exhibition optimization unit also includes AI processing and can use AI to adjust the placement and lighting of artworks. Step 3: The guided tour team guides visitors to the most suitable works based on their interests. If a visitor is interested in a particular artist or theme, the guided tour team can use that information to guide them to the most suitable works. The guided tour team also incorporates AI processing, using AI to analyze visitors' interests and preferences and provide personalized tours. Step 4: The interaction area allows artists and visitors to interact in real time through avatars. In the interaction area, artists can explain their work and visitors can ask questions. The interaction area also includes AI processing and can use AI to optimize the way interactions are conducted. Step 5: The trading section allows for the purchase and trading of artwork within the gallery. Visitors can purchase artwork they like on the spot and own it as an NFT. The trading section also incorporates AI processing and can use AI to optimize the trading process. Step 6: The Data Analysis Department collects and analyzes visitor behavior data. The Data Analysis Department can analyze data such as which works were viewed by the most visitors and which works resulted in the most purchases. The Data Analysis Department also includes AI processing and can use AI to collect and analyze behavioral data.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] Each of the multiple elements described above, including the upload unit, exhibition optimization unit, guided tour unit, interaction unit, transaction unit, and data analysis unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the upload unit is implemented by the control unit 46A of the smart device 14, allowing artists to input information such as the title, description, and price of their NFT artworks when uploading them. The exhibition optimization unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, optimizing the placement of uploaded artworks and the gallery design. The guided tour unit is implemented by, for example, the control unit 46A of the smart device 14, guiding visitors to the most suitable artworks according to their interests. The interaction unit is implemented by, for example, the control unit 46A of the smart device 14, allowing artists and visitors to interact in real time through avatars. The transaction unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, enabling the purchase and transaction of artworks within the gallery. The data analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, collecting and analyzing visitor behavior data. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0120] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] Each of the multiple elements described above, including the upload unit, exhibition optimization unit, guided tour unit, interaction unit, transaction unit, and data analysis unit, is implemented by, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the upload unit is implemented by the control unit 46A of the smart glasses 214, allowing artists to input information such as the title, description, and price of their NFT artworks when uploading them. The exhibition optimization unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, optimizing the placement of uploaded artworks and the gallery design. The guided tour unit is implemented by, for example, the control unit 46A of the smart glasses 214, guiding visitors to the most suitable artworks according to their interests. The interaction unit is implemented by, for example, the control unit 46A of the smart glasses 214, allowing artists and visitors to interact in real time through avatars. The transaction unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, enabling the purchase and transaction of artworks within the gallery. The data analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and collects and analyzes visitor behavior data. 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.

[0136] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] Each of the multiple elements described above, including the upload unit, exhibition optimization unit, guided tour unit, interaction unit, transaction unit, and data analysis unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the upload unit is implemented by the control unit 46A of the headset terminal 314, allowing artists to input information such as the title, description, and price of their NFT artworks when uploading them. The exhibition optimization unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, optimizing the placement of uploaded artworks and the gallery design. The guided tour unit is implemented by, for example, the control unit 46A of the headset terminal 314, guiding visitors to the most suitable artworks according to their interests. The interaction unit is implemented by, for example, the control unit 46A of the headset terminal 314, allowing artists and visitors to interact in real time through avatars. The transaction unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, enabling the purchase and transaction of artworks within the gallery. The data analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and collects and analyzes visitor behavior data. 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.

[0152] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] Each of the multiple elements described above, including the upload unit, exhibition optimization unit, guided tour unit, interaction unit, transaction unit, and data analysis unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the upload unit is implemented by the control unit 46A of the robot 414, allowing artists to input information such as the title, description, and price of their NFT artworks when uploading them. The exhibition optimization unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, optimizing the placement of uploaded artworks and the gallery design. The guided tour unit is implemented by, for example, the control unit 46A of the robot 414, guiding visitors to the most suitable artworks according to their interests. The interaction unit is implemented by, for example, the control unit 46A of the robot 414, allowing artists and visitors to interact in real time through avatars. The transaction unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, enabling the purchase and transaction of artworks within the gallery. The data analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, collecting and analyzing visitor behavior data. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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."

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] (Note 1) The upload section for artists to upload their works, An exhibition optimization unit optimizes the placement of works uploaded by the aforementioned upload unit and the gallery design, The guided tour department will show visitors the most suitable works of art according to their interests, An interaction section where artists and visitors can interact in real time through avatars, The gallery has a trading department that handles the purchase and transaction of artworks, It includes a data analysis department that collects and analyzes visitor behavior data. A system characterized by the following features. (Note 2) The aforementioned display optimization unit, We propose the optimal placement and lighting based on the theme and color scheme of the artwork. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned guided tour department, We collect visitor behavior data and analyze visitors' interests and preferences. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned AC unit is Artists can explain their work, and visitors can ask questions. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned trading department, Visitors can purchase their favorite artwork on the spot and own it as an NFT. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned data analysis unit, We analyze data such as which works were viewed by the most visitors and which works resulted in the most purchases. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned upload unit is It estimates the artist's emotions and adjusts the upload timing based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned upload unit is Analyze the artist's past upload history to select the optimal upload method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned upload unit is When uploading, filtering is performed based on the artist's current creative status and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned upload unit is It estimates the artist's emotions and prioritizes uploading works based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned upload unit is When uploading, the system prioritizes uploading works that are highly relevant to the artist, taking into account their geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned upload unit is When uploading, the system analyzes the artist's social media activity and uploads relevant works. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned display optimization unit, The system estimates the artist's emotions and adjusts the exhibition layout and lighting based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned display optimization unit, When optimizing the display, adjust the level of detail in placement based on the importance of each artwork. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned display optimization unit, When optimizing the exhibition, different optimization algorithms are applied depending on the category of the artwork. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned guided tour department, The system estimates the visitor's emotions and adjusts the content of the guided tour based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned guided tour department, During guided tours, the optimal tour content is selected by referring to past visitor behavior data. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned guided tour department, During guided tours, different tour algorithms are applied depending on the visitor's interests and preferences. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned AC unit is The system estimates the emotions of artists and visitors and adjusts the interaction method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned AC unit is During interaction, the system selects the most suitable method of interaction by referring to the past interaction history between the artist and the visitor. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned AC unit is During interactions, the means of interaction are customized based on the attribute information of the artist and the visitor. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned trading department, Estimate the visitor's emotions and adjust the transaction method based on the estimated visitor's emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned trading department, During a transaction, the system selects the most suitable transaction method by referring to the visitor's past purchase history. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned trading department, During a transaction, the transaction method is customized based on the visitor's current financial situation. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned data analysis unit, We estimate visitors' emotions and adjust our data analysis methods based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned data analysis unit, When analyzing data, refer to past analysis data to optimize the analysis algorithm. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned data analysis unit, When analyzing data, customize the analysis methods based on visitor attribute information. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned data analysis unit, We estimate the visitor's emotions and prioritize analysis based on the estimated visitor emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned data analysis unit, When analyzing data, the optimal analysis method is selected by considering the visitor's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned data analysis unit, When analyzing data, we analyze visitors' social media activity and propose analytical methods. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0188] 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 upload section for artists to upload their works, An exhibition optimization unit optimizes the placement of works uploaded by the aforementioned upload unit and the gallery design, The guided tour department will show visitors the most suitable works of art according to their interests, An interaction section where artists and visitors can interact in real time through avatars, The gallery has a trading department that handles the purchase and transaction of artworks, It includes a data analysis department that collects and analyzes visitor behavior data. A system characterized by the following features.

2. The aforementioned display optimization unit, We propose the optimal placement and lighting based on the theme and color scheme of the artwork. The system according to feature 1.

3. The aforementioned guided tour department, We collect visitor behavior data and analyze visitors' interests and preferences. The system according to feature 1.

4. The aforementioned AC unit is Artists can explain their work, and visitors can ask questions. The system according to feature 1.

5. The aforementioned trading department, Visitors can purchase their favorite artwork on the spot and own it as an NFT. The system according to feature 1.

6. The aforementioned data analysis unit, We analyze data such as which works were viewed by the most visitors and which works resulted in the most purchases. The system according to feature 1.

7. The aforementioned upload unit is It estimates the artist's emotions and adjusts the upload timing based on the estimated emotions. The system according to feature 1.

8. The aforementioned upload unit is Analyze the artist's past upload history to select the optimal upload method. The system according to feature 1.

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