System

Abstract

To provide a system that generates original works of art based on a user's individual preferences to enable creators to obtain revenue.SOLUTION: A system according to the embodiment comprises a reception unit, a generation unit, a provision unit, a feedback reception unit, and a revenue unit. The reception unit inputs a user's preferences. The generation unit analyzes information input by the reception unit, and generates an original work of art. The provision unit provides the work generated by the generation unit to the user. The feedback reception unit accepts user feedback. The revenue unit manages revenue so that creators can earn revenue.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including a description and related instruction sentence regarding the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has the drawback of not providing a mechanism for generating original works of art based on a user's individual preferences and allowing creators to earn revenue.

[0005] The system according to the embodiment aims to generate original works of art based on the individual tastes of users and to allow creators to earn revenue. [Means for solving the problem]

[0006] The system according to the embodiment includes a receiving unit, a generating unit, a providing unit, a feedback receiving unit, and a profit unit. The receiving unit inputs a user's preferences. The generating unit analyzes the information input by the receiving unit and generates an original work of art. The providing unit provides the work generated by the generating unit to the user. The feedback receiving unit receives user feedback. The profit unit manages profits for creators to earn profits. Effect of the Invention

[0007] The system according to the embodiment generates original works of art based on the individual tastes of the user, and allows creators to earn revenue. [Brief description of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10]1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

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

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

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

[0012] In the following embodiments, a signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "and / or."

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

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

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

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

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (e.g., a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

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

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

[0028] (Example 1) The art generation platform according to the embodiment of the present invention is a system in which an AI generates original artworks based on the individual preferences of users, allowing creators to obtain new revenue streams. In this system, a user inputs their preferences and the style of art they desire, and an AI analyzes the input to generate original artworks that match the user's preferences. The generated artworks are provided to the user, who provides feedback on the artworks. This feedback is learned by the AI ​​and reflected in the next and subsequent work generation. This allows users to obtain their own original art, and creators to obtain new revenue streams. This service also respects individual expression and contributes to the development of the creative industry. For example, a user can specify a style such as "abstract painting" or "landscape painting." This information is input into the AI. The AI ​​then analyzes the input information and generates original artworks that match the user's preferences. The AI ​​generates artworks that best suit the user's preferences based on past data and learned models. For example, if a user requests an "abstract painting," the AI ​​generates artworks that incorporate abstract elements. The generated artworks are provided to the user, who provides feedback on the artworks. For example, users can provide feedback such as "I don't like the color" or "I would like a design with more movement." This feedback is learned by the AI ​​and reflected in the next and subsequent work generation. This mechanism allows users to obtain their own original art. For example, if a user requests a "landscape painting," the AI ​​will generate a landscape painting that matches the user's preferences and provide it to the user. Creators can also obtain new revenue sources by selling the generated works. For example, creators can sell works generated using AI on online platforms. This service respects individual expression and contributes to the development of the creative industry. For example, when users obtain works that match their preferences, their interest in art increases, which is expected to revitalize the entire creative industry. This allows the art generation platform to generate original works of art based on the individual preferences of users, allowing creators to obtain new revenue sources.

[0029] The art generation platform according to the embodiment includes a reception unit, a generation unit, a provision unit, a feedback reception unit, and a revenue unit. The reception unit inputs the user's preferences. The user's preferences include, for example, color, theme, style, etc., but are not limited to such examples. The reception unit allows the user to specify a style such as "abstract painting" or "landscape painting". The generation unit uses a generation AI to analyze the information input by the reception unit and generate an original work of art. The generation unit generates a work that is optimal for the user's preferences, for example, based on past data and a learned model. For example, if the user requests an "abstract painting," the generation AI generates a work that incorporates abstract elements. The generation unit can also use the generation AI to generate a landscape painting that matches the user's preferences. The provision unit provides the user with the work generated by the generation unit. The provision unit provides the user with the generated work in a digital format, for example. The provision unit can also provide the user with the generated work in a physical format. The feedback reception unit accepts the user's feedback. The feedback includes, for example, color shades, composition, theme, etc., but are not limited to such examples. The feedback receiving unit receives, for example, feedback from the user such as "I don't like the color" or "I prefer a design with more movement." The revenue unit manages a mechanism for the creator to earn revenue. The revenue unit manages, for example, a method for selling the generated work and a mechanism for revenue distribution. For example, the revenue unit can sell the work generated by the creator using AI on an online platform. In this way, the art generation platform according to the embodiment can generate original works of art based on the individual preferences of the user, allowing the creator to obtain a new source of revenue. Some or all of the above-mentioned processing in the revenue unit may be performed, for example, using AI, or may be performed without using AI. For example, the revenue unit can manage revenue using an AI model that manages a method for selling the generated work and a mechanism for revenue distribution.

[0030] The reception unit inputs the user's preferences. The user's preferences include, but are not limited to, for example, color, theme, style, etc. The reception unit allows the user to specify a style such as "abstract painting" or "landscape painting". Specifically, the reception unit is designed to allow the user to intuitively input preferences through a user interface. For example, a color picker can be used to select a color, and a drop-down menu or check box can be used to select a theme or style. The reception unit also has a function of remembering preferences previously input by the user and automatically suggesting them the next time the user uses the system. Furthermore, even if the user does not have a specific image, the reception unit presents several sample images and keywords, allowing the user to input preferences by selecting them. This allows the user to easily input their preferences, improving the quality of the generated work.

[0031] The generation unit uses the generation AI to analyze the information input by the reception unit and generate an original work of art. The generation unit generates a work that best suits the user's preferences, for example, based on past data and learned models. Specifically, the generation AI uses deep learning technology, takes in millions of works of art as learning data, and generates a work according to the user's preferences. For example, if the user requests an "abstract painting," the generation AI generates a work that incorporates abstract elements. The generation AI takes into account factors such as color balance, composition, and texture to generate a work that is closest to the user's preferences. The generation unit can also use the generation AI to generate a landscape painting that matches the user's preferences. In the case of a landscape painting, the generation AI generates a work based on a theme specified by the user, such as a natural landscape or a cityscape. In addition, the generation unit is equipped with an internal evaluation system for evaluating the quality of the generated work and checks whether the generated work meets certain quality standards. This allows the generation unit to provide the user with a high-quality original work of art.

[0032] The providing unit provides the work generated by the generating unit to the user. For example, the providing unit provides the generated work to the user in a digital format. Specifically, the providing unit provides the generated work to the user as a high-resolution digital file through a download link. The providing unit can also provide the generated work to the user in a physical format. For example, the providing unit provides a service of printing the generated work as a canvas print or poster and delivering it to an address specified by the user. Furthermore, the providing unit also has a function that allows the user to share the generated work on social media. This allows the user to share his or her work with friends and family and get feedback. The providing unit provides multiple providing methods in consideration of the user's convenience, allowing the user to receive the work in the most suitable format.

[0033] The feedback receiving unit receives user feedback. The feedback includes, but is not limited to, color, composition, theme, and the like. For example, the feedback receiving unit receives feedback such as "I don't like the color" or "I prefer a design with more movement." Specifically, the feedback receiving unit is designed to allow the user to easily input feedback through a user interface. For example, the feedback form is provided with text boxes and evaluation sliders so that the user can input specific opinions and evaluations. The feedback receiving unit also has a function of recording feedback provided by the user in the past and referring to it for the next generation. This allows the generation AI to more accurately grasp the user's preferences and reflect them in the generation of the next work. Furthermore, the feedback receiving unit also has a data analysis function for analyzing the user's feedback and identifying common improvements and trends. This can contribute to improving the quality of the entire platform.

[0034] The revenue division manages the mechanism by which creators can earn revenue. For example, the revenue division manages the sales method of the generated works and the mechanism of revenue distribution. Specifically, the revenue division provides a marketplace for selling the generated works through the online platform. Creators can earn revenue by exhibiting their works on this marketplace and users purchasing them. The revenue division manages the setting of the sales price and the revenue distribution ratio, and realizes a fair revenue distribution between creators and platform operators. The revenue division also provides promotion and marketing support for creators to maximize their revenue. For example, it may implement a campaign to offer works at a discounted price during a certain period of time, or promote works using social media. In addition, the revenue division may explore new ways for creators to earn revenue, and provide revenue sources such as subscription models and sponsorship contracts. This allows the revenue division to provide diverse means for creators to earn stable revenue and improve the sustainability of the entire platform.

[0035] The reception unit can receive detailed input items of the user's preferences. The detailed input items include, for example, color, theme, style, etc., but are not limited to such examples. The reception unit can, for example, allow the user to specify a style such as "abstract painting" or "landscape painting". The reception unit can also input the user's desired color tone or theme in detail. For example, the user can input detailed requests such as "blue abstract painting" or "sunset landscape painting". This allows the user to receive detailed input items of the user's preferences, thereby enabling the creation of a work with higher accuracy. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can receive detailed input items using an AI model that analyzes the detailed input items of the user's preferences.

[0036] The feedback receiving unit can receive feedback items from the user. The feedback items include, but are not limited to, for example, color, composition, theme, and the like. The feedback receiving unit receives, for example, feedback from the user such as "I don't like the color" or "I would like a design with more movement." The feedback receiving unit can also input detailed changes and improvements desired by the user. For example, the user can input detailed feedback such as "I would like the color to be brighter" or "I would like the composition to be changed." By this, by receiving the feedback items from the user, the items are reflected in the generation of works from the next time onwards. A part or all of the above-mentioned processing in the feedback receiving unit may be performed, for example, using AI, or may be performed without using AI. For example, the feedback receiving unit can receive feedback using an AI model that analyzes the feedback items from the user.

[0037] The revenue section can manage the sales method of the generated work and the mechanism of revenue distribution. The sales method includes, for example, online sales, auctions, gallery exhibitions, etc., but is not limited to such examples. The revenue distribution mechanism includes, for example, a percentage of sales, fixed remuneration, incentives, etc., but is not limited to such examples. The revenue section can, for example, sell the generated work on an online platform. The revenue section can also sell the generated work in an auction format. Furthermore, the revenue section can exhibit and sell the generated work in a gallery. In this way, the creator can earn revenue by managing the sales method of the generated work and the mechanism of revenue distribution. Some or all of the above-mentioned processing in the revenue section may be performed, for example, using AI, or may be performed without using AI. For example, the revenue section can manage revenue using an AI model that manages the sales method of the generated work and the mechanism of revenue distribution.

[0038] The generation unit can generate a work that matches the user's preferences based on past data and a learned model. The past data includes, for example, past work data, user preference data, etc., but is not limited to such examples. The learned model includes, for example, machine learning models, neural network models, etc., but is not limited to such examples. The generation unit generates a work that matches the user's preferences based on, for example, past data. For example, if the user desires an "abstract painting," the generation AI generates a work that incorporates abstract elements based on past abstract painting data. The generation unit can also generate a work that matches the user's preferences based on a learned model. For example, the generation AI uses a neural network model to generate a work that is optimal for the user's preferences. As a result, a work that matches the user's preferences can be provided by generating a work based on past data and a learned model. A part or all of the above-mentioned processing in the generation unit is performed using the generation AI. For example, the generation unit inputs a prompt to the generation AI based on past data and a learned model, and the generation AI generates a work that matches the user's preferences.

[0039] The providing unit can provide the generated work to the user. The providing method includes, for example, digital distribution, physical delivery, and the like, but is not limited to such examples. The providing unit, for example, provides the generated work to the user in a digital format. The providing unit can also provide the generated work to the user in a physical format. For example, the providing unit provides the generated work to the user as a high-resolution digital file. The providing unit can also print the generated work on a canvas and deliver it to the user. In this way, by providing the generated work to the user, the user can obtain an original work of art. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can select a providing method using an AI model that provides the generated work to the user.

[0040] The reception unit can analyze the user's past art purchase history and suggest optimal input items. The reception unit, for example, analyzes the user's past art purchase history and suggests optimal input items. For example, based on the style and theme of the art work the user has purchased in the past, related input items are automatically displayed. In addition, the color and composition that the user has liked in the past can be analyzed and input items based on the analysis can be suggested. Furthermore, input items related to a specific artist or genre can be preferentially displayed from the user's past purchase history. This makes it possible to generate a work that matches the user's preferences by suggesting optimal input items based on the user's past purchase history. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can suggest optimal input items using an AI model that analyzes the user's past art purchase history.

[0041] The reception unit can customize the input items based on the user's current interests and trends. The reception unit customizes the input items based on the user's current interests and trends, for example. For example, based on art-related keywords recently searched by the user, related input items are suggested. In addition, the reception unit can analyze artists and trends that the user follows on social media and provide input items based on the analysis. Furthermore, related input items can be customized based on information on art events and exhibitions that the user has participated in. This allows the creation of works that correspond to the latest preferences by customizing the input items based on the user's current interests and trends. Some or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can customize the input items using an AI model that analyzes the user's current interests and trends.

[0042] The reception unit can propose an art style specific to a region by taking into account the geographical location information of the user. The reception unit proposes an art style specific to a region by taking into account, for example, the geographical location information of the user. For example, if the user lives in a specific region, the reception unit proposes a traditional art style of the region. In addition, if the user is traveling, the reception unit can also propose an art style of the region visited. Furthermore, if the user is in a specific city, the reception unit can also propose an art style that is popular in the city. In this way, the reception unit can propose an art style specific to a region by taking into account the geographical location information of the user. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can propose an art style specific to a region by using an AI model that analyzes the geographical location information of the user.

[0043] The reception unit can analyze the user's social media activity and suggest a related art style. The reception unit, for example, analyzes the user's social media activity and suggests a related art style. For example, based on the style of the artwork that the user "liked" on social media, the reception unit can suggest a related input item. In addition, the style of the artist the user follows can be analyzed and an input item based on the style can be provided. Furthermore, based on the theme and style of the artwork shared by the user, the related input item can be customized. In this way, a related art style can be suggested by analyzing the user's social media activity. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can suggest a related art style using an AI model that analyzes the user's social media activity.

[0044] The generating unit can optimize the generating algorithm by referring to the user's past feedback at the time of generation. The generating unit optimizes the generating algorithm by referring to the user's past feedback at the time of generation, for example. For example, the generating algorithm is adjusted based on the color or theme that the user liked in the past. In addition, the generating algorithm can be optimized based on the feedback provided by the user in the past. Furthermore, the generating algorithm can be improved by incorporating features of artworks that the user evaluated in the past. In this way, the generating algorithm can be optimized by referring to the user's past feedback, and more accurate art can be generated. Part or all of the above-mentioned processing in the generating unit may be performed, for example, using AI, or may be performed without using AI. For example, the generating unit can optimize the generating algorithm using an AI model that optimizes the generating algorithm by referring to the user's past feedback.

[0045] The generating unit can apply different generating algorithms according to the user's preferences at the time of generation. The generating unit, for example, applies different generating algorithms according to the user's preferences at the time of generation. For example, if the user likes abstract paintings, a generating algorithm that emphasizes abstract elements can be applied. Also, if the user likes landscape paintings, a generating algorithm that incorporates natural scenery can be applied. Furthermore, if the user likes portraits, a generating algorithm that emphasizes human features can be applied. In this way, by applying different generating algorithms according to the user's preferences, art that matches the user's preferences can be generated. Part or all of the above-mentioned processing in the generating unit may be performed, for example, using AI, or may be performed without using AI. For example, the generating unit can apply a generating algorithm using an AI model that applies different generating algorithms according to the user's preferences.

[0046] The generation unit can determine the priority of generation based on the user's submission time at the time of generation. The generation unit, for example, determines the priority of generation based on the user's submission time at the time of generation. For example, if the user is in a hurry, the priority of generation is increased taking into account the submission time. In addition, if the user desires art for a specific event, the priority of generation can be determined taking into account the date and time of the event. Furthermore, if the user desires art as part of a long-term project, the schedule of generation can be adjusted based on the submission time. In this way, by determining the priority of generation based on the user's submission time, timely art can be generated according to the user's needs. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can determine the priority of generation using an AI model that analyzes the user's submission time.

[0047] The generation unit can improve the accuracy of generation by referring to the user's related artworks at the time of generation. The generation unit can improve the accuracy of generation by referring to the user's related artworks at the time of generation, for example. For example, the generation unit refers to artworks created by the user in the past and generates a new work based on the artworks. In addition, if the user refers to the work of another artist, the accuracy of generation can be improved by referring to the work. Furthermore, if the user likes a particular theme or style, the accuracy of generation can be improved by referring to related artworks. As a result, by referring to the user's related artworks, the accuracy of generation can be improved and art that better suits the user's preferences can be generated. Part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI or may be performed without using AI. For example, the generation unit can improve the accuracy of generation by using an AI model that refers to the user's related artworks.

[0048] The providing unit can select the optimal display method by referring to the user's past art purchase history at the time of providing. For example, the providing unit selects the optimal display method by referring to the user's past art purchase history at the time of providing. For example, the providing unit selects a related display method based on the style and theme of the art work purchased by the user in the past. In addition, the display method based on the color and composition that the user liked in the past can be analyzed. Furthermore, it is also possible to preferentially select a display method related to a specific artist or genre from the user's past purchase history. As a result, art that matches the user's preferences can be provided by selecting the optimal display method based on the user's past purchase history. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can select the optimal display method using an AI model that refers to the user's past art purchase history.

[0049] The providing unit can customize the display content based on the user's current interests at the time of providing. The providing unit, for example, customizes the display content based on the user's current interests at the time of providing. For example, the related display content is customized based on art-related keywords recently searched by the user. In addition, the display content can be provided based on the artists and trends that the user follows on social media. Furthermore, the related display content can be customized based on information on art events and exhibitions that the user has participated in. As a result, art that matches the user's preferences can be provided by customizing the display content based on the user's current interests. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI or may be performed without using AI. For example, the providing unit can customize the display content using an AI model that analyzes the user's current interests.

[0050] The providing unit can select the optimal display method in consideration of the geographical location information of the user at the time of providing. For example, the providing unit selects the optimal display method in consideration of the geographical location information of the user at the time of providing. For example, if the user lives in a specific region, the traditional art style of the region is displayed. Also, if the user is traveling, the art style of the visited region can be displayed. Furthermore, if the user is in a specific city, the art style popular in the city can be displayed. In this way, by considering the geographical location information of the user, an art style unique to the region can be displayed. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can select the optimal display method using an AI model that analyzes the geographical location information of the user.

[0051] The providing unit can select the optimal display method in consideration of the device information of the user at the time of providing. For example, the providing unit selects the optimal display method in consideration of the device information of the user at the time of providing. For example, when the user is using a smartphone, a display method according to the screen size is provided. In addition, when the user is using a tablet, a display method optimized for a large screen can be provided. Furthermore, when the user is using a smart watch, a display method that is simple and has high visibility can be provided. In this way, the optimal display method can be provided by considering the device information of the user. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can select the optimal display method using an AI model that analyzes the device information of the user.

[0052] The feedback receiving unit can select the optimal receiving method by referring to the user's past feedback history when receiving feedback. The feedback receiving unit, for example, selects the optimal receiving method by referring to the user's past feedback history when receiving feedback. For example, based on the content of feedback provided by the user in the past, related feedback items are automatically displayed. In addition, it is also possible to preferentially suggest feedback methods (audio, text, etc.) used by the user in the past. Furthermore, it is also possible to preferentially display feedback items related to a specific theme or style from the user's past feedback history. In this way, it is possible to select the optimal feedback receiving method by referring to the user's past feedback history. A part or all of the above-mentioned processing in the feedback receiving unit may be performed, for example, using AI, or may be performed without using AI. For example, the feedback receiving unit can select the optimal receiving method by using an AI model that refers to the user's past feedback history.

[0053] The feedback receiving unit can customize the reception contents based on the user's current interests when receiving feedback. For example, the feedback receiving unit customizes the reception contents based on the user's current interests when receiving feedback. For example, based on art-related keywords recently searched by the user, related feedback items are suggested. In addition, it is also possible to analyze artists and trends that the user follows on social media and provide feedback items based on the analysis. Furthermore, it is also possible to customize related feedback items based on information on art events and exhibitions that the user has participated in. As a result, by customizing the reception contents based on the user's current interests, it is possible to receive feedback that matches the user's preferences. Some or all of the above-mentioned processing in the feedback receiving unit may be performed, for example, using AI, or may be performed without using AI. For example, the feedback receiving unit can customize the reception contents using an AI model that analyzes the user's current interests.

[0054] The feedback reception unit can select the optimal reception method in consideration of the geographical location information of the user when receiving feedback. For example, the feedback reception unit selects the optimal reception method in consideration of the geographical location information of the user when receiving feedback. For example, if the user lives in a specific area, feedback items related to the traditional art style of the area can be provided. In addition, if the user is traveling, feedback items related to the art style of the area visited can also be provided. Furthermore, if the user is in a specific city, feedback items related to the art style popular in the city can also be provided. In this way, the optimal feedback reception method can be selected by considering the geographical location information of the user. Part or all of the above-mentioned processing in the feedback reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the feedback reception unit can select the optimal reception method using an AI model that analyzes the geographical location information of the user.

[0055] The feedback receiving unit can analyze the social media activity of the user and receive related feedback when receiving feedback. For example, the feedback receiving unit can analyze the social media activity of the user and receive related feedback when receiving feedback. For example, based on the style of the artwork that the user "liked" on social media, the feedback receiving unit can suggest related feedback items. In addition, the style of the artist that the user follows can be analyzed and feedback items based on the style can be provided. Furthermore, based on the theme or style of the artwork shared by the user, related feedback items can be customized. In this way, the user's social media activity can be analyzed to receive related feedback. Part or all of the above-mentioned processing in the feedback receiving unit may be performed, for example, using AI, or may be performed without using AI. For example, the feedback receiving unit can receive related feedback using an AI model that analyzes the user's social media activity.

[0056] The revenue unit can select the optimal distribution method by referring to the creator's past revenue history at the time of revenue distribution. For example, the revenue unit selects the optimal distribution method by referring to the creator's past revenue history at the time of revenue distribution. For example, the revenue unit proposes the optimal revenue distribution method based on the data of the revenue the creator has earned in the past. In addition, a specific revenue distribution method can be preferentially selected from the creator's past revenue history. Furthermore, the creator's past revenue history can be analyzed to select the most efficient revenue distribution method. In this way, the optimal revenue distribution method can be selected by referring to the creator's past revenue history. A part or all of the above-mentioned processing in the revenue unit may be performed, for example, using AI, or may be performed without using AI. For example, the revenue unit can select the optimal distribution method using an AI model that refers to the creator's past revenue history.

[0057] The revenue division can customize the distribution content based on the current activity status of the creator at the time of revenue distribution. For example, the revenue division customizes the distribution content based on the current activity status of the creator at the time of revenue distribution. For example, the revenue distribution content is adjusted based on the project currently being carried out by the creator. In addition, the priority of the revenue distribution can be determined taking into account the current activity status of the creator. Furthermore, if the creator is participating in a specific event or campaign, the revenue distribution content can be customized based on the activity status. This enables optimal revenue distribution by customizing the distribution content based on the current activity status of the creator. Some or all of the above-mentioned processing in the revenue division may be performed, for example, using AI or may be performed without using AI. For example, the revenue division can customize the distribution content using an AI model that analyzes the current activity status of the creator.

[0058] The revenue unit can select the optimal distribution method in consideration of the geographical location information of the creator when distributing revenue. For example, the revenue unit selects the optimal distribution method in consideration of the geographical location information of the creator when distributing revenue. For example, if the creator lives in a specific area, the revenue distribution method is selected in consideration of the economic situation of the area. In addition, if the creator is traveling, the revenue distribution method can be selected in consideration of the economic situation of the area to be visited. Furthermore, if the creator is in a specific city, the revenue distribution method can be selected in consideration of the economic situation of the city. In this way, the optimal revenue distribution method can be selected by considering the geographical location information of the creator. Part or all of the above-mentioned processing in the revenue unit may be performed, for example, using AI, or may be performed without using AI. For example, the revenue unit can select the optimal distribution method using an AI model that analyzes the geographical location information of the creator.

[0059] The revenue unit may analyze the social media activity of the creator at the time of revenue distribution to perform the relevant revenue distribution. For example, the revenue unit may analyze the social media activity of the creator at the time of revenue distribution to perform the relevant revenue distribution. For example, the revenue unit may suggest a relevant revenue distribution method based on the style of the artwork that the creator "liked" on social media. The revenue unit may also analyze the style of the artist that the creator follows and provide a revenue distribution method based thereon. Furthermore, the revenue unit may customize the relevant revenue distribution method based on the theme or style of the artwork that the creator shared. In this way, the relevant revenue distribution can be performed by analyzing the social media activity of the creator. Some or all of the above-mentioned processing in the revenue unit may be performed, for example, using AI or may be performed without using AI. For example, the revenue unit may perform the relevant revenue distribution using an AI model that analyzes the social media activity of the creator.

[0060] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0061] When inputting the user's preferences, the reception unit can suggest optimal input items by referring to the user's past art purchase history. For example, based on the style and theme of art works purchased by the user in the past, related input items can be automatically displayed. In addition, the reception unit can analyze the colors and compositions that the user liked in the past and suggest input items based on them. Furthermore, it can preferentially display input items related to a specific artist or genre from the user's past purchase history. This makes it possible to generate a work that matches the user's preferences by suggesting optimal input items based on the user's past purchase history.

[0062] During generation, the generation unit can optimize the generation algorithm by referring to the user's past feedback. For example, the generation algorithm can be adjusted based on the user's favorite colors or themes in the past. In addition, the generation algorithm can be optimized based on the analysis of feedback provided by the user in the past. Furthermore, the generation algorithm can be improved by incorporating features of artworks that the user has evaluated in the past. In this way, the generation algorithm can be optimized by referring to the user's past feedback, and more accurate art can be generated.

[0063] When providing the art, the providing unit can select the optimal display method by referring to the user's past art purchase history. For example, the providing unit can select a related display method based on the style or theme of art works purchased by the user in the past. The providing unit can also analyze the colors and compositions that the user has liked in the past and select a display method based on the analysis. Furthermore, the providing unit can preferentially select a display method related to a specific artist or genre from the user's past purchase history. This allows the providing unit to provide art that matches the user's preferences by selecting the optimal display method based on the user's past purchase history.

[0064] When distributing revenue, the revenue department can select the optimal distribution method by referring to the creator's past revenue history. For example, the revenue department can suggest the optimal revenue distribution method based on the data of the creator's past revenue. In addition, the revenue department can preferentially select a specific revenue distribution method from the creator's past revenue history. Furthermore, the revenue department can analyze the creator's past revenue history and select the most efficient revenue distribution method. In this way, the optimal revenue distribution method can be selected by referring to the creator's past revenue history.

[0065] When receiving feedback, the feedback receiving unit can select the optimal feedback receiving method by referring to the user's past feedback history. For example, based on the content of feedback provided by the user in the past, related feedback items can be automatically displayed. Also, it is possible to preferentially suggest feedback methods (audio, text, etc.) that the user has used in the past. Furthermore, it is possible to preferentially display feedback items related to a specific theme or style from the user's past feedback history. In this way, it is possible to select the optimal feedback receiving method by referring to the user's past feedback history.

[0066] The process flow of the first embodiment will be briefly described below.

[0067] Step 1: The reception unit inputs the user's preferences. The user's preferences include, for example, color, theme, style, etc. The user can specify a style such as "abstract painting" or "landscape painting." Step 2: The generator uses a generative AI to analyze the information entered by the reception unit and generate an original work of art. The generator generates a work that best suits the user's preferences based on past data and learned models. For example, if the user requests an "abstract painting," it generates a work that incorporates abstract elements. Step 3: The providing unit provides the work generated by the generating unit to the user. The providing unit can provide the generated work to the user in a digital or physical form. Step 4: The feedback receiving unit receives user feedback. The feedback includes color, composition, theme, etc. The user can provide feedback such as "I don't like the color" or "I would like a design with more movement." Step 5: The revenue division manages the mechanism by which creators can earn revenue. The revenue division manages the method of selling the generated works and the mechanism for revenue distribution. For example, creators can sell works generated using AI on online platforms.

[0068] (Example 2) The art generation platform according to the embodiment of the present invention is a system in which an AI generates original artworks based on the individual preferences of users, allowing creators to obtain new revenue streams. In this system, a user inputs their preferences and the style of art they desire, and an AI analyzes the input to generate original artworks that match the user's preferences. The generated artworks are provided to the user, who provides feedback on the artworks. This feedback is learned by the AI ​​and reflected in the next and subsequent work generation. This allows users to obtain their own original art, and creators to obtain new revenue streams. This service also respects individual expression and contributes to the development of the creative industry. For example, a user can specify a style such as "abstract painting" or "landscape painting." This information is input into the AI. The AI ​​then analyzes the input information and generates original artworks that match the user's preferences. The AI ​​generates artworks that best suit the user's preferences based on past data and learned models. For example, if a user requests an "abstract painting," the AI ​​generates artworks that incorporate abstract elements. The generated artworks are provided to the user, who provides feedback on the artworks. For example, users can provide feedback such as "I don't like the color" or "I would like a design with more movement." This feedback is learned by the AI ​​and reflected in the next and subsequent work generation. This mechanism allows users to obtain their own original art. For example, if a user requests a "landscape painting," the AI ​​will generate a landscape painting that matches the user's preferences and provide it to the user. Creators can also obtain new revenue sources by selling the generated works. For example, creators can sell works generated using AI on online platforms. This service respects individual expression and contributes to the development of the creative industry. For example, when users obtain works that match their preferences, their interest in art increases, which is expected to revitalize the entire creative industry. This allows the art generation platform to generate original works of art based on the individual preferences of users, allowing creators to obtain new revenue sources.

[0069] The art generation platform according to the embodiment includes a reception unit, a generation unit, a provision unit, a feedback reception unit, and a revenue unit. The reception unit inputs the user's preferences. The user's preferences include, for example, color, theme, style, etc., but are not limited to such examples. The reception unit allows the user to specify a style such as "abstract painting" or "landscape painting". The generation unit uses a generation AI to analyze the information input by the reception unit and generate an original work of art. The generation unit generates a work that is optimal for the user's preferences, for example, based on past data and a learned model. For example, if the user requests an "abstract painting," the generation AI generates a work that incorporates abstract elements. The generation unit can also use the generation AI to generate a landscape painting that matches the user's preferences. The provision unit provides the user with the work generated by the generation unit. The provision unit provides the user with the generated work in a digital format, for example. The provision unit can also provide the user with the generated work in a physical format. The feedback reception unit accepts the user's feedback. The feedback includes, for example, color shades, composition, theme, etc., but are not limited to such examples. The feedback receiving unit receives, for example, feedback from the user such as "I don't like the color" or "I prefer a design with more movement." The revenue unit manages a mechanism for the creator to earn revenue. The revenue unit manages, for example, a method for selling the generated work and a mechanism for revenue distribution. For example, the revenue unit can sell the work generated by the creator using AI on an online platform. In this way, the art generation platform according to the embodiment can generate original works of art based on the individual preferences of the user, allowing the creator to obtain a new source of revenue. Some or all of the above-mentioned processing in the revenue unit may be performed, for example, using AI, or may be performed without using AI. For example, the revenue unit can manage revenue using an AI model that manages a method for selling the generated work and a mechanism for revenue distribution.

[0070] The reception unit inputs the user's preferences. The user's preferences include, but are not limited to, for example, color, theme, style, etc. The reception unit allows the user to specify a style such as "abstract painting" or "landscape painting". Specifically, the reception unit is designed to allow the user to intuitively input preferences through a user interface. For example, a color picker can be used to select a color, and a drop-down menu or check box can be used to select a theme or style. The reception unit also has a function of remembering preferences previously input by the user and automatically suggesting them the next time the user uses the system. Furthermore, even if the user does not have a specific image, the reception unit presents several sample images and keywords, allowing the user to input preferences by selecting them. This allows the user to easily input their preferences, improving the quality of the generated work.

[0071] The generation unit uses the generation AI to analyze the information input by the reception unit and generate an original work of art. The generation unit generates a work that best suits the user's preferences, for example, based on past data and learned models. Specifically, the generation AI uses deep learning technology, takes in millions of works of art as learning data, and generates a work according to the user's preferences. For example, if the user requests an "abstract painting," the generation AI generates a work that incorporates abstract elements. The generation AI takes into account factors such as color balance, composition, and texture to generate a work that is closest to the user's preferences. The generation unit can also use the generation AI to generate a landscape painting that matches the user's preferences. In the case of a landscape painting, the generation AI generates a work based on a theme specified by the user, such as a natural landscape or a cityscape. In addition, the generation unit is equipped with an internal evaluation system for evaluating the quality of the generated work and checks whether the generated work meets certain quality standards. This allows the generation unit to provide the user with a high-quality original work of art.

[0072] The providing unit provides the work generated by the generating unit to the user. For example, the providing unit provides the generated work to the user in a digital format. Specifically, the providing unit provides the generated work to the user as a high-resolution digital file through a download link. The providing unit can also provide the generated work to the user in a physical format. For example, the providing unit provides a service of printing the generated work as a canvas print or poster and delivering it to an address specified by the user. Furthermore, the providing unit also has a function that allows the user to share the generated work on social media. This allows the user to share his or her work with friends and family and get feedback. The providing unit provides multiple providing methods in consideration of the user's convenience, allowing the user to receive the work in the most suitable format.

[0073] The feedback receiving unit receives user feedback. The feedback includes, but is not limited to, color, composition, theme, and the like. For example, the feedback receiving unit receives feedback such as "I don't like the color" or "I prefer a design with more movement." Specifically, the feedback receiving unit is designed to allow the user to easily input feedback through a user interface. For example, the feedback form is provided with text boxes and evaluation sliders so that the user can input specific opinions and evaluations. The feedback receiving unit also has a function of recording feedback provided by the user in the past and referring to it for the next generation. This allows the generation AI to more accurately grasp the user's preferences and reflect them in the generation of the next work. Furthermore, the feedback receiving unit also has a data analysis function for analyzing the user's feedback and identifying common improvements and trends. This can contribute to improving the quality of the entire platform.

[0074] The revenue division manages the mechanism by which creators can earn revenue. For example, the revenue division manages the sales method of the generated works and the mechanism of revenue distribution. Specifically, the revenue division provides a marketplace for selling the generated works through the online platform. Creators can earn revenue by exhibiting their works on this marketplace and users purchasing them. The revenue division manages the setting of the sales price and the revenue distribution ratio, and realizes a fair revenue distribution between creators and platform operators. The revenue division also provides promotion and marketing support for creators to maximize their revenue. For example, it may implement a campaign to offer works at a discounted price during a certain period of time, or promote works using social media. In addition, the revenue division may explore new ways for creators to earn revenue, and provide revenue sources such as subscription models and sponsorship contracts. This allows the revenue division to provide diverse means for creators to earn stable revenue and improve the sustainability of the entire platform.

[0075] The reception unit can receive detailed input items of the user's preferences. The detailed input items include, for example, color, theme, style, etc., but are not limited to such examples. The reception unit can, for example, allow the user to specify a style such as "abstract painting" or "landscape painting". The reception unit can also input the user's desired color tone or theme in detail. For example, the user can input detailed requests such as "blue abstract painting" or "sunset landscape painting". This allows the user to receive detailed input items of the user's preferences, thereby enabling the creation of a work with higher accuracy. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can receive detailed input items using an AI model that analyzes the detailed input items of the user's preferences.

[0076] The feedback receiving unit can receive feedback items from the user. The feedback items include, but are not limited to, for example, color, composition, theme, and the like. The feedback receiving unit receives, for example, feedback from the user such as "I don't like the color" or "I would like a design with more movement." The feedback receiving unit can also input detailed changes and improvements desired by the user. For example, the user can input detailed feedback such as "I would like the color to be brighter" or "I would like the composition to be changed." By this, by receiving the feedback items from the user, the items are reflected in the generation of works from the next time onwards. A part or all of the above-mentioned processing in the feedback receiving unit may be performed, for example, using AI, or may be performed without using AI. For example, the feedback receiving unit can receive feedback using an AI model that analyzes the feedback items from the user.

[0077] The revenue section can manage the sales method of the generated work and the mechanism of revenue distribution. The sales method includes, for example, online sales, auctions, gallery exhibitions, etc., but is not limited to such examples. The revenue distribution mechanism includes, for example, a percentage of sales, fixed remuneration, incentives, etc., but is not limited to such examples. The revenue section can, for example, sell the generated work on an online platform. The revenue section can also sell the generated work in an auction format. Furthermore, the revenue section can exhibit and sell the generated work in a gallery. In this way, the creator can earn revenue by managing the sales method of the generated work and the mechanism of revenue distribution. Some or all of the above-mentioned processing in the revenue section may be performed, for example, using AI, or may be performed without using AI. For example, the revenue section can manage revenue using an AI model that manages the sales method of the generated work and the mechanism of revenue distribution.

[0078] The generation unit can generate a work that matches the user's preferences based on past data and a learned model. The past data includes, for example, past work data, user preference data, etc., but is not limited to such examples. The learned model includes, for example, machine learning models, neural network models, etc., but is not limited to such examples. The generation unit generates a work that matches the user's preferences based on, for example, past data. For example, if the user desires an "abstract painting," the generation AI generates a work that incorporates abstract elements based on past abstract painting data. The generation unit can also generate a work that matches the user's preferences based on a learned model. For example, the generation AI uses a neural network model to generate a work that is optimal for the user's preferences. As a result, a work that matches the user's preferences can be provided by generating a work based on past data and a learned model. A part or all of the above-mentioned processing in the generation unit is performed using the generation AI. For example, the generation unit inputs a prompt to the generation AI based on past data and a learned model, and the generation AI generates a work that matches the user's preferences.

[0079] The providing unit can provide the generated work to the user. The providing method includes, for example, digital distribution, physical delivery, and the like, but is not limited to such examples. The providing unit, for example, provides the generated work to the user in a digital format. The providing unit can also provide the generated work to the user in a physical format. For example, the providing unit provides the generated work to the user as a high-resolution digital file. The providing unit can also print the generated work on a canvas and deliver it to the user. In this way, by providing the generated work to the user, the user can obtain an original work of art. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can select a providing method using an AI model that provides the generated work to the user.

[0080] The reception unit can estimate the user's emotion and adjust the preferred input items based on the estimated user's emotion. The reception unit, for example, estimates the user's emotion and dynamically adjusts the preferred input items based on the estimated user's emotion. For example, when the user is relaxed, detailed input items can be provided and customizable options can be increased. Also, when the user is stressed, a simple interface can be provided and input procedures can be minimized. Furthermore, when the user is in a hurry, voice input can be prioritized and the preferred input items can be set quickly. This allows optimal input according to the user's situation by dynamically adjusting the input items based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can adjust the input items based on the emotion using an AI model that estimates the user's emotion.

[0081] The reception unit can analyze the user's past art purchase history and suggest optimal input items. The reception unit, for example, analyzes the user's past art purchase history and suggests optimal input items. For example, based on the style and theme of the art work the user has purchased in the past, related input items are automatically displayed. In addition, the color and composition that the user has liked in the past can be analyzed and input items based on the analysis can be suggested. Furthermore, input items related to a specific artist or genre can be preferentially displayed from the user's past purchase history. This makes it possible to generate a work that matches the user's preferences by suggesting optimal input items based on the user's past purchase history. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can suggest optimal input items using an AI model that analyzes the user's past art purchase history.

[0082] The reception unit can customize the input items based on the user's current interests and trends. The reception unit customizes the input items based on the user's current interests and trends, for example. For example, based on art-related keywords recently searched by the user, related input items are suggested. In addition, the reception unit can analyze artists and trends that the user follows on social media and provide input items based on the analysis. Furthermore, related input items can be customized based on information on art events and exhibitions that the user has participated in. This allows the creation of works that correspond to the latest preferences by customizing the input items based on the user's current interests and trends. Some or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can customize the input items using an AI model that analyzes the user's current interests and trends.

[0083] The reception unit can estimate the user's emotion and determine the priority of the input items based on the estimated user's emotion. The reception unit, for example, estimates the user's emotion and determines the priority of the input items based on the estimated user's emotion. For example, when the user is relaxed, detailed input items can be preferentially displayed. Also, when the user is stressed, basic input items can be preferentially displayed. Furthermore, when the user is in a hurry, the most important input items can be preferentially displayed. In this way, by determining the priority of the input items based on the user's emotion, optimal input according to the user's situation is possible. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can determine the priority of the input items based on the emotion using an AI model that estimates the user's emotion.

[0084] The reception unit can propose an art style specific to a region by taking into account the geographical location information of the user. The reception unit proposes an art style specific to a region by taking into account, for example, the geographical location information of the user. For example, if the user lives in a specific region, the reception unit proposes a traditional art style of the region. In addition, if the user is traveling, the reception unit can also propose an art style of the region visited. Furthermore, if the user is in a specific city, the reception unit can also propose an art style that is popular in the city. In this way, the reception unit can propose an art style specific to a region by taking into account the geographical location information of the user. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can propose an art style specific to a region by using an AI model that analyzes the geographical location information of the user.

[0085] The reception unit can analyze the user's social media activity and suggest a related art style. The reception unit, for example, analyzes the user's social media activity and suggests a related art style. For example, based on the style of the artwork that the user "liked" on social media, the reception unit can suggest a related input item. In addition, the style of the artist the user follows can be analyzed and an input item based on the style can be provided. Furthermore, based on the theme and style of the artwork shared by the user, the related input item can be customized. In this way, a related art style can be suggested by analyzing the user's social media activity. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can suggest a related art style using an AI model that analyzes the user's social media activity.

[0086] The generation unit can estimate the user's emotion and adjust the expression method of the generated art based on the estimated user's emotion. The generation unit, for example, estimates the user's emotion and adjusts the expression method of the generated art based on the estimated user's emotion. For example, when the user is relaxed, art with soft colors and a gentle composition can be generated. Also, when the user is excited, art with vivid colors and a moving composition can be generated. Furthermore, when the user is sad, art with calm colors and a quiet composition can be generated. In this way, by adjusting the expression method of the art based on the user's emotion, optimal art according to the user's situation can be generated. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the generation unit is performed using the generation AI. For example, the generation unit can adjust the expression method of the art based on the emotion using an AI model that estimates the user's emotion.

[0087] The generating unit can optimize the generating algorithm by referring to the user's past feedback at the time of generation. The generating unit optimizes the generating algorithm by referring to the user's past feedback at the time of generation, for example. For example, the generating algorithm is adjusted based on the color or theme that the user liked in the past. In addition, the generating algorithm can be optimized based on the feedback provided by the user in the past. Furthermore, the generating algorithm can be improved by incorporating features of artworks that the user evaluated in the past. In this way, the generating algorithm can be optimized by referring to the user's past feedback, and more accurate art can be generated. Part or all of the above-mentioned processing in the generating unit may be performed, for example, using AI, or may be performed without using AI. For example, the generating unit can optimize the generating algorithm using an AI model that optimizes the generating algorithm by referring to the user's past feedback.

[0088] The generating unit can apply different generating algorithms according to the user's preferences at the time of generation. The generating unit, for example, applies different generating algorithms according to the user's preferences at the time of generation. For example, if the user likes abstract paintings, a generating algorithm that emphasizes abstract elements can be applied. Also, if the user likes landscape paintings, a generating algorithm that incorporates natural scenery can be applied. Furthermore, if the user likes portraits, a generating algorithm that emphasizes human features can be applied. In this way, by applying different generating algorithms according to the user's preferences, art that matches the user's preferences can be generated. Part or all of the above-mentioned processing in the generating unit may be performed, for example, using AI, or may be performed without using AI. For example, the generating unit can apply a generating algorithm using an AI model that applies different generating algorithms according to the user's preferences.

[0089] The generation unit can estimate the user's emotion and adjust the color tone and composition of the generated art based on the estimated user's emotion. The generation unit, for example, estimates the user's emotion and adjusts the color tone and composition of the generated art based on the estimated user's emotion. For example, when the user is relaxed, art with soft color tone and gentle composition is generated. Also, when the user is excited, art with vivid color tone and moving composition can be generated. Furthermore, when the user is sad, art with calm color tone and quiet composition can be generated. In this way, by adjusting the color tone and composition of the art based on the user's emotion, optimal art according to the user's situation can be generated. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the generation unit is performed using the generation AI. For example, the generation unit can adjust the color tone and composition of the art based on the emotion using an AI model that estimates the user's emotion.

[0090] The generation unit can determine the priority of generation based on the user's submission time at the time of generation. The generation unit, for example, determines the priority of generation based on the user's submission time at the time of generation. For example, if the user is in a hurry, the priority of generation is increased taking into account the submission time. In addition, if the user desires art for a specific event, the priority of generation can be determined taking into account the date and time of the event. Furthermore, if the user desires art as part of a long-term project, the schedule of generation can be adjusted based on the submission time. In this way, by determining the priority of generation based on the user's submission time, timely art can be generated according to the user's needs. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can determine the priority of generation using an AI model that analyzes the user's submission time.

[0091] The generation unit can improve the accuracy of generation by referring to the user's related artworks at the time of generation. The generation unit can improve the accuracy of generation by referring to the user's related artworks at the time of generation, for example. For example, the generation unit refers to artworks created by the user in the past and generates a new work based on the artworks. In addition, if the user refers to the work of another artist, the accuracy of generation can be improved by referring to the work. Furthermore, if the user likes a particular theme or style, the accuracy of generation can be improved by referring to related artworks. As a result, by referring to the user's related artworks, the accuracy of generation can be improved and art that better suits the user's preferences can be generated. Part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI or may be performed without using AI. For example, the generation unit can improve the accuracy of generation by using an AI model that refers to the user's related artworks.

[0092] The providing unit can estimate the user's emotion and adjust the display method of the art to be provided based on the estimated user's emotion. The providing unit, for example, estimates the user's emotion and adjusts the display method of the art to be provided based on the estimated user's emotion. For example, when the user is relaxed, art with soft colors and a gentle composition can be displayed. Also, when the user is excited, art with vivid colors and a moving composition can be displayed. Furthermore, when the user is sad, art with calm colors and a quiet composition can be displayed. In this way, by adjusting the display method of the art based on the user's emotion, it is possible to provide optimal art according to the user's situation. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can adjust the display method of the art based on the emotion using an AI model that estimates the user's emotion.

[0093] The providing unit can select the optimal display method by referring to the user's past art purchase history at the time of providing. For example, the providing unit selects the optimal display method by referring to the user's past art purchase history at the time of providing. For example, the providing unit selects a related display method based on the style and theme of the art work purchased by the user in the past. In addition, the display method based on the color and composition that the user liked in the past can be analyzed. Furthermore, it is also possible to preferentially select a display method related to a specific artist or genre from the user's past purchase history. As a result, art that matches the user's preferences can be provided by selecting the optimal display method based on the user's past purchase history. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can select the optimal display method using an AI model that refers to the user's past art purchase history.

[0094] The providing unit can customize the display content based on the user's current interests at the time of providing. The providing unit, for example, customizes the display content based on the user's current interests at the time of providing. For example, the related display content is customized based on art-related keywords recently searched by the user. In addition, the display content can be provided based on the artists and trends that the user follows on social media. Furthermore, the related display content can be customized based on information on art events and exhibitions that the user has participated in. As a result, art that matches the user's preferences can be provided by customizing the display content based on the user's current interests. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI or may be performed without using AI. For example, the providing unit can customize the display content using an AI model that analyzes the user's current interests.

[0095] The providing unit can estimate the user's emotion and determine the priority of the art to be provided based on the estimated user's emotion. The providing unit, for example, estimates the user's emotion and determines the priority of the art to be provided based on the estimated user's emotion. For example, when the user is relaxed, detailed artworks can be provided preferentially. Also, when the user is excited, visually stimulating artworks can be provided preferentially. Furthermore, when the user is sad, artworks with calm colors and quiet compositions can be provided preferentially. In this way, by determining the priority of art based on the user's emotion, optimal art according to the user's situation can be provided. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can determine the priority of art based on emotion using an AI model that estimates the user's emotion.

[0096] The providing unit can select the optimal display method in consideration of the geographical location information of the user at the time of providing. For example, the providing unit selects the optimal display method in consideration of the geographical location information of the user at the time of providing. For example, if the user lives in a specific region, the traditional art style of the region is displayed. Also, if the user is traveling, the art style of the visited region can be displayed. Furthermore, if the user is in a specific city, the art style popular in the city can be displayed. In this way, by considering the geographical location information of the user, an art style unique to the region can be displayed. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can select the optimal display method using an AI model that analyzes the geographical location information of the user.

[0097] The providing unit can select the optimal display method in consideration of the device information of the user at the time of providing. For example, the providing unit selects the optimal display method in consideration of the device information of the user at the time of providing. For example, when the user is using a smartphone, a display method according to the screen size is provided. In addition, when the user is using a tablet, a display method optimized for a large screen can be provided. Furthermore, when the user is using a smart watch, a display method that is simple and has high visibility can be provided. In this way, the optimal display method can be provided by considering the device information of the user. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can select the optimal display method using an AI model that analyzes the device information of the user.

[0098] The feedback receiving unit can estimate the user's emotion and adjust the feedback receiving method based on the estimated user's emotion. The feedback receiving unit, for example, estimates the user's emotion and adjusts the feedback receiving method based on the estimated user's emotion. For example, when the user is relaxed, detailed feedback items are provided and customizable options are increased. Also, when the user is stressed, a simple interface can be provided and feedback procedures can be minimized. Furthermore, when the user is in a hurry, voice input can be prioritized and feedback can be provided quickly. In this way, by adjusting the feedback receiving method based on the user's emotion, optimal feedback according to the user's situation can be received. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the feedback receiving unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the feedback receiving unit can adjust the feedback receiving method based on the emotion using an AI model that estimates the user's emotion.

[0099] The feedback receiving unit can select the optimal receiving method by referring to the user's past feedback history when receiving feedback. The feedback receiving unit, for example, selects the optimal receiving method by referring to the user's past feedback history when receiving feedback. For example, based on the content of feedback provided by the user in the past, related feedback items are automatically displayed. In addition, it is also possible to preferentially suggest feedback methods (audio, text, etc.) used by the user in the past. Furthermore, it is also possible to preferentially display feedback items related to a specific theme or style from the user's past feedback history. In this way, it is possible to select the optimal feedback receiving method by referring to the user's past feedback history. A part or all of the above-mentioned processing in the feedback receiving unit may be performed, for example, using AI, or may be performed without using AI. For example, the feedback receiving unit can select the optimal receiving method by using an AI model that refers to the user's past feedback history.

[0100] The feedback receiving unit can customize the reception contents based on the user's current interests when receiving feedback. For example, the feedback receiving unit customizes the reception contents based on the user's current interests when receiving feedback. For example, based on art-related keywords recently searched by the user, related feedback items are suggested. In addition, it is also possible to analyze artists and trends that the user follows on social media and provide feedback items based on the analysis. Furthermore, it is also possible to customize related feedback items based on information on art events and exhibitions that the user has participated in. As a result, by customizing the reception contents based on the user's current interests, it is possible to receive feedback that matches the user's preferences. Some or all of the above-mentioned processing in the feedback receiving unit may be performed, for example, using AI, or may be performed without using AI. For example, the feedback receiving unit can customize the reception contents using an AI model that analyzes the user's current interests.

[0101] The feedback receiving unit can estimate the user's emotion and determine the priority of the feedback based on the estimated user's emotion. The feedback receiving unit, for example, estimates the user's emotion and determines the priority of the feedback based on the estimated user's emotion. For example, when the user is relaxed, detailed feedback items can be preferentially displayed. Also, when the user is excited, visually stimulating feedback items can be preferentially displayed. Furthermore, when the user is sad, feedback items with calm colors and quiet compositions can be preferentially displayed. In this way, by determining the priority of the feedback based on the user's emotion, optimal feedback according to the user's situation can be received. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the feedback receiving unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the feedback receiving unit can determine the priority of the feedback based on the emotion using an AI model that estimates the user's emotion.

[0102] The feedback reception unit can select the optimal reception method in consideration of the geographical location information of the user when receiving feedback. For example, the feedback reception unit selects the optimal reception method in consideration of the geographical location information of the user when receiving feedback. For example, if the user lives in a specific area, feedback items related to the traditional art style of the area can be provided. In addition, if the user is traveling, feedback items related to the art style of the area visited can also be provided. Furthermore, if the user is in a specific city, feedback items related to the art style popular in the city can also be provided. In this way, the optimal feedback reception method can be selected by considering the geographical location information of the user. Part or all of the above-mentioned processing in the feedback reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the feedback reception unit can select the optimal reception method using an AI model that analyzes the geographical location information of the user.

[0103] The feedback receiving unit can analyze the social media activity of the user and receive related feedback when receiving feedback. For example, the feedback receiving unit can analyze the social media activity of the user and receive related feedback when receiving feedback. For example, based on the style of the artwork that the user "liked" on social media, the feedback receiving unit can suggest related feedback items. In addition, the style of the artist that the user follows can be analyzed and feedback items based on the style can be provided. Furthermore, based on the theme or style of the artwork shared by the user, related feedback items can be customized. In this way, the user's social media activity can be analyzed to receive related feedback. Part or all of the above-mentioned processing in the feedback receiving unit may be performed, for example, using AI, or may be performed without using AI. For example, the feedback receiving unit can receive related feedback using an AI model that analyzes the user's social media activity.

[0104] The revenue unit can estimate the user's emotion and adjust the revenue distribution method based on the estimated user's emotion. The revenue unit, for example, estimates the user's emotion and adjusts the revenue distribution method based on the estimated user's emotion. For example, when the user is relaxed, a detailed revenue distribution method can be provided and customizable options can be increased. Also, when the user is stressed, a simple revenue distribution method can be provided and the steps can be minimized. Furthermore, when the user is in a hurry, a method for quickly distributing revenue can be provided. This allows optimal revenue distribution according to the user's situation by adjusting the revenue distribution method based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the revenue unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the revenue unit can adjust the revenue distribution method based on the emotion using an AI model that estimates the user's emotion.

[0105] The revenue unit can select the optimal distribution method by referring to the creator's past revenue history at the time of revenue distribution. For example, the revenue unit selects the optimal distribution method by referring to the creator's past revenue history at the time of revenue distribution. For example, the revenue unit proposes the optimal revenue distribution method based on the data of the revenue the creator has earned in the past. In addition, a specific revenue distribution method can be preferentially selected from the creator's past revenue history. Furthermore, the creator's past revenue history can be analyzed to select the most efficient revenue distribution method. In this way, the optimal revenue distribution method can be selected by referring to the creator's past revenue history. A part or all of the above-mentioned processing in the revenue unit may be performed, for example, using AI, or may be performed without using AI. For example, the revenue unit can select the optimal distribution method using an AI model that refers to the creator's past revenue history.

[0106] The revenue division can customize the distribution content based on the current activity status of the creator at the time of revenue distribution. For example, the revenue division customizes the distribution content based on the current activity status of the creator at the time of revenue distribution. For example, the revenue distribution content is adjusted based on the project currently being carried out by the creator. In addition, the priority of the revenue distribution can be determined taking into account the current activity status of the creator. Furthermore, if the creator is participating in a specific event or campaign, the revenue distribution content can be customized based on the activity status. This enables optimal revenue distribution by customizing the distribution content based on the current activity status of the creator. Some or all of the above-mentioned processing in the revenue division may be performed, for example, using AI or may be performed without using AI. For example, the revenue division can customize the distribution content using an AI model that analyzes the current activity status of the creator.

[0107] The revenue unit can estimate the user's emotion and determine the priority of revenue distribution based on the estimated user's emotion. The revenue unit, for example, estimates the user's emotion and determines the priority of revenue distribution based on the estimated user's emotion. For example, if the user is relaxed, a detailed revenue distribution method can be provided preferentially. In addition, if the user is excited, a visually stimulating revenue distribution method can be provided preferentially. Furthermore, if the user is sad, a revenue distribution method with calm colors and a quiet composition can be provided preferentially. In this way, by determining the priority of revenue distribution based on the user's emotion, optimal revenue distribution according to the user's situation is possible. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the revenue unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the revenue unit can determine the priority of revenue distribution based on emotion using an AI model that estimates the user's emotion.

[0108] The revenue unit can select the optimal distribution method in consideration of the geographical location information of the creator when distributing revenue. For example, the revenue unit selects the optimal distribution method in consideration of the geographical location information of the creator when distributing revenue. For example, if the creator lives in a specific area, the revenue distribution method is selected in consideration of the economic situation of the area. In addition, if the creator is traveling, the revenue distribution method can be selected in consideration of the economic situation of the area to be visited. Furthermore, if the creator is in a specific city, the revenue distribution method can be selected in consideration of the economic situation of the city. In this way, the optimal revenue distribution method can be selected by considering the geographical location information of the creator. Part or all of the above-mentioned processing in the revenue unit may be performed, for example, using AI, or may be performed without using AI. For example, the revenue unit can select the optimal distribution method using an AI model that analyzes the geographical location information of the creator.

[0109] The revenue unit may analyze the social media activity of the creator at the time of revenue distribution to perform the relevant revenue distribution. For example, the revenue unit may analyze the social media activity of the creator at the time of revenue distribution to perform the relevant revenue distribution. For example, the revenue unit may suggest a relevant revenue distribution method based on the style of the artwork that the creator "liked" on social media. The revenue unit may also analyze the style of the artist that the creator follows and provide a revenue distribution method based thereon. Furthermore, the revenue unit may customize the relevant revenue distribution method based on the theme or style of the artwork that the creator shared. In this way, the relevant revenue distribution can be performed by analyzing the social media activity of the creator. Some or all of the above-mentioned processing in the revenue unit may be performed, for example, using AI or may be performed without using AI. For example, the revenue unit may perform the relevant revenue distribution using an AI model that analyzes the social media activity of the creator.

[0110] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0111] When inputting the user's preferences, the reception unit can suggest optimal input items by referring to the user's past art purchase history. For example, based on the style and theme of art works purchased by the user in the past, related input items can be automatically displayed. In addition, the reception unit can analyze the colors and compositions that the user liked in the past and suggest input items based on them. Furthermore, it can preferentially display input items related to a specific artist or genre from the user's past purchase history. This makes it possible to generate a work that matches the user's preferences by suggesting optimal input items based on the user's past purchase history.

[0112] During generation, the generation unit can optimize the generation algorithm by referring to the user's past feedback. For example, the generation algorithm can be adjusted based on the user's favorite colors or themes in the past. In addition, the generation algorithm can be optimized based on the analysis of feedback provided by the user in the past. Furthermore, the generation algorithm can be improved by incorporating features of artworks that the user has evaluated in the past. In this way, the generation algorithm can be optimized by referring to the user's past feedback, and more accurate art can be generated.

[0113] When providing the art, the providing unit can select the optimal display method by referring to the user's past art purchase history. For example, the providing unit can select a related display method based on the style or theme of art works purchased by the user in the past. The providing unit can also analyze the colors and compositions that the user has liked in the past and select a display method based on the analysis. Furthermore, the providing unit can preferentially select a display method related to a specific artist or genre from the user's past purchase history. This allows the providing unit to provide art that matches the user's preferences by selecting the optimal display method based on the user's past purchase history.

[0114] When distributing revenue, the revenue department can select the optimal distribution method by referring to the creator's past revenue history. For example, the revenue department can suggest the optimal revenue distribution method based on the data of the creator's past revenue. In addition, the revenue department can preferentially select a specific revenue distribution method from the creator's past revenue history. Furthermore, the revenue department can analyze the creator's past revenue history and select the most efficient revenue distribution method. In this way, the optimal revenue distribution method can be selected by referring to the creator's past revenue history.

[0115] When receiving feedback, the feedback receiving unit can select the optimal feedback receiving method by referring to the user's past feedback history. For example, based on the content of feedback provided by the user in the past, related feedback items can be automatically displayed. Also, it is possible to preferentially suggest feedback methods (audio, text, etc.) that the user has used in the past. Furthermore, it is possible to preferentially display feedback items related to a specific theme or style from the user's past feedback history. In this way, it is possible to select the optimal feedback receiving method by referring to the user's past feedback history.

[0116] The reception unit can estimate the user's emotions and adjust the preferred input items based on the estimated user's emotions. For example, when the user is relaxed, detailed input items can be provided and customizable options can be increased. Also, when the user is stressed, a simple interface can be provided and input procedures can be minimized. Furthermore, when the user is in a hurry, voice input can be prioritized so that the preferred input items can be set quickly. In this way, the input items can be dynamically adjusted based on the user's emotions, enabling optimal input according to the user's situation.

[0117] The generation unit can estimate the user's emotion and adjust the expression method of the generated art based on the estimated user's emotion. For example, when the user is relaxed, art with soft colors and a calm composition can be generated. When the user is excited, art with vivid colors and a moving composition can be generated. Furthermore, when the user is sad, art with calm colors and a quiet composition can be generated. In this way, by adjusting the expression method of the art based on the user's emotion, optimal art according to the user's situation can be generated.

[0118] The providing unit can estimate the user's emotions and adjust the display method of the art to be provided based on the estimated user's emotions. For example, when the user is relaxed, art with soft colors and a calm composition can be displayed. When the user is excited, art with vivid colors and a moving composition can be displayed. Furthermore, when the user is sad, art with calm colors and a quiet composition can be displayed. In this way, by adjusting the display method of the art based on the user's emotions, it is possible to provide optimal art according to the user's situation.

[0119] The feedback receiving unit can estimate the user's emotion and adjust the feedback receiving method based on the estimated user's emotion. For example, when the user is relaxed, detailed feedback items can be provided and customizable options can be increased. Also, when the user is stressed, a simple interface can be provided and feedback procedures can be minimized. Furthermore, when the user is in a hurry, voice input can be prioritized so that feedback can be provided quickly. In this way, by adjusting the feedback receiving method based on the user's emotion, it is possible to receive optimal feedback according to the user's situation.

[0120] The revenue unit can estimate the user's emotions and adjust the revenue sharing method based on the estimated user's emotions. For example, if the user is relaxed, a detailed revenue sharing method can be provided to increase customizable options. Also, if the user is stressed, a simple revenue sharing method can be provided to minimize steps. Furthermore, if the user is in a hurry, a method for quick revenue sharing can be provided. In this way, by adjusting the revenue sharing method based on the user's emotions, optimal revenue sharing according to the user's situation can be achieved.

[0121] The process flow of the second embodiment will be briefly described below.

[0122] Step 1: The reception unit inputs the user's preferences. The user's preferences include, for example, color, theme, style, etc. The user can specify a style such as "abstract painting" or "landscape painting." Step 2: The generator uses a generative AI to analyze the information entered by the reception unit and generate an original work of art. The generator generates a work that best suits the user's preferences based on past data and learned models. For example, if the user requests an "abstract painting," it generates a work that incorporates abstract elements. Step 3: The providing unit provides the work generated by the generating unit to the user. The providing unit can provide the generated work to the user in a digital or physical form. Step 4: The feedback receiving unit receives user feedback. The feedback includes color, composition, theme, etc. The user can provide feedback such as "I don't like the color" or "I would like a design with more movement." Step 5: The revenue division manages the mechanism by which creators can earn revenue. The revenue division manages the method of selling the generated works and the mechanism for revenue distribution. For example, creators can sell works generated using AI on online platforms.

[0123] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0124] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0125] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0126] Each of the multiple elements including the above-mentioned reception unit, generation unit, provision unit, feedback reception unit, and profit unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14 and inputs the user's preferences. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates an original work of art using a generation AI. The provision unit is realized by the output device 40 of the smart device 14 and provides the generated work to the user. The feedback reception unit is realized by the reception device 38 of the smart device 14 and accepts user feedback. The profit unit is realized by the specific processing unit 290 of the data processing device 12 and manages the sales method of the generated work and the mechanism of profit distribution. The correspondence between each unit and the device and the control unit is not limited to the above-mentioned example, and various changes are possible.

[0127] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0128] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0129] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0130] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0131] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0133] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0134] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0135] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0136] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0137] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0138] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0139] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0140] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0141] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0142] Each of the multiple elements including the above-mentioned reception unit, generation unit, provision unit, feedback reception unit, and revenue unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and inputs the user's preferences. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates an original work of art using a generation AI. The provision unit is realized by the speaker 240 of the smart glasses 214 and provides the generated work to the user. The feedback reception unit is realized by the microphone 238 of the smart glasses 214 and accepts user feedback. The revenue unit is realized by the specific processing unit 290 of the data processing device 12 and manages the sales method of the generated work and the mechanism of revenue distribution. The correspondence between each unit and the device and the control unit is not limited to the above-mentioned example, and various changes are possible.

[0143] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0144] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0145] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0146] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0147] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0149] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0150] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0151] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0152] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0153] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0154] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0155] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0156] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0157] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0158] Each of the multiple elements including the above-mentioned reception unit, generation unit, provision unit, feedback reception unit, and revenue unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset type terminal 314 and inputs the user's preferences. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates an original work of art using a generation AI. The provision unit is realized by the display 343 of the headset type terminal 314 and provides the generated work to the user. The feedback reception unit is realized by the microphone 238 of the headset type terminal 314 and accepts user feedback. The revenue unit is realized by the specific processing unit 290 of the data processing device 12 and manages the sales method of the generated work and the mechanism of revenue distribution. The correspondence between each unit and the device and the control unit is not limited to the above-mentioned example, and various changes are possible.

[0159] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0160] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0161] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0162] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0163] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0164] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0165] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0166] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0167] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0168] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0169] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0170] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0171] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0172] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0173] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0174] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0175] Each of the multiple elements including the above-mentioned reception unit, generation unit, provision unit, feedback reception unit, and profit unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414 and inputs the user's preferences. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates an original work of art using a generation AI. The provision unit is realized by the speaker 240 of the robot 414 and provides the generated work to the user. The feedback reception unit is realized by the microphone 238 of the robot 414 and accepts user feedback. The profit unit is realized by the specific processing unit 290 of the data processing device 12 and manages the sales method of the generated work and the mechanism of profit distribution. The correspondence between each unit and the device and the control unit is not limited to the above-mentioned example, and various changes are possible.

[0176] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0177] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0178] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0179] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out on emotion map 400 you go, the more visible the emotions become (the more they are expressed in behavior).

[0180] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0181] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0182] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0183] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

[0184] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0185] In addition, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 upon request from the data processing device 12.

[0186] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0187] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0188] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0189] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0190] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0191] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0192] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0193] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0194] (Appendix 1) A reception unit for inputting user preferences; a generating unit that analyzes the information input by the receiving unit and generates an original work of art; a providing unit that provides a work generated by the generating unit to a user; a feedback receiving unit for receiving feedback from a user; Providing a revenue section for creators to earn revenue A system characterized by: (Appendix 2) The reception unit is Accepts detailed input of user preferences 2. The system of claim 1, (Appendix 3) The feedback receiving unit, Accept user feedback items 2. The system of claim 1, (Appendix 4) The revenue section includes: Manage how your creations are sold and how revenue is shared 2. The system of claim 1, (Appendix 5) The generation unit is Generate works that match the user's preferences based on past data and learned models 2. The system of claim 1, (Appendix 6) The providing unit is Providing the generated work to the user 2. The system of claim 1, (Appendix 7) The reception unit is Inferring user emotions and adjusting preference input items based on the estimated user emotions 2. The system of claim 1, (Appendix 8) The reception unit is Analyze the user's past art purchase history and suggest optimal input items 2. The system of claim 1, (Appendix 9) The reception unit is Customize input fields based on the user's current interests and trends 2. The system of claim 1, (Appendix 10) The reception unit is Infer user emotions and rank input items based on the estimated user emotions 2. The system of claim 1, (Appendix 11) The reception unit is Considering the user's geographic location, it suggests regional art styles 2. The system of claim 1, (Appendix 12) The reception unit is Analyze users' social media activity and suggest relevant art styles 2. The system of claim 1, (Appendix 13) The generation unit is Estimate the user's emotions and adjust the way the art is generated based on the estimated user emotions 2. The system of claim 1, (Appendix 14) The generation unit is During generation, the generation algorithm is optimized by referring to past user feedback. 2. The system of claim 1, (Appendix 15) The generation unit is During generation, different generation algorithms can be applied depending on the user's preferences. 2. The system of claim 1, (Appendix 16) The generation unit is Estimate the user's emotions and adjust the color tone of the generated art based on the estimated user's emotions. 2. The system of claim 1, (Appendix 17) The generation unit is At generation time, prioritize generation based on user submission timing 2. The system of claim 1, (Appendix 18) The generation unit is During generation, the accuracy of the generation is improved by referencing related artworks of the user. 2. The system of claim 1, (Appendix 19) The providing unit is Estimate the user's emotions and adjust the way the art is presented based on the estimated user's emotions 2. The system of claim 1, (Appendix 20) The providing unit is When providing the content, the optimal display method is selected by referring to the user's past art purchase history. 2. The system of claim 1, (Appendix 21) The providing unit is At the time of delivery, customize the content displayed based on the user's current interests 2. The system of claim 1, (Appendix 22) The providing unit is Estimate the user's emotions and determine the ranking of the art to be provided based on the estimated user's emotions. 2. The system of claim 1, (Appendix 23) The providing unit is When providing the content, the optimal display method is selected taking into account the user's geographic location information. 2. The system of claim 1, (Appendix 24) The providing unit is When providing the content, the optimal display method is selected taking into account the user's device information. 2. The system of claim 1, (Appendix 25) The feedback receiving unit, Estimate user's emotions and adjust the feedback method based on the estimated user's emotions 2. The system of claim 1, (Appendix 26) The feedback receiving unit, When receiving feedback, select the optimal method of receiving feedback by referring to the user's past feedback history. 2. The system of claim 1, (Appendix 27) The feedback receiving unit, When collecting feedback, customize it based on the user's current interests 2. The system of claim 1, (Appendix 28) The feedback receiving unit, Estimate the user's emotions and determine the ranking of feedback based on the estimated user emotions 2. The system of claim 1, (Appendix 29) The feedback receiving unit, When receiving feedback, the optimal method of reception is selected taking into account the user's geographic location information. 2. The system of claim 1, (Appendix 30) The feedback receiving unit, When receiving feedback, analyze users' social media activity to receive relevant feedback 2. The system of claim 1, (Appendix 31) The revenue section includes: Inferring user sentiment and adjusting revenue sharing methodology based on the inferred user sentiment 2. The system of claim 1, (Appendix 32) The revenue section includes: When sharing revenue, we will choose the optimal distribution method based on the creator's past revenue history. 2. The system of claim 1, (Appendix 33) The revenue section includes: Customize revenue sharing based on a creator's current activity 2. The system of claim 1, (Appendix 34) The revenue section includes: Infer user sentiment and determine revenue sharing ranking based on the estimated user sentiment 2. The system of claim 1, (Appendix 35) The revenue section includes: When distributing revenue, the optimal distribution method will be selected taking into account the creator's geographic location information. 2. The system of claim 1, (Appendix 36) The revenue section includes: At the time of revenue sharing, we analyze the social media activity of creators to make relevant revenue sharing 2. The system of claim 1, [Explanation of symbols]

[0195] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A reception unit for inputting user preferences; a generating unit that analyzes the information input by the receiving unit and generates an original work of art; a providing unit that provides a work generated by the generating unit to a user; a feedback receiving unit for receiving feedback from a user; Providing a revenue section for creators to earn revenue A system characterized by:

2. The reception unit is Accepts detailed input of user preferences 2. The system of claim 1 .

3. The feedback receiving unit, Accept user feedback items 2. The system of claim 1 .

4. The revenue section includes: Manage the sales method and revenue distribution mechanism of the works generated by the generation unit 2. The system of claim 1 .

5. The generation unit is Generate works that match the user's preferences based on past data and learned models 2. The system of claim 1 .

6. The reception unit is Inferring user emotions and adjusting preference input items based on the estimated user emotions 2. The system of claim 1 .

7. The reception unit is Analyze the user's past art purchase history and suggest optimal input items 2. The system of claim 1 .

8. The reception unit is Customize input fields based on the user's current interests and trends 2. The system of claim 1 .

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