System

Abstract

To provide a system according to an embodiment, capable of manufacturing a content that can be easily sold by anyone on a metaverse, and making profits.SOLUTION: A system according to an embodiment comprises a content creation part, a conversion part, a selling part, and a profit feedback part. The content creation part creates a content. The conversion part converts the content created by the content creation part so as to conform to a specification of a metaverse. The selling part sells the content converted by the conversion part. The profit feedback part feedbacks the profit of the content sold by the selling part.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] With conventional technology, specialized knowledge and advanced skills were required to create content that could be sold on the metaverse, making it difficult for general users to access.

[0005] The system according to the embodiment aims to enable anyone to easily create and monetize content that can be sold on the metaverse. [Means for solving the problem]

[0006] The system according to the embodiment includes a content creation unit, a conversion unit, a sales unit, and a revenue return unit. The content creation unit creates content. The conversion unit converts the content created by the content creation unit so that it complies with the specifications of the metaverse. The sales unit sells the content converted by the conversion unit. The revenue return unit returns the revenue from the content sold by the sales unit. Effect of the Invention

[0007] The system according to the embodiment enables anyone to easily create and monetize content that can be sold on the metaverse. [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 content creation platform according to the embodiment of the present invention is a system that allows anyone to easily create content that can be sold on the metaverse. In this system, users can access the platform and create content with simple operations. For example, they can place 3D objects by drag and drop, and customize designs using templates. The created content is then automatically converted to comply with the specifications of major metaverses. This allows users to sell the same content in multiple metaverses. In addition, the platform also provides a sales function, allowing users to upload the content they create to a store in the metaverse and set a price to sell it. Revenue from the sold content is returned to the user through the platform. The introduction of this platform is expected to increase the number of IT human resources in Japan, and also contribute to the promotion of the spread of the metaverse. This allows the content creation platform to allow users to easily create content, sell it, and earn revenue.

[0029] The content creation platform according to the embodiment includes a content creation unit, a conversion unit, a sales unit, and a revenue return unit. The content creation unit provides a function for a user to create content. For example, the content creation unit includes a function for arranging 3D objects by drag and drop. A user can intuitively arrange 3D objects using, for example, a mouse operation or a touch operation. The content creation unit also includes a function for customizing a design using a template. A user can, for example, select a design template or a layout template and adjust customizable elements. The conversion unit converts the content created by the content creation unit to comply with the specifications of a major metaverse. For example, the conversion unit automatically converts the content using an algorithm to be used. The conversion unit can adjust the accuracy of the conversion to correspond to, for example, the technical requirements and formats of multiple metaverses. The sales unit provides a function for selling the content converted by the conversion unit. For example, the sales unit uploads the created content to a store in the metaverse, sets a price, and sells it. A user can set an appropriate price based on, for example, a price range or a market price. The revenue return unit provides a function of returning revenue from the content sold by the sales unit to the user. For example, the revenue return unit sets a return rate and timing, and returns the revenue to the user in the form of cash, points, etc. In this way, the content production platform according to the embodiment allows users to easily create and sell content and earn revenue.

[0030] The content creation unit provides a function for a user to create content. For example, the content creation unit has a function for placing 3D objects by dragging and dropping. A user can intuitively place a 3D object using, for example, a mouse operation or a touch operation. Specifically, a user can drag and drop a 3D object selected from a library onto a canvas and freely adjust the position, angle, and size. In addition, the content creation unit has a function for customizing a design using a template. A user can select, for example, a design template or a layout template, and adjust customizable elements. This allows a user, even a beginner, to easily create a professional design. In addition, the content creation unit provides a variety of editing functions, such as a text editor, an image editing tool, and an animation creation tool. A user can use these tools to change the font, color, and size of text, crop images, apply filters, and set the timing and movement of animations. In this way, the content creation unit provides strong support for a user to demonstrate his or her creativity and create original content. In addition, the content creation unit has a real-time preview function, allowing a user to instantly check the content being created. This allows users to check the appearance and behavior of the content they have created on the spot and make modifications as necessary. Furthermore, the content creation section works with cloud storage to safely store the content created by users and make it accessible anytime, anywhere. This allows users to continue working across different devices and create content efficiently.

[0031] The conversion unit converts the content created by the content creation unit to comply with the specifications of major metaverses. For example, the conversion unit automatically converts the content using the algorithm used. Specifically, the conversion unit converts the format of 3D models, optimizes textures, converts animation data, etc. This ensures that the content created by the user works without problems on different metaverse platforms. The conversion unit can adjust the accuracy of the conversion to accommodate the technical requirements and formats of multiple metaverses, for example. This allows users to use content on a wide range of platforms without relying on a specific metaverse platform. Furthermore, the conversion unit optimizes the conversion process using AI. For example, the AI ​​learns from past conversion data and automatically selects optimal conversion parameters to improve the accuracy and efficiency of the conversion. In addition, the conversion unit has a function to monitor the conversion status in real time and immediately notify if an error or malfunction occurs. This allows users to understand the progress of the conversion process and respond as necessary. Furthermore, the conversion unit provides a function to preview the converted content, allowing users to check the conversion results and make corrections as necessary. As a result, the conversion unit plays an important role in making the content created by users compatible with various metaverse platforms with high quality.

[0032] The sales department provides a function to sell the content converted by the conversion department. For example, the sales department uploads the created content to a store in the metaverse, sets a price, and sells it. Specifically, the user can set detailed information and preview images of the content, and set a price through the interface of the sales department. Based on this information, the sales department posts the content in the store in the metaverse so that the user can purchase it. The user can set an appropriate price, for example, based on a price range or a market price. Furthermore, the sales department provides a promotion and discount function, and the user can promote the sales of the content. For example, by setting a time-limited sale or bundle sale, sales opportunities can be increased. In addition, the sales department has a sales data analysis function, and provides information for the user to grasp the sales situation and optimize the sales strategy. This allows the user to analyze which content is popular and which price range is most effective, and improve sales activities. Furthermore, the sales department supports multiple payment methods, and allows the user to purchase the content using a credit card, a digital wallet, virtual currency, etc. In this way, the sales department provides comprehensive support for the user to easily and safely sell the content and earn revenue.

[0033] The revenue return unit provides a function of returning the revenue of the content sold by the sales unit to the user. For example, the revenue return unit sets the rate and timing of return, and returns the revenue to the user in the form of cash, points, or the like. Specifically, the revenue return unit calculates the revenue based on the sales data, and reflects the return amount in the user's account. The user can receive the revenue, for example, monthly or quarterly. In addition, the revenue return unit can flexibly set the form of return. For example, in addition to cash return, it is also possible to return the revenue as points or credits that can be used within the platform. This allows the user to reinvest the revenue and use it as funds to create new content. Furthermore, the revenue return unit provides a detailed revenue report to ensure the transparency of the revenue. Through the revenue report, the user can check which content generated how much revenue and how the return amount was calculated. This allows the revenue return unit to accurately grasp the revenue and receive a reliable return. In addition, the revenue return unit also handles tax processing and legal requirements, simplifying the procedures for the user to receive the revenue. In this way, the revenue return unit provides important support for users to receive revenue with peace of mind and continue their content production activities.

[0034] The content creation unit has a function of arranging a 3D object by drag and drop. The content creation unit can intuitively arrange the 3D object by using, for example, a mouse operation or a touch operation. For example, a user can arrange a 3D object on a canvas by using a drag and drop operation. The content creation unit also has a function of adjusting the position and size of the arranged 3D object. For example, a user can select a 3D object and move the position by a drag operation or change the size by a resize operation. Furthermore, the content creation unit also provides a customization function such as rotating the 3D object or changing the color. For example, a user can freely customize the design by rotating the 3D object or changing the color. This allows a user to intuitively arrange the 3D object and customize the design.

[0035] The content creation unit has a function of customizing a design using a template. The content creation unit provides, for example, design templates and layout templates. A user can select these templates and adjust customizable elements. For example, a user can select a design template, add text and images, and change colors and fonts. The content creation unit also provides a function of easily customizing the placement of content using a layout template. For example, a user can select a layout template and adjust the position of content by a drag-and-drop operation. Furthermore, the content creation unit also has a function of saving and reusing a customized design. For example, a user can save a customized design as a template and reuse it in other projects. This allows a user to easily customize a design and efficiently create content.

[0036] The conversion unit has a function of automatically converting the content to comply with the specifications of the metaverse. For example, the conversion unit automatically converts the content using an algorithm to be used. For example, the conversion unit converts the content to correspond to the technical requirements and formats of major metaverses. The conversion unit can adjust the accuracy of the conversion to comply with the specifications of multiple metaverses. For example, the conversion unit adjusts the number of polygons of a 3D object and the resolution of a texture to match the format of a specific metaverse. The conversion unit also has a function of adding necessary metadata to ensure compatibility of the content. For example, the conversion unit adds metadata to the content to optimize the display and operation in the metaverse. Furthermore, the conversion unit also provides a function of displaying the progress of the conversion process. For example, the conversion unit displays the progress of the conversion in real time and notifies the user of the completion of the conversion. This allows the user to sell the content in multiple metaverses.

[0037] The sales department has a function of uploading the created content to a store in the metaverse, setting a price, and selling it. The sales department, for example, provides a function of uploading the created content to a store in the metaverse. For example, a user can select a content file and upload it to a store. The sales department also provides a function of setting a price. For example, a user can set an appropriate price based on a price range or a market price. Furthermore, the sales department also has a function of monitoring the sales situation. For example, the sales department displays the number of sales and revenue in real time and provides feedback to the user. The sales department also provides a function of setting promotions and discounts. For example, a user can set promotions and discounts based on a specific period or condition. This allows the user to sell content in the metaverse and earn revenue.

[0038] The revenue return unit has a function of returning the revenue of sold content to the user. The revenue return unit provides, for example, a function of returning the revenue of sold content to the user. For example, the revenue return unit sets the rate and timing of return, and returns the revenue to the user in the form of cash, points, or the like. The revenue return unit can set, for example, a method of calculating revenue and a standard of return. For example, the revenue return unit calculates and returns revenue based on the number of sales and the price. The revenue return unit also provides a function of displaying the progress of return. For example, the revenue return unit displays the progress of return in real time, and notifies the user of the completion of return. Furthermore, the revenue return unit also has a function of managing the history of return. For example, the revenue return unit displays the history of past return so that the user can check it. This allows the user to receive the sales revenue.

[0039] The content creation unit has a function of analyzing the user's past content creation history and automatically suggesting appropriate templates and tools. The content creation unit provides, for example, a function of analyzing the user's past content creation history. For example, the content creation unit collects and analyzes data such as the type of content created by the user in the past, the tools used, and the creation date and time. The content creation unit automatically suggests optimal templates and tools based on the analysis results. For example, the optimal template is suggested based on the templates used by the user in the past. In addition, it is also possible to preferentially display frequently used tools from the user's past creation history. Furthermore, the content creation unit analyzes the user's past content creation patterns and suggests optimal creation procedures. For example, an efficient creation procedure is suggested based on the patterns of content created by the user in the past. This allows optimal suggestions to be made based on the user's past history.

[0040] The content creation unit has a function of automatically providing related materials and objects based on the user's current project and field of interest when creating content. The content creation unit provides, for example, a function of identifying the user's current project and field of interest. For example, the content creation unit analyzes the content of the project currently being carried out by the user and the category of the field of interest. The content creation unit automatically provides related materials and objects based on the analysis result. For example, the content creation unit automatically suggests materials related to the project currently being carried out by the user. In addition, the content creation unit can provide related 3D objects based on the user's field of interest. Furthermore, the content creation unit suggests related design elements based on content created by the user in the past. For example, the content creation unit suggests related materials and objects based on the design elements of content created by the user in the past. This makes it possible to provide materials according to the user's projects and fields of interest.

[0041] The content creation unit has a function of proposing region-specific materials and designs in consideration of the user's geographical location information when creating content. The content creation unit provides, for example, a function of acquiring the user's geographical location information. For example, the content creation unit identifies the user's location information using GPS data or an IP address. The content creation unit proposes region-specific materials and designs based on the acquired geographical location information. For example, when the user is in a specific region, materials related to the culture and scenery of the region are proposed. In addition, region-specific design elements can also be provided based on the user's location information. Furthermore, when the user is traveling, content related to the region visited is proposed. For example, materials related to the culture and tourist attractions of the region where the user is traveling are proposed. In this way, materials and designs based on the user's geographical location information can be provided.

[0042] The content creation unit has a function of analyzing the user's social media activity when creating content and proposing related trends and popular designs. The content creation unit provides, for example, a function of analyzing the user's social media activity. For example, the content creation unit collects and analyzes data such as the user's posted content, the number of likes, and the number of followers. The content creation unit proposes related trends and popular designs based on the analysis results. For example, related designs are proposed based on the trends of accounts the user follows. In addition, the content of the user's social media activity can be analyzed to provide designs that the user may be interested in. Furthermore, popular designs are proposed based on trends in communities in which the user participates. For example, trends in communities in which the user participates are analyzed to propose related designs. In this way, designs based on the user's social media activity can be provided.

[0043] The conversion unit has a function of selecting an appropriate conversion algorithm by referring to past conversion data during conversion. The conversion unit provides, for example, a function of referring to past conversion data. For example, the conversion unit collects and analyzes conversion data and conversion history that were successful in the past. The conversion unit selects an optimal conversion algorithm based on the analysis results. For example, the conversion unit selects an optimal algorithm based on conversion data that was successful in the past. The conversion unit can also analyze the user's past conversion history and suggest an optimal algorithm. Furthermore, an algorithm with fewer errors is selected from the past conversion data. For example, the conversion unit selects an algorithm with fewer errors based on the past conversion data to improve the accuracy of the conversion. This makes it possible to select an optimal conversion algorithm based on past data.

[0044] The conversion unit has a function of applying an appropriate conversion method according to the category of the content at the time of conversion. The conversion unit provides, for example, a function of identifying the category of the content. For example, the conversion unit identifies which category the content belongs to, such as text, image, video, or 3D model. The conversion unit applies an appropriate conversion method according to the identified category. For example, in the case of a 3D object, an optimal conversion method is applied to maintain quality. In addition, in the case of text content, a method of conversion while maintaining the format can also be applied. Furthermore, in the case of video content, the compression rate is adjusted to apply the optimal conversion method. For example, the conversion unit adjusts the number of polygons and the resolution of the texture to maintain quality when converting a 3D object. In addition, when converting text content, conversion is performed while maintaining the format. In the case of converting video content, the compression rate is adjusted and the optimal conversion method is applied. This makes it possible to apply the optimal conversion method according to the category of the content.

[0045] The conversion unit has a function of converting the content to conform to region-specific specifications while taking into account the geographical location information of the user. The conversion unit provides, for example, a function of acquiring the geographical location information of the user. For example, the conversion unit identifies the user's location information using GPS data or an IP address. The conversion unit converts the content to conform to region-specific specifications based on the acquired geographical location information. For example, when the user is in a specific region, the conversion unit converts the content to conform to the specifications of the region. The conversion unit can also convert the content to a region-specific format based on the user's location information. Furthermore, when the user is traveling, the conversion unit converts the content to conform to the specifications of the region the user is visiting. For example, the content is converted to conform to the technical requirements and format of the region the user is traveling to. This allows the conversion to conform to specifications based on the user's geographical location information.

[0046] The conversion unit has a function of analyzing the user's social media activity at the time of conversion and proposing related specifications and formats. The conversion unit provides, for example, a function of analyzing the user's social media activity. For example, the conversion unit collects and analyzes data such as the user's posted content, the number of likes, and the number of followers. The conversion unit proposes related specifications and formats based on the analysis results. For example, the conversion unit converts the content into an optimal format on the assumption that the user will share it on social media. The conversion unit can also analyze the user's social media activity and propose related specifications. Furthermore, the conversion unit converts the content to comply with the specifications of the accounts the user follows. For example, the content is converted to match the technical requirements and format of the accounts the user follows. This makes it possible to propose specifications and formats based on the user's social media activity.

[0047] The sales department has a function of proposing appropriate pricing and promotions by referring to past sales data at the time of sale. The sales department, for example, provides a function of referring to past sales data. For example, the sales department collects and analyzes past sales history and sales results. The sales department proposes optimal pricing and promotions based on the analysis results. For example, optimal pricing is proposed based on past sales data. In addition, the sales department can analyze the user's past sales history and propose effective promotions. Furthermore, pricing according to seasons and events is proposed based on past sales data. For example, the sales department sets prices according to seasons and events based on past sales data to promote sales. This makes it possible to propose optimal pricing and promotions based on past sales data.

[0048] The sales department has a function of applying an appropriate sales strategy according to the category of the content at the time of sale. The sales department provides, for example, a function of identifying the category of the content. For example, the sales department identifies which category the content belongs to, such as text, image, video, or 3D model. The sales department applies an appropriate sales strategy according to the identified category. For example, in the case of a 3D object, a visually attractive presentation is provided. Also, in the case of text content, a sample can be provided to increase purchasing motivation. Furthermore, in the case of video content, a preview is provided to increase purchasing motivation. For example, the sales department provides a visually attractive presentation when selling a 3D object to increase purchasing motivation. Also, in the case of selling text content, a sample is provided to increase purchasing motivation. Furthermore, in the case of selling video content, a preview is provided to increase purchasing motivation. This makes it possible to apply an optimal sales strategy according to the category of the content.

[0049] The sales department has a function of preferentially posting on a region-specific marketplace in consideration of the geographical location information of the user at the time of sale. The sales department provides, for example, a function of acquiring the geographical location information of the user. For example, the sales department identifies the user's location information using GPS data or an IP address. The sales department preferentially posts on a region-specific marketplace based on the acquired geographical location information. For example, when the user is in a specific region, the content is preferentially posted on the marketplace of that region. In addition, region-specific promotions can be provided based on the user's location information. Furthermore, when the user is traveling, the content is posted on a marketplace in the region the user is visiting. For example, the content is posted on a marketplace in the region the user is traveling to, promoting sales. This allows the content to be preferentially posted on a marketplace based on the user's geographical location information.

[0050] The sales department has a function of analyzing the user's social media activity at the time of sale and proposing related promotions and advertisements. The sales department provides, for example, a function of analyzing the user's social media activity. For example, the sales department collects and analyzes data such as the user's posted content, number of likes, number of followers, etc. The sales department proposes related promotions and advertisements based on the analysis results. For example, the sales department proposes optimal promotions on the premise that the user will share on social media. In addition, the sales department can analyze the user's social media activity and provide related advertisements. Furthermore, promotions are proposed based on trends of accounts followed by the user. For example, related promotions are proposed based on trends of accounts followed by the user. This makes it possible to propose promotions and advertisements based on the user's social media activity.

[0051] The revenue return unit has a function of selecting an appropriate return method by referring to past revenue data at the time of revenue return. The revenue return unit, for example, provides a function of referring to past revenue data. For example, the revenue return unit collects and analyzes past revenue history and revenue results. The revenue return unit selects an optimal return method based on the analysis results. For example, the optimal return method is selected based on past revenue data. In addition, the revenue return unit can analyze the user's past revenue history and propose optimal return timing. Furthermore, a choice of return methods is provided based on the past revenue data. For example, the revenue return unit provides a choice of return methods based on the past revenue data, allowing the user to select the optimal method. This allows the optimal return method to be selected based on the past revenue data.

[0052] The revenue return unit has a function of customizing the timing and method of return based on the user's current revenue situation at the time of revenue return. The revenue return unit provides, for example, a function of analyzing the user's current revenue situation. For example, the revenue return unit identifies the amount of revenue of the user and the type of revenue (cash, points, etc.). The revenue return unit customizes the timing and method of return based on the analysis result. For example, the revenue return unit analyzes the user's current revenue situation and proposes the optimal timing of return. The revenue return unit can also customize the return method based on the user's revenue situation. Furthermore, if the user is in a hurry to receive the revenue, a quick return method is provided. For example, the revenue return unit provides a quick return method based on the user's revenue situation so that the user can receive the revenue early. This makes it possible to provide the timing and method of return based on the user's current revenue situation.

[0053] The revenue return unit has a function of proposing a region-specific return method in consideration of the geographical location information of the user at the time of revenue return. The revenue return unit provides, for example, a function of acquiring the geographical location information of the user. For example, the revenue return unit identifies the user's location information using GPS data or an IP address. The revenue return unit proposes a region-specific return method based on the acquired geographical location information. For example, when the user is in a specific region, a return method for that region is proposed. In addition, region-specific return options can be provided based on the user's location information. Furthermore, when the user is traveling, a return method for the region visited is proposed. For example, the optimal return method is proposed based on the return method for the region where the user is traveling. In this way, a return method based on the user's geographical location information can be provided.

[0054] The revenue return unit has a function of analyzing the user's social media activity at the time of revenue return and proposing a related return method and promotion. The revenue return unit provides, for example, a function of analyzing the user's social media activity. For example, the revenue return unit collects and analyzes data such as the user's posted content, the number of likes, and the number of followers. The revenue return unit proposes a related return method and promotion based on the analysis result. For example, the revenue return unit proposes an optimal return method on the premise that the user shares on social media. In addition, the revenue return unit can analyze the user's social media activity and provide a related promotion. Furthermore, the revenue return unit proposes a return method based on the trend of the accounts the user follows. For example, the revenue return unit proposes a related return method based on the trend of the accounts the user follows. This makes it possible to provide a return method and promotion based on the user's social media activity.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] The content creation platform may further include a skill determination unit that determines the skill level of a user. The skill determination unit analyzes the user's past content creation history, operation speed, types of tools used, and the like to determine the user's skill level. For example, basic tools and a simple interface can be provided to novice users, and advanced tools and a customizable interface can be provided to advanced users. The skill determination unit may also provide tutorials and guides according to the user's skill level. This allows users to efficiently create content in an environment that matches their own skill level.

[0057] The content creation unit can further include an auto-completion function for improving the user's work efficiency. The auto-completion function automatically adjusts the position, size, color, etc. of a 3D object placed by the user to maintain consistency in the design. For example, when the user places multiple objects, the auto-completion function automatically adjusts the spacing between the objects to keep them even. Also, based on the color selected by the user, the colors of other objects can be automatically adjusted to unify the overall design. Furthermore, the auto-completion function can learn operations frequently used by the user and predict and suggest operations for the next time and thereafter. This allows the user to create content efficiently.

[0058] The content creation unit can further take into account the user's geographic location information to suggest region-specific materials and designs. For example, if the user is in a particular region, materials related to the culture and scenery of that region are suggested. Region-specific design elements can also be provided based on the user's location information. Furthermore, if the user is traveling, content related to the region visited can be suggested. This makes it possible to provide materials and designs based on the user's geographic location information.

[0059] The conversion unit can further select an appropriate conversion algorithm by referring to past conversion data. For example, an optimal algorithm is selected based on conversion data that was successful in the past. Also, the conversion unit can analyze the user's past conversion history and suggest an optimal algorithm. Furthermore, an algorithm with fewer errors can be selected from the past conversion data. This makes it possible to select an optimal conversion algorithm based on past data.

[0060] The sales department can further refer to past sales data to propose appropriate pricing and promotions. For example, optimal pricing can be proposed based on past sales data. In addition, it is also possible to analyze the user's past sales history and propose effective promotions. Furthermore, it is possible to propose pricing according to seasons and events based on past sales data. In this way, it is possible to propose optimal pricing and promotions based on past sales data.

[0061] The revenue return unit can further customize the timing and method of return based on the user's current revenue situation. For example, the revenue return unit can analyze the user's current revenue situation and propose the optimal timing of return. The revenue return unit can also customize the return method based on the user's revenue situation. Furthermore, if the user is in a hurry to return revenue, a quick return method can be provided. This makes it possible to provide the timing and method of return based on the user's current revenue situation.

[0062] The process flow of the first embodiment will be briefly described below.

[0063] Step 1: The content creation unit provides a function for a user to create content. For example, the content creation unit has a function for placing 3D objects by drag and drop. The user can intuitively place the 3D objects by using, for example, a mouse operation or a touch operation. The content creation unit also has a function for customizing a design using a template. The user can, for example, select a design template or a layout template and adjust customizable elements. Step 2: The converter converts the content created by the content creator to conform to the specifications of the major metaverses. For example, the converter automatically converts the content using algorithms it uses. The converter can adjust the accuracy of the conversion to accommodate the technical requirements and formats of multiple metaverses, for example. Step 3: The sales department provides a function to sell the content converted by the conversion department. For example, the sales department uploads the created content to a store in the metaverse, sets a price, and sells it. Users can set an appropriate price based on, for example, a price range or market price. Step 4: The revenue return unit provides a function of returning the revenue of the content sold by the sales unit to the user. For example, the revenue return unit sets the rate and timing of return, and returns the revenue to the user in the form of cash, points, etc.

[0064] (Example 2) The content creation platform according to the embodiment of the present invention is a system that allows anyone to easily create content that can be sold on the metaverse. In this system, users can access the platform and create content with simple operations. For example, they can place 3D objects by drag and drop, and customize designs using templates. The created content is then automatically converted to comply with the specifications of major metaverses. This allows users to sell the same content in multiple metaverses. In addition, the platform also provides a sales function, allowing users to upload the content they create to a store in the metaverse and set a price to sell it. Revenue from the sold content is returned to the user through the platform. The introduction of this platform is expected to increase the number of IT human resources in Japan, and also contribute to the promotion of the spread of the metaverse. This allows the content creation platform to allow users to easily create content, sell it, and earn revenue.

[0065] The content creation platform according to the embodiment includes a content creation unit, a conversion unit, a sales unit, and a revenue return unit. The content creation unit provides a function for a user to create content. For example, the content creation unit includes a function for arranging 3D objects by drag and drop. A user can intuitively arrange 3D objects using, for example, a mouse operation or a touch operation. The content creation unit also includes a function for customizing a design using a template. A user can, for example, select a design template or a layout template and adjust customizable elements. The conversion unit converts the content created by the content creation unit to comply with the specifications of a major metaverse. For example, the conversion unit automatically converts the content using an algorithm to be used. The conversion unit can adjust the accuracy of the conversion to correspond to, for example, the technical requirements and formats of multiple metaverses. The sales unit provides a function for selling the content converted by the conversion unit. For example, the sales unit uploads the created content to a store in the metaverse, sets a price, and sells it. A user can set an appropriate price based on, for example, a price range or a market price. The revenue return unit provides a function of returning revenue from the content sold by the sales unit to the user. For example, the revenue return unit sets a return rate and timing, and returns the revenue to the user in the form of cash, points, etc. In this way, the content production platform according to the embodiment allows users to easily create and sell content and earn revenue.

[0066] The content creation unit provides a function for a user to create content. For example, the content creation unit has a function for placing 3D objects by dragging and dropping. A user can intuitively place a 3D object using, for example, a mouse operation or a touch operation. Specifically, a user can drag and drop a 3D object selected from a library onto a canvas and freely adjust the position, angle, and size. In addition, the content creation unit has a function for customizing a design using a template. A user can select, for example, a design template or a layout template, and adjust customizable elements. This allows a user, even a beginner, to easily create a professional design. In addition, the content creation unit provides a variety of editing functions, such as a text editor, an image editing tool, and an animation creation tool. A user can use these tools to change the font, color, and size of text, crop images, apply filters, and set the timing and movement of animations. In this way, the content creation unit provides strong support for a user to demonstrate his or her creativity and create original content. In addition, the content creation unit has a real-time preview function, allowing a user to instantly check the content being created. This allows users to check the appearance and behavior of the content they have created on the spot and make modifications as necessary. Furthermore, the content creation section works with cloud storage to safely store the content created by users and make it accessible anytime, anywhere. This allows users to continue working across different devices and create content efficiently.

[0067] The conversion unit converts the content created by the content creation unit to comply with the specifications of major metaverses. For example, the conversion unit automatically converts the content using the algorithm used. Specifically, the conversion unit converts the format of 3D models, optimizes textures, converts animation data, etc. This ensures that the content created by the user works without problems on different metaverse platforms. The conversion unit can adjust the accuracy of the conversion to accommodate the technical requirements and formats of multiple metaverses, for example. This allows users to use content on a wide range of platforms without relying on a specific metaverse platform. Furthermore, the conversion unit optimizes the conversion process using AI. For example, the AI ​​learns from past conversion data and automatically selects optimal conversion parameters to improve the accuracy and efficiency of the conversion. In addition, the conversion unit has a function to monitor the conversion status in real time and immediately notify if an error or malfunction occurs. This allows users to understand the progress of the conversion process and respond as necessary. Furthermore, the conversion unit provides a function to preview the converted content, allowing users to check the conversion results and make corrections as necessary. As a result, the conversion unit plays an important role in making the content created by users compatible with various metaverse platforms with high quality.

[0068] The sales department provides a function to sell the content converted by the conversion department. For example, the sales department uploads the created content to a store in the metaverse, sets a price, and sells it. Specifically, the user can set detailed information and preview images of the content, and set a price through the interface of the sales department. Based on this information, the sales department posts the content in the store in the metaverse so that the user can purchase it. The user can set an appropriate price, for example, based on a price range or a market price. Furthermore, the sales department provides a promotion and discount function, and the user can promote the sales of the content. For example, by setting a time-limited sale or bundle sale, sales opportunities can be increased. In addition, the sales department has a sales data analysis function, and provides information for the user to grasp the sales situation and optimize the sales strategy. This allows the user to analyze which content is popular and which price range is most effective, and improve sales activities. Furthermore, the sales department supports multiple payment methods, and allows the user to purchase the content using a credit card, a digital wallet, virtual currency, etc. In this way, the sales department provides comprehensive support for the user to easily and safely sell the content and earn revenue.

[0069] The revenue return unit provides a function of returning the revenue of the content sold by the sales unit to the user. For example, the revenue return unit sets the rate and timing of return, and returns the revenue to the user in the form of cash, points, or the like. Specifically, the revenue return unit calculates the revenue based on the sales data, and reflects the return amount in the user's account. The user can receive the revenue, for example, monthly or quarterly. In addition, the revenue return unit can flexibly set the form of return. For example, in addition to cash return, it is also possible to return the revenue as points or credits that can be used within the platform. This allows the user to reinvest the revenue and use it as funds to create new content. Furthermore, the revenue return unit provides a detailed revenue report to ensure the transparency of the revenue. Through the revenue report, the user can check which content generated how much revenue and how the return amount was calculated. This allows the revenue return unit to accurately grasp the revenue and receive a reliable return. In addition, the revenue return unit also handles tax processing and legal requirements, simplifying the procedures for the user to receive the revenue. In this way, the revenue return unit provides important support for users to receive revenue with peace of mind and continue their content production activities.

[0070] The content creation unit has a function of arranging a 3D object by drag and drop. The content creation unit can intuitively arrange the 3D object by using, for example, a mouse operation or a touch operation. For example, a user can arrange a 3D object on a canvas by using a drag and drop operation. The content creation unit also has a function of adjusting the position and size of the arranged 3D object. For example, a user can select a 3D object and move the position by a drag operation or change the size by a resize operation. Furthermore, the content creation unit also provides a customization function such as rotating the 3D object or changing the color. For example, a user can freely customize the design by rotating the 3D object or changing the color. This allows a user to intuitively arrange the 3D object and customize the design.

[0071] The content creation unit has a function of customizing a design using a template. The content creation unit provides, for example, design templates and layout templates. A user can select these templates and adjust customizable elements. For example, a user can select a design template, add text and images, and change colors and fonts. The content creation unit also provides a function of easily customizing the placement of content using a layout template. For example, a user can select a layout template and adjust the position of content by a drag-and-drop operation. Furthermore, the content creation unit also has a function of saving and reusing a customized design. For example, a user can save a customized design as a template and reuse it in other projects. This allows a user to easily customize a design and efficiently create content.

[0072] The conversion unit has a function of automatically converting the content to comply with the specifications of the metaverse. For example, the conversion unit automatically converts the content using an algorithm to be used. For example, the conversion unit converts the content to correspond to the technical requirements and formats of major metaverses. The conversion unit can adjust the accuracy of the conversion to comply with the specifications of multiple metaverses. For example, the conversion unit adjusts the number of polygons of a 3D object and the resolution of a texture to match the format of a specific metaverse. The conversion unit also has a function of adding necessary metadata to ensure compatibility of the content. For example, the conversion unit adds metadata to the content to optimize the display and operation in the metaverse. Furthermore, the conversion unit also provides a function of displaying the progress of the conversion process. For example, the conversion unit displays the progress of the conversion in real time and notifies the user of the completion of the conversion. This allows the user to sell the content in multiple metaverses.

[0073] The sales department has a function of uploading the created content to a store in the metaverse, setting a price, and selling it. The sales department, for example, provides a function of uploading the created content to a store in the metaverse. For example, a user can select a content file and upload it to a store. The sales department also provides a function of setting a price. For example, a user can set an appropriate price based on a price range or a market price. Furthermore, the sales department also has a function of monitoring the sales situation. For example, the sales department displays the number of sales and revenue in real time and provides feedback to the user. The sales department also provides a function of setting promotions and discounts. For example, a user can set promotions and discounts based on a specific period or condition. This allows the user to sell content in the metaverse and earn revenue.

[0074] The revenue return unit has a function of returning the revenue of sold content to the user. The revenue return unit provides, for example, a function of returning the revenue of sold content to the user. For example, the revenue return unit sets the rate and timing of return, and returns the revenue to the user in the form of cash, points, or the like. The revenue return unit can set, for example, a method of calculating revenue and a standard of return. For example, the revenue return unit calculates and returns revenue based on the number of sales and the price. The revenue return unit also provides a function of displaying the progress of return. For example, the revenue return unit displays the progress of return in real time, and notifies the user of the completion of return. Furthermore, the revenue return unit also has a function of managing the history of return. For example, the revenue return unit displays the history of past return so that the user can check it. This allows the user to receive the sales revenue.

[0075] The content creation unit has a function of estimating the user's emotion and customizing the content creation interface based on the emotion. The content creation unit estimates the user's emotion, for example, using an emotion estimation algorithm. For example, the content creation unit analyzes data such as the user's facial expression, voice, and text to estimate the emotion. The emotion estimation is realized by 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. The content creation unit customizes the interface based on the estimated user's emotion. For example, when the user is stressed, a simple and intuitive interface is provided to simplify the operation. Also, when the user is relaxed, detailed customization options are provided to allow the user to demonstrate creativity. Furthermore, when the user is in a hurry, templates are preferentially displayed to allow the user to quickly create content. This makes it possible to provide an interface according to the user's emotion.

[0076] The content creation unit has a function of analyzing the user's past content creation history and automatically suggesting appropriate templates and tools. The content creation unit provides, for example, a function of analyzing the user's past content creation history. For example, the content creation unit collects and analyzes data such as the type of content created by the user in the past, the tools used, and the creation date and time. The content creation unit automatically suggests optimal templates and tools based on the analysis results. For example, the optimal template is suggested based on the templates used by the user in the past. In addition, it is also possible to preferentially display frequently used tools from the user's past creation history. Furthermore, the content creation unit analyzes the user's past content creation patterns and suggests optimal creation procedures. For example, an efficient creation procedure is suggested based on the patterns of content created by the user in the past. This allows optimal suggestions to be made based on the user's past history.

[0077] The content creation unit has a function of automatically providing related materials and objects based on the user's current project and field of interest when creating content. The content creation unit provides, for example, a function of identifying the user's current project and field of interest. For example, the content creation unit analyzes the content of the project currently being carried out by the user and the category of the field of interest. The content creation unit automatically provides related materials and objects based on the analysis result. For example, the content creation unit automatically suggests materials related to the project currently being carried out by the user. In addition, the content creation unit can provide related 3D objects based on the user's field of interest. Furthermore, the content creation unit suggests related design elements based on content created by the user in the past. For example, the content creation unit suggests related materials and objects based on the design elements of content created by the user in the past. This makes it possible to provide materials according to the user's projects and fields of interest.

[0078] The content creation unit has a function of estimating the user's emotion and providing a guide or tutorial for content creation based on the estimated user's emotion. The content creation unit estimates the user's emotion, for example, using an emotion estimation algorithm. For example, the content creation unit analyzes data such as the user's facial expression, voice, and text to estimate the emotion. The emotion estimation is realized by 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. The content creation unit provides a guide or tutorial based on the estimated user's emotion. For example, if the user feels anxious, a step-by-step guide is provided to allow the user to create with peace of mind. Also, if the user is confident, a detailed tutorial is omitted and the user is allowed to create freely. Furthermore, if the user is a first-time user, a tutorial that explains basic operation methods in detail is provided. This makes it possible to provide a guide or tutorial according to the user's emotion.

[0079] The content creation unit has a function of proposing region-specific materials and designs in consideration of the user's geographical location information when creating content. The content creation unit provides, for example, a function of acquiring the user's geographical location information. For example, the content creation unit identifies the user's location information using GPS data or an IP address. The content creation unit proposes region-specific materials and designs based on the acquired geographical location information. For example, when the user is in a specific region, materials related to the culture and scenery of the region are proposed. In addition, region-specific design elements can also be provided based on the user's location information. Furthermore, when the user is traveling, content related to the region visited is proposed. For example, materials related to the culture and tourist attractions of the region where the user is traveling are proposed. In this way, materials and designs based on the user's geographical location information can be provided.

[0080] The content creation unit has a function of analyzing the user's social media activity when creating content and proposing related trends and popular designs. The content creation unit provides, for example, a function of analyzing the user's social media activity. For example, the content creation unit collects and analyzes data such as the user's posted content, the number of likes, and the number of followers. The content creation unit proposes related trends and popular designs based on the analysis results. For example, related designs are proposed based on the trends of accounts the user follows. In addition, the content of the user's social media activity can be analyzed to provide designs that the user may be interested in. Furthermore, popular designs are proposed based on trends in communities in which the user participates. For example, trends in communities in which the user participates are analyzed to propose related designs. In this way, designs based on the user's social media activity can be provided.

[0081] The conversion unit has a function of estimating the user's emotion and adjusting the method of displaying the progress of the conversion process based on the emotion. The conversion unit estimates the user's emotion, for example, using an emotion estimation algorithm. For example, the conversion unit analyzes data such as the user's facial expression, voice, and text to estimate the emotion. The emotion estimation is realized by 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. The conversion unit adjusts the method of displaying the progress of the conversion process based on the estimated user's emotion. For example, when the user feels anxious, the progress is displayed in detail to provide a sense of security. Also, when the user is relaxed, the progress is displayed briefly to reduce stress. Furthermore, when the user is in a hurry, the progress is displayed quickly to shorten the time until the conversion is completed. This makes it possible to display the progress according to the user's emotion.

[0082] The conversion unit has a function of selecting an appropriate conversion algorithm by referring to past conversion data during conversion. The conversion unit provides, for example, a function of referring to past conversion data. For example, the conversion unit collects and analyzes conversion data and conversion history that were successful in the past. The conversion unit selects an optimal conversion algorithm based on the analysis results. For example, the conversion unit selects an optimal algorithm based on conversion data that was successful in the past. The conversion unit can also analyze the user's past conversion history and suggest an optimal algorithm. Furthermore, an algorithm with fewer errors is selected from the past conversion data. For example, the conversion unit selects an algorithm with fewer errors based on the past conversion data to improve the accuracy of the conversion. This makes it possible to select an optimal conversion algorithm based on past data.

[0083] The conversion unit has a function of applying an appropriate conversion method according to the category of the content at the time of conversion. The conversion unit provides, for example, a function of identifying the category of the content. For example, the conversion unit identifies which category the content belongs to, such as text, image, video, or 3D model. The conversion unit applies an appropriate conversion method according to the identified category. For example, in the case of a 3D object, an optimal conversion method is applied to maintain quality. In addition, in the case of text content, a method of conversion while maintaining the format can also be applied. Furthermore, in the case of video content, the compression rate is adjusted to apply the optimal conversion method. For example, the conversion unit adjusts the number of polygons and the resolution of the texture to maintain quality when converting a 3D object. In addition, when converting text content, conversion is performed while maintaining the format. In the case of converting video content, the compression rate is adjusted and the optimal conversion method is applied. This makes it possible to apply the optimal conversion method according to the category of the content.

[0084] The conversion unit has a function of estimating the user's emotion and determining the priority of the conversion process based on the estimated user's emotion. The conversion unit estimates the user's emotion, for example, using an emotion estimation algorithm. For example, the conversion unit analyzes data such as the user's facial expression, voice, and text to estimate the emotion. The emotion estimation is realized by 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. The conversion unit determines the priority of the conversion process based on the estimated user's emotion. For example, when the user is in a hurry, the priority of the conversion process is set high and conversion is performed quickly. Also, when the user is relaxed, the priority of the conversion process is set low and other tasks are prioritized. Furthermore, when the user feels anxious, the priority of the conversion process is set to a medium level and the progress is displayed in detail. This makes it possible to determine the priority of the conversion process according to the user's emotion.

[0085] The conversion unit has a function of converting the content to conform to region-specific specifications while taking into account the geographical location information of the user. The conversion unit provides, for example, a function of acquiring the geographical location information of the user. For example, the conversion unit identifies the user's location information using GPS data or an IP address. The conversion unit converts the content to conform to region-specific specifications based on the acquired geographical location information. For example, when the user is in a specific region, the conversion unit converts the content to conform to the specifications of the region. The conversion unit can also convert the content to a region-specific format based on the user's location information. Furthermore, when the user is traveling, the conversion unit converts the content to conform to the specifications of the region the user is visiting. For example, the content is converted to conform to the technical requirements and format of the region the user is traveling to. This allows the conversion to conform to specifications based on the user's geographical location information.

[0086] The conversion unit has a function of analyzing the user's social media activity at the time of conversion and proposing related specifications and formats. The conversion unit provides, for example, a function of analyzing the user's social media activity. For example, the conversion unit collects and analyzes data such as the user's posted content, the number of likes, and the number of followers. The conversion unit proposes related specifications and formats based on the analysis results. For example, the conversion unit converts the content into an optimal format on the assumption that the user will share it on social media. The conversion unit can also analyze the user's social media activity and propose related specifications. Furthermore, the conversion unit converts the content to comply with the specifications of the accounts the user follows. For example, the content is converted to match the technical requirements and format of the accounts the user follows. This makes it possible to propose specifications and formats based on the user's social media activity.

[0087] The sales department has a function of estimating the user's emotion and adjusting the design and layout of the sales page based on the emotion. The sales department estimates the user's emotion, for example, using an emotion estimation algorithm. For example, the sales department analyzes data such as the user's facial expression, voice, and text to estimate the emotion. The emotion estimation is realized using an emotion estimation function, for example, using 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. The sales department adjusts the design and layout of the sales page based on the estimated user's emotion. For example, if the user is excited, a visually stimulating design is provided. Also, if the user is relaxed, a calm design is provided. Furthermore, if the user is anxious, a simple and intuitive layout is provided. This makes it possible to provide the design and layout of the sales page according to the user's emotion.

[0088] The sales department has a function of proposing appropriate pricing and promotions by referring to past sales data at the time of sale. The sales department, for example, provides a function of referring to past sales data. For example, the sales department collects and analyzes past sales history and sales results. The sales department proposes optimal pricing and promotions based on the analysis results. For example, optimal pricing is proposed based on past sales data. In addition, the sales department can analyze the user's past sales history and propose effective promotions. Furthermore, pricing according to seasons and events is proposed based on past sales data. For example, the sales department sets prices according to seasons and events based on past sales data to promote sales. This makes it possible to propose optimal pricing and promotions based on past sales data.

[0089] The sales department has a function of applying an appropriate sales strategy according to the category of the content at the time of sale. The sales department provides, for example, a function of identifying the category of the content. For example, the sales department identifies which category the content belongs to, such as text, image, video, or 3D model. The sales department applies an appropriate sales strategy according to the identified category. For example, in the case of a 3D object, a visually attractive presentation is provided. Also, in the case of text content, a sample can be provided to increase purchasing motivation. Furthermore, in the case of video content, a preview is provided to increase purchasing motivation. For example, the sales department provides a visually attractive presentation when selling a 3D object to increase purchasing motivation. Also, in the case of selling text content, a sample is provided to increase purchasing motivation. Furthermore, in the case of selling video content, a preview is provided to increase purchasing motivation. This makes it possible to apply an optimal sales strategy according to the category of the content.

[0090] The sales department has a function of estimating the user's emotion and adjusting the timing and method of sales based on the estimated user's emotion. The sales department estimates the user's emotion, for example, using an emotion estimation algorithm. For example, the sales department analyzes data such as the user's facial expression, voice, and text to estimate the emotion. The emotion estimation is realized using an emotion estimation function, for example, using 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. The sales department adjusts the timing and method of sales based on the estimated user's emotion. For example, if the user is excited, it promotes immediate sales. Also, if the user is relaxed, it provides a slow sales process. Furthermore, if the user is anxious, it provides a sales method that gives a sense of security. This makes it possible to provide the timing and method of sales according to the user's emotion.

[0091] The sales department has a function of preferentially posting on a region-specific marketplace in consideration of the geographical location information of the user at the time of sale. The sales department provides, for example, a function of acquiring the geographical location information of the user. For example, the sales department identifies the user's location information using GPS data or an IP address. The sales department preferentially posts on a region-specific marketplace based on the acquired geographical location information. For example, when the user is in a specific region, the content is preferentially posted on the marketplace of that region. In addition, region-specific promotions can be provided based on the user's location information. Furthermore, when the user is traveling, the content is posted on a marketplace in the region the user is visiting. For example, the content is posted on a marketplace in the region the user is traveling to, promoting sales. This allows the content to be preferentially posted on a marketplace based on the user's geographical location information.

[0092] The sales department has a function of analyzing the user's social media activity at the time of sale and proposing related promotions and advertisements. The sales department provides, for example, a function of analyzing the user's social media activity. For example, the sales department collects and analyzes data such as the user's posted content, number of likes, number of followers, etc. The sales department proposes related promotions and advertisements based on the analysis results. For example, the sales department proposes optimal promotions on the premise that the user will share on social media. In addition, the sales department can analyze the user's social media activity and provide related advertisements. Furthermore, promotions are proposed based on trends of accounts followed by the user. For example, related promotions are proposed based on trends of accounts followed by the user. This makes it possible to propose promotions and advertisements based on the user's social media activity.

[0093] The revenue return unit has a function of estimating the user's emotion and adjusting the notification method of the revenue return based on the emotion. The revenue return unit estimates the user's emotion, for example, using an emotion estimation algorithm. For example, the revenue return unit analyzes data such as the user's facial expression, voice, and text to estimate the emotion. The emotion estimation is realized, for example, using an emotion estimation function using 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. The revenue return unit adjusts the notification method of the revenue return based on the estimated user's emotion. For example, if the user is excited, a visually stimulating notification is provided. Also, if the user is relaxed, a calming notification is provided. Furthermore, if the user is anxious, a simple and intuitive notification is provided. This makes it possible to provide a notification method of the revenue return according to the user's emotion.

[0094] The revenue return unit has a function of selecting an appropriate return method by referring to past revenue data at the time of revenue return. The revenue return unit, for example, provides a function of referring to past revenue data. For example, the revenue return unit collects and analyzes past revenue history and revenue results. The revenue return unit selects an optimal return method based on the analysis results. For example, the optimal return method is selected based on past revenue data. In addition, the revenue return unit can analyze the user's past revenue history and propose optimal return timing. Furthermore, a choice of return methods is provided based on the past revenue data. For example, the revenue return unit provides a choice of return methods based on the past revenue data, allowing the user to select the optimal method. This allows the optimal return method to be selected based on the past revenue data.

[0095] The revenue return unit has a function of customizing the timing and method of return based on the user's current revenue situation at the time of revenue return. The revenue return unit provides, for example, a function of analyzing the user's current revenue situation. For example, the revenue return unit identifies the amount of revenue of the user and the type of revenue (cash, points, etc.). The revenue return unit customizes the timing and method of return based on the analysis result. For example, the revenue return unit analyzes the user's current revenue situation and proposes the optimal timing of return. The revenue return unit can also customize the return method based on the user's revenue situation. Furthermore, if the user is in a hurry to receive the revenue, a quick return method is provided. For example, the revenue return unit provides a quick return method based on the user's revenue situation so that the user can receive the revenue early. This makes it possible to provide the timing and method of return based on the user's current revenue situation.

[0096] The revenue return unit has a function of estimating the user's emotion and determining the priority of revenue return based on the estimated user's emotion. The revenue return unit estimates the user's emotion, for example, using an emotion estimation algorithm. For example, the revenue return unit analyzes data such as the user's facial expression, voice, and text to estimate the emotion. The emotion estimation is realized by 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. The revenue return unit determines the priority of revenue return based on the estimated user's emotion. For example, if the user is in a hurry, the priority of revenue return is set high and the return is performed quickly. Also, if the user is relaxed, the priority of revenue return is set low and other tasks are prioritized. Furthermore, if the user is feeling anxious, the priority of revenue return is set to a medium level and the progress is displayed in detail. This makes it possible to determine the priority of revenue return according to the user's emotion.

[0097] The revenue return unit has a function of proposing a region-specific return method in consideration of the geographical location information of the user at the time of revenue return. The revenue return unit provides, for example, a function of acquiring the geographical location information of the user. For example, the revenue return unit identifies the user's location information using GPS data or an IP address. The revenue return unit proposes a region-specific return method based on the acquired geographical location information. For example, when the user is in a specific region, a return method for that region is proposed. In addition, region-specific return options can be provided based on the user's location information. Furthermore, when the user is traveling, a return method for the region visited is proposed. For example, the optimal return method is proposed based on the return method for the region where the user is traveling. In this way, a return method based on the user's geographical location information can be provided.

[0098] The revenue return unit has a function of analyzing the user's social media activity at the time of revenue return and proposing a related return method and promotion. The revenue return unit provides, for example, a function of analyzing the user's social media activity. For example, the revenue return unit collects and analyzes data such as the user's posted content, the number of likes, and the number of followers. The revenue return unit proposes a related return method and promotion based on the analysis result. For example, the revenue return unit proposes an optimal return method on the premise that the user shares on social media. In addition, the revenue return unit can analyze the user's social media activity and provide a related promotion. Furthermore, the revenue return unit proposes a return method based on the trend of the accounts the user follows. For example, the revenue return unit proposes a related return method based on the trend of the accounts the user follows. This makes it possible to provide a return method and promotion based on the user's social media activity.

[0099] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0100] The content creation platform may further include a skill determination unit that determines the skill level of a user. The skill determination unit analyzes the user's past content creation history, operation speed, types of tools used, and the like to determine the user's skill level. For example, basic tools and a simple interface can be provided to novice users, and advanced tools and a customizable interface can be provided to advanced users. The skill determination unit may also provide tutorials and guides according to the user's skill level. This allows users to efficiently create content in an environment that matches their own skill level.

[0101] The content creation unit can further include an auto-completion function for improving the user's work efficiency. The auto-completion function automatically adjusts the position, size, color, etc. of a 3D object placed by the user to maintain consistency in the design. For example, when the user places multiple objects, the auto-completion function automatically adjusts the spacing between the objects to keep them even. Also, based on the color selected by the user, the colors of other objects can be automatically adjusted to unify the overall design. Furthermore, the auto-completion function can learn operations frequently used by the user and predict and suggest operations for the next time and thereafter. This allows the user to create content efficiently.

[0102] The content creation unit can further estimate the user's emotions and suggest templates based on the estimated emotions. For example, if the user is feeling stressed, a simple and easy-to-use template can be preferentially suggested. If the user is feeling relaxed, a more complex and customizable template can be suggested. Furthermore, if the user is feeling creative, a template incorporating original design elements can be suggested. In this way, an optimal template can be provided according to the user's emotions.

[0103] The conversion unit can further estimate the user's emotions and adjust the way in which the progress of the conversion process is displayed based on the estimated emotions. For example, if the user feels anxious, the progress is displayed in detail to provide a sense of security. If the user feels relaxed, the progress is displayed briefly to reduce stress. If the user is in a hurry, the progress is displayed quickly to reduce the time it takes to complete the conversion. This allows the progress to be displayed according to the user's emotions.

[0104] The sales department can further estimate the user's emotions and adjust the design and layout of the sales page based on the estimated emotions. For example, if the user is excited, a visually stimulating design can be provided. If the user is relaxed, a calming design can be provided. If the user is anxious, a simple and intuitive layout can be provided. This makes it possible to provide the design and layout of the sales page according to the user's emotions.

[0105] The revenue return unit can further estimate the user's emotion and adjust the revenue return notification method based on the estimated emotion. For example, if the user is excited, a visually stimulating notification is provided. If the user is relaxed, a calming notification is provided. If the user is anxious, a simple and intuitive notification can be provided. This makes it possible to provide a revenue return notification method according to the user's emotion.

[0106] The content creation unit can further take into account the user's geographic location information to suggest region-specific materials and designs. For example, if the user is in a particular region, materials related to the culture and scenery of that region are suggested. Region-specific design elements can also be provided based on the user's location information. Furthermore, if the user is traveling, content related to the region visited can be suggested. This makes it possible to provide materials and designs based on the user's geographic location information.

[0107] The conversion unit can further select an appropriate conversion algorithm by referring to past conversion data. For example, an optimal algorithm is selected based on conversion data that was successful in the past. Also, the conversion unit can analyze the user's past conversion history and suggest an optimal algorithm. Furthermore, an algorithm with fewer errors can be selected from the past conversion data. This makes it possible to select an optimal conversion algorithm based on past data.

[0108] The sales department can further refer to past sales data to propose appropriate pricing and promotions. For example, optimal pricing can be proposed based on past sales data. In addition, it is also possible to analyze the user's past sales history and propose effective promotions. Furthermore, it is possible to propose pricing according to seasons and events based on past sales data. In this way, it is possible to propose optimal pricing and promotions based on past sales data.

[0109] The revenue return unit can further customize the timing and method of return based on the user's current revenue situation. For example, the revenue return unit can analyze the user's current revenue situation and propose the optimal timing of return. The revenue return unit can also customize the return method based on the user's revenue situation. Furthermore, if the user is in a hurry to return revenue, a quick return method can be provided. This makes it possible to provide the timing and method of return based on the user's current revenue situation.

[0110] The process flow of the second embodiment will be briefly described below.

[0111] Step 1: The content creation unit provides a function for a user to create content. For example, the content creation unit has a function for placing 3D objects by drag and drop. The user can intuitively place the 3D objects by using, for example, a mouse operation or a touch operation. The content creation unit also has a function for customizing a design using a template. The user can, for example, select a design template or a layout template and adjust customizable elements. Step 2: The converter converts the content created by the content creator to conform to the specifications of the major metaverses. For example, the converter automatically converts the content using algorithms it uses. The converter can adjust the accuracy of the conversion to accommodate the technical requirements and formats of multiple metaverses, for example. Step 3: The sales department provides a function to sell the content converted by the conversion department. For example, the sales department uploads the created content to a store in the metaverse, sets a price, and sells it. Users can set an appropriate price based on, for example, a price range or market price. Step 4: The revenue return unit provides a function of returning the revenue of the content sold by the sales unit to the user. For example, the revenue return unit sets the rate and timing of return, and returns the revenue to the user in the form of cash, points, etc.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] Each of the multiple elements including the above-mentioned content creation unit, conversion unit, sales unit, and revenue return unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the content creation unit is realized by the control unit 46A of the smart device 14, and provides a function for a user to arrange 3D objects by drag and drop. The conversion unit is realized, for example, by a specific processing unit 290 of the data processing device 12, and converts content to comply with the specifications of the main metaverse. The sales unit is realized, for example, by the control unit 46A of the smart device 14, and uploads the created content to a store in the metaverse, sets a price, and sells it. The revenue return unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and returns the revenue of the sold content to the user. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0116] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] Each of the multiple elements including the above-mentioned content creation unit, conversion unit, sales unit, and revenue return unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the content creation unit is realized by the control unit 46A of the smart glasses 214, and provides a function for a user to arrange 3D objects by drag and drop. The conversion unit is realized, for example, by a specific processing unit 290 of the data processing device 12, and converts content to comply with the specifications of the main metaverse. The sales unit is realized, for example, by the control unit 46A of the smart glasses 214, and uploads the created content to a store in the metaverse, sets a price, and sells it. The revenue return unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and returns the revenue of the sold content to the user. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0132] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] Each of the multiple elements including the above-mentioned content creation unit, conversion unit, sales unit, and revenue return unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the content creation unit is realized by the control unit 46A of the headset type terminal 314, and provides a function for a user to arrange a 3D object by drag and drop. The conversion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and converts the content so as to conform to the specifications of the main metaverse. The sales unit is realized, for example, by the control unit 46A of the headset type terminal 314, and uploads the created content to a store in the metaverse, sets a price, and sells it. The revenue return unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and returns the revenue of the sold content to the user. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0148] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] Each of the multiple elements including the above-mentioned content creation unit, conversion unit, sales unit, and revenue return unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the content creation unit is realized by the control unit 46A of the robot 414, and provides a function for a user to arrange a 3D object by drag and drop. The conversion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and converts the content so as to conform to the specifications of the main metaverse. The sales unit is realized, for example, by the control unit 46A of the robot 414, and uploads the created content to a store in the metaverse, sets a price, and sells it. The revenue return unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and returns the revenue of the sold content to the user. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0165] 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.

[0166] 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.

[0167] These emotions are distributed in the three 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.

[0168] 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 you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0169] 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.

[0170] 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."

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] (Appendix 1) a content creation unit that creates content; A conversion unit that converts the content created by the content creation unit into a content that complies with a metaverse specification; a sales unit that sells the content converted by the conversion unit; A profit return unit that returns profits from the content sold by the sales unit. A system characterized by: (Appendix 2) The content creation unit Equipped with drag and drop function to place 3D objects 2. The system of claim 1, (Appendix 3) The content creation unit Ability to customize designs using templates 2. The system of claim 1, (Appendix 4) The conversion unit is Equipped with automatic conversion function to comply with metaverse specifications 2. The system of claim 1, (Appendix 5) The sales department: It will have the ability to upload the content you create to a store in the metaverse, set prices and sell them. 2. The system of claim 1, (Appendix 6) The revenue return unit is Equipped with a function to return revenue from sold content to users 2. The system of claim 1, (Appendix 7) The content creation unit Inferring user emotions and customizing the content creation interface based on those emotions 2. The system of claim 1, (Appendix 8) The content creation unit Analyze users' past content creation history and automatically suggest appropriate templates and tools 2. The system of claim 1, (Appendix 9) The content creation unit During content creation, automatically provide relevant materials and objects based on the user's current projects and interests 2. The system of claim 1, (Appendix 10) The content creation unit Estimate user emotions and provide content creation guides and tutorials based on the estimated user emotions 2. The system of claim 1, (Appendix 11) The content creation unit When creating content, the app takes into account the user's geographic location to suggest localized materials and designs. 2. The system of claim 1, (Appendix 12) The content creation unit When creating content, it analyzes users' social media activity to suggest relevant trends and popular designs. 2. The system of claim 1, (Appendix 13) The conversion unit is Infer the user's emotions and adjust how the conversion process progress is displayed based on those emotions 2. The system of claim 1, (Appendix 14) The conversion unit is When converting, select the appropriate conversion algorithm by referring to past conversion data. 2. The system of claim 1, (Appendix 15) The conversion unit is During conversion, apply appropriate conversion techniques depending on the content category 2. The system of claim 1, (Appendix 16) The conversion unit is Inferring user sentiment and prioritizing conversion processes based on the estimated user sentiment 2. The system of claim 1, (Appendix 17) The conversion unit is During conversion, the geographic location of the user is taken into account to convert to conform to region-specific specifications. 2. The system of claim 1, (Appendix 18) The conversion unit is During conversion, it analyzes your social media activity and suggests relevant specifications and formats 2. The system of claim 1, (Appendix 19) The sales department: Infer user sentiment and adjust the design and layout of your sales page based on that sentiment 2. The system of claim 1, (Appendix 20) The sales department: When it comes time to make a sale, look at past sales data to suggest the right pricing and promotions 2. The system of claim 1, (Appendix 21) The sales department: When selling, apply the right sales strategy depending on the content category 2. The system of claim 1, (Appendix 22) The sales department: Infer user sentiment and adjust the timing and method of sales based on the inferred user sentiment 2. The system of claim 1, (Appendix 23) The sales department: At the time of sale, consider the user's geographic location to prioritize listings in region-specific marketplaces 2. The system of claim 1, (Appendix 24) The sales department: At the point of sale, analyze your social media activity to suggest relevant promotions and advertisements 2. The system of claim 1, (Appendix 25) The revenue return unit is Infer user sentiment and adjust the way you notify them of their earnings based on that sentiment 2. The system of claim 1, (Appendix 26) The revenue return unit is When distributing revenue, select the appropriate method of distributing revenue by referring to past revenue data. 2. The system of claim 1, (Appendix 27) The revenue return unit is Customize the timing and method of redemption based on the user's current earnings status 2. The system of claim 1, (Appendix 28) The revenue return unit is Estimate user sentiment and determine revenue distribution priorities based on the estimated user sentiment 2. The system of claim 1, (Appendix 29) The revenue return unit is When distributing revenue, the system considers the user's geographic location to suggest region-specific methods of distributing revenue. 2. The system of claim 1, (Appendix 30) The revenue return unit is Analyze users' social media activity at the time of redemption to suggest relevant redemption methods and promotions 2. The system of claim 1, [Explanation of symbols]

[0184] 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 content creation unit that creates content; A conversion unit that converts the content created by the content creation unit into a content that complies with a metaverse specification; a sales unit that sells the content converted by the conversion unit; A profit return unit that returns profits from the content sold by the sales unit. A system characterized by:

2. The content creation unit Equipped with drag and drop function to place 3D objects 2. The system of claim 1 .

3. The content creation unit Ability to customize designs using templates 2. The system of claim 1 .

4. The conversion unit is Equipped with automatic conversion function to comply with metaverse specifications 2. The system of claim 1 .

5. The sales department: It will have the ability to upload the content you create to a store in the metaverse, set prices and sell them.

2. The system of claim 1 .

6. The revenue return unit is Equipped with a function to return revenue from sold content to users 2. The system of claim 1 .

7. The content creation unit Inferring user emotions and customizing the content creation interface based on those emotions 2. The system of claim 1 .

8. The content creation unit Analyze users' past content creation history and automatically suggest appropriate templates and tools 2. The system of claim 1 .

9. The content creation unit During content creation, automatically provide relevant materials and objects based on the user's current projects and interests 2. The system of claim 1 .

Images