Method for making asset, content, or template recommendations, through keywords and user experience, and device therefor

Abstract

The present specification relates to a method by which a recommendation device makes asset, content, or template recommendations to a user, the method comprising the steps of: receiving an input of a sentence from a user, based on which asset, content, or template recommendations are to be offered; generating keywords for making the asset, content, or template recommendations on the basis of the sentence; generating user characteristic data on the basis of 1) history information of content used by the user through a platform, 2) information on components of the content created by the user, and 3) information on the content the user is currently working on; and generating a list of assets, content, or templates that can be recommended to the user on the basis of the keywords and the user characteristic data.

Description

Method and device for recommending assets, content, or templates through keywords and user experience

[0001] This specification relates to a method for utilizing keywords, characteristics of content used and produced, and characteristics of real-time data to supplement the problems of technology that provides customized content by analyzing users' interests.

[0002] User activity-based recommendation systems are services that recommend content based on user activity history. However, this approach can lead to problems such as bias, information limitations, and a lack of diversity. For example, bias can arise when determining content similarity by considering only certain types of information. Furthermore, because the system relies solely on user activity information, new content or content with insufficient activity information cannot be recommended. This can lead to recommendation systems failing to provide a wide range of information and resulting in a lack of diversity.

[0003] The purpose of this specification is to accumulate real-time information about the details and composition of the content the user is currently working on.

[0004] Additionally, the purpose of this specification is to enhance content recommendation information and to take into account the characteristics of content according to user search or activity information.

[0005] Additionally, the purpose of this specification is to construct a system that recommends content with various keywords by utilizing a large-scale language model.

[0006] The technical problems to be solved by this specification are not limited to the technical problems mentioned above, and other technical problems not mentioned can be clearly understood by a person having ordinary skill in the technical field to which this specification belongs from the detailed description of the specification below.

[0007] One aspect of the present specification may include a method for a recommendation device to recommend an asset, content, or template to a user, the method comprising: receiving, from the user, a sentence for recommending the asset, the content, or the template; generating a keyword for recommending the asset, the content, or the template based on the sentence; generating user characteristic data based on 1) history information of content used by the user through a platform, 2) information on components of content created by the user, and 3) information on content currently being created by the user; and generating a list of the asset, the content, or the template that can be recommended to the user based on the keyword and the user characteristic data.

[0008] In addition, the step of generating the user characteristic data may include: a step of obtaining an image of an asset based on history information of the content, information on components of content created by the user, and information on content currently being created by the user; a step of generating an image caption based on the image of the asset; a step of generating text related to the image of the asset based on the image caption; a step of extracting a mood of the text based on the text; a step of extracting a color based on the image of the asset; a step of extracting a mood of the color based on the extracted color; a step of extracting an asset characteristic related to the image of the asset based on the mood of the text, the color, and the mood of the color; and a step of generating the user characteristic data based on the asset characteristic.

[0009] In addition, the method may further include: a step of extracting a language mood and obtaining a thumbnail image based on the history information of the content, information on components of the content created by the user, and information on the content currently being created by the user; a step of extracting a color of the thumbnail image and a mood of the color based on the thumbnail image; a step of extracting an asset characteristic included in the content related to the thumbnail; a step of extracting a content characteristic based on the language mood, the color of the image, the mood of the color, and the asset characteristic included in the content related to the thumbnail; and a step of generating the user characteristic data based on the content characteristic.

[0010] In addition, the step of generating the keyword may include a step of converting the sentence into a story-type text through a language model; a step of converting the story-type text into a plot including 1) a background, 2) a main character, and 3) main elements through the language model; a step of converting the plot into sentence data associated with language-based data included in information related to the asset, the content, or the template through the language model; and a step of generating the keyword based on the sentence data.

[0011] In addition, the method may further include a step of applying a weight related to the user's preference to the asset characteristics and the content characteristics; and a step of generating the user characteristic data based on the weight.

[0012] In addition, the method may further include a step of displaying a list of the assets, the content, or the templates to the user through the platform.

[0013] In addition, the method may further include a step of displaying a list of the assets, the content, or the templates to the user through the fusion production device.

[0014] Another aspect of the present specification is a recommendation device for recommending an asset, content, or template to a user, comprising: a communication unit; a memory including a language model; and a processor for functionally controlling the communication unit and the memory; wherein the processor receives a sentence for recommending the asset, the content, or the template from the user, generates a keyword for recommending the asset, the content, or the template based on the sentence, and generates user characteristic data based on 1) history information of content used by the user through a platform, 2) information on components of content created by the user, and 3) information on content currently being created by the user, and generates a list of the asset, the content, or the template that can be recommended to the user based on the keyword and the user characteristic data.

[0015] According to embodiments of the present specification, information can be accumulated in real time according to details and composition of content that a user is currently working on.

[0016] Additionally, according to embodiments of the present specification, recommendation information of content can be enhanced and characteristics of content can be considered according to user search or activity information.

[0017] Additionally, according to an embodiment of the present specification, a system for recommending content using various keywords can be configured by utilizing a large-scale language model.

[0018] The effects that can be obtained from this specification are not limited to the effects mentioned above, and other effects that are not mentioned can be clearly understood by a person having ordinary skill in the technical field to which this specification belongs from the description below.

[0019] Figure 1 is a block diagram illustrating an electronic device related to the present specification.

[0020] Figure 2 is an example of an embodiment to which the present specification can be applied.

[0021] Figure 3 is an example of page creation to which this specification can be applied.

[0022] Figure 4 is an example of a controller (400) applicable to this specification.

[0023] Figure 5 is an example of a list of pages to which this specification can be applied.

[0024] Figure 6 is an example of elements to which this specification can be applied.

[0025] Figure 7 is an example of event detection of a fusion manufacturing device to which the present specification can be applied.

[0026] Figure 8 is an example of a result execution method to which this specification can be applied.

[0027] Figure 9 is an example of element management to which this specification can be applied.

[0028] Figures 10 and 11 are examples of element uploads to which the present specification can be applied.

[0029] FIG. 12 is a block diagram of an AI device according to one embodiment of the present specification.

[0030] Figure 13 is an example of a recommended method pipeline to which the present specification can be applied.

[0031] Figure 14 is an example of visual coding to which the present specification can be applied.

[0032] Figure 15 is an example of a recommended device to which the present specification can be applied.

[0033] Figure 16 is an example of a sentence input to which this specification can be applied.

[0034] Figure 17 is an example of a recommended method to which this specification can be applied.

[0035] Figure 18 is an example of user characteristic data generation to which this specification can be applied.

[0036] Figures 19 and 20 illustrate the collection of characteristic data that may be included in a database to which the present specification may be applied.

[0037] Figures 21 and 22 are examples of information related to content to which this specification can be applied.

[0038] Figure 23 is an example of asset feature extraction to which the present specification can be applied.

[0039] Figure 24 is an example of content / template feature extraction to which this specification can be applied.

[0040] Figure 25 is another embodiment of a recommended device to which the present specification can be applied.

[0041] Figure 26 is an example of a platform to which this specification can be applied.

[0042] Figure 27 is an example of a fusion manufacturing device to which the present specification can be applied.

[0043] The accompanying drawings, which are incorporated in and constitute a part of the detailed description to aid in understanding the present specification, provide embodiments of the present specification and, together with the detailed description, explain the technical features of the present specification.

[0044] Hereinafter, the embodiments disclosed in this specification will be described in detail with reference to the attached drawings. Regardless of the drawing numbers, identical or similar components will be given the same reference numbers and redundant descriptions thereof will be omitted. The suffixes "module" and "part" used for components in the following description are assigned or used interchangeably only for the convenience of writing the specification, and do not in themselves have distinct meanings or roles. In addition, when describing the embodiments disclosed in this specification, if it is determined that a specific description of a related known technology may obscure the gist of the embodiments disclosed in this specification, a detailed description thereof will be omitted. In addition, the attached drawings are only intended to facilitate easy understanding of the embodiments disclosed in this specification, and the technical ideas disclosed in this specification are not limited by the attached drawings, and should be understood to include all modifications, equivalents, and substitutes included in the spirit and technical scope of this specification.

[0045] Terms that include ordinal numbers, such as first, second, etc., may be used to describe various components, but the components are not limited by these terms. These terms are used solely to distinguish one component from another.

[0046] When a component is referred to as being "connected" or "connected" to another component, it should be understood that it may be directly connected or connected to that other component, but that there may be other components intervening. Conversely, when a component is referred to as being "directly connected" or "connected" to another component, it should be understood that there are no other components intervening.

[0047] Singular expressions include plural expressions unless the context clearly indicates otherwise.

[0048] In this application, terms such as “include” or “have” are intended to specify the presence of a feature, number, step, operation, component, part or combination thereof described in the specification, but should be understood not to exclude in advance the possibility of the presence or addition of one or more other features, numbers, steps, operations, components, parts or combinations thereof.

[0049] Figure 1 is a block diagram illustrating an electronic device related to the present specification.

[0050] The above electronic device (100) may include a wireless communication unit (110), an input unit (120), a sensing unit (140), an output unit (150), an interface unit (160), a memory (170), a control unit (180), and a power supply unit (190). The components illustrated in FIG. 1 are not essential for implementing the electronic device, and thus the electronic device described in this specification may have more or fewer components than the components listed above.

[0051] More specifically, among the above components, the wireless communication unit (110) may include one or more modules that enable wireless communication between the electronic device (100) and a wireless communication system, between the electronic device (100) and another electronic device (100), or between the electronic device (100) and an external server. In addition, the wireless communication unit (110) may include one or more modules that connect the electronic device (100) to one or more networks.

[0052] This wireless communication unit (110) may include at least one of a broadcast reception module (111), a mobile communication module (112), a wireless Internet module (113), a short-range communication module (114), and a location information module (115).

[0053] The input unit (120) may include a camera (121) or a video input unit for inputting a video signal, a microphone (122) or an audio input unit for inputting an audio signal, and a user input unit (123, for example, a touch key, a mechanical key, etc.) for receiving information from a user. Voice data or image data collected by the input unit (120) may be analyzed and processed into a user's control command.

[0054] The sensing unit (140) may include one or more sensors for sensing at least one of information within the electronic device, information about the surrounding environment surrounding the electronic device, and user information. For example, the sensing unit (140) may include at least one of a proximity sensor (141), an illumination sensor (142), a touch sensor, an acceleration sensor, a magnetic sensor, a gravity sensor (G-sensor), a gyroscope sensor, a motion sensor, an RGB sensor, an infrared sensor (IR sensor), a fingerprint recognition sensor, an ultrasonic sensor, an optical sensor (e.g., a camera (see 121)), a microphone (see 122), a battery gauge, an environmental sensor (e.g., a barometer, a hygrometer, a thermometer, a radiation detection sensor, a heat detection sensor, a gas detection sensor, etc.), and a chemical sensor (e.g., an electronic nose, a healthcare sensor, a biometric recognition sensor, etc.). Meanwhile, the electronic device disclosed in this specification can utilize information sensed by at least two of these sensors in combination.

[0055] The output unit (150) is for generating output related to visual, auditory, or tactile sensations, and may include at least one of a display unit (151), an audio output unit (152), a haptic module (153), and an optical output unit (154). The display unit (151) may be formed as a layer structure with a touch sensor or formed as an integral part, thereby implementing a touch screen. This touch screen may function as a user input unit (123) that provides an input interface between the electronic device (100) and the user, and at the same time, provide an output interface between the electronic device (100) and the user.

[0056] The interface unit (160) serves as a passageway for various types of external devices connected to the electronic device (100). This interface unit (160) may include at least one of a wired / wireless headset port, an external charger port, a wired / wireless data port, a memory card port, a port for connecting a device equipped with an identification module, an audio I / O (Input / Output) port, a video I / O (Input / Output) port, and an earphone port. In the electronic device (100), appropriate control related to the connected external device can be performed in response to the external device being connected to the interface unit (160).

[0057] In addition, the memory (170) stores data that supports various functions of the electronic device (100). The memory (170) can store a plurality of application programs (or applications) that run on the electronic device (100), data for the operation of the electronic device (100), and commands. At least some of these application programs can be downloaded from an external server via wireless communication. In addition, at least some of these application programs can exist on the electronic device (100) from the time of shipment for the basic functions of the electronic device (100) (e.g., call receiving and sending functions, message receiving and sending functions). Meanwhile, the application programs can be stored in the memory (170), installed on the electronic device (100), and driven by the control unit (180) to perform the operations (or functions) of the electronic device.

[0058] In addition to the operations related to the above application program, the control unit (180) typically controls the overall operation of the electronic device (100). The control unit (180) can provide or process appropriate information or functions to the user by processing signals, data, information, etc. input or output through the components discussed above or by operating an application program stored in the memory (170).

[0059] In addition, the control unit (180) can control at least some of the components discussed with reference to FIG. 1 to drive an application program stored in the memory (170). Furthermore, the control unit (180) can operate at least two or more of the components included in the electronic device (100) in combination to drive the application program.

[0060] The power supply unit (190) receives external power and internal power under the control of the control unit (180) and supplies power to each component included in the electronic device (100). The power supply unit (190) includes a battery, and the battery may be a built-in battery or a replaceable battery.

[0061] At least some of the above components may cooperate with each other to implement the operation, control, or control method of the electronic device according to the various embodiments described below. In addition, the operation, control, or control method of the electronic device may be implemented on the electronic device by driving at least one application program stored in the memory (170).

[0062] In this specification, the electronic device (100) may include a recommendation device, a terminal, a visual coding device, and a fusion manufacturing device.

[0063] Figure 2 is an example of an embodiment to which the present specification can be applied.

[0064] Referring to Figure 2, a user can communicate with a convergence production device via a terminal. For example, the terminal can connect to the convergence production device via the web without a separate application, and the user can simultaneously produce 2D and 3D content via the terminal.

[0065] The recommendation device includes an AI device (20) described below, is connected to the user's terminal and the fusion production device, and uses meta information generated from the terminal, platform, and fusion production device to analyze the user's tendencies, and utilizes the analysis results to recommend content, elements (Assets), and templates suitable for the user.

[0066] The recommendation device can assign weights to content activities used by users through the platform, analyze information on content created through the fusion production device, extract characteristics, and use them as learning data for the recommendation algorithm.

[0067] Additionally, the recommendation device can analyze information on the content the user is currently working on through the fusion production device and extract real-time characteristics.

[0068] Additionally, the recommendation device can generate JSON sentence data based on sentences entered by the user in the input field via the terminal, utilizing a large-scale language model framework. For example, the recommendation device can extract various keywords by processing the sentences entered by the user using a large-scale language model framework.

[0069] Using this, the recommendation device can recommend content, assets, and templates to the integrated production device, and templates and content to the platform. Furthermore, the recommendation device can analyze uploaded files, generate JSON sentence data through a language model framework, and store this data in asset metadata.

[0070] The fusion production device receives content creation commands from a terminal via the web (S2010). For example, the content may include 2D and / or 3D objects.

[0071] The convergence production device creates pages for content creation (S2020). For example, pages can be expressed in 2D or 3D formats, and the convergence production device can configure the screen by adding and arranging predefined elements (Assets) or templates to each page. More specifically, events can be registered for the added elements, allowing for additional interaction with content users. This allows users to create immersive and creative content.

[0072] Figure 3 is an example of page creation to which this specification can be applied.

[0073] Referring to FIG. 3, a user can receive a page display screen (300) from a convergence production device via a terminal. For example, a single piece of content may include one or more pages. Furthermore, the user can change the page format to 2D or 3D via a layout selection window (310) that may appear on the page display screen (300), and can separately add a virtual space with special functions, such as an AR mode, according to the content user's needs. The convergence production device can register a separate controller (400) depending on the page format. Furthermore, the user can change the size and ratio of the page via the layout selection window (310).

[0074] Referring again to FIG. 2, the fusion manufacturing device registers a controller (400) based on the page (S2030). For example, the fusion manufacturing device may register a controller (400) capable of controlling and interacting with elements based on the page's form.

[0075] Figure 4 is an example of a controller (400) applicable to this specification.

[0076] Referring to FIG. 4, when a user selects an element through a terminal, properties corresponding to the element are displayed in a property window (410), and the fusion production device displays a registered controller (400), and the user can easily edit the properties using a mouse or touch using the controller (400). For example, the user can precisely modify the property values ​​of the element by entering an exact numerical value in the property window (410). The user can perform additional connected functions by adding a tab to the property window (410). For example, the additional connected functions may include the source of the element's creator or media playback information.

[0077] Referring again to FIG. 2, the fusion production device places elements on a page (S2040). For example, depending on the page format, the fusion production device may place predefined elements / templates and / or additionally uploaded elements.

[0078] Figure 5 is an example of a list of pages to which this specification can be applied.

[0079] Referring to Figure 5, one content is composed of a bundle of multiple pages (screens), and a 2D or 3D screen can be selected according to the user's needs.

[0080] For example, a page contains (1) page properties, (2) an event list, and (3) a resource list. More specifically, the event list contains information about events assigned to elements, and the resource list contains information about elements added to the page.

[0081] Figure 6 is an example of elements to which this specification can be applied.

[0082] Referring to Figure 6, the fusion production device can provide users with predefined elements / templates based on page format via a terminal. Additionally, users can upload and arrange additional elements.

[0083] Referring again to FIG. 2, the fusion manufacturing device modifies the attribute values ​​of the element (S2050).

[0084] For example, when a user selects an element placed on a page display screen (300), the fusion production device displays a property window (410) corresponding to the element, and the user can modify the property value by using a mouse, touch, numerical input, etc., using a controller (400) registered based on the page.

[0085] The fusion production device registers events corresponding to elements (S2060). For example, an event may include a set of "actions" and "results." More specifically, an action may define the conditions under which the event occurs. For example, an "action" is a condition for a function to be performed and may include various types of events or calls, such as keyboard events, mouse / touch events, gesture events, area events, value events, and call events.

[0086] Additionally, the results may include “features” and “targets”.

[0087] More specifically, a “function” can define property changes and specific actions to be performed on the “target” that is the purpose of the function when an event is activated, and can include basic properties of elements such as position, size, rotation, and transparency, as well as control functions for media elements such as view, hide, play, stop, and pause. It can also include the user terminal’s camera, GPS, and accelerometer to utilize information from the external environment.

[0088] The fusion production device registers actions, functions, and / or targets corresponding to an event (S2070). For example, the fusion production device may register actions, functions, and / or targets based on the attribute values ​​of an element.

[0089] The fusion production device executes a result when an event for an element is detected based on a registered action (S2080).

[0090] Figure 7 is an example of event detection of a fusion manufacturing device to which the present specification can be applied.

[0091] Referring to Figure 7, the fusion fabrication device can detect events by identifying events corresponding to elements, monitoring the events, and executing the corresponding functions. When an event is detected, the fusion fabrication device can identify properties and execute the results based on the functions and properties to execute the corresponding functions.

[0092] Figure 8 is an example of a result execution method to which this specification can be applied.

[0093] Referring to FIG. 8, the fusion production device can execute “results” simultaneously, and lower “results” can be connected to upper “results” and executed continuously. In addition, there is no limit to the connection of “results”, and the execution result of the upper “result” can be used to perform a continuous function in the next step. Unlike an execution method that is simply driven by a single timeline, this result execution method can provide the user with an environment identical to an actual programming technique in relation to the operation of elements, and can help in naturally learning the programming environment.

[0094] Figure 9 is an example of element management to which this specification can be applied.

[0095] Referring to Fig. 9, since 2D elements and 3D elements have different properties, it is difficult for the fusion production device to control them in the same way. Therefore, the fusion production device first wraps the 2D elements and 3D elements through an object called a "basic element", and manages the elements so that they can be used by expanding them into "use elements" used in the authoring tool of the fusion production device based on the "basic elements". For example, the function of the "use elements" can be configured to control and use the properties of the "primitive elements". More specifically, in Fig. 9, the "use elements" are exemplified as images, videos, shapes, and 3D models, but any form that is advantageous for controlling and displaying the properties of 2D elements and 3D elements can be displayed as a "use element."

[0096] Figures 10 and 11 are examples of element uploads to which the present specification can be applied.

[0097] Referring to FIGS. 10 and 11, the fusion production device (or platform) loads an uploaded element file through a loader and registers an object of the loaded element file in a "use element" that can be corresponding to the loader. The fusion production device can place the uploaded element using the registered "use element."

[0098] Referring to FIG. 10, if the uploaded element file is an image file, the fusion manufacturing device can load the image file through the image loader and register the object of the loaded image file as an image element.

[0099] Referring to FIG. 11, if the uploaded element file is a 3D model file, the fusion manufacturing device can load the 3D model file through a 3D loader, create an object of the loaded 3D model file, create an animation, add the animation to the created object, and register the object as a 3D element.

[0100] For example, if the uploaded element file is a video file, the fusion production device can create an HTMLVideoElement, add it to the screen, and then register the object in the "use element" to control it.

[0101] A single piece of content can contain multiple pages, and each page can contain a "resourceManager" to manage elements and an "eventManager" to manage action events.

[0102] Elements placed on a page have separate depths, so the fusion production device can adjust the depth according to the user's needs and adjust the order in which elements are displayed on the screen.

[0103] For example, when an element is placed in a fusion production device, it registers the element in the Resource Manager of the corresponding page, and can manage it through changes in its properties, status, etc., or through registration and deletion. The Resource Manager not only manages the placed elements, but also generates events for changes, and can call connected functions according to resource changes based on the resource list.

[0104] When an element is added, the fusion fabrication device adds it to the management list via the controller registered on the page, allowing the controller to recognize and manage it as a controllable element. Elements registered to the controller can have their properties modified or controlled using the user's mouse, gestures, touch, or an external controller.

[0105] Additionally, Redo / Undo functionality may be required during the user's control of an element. To achieve this, the fusion authoring device can be configured to record changes to the state of the corresponding element by saving changes made by the controller in the page's History Manager. Upon request, the History Manager can retrieve the element's properties from a list of saved changes and update the current element properties to restore or update the element's state. Since the History Manager saves changes for each element, memory issues can arise. Therefore, to prevent this, the fusion authoring device can limit the number of saved change lists depending on the situation.

[0106] Additionally, the converged production device can designate users to share content during production, allowing them to work simultaneously. For example, when content is initially created, the converged production device can create a unique channel corresponding to the content. The converged production device can designate shared users who can use the unique channel, and when those users access the content, they can be added to the same channel.

[0107] Users on the same channel can exchange changes in real time and synchronize authoring data in real time. To achieve this, communication can be done in real time via websocket or webrtc.

[0108] However, when a new user accesses unsaved content, the content will be synchronized to the previous version, potentially resulting in version discrepancies. To prevent this, when a new user is added to a shared channel and accesses the content for the first time, an initial synchronization can be performed by a specific user among the existing users, who will perform a comprehensive update of any changes made. After this initial synchronization, the converged production device can resolve synchronization issues with unsaved content by sharing data on changes in real time.

[0109] FIG. 12 is a block diagram of an AI device according to one embodiment of the present specification.

[0110] The AI ​​device (20) may include an electronic device including an AI module capable of performing AI processing, a server including the AI ​​module, or the like. In addition, the AI ​​device (20) may be included as a component of at least a portion of the electronic device (100) illustrated in FIG. 1 and may be configured to perform at least a portion of the AI ​​processing.

[0111] The above AI device (20) may include an AI processor (21), a memory (25) and / or a communication unit (27).

[0112] The above AI device (20) is a computing device capable of learning a neural network, and can be implemented as various electronic devices such as a server, desktop PC, laptop PC, tablet PC, etc.

[0113] The AI ​​processor (21) can learn a neural network using a program stored in the memory (25). For example, the AI ​​processor (21) can create a Recurrent Neural Networks model utilizing Tensorflow on the memory (25), and can train such an artificial intelligence model using data that can be collected from a user terminal and a fusion manufacturing device.

[0114] For example, a trained AI model may include a language model and generate sub-models with tasks such as:

[0115] 1. Content / Template Recommendation Model: Recommends / generates customized content and templates to users by utilizing data analyzed from content usage and content creation information and a user characteristic model.

[0116] 2. Element Recommendation Model: Customized asset recommendations to content creators by utilizing user characteristic models and asset metadata.

[0117] 3. User Characteristics Analysis Model: Analyze user characteristics through analysis of user learning interest, participation, immersion, user usage, and production data.

[0118] Figure 13 is an example of a recommended method pipeline to which the present specification can be applied.

[0119] Referring to FIG. 13, the artificial intelligence model (1300) can be trained using data collected from the platform and the fusion manufacturing device.

[0120] For example, the platform can be provided to terminals via the web and connected to a convergence production device, providing users with a content creation environment. Furthermore, it can display content created by other users and provide a search function for this content. Furthermore, the platform and convergence production device can provide users with templates that can be used in the content creation environment and provide a search function for this.

[0121] The trained artificial intelligence model can generate 1) a user characteristic analysis model, 2) a content / template recommendation model, and 3) an element recommendation model. The user characteristics output by the user characteristic analysis model can be used for training the artificial intelligence model (1300).

[0122] Additionally, the content / template recommendation model can receive sentences from users, convert them into JSON-format sentence data, and provide automatically generated content and templates to users through the platform and integrated production device.

[0123] Additionally, the element recommendation model can recommend assets to users through a fusion production device using sentence data in JSON format.

[0124] Figure 14 is an example of visual coding to which the present specification can be applied.

[0125] Referring to FIG. 14, a user can communicate with a visual coding device via a terminal. For example, a fusion manufacturing device may include a visual coding device.

[0126] Additionally, the terminal can be connected to a visual coding device via the WEB without a separate application, and users can create content through visual coding via the terminal.

[0127] The terminal creates a page for visual coding and places elements (asset) on the page (S1410). For example, the terminal may display a list of elements and / or a template containing the elements to the user. The user can select elements to be placed on the page from the template.

[0128] The terminal sets the target element for visual coding based on the placed elements (S1420). For example, a user can select a target element by clicking on it among the elements placed on the page.

[0129] The terminal sets user actions related to user interaction (S1430). For example, a user action may be a condition for an event to occur in a target element.

[0130] The terminal sets a result related to the target element based on the user action (S1440). For example, the result may refer to an operation performed in relation to the target element when the terminal receives input from the user corresponding to the user action. The result may include a "function" that controls the size, position, and status of the target element, an "operation" that operates variables related to the target element, and a "function page" that indicates page movement.

[0131] The terminal displays the result of the target element based on the user action input (S1450). For example, if the user inputs a single click on the target element, the terminal may display a screen that moves the target element for one second or moves to a different page as a result.

[0132] Figure 15 is an example of a recommended device to which the present specification can be applied.

[0133] Referring to Figure 15, the recommendation device is connected to a user terminal and a convergence production device, learns from collected data, and provides predicted output values ​​from the learned AI model to the platform and convergence production device. For example, the convergence production device may include an authoring tool for content creation.

[0134] The recommendation device receives sentences from the user via the convergence production device and / or platform (S1510). For example, the user may input sentences about a specific topic or story for content creation into the convergence production device. More specifically, the user may input the sentence "Blue sky and grass with a smiling sun rising" into the convergence production device. In this case, the input language may primarily be Korean, and the recommendation device may store the input sentences for subsequent processing.

[0135] Figure 16 is an example of a sentence input to which this specification can be applied.

[0136] Referring to Figure 16, a user can select a chat icon (1610) for entering sentences on the content creation screen. Upon receiving the chat icon (1610), the integrated production device can activate the chat function. Through the terminal, the user can enter sentences for content creation through the displayed chat window (1620).

[0137] Referring again to Figure 15, the recommendation device converts the input sentence into English (S1520). If the input sentence is in a language other than English, the input sentence may be converted into English to ensure the accuracy of the language model included in the recommendation device.

[0138] For example, the sentence "Blue sky and grass with a smiling sun" can be converted to the English sentence "Blue sky and grass with a smile."

[0139] The recommendation device converts sentences into story-like text through a language model (S1530). More specifically, the language model's template can be configured to issue the command "Write a synopsis about the "blue sky and grass with a smile" for the sentence "Blue sky and grass with a smile."

[0140] For example, "Blue sky and grass with a smile"

[0141] In the enchanting world of "Blue Sky and Grass with a Smile," we are transported to a serene and picturesque landscape where the beauty of the blue sky and lush green grass becomes a catalyst for transformation. The story revolves around Emily, a young girl grappling with the hardships of life and burdened by sorrow. However, one fateful day, she stumbles upon a hidden meadow, bathed in the golden sunlight, where the sky is a vivid shade of blue and the grass dances with an infectious joy.

[0142] As Emily immerses herself in this magical haven, she starts to notice how the vibrant hues of the sky and grass affect her mood. The brilliant blue sky acts as a gentle reminder to look beyond her troubles and find hope, while the cheerful green grass inspires her to embrace the present moment and seek happiness. Slowly but surely, a smile starts to grace Emily's face as she discovers the power of perspective and the healing balm of nature.

[0143] With each visit to the meadow, Emily's connection to the blue sky and grass deepens. She begins to cultivate a profound sense of gratitude for the beauty surrounding her, finding solace and strength in the simplicity of life's wonders. The radiant smile that forms on her lips becomes a beacon of optimism, influencing those around her and spreading contagious joy.

[0144] Through this poignant journey, “Blue Sky and Grass with a Smile” highlights the transformative power of nature and the profound impact it can have on our emotional well-being. It reminds us to find solace in the smallest of moments, to appreciate the beauty that surrounds us, and to nurture a smile that can illuminate even the darkest of days.

[0145] This can be converted into a story-like text. This is to compensate for the fact that the sentences entered by the initial user lack information for content generation, templates, and / or asset recommendations.

[0146] The recommender uses a language model to transform a story-like text into a plot, including the setting, main character, and key elements (S1540). The recommender can then analyze the story-like text and summarize it into a single scene, including the setting, main character, and key elements, to create a plot. This is intended to extract core elements of the content. The recommender can extract the core elements—the setting, main character, and key elements—from the vast amount of data contained in the story-like text and generate a plot.

[0147] For this purpose, the template of the language model can be set to Summarize the plot in three lines, including the background, main characters, and main elements.

[0148] For example, the story-type text exemplified in S1530 mentioned above

[0149] In "Blue Sky and Grass with a Smile," Emily, a young girl burdened by sorrow, discovers a hidden meadow where the vibrant blue sky and dancing green grass become catalysts for transformation. Immersed in this magical haven, she learns to find hope, embrace the present, and cultivate gratitude. Through the healing power of nature, Emily's radiant smile becomes a symbol of optimism, inspiring those around her and reminding us to appreciate life's simple wonders.

[0150] It can be converted into a plot called .

[0151] The recommendation device converts the plot into sentence data for content, template, and / or asset recommendations (S1550). If necessary, the recommendation device can translate the plot back into Korean. For example, the plot illustrated in S1540 above is

[0152] "In "With the Smile of the Sky and Grass," Emily, a girl burdened by grief, discovers a hidden meadow where vibrant blue skies and dancing green grass become the starting point of change. Immersed in this magical place, she learns to find hope, embrace the present, and cultivate gratitude. Through the healing power of nature, Emily's radiant smile becomes a symbol of optimism, inspiring those around her and reminding us to recognize the simple beauty in life."

[0153] Additionally, the recommendation device can use a language model to convert the plot into JSON format sentence data based on the original words and English words, classification and zero classification, importance, and part of speech.

[0154] Table 1 below provides examples of sentence data.

[0155] {"original":"Blue Sky and Grass with a Smiling Sun""sentence": "In Sky and Grass's Smile, Emily, a girl burdened by grief, discovers a hidden meadow where vibrant blue skies and dancing green grass become the starting point of change. Immersed in this magical place, she learns to find hope, embrace the present, and cultivate gratitude. Through the healing power of nature, Emily's radiant smile becomes a symbol of optimism, inspiring those around her and reminding us to recognize the simple beauties of life.","words": [{"text": "sky and","english": "sky and","classification": "noun","english_classification": "noun","importance": 3,"part_of_speech": "NNG"},{"text": "grass with","english": "grass with","classification": "noun","english_classification": "noun","importance": 3,"part_of_speech": "NNG"},{"text": "smile and","english": "smile and","classification": "noun","english_classification": "noun","importance": 3,"part_of_speech": "NNG"},{"text": "with a smile","english": "with a smile","classification": "adverb","english_classification": "adverb","importance": 2,"part_of_speech": "MAG"},{"text": "by sorrow","english": "by sorrow","classification": "noun","english_classification": "noun","importance": 3,"part_of_speech": "NNG"},{"text": "heavy","english": "burdened","classification": "adjective","english_classification": "adjective","importance": 3,"part_of_speech": "VA"},{"text": "girl","english": "girl","classification": "noun","english_classification": "noun","importance": 3,"part_of_speech": "NNG"},{"text": "Emily","english": "Emily,","classification": "noun","english_classification": "noun","importance": 3,"part_of_speech": "NNP"},{"text": "vibrant","english": "vibrant","classification": "adjective","english_classification": "adjective","importance": 3,"part_of_speech": "VA"},{"text": "Blue","english": "blue","classification": "Adjective","english_classification": "adjective","importance": 3,"part_of_speech": "VA"},{"text": "Sky and","english": "sky","classification": "Noun","english_classification": "noun","importance": 3,"part_of_speech": "NNG"},{"text": "Dancing","english": "dancing","classification": "Verb","english_classification": "verb","importance": 3,"part_of_speech": "VV+ETM"},{"text": "Blue","english": "green","classification": "Adjective","english_classification": "adjective","importance": 3,"part_of_speech": "VA"},{"text": "Solution","english": "grass","classification": "noun","english_classification": "noun","importance": 3,"part_of_speech": "NNG"},{"text": "of change","english": "transformation","classification": "noun","english_classification": "noun","importance": 3,"part_of_speech": "NNG"},{"text": "starting point","english": "catalysts for","classification": "noun","english_classification": "noun","importance": 3,"part_of_speech": "NNG"},{"text": "being","english": "become","classification": "Verb","english_classification": "verb","importance": 3,"part_of_speech": "VV+ETM"},{"text": "Hidden","english": "hidden","classification": "Adjective","english_classification": "adjective","importance": 3,"part_of_speech": "VA"},{"text": "Meadow","english": "meadow","classification": "Noun","english_classification": "noun","importance": 3,"part_of_speech": "NNG"},{"text": "Discovers","english": "discovers","classification": "Verb","english_classification": "verb","importance": 3,"part_of_speech": "VV+EC"},{"text": "this","english": "this","classification": "pronoun","english_classification": "pronoun","importance": 1,"part_of_speech": "MM"},{"text": "magic","english": "magical","classification": "noun","english_classification": "noun","importance": 3,"part_of_speech": "NNG"},{"text": "like","english": "like","classification": "adjective","english_classification": "adjective","importance": 3,"part_of_speech": "VA"},{"text": "Where","english": "place","classification": "noun","english_classification": "noun","importance": 2,"part_of_speech": "NNG"},{"text": "immersed,","english": "immersing","classification": "verb","english_classification": "verb","importance": 2,"part_of_speech": "VV+EC"},{"text": "she","english": "she","classification": "pronoun","english_classification": "pronoun","importance": 2,"part_of_speech": "NP"},{"text": "hope","english": "hope","classification": "noun","english_classification": "noun","importance": 3,"part_of_speech": "NNG"},{"text": "looking for","english": "finds","classification": "verb","english_classification": "verb","importance": 2,"part_of_speech": "VV+EC"},{"text": "the present","english": "the present","classification": "noun","english_classification": "noun","importance": 2,"part_of_speech": "NNG"},{"text": "accepting","english": "accepting","classification": "verb","english_classification": "verb","importance": 2,"part_of_speech": "VV+EC"},{"text": "thank you","english": "gratitude","classification": "noun","english_classification": "noun","importance": 2,"part_of_speech": "NNG"},{"text": "Cultivating","english": "cultivating","classification": "Verb","english_classification": "verb","importance": 2,"part_of_speech": "VV+ETM"},{"text": "Law","english": "the way","classification": "Noun","english_classification": "noun","importance": 2,"part_of_speech": "NNG"},{"text": "Learn","english": "learn","classification": "Verb","english_classification": "verb","importance": 2,"part_of_speech": "VV+EC"},{"text": "Become","english": "become","classification": "Verb","english_classification": "verb","importance": 2,"part_of_speech": "VV+EC"},{"text": "nature's","english": "nature's","classification": "noun","english_classification": "noun","importance": 3,"part_of_speech": "NNG"},{"text": "healing power","english": "healing power","classification": "noun","english_classification": "noun","importance": 3,"part_of_speech": "NNG"},{"text": "through","english": "through","classification": "investigation","english_classification": "particle","importance": 1,"part_of_speech": "JKM"},{"text": "Emily's","english": "Emily's","classification": "noun","english_classification": "noun","importance": 2,"part_of_speech": "NNG"},{"text": "shining","english": "shining","classification": "adjective","english_classification": "adjective","importance": 3,"part_of_speech": "VA"},{"text": "smile","english": "smile","classification": "noun","english_classification": "noun","importance": 2,"part_of_speech": "NNG"},{"text": "optimism","english": "optimism's","classification": "noun","english_classification": "noun","importance": 3,"part_of_speech": "NNG"},{"text": "상징이","english": "symbol","classification": "명사","english_classification": "noun","importance": 2,"part_of_speech": "NNG"},{"text": "되어","english": "becomes","classification": "동사","english_classification": "verb","importance": 2,"part_of_speech": "VV+EC"},{"text": "그","english": "that","classification": "대명사","english_classification": "pronoun","importance": 1,"part_of_speech": "NP"},{"text": "주변","english": "surrounding","classification": "명사","english_classification": "noun","importance": 2,"part_of_speech": "NNG"},{"text": "사람들에게","english": "to people","classification": "명사","english_classification": "noun","importance": 2,"part_of_speech": "NNG+JKO"},{"text": "영감을","english": "inspiration","classification": "명사","english_classification": "noun","importance": 2,"part_of_speech": "NNG"},{"text": "주며,","english": "and","classification": "접속사","english_classification": "conjunction","importance": 1,"part_of_speech": "JC"},{"text": "To us","english": "to us","classification": "Pronoun","english_classification": "pronoun","importance": 2,"part_of_speech": "NP+JKO"},{"text": "Life's","english": "life's","classification": "Noun","english_classification": "noun","importance": 2,"part_of_speech": "NNG"},{"text": "Simple","english": "simple","classification": "Adjective","english_classification": "adjective","importance": 3,"part_of_speech": "VA"},{"text": "Beauty","english": "beauty","classification": "Noun","english_classification": "noun","importance": 2,"part_of_speech": "NNG"},{"text": "Realize","english": "realize","classification": "Verb","english_classification": "verb","importance": 2,"part_of_speech": "VV+EC"},{"text": "Remind","english": "remind","classification": "Verb","english_classification": "verb","importance": 2,"part_of_speech": "VV+EC"},{"text": "Gives","english": "us","classification": "Pronoun","english_classification": "pronoun","importance": 1,"part_of_speech": "NP"}]},

[0156] Referring to Table 1, the recommendation device can convert the synopsis of the initial input sentence into JSON data. The JSON data can be used to recommend content, templates, and / or assets. This JSON data can include sentence data information such as background, main character, and key elements. Based on the sentence data, the recommendation device recommends content, templates, and / or assets (S1560).

[0157] Figure 17 is an example of a recommended method to which this specification can be applied.

[0158] Referring to FIG. 17, the recommendation device may recommend assets, content, and / or templates using not only the aforementioned sentence data, but also keywords (e.g., "text") in the sentence data and user characteristic data. For example, the keyword matching system may include all or part of the components of the artificial intelligence model (1300).

[0159] The recommendation device can recommend templates / contents to the platform by configuring keyword and user characteristic data as input data for the keyword matching system, and can recommend templates / contents / assets to the convergence production device.

[0160] Figure 18 is an example of user characteristic data generation to which this specification can be applied.

[0161] Referring to FIG. 18, the recommendation device can extract asset characteristics and / or content / template characteristics from a database that manages data of a platform and / or a convergence production device. The recommendation device can generate user characteristic data from the extracted asset characteristics and / or content / template characteristics.

[0162] Figures 19 and 20 illustrate the collection of characteristic data that may be included in a database to which the present specification may be applied.

[0163] Referring to Figure 19, the platform can display (1910) content produced through a fusion production device. The platform can also display information about the content (1920). Information about the content can be generated based on users' history of using the content through the platform, and can include information such as the number of plays, likes, dislikes, saves, progress, duration of stay, and follows. Such information about the content can be included and managed in a database.

[0164] Referring to Figure 20, the platform can display content created by each user (2010). The recommendation device can collect information on the components of the content created by the user. Unlike information on content created by other users, this information can include information on the components used in the creation by the user through a convergence creation device. For example, the information on the components can include the content's name, description, category, hashtags, metatags of assets used to create the content, the structured actions, the background, and other information. Information on these components can also be included and managed in a database.

[0165] Additionally, the recommendation device can collect information about the content currently being created by the user through the integrated production device. For example, information about the content currently being created may include the content's name, description, category, hashtags, metatags for assets used in its creation, the content's structure, and other elements, such as the motion and background. This information can be stored and managed in a database.

[0166] Figures 21 and 22 are examples of information related to content to which this specification can be applied.

[0167] Referring to FIG. 21, information about content that has been created or is being created may include the size of the content and the type of the content (e.g., 2D / 3D / VR / AR).

[0168] Referring to Figure 22, information on elements included in content that has been created or is being produced can be managed through a separate ID value (uuid), and the database can manage the name and tag information of assets using the ID value, through which asset characteristics / content characteristics can be extracted.

[0169] Figure 23 is an example of asset feature extraction to which the present specification can be applied.

[0170] Referring to Figure 23, a method for extracting asset characteristics based on an image of an asset is illustrated in more detail. The image of the asset can be obtained from a database using the ID value of the asset described above. For example, the image of the asset can be obtained from a database based on 1) the history of content used by the user through the platform, 2) information about the components of content created by the user, and 3) information about content currently being created by the user.

[0171] The recommendation device generates an image caption based on the asset image (S2310). Image caption generation (Automatic Image Captioning) is a technology that combines computer vision and natural language processing. Through this, the recommendation device can automatically generate descriptions or captions for the asset images.

[0172] The recommendation device generates text related to the image of the asset based on the image caption (S2320). Image caption generation can perform two main tasks: image recognition and natural language processing. For example, the recommendation device uses an image recognition algorithm to detect and recognize objects, scenes, and properties in the image of the asset. The recommendation device can then use a natural language processing model to generate text for the image. The generated text can be used as a caption describing the image of the asset.

[0173] Additionally, the recommender can process text to enrich its information. For example, the recommender can increase the amount of information in the text through a language model and extract keywords from the processed text.

[0174] The recommendation device extracts the text's mood based on the text (S2330). For example, the recommendation device can extract the mood of text or processed text through sentiment analysis.

[0175] For example, sentiment analysis can refer to technologies that recognize and classify emotions and sentiments in data such as text, images, and voice. Recommender devices can perform text sentiment analysis using natural language processing (NLP) techniques. For this purpose, various approaches and algorithms, such as sentiment dictionaries, machine learning, and deep learning, can be used.

[0176] The recommendation device extracts the color of the asset based on the image of the asset (S2340).

[0177] The recommendation device extracts color mood based on the colors of the extracted assets (S2350). For example, the recommendation device can extract color mood using sentiment analysis technology. For example, color mood may include values ​​representing light, medium, and dark, and the ColorThief library can be used for this purpose.

[0178] The recommendation device extracts asset features related to the image of the asset based on the mood of the text, the color of the asset, and the mood of the color (S2360). For example, the asset features may include numerical values ​​that can be defined using a numerical value corresponding to the mood of the text (e.g., positive, negative), the degree of the mood of the color (e.g., bright, dark), and the degree of the color (e.g., brightness), corresponding to an ID value representing the asset. Using the numerical values ​​defined in this way, the asset can be classified.

[0179] The characteristics of the extracted assets can be stored and managed through a database.

[0180] Figure 24 is an example of content / template feature extraction to which this specification can be applied.

[0181] Referring to Figure 24, the recommendation device extracts content-related information (S2410). The recommendation device can extract content-related information from a database. For example, the content-related information may include information about the content described above in Figures 19 and 20, information about components of content created by the user, and / or information about content currently being created.

[0182] The recommendation device extracts the color and color mood of the image based on the thumbnail image of the content (S2420). For example, the recommendation device can extract the color of the thumbnail image and, using sentiment analysis technology, extract the mood of the color.

[0183] The recommendation device extracts linguistic sentiment based on content-related information (S2430). For example, the recommendation device can extract linguistic sentiment using sentiment analysis technology from text-based content-related information (e.g., content name, description, category, etc.).

[0184] The recommendation device extracts asset characteristics contained in the content (S2450). For example, the characteristics of these assets can be extracted based on information about elements contained in the content related to the thumbnail, through the operation of FIG. 23 described above.

[0185] The recommendation device extracts content characteristics based on the color of the image, the color mood, the language mood, and the asset characteristics included in the content (S2460).

[0186] For example, content characteristics may include numerical values ​​that can be defined using the color (e.g., brightness) of the content's thumbnail image, the mood of the color (e.g., light, dark), the degree of the language mood (e.g., positive, negative), and the numerical values ​​corresponding to the asset characteristics included in the content. Using the numerical values ​​defined in this way, the content can be classified.

[0187] If the content in question is a specific template, the template's characteristics can also be extracted in the same manner. The extracted content and template characteristics can be stored and managed through a database.

[0188] Referring again to Figure 18, the recommendation device can generate user characteristic data by applying weights to extracted asset, content, and template characteristics. For example, the weights can be applied using user behavioral data, such as the user's number of plays, likes, and dwell time, which may indicate the user's preference for the asset, content, or template.

[0189] Figure 25 is another embodiment of a recommended device to which the present specification can be applied.

[0190] Referring to FIG. 25, the recommendation device can generate a list of assets, contents, or templates by inputting keywords (e.g., text fields) and user characteristic data included in the sentence data in FIG. 15 described above into the keyword matching system.

[0191] The recommendation device generates keywords for creating a list of recommended assets, content, or templates based on a sentence input by the user (S2510). For example, the recommendation device may receive a sentence for recommending assets, content, or templates from the user via a terminal, and generate keywords for recommending the assets, content, or templates based on the operations of FIG. 15.

[0192] Based on keyword and user characteristic data, a list of assets, content and / or templates that can be recommended to the user is generated (S2520).

[0193] For example, a recommendation device can generate user characteristic data based on a user's history of content used through the platform, information about the components of content created by the user, and information about content currently being created by the user. Furthermore, keywords can be associated with language-based data contained in information related to assets, content, and / or templates. Based on these associations, the recommendation device can generate a list of assets, content, and / or templates that can be recommended to the user based on preferences based on the user characteristic data.

[0194] Table 2 below provides an example list of assets, content, and / or templates that may be recommended to users.

[0195] {"original": "Blue Sky and Grass with a Smiling Sun","sentence": "In With the Smile of the Sky and Grass, Emily, a girl burdened by grief, discovers a hidden meadow where vibrant blue skies and dancing green grass become the starting point of change. Immersed in this magical place, she learns to find hope, embrace the present, and cultivate gratitude. Through the healing power of nature, Emily's radiant smile becomes a symbol of optimism, inspiring those around her and reminding us to recognize the simple beauty in life.","contentList": ["a7e884bb-3d4c-46e2-9cf9-640460eb44d0","a7e884bb-3d4c-46e2-9cf9-640460eb44d0","a7e884bb -3d4c-46e2-9cf9-640460eb44d0","a7e884bb-3d4c-46e2-9cf9-640460eb44d0",...],"TemplateList": ["a7e884bb-3d4c-46e2-9cf9-640460eb44d0","a7e884bb-3d4c-46e2-9cf9-640460eb44d0", "a7e884bb-3d4c-46e2-9cf9-640460eb44d0","7e884bb-3d4c-46e2-9cf9-640460eb44d0",...],"assetList": [{"assetIdx": "0b70763b-4e22-43bb-a163-68c80f9b9e37","metaData": {"words": [{"text": "하늘과*","english": "sky and","classification": "명사","english_classification": "noun","importance": 3,"part_of_speech": "NNG"},{"text": "풀의","english": "grass with","classification": "명사","english_classification": "noun","importance": 3,"part_of_speech": "NNG"},{"text": "미소와","english": "smile and","classification": "명사","english_classification": "noun","importance": 3,"part_of_speech": "NNG"}, ...]}},{"assetIdx": "0b70763b-4e22-43bb-a163-68c80f9b9e37","metaData": {"words": [{"text": "춤추는*","english": "dancing","classification": "동사","english_classification": "verb","importance": 3,"part_of_speech": "VV+ETM"},{"text": "푸른","english": "green","classification": "형용사","english_classification": "adjective","importance": 3,"part_of_speech": "VA"},{"text": "풀이","english": "grass","classification": "명사","english_classification": "noun","importance": 3,"part_of_speech": "NNG"}, ...]}, ...}]}.

[0196] Referring to Table 2, the list that can be recommended to the user may include a sentence entered by the user, a story based on the sentence, a content list, a template list, and / or an asset list, and the asset list may include metadata of the asset.

[0197] Figure 26 is an example of a platform to which this specification can be applied.

[0198] Referring to Figure 26, a user can search for desired content by entering a sentence in the search box (2610) on the main screen of the platform, and the recommendation device can recommend and display content based on keywords and user characteristic data in the entered sentence (2620). Asset and template recommendations can also be performed in the same manner.

[0199] Figure 27 is an example of a fusion manufacturing device to which the present specification can be applied.

[0200] Referring to FIG. 27, a user can receive a content creation screen from a convergence production device via a terminal. The user can input sentences for content creation on the content creation screen (2710). The recommendation device can recommend and display assets (2720) based on keywords and user characteristic data of the input sentences, and recommended content / templates can be displayed (2740) after the canvas (2730) on which the user is currently positioned.

[0201] The above-described specification can be implemented as computer-readable code on a program-recorded medium. A computer-readable medium includes all types of recording devices that store data that can be read by a computer system. Examples of computer-readable media include hard disk drives (HDDs), solid-state disks (SSDs), silicon disk drives (SDDs), ROMs, RAMs, CD-ROMs, magnetic tapes, floppy disks, optical data storage devices, etc., and also includes media implemented in the form of carrier waves (e.g., transmission via the Internet). Therefore, the above detailed description should not be construed as limiting in all respects, but rather as illustrative. The scope of this specification should be determined by a reasonable interpretation of the appended claims, and all changes within the equivalency range of this specification are intended to be included within the scope of this specification.

[0202] In addition, while the above description focuses on services and embodiments, these are merely examples and do not limit the present specification. Those skilled in the art to which this specification pertains will appreciate that various modifications and applications not exemplified above are possible without departing from the essential characteristics of the present service and embodiments. For example, each component specifically shown in the embodiments can be modified and implemented. In addition, differences related to such modifications and applications should be construed as being included within the scope of this specification as defined in the appended claims.

Claims

1. In the method of recommending assets, content, or templates to users, A step of receiving a sentence for recommending the asset, the content, or the template from the user; A step of generating keywords for recommending the asset, the content, or the template based on the above sentence; 1) a step of generating user characteristic data based on the history information of content used by the user through the platform, 2) information on components of content created by the user, and 3) information on content currently being created by the user; and A step of generating a list of the assets, the content, or the templates that can be recommended to the user based on the keywords and the user characteristic data; A recommended method including:

2. In paragraph 1, The steps for generating the above user characteristic data are A step of acquiring an image of an asset based on the history information of the content, information on components of the content created by the user, and information on the content currently being created by the user; A step of generating an image caption based on an image of the above asset; A step of generating text related to the image of the asset based on the image caption; A step of extracting the mood of the text based on the above text; A step of extracting a color based on an image of the above asset; A step of extracting the mood of the color based on the extracted color; A step of extracting asset characteristics related to the image of the asset based on the mood of the text, the color, and the mood of the color; and A step of generating the user characteristic data based on the above asset characteristics; A recommended method including:

3. In paragraph 2, A step of extracting a language atmosphere and obtaining a thumbnail image based on the history information of the content, information on components of the content created by the user, and information on the content currently being created by the user; A step of extracting the color of the thumbnail image and the mood of the color based on the thumbnail image; A step of extracting asset characteristics included in content related to the above thumbnail; A step of extracting content characteristics based on the language mood, the color of the image, the mood of the color, and the asset characteristics included in the content related to the thumbnail; and A step of generating the user characteristic data based on the above content characteristics; A recommended method that includes more.

4. In paragraph 3, The steps to generate the above keywords are A step of converting the above sentence into a story-type text through a language model; A step of converting the above story-type text into a plot including 1) background, 2) main character, and 3) main elements through the above language model; A step of converting the plot into sentence data associated with language-based data included in the asset, the content, or information related to the template through the language model; and A step of generating the keyword based on the above sentence data; A recommended method including:

5. In paragraph 3, A step of applying a weight related to the user's preference to the above asset characteristics and the above content characteristics; and A step of generating the user characteristic data based on the above weights; A recommended method that includes more.

6. In paragraph 1, A step of displaying a list of the assets, the content, or the templates to the user through the platform; A recommended method that includes more.

7. In paragraph 1, A step of displaying a list of the assets, the contents, or the templates to the user through a fusion production device; A recommended method that includes more.

8. In a recommendation device for recommending assets, content, or templates to users, Department of Communications; Memory containing the language model; and A processor for functionally controlling the communication unit and the memory; The above processor Receiving a sentence for recommending the asset, the content, or the template from the user, and generating a keyword for recommending the asset, the content, or the template based on the sentence; 1) User characteristic data is generated based on the history information of content used by the user through the platform, 2) information on components of content created by the user, and 3) information on content currently being created by the user. A recommendation device that generates a list of the assets, the content, or the templates that can be recommended to the user based on the keywords and the user characteristic data.

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