Owned media platform managing server and method

Abstract

An owned media platform management server according to an embodiment includes a memory for storing at least one instruction for managing an owned media platform; and a processor that performs an operation according to the instruction. The processor constructs an enterprise owned media, collects original content of the enterprise, displays it on the owned media, and when user information for operating the enterprise owned media is collected, selects customized content for each user based on the user information and adjusts the display of the selected customized content.

Description

Technology Field

[0001] The technical concept of the present disclosure relates to an owned media platform management server and method, and more specifically, to a server and method for generating and managing an owned media of a company that serves as the company's social media based on the company's original content. Background Technology

[0002] Unless otherwise indicated in this specification, the contents described in this section are not prior art for the claims of this application, and are not to be recognized as prior art simply because they are included in this section.

[0003] Owned media refers to media channels directly owned and managed by companies or individuals. As digital assets under a company's direct control, owned media is used to effectively deliver brand messages and content. Owned media enables companies to convey their desired messages and allows for consistent management of brand image, unlike third-party media or advertising. Furthermore, it facilitates direct interaction with customers and helps reduce advertising and promotional costs.

[0004] However, traditionally, the initial setup of owned media involves significant costs and time. For instance, establishing owned media through websites, blogs, or mobile apps can incur substantial initial expenses. This is because the design, development, and content creation required for building owned media are all time-consuming and costly tasks. Furthermore, substantial resources must be invested in continuously creating and updating content during the initial phase. In addition, owned media requires the continuous generation of new content, which is a time- and manpower-intensive task, while consistently maintaining and improving content quality remains a challenging task. The problem to be solved

[0005] The problem that the technical concept of the present disclosure aims to solve is to provide an owned media platform management server and method that reduce the time and cost invested in managing and maintaining an owned media platform, more accurately generate company-specific owned content using the company's original content, and satisfy content quality and reader needs.

[0006] In addition, the problem that the technical concept of the present disclosure aims to solve is to provide a server and a method that enable readers to use content for free and for companies to pay usage fees for the owned media platform, thereby further activating the owned media platform.

[0007] However, the problem to be solved according to one embodiment is not limited only to that mentioned above. means of solving the problem

[0009] An owned media platform management server according to an embodiment includes a memory for storing at least one instruction for managing an owned media platform; and a processor that performs an operation according to the instruction. The processor constructs an enterprise owned media, collects original content of the enterprise, displays it on the owned media, and when user information for operating the enterprise owned media is collected, selects customized content for each user based on the user information and adjusts the display of the selected customized content.

[0010] In addition, when the processor receives a request from a company to generate content to be published on owned media, it generates content to be registered on said owned media based on original content using generative artificial intelligence, and after publishing the generated content to said owned media, analyzes the publication results of the content, calculates an influence index representing the corporate promotional effect of said content from said publication results, and trains said generative artificial intelligence with excellent content having an influence index above a certain level to generate new content to be registered on said owned media.

[0011] In addition, the publication results of the content include the number of shares, completion rate, gender and age ratio of readers, number of subscriptions, and subsequent actions by content, and the subsequent actions by content may include subscribing, sharing, and saving.

[0012] In addition, the processor can extract key items indicating the popularity of the content from among the items included in the content publication results, set a weight for each of the extracted key items, and calculate an influence index using the set weight and the number of key items.

[0013] In addition, the processor can select customized content for each user based on user information of enterprise owned media and dynamically adjust the display of the selected customized content.

[0014] In addition, the processor can analyze behavioral data including links clicked, articles read, videos watched, and content liked by the user on owned media to calculate preferences for personalized content for each user, and display personalized content in order according to said preferences.

[0015] In addition, the processor can analyze user information to extract users who wish to become content writers, analyze content reviews written by the extracted users who wish to become content writers to evaluate the quality of the reviews, and generate a group of content writer candidates based on the results of the review quality evaluation.

[0016] In addition, the processor performs sentiment analysis, keyword extraction, and topic modeling of user-written content reviews through natural language processing to identify the categories of the content reviews, calculate an influence index based on the publication results of the content reviews, and evaluate the quality of the reviews using the category identification results and the influence index.

[0017] In addition, the processor calculates an influence index based on the publication results of content reviews written by users, and can include users whose calculated influence index exceeds a certain level in the content author candidate group.

[0018] In addition, the processor can calculate the rewards paid by the company based on the number of contents whose influence index exceeds a certain level.

[0019] In addition, generative AI uses pre-published content as training data and can adjust the training weight of the said published content according to the influence index of the published content. Effects of the invention

[0020] The owned media platform management server and method according to the embodiment enable rapid owned media launching, thereby significantly reducing the time and cost required to build a website or blog directly.

[0021] In addition, content can be managed and analyzed efficiently through the embodiments. For example, by analyzing content publishing results (sharing, completion rate, reader gender ratio, age group, preference, number of subscriptions, next action, etc.) in real time, it supports strategic decision-making by enabling the company to immediately adjust its content strategy.

[0022] In addition, the embodiment efficiently performs content creation with AI support, thereby supporting the research and content creation processes, reducing the resources of content managers within the company, and providing higher efficiency.

[0023] In addition, through the embodiments, the manpower and time required for content creation can be reduced, thereby saving the company's human resources and costs.

[0024] In addition, the embodiment operates customized categories for each company, allowing companies to select and operate only the categories they desire, thereby enabling the provision of specialized content focused on specific topics.

[0025] In addition, the embodiment ensures reader influx, which can increase brand awareness as more readers are attracted.

[0026] In addition, it allows for easy identification of content from similar companies or competitors, enabling monitoring of the competitive landscape and the formulation of response strategies.

[0027] In addition, through the embodiments, readers can conveniently access various customized content for free.

[0028] Furthermore, in the embodiment, all original content published by the company can be read for free, offering higher accessibility compared to paid content platforms. Additionally, it allows users to easily save content of interest and conveniently access it in one place without the need to search for it on other portals.

[0029] In addition, the embodiment recommends content tailored to the reader's interests, thereby providing the reader with greater satisfaction.

[0030] In addition, the embodiment provides readers with the opportunity to create content by matching them with companies related to their field, thereby offering new opportunities to people who enjoy writing.

[0031] In addition, through the embodiments, companies do not need to directly hire personnel required for content creation, thereby reducing HR risks, and the platform handles writer matching and management, allowing companies to reduce the burden of personnel management.

[0032] In addition, the embodiment provides a team of professional writers with diverse backgrounds, such as freelance writers, women raising children, aspiring digital nomads, and office workers seeking side jobs, to meet the diverse content needs of companies.

[0033] In addition, in the embodiment, the platform recommends and manages authors, thereby guaranteeing the quality of the content.

[0034] In addition, through the examples, it is possible to strengthen the company's brand image and enhance credibility through high-quality customized content, as well as increase customer engagement and brand loyalty through interaction with readers.

[0035] Furthermore, through the embodiments, cost efficiency is maximized in various aspects such as content creation, management, and marketing. In addition, by monitoring and analyzing competitor content, it enables the establishment of a more competitive content strategy.

[0036] The effects obtainable from the exemplary embodiments of the present disclosure are not limited to those mentioned above, and other unmentioned effects can be clearly derived and understood by those skilled in the art to which the exemplary embodiments of the present disclosure belong from the description below. That is, unintended effects resulting from the implementation of the exemplary embodiments of the present disclosure can also be derived by those skilled in the art from the exemplary embodiments of the present disclosure. Brief explanation of the drawing

[0037] FIG. 1 is a diagram showing an owned media platform management system according to an embodiment. FIG. 2 is a block diagram of an owned media platform management server according to an embodiment. Figure 3 is a diagram illustrating the RAG model used in the embodiment. Figure 4 is a diagram showing the interface of an owned media according to an embodiment. Figure 5 is a diagram illustrating the management process of an owned media platform according to an embodiment. Specific details for implementing the invention

[0038] Hereinafter, various embodiments of the present disclosure are described in conjunction with the accompanying drawings. As various embodiments of the present disclosure may be subject to various modifications and may have various forms, specific embodiments are illustrated in the drawings and described in detail. However, this is not intended to limit the various embodiments of the present disclosure to specific forms, and it should be understood that they include all modifications and / or equivalents and substitutions that fall within the spirit and scope of the various embodiments of the present disclosure. In relation to the description of the drawings, similar reference numerals have been used for similar components.

[0039] In various embodiments of the present disclosure, terms such as “comprising” or “having” are intended to indicate the existence of the features, numbers, steps, actions, components, parts, or combinations thereof described in the specification, and should be understood as not precluding the existence or addition of one or more other features, numbers, steps, actions, components, parts, or combinations thereof.

[0040] In various embodiments of the present disclosure, expressions such as “or” include any and all combinations of the words listed together. For example, “A or B” may include A, may include B, or may include both A and B.

[0041] Expressions such as "first," "second," "first," or "second" used in various embodiments of the present disclosure may modify various components of the various embodiments, but do not limit such components. For example, such expressions do not limit the order and / or importance of such components and may be used to distinguish one component from another.

[0042] When it is mentioned that a component is "connected" or "joined" to another component, it should be understood that the component may be directly connected or joined to the other component, but that a new component may also exist between the component and the other component.

[0043] In the embodiments of the present disclosure, terms such as "module," "unit," "part," etc. are used to refer to a component that performs at least one function or operation, and such component may be implemented in hardware or software, or in a combination of hardware and software. Additionally, a plurality of "modules," "units," "parts," etc. may be integrated into at least one module or chip and implemented as at least one processor, except where each needs to be implemented in specific individual hardware.

[0044] Terms such as those defined in commonly used dictionaries should be interpreted as having meanings consistent with their meanings in the context of the relevant technology, and should not be interpreted in an ideal or overly formal sense unless explicitly defined in the various embodiments of the present disclosure.

[0045] Hereinafter, embodiments of the present disclosure will be described in detail with reference to the attached drawings.

[0046] FIG. 1 is a diagram showing an owned media platform management system according to an embodiment.

[0047] Referring to FIG. 1, an owned media platform management system according to an embodiment may be configured to include an owned media platform management server (100), a user terminal (200), and a corporate terminal (300).

[0048] A corporate terminal (300) may request the corporate owned media or the creation of content to be published on the owned media to the owned media platform management server (100). When the corporate terminal (300) requests the creation of the corporate owned media or the creation of content to be published on the owned media to the owned media platform management server (100), the corporate detailed information, condition information regarding the owned media and content, and content previously created by the corporate may be provided as the corporate original content. In an embodiment, the condition information regarding the owned media and content may include the type of owned media, the subject matter of the content, information regarding target readers, desired view counts, etc.

[0049] In the embodiment, the owned media platform management server (100) builds corporate owned media, collects the company's original content, displays it on the owned media, and, when user information for operating the corporate owned media is collected, selects customized content for each user based on the user information. In the embodiment, user information is information of users exposed to the owned media and users who subscribe to or sign up for the owned media. In the embodiment, user information includes, but is not limited to, the user's age, gender, whether they subscribe to the owned media, and behavioral data.

[0050] Subsequently, the owned media platform management server (100) adjusts the display of the selected customized content. Additionally, in the embodiment, when the owned media platform management server (100) receives a request from a company to create content to be published on the owned media, it uses generative artificial intelligence to create content to be registered on the owned media based on the company's original content. In the embodiment, the company's original content refers to content that the company plans, produces, and distributes internally. The company's original content is creatively developed within the company without relying on other companies or external producers. Original content plays an important role in increasing brand awareness, strengthening relationships with customers, and conveying the company's values ​​and vision.

[0051] In the embodiment, the owned media platform management server (100) establishes a stable and scalable server infrastructure using cloud services (AWS, Google Cloud, Azure) or on-premises servers, etc. Additionally, it sets up a database (MySQL, PostgreSQL, MongoDB, etc.) for storing and managing content. Furthermore, the owned media platform management server (100) designs and develops a website considering the user interface (UI) and user experience (UX). In addition, it integrates a content management system (CMS) to provide functions for creating, modifying, deleting, and managing content. Furthermore, the owned media platform management server (100) according to the embodiment selects an artificial intelligence model that generates content to be registered on the owned media. In the embodiment, the artificial intelligence model is a generative AI, and can be trained by selecting GPT (Generative Pre-trained Transformer) and BERT (Bidirectional Encoder Representations from Transformers) for text generation, GANs (Generative Adversarial Networks) for audio generation, and VGAN (Video Generative Adversarial Networks) for video generation. In addition, in the embodiment, the owned media platform management server (100) can generate content registered in the company's owned media based on a RAG (Retrieval Augmented Generation) model.

[0052] The user terminal (200) is a terminal of a writer or customer who subscribes to the company's owned media or creates content to be registered on the owned media. In an embodiment, the user terminal (200) can collect user behavior data, such as whether the content registered on the owned media is subscribed to, liked, or shared, and transmit it to the owned media platform management server (100). Additionally, the user terminal (200) provides the user with customized content extracted based on the user's history of viewing the owned media.

[0053] In the embodiment, the owned media platform management system improves the time and cost invested in managing and maintaining the owned media platform, more accurately generates company-specific owned content using the company's original content, and provides an owned media platform management server that satisfies content quality and reader needs. Furthermore, through the owned media platform management system according to the embodiment, readers can use content for free, while only the company pays the usage fee, thereby enabling the owned media platform to be more active.

[0054] FIG. 2 is a block diagram of an owned media platform management server according to an embodiment.

[0055] In the embodiment, a server is a computing system that provides services to other computers or devices in a computer network or stores and manages data. The server (200) accepts requests from other computers or devices called clients and provides responses or data to those requests. The configuration of the server (200) shown in FIG. 2 is merely a simplified example.

[0056] The communication module (110) can be configured regardless of the mode of communication, such as wired or wireless, and can be configured with various communication networks, such as a Personal Area Network (PAN) or a Wide Area Network (WAN). Additionally, the communication module (110) can operate based on the known World Wide Web (WWW) and may utilize wireless transmission technologies used for short-range communication, such as Infrared Data Association (IrDA) or Bluetooth. For example, the communication module (110) may be responsible for transmitting and receiving data necessary to perform a technique according to one embodiment of the present disclosure.

[0057] Memory (120) may refer to any type of storage medium. For example, memory (120) may include at least one type of storage medium among flash memory type, hard disk type, multimedia card micro type, card type memory (e.g., SD or XD memory, etc.), RAM (Random Access Memory), SRAM (Static Random Access Memory), ROM (Read-Only Memory), EEPROM (Electrically Erasable Programmable Read-Only Memory), PROM (Programmable Read-Only Memory), magnetic memory, magnetic disk, and optical disk. Such memory (120) may also constitute the database shown in FIG. 1.

[0058] Memory (120) can store at least one instruction that can be executed by the processor (130). Additionally, memory (120) can store any form of information generated or determined by the processor (130) and any form of information received by the server (200). For example, memory (120) stores RM data and RM protocols according to the user, as will be described later. Additionally, memory (120) stores various types of modules, instruction sets, or models.

[0059] The processor (130) can perform technical features according to embodiments of the present disclosure to be described below by executing at least one instruction stored in memory (120). In one embodiment, the processor (130) may be composed of at least one core and may include a processor for data analysis and / or processing, such as a central processing unit (CPU) of a computer device, a general purpose graphics processing unit (GPGPU), or a tensor processing unit (TPU).

[0060] This processor (130) can train a neural network or model designed in a machine learning or deep learning manner. To this end, the processor (130) can perform calculations for training the neural network, such as processing input data for training, extracting features from input data, calculating errors, and updating the weights of the neural network using backpropagation. Additionally, the processor (130) can perform inference for a specific purpose using a model implemented in an artificial neural network manner.

[0061] In the embodiment, the processor (130) generates content to be registered on owned media through generative artificial intelligence based on the company's original content. In the embodiment, the processor (130) can generate content to be registered on owned media using generative artificial intelligence. To this end, the processor (130) trains a generative artificial intelligence model using training data. In the embodiment, the training data may include the company's original content, content already published on owned media, etc. In the embodiment, the processor (130) can train a generative artificial intelligence model by using content generation elements including keywords, sentiments, and topics as input data, and using already published content regarding the input generation elements as output data. Subsequently, content to be registered on owned media is generated using the trained generative artificial intelligence model.

[0062] In addition, in the embodiment, the processor (130) can generate content registered in owned media using the company's original content based on a RAG (Retrieval Augmented Generation) model.

[0063] In the embodiment, when the generative AI model receives content generation elements, the generative AI model analyzes existing content from various companies to generate content optimized for the company's owned media. Additionally, in the embodiment, input information selected or entered by the generative AI model can be presented to the user terminal for reference. Furthermore, the generative AI model according to the embodiment may replace what the user has selected or entered, or may be combined based on context and provided to the user.

[0064] In the embodiment, the processor (130) learns numerous existing corporate original content and already published content using a language model based on generative artificial intelligence. In this embodiment, by using RAG-based technology, it is possible to generate relevant content by considering the items desired by a specific company and market conditions.

[0065] Retrieval-Augmented Generation (RAG) models are artificial intelligence models that combine information retrieval and generation. RAG models are generally used in text generation tasks. RAG-based models generate sentences by utilizing retrieved information, thereby improving the consistency and usefulness of the generated text. Furthermore, RAG models typically build language models by training on large amounts of text data and can additionally use search functions to retrieve information regarding specific topics or contexts, integrating this into the generated text.

[0066] Figure 3 is a diagram illustrating the RAG model used in the embodiment.

[0067] The RAG model embeds text data sources regarding new knowledge and stores them in vector storage. When constructing a prompt, it uses the text data obtained from an external data source to construct the prompt and then obtains an answer from the Large Language Model (LM). The RAG model generates an answer to the question by combining the external text data source with the question given to the LLM. To this end, the RAG model may be configured to include a document loading module, a splitting module, a storage module, a retrieval module, and an output module, as illustrated in FIG. 10.

[0068] Subsequently, the processor (130) publishes the content generated by the generative artificial intelligence model to owned media and determines the results of the content publication. In the embodiment, the results of the content publication are information for recognizing the degree of achievement of the content publication goal, and include, but are not limited to, the number of shares of the published content, the completion rate, the gender ratio and age of the readers, the number of subscriptions, and the next action per content. In the embodiment, the next action per content is a user motion performed immediately after the user reads the content, and the next action per content includes, but is not limited to, subscribing, sharing, and saving.

[0069] In the embodiment, the processor (130) collects data from various platforms in real time to analyze the results of publishing content. For example, the processor (130) collects data using APIs from various platforms, such as social media, website analysis tools, and email marketing tools. If no API is provided, the processor (130) collects data using a web scraping tool. Additionally, the processor (130) collects data on the next action for each content in real time by collecting web server logs, database logs, etc.

[0070] Additionally, in the embodiment, the processor (130) can determine the completion rate among the publication results of the content by tracking at least one of scroll depth, session time, page leave event and user interaction.

[0071] In the embodiment, determining the completion rate through scroll depth tracking is to estimate the completion rate of the content by tracking how much the user has scrolled the page. For example, the processor (130) can estimate whether the user has finished reading by using a scroll depth plugin, etc., to check whether the user has scrolled to the end of the page.

[0072] Additionally, in the embodiment, the processor (130) indirectly determines the completion rate by tracking the session time, which is the time a user spends on specific content. For example, the processor (130) calculates the time spent by recording the time the user loaded the page and the time they left, and can consider a user who has stayed for a certain amount of time or longer as having finished reading.

[0073] Additionally, the processor (130) can determine the page dwell time by recording an event when the user leaves the page. Subsequently, the time when the user leaves the page can be recorded to calculate the content consumption pattern and the completion rate. In the embodiment, the completion rate of the content displayed on the owned media can be determined through a completion rate calculation algorithm proportional to the page dwell time.

[0074] Additionally, the processor (130) can track various interactions (clicks, highlights, etc.) performed by the user within the page to identify content consumption patterns and determine the completion rate of the content. For example, the processor (130) can track specific events to record user activity and determine whether the content has been fully read.

[0075] Additionally, in the embodiment, the processor (130) may set up event tracking on the content to determine the user's next action after the content displayed on the owned media has been fully read. This involves tracking specific user actions, such as tracking the user's subscription, save, and share events, and recording user actions by setting event categories, actions, and labels. Subsequently, the next action after the content has been fully read is determined based on the timing of the user's actions.

[0076] Additionally, the processor (130) can use a heatmap tool to visually determine how a user interacts with the page and thereby recognize the next action after subscribing to the content. In the embodiment, the heatmap tool analyzes the consumption pattern of the content by visualizing the user's clicks, scrolls, mouse movements, etc.

[0077] Additionally, the processor (130) can collect gender and age information through the subscription records or subscription forms of readers who have subscribed to the content, or retrieve such information through a social media login function, in order to determine the gender ratio and age of readers who have subscribed to the content. Afterward, the processor (130) refines the collected data and classifies and analyzes it by gender and age.

[0078] In an embodiment, the processor (130) calculates an influence index representing the corporate promotional effect of the content from the publication result, and creates new content to be registered on owned media by taking the influence index into consideration.

[0079] For example, the processor (130) may set a weight for each item included in the content publication result and calculate an influence index using the set weight and the number of items. To do this, the processor (130) selects key items to evaluate the content publication result from the content publication result and sets a weight for each selected item. For example, the processor (130) selects items indicating the popularity of the content, such as views, comments, likes, page views, and shares, as key items among the content publication results and assigns a weight to each item. Specifically, a weight of 0.2 may be assigned to the number of views, 0.3 to the number of comments, and 0.2 to the number of likes. Afterward, the processor (130) may calculate the content influence index by multiplying the weight of the key items by the actual value of each item and summing them.

[0080] Additionally, the processor (130) evaluates the positivity and negativity of comments registered on the content and adjusts the influence index according to the evaluation results. For example, the processor (130) can identify the positivity and negativity of the comments by analyzing the text of all comments registered on the content through text-entailment recognition, which is one of the natural language processing technologies. In the embodiment, positivity and negativity are the evaluation results of the content, and if the positive evaluation of the content registered on the owned media is high as a result of the comment analysis, the positivity appears high. Conversely, if the negative evaluation of the content registered on the owned media is high, the negativity appears high. In the embodiment, the processor (130) can calculate the influence index for each content by converting the positivity and negativity of the comments into a ratio and adding an additional index equal to the ratio of positivity to the influence index of the content.

[0081] Additionally, in the embodiment, when the processor (130) trains a generative artificial intelligence using pre-published content as training data, it can adjust the training weight of the published content according to the influence index of the published content. For example, the processor (130) can set the training weight of excellent content with an influence index exceeding a certain level and non-excellent content with an influence index below a certain level to 50 percent or more, and the training weight of general content with an influence index below excellent content and above non-excellent content to less than 50 percent. Additionally, the processor (130) can set the training weight of general content to 50 percent and the training weight of excellent content to 50 percent.

[0082] The processor (130) according to the embodiment can increase the accuracy and reliability of the generated content by adjusting the learning weight of the generative artificial intelligence model according to the influence index of the content. In addition, the embodiment allows for the reduction of data bias by adjusting the weight according to the influence index.

[0083] Additionally, the processor (130) selects customized content for each user based on user information of the enterprise-owned media. For example, the processor (130) collects the user's name, age, gender, occupation information, etc., to build a user profile, and analyzes user-specific behavioral data. In the embodiment, user behavioral data includes, but is not limited to, content previously viewed by the user, links clicked, and time spent. Additionally, the processor (130) identifies topics the user likes, areas of interest, and frequently searched keywords based on the analysis results of the behavioral data. Furthermore, the processor (130) can collect additional information such as the user's access location, device information, and time of visit, and analyze this information.

[0084] In the embodiment, the processor (130) analyzes the user's behavioral patterns and preferences to predict the most suitable content for each user. At this time, the processor (130) can predict the most suitable customized content for the user by using various artificial intelligence models, such as collaborative filtering, content-based filtering, and deep learning-based models. In addition, the processor (130) groups users with similar interests to enable more precise customized content recommendations.

[0085] Subsequently, the processor (130) provides the predicted customized content to the user. At this time, the display of the selected customized content can be dynamically adjusted to provide the customized content to the user. For example, the processor (130) can analyze behavioral data including links clicked, articles read, videos watched, and content liked by the user on owned media to calculate the preference for customized content for each user, and adjust the display of customized content in order according to the preference. Additionally, if additional information corresponding to the user's access location is collected, the processor (130) can dynamically adjust the display so that content related to the user's access location is displayed first.

[0086] In an embodiment, the processor (130) can calculate a preference by assigning a score to each item of behavioral data. For example, a score is assigned to each item of behavioral data, such as 1 point for a clicked link, 2 points for an article read, 3 points for a video watched, and 5 points for content liked. Subsequently, a preference score is calculated based on each user's behavioral data. Additionally, the processor (130) can provide other content as customized content that has a similarity level above a certain level to the content the user likes.

[0087] Additionally, the processor (130) analyzes user information to extract users who wish to become content writers, and analyzes content reviews written by the extracted users who wish to become content writers to evaluate the quality of the reviews. Afterward, a group of content writer candidates is created based on the results of the review quality evaluation.

[0088] In the embodiment, the processor (130) checks an item in the user's profile indicating whether they "aspire to be a writer" in order to evaluate the quality of the review. Subsequently, it analyzes the user's behavioral data (e.g., frequently written comments, reviews, engagement, etc.) to identify users interested in writing activities. Additionally, in the embodiment, the processor (130) conducts a survey of users to ask whether they aspire to be a writer and analyzes the results. Subsequently, the processor (130) creates a list of users aspiring to be writers based on the collected data and stores their unique IDs. Subsequently, the processor (130) collects all reviews written by users aspiring to be writers. It also collects metadata including the time of writing, evaluated content, and length of each review.

[0089] Subsequently, the processor (130) evaluates the quality of the review of the content written by the user. In the embodiment, the processor (130) sets quality evaluation criteria, analyzes the review text by applying natural language processing (NLP) technology, and calculates a score according to the quality evaluation criteria.

[0090] In the embodiments, quality evaluation criteria may include depth of content, clarity, objectivity, creativity, accuracy, etc. Depth of content is an evaluation of how in-depth the analysis provided by the review is, and clarity is an evaluation of whether the review is clear and easy to understand. Objectivity is an evaluation of whether the review is objective and unbiased, and creativity is an evaluation of whether the review is original and creative. Accuracy is an evaluation of whether the content of the review is based on facts and is accurate.

[0091] Additionally, the processor (130) evaluates the depth and clarity of the review through topic modeling or sentiment analysis, and evaluates grammatical accuracy and the appropriateness of vocabulary usage. In an embodiment, the processor (130) can automatically evaluate the quality of the review using a machine learning model trained according to the quality evaluation criteria of the review.

[0092] Additionally, the processor (130) can perform sentiment analysis, keyword extraction, and topic modeling of content reviews written by users through natural language processing to identify the category of the content review, calculate an influence index based on the publication result of the content review, and evaluate the quality of the review using the category identification result and the influence index.

[0093] To this end, the processor (130) removes unnecessary symbols, HTML tags, stop words, etc. from the review text and divides the review text into words. Then, it applies a sentiment analysis model to the review text to calculate positive, negative, and neutral sentiment scores. In addition, it extracts important keywords from the review text and stores the extracted key keywords for each review. Furthermore, the processor (130) applies an LDA algorithm to extract the main topic from the review text. Afterward, it classifies each review into a specific category based on the topic modeling results. In addition, in the embodiment, the extracted keywords can be used to classify the review into an appropriate category. Additionally, the processor (130) supplementarily classifies which category the review belongs to by referring to the sentiment analysis results. Afterward, the processor (130) analyzes the publication results of the review and calculates an influence index. For example, it collects the number of views, comments, likes and recommendations, and shares of the review.

[0094] In the embodiment, the processor (130) evaluates whether a review belongs to an appropriate category and evaluates the consistency of the review's sentiment score and whether the extracted keywords and topics adequately represent the content of the review. Additionally, it evaluates the influence index calculated based on the publication results. In the embodiment, the processor (130) calculates the quality score of the review by combining the results of sentiment analysis, keyword extraction, and topic modeling with the influence index. In the embodiment, the quality evaluation results of each review are stored, and the ranking of the reviews can be set based on this. Furthermore, the processor (130) calculates the influence index based on the publication results of content reviews written by users and includes users whose calculated influence index exceeds a certain level in the content author candidate group.

[0095] Additionally, the processor (130) calculates the rewards paid by the company based on the number of contents whose influence index exceeds a certain level.

[0096] To this end, the processor (130) sets a threshold value for the influence index of the content to exceed a certain level. For example, content with an influence index exceeding 50 or higher may be eligible for rewards. Subsequently, the influence index of each piece of content is collected and stored. In addition, the company's current reward policy, budget, and reward payment criteria are collected.

[0097] Afterward, the processor (130) uses the influence index of each calculated content to identify content that exceeds a threshold and aggregates the number of content whose influence index exceeds the threshold.

[0098] In the embodiment, the processor (130) sets a reward unit price to be paid per piece of content that exceeds a threshold for setting a reward policy. Additionally, a reward tier can be set that pays differentially based on the number of pieces of content that exceed the threshold. For example, the reward tier can be set such that when there are 1 to 10 pieces of content with an influence index exceeding the threshold, the reward is $100 per piece of content; when there are 11 to 20 pieces of content, the reward is $120 per piece of content; and when there are 21 or more pieces of content, the reward is $150 per piece of content.

[0099] In the example, the total reward is calculated by multiplying the number of contents exceeding the threshold by the reward unit price. Additionally, if the number of excess contents spans multiple tiers, the corresponding reward is calculated for each tier to arrive at the total sum.

[0100] In addition, in the embodiment, when a company requests the creation of owned media and content, and the processor (130) creates content to be published on the company's owned media by a generative artificial intelligence or a group of user writers, the processor (130) can calculate a fee based on the number of created content. For example, the processor (130) sets a basic unit price for each piece of content. Specifically, if the basic fee per piece of content is $100, the basic unit price becomes $100. In addition, in the embodiment, the basic unit price can be applied differentially depending on the type of content (e.g., blog post, social media post, newsletter, video, etc.).

[0101] Additionally, the processor (130) may charge additional fees based on the length of the content. For example, a basic fee may be applied to blog posts of 500 words or less, and an additional fee may be charged for every 500 words exceeded. Furthermore, the processor (130) may be configured to charge additional fees for topics that are technical or require expertise. Additionally, when a certain number of pieces of content are commissioned, a discount fee may be applied to calculate the fee charged to the company. For example, a 5% discount may be applied for orders of 10 or more, and a 10% discount for orders of 20 or more.

[0102] Figure 4 is a diagram showing the interface of an owned media according to an embodiment.

[0103] Referring to FIG. 4, the owned media platform management server according to the embodiment builds the company's owned media and generates and uploads various content to be registered in the owned media through generative artificial intelligence or user authors. In addition, the embodiment calculates an influence index based on the publication results for each published content and increases the learning weight of content with a high influence index to improve the quality of content provided by generative artificial intelligence. Furthermore, by tracking user behavior data and dynamically displaying customized content, user satisfaction can be improved.

[0104] Below, we will look at FIG. 5. The owned media platform management method illustrated in FIG. 5 can be performed by an owned media platform management server (100) including a processor (130).

[0105] Meanwhile, FIG. 5 is merely illustrative, and the concept of the present invention is not to be interpreted as being limited to that illustrated in FIG. 5. For example, each step may be configured in a different order than that illustrated in FIG. 5, at least one of the steps illustrated in FIG. 3 may not be performed, or one or more steps not illustrated in FIG. 5 may be additionally performed.

[0106] Below, the method for managing an owned media platform will be explained in turn. Since the operation (function) of the method according to the embodiment is essentially the same as the function of the system, descriptions that overlap with FIGS. 1 to 4 will be omitted.

[0107] Figure 5 is a diagram illustrating the management process of an owned media platform according to an embodiment.

[0108] Referring to Fig. 5, in step S100, enterprise owned media is established, and in step S200, the enterprise's original content is collected. In step S300, the collected content is displayed on the owned media, and in step S400, if user information regarding subscribers to the enterprise owned media is collected, customized content for each user is selected based on the user information. Subsequently, in step S500, the display of the selected customized content is adjusted.

[0109] In the owned media platform management system according to the embodiment, the reader uses the content for free, while the company produces and distributes the content. Accordingly, in the embodiment, the company pays a platform usage fee, and if the company also commissions content production, it pays additional content production costs.

[0110] The owned media platform management server and method according to the embodiment enable rapid owned media launching, thereby significantly reducing the time and cost required to build a website or blog directly.

[0111] In addition, content can be managed and analyzed efficiently through the embodiments. For example, by analyzing content publishing results (sharing, completion rate, reader gender ratio, age group, preference, number of subscriptions, next action, etc.) in real time, it supports strategic decision-making by enabling the company to immediately adjust its content strategy.

[0112] In addition, the embodiment efficiently performs content creation with AI support, thereby supporting the research and content creation processes, reducing the resources of content managers within the company, and providing higher efficiency.

[0113] In addition, through the embodiments, the manpower and time required for content creation can be reduced, thereby saving the company's human resources and costs.

[0114] In addition, the embodiment operates customized categories for each company, allowing companies to select and operate only the categories they desire, thereby enabling the provision of specialized content focused on specific topics.

[0115] In addition, the embodiment ensures reader influx, which can increase brand awareness as more readers are attracted.

[0116] In addition, it allows for easy identification of content from similar companies or competitors, enabling monitoring of the competitive landscape and the formulation of response strategies.

[0117] In addition, through the embodiments, readers can conveniently access various customized content for free.

[0118] Furthermore, in the embodiment, all original content published by the company can be read for free, offering higher accessibility compared to paid content platforms. Additionally, it allows users to easily save content of interest and conveniently access it in one place without the need to search for it on other portals.

[0119] In addition, the embodiment recommends content tailored to the reader's interests, thereby providing the reader with greater satisfaction.

[0120] In addition, the embodiment provides readers with the opportunity to create content by matching them with companies related to their field, thereby offering new opportunities to people who enjoy writing.

[0121] In addition, through the embodiments, companies do not need to directly hire personnel required for content creation, thereby reducing HR risks, and the platform handles writer matching and management, allowing companies to reduce the burden of personnel management.

[0122] In addition, the embodiment provides a team of professional writers with diverse backgrounds, such as freelance writers, women raising children, aspiring digital nomads, and office workers seeking side jobs, to meet the diverse content needs of companies.

[0123] In addition, in the embodiment, the platform recommends and manages authors, thereby guaranteeing the quality of the content.

[0124] In addition, through the examples, it is possible to strengthen the company's brand image and enhance credibility through high-quality customized content, as well as increase customer engagement and brand loyalty through interaction with readers.

[0125] Furthermore, through the embodiments, cost efficiency is maximized in various aspects such as content creation, management, and marketing. In addition, by monitoring and analyzing competitor content, it enables the establishment of a more competitive content strategy.

[0126] As used herein, a model may refer to any form of computer program that operates based on a network function, an artificial neural network, and / or a neural network. Throughout this specification, the terms model, neural network, network function, and neural network may be used interchangeably. A neural network is formed in which one or more nodes are interconnected through one or more links to form input and output node relationships within the neural network. The characteristics of a neural network may be determined by the number of nodes and links within the neural network, the relationships between the nodes and links, and the values ​​of the weights assigned to each link. A neural network may be composed of a set of one or more nodes. A subset of nodes constituting a neural network may form a layer.

[0127] A deep neural network (DNN) may refer to a neural network that includes multiple hidden layers in addition to an input layer and an output layer. Deep neural networks may include convolutional neural networks (CNN), recurrent neural networks (RNN), autoencoders, Generative Adversarial Networks (GAN), restricted Boltzmann machines (RBM), deep belief networks (DBN), Q networks, U networks, Siamese networks, Generative Adversarial Networks (GAN), transformers, etc. The description of deep neural networks described above is merely illustrative and the present disclosure is not limited thereto.

[0128] Neural networks can be trained in at least one of supervised learning, unsupervised learning, semi-supervised learning, self-supervised learning, or reinforcement learning. The training of a neural network may be a process of applying knowledge to the neural network to perform a specific action.

[0129] Neural networks can be trained to minimize output errors. The training process involves repeatedly inputting training data into the network, calculating the error between the network's output and the target for the training data, and updating the weights of each node by backpropagating the error from the output layer to the input layer in a direction that reduces the error. In supervised learning, labeled data with correct answers is used for each training point, whereas in unsupervised learning, unlabeled data can be used. The amount of change in the connection weights of each updated node can be determined by the learning rate. The neural network's calculation of the input data and the backpropagation of the error can constitute a training cycle (epoch). The learning rate can be applied differently depending on the number of iterations of the neural network's training cycle. In addition, to prevent overfitting, methods such as increasing training data, regularization, dropout (which disables some nodes), and batch normalization layers can be applied.

[0130] In one embodiment, the model may borrow at least a part of a transformer. The transformer may be composed of an encoder that encodes embedded data and a decoder that decodes the encoded data. The transformer may have a structure that receives a series of data and outputs a series of data of different types after undergoing encoding and decoding steps. In one embodiment, the series of data may be processed into a form that the transformer can compute. The process of processing the series of data into a form that the transformer can compute may include an embedding process. Expressions such as data token, embedding vector, embedding token, etc., may refer to data embedded in a form that the transformer can process.

[0131] To encode and decode a series of data, the encoders and decoders within the transformer can be processed using an attention algorithm. An attention algorithm can refer to an algorithm that calculates the similarity between one or more keys for a given query, applies this similarity to the values ​​corresponding to each key, and then calculates an attention value by performing a weighted sum of the similarity-applied values.

[0132] Various types of attention algorithms can be classified depending on how the query, key, and value are configured. For example, if attention is calculated by setting the query, key, and value identically, this can be referred to as a self-attention algorithm. If attention is calculated by reducing the dimensionality of embedding vectors to process a series of input data in parallel and determining an individual attention head for each partitioned embedding vector, this can be referred to as a multi-head attention algorithm.

[0133] In one embodiment, the transformer may be composed of modules that perform a plurality of multi-head self-attention algorithms or multi-head encoder-decoder algorithms. In one embodiment, the transformer may also include additional components other than attention algorithms, such as embeddings, normalization, and softmax. A method for constructing the transformer using an attention algorithm may include the method disclosed in Vaswani et al., Attention Is All You Need, 2017 NIPS, which is incorporated herein by reference.

[0134] A transformer can be applied to various data domains, such as embedded natural language, segmented image data, and audio waveforms, to convert a series of input data into a series of output data. To convert data with various data domains into a series of data that can be input to the transformer, the transformer can embed the data. The transformer can process additional data that represents the relative positional or phase relationships between the series of input data. Alternatively, the series of input data may be embedded by additionally reflecting vectors that represent the relative positional or phase relationships between the input data. In one example, the relative positional relationships between the series of input data may include, but are not limited to, word order within a natural language sentence, the relative positional relationships of each segmented image, and the temporal order of segmented audio waveforms. The process of adding information that represents the relative positional or phase relationships between the series of input data may be referred to as positional encoding.

[0135] In one embodiment, the model may include, but is not limited to, at least one of a Recurrent Neural Network (RNN), a Long Short Term Memory (LSTM) network, a Deep Neural Network (DNN), a Convolutional Neural Network (CNN), and a Bidirectional Recurrent Deep Neural Network (BRDNN).

[0136] In one embodiment, the model may be a model trained using a transfer learning method. Here, transfer learning refers to a learning method in which a pre-trained model having a first task is obtained by pre-training a large amount of unlabeled training data using a semi-supervised or self-learning method, and the pre-trained model is fine-tuned to be suitable for a second task, and a target model is implemented by training the labeled training data using a supervised learning method.

[0137] Meanwhile, the methods according to the various embodiments of the present invention described above can be implemented in the form of an application or software program that can be installed on an existing electronic device.

[0138] In addition, the whole or part of the method may be composed of multiple software function modules and implemented on an operating system (OS). Alternatively, each step may be composed of a single software function module, or each step may be combined to form a single software function module and implemented on an operating system. Therefore, even if all of the embodiments of the present disclosure are not implemented as a single software function module, if multiple software function modules implement each step of the present disclosure and multiple software function modules are implemented on a single operating system, it can be understood that the method of the present disclosure has been implemented.

[0139] In addition, the methods according to the various embodiments of the present invention described above can be implemented solely through software upgrades or hardware upgrades of existing electronic devices. Furthermore, the various embodiments of the present invention described above can also be performed through an embedded server equipped in an electronic device or an external server of the electronic device.

[0140] Meanwhile, according to one embodiment of the present invention, the various embodiments described above may be implemented as software comprising instructions stored on a computer-readable recording medium using software, hardware, or a combination thereof. In some cases, the embodiments described herein may be implemented as the processor itself. According to the software implementation, embodiments such as the procedures and functions described herein may be implemented as separate software modules. Each of the software modules may perform one or more functions and operations described herein.

[0141] Meanwhile, a computer or a similar device may include a device according to the disclosed embodiments, which is capable of calling instructions stored from a storage medium and operating according to the called instructions. When said instructions are executed by a processor, the processor may perform a function corresponding to said instructions directly or by using other components under the control of said processor. The instructions may include code generated or executed by a compiler or an interpreter.

[0142] A computer-readable recording medium may be provided in the form of a non-transitory computer-readable recording medium. Here, "non-transitory" simply means that the storage medium does not contain a signal and is tangible, without distinguishing whether data is stored semi-permanently or temporarily on the storage medium. In this context, a non-transitory computer-readable medium refers to a medium that stores data semi-permanently and is readable by a device, rather than a medium that stores data for a short moment, such as registers, caches, or memory. Specific examples of non-transitory computer-readable media may include CDs, DVDs, hard disks, Blu-ray discs, USBs, memory cards, and ROMs.

[0143] As described above, exemplary embodiments have been disclosed in the drawings and specification. Although specific terms have been used to describe the embodiments in this specification, they are used only for the purpose of explaining the technical concept of this disclosure and are not intended to limit the meaning or the scope of this disclosure as defined in the claims. Therefore, those skilled in the art will understand that various modifications and equivalent alternative embodiments are possible therefrom. Accordingly, the true technical scope of protection of this disclosure should be determined by the technical concept of the appended claims.

Claims

Claim 1 An owned media platform management server comprising: a memory storing at least one instruction for managing an owned media platform; and a processor that performs an operation according to said instruction, wherein the processor constructs an enterprise owned media, collects original content of said enterprise and displays it on said owned media, and when user information subscribing to the enterprise owned media is collected, selects customized content for each user based on said user information, adjusts the display of said selected customized content, analyzes said user information to extract users who wish to become content writers, analyzes content reviews written by said extracted users who wish to become content writers, evaluates the quality of said content reviews, and creates a content writer candidate group according to the result of said evaluation. Claim 2 In claim 1, the processor, upon receiving a request from a company to create content to be published on the owned media, generates content to be registered on the owned media based on the original content through a generative artificial intelligence, publishes the generated content to the owned media, identifies the publication result of the content, calculates an influence index representing the corporate promotional effect of the content from the publication result, trains the generative artificial intelligence with excellent content having an influence index above a certain level, and generates new content to be registered on the owned media, an owned media platform management server. Claim 3 In paragraph 2, the publication results of the content include the number of shares, completion rate, gender ratio and age of readers, number of subscriptions, and subsequent actions per content, and the subsequent actions per content include subscription, sharing, and saving, an owned media platform management server. Claim 4 In paragraph 2, the processor extracts key items indicating the popularity of the content among the items included in the publication result of the content, sets a weight for each of the extracted key items, and calculates an influence index using the set weight and the number of key items, an owned media platform management server. Claim 5 In claim 1, the processor selects user-specific customized content based on user information of enterprise owned media, and to dynamically adjust the display of the selected customized content, analyzes behavioral data including links clicked, articles read, videos watched, and content liked by the user in the owned media, calculates the preference for user-specific customized content, and displays the customized content in order according to the preference, an owned media platform management server. Claim 6 delete Claim 7 An owned media platform management server according to claim 1, wherein the processor performs sentiment analysis, keyword extraction, and topic modeling on the content review written by the user through natural language processing, identifies the category of the content review, calculates an influence index based on the publication result of the content review, and evaluates the quality of the content review using the category identification result and the influence index.

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