A method, device and system for providing integrated scalable business support platform services based on brand licensing

Abstract

The present application provides a method, device and system for providing integrated scalable business support platform services based on brand licensing, which comprises the following steps: receiving a brand licensing recommendation request from a first party terminal; encoding the first party information to generate a first party input; inputting the first input signal into a first AI model, training according to the analysis of the list, and selecting a recommended license; if the recommended license of the first company is selected as the license of the first brand through the first input signal, obtaining a first output signal representing the first brand from the first artificial intelligence model; on the basis of the first output signal, confirming that the license recommended by the first company is the license of the first brand; and transmitting the recommendation information of the recommended first brand license to the first company terminal.

Description

Technical Field

[0001] The following examples relate to technologies that provide integrated, scalable, business-supporting platform services based on brand licenses. Background Technology

[0002] Brand licensing refers to licensing goods using an existing brand, which may mean granting commercial rights to use the brand in a specific country and industry.

[0003] The market for licensed products is growing, but it is difficult to connect sellers who want to sell licenses with buyers who want to purchase licenses, and license buyers often find it difficult to know which licenses they need.

[0004] Therefore, there is a growing need to support the use of brand licenses by providing buyers with the necessary information about them, and research into the relevant technologies is required.

[0005] Existing technical documents

[0006] Patent documents

[0007] (Patent Document 1) Korean Patent Registration No. 10-2548480

[0008] (Patent Document 2) Korean Patent Registration No. 10-2625227

[0009] (Patent Document 3) Korean Patent Registration No. 10-2346727

[0010] (Patent Document 4) Korean Patent No. 10-2023-0115604 Summary of the Invention

[0011] The problem that the invention aims to solve

[0012] According to one embodiment, the aim is to provide a platform service for integrated, scalable business support based on brand licenses, which provides methods, devices, and systems.

[0013] The purpose of this invention is not limited to the above-mentioned purposes, and other purposes not mentioned can be clearly understood from the following description.

[0014] means for solving problems

[0015] According to one embodiment, in a method for providing an integrated and scalable business support platform service based on brand licensing executed by a device, a brand license recommendation request is received from a first-party terminal; the first-party information is encoded to generate a first-party input; a first input signal is input into a first AI model, trained based on analysis of a list, and a recommended license is selected; if the recommended license of the first company is selected as the license of the first brand through the first input signal, a first output signal representing the first brand is obtained from the first artificial intelligence model; based on the first output signal, it is confirmed that the license recommended by the first company is the license of the first brand; and a method for providing an integrated and scalable business support platform service based on brand licensing includes the step of transmitting recommendation information recommending the first brand license to the first company terminal.

[0016] The first AI model, based on the first input signal, determines the location, industry, and vision of the first company. When the first input signal is input, if the location of the first company is confirmed to be in the first country, then the license registered as available in the first country is classified into the first candidate group among the brand licenses registered as available licenses. If the industry of the first company is confirmed to be the first industry, then the licenses registered as available in the first industry among the licenses classified into the first candidate group are classified into the second candidate group. For each license classified into the second candidate group, the higher the similarity between the brand image and the vision of the first company, the higher the score of the first company. Among the licenses in the second candidate group, the license with the highest score is likely to be selected as the recommended license of the first company.

[0017] The method for providing an integrated and scalable business support platform service based on brand licensing involves transmitting the recommendation information of the first brand to the terminal of the first company, then confirming the product category of the products manufactured by the first company; if the first product is manufactured as a product of the first category, confirming that the target customer group for the first product has been set as the first customer group; identifying users in the SNS users marked as belonging to the first customer group as the first user group; reclassifying users identified as having uploaded posts about the first product from those in the first user group to the second user group, and distinguishing users who have not uploaded posts about the first product from those in the third user group; and reclassifying users in the first user group who have been identified as having uploaded posts about the first brand as the fourth user group. Users in the first user group who have not uploaded posts for the first brand are grouped into the fifth user group; users included in the third user group and users also included in the fourth user group are separated into the sixth user group; the number of users in the sixth user group is identified as the first user number; the higher the number of the first value, the higher the setting of the first value within the preset value range; the utilization rate of the first brand in the first category is set to the first number; if it is confirmed by comparing the utilization rate of the first brand sets in each category that the first brand has the highest utilization rate for the first category, further steps may be included, such as when licensing the first brand, recommending that the first category of products be published to the first brand's terminal.

[0018] Invention Effects

[0019] According to one embodiment, it serves to provide purchasers with information about the brand licenses they require, which can help them activate the use of the brand licenses.

[0020] On the other hand, the effects of the embodiments are not limited to those described above, and other unmentioned effects can be clearly understood by those skilled in the art from the following description. Attached Figure Description

[0021] Figure 1 It is a diagram that outlines the system configuration based on the diaphragm.

[0022] Figure 2 This is a flowchart illustrating the process of providing integrated and scalable business support platform services based on a brand license, according to a single embodiment.

[0023] Figure 3 This is a flowchart illustrating the process of selecting a recommended license based on an embodiment.

[0024] Figure 4 This is a flowchart illustrating the process of recommending product categories when licensing brands according to an embodiment.

[0025] Figure 5 It is a flowchart illustrating the process of calculating the similarity between brand image and corporate vision based on an example.

[0026] Figure 6 It is a flowchart illustrating the process of adjusting brand utilization figures based on routine implementation methods.

[0027] Figure 7 This is a preliminary schematic diagram of a device configuration according to a single embodiment. Detailed Implementation

[0028] The embodiments are described in detail below with reference to the accompanying drawings. However, various modifications can be made to the embodiments, and therefore the scope of the patent application is not limited to or restricted by these embodiments. Any changes, equivalents, or substitutions to the embodiments should be understood to be included within the scope of the claims.

[0029] The specific structural or functional descriptions of the embodiments are provided for illustrative purposes only and may be modified and implemented in various forms. Therefore, the embodiments are not limited to a particular form of disclosure, and the scope of this specification includes changes, uniformities, or substitutions incorporated into the descriptive concepts.

[0030] Terms such as "first" or "second" can be used to describe various components, but the interpretation of these terms should only be used to distinguish one component from another. For example, the first component can be named the second component, and similarly, the second component can be named the first component.

[0031] When a component is said to be "connected" to another component, it should be understood that it may be directly connected to or connected to another component, but there may be another component between them.

[0032] The terminology used in the embodiments is for illustrative purposes only and should not be construed as restrictive. Singular expressions include plural expressions unless the context clearly implies otherwise. In this specification, the terms "comprising" or "having" should be understood to mean the presence of the functions, numbers, steps, actions, components, parts, or combinations thereof described herein, and should not exclude the presence or addition of one or more other functions or numbers, steps, actions, components, parts, or combinations thereof.

[0033] Unless otherwise defined, all terms used herein, including technical or scientific terms, shall have the same meaning as commonly understood by one of ordinary skill in the art to which the embodiments pertain. Terms such as those defined in common dictionaries shall be interpreted as having the meaning consistent with their meaning in the relevant descriptive context and shall not be interpreted in an ideal or overly formal sense unless expressly defined herein.

[0034] Furthermore, when describing the accompanying drawings, regardless of the drawing code, the same reference numerals should be assigned to the same elements, and identical repetitive descriptions should be omitted. When describing embodiments, detailed descriptions should be omitted if it is determined that a specific description of the relevant technical notifications may unnecessarily obscure the essential points of the embodiment.

[0035] The embodiments can be implemented in various types of products, including personal computers, laptops, tablets, smartphones, televisions, smart home appliances, smart cars, kiosks, and wearable devices.

[0036] In this embodiment, the artificial intelligence (AI) system is a computer system that achieves human-level intelligence. Unlike existing rule-based intelligent systems, it is a system in which machines learn and make judgments independently. As AI systems improve their recognition rates and more accurately understand sellers' preferences, existing rule-based intelligent systems are gradually being replaced by deep learning-based AI systems.

[0037] Artificial intelligence technology includes machine learning and elemental technologies that use machine learning. Machine learning is an algorithmic technique that classifies / learns the features of input data on its own. Elemental technologies are techniques that use machine learning algorithms such as deep learning to simulate the cognitive and judgmental functions of the human brain, and consist of technical fields such as language understanding, visual understanding, reasoning / prediction, knowledge representation, and motion control.

[0038] The various fields where artificial intelligence technology is applied are as follows: Language understanding is a technology for recognizing, adapting to, and processing human language / text, including natural language processing, machine translation, dialogue systems, question answering, and speech recognition / synthesis. Visual understanding is a technology for recognizing and processing objects like human vision, including object recognition, object tracking, image search, human recognition, scene understanding, spatial understanding, and image improvement. Reasoning and prediction is a technology for making logical inferences and predictions by judging information, including knowledge / probability-based reasoning, optimization prediction, preference-based planning, and recommendation. Knowledge representation is a technology for automatically processing human experience information into knowledge data, including knowledge construction (data generation / classification) and knowledge management (data utilization). Motion control is a technology for controlling the autonomous driving of vehicles and the movement of robots, including motion control (navigation, collision, driving) and action control (behavioral control).

[0039] Generally, to apply machine learning algorithms to real-world applications, training is conducted through trial and error due to the nature of basic machine learning methods. Deep learning, in particular, requires hundreds of thousands of iterations. Since this is impossible to achieve in a real physical environment, the actual physical environment is virtualized on a computer, and learning is conducted through simulation.

[0040] Figure 1It is a diagram that outlines the system configuration based on the diaphragm.

[0041] Reference Figure 1 According to one embodiment, the system may include multiple enterprise terminals 100 and devices 200, which can communicate with each other through a communication network.

[0042] First, regardless of whether the communication method is wired or wireless, a communication network can be configured and implemented in various forms to enable communication between servers and between servers and terminals.

[0043] Each of the multiple manufacturers' terminals 100 can be implemented as a computing device with communication functions, such as a mobile phone, desktop PC, laptop, tablet, smartphone, etc., but not limited to, and can be implemented as various types of communication devices that can connect to external servers.

[0044] Multiple company terminals 100 are terminals used by company leaders to purchase brand licenses, and may include a first company terminal 110 used by a first company leader and a second company terminal 120 used by a second company leader.

[0045] Each of the plurality of vendor terminals 100 can be configured to perform all or part of the computing, storage / reference, input / output, and control functions of a general-purpose computer. The plurality of vendor terminals 100 can be configured to communicate with device 200 via wired and wireless means.

[0046] Each of the plurality of company terminals 100 can access a webpage created by an individual or organization providing services using device 200, or can install an application developed and distributed by an individual or group providing services using device 200. Each of the plurality of vendor terminals 100 can link to device 200 via a webpage or an application.

[0047] Each of the multiple manufacturers' terminals 100 can access device 200 through a webpage or application provided by device 200.

[0048] For ease of explanation below, the operation of the first manufacturer's terminal 110 will be described in detail, but of course the operation of the first company's terminal 110 can be performed in other manufacturers' terminals (such as the second company's terminal 120).

[0049] Device 200 may be its own server, owned by an individual or organization using services provided by device 200, or it may be a cloud server, or it may be a peer-to-peer (P2P) collection of distributed nodes. Device 200 may be configured to perform all or part of the computing, storage / reference, input / output, and control functions of a conventional computer. Device 200 may be equipped with at least one artificial intelligence model that performs inference functions.

[0050] The device 200 can be configured to communicate with multiple vendor terminals 100 via wired or wireless communication, and can control the operation of each of the vendor terminals 100 and control what information is displayed on each of the vendor terminals 100.

[0051] Device 200 can be implemented as a server that provides integrated and scalable business support platform services based on brand licensing. When brand licensing is carried out, it can provide various services such as product planning, distribution support, marketing planning and retail, as well as act as an intermediary between licensee and seller.

[0052] On the other hand, for ease of explanation, in Figure 1 The image shows only the first company terminal 110 and the second company terminal 120 among the multiple company terminals 100, but the number of terminals may vary depending on the embodiment. There is no particular limitation on the number of terminals as long as the processing power of the device 200 allows.

[0053] According to one embodiment, device 200 can select a recommended license based on AI-based business information analysis and provide recommended information for that license.

[0054] In this invention, artificial intelligence (AI) refers to the technology of mimicking human learning, reasoning, and perception abilities and implementing them in computers, and may include concepts such as machine learning and symbolic logic. Machine learning (ML) is an algorithmic technique that can classify or learn features of input data on its own. Artificial intelligence technology is a machine learning algorithm that analyzes input data, learns from the analysis results, and can make judgments or predictions based on the learning results. Furthermore, the technology of using machine learning algorithms to mimic the cognitive and judgmental functions of the human brain can also be understood as falling within the scope of artificial intelligence. For example, it may include technical fields such as language understanding, visual understanding, reasoning / prediction, knowledge representation, and motion control.

[0055] Machine learning can refer to the process of training neural network models using experience in processing data. Machine learning can mean that computer software improves its ability to process data on its own. Neural network models are built by modeling the correlations between data, which can be represented by multiple parameters. A neural network model extracts features from given data, analyzes it, and derives the correlations between the data. Machine learning can be said to repeat this process to optimize the parameters of the neural network model. For example, a neural network model can learn the mapping (correlation) between the inputs and outputs of data given in the form of input / output pairs. Alternatively, a neural network model can deduce regularities between given datasets and learn relationships, even if only the input data is given.

[0056] Artificial intelligence learning models, or neural network models, can be designed to replicate the structure of the human brain on a computer and can include multiple network nodes that simulate and weight neurons in a human neural network. These multiple network nodes can simulate the synaptic activity of neurons sending and receiving signals through synapses and are interconnected. In an AI learning model, multiple network nodes can reside in layers of different depths and send and receive data based on their convolutional connections. For example, an AI learning model can be an artificial neural network, a convolutional neural network (CNN), etc. As an example, the AI ​​learning model can perform machine learning using methods such as supervised learning, unsupervised learning, and reinforcement learning. Machine learning algorithms used to perform machine learning can include decision trees, Bayesian networks, support vector machines, artificial neural networks, Ada-boost, perceptrons, genetic programming, and clustering.

[0057] A CNN (Neural Network Array) is a multilayer perceptron designed to use minimal preprocessing. A CNN consists of one or more convolutional layers and typical neural network layers above them, with additional weights and pooling layers. Due to this structure, CNNs can fully utilize input data from two-dimensional structures. Compared to other deep learning architectures, CNNs perform well in both video and audio domains. CNNs can also be trained using standard backpropagation. CNNs are easier to train than other feedforward neural network techniques and have the advantage of using fewer parameters.

[0058] Convolutional networks are neural networks consisting of a set of nodes with bound parameters. Increased availability of training data and computational power, coupled with advancements in algorithms such as discriminative linear units and dropout training, have significantly improved many computer vision tasks. In large datasets (such as those available for many tasks today), overfitting is not critical, and increasing network size can improve test accuracy. Optimal use of computational resources is a limiting factor. To address this, decentralized, scalable implementations of deep neural networks can be used.

[0059] Figure 2 This is a flowchart illustrating the process of providing integrated and scalable business support platform services based on a brand license, according to a single embodiment.

[0060] refer to Figure 2 First, in step S201, device 200 may receive a brand license recommendation request from first manufacturer terminal 110. In this case, the recommendation request may refer to a request recommending a brand license that can be purchased and used by a first-party company.

[0061] In step S202, device 200 may encode the first connection information to generate a first input signal. Here, the first company information may include information related to the first company, such as the location of the first company, the business type of the first company, the vision of the first company, etc., and may be obtained through the first company's terminal 110 or the database of device 200.

[0062] Specifically, the device 200 can generate a first input signal by performing normal information processing on the first entity information, so as to use the first entity information as input to the first artificial intelligence model.

[0063] In step S203, device 200 may input a first input signal to a trained first AI model, which selects a recommended license based on analysis of business information. Here, the first AI model may be trained to recommend licenses based on the company's location, industry, and vision.

[0064] According to one embodiment, the first AI model may be an algorithm that receives an input signal generated by encoding company information, then selects a recommended license based on the input signal, and outputs an output signal indicating the selected license.

[0065] In other words, the first AI model can consider a company's location, industry, and vision identified through company information, select recommended licenses that the company anticipates need, and output a signal indicating the selected recommended licenses. The process of selecting recommended licenses in the first AI model will be discussed later. Figure 3 Please provide a detailed explanation.

Claims

1. A method for providing scalable business support platform services based on brand licensing, wherein, The method is performed by the device. The method includes the following steps: Receive brand licensing recommendation requests from first-party terminals; Encode the first-party information to generate the first-party input; The first input signal is fed into the first AI model, which is trained based on the analysis of the list to select a recommended license. If the recommended license of the first company is selected as the license of the first brand through the first input signal, then the first output signal representing the first brand is obtained from the first artificial intelligence model; Based on the first output signal, it was confirmed that the license recommended by the first company is the license for the first brand; and The recommended information will be transmitted to the first company's terminal along with the license of the leading brand.

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