Method and system for operating emotion economy using web3-based emotion coin and nft

Abstract

An emotional economy operating system utilizing Web3-based emotional coins and NFTs may include memory and a processor for storing instructions. When executed by the processor, the instructions can control the system to evaluate a recipient's resonance score regarding a user's emotional message or interaction, generate an emotional coin based on the evaluated resonance score and scarcity coefficient, issue an ERC-721 based NFT and record it in IPFS when a preset condition is satisfied by linking the generated emotional coin with a specific emotional moment, control the token circulation amount through the choice of consumption or burning of the issued NFT, dynamically update the resonance ranking and scarcity index by analyzing the user's emotional consumption history, create an AI character by learning the speech patterns and emotional patterns of a celebrity or fan, and build a fandom-based emotional economy model by converting emotional messages into NFTs through emotional interaction with fans, and operate a circular economy model that promotes the balance and value stability of the token economy based on the circulation amount obtained by subtracting the amount burned from the total amount of tokens issued.

Description

Technology Field

[0001] The present invention relates to the fields of artificial intelligence, blockchain, and the digital economy. More specifically, it relates to a circular emotional economy system and a method of operating the same, which quantifies emotional resonance generated from a user's emotional messages or interactions and, based on this, generates, distributes, consumes, and burns emotional coins and non-fungible tokens (NFTs) in conjunction with Web3 technology.

[0002] In addition, the present invention can be applied to the field of technology for building an expanded emotional economy ecosystem that combines with the fandom economy by utilizing an Emotion OS, celebrity AI, or fan AI character, and includes linkage with the real economy, distribution protection, recommendation of emotional consumption, and linkage with global emotional currency. Background Technology

[0004] With the recent advancement of artificial intelligence technology, attempts to recognize and analyze user emotions have become increasingly active. However, existing technologies have primarily focused on analyzing emotions to provide feedback within digital environments or to enhance the user experience of specific services. Efforts to convert the intangible value of emotion into tangible economic value, manage it transparently and securely, and build a sustainable ecosystem have been insufficient.

[0005] Meanwhile, although blockchain technology and NFTs have presented new methods for proving ownership and trading digital assets, their application has been limited primarily to specific fields such as artworks, game items, and financial products. There has been a lack of a comprehensive approach to directly connect 'emotional exchange,' a fundamental human activity, with a blockchain-based economic system.

[0006] Furthermore, the relationship between celebrities and fans has primarily consisted of one-way content consumption, traditional fan club activities, and merchandise purchases. It has been difficult to find a systematic system that allows fans to own their emotional bond with celebrities as a tangible digital asset or participate in economic activities based on it.

[0007] Consequently, there is a growing need for integrated systems and methods that quantify the value of emotions, combine them with Web3 technologies to build a transparent and sustainable economic model, and further deepen this in specific areas, particularly the fandom economy. The problem to be solved

[0009] The present invention aims to provide a system and method for quantitatively measuring the degree of emotion generated from a user's emotional message or interaction, and generating a unique emotion coin using Web3 technology based on this measured value.

[0010] Furthermore, the present invention aims to provide a system and method that guarantees the permanence and transparency of assets by linking a generated emotion coin with a specific emotional moment or content to issue an NFT (Non-Fungible Token) having uniqueness and scarcity, and recording it in a distributed storage system such as IPFS.

[0011] The present invention aims to provide a complete circular economy model that promotes the balance and value stability of the token economy by introducing consumption and burning mechanisms to regulate the circulation of issued sentiment coins and NFTs, and by dynamically updating resonance rankings and scarcity indices through user history analysis.

[0012] The present invention aims to provide a system and method for building a new fandom-based emotional economy model by generating an AI character that learns the emotional patterns of celebrities or fans, facilitating emotional interaction with fans through this, and converting the emotional messages generated during this process into NFTs.

[0013] The present invention aims to provide a comprehensive solution that expands the emotional economy system into the real economy and global environment by linking emotional NFTs with physical product payments, providing anti-piracy and license management functions, introducing a recommendation algorithm based on user emotional consumption patterns, and further calculating a global emotional index to differentiate emotional currency exchange rates by country. means of solving the problem

[0015] The emotional economy operating system utilizing Web3-based emotional coins and NFTs of this document may include memory and a processor for storing instructions. When executed by the processor, the instructions can control the system to evaluate a recipient's resonance score regarding a user's emotional message or interaction, generate an emotional coin based on the evaluated resonance score and scarcity coefficient, issue an ERC-721 based NFT and record it in IPFS when a preset condition is satisfied by linking the generated emotional coin with a specific emotional moment, control the token circulation amount through the choice of consumption or burning of the issued NFT, dynamically update the resonance ranking and scarcity index by analyzing the user's emotional consumption history, create an AI character by learning the speech patterns and emotional patterns of a celebrity or fan, and build a fandom-based emotional economy model by converting emotional messages into NFTs through emotional interaction with fans, and operate a circular economy model that promotes the balance and value stability of the token economy based on the circulation amount obtained by subtracting the amount burned from the total amount of tokens issued. Effects of the invention

[0017] The method and system for operating an emotion economy utilizing Web3-based emotion coins and NFTs according to the present invention quantifies the intangible emotional exchange of users and converts it into tangible digital assets such as coins and NFTs, thereby redefining the value of emotion and creating a new concept economic ecosystem based thereon.

[0018] In addition, the present invention utilizes blockchain technology to transparently and securely manage the creation, distribution, and ownership transfer of emotional assets, and can establish a sustainable and stable circular economy model through token total supply control and burning mechanisms.

[0019] The present invention introduces a celebrity AI and fan AI system, enabling fans to own and trade emotional connection with celebrities in the form of unique NFTs, thereby presenting a new value creation and monetization model for the fandom economy and having the effect of maximizing the bond with fans.

[0020] The present invention utilizes emotional assets as a means of payment in the real economy, protects the value of assets using IPFS and a hash-based protection system, and has the effect of significantly expanding the scope of the emotional economy and enhancing the user experience through personalized recommendations and global integration functions. Brief explanation of the drawing

[0022] FIG. 1 is a diagram illustrating the overall structure of an artificial intelligence-based system according to one embodiment. FIG. 2 is a diagram illustrating the learning of a neural network according to one embodiment. FIG. 3 is a diagram illustrating the configuration of an artificial intelligence model according to one embodiment. FIG. 4 is a block diagram showing the configuration of an emotional economy operating system utilizing Web3-based emotional coins and NFTs according to one embodiment. FIG. 5 is a flowchart illustrating a method for operating an emotion economy using Web3-based emotion coins and NFTs according to one embodiment. FIG. 6 is a flowchart illustrating a method for operating an emotion economy using Web3-based emotion coins and NFTs according to one embodiment. FIG. 7 is a flowchart illustrating a method for operating an emotion economy using Web3-based emotion coins and NFTs according to one embodiment. Specific details for implementing the invention

[0023] Hereinafter, embodiments are described in detail with reference to the attached drawings. However, various modifications may be made to the embodiments, and thus the scope of the patent application is not limited or restricted by these embodiments. It should be understood that all modifications, equivalents, and substitutions to the embodiments are included within the scope of the rights.

[0024] Specific structural or functional descriptions of the embodiments are disclosed for illustrative purposes only and may be modified and implemented in various forms. Accordingly, the embodiments are not limited to the specific disclosed forms, and the scope of this specification includes modifications, equivalents, or substitutions that fall within the technical concept.

[0025] Terms such as "first" or "second" may be used to describe various components, but these terms should be interpreted solely for the purpose of distinguishing one component from another. For example, the first component may be named the second component, and similarly, the second component may be named the first component.

[0026] When it is stated that a component is "connected" to another component, it should be understood that it may be directly connected to or joined to that other component, or that there may be other components in between.

[0027] The terms used in the embodiments are for illustrative purposes only and should not be interpreted as intended to be limiting. Singular expressions include plural expressions unless the context clearly indicates otherwise. In this specification, 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.

[0028] Unless otherwise defined, all terms used herein, including technical or scientific terms, have the same meaning as generally understood by those skilled in the art to which the embodiments pertain. Terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant technology, and should not be interpreted in an ideal or overly formal sense unless explicitly defined in this application.

[0029] In addition, when describing with reference to the attached drawings, identical components are assigned the same reference numeral regardless of drawing symbols, and redundant descriptions thereof are omitted. In describing the embodiments, if it is determined that a detailed description of related prior art could unnecessarily obscure the essence of the embodiments, such detailed description is omitted.

[0030] The embodiments can be implemented in various forms of products such as personal computers, laptop computers, tablet computers, smartphones, televisions, smart home appliances, intelligent automobiles, kiosks, and wearable devices.

[0031] Artificial Intelligence (AI) systems are computer systems that implement human-level intelligence; unlike existing rule-based smart systems, they are systems in which machines learn and make decisions autonomously. As AI systems improve in recognition accuracy and gain a more accurate understanding of user preferences with continued use, existing rule-based smart systems are gradually being replaced by deep learning-based AI systems.

[0032] Artificial intelligence technology consists of machine learning and component technologies utilizing machine learning. Machine learning is an algorithmic technology that autonomously classifies and learns the characteristics of input data, while component technologies are technologies that mimic the cognitive and judgmental functions of the human brain by utilizing machine learning algorithms such as deep learning, and are comprised of technological fields such as linguistic understanding, visual understanding, reasoning / prediction, knowledge representation, and motion control.

[0033] The various fields where artificial intelligence technology is applied are as follows. Linguistic understanding refers to technologies that recognize, apply, and process human language and text, including natural language processing, machine translation, dialogue systems, question answering, and speech recognition / synthesis. Visual understanding refers to technologies that perceive and process objects like human vision, including object recognition, object tracking, image search, people recognition, scene understanding, spatial understanding, and image enhancement. Inference and prediction refers to technologies that logically reason and predict by judging information, including knowledge / probability-based inference, optimization prediction, preference-based planning, and recommendation. Knowledge representation refers to technologies that automatically process human experiential information into knowledge data, including knowledge construction (data generation / classification) and knowledge management (data utilization). Motion control refers to technologies that control the autonomous driving of vehicles and the movement of robots, including motion control (navigation, collision, driving) and manipulation control (behavior control).

[0034] Generally, to apply machine learning algorithms to real-world situations, training is performed using a trial-and-error method due to the inherent characteristics of the fundamental methodologies. In particular, deep learning requires hundreds of thousands of iterations. Since it is impossible to execute this in a real physical external environment, training is instead performed through simulations that virtually recreate the actual physical environment on a computer.

[0035] In the present invention, Artificial Intelligence (AI) refers to a technology that imitates human learning ability, reasoning ability, and perceptual ability, and implements them on a computer, and may include concepts such as machine learning and symbolic logic. Machine Learning (ML) is an algorithmic technology that classifies or learns the characteristics of input data on its own. AI technology can analyze input data as a machine learning algorithm, learn from the results of the analysis, and make judgments or predictions based on the results of the learning. Furthermore, technologies that mimic the functions of the human brain, such as cognition and judgment, by utilizing machine learning algorithms can also be understood as falling within the category of AI. For example, technological fields such as linguistic understanding, visual understanding, reasoning / prediction, knowledge representation, and motion control may be included.

[0036] Machine learning can refer to the process of training neural network models using experience in processing data. Through machine learning, computer software can improve its own data processing capabilities. A neural network model is constructed by modeling the correlations between data, and these correlations can be expressed by multiple parameters. A neural network model extracts and analyzes features from given data to derive correlations between them; machine learning can be defined as the process of optimizing the model's parameters by repeating this process. For example, a neural network model can learn the mapping (correlation) between inputs and outputs for data given as input-output pairs. Alternatively, even when only input data is provided, a neural network model can derive regularities between the given data and learn those relationships.

[0037] An artificial intelligence learning model or neural network model can be designed to implement the structure of the human brain on a computer and may include multiple network nodes that have weights and simulate neurons of a human neural network. The multiple network nodes may have interconnected relationships by simulating the synaptic activity of neurons, where neurons exchange signals through synapses. In an artificial intelligence learning model, multiple network nodes may be located in layers of different depths and exchange data according to convolutional connections. The artificial intelligence learning model may be, for example, an Artificial Neural Network (ANN) or a Convolutional Neural Network (CNN). As an embodiment, the artificial intelligence learning model may be machine learned according to methods such as supervised learning, unsupervised learning, and reinforcement learning. Machine learning algorithms for performing machine learning may include Decision Tree, Bayesian Network, Support Vector Machine, Artificial Neural Network, Ada-boost, Perceptron, Genetic Programming, and Clustering.

[0038] Among these, CNNs are a type of multilayer perceptron designed to use minimal preprocessing. CNNs consist of one or more convolutional layers and standard artificial neural network layers stacked on top, additionally utilizing weights and pooling layers. Thanks to this structure, CNNs can fully utilize two-dimensional input data. Compared to other deep learning architectures, CNNs demonstrate good performance in both image and audio fields. CNNs can also be trained using standard backpropagation. CNNs have the advantage of being easier to train than other feedforward artificial neural network techniques and using a small number of parameters.

[0039] Convolutional networks are neural networks comprising sets of nodes with bounded parameters. Many computer vision tasks have been significantly improved, driven by the increased size of available training data and the availability of computational power, combined with algorithmic advancements such as discriminative linear units and dropout training. In the case of massive datasets, such as those available for many tasks today, outfitting is not critical, and increasing the network size improves test accuracy. Optimal utilization of computing resources becomes a limiting factor. To address this, distributed, scalable implementations of deep neural networks can be employed.

[0041] FIG. 1 is a diagram illustrating the overall structure of an artificial intelligence-based system according to one embodiment.

[0042] As illustrated in FIG. 1, an artificial intelligence-based system (100) may include a plurality of user terminals (110-1, 110-n), a server (120), and a database (130). This system adopts a distributed architecture and operates based on a client-server model, with each component playing a unique role and contributing to increasing the efficiency and scalability of the entire system. According to one embodiment, the database (130) is depicted as being configured separately from the server (120), but is not limited thereto; depending on system design and operational efficiency, the database (130) may be integrated within the server (120). This integrated configuration has the advantage of improving data access speed and reducing system complexity. For example, the server (120) may include a plurality of artificial intelligence models and processing units for performing machine learning algorithms, and these can implement various types of deep learning and machine learning technologies (e.g., CNN, RNN, Transformer, reinforcement learning, etc.) to respond to user requests and provide intelligent services. According to one embodiment, a plurality of user terminals (110-1, 110-n), a server (120), and a database (130) can be connected to communicate with each other through a network (N), which enables real-time data exchange and smooth interaction between system components.

[0043] According to one embodiment, the network (N) may perform wireless or wired communication between a plurality of user terminals (110-1, 110-n), a server (120), a database (130), etc., and is located in the center of FIG. 1 and serves as a hub connecting all components. For example, the network may perform wireless communication according to methods such as 5G, LTE (Long-Term Evolution), LTE-A (LTE Advanced), CDMA (Code Division Multiple Access), WCDMA (Wideband CDMA), WiBro (Wireless BroadBand), WiFi (Wireless Fidelity), Bluetooth, NFC (Near Field Communication), GPS (Global Positioning System), or GNSS (Global Navigation Satellite System). The 5G network supports high-speed data transmission (up to 20Gbps), ultra-low latency (1ms or less), and large-scale connectivity, making it suitable for real-time processing of large-capacity AI models. For example, the network (N) may be configured to perform wired communication using methods such as USB (Universal Serial Bus), HDMI (High Definition Multimedia Interface), RS-232 (Recommended Standard 232), Ethernet, fiber optic cable, or POTS (Plain Old Telephone Service). In particular, in data center environments, high-performance network technologies such as 400Gbps Ethernet or InfiniBand can be utilized for communication between large-scale AI computing clusters.

[0044] According to one embodiment, user terminals (110-1, 110-n) are various client devices that access the system and can be implemented in various forms such as smartphones, tablets, desktop computers, wearable devices, and IoT devices. These terminals are responsible for transmitting service requests through a user interface and receiving and displaying results processed by a server (120). Each terminal may also run a lightweight AI model locally, which can reduce network latency and enhance privacy protection. Direct connection or peer-to-peer (P2P) communication between terminals may be possible, as indicated by the dotted line, which is particularly useful in distributed learning or edge computing scenarios.

[0045] According to one embodiment, the server (120) is the central processing unit of the system, responsible for processing user requests, executing artificial intelligence models, and managing data. The server is equipped with a high-performance processor (CPU, GPU, TPU, NPU, etc.) for large-scale computation, large-capacity memory, and a stable operating system, and may be operated in a virtualized manner on a cloud infrastructure. The server performs training and inference functions for complex deep learning models and transmits the results to user terminals or stores them in a database. In addition, the stability and reliability of the system can be ensured through functions such as load balancing, error recovery, and security management.

[0046] According to one embodiment, a database (130) is a storage facility for storing and managing various data, and is depicted in a cylindrical shape in FIG. 1. Data stored in the database (130) is data acquired, processed, or used by at least one component of a plurality of user terminals (110-1, 110-n) or a server (120), and may include software (e.g., programs), user profiles, training datasets, model weights, log records, etc. Structured relational data may be stored in a SQL-based database (MySQL, PostgreSQL, etc.), and unstructured large-scale data may be stored in a NoSQL database (MongoDB, Cassandra, etc.) or a distributed file system (Hadoop HDFS, etc.). The database (130) may include volatile and / or non-volatile memory, where volatile memory (RAM) is used for caching or temporary data processing requiring fast data access, and non-volatile memory (SSD, HDD, tape drive, etc.) is used for permanent data storage. Databases also ensure the security and integrity of data through data encryption, access control, and backup and recovery mechanisms.

[0047] This artificial intelligence-based system (100) can be utilized in various application fields and can provide services such as user behavior analysis, recommendation systems, natural language processing, image and video recognition, and predictive analysis. In particular, the distributed architecture and scalable design of the system enable stable performance to be maintained even as the number of users and data volume increase.

[0049] FIG. 2 is a diagram illustrating the learning of a neural network according to one embodiment.

[0050] As illustrated in FIG. 2, the learning device can train a neural network (123) to classify review responses received from multiple user terminals (110-1, ���) by item. Additionally, the learning device can train a neural network (123) to extract user stay history from user movement path information. The neural network (123) is also called an artificial neural network and is a computational model designed inspired by the neural structure of the human brain, specialized in recognizing and learning complex patterns within data. According to one embodiment, the learning device may be a separate entity from the server (120), but it may also be implemented integrated into the same system, so it is not limited thereto.

[0051] According to one embodiment, the neural network (123) includes an input layer (121) into which training samples are input and an output layer (125) that outputs training outputs, and can be learned based on the difference between the training outputs and labels (i.e., actual correct data). Here, the labels are defined based on items corresponding to review responses (e.g., service quality, price satisfaction, cleanliness, etc.) and can be defined based on user dwell history (place visited, time spent, frequency of visit, etc.) corresponding to movement path information. The neural network (123) is connected as a group of multiple nodes and is defined by weights between the connected nodes and an activation function that activates the nodes. Various forms of the activation function may be used, such as sigmoid, hyperbolic tangent (tanh), and ReLU, which enables the network to learn non-linear patterns.

[0052] According to one embodiment, the learning device can train a neural network (123) using a Gradient Descent (GD) technique or a Stochastic Gradient Descent (SGD) technique. The GD technique is a method of updating weights all at once by calculating gradients based on the entire dataset, while the SGD technique is a method of increasing computational efficiency and the possibility of escaping a local optimum by updating weights more frequently using only a randomly selected portion of data (mini-batch). The learning device can use a loss function designed by the outputs and labels of the neural network. The loss function is an important element that quantifies the difference between the model's predicted value and the actual correct answer to suggest the direction of learning.

[0053] The learning device can calculate the training error using a predefined loss function. The loss function can be predefined with labels, outputs, and parameters as input variables, where the parameters can be set by weights within the neural network (123). For example, the loss function can be designed in the form of Mean Square Error (MSE), entropy, etc. MSE is calculated as the squared mean of the difference between the predicted value and the actual value and is mainly used for regression problems, while cross-entropy is a function that measures the difference between the predicted probability distribution and the actual distribution and is suitable for classification problems. In addition, loss functions that are less sensitive to outliers, such as Huber Loss, can be utilized, and various techniques or methods can be employed in the embodiments where the loss function is designed.

[0054] According to one embodiment, the learning device can identify weights that influence the training error using a backpropagation technique. Backpropagation is a process of calculating the degree to which each weight contributes to the final error while propagating the error calculated in the output layer toward the input layer, and is performed through differential calculations using the chain rule. Here, the weights are relationships between nodes within the neural network (123). The learning device may use an SGD technique using labels and outputs to optimize the weights found through the backpropagation technique. For example, the learning device may update the weights of a loss function defined based on labels, outputs, and weights using an SGD technique. This process operates by calculating the gradient (the derivative of the loss function with respect to the weights) and adjusting the weights in the direction of this gradient to gradually decrease the value of the loss function.

[0055] According to one embodiment, the learning device extracts first objects from a review response, obtains first labels which are items corresponding to the first objects, applies the first objects to a first neural network to generate first training outputs corresponding to the first objects, and can train the first neural network based on the first training outputs and the first labels. At this time, the first objects may include key keywords, sentence structures, sentiment expressions, etc. extracted from the review text, and thereby learn the ability to identify the core content of the review and classify it into the corresponding items.

[0056] According to one embodiment, the learning device extracts second objects from movement path information, obtains second labels which are user dwell history corresponding to the second objects, applies the second objects to a second neural network to generate second training outputs corresponding to the second objects, and can train the second neural network based on the second training outputs and the second labels. The second objects may consist of data such as the user's GPS coordinate sequence, movement speed, stopping point, and movement pattern, thereby learning the ability to determine where and for how long the user stayed.

[0057] According to one embodiment, a learning device can generate first training feature vectors based on constituent features (e.g., sentence structure, word frequency, part-of-speech distribution), positional features (e.g., location of important words, location of key sentences), and pattern features (e.g., repetitive expressions, usage patterns of specific phrases) of a review response. These feature vectors are generated through a process of converting raw text data into a numerical form that can be processed by a neural network, and various methods such as TF-IDF, Word2Vec, and BERT may be employed to extract features.

[0058] According to one embodiment, the learning device can generate second training feature vectors based on constituent features of movement path information (e.g., shape of the path, estimation of the means of movement), length features (e.g., total travel distance, travel time, distance between each point), and pattern features (e.g., repeated visit patterns, movement patterns by day of the week / time of the day). Since location data has continuous values ​​over time, time-series data processing techniques or spatial data analysis methodologies may be applied, and various methods may be employed to extract features.

[0059] According to one embodiment, the learning device can obtain training outputs by applying first training feature vectors to the neural network (123). This process is also called feedforward and is a process in which input data passes through each layer of the network to generate a final prediction value. The learning device can train the review item extraction algorithm of the neural network (123) based on the training outputs and first labels. The learning device can train the review item extraction algorithm of the neural network (123) by calculating training errors corresponding to the training outputs and optimizing the connection relationships of nodes within the neural network (123) to minimize the training errors. This process is repeated over several epochs, and in each epoch, the entire training dataset is processed and weights are updated. The server (120) can automatically extract and classify items from new review responses using the first neural network that has been trained.

[0060] According to one embodiment, the learning device can obtain training outputs by applying second training feature vectors to the neural network (123). The learning device can train the user stay history acquisition algorithm of the neural network (123) based on the training outputs and second labels. The learning device can train the user stay history acquisition algorithm of the neural network (123) by calculating training errors corresponding to the training outputs and optimizing the connection relationships of nodes within the neural network (123) to minimize the training errors. Since movement path data has temporal continuity, a recurrent neural network (RNN) structure such as LSTM (Long Short-Term Memory) or GRU (Gated Recurrent Unit) can be applied, which enables effective learning of the patterns of the sequence data. The server (120) can obtain user stay history from movement path information using the second neural network after training is complete, and the information thus obtained can be utilized in various application fields such as improving location-based services, building customized recommendation systems, and analyzing traffic patterns.

[0062] FIG. 3 is a diagram illustrating the configuration of an artificial intelligence model according to one embodiment.

[0063] An artificial intelligence model according to one embodiment may include an input layer, a hidden layer, and an output layer. This multi-layer structure is effective for learning complex patterns and solving non-linear problems, and is utilized as a core structure, particularly in deep learning models. The input layer is a layer related to input values ​​input into the artificial intelligence model and serves to receive data coming from the outside. As illustrated in FIG. 3, the input layer consists of three nodes, and each node may represent a specific feature or attribute of the data. The number of nodes in the input layer may vary depending on the dimension or characteristics of the data to be processed, and may be determined by the number of pixels in the case of image data, the dimension of the word vector in the case of text data, etc.

[0064] According to one embodiment, a feature map can be output by performing a MAC (multiply-accumulate) operation and an activation operation on the input values ​​in the hidden layer. In FIG. 3, the hidden layer consists of four nodes, and these nodes are connected to all nodes of the input layer to form a fully connected structure. The MAC operation may be an operation that multiplies the input values ​​by their corresponding weights and sums the multiplied values, and this acts as the basic unit of information processing in a neural network.

[0065] According to one embodiment, the activation operation may be an operation that inputs the result of a MAC operation into an activation function and outputs a result value. The activation function is a key element that enables a model to learn complex patterns by introducing non-linearity into the result of a linear operation. The activation function may be of various types. For example, the activation function may include a sigmoid function (which outputs a value between 0 and 1 and is useful for expressing probability), a tangent function (which outputs a value between -1 and 1 and is suitable for balanced data), a ReLU function (which outputs 0 for negative inputs and outputs it as is for positive inputs, mitigating the vanishing gradient problem), a Leaky ReLU function (which applies a linear function with a small gradient to negative inputs), a MaxOut function (which selects the maximum value among several linear functions), and / or an ELU function (which is an exponential linear unit function that provides a smooth saturation curve for negative inputs), but there are no limitations on the types thereof. Recently, new activation functions such as Swish and Mish are also being studied and are contributing to the improvement of model performance.

[0066] According to one embodiment, the hidden layer may be composed of at least one layer, and the more layers there are, the more complex patterns can be learned, resulting in a deep learning structure. For example, when the hidden layer is composed of a first hidden layer and a second hidden layer, the first hidden layer performs MAC operations and activation operations based on the input value of the input layer to output a feature map, and the feature map, which is the result value from the first hidden layer, can become the input value for the second hidden layer. The feature map is an intermediate representation representing features extracted from input data; in image processing, it can represent visual features such as edges, textures, and shapes, and in text processing, it can represent semantic patterns or syntactic structures. The second hidden layer can perform MAC operations and activation operations based on the feature map, which is the result value of the first hidden layer, and through this hierarchical processing, increasingly abstract and high-dimensional features can be extracted.

[0067] According to one embodiment, the output layer may be a layer associated with the result of an operation performed in the hidden layer, and in FIG. 3, it consists of three nodes. The number of nodes in the output layer varies depending on the type of problem to be solved; for binary classification problems, it may be one node, for multi-class classification problems, it may be equal to the number of classes, for regression problems, it may be determined by the dimension of the value to be predicted, and for sequence generation problems, it may be determined by the length of the output sequence. In the output layer, a special activation function such as a softmax function or a sigmoid function is generally used to generate a final predicted value or a probability distribution.

[0068] In one embodiment, the learning model learns syllable (character) patterns that are frequently combined and used in a given corpus to automatically learn the boundaries of compound words and named entities, integrates object information from a first UI source with object information rendered in a browser to create a learning object information file, and uses the learning object information file to generate training data for training a deep learning network. In this process, natural language processing (NLP) technology is utilized, and architectures such as word embedding, recurrent neural networks (RNN), and Transformers may be applied. Additionally, the learning model receives data from various domains of a support system, standardizes the data from the various domains into an integrated format based on at least one standardization method corresponding to each of the various domains, learns and infers data from a specific domain, determines information to be transmitted for standardization from the data of the specific domain, and performs post-processing on the data from the various domains.

[0069] According to one embodiment, the first UI source includes an XML file, and the training object information file includes an input JSON file for feature learning and an output JSON file that serves as label data during training. This structured data format allows the model to process it easily and is particularly useful for analyzing UI elements of web-based applications. The output JSON file includes a file containing HTML DOM Tree information implemented in compliance with web standards, and the various domains include at least one of a RAN (radio access network), a transport, or a core. A RAN refers to a wireless access network where a mobile device connects to a cellular network; a transport refers to a network layer responsible for data transmission; and a core refers to the central part of a network responsible for central processing and routing. Post-processing may include a correlation function, which plays an important role in analyzing relationships between data collected from various domains and discovering meaningful patterns. Through correlation analysis, the model can identify the causes of anomalies or performance degradation occurring in complex systems and improve prediction accuracy.

[0071] FIG. 4 is a block diagram showing the configuration of an emotional economy operating system utilizing Web3-based emotional coins and NFTs according to one embodiment.

[0072] A system (400) according to one embodiment may include a processor (420) and memory (430), and some of the illustrated components may be omitted or substituted. Although the system diagram briefly depicts only the core components, the actual implementation may include various additional components such as an input / output controller, a system bus, a graphics processing unit (GPU), a network interface card (NIC), and a storage device controller. A system (400) according to one embodiment may be a server or a terminal; if implemented as a server, it may be operated in a virtualized environment as part of a cloud infrastructure or implemented as a physical hardware server, and if implemented as a terminal, it may be implemented in various forms such as a desktop computer, laptop, tablet, smartphone, embedded system, or IoT device. According to one embodiment, the processor (420) is a component capable of performing operations or data processing regarding the control and / or communication of each component of the system (400), and may be composed of one or more processors. In modern processor architectures, multi-core designs are common, which improve parallel processing performance by integrating multiple independent processor cores within a single chip. For example, configurations such as dual-core, quad-core, and octa-core are possible, and each core has independent cache memory (L1, L2), and some cache (L3) can be shared among cores.

[0073] The memory (430) can store information related to the method described above or a program in which the method described above is implemented. The memory subsystem is designed with a hierarchical structure and is composed of several levels according to access speed and capacity. The memory (430) may be volatile memory or non-volatile memory. Volatile memory is memory in which stored data is lost when the power is turned off, and is mainly implemented in the form of DRAM (Dynamic RAM) or SRAM (Static RAM). DRAM stores each bit in the form of charge in a capacitor and requires periodic refreshing, whereas SRAM uses flip-flop circuits to maintain data, so it does not require refreshing but has higher cost and power consumption due to the use of more transistors. The memory (430) can store various file data, and the stored file data can be updated according to the operation of the processor (420). The memory management unit (MMU) is responsible for converting virtual memory addresses to physical memory addresses, managing page tables, and maintaining memory protection and cache consistency.

[0074] According to one embodiment, the processor (420) can execute a program and control the device (400). The processor can operate through basic pipeline stages of instruction fetch, decode, execute, and write-back. The code of the program executed by the processor (420) can be stored in memory (430). In addition to the application, the operating system, device driver, middleware, system services, etc. are loaded into memory and executed, and these provide basic system functions such as hardware resource management, process scheduling, interrupt handling, and file system management. The operations of the processor (420) can be performed by loading instructions stored in memory (430). In this process, the program counter (PC) points to the address of the next instruction to be executed, the instruction register (IR) stores the instruction currently being executed, and various general-purpose registers store operation data and intermediate results. The system (400) can be connected to an external device (e.g., a personal computer or a network) through an input / output device (not shown in the drawing) and exchange data. The input / output system can operate on an interrupt-based or polling-based basis and can transfer data directly between memory and the input / output device without the intervention of a processor through a Direct Memory Access (DMA) controller.

[0075] According to one embodiment, data exchange between memory (430) and processor (420) is performed via a system bus, which consists of an address bus, a data bus, and a control bus. The address bus specifies the memory location that the processor intends to access, the data bus transmits actual data, and the control bus transmits control information such as read / write signals. The system (400) may be operated under an operating system that supports advanced features such as multitasking, virtual memory, memory protection, and separation of privileges, and if a real-time operating system (RTOS) is used, it may satisfy deterministic response times and strict time constraints. For artificial intelligence applications, the memory (430) stores a neural network model, weight parameters, a training dataset, intermediate calculation results, etc., and the processor (420) may perform inference and learning by executing forward propagation and backpropagation algorithms. The system (400) can ensure high availability and fault tolerance by applying technologies such as clustering, load balancing, and failover mechanisms for scalability, and can implement technologies such as data encryption, access control, secure booting, and memory protection for security.

[0077] FIG. 5 is a flowchart illustrating a method for operating an emotion economy using Web3-based emotion coins and NFTs according to one embodiment.

[0078] The operations described through FIG. 5 can be implemented based on instructions that can be stored in a computer recording medium or memory (e.g., memory (430) of FIG. 4). The order of each operation of FIG. 5 may be changed, some operations may be omitted, and some operations may be performed simultaneously.

[0079] In operation 510, the system (400) can evaluate the recipient's resonance score for the user's emotional message or interaction. The resonance score may be a quantitative indicator of how emotionally the recipient responds to the emotional message or interaction sent by the user. The system (400) can calculate the resonance score by analyzing the emotional content of the message, the frequency and depth of the interaction, and the recipient's response pattern. Natural language processing and sentiment analysis technologies may be utilized in this process, which may be an important step that forms the basis of the emotion economy.

[0080] In operation 520, the system (400) can generate emotion coins based on an evaluated resonance score and a scarcity coefficient. The scarcity coefficient may be a variable determined by considering the frequency of occurrence of the corresponding emotion type, the total token circulation, market demand, etc. The system (400) can determine the amount of emotion coins to be generated by applying a formula that multiplies the resonance score, the scarcity coefficient, and a preset constant. For example, it can be calculated in a manner such as: Amount of emotion coins generated = Resonance score × Scarcity coefficient × 10. The formula is merely an example and is not limited to this, and may vary depending on the settings. The emotion coins generated in this way can serve to convert the value of emotion exchange into a blockchain-based digital asset.

[0081] In operation 530, the system (400) can link a generated emotion coin with a specific emotional moment and, if a preset condition is satisfied, issue an ERC-721-based NFT and record it in IPFS. Additionally, the system (400) can control the token circulation amount by selecting to consume or burn the issued NFT. The preset conditions may include cases where the resonance score is above a specific threshold or where the elapsed time after an emotional exchange exceeds a specific period. When issuing the NFT, the system (400) can access the Ethereum blockchain through the Web3.py library and a blockchain node connection service and store the metadata of the emotion content in IPFS. This enables the permanent recording of the emotional moment and proof of ownership.

[0082] According to one embodiment, an emotional economy operating system (400) utilizing Web3-based emotional coins and NFTs evaluates the resonance score of a recipient for a user's emotional message or interaction, generates an emotional coin based on the evaluated resonance score and scarcity coefficient, links the generated emotional coin with a specific emotional moment and, when a preset condition is satisfied, issues an ERC-721-based NFT and records it in IPFS, controls the token circulation amount through the choice of consumption or burning of the issued NFT, dynamically updates the resonance ranking and scarcity index by analyzing the user's emotional consumption history, creates an AI character by learning the speech and emotional patterns of a celebrity or fan, and builds a fandom-based emotional economy model by converting emotional messages into NFTs through emotional interaction with fans, and controls the operation of a circular economy model that promotes the balance and value stability of the token economy based on the circulation amount obtained by subtracting the amount burned from the total amount of tokens issued.

[0083] According to one embodiment, an emotional economy operating system (400) utilizing Web3-based emotional coins and NFTs can evaluate a resonance score that quantifies the depth of emotional impact that a user's emotional message or interaction has on a recipient. This score can be calculated by comprehensively analyzing the emotional intensity of the message, the authenticity of the content, the recipient's reaction pattern, and the relationship between them, thereby converting subjective emotions into objective indicators and establishing a basis for measuring emotional value.

[0084] According to one embodiment, the system (400) can generate emotion coins by reflecting a scarcity coefficient of the corresponding emotion or interaction in the resonance score evaluated in this way. The scarcity coefficient can be dynamically adjusted by considering the frequency of occurrence of a specific type of emotion or the specialness of interactions between specific users, and this can serve as a core mechanism for evaluating the value of emotions from various angles and directly linking it to blockchain-based token rewards.

[0085] According to one embodiment, the system (400) may link the generated emotion coin with a specific emotional moment or message that serves as its basis, and issue an NFT based on the Ethereum ERC-721 standard when pre-set conditions, such as a resonance score or elapsed time, are satisfied. At this time, the metadata and unique identifier of the emotion content may be recorded in a distributed file system such as IPFS (InterPlanetary File System), which can have the effect of permanently proving the uniqueness and ownership of the emotional moment on the blockchain and converting it into an asset.

[0086] According to one embodiment, the system (400) may allow a user to choose to 'consume' the issued NFT, which is to exchange it for other goods or services, or 'burn' it, which is to permanently remove it from the blockchain. This selection mechanism serves as a means to regulate the circulation of the entire Emotion Coin and NFT, and can contribute to maintaining the balance of the token economy and preventing inflation, such as by increasing scarcity through burning or activating the ecosystem through consumption.

[0087] According to one embodiment, the system (400) records all activity history of the user, such as the creation, issuance, consumption, and burning of emotion coins, in a database and analyzes it to dynamically update the user's resonance ranking or the scarcity index of a specific emotion. This can provide feedback to the user regarding their activities, induce higher resonance or scarce emotion exchanges, and support the overall economic model of the system to operate adaptively based on real-time data.

[0088] According to one embodiment, the system (400) can generate an AI character that mimics the emotional characteristics of a specific person (e.g., a celebrity) or the user themselves by learning their speech patterns, emotional expression styles, message patterns, etc., through natural language processing and machine learning techniques (e.g., BERT). This AI character can interact with fans or other users and generate emotional messages, and these unique messages can be issued as NFTs that fans can own or trade, thereby establishing a new fandom-based emotional economy model. This can have the effect of strengthening emotional bonds with fans and creating new revenue models.

[0089] According to one embodiment, the system (400) calculates the current circulating supply by subtracting the burning amount from the total issuance amount in real time, and controls the system to continuously operate and manage a circular economy model that promotes the overall balance of the token economy and the stability of the value of emotional assets, such as by adjusting the scarcity coefficient applied when issuing new coins based on this circulating supply information or planning burning events.

[0091] FIG. 6 is a flowchart illustrating a method for operating an emotion economy using Web3-based emotion coins and NFTs according to one embodiment.

[0092] The operations described through FIG. 6 can be implemented based on instructions that can be stored in a computer recording medium or memory (e.g., memory (430) of FIG. 4). The order of each operation of FIG. 6 may be changed, some operations may be omitted, and some operations may be performed simultaneously.

[0093] In operation 610, the system (400) can extract emotion labels and intensity from a user's emotion message using a HuggingFace Transformers-based natural language processing model. In this process, the system (400) can recognize and classify emotion types and their intensity, such as joy, sadness, anger, and surprise, from text data. HuggingFace Transformers is a modern natural language processing library that provides various pre-trained models, such as BERT and GPT, which can enable emotion analysis with high accuracy. This step serves as the basis for the quantification and objective evaluation of emotion data, thereby ensuring the reliability of subsequent processing.

[0094] In operation 620, the system (400) can calculate a resonance score, including an emotion matching rate, an authenticity index, and a response pattern, as a normalized value between 0 and 1 by comparing and analyzing the extracted emotion data with the recipient's past emotion patterns. Additionally, the system (400) can calculate the amount of emotion coins generated by applying a formula that multiplies this resonance score by a scarcity coefficient and a preset constant. The emotion matching rate may reflect the degree of emotional agreement between the sender and the recipient, the authenticity index may reflect the truthfulness of the emotional expression, and the response pattern may reflect the qualitative characteristics of the interaction. This process can quantify the value of emotional exchange and provide a basis for fair and transparent coin issuance.

[0095] In operation 630, the system (400) may determine that an NFT issuance is possible if the conditions are satisfied that the resonance score is above a preset first threshold and the elapsed time after emotional exchange is above a preset second threshold. Additionally, when issuing an NFT, the system (400) may connect to the Ethereum blockchain through the Web3.py library and the blockchain node connection service, configure the metadata of the emotional content in JSON format, upload it to IPFS, and set the returned hash value as the tokenURI of the ERC-721 smart contract. This metadata may include the type of emotion, intensity, time of occurrence, participant information, etc., and the distributed storage and persistence of the content can be ensured through IPFS storage.

[0096] In operation 640, the system (400) can store each user's sentiment coin issuance history, NFT creation and burning records, and resonance score changes in a database along with timestamps. Additionally, the system (400) can periodically analyze the stored history and, if the average resonance score per user corresponds to a preset upper percentile, raise the resonance ranking by a preset step, and if the frequency of issuing NFTs of a specific sentiment type is below a preset ratio relative to the overall average, increase the scarcity index of that sentiment by a preset increase value to reflect this in the future coin creation amount. This dynamic update mechanism can contribute to increasing the sustainability of the system and the motivation for user participation.

[0097] According to one embodiment, the system (400) uses a HuggingFace Transformers-based natural language processing model to extract emotion labels and intensity from a user's emotion message, compares and analyzes the extracted emotion data with the recipient's past emotion patterns to calculate a resonance score including an emotion matching rate, an authenticity index, and a response pattern as a normalized value between 0 and 1, calculates the amount of emotion coins generated by applying a formula that multiplies the resonance score by a scarcity coefficient and a preset constant, determines that an NFT issuance state is possible if the conditions are satisfied that the resonance score is above a preset first threshold and the elapsed time after emotion exchange is above a preset second threshold, and when issuing an NFT, connects to the Ethereum blockchain through the Web3.py library and a blockchain node connection service, configures the metadata of the emotion content in JSON format and uploads it to IPFS, and sets the returned hash value as the tokenURI of the ERC-721 smart contract, and if the user chooses to burn the NFT, calls a burn function on the blockchain to permanently remove the corresponding token and deducts it from the total circulating supply, and stores each user's emotion coin issuance history, NFT creation and burning records, Changes in resonance scores are stored along with timestamps, and the stored history is periodically analyzed to raise the resonance ranking by a preset level if the average resonance score per user corresponds to a preset upper percentile. If the issuance frequency of a specific emotion type NFT is below a preset ratio relative to the overall average, the scarcity index of that emotion is increased by a preset increment value and reflected in the future coin generation amount. Additionally, the net circulating supply is calculated in real time by subtracting the total amount of burned coins from the total amount of issued coins in the entire system, and based on this, the scarcity coefficient is dynamically adjusted upon new issuance, thereby preventing inflation in the emotion economy and ensuring the stability of the token value.

[0098] According to one embodiment, the system (400) can utilize a modern natural language processing model, such as HuggingFace Transformers, to identify specific emotion labels, such as 'joy', 'sadness', 'anger', etc., from text-based emotion messages transmitted by a user, and quantitatively extract the intensity of the emotion. This may be a process of generating basic input data for the emotion economy.

[0099] According to one embodiment, the system (400) can compare and analyze the extracted emotion data with the user's past emotion history and reaction patterns receiving the message. Through this, a resonance score normalized to a value between 0 and 1 can be calculated by combining an 'emotion matching rate' indicating the degree of agreement between the sender's emotional expression and the recipient's emotional receptiveness, a 'sincerity index' estimating the degree of sincerity contained in the message, and a 'reaction pattern' considering the recipient's general reaction speed or form. This may be intended to measure the quality of interaction beyond simple emotion recognition.

[0100] According to one embodiment, the system (400) can calculate the amount of emotional coins to be generated to be paid as compensation for the emotional exchange by applying a formula that multiplies the calculated resonance score by the current scarcity coefficient and a constant predetermined by the system. This calculation method may aim to provide fair compensation by reflecting both the quality and scarcity of the emotional exchange.

[0101] According to one embodiment, the system (400) may determine whether conditions are satisfied, such as the calculated resonance score being higher than a preset threshold (first threshold) and a certain amount of time (second threshold) having elapsed after the emotional exchange occurred, and thereby grant eligibility to issue the emotional moment as an NFT. This may be intended to place a higher value on meaningful and continuous emotional exchanges rather than on one-off emotions.

[0102] According to one embodiment, when the issuance of an NFT is decided, the system (400) can connect to a blockchain network such as Ethereum using a Python library such as Web3.py and a blockchain node connection service such as Infura. Subsequently, metadata containing information such as an emotional message, sender, recipient, and timestamp is configured in JSON format, uploaded to IPFS to obtain a unique content hash (CID), and then the NFT can be issued (minted) by setting this hash value as the tokenURI of a smart contract that follows the ERC-721 standard. This may be intended to ensure the immutability of the NFT and the distributed storage of data.

[0103] According to one embodiment, if the system (400) chooses to burn an NFT held by a user, it may call the burn function of a smart contract with the tokenID of the NFT as an argument. Execution of this function permanently destroys the NFT on the blockchain, and the system may subtract the NFT from the calculation of the total token circulation amount by reflecting this information. This can be used as an economic control measure to reflect the user's intention to clear their mind or to increase the value of other NFTs by increasing scarcity.

[0104] According to one embodiment, the system (400) can systematically store the history of all users' emotional coin issuance, records of NFT creation and burning, and trends in resonance scores along with timestamps in a database (e.g., SQLite). This may be intended to ensure transparency of all economic activities and to secure basic data for user-specific activity analysis and system improvement.

[0105] According to one embodiment, the system (400) may periodically analyze the stored history and assign a rating based on user activity, such as raising the user's resonance ranking by one level when the user's average resonance score reaches a certain percentile (e.g., 10%) among all users. This can serve as an incentive system to encourage positive and deep emotional exchanges among users.

[0106] According to one embodiment, the system (400) may also slightly increase the scarcity index of a specific emotion (e.g., 'gratitude') when the number of NFTs issued based on a specific emotion type is lower than a certain percentage of the total NFT issuance volume, thereby adjusting to receive more rewards when generating coins with the corresponding emotion thereafter.

[0107] According to one embodiment, the system (400) calculates the net circulation amount by subtracting the total number of burned coins from the total number of issued coins in the system in real time, and monitors the trend of change in this net circulation amount to dynamically adjust the scarcity coefficient applied to the issuance of new coins when necessary. This can aim to suppress inflation caused by a rapid increase in the money supply within the emotional economy system and to build a reliable economic ecosystem by maintaining the value of emotional coins stably in the long term.

[0109] FIG. 7 is a flowchart illustrating a method for operating an emotion economy using Web3-based emotion coins and NFTs according to one embodiment.

[0110] The operations described through FIG. 7 can be implemented based on instructions that can be stored in a computer recording medium or memory (e.g., memory (430) of FIG. 4). The order of each operation of FIG. 7 may be changed, some operations may be omitted, and some operations may be performed simultaneously.

[0111] In operation 710, the system (400) can generate an emotion rhythm vector by learning the speech patterns, emotion rhythms, and message patterns of a celebrity or a specific user using a BERT-based emotion label sequencing technique. BERT (Bidirectional Encoder Representations from Transformers) is a high-performance language model that understands bidirectional context, and the system (400) can utilize it to analyze and learn the subject's language usage patterns, manner of expressing emotions, and conversation flow in detail. The generated emotion rhythm vector represents the unique emotional characteristics of the person in a quantified form and can be used as core data for AI character generation.

[0112] In operation 720, the system (400) can generate a celebrity AI or fan AI character that mimics the emotional characteristics of the person based on the generated emotional rhythm vector. This AI character can implement an emotional communication style similar to that of a real person by reflecting learned emotional patterns, language styles, and response methods. The system (400) can generate the character's responses and manage interactions by utilizing natural language generation technology, a conversation management system, a context awareness algorithm, etc. Through this, fans can have the experience of communicating with an AI that reflects the emotional characteristics of a celebrity, or share an AI that reflects their own emotional patterns with other fans.

[0113] In operation 730, the system (400) may provide a subscription system that allows fans to acquire the right to interact with the AI ​​character in the form of a monthly subscription or gift. This subscription system may include various tiers or options, and depending on each tier, differentiated benefits such as interaction frequency, access to exclusive content, and participation in special events may be provided. The system (400) may support stable service operation by implementing functions such as subscription status management, payment processing, and access control. Through this, a sustainable revenue model based on the fandom can be established, and a new form of connection experience between fans and celebrities can be created.

[0114] In operation 740, the system (400) can convert the emotional message generated by the AI ​​character into an NFT-capable form by generating a unique identifier using the SHA256 hash algorithm along with a timestamp. Additionally, the system (400) can issue the generated emotional message NFT on the blockchain according to the ERC-721 standard, allowing fans to own the unique emotional message as a digital asset. In this process, the system (400) can automatically filter out inappropriate content by verifying the content at an ethical judgment layer before issuing the message. Fan reaction data is continuously collected to update the emotional rhythm of the AI ​​character and can be used for personalized content recommendations, which can help maintain a continuous emotional connection between fans and celebrities.

[0115] According to one embodiment, the system (400) generates an emotional rhythm vector by learning the speech patterns, emotional rhythms, and message patterns of a celebrity or a specific user using a BERT-based emotional label sequencing technique, generates a celebrity AI or fan AI character that mimics the emotional characteristics of the person based on the generated emotional rhythm vector, provides a subscription system so that fans can acquire the right to interact with the AI ​​character in the form of a monthly subscription or gift, generates a unique identifier using the SHA256 hash algorithm along with a timestamp for the emotional message generated by the AI ​​character and converts it into an NFT-capable form, issues the generated emotional message NFT on a blockchain according to the ERC-721 standard so that fans can own the unique emotional message as a digital asset, verifies the content of the emotional message at an ethical judgment layer before issuing the message and automatically filters it if inappropriate content is included, and collects fan reaction data to periodically update the emotional rhythm of the AI ​​character and recommends customized content for each fan, thereby controlling the maintenance of a continuous emotional connection between the celebrity and the fan.

[0116] According to one embodiment, the system (400) can analyze vast amounts of text data generated by celebrities or specific users using a language model such as BERT (Bidirectional Encoder Representations from Transformers) and an emotion label sequencing technique. This goes beyond simply understanding the meaning of words or sentences; it can learn specific word selection tendencies, sentence structures, and patterns of emotional change within conversations (emotional rhythms), and generate these as unique numerical representations called 'emotional rhythm vectors'. This may be a process of digitizing an individual's emotional characteristics to provide a foundation for AI to mimic them.

[0117] According to one embodiment, the system (400) can generate a celebrity AI or fan AI character that mimics the speech and emotional response of the person in question by utilizing the emotional rhythm vector thus generated as a key parameter. When interacting with the user, this AI character can provide the user with a high level of immersion and emotional bonding by generating a response based on the learned emotional rhythm vector.

[0118] According to one embodiment, the system (400) may provide a subscription-based economic model to enable fans to experience interaction with a generated AI character. Fans may obtain access to differentiated experiences, such as one-on-one conversations with the AI ​​character, receiving limited edition messages, and participating in special events, by paying a monthly subscription fee or purchasing a gift of a specific value, which can function as a practical monetization model for the fandom economy.

[0119] According to one embodiment, the system (400) can generate a unique identifier for a special or meaningful emotional message generated by an AI character during interaction with a fan by combining the message content and the time of creation (timestamp), etc., through a cryptographic hash algorithm such as SHA256. This process may be a preparatory step to make the message into a unique 'fingerprint' of an NFT to be recorded on a blockchain, thereby guaranteeing its value and authenticity as an NFT thereafter.

[0120] According to one embodiment, the system (400) can issue an emotional message with a unique identifier as an NFT on the blockchain according to the ERC-721 standard and transfer it to the fan's digital wallet. Through this, the fan can own the special message received from the celebrity AI not as a simple text, but as a unique digital asset with proven ownership and tradable value, which can enable a new form of assetization and collection activity based on fan sentiment.

[0121] According to one embodiment, the system (400) may allow an AI character to pass through an 'ethical judgment layer' that verifies the content of a message according to predefined ethical standards and filtering rules before generating an emotional message and sending it to a fan or issuing it as an NFT. This layer can serve as an important safeguard to prevent misuse of the AI ​​system and protect the celebrity's image and fans by automatically detecting and filtering inappropriate language, hate speech, content that may infringe on privacy, etc.

[0122] According to one embodiment, the system (400) can continuously collect and analyze data on fans' reactions (reply content, emotional expression, whether to purchase NFTs, etc.) to messages sent by the AI ​​character. Based on the results of this analysis, the AI ​​character's emotional rhythm vector can be periodically updated to evolve and generate more sophisticated reactions that suit the fans' tastes, and at the same time, by recommending customized content or NFTs that suit each fan's preferences, it can contribute to maintaining and strengthening a continuous and positive emotional connection between the celebrity and the fans.

[0124] According to one embodiment, the system (400) establishes a payment API that links the appraisal NFT with the physical product and executes a smart contract that automatically converts a preset quantity of appraisal coins into a physical product voucher. The smart contract includes a mapping table that defines the exchange ratio between the appraisal coins and the physical product, thereby implementing a direct exchange of value between the appraisal economy and the physical economy. When issuing an NFT, the system operates a protection system that records the hash value of the content stored in IPFS on the blockchain and detects in real time whether content with the same hash value is duplicated based on this. It also automatically manages usage rights by specifying license types, including non-commercial use, limited use, and permanent retention, in the license field of the smart contract for each NFT. Furthermore, if a license violation is detected, it can control the system to protect the value and rights of the appraisal asset by deactivating the transfer function of the NFT or executing a function that automatically returns ownership to the original owner.

[0125] According to one embodiment, the system (400) can establish a payment API that links an emotional NFT with a physical product. This payment API acts as an intermediary between blockchain technology and a traditional e-commerce system and can function as a bridge connecting the real economy and the emotional economy. Through this API, the system (400) can improve the user experience and increase the practical utility of emotional assets by automating the process of converting the value of emotional coins into physical products.

[0126] According to one embodiment, the system (400) can execute a smart contract that automatically converts a preset quantity of appraisal coins into physical product vouchers. This smart contract is an automatically executable program code that can be automatically executed on the blockchain when conditions are met to process the exchange between appraisal coins and vouchers. The smart contract may include a mapping table that defines the exchange rate between appraisal coins and physical products, and this table may define various exchange rates according to product category, price range, scarcity, etc.

[0127] According to one embodiment, the system (400) can record the hash value of content stored in the InterPlanetary File System (IPFS) on the blockchain when issuing an NFT. IPFS is a distributed file storage system that can guarantee data integrity and accessibility without a central server by referencing content with a unique hash value. Based on this hash value, the system (400) can operate a protection system that detects in real time whether content with the same hash value has been duplicated. This protection system can preserve the scarcity and value of the NFT by utilizing a hash collision detection algorithm and a blockchain query function to verify the uniqueness of the content and prevent duplicate issuance.

[0128] According to one embodiment, the system (400) may specify a license type for each NFT in the license field of a smart contract. The license type may include various levels of rights, such as non-commercial use, limited use, and perpetual retention, which may define how the NFT owner can utilize the digital asset. A non-commercial use license may permit only personal appreciation or non-profit use, a limited use license may permit commercial use under specific conditions, and a perpetual retention license may grant all rights to the owner.

[0129] According to one embodiment, the system (400) may execute a function to disable the transfer function of the NFT or automatically return ownership to the original owner when a license violation is detected. To this end, the system (400) may implement a violation detection mechanism that monitors transactions on the blockchain and compares and analyzes them with license conditions defined in the smart contract. When a violation is detected, a conditional function of the smart contract is automatically executed to lock the transfer function of the NFT, or, in the case of a more serious violation, to return ownership to the original issuer or previous owner, thereby protecting the value and rights of the emotional asset. Through the implementation of such a physical product linkage and NFT protection system, the system (400) can expand the scope of application of the emotional economy to the real economy, ensure the safe management and trading of emotional assets, and protect creators and owners from infringement of rights, thereby enhancing the reliability and sustainability of the entire ecosystem.

[0131] According to one embodiment, the system (400) executes an algorithm that analyzes the user's emotional consumption history to extract content selected by other users with similar emotional patterns, sorts it based on emotional scores, and recommends a preset number of top content; it constructs a global emotional currency system that analyzes the emotional resonance patterns of users by country to calculate an average resonance index by country, calculates the ratio between the calculated average resonance indices by country, and dynamically determines the emotional currency exchange rate between countries; it applies an emotional analysis model that reflects multilingual emotional differences to correct differences in emotional expression according to cultural context by referring to an emotional expression weight table by language, and constructs a global emotional flow database to collect the issuance amount, burning amount, and average resonance index of each country's emotional coins in real time, and controls the system to maintain the balance of the global emotional economy ecosystem by periodically recalculating and updating the international value of the emotional coins based on this.

[0132] According to one embodiment, the system (400) can analyze the user's emotional consumption history to extract content selected by other users with similar emotional patterns. In this process, the system (400) can utilize a hybrid recommendation model that combines collaborative filtering and content-based filtering techniques. The emotional consumption history is a dataset containing the type, intensity, frequency, preference, etc., of emotional messages that the user has created, consumed, or shared in the past, through which the user's emotional tastes and patterns can be identified. The system (400) can personalize the user experience and improve the quality of emotional exchange by sorting the extracted content based on emotional scores and recommending a preset number of top content.

[0133] According to one embodiment, the system (400) can calculate an average resonance index for each country by analyzing the emotional resonance patterns of users in each country. The average resonance index for each country is an indicator that comprehensively quantifies the responsiveness, intensity of emotional expression, and empathy shown by users in emotional exchange within a specific region or cultural sphere, and can reflect cultural differences and regional characteristics. The system (400) can construct a global emotional currency system that dynamically determines the emotional currency exchange rate between countries by calculating the ratio between the calculated average resonance indices for each country. This approach can present a new paradigm for converting differences in emotional expression between different cultural spheres into economic value.

[0134] According to one embodiment, the system (400) may apply an emotion analysis model that reflects multilingual emotional differences. This model may correct for differences in emotional expression according to cultural context by referring to a language-specific emotional expression weight table. For example, since emotional expression tends to be restrained in some cultures and more extroverted expression may be common in others, the system (400) may normalize emotional intensity by taking these differences into account. The language-specific emotional expression weight table may be constructed based on linguistic research, cross-cultural psychological data, multilingual emotion corpus analysis, etc., and may be continuously updated through machine learning techniques.

[0135] According to one embodiment, the system (400) can build a global sentiment flow database to collect the issuance amount, burning amount, and average resonance index of sentiment coins for each country in real time. This database can utilize distributed registry technology to collect data from multiple regional nodes and aggregate it in a central repository. The system (400) can maintain the balance of the global sentiment economy ecosystem by periodically recalculating and updating the international value of sentiment coins based on the collected data. Time series analysis, economic modeling, and market supply-demand mechanisms may be applied to the recalculation process, thereby ensuring that the value of sentiment coins is not concentrated in specific regions or cultural spheres but is distributed evenly within the global ecosystem. The system (400) can enhance the scalability and inclusiveness of the global sentiment economy by personalizing user experience, respecting cultural diversity, and promoting cross-border sentiment exchange and value exchange.

[0137] According to one embodiment, the system (400) generates a unique emotional DNA fingerprint including the periodicity, amplitude, and transition probability between major emotions of emotional changes by time-series analysis of each user's emotional expression pattern, and establishes a verification system that distinguishes between fake emotions generated by AI and real human emotions by encoding the emotional DNA fingerprint into a vector of a preset bit length and utilizing it as an authenticity verification code embedded in each NFT, detects a collective resonance phenomenon that occurs when more than a preset number of users simultaneously share a similar emotional state, calculates a collective resonance index by multiplying the number of participating users by the emotional intensity of each user, executes a fractional NFT mechanism in which, if the collective resonance index exceeds a preset threshold, the emotional DNA fingerprints of the participants are combined in a Merkle tree structure to generate a single root hash and automatically generate a community emotional NFT based thereon, divides ownership of the generated community emotional NFT in proportion to a contribution score obtained by multiplying each participant's emotional intensity and duration, and automatically distributes them to each person's wallet using the ERC-1155 standard, and the time of occurrence, duration, number of participants, and average emotional intensity of the collective resonance event are used as events on the blockchain. It is permanently recorded in a log and stored in a searchable format, and can be controlled to execute an algorithm that learns collective emotion patterns that repeatedly occur in specific regions or communities using machine learning algorithms to extract patterns by time of day, day of the week, and season, and predicts the probability of future collective emotion events based on these patterns.

[0138] According to one embodiment, the system (400) can generate a unique emotional DNA fingerprint by time-series analyzing each user's emotional expression pattern. The emotional DNA fingerprint is a data structure that quantifies unique characteristics appearing in an individual's emotional expression, such as the periodicity of emotional changes, amplitude, and the probability of transition between major emotions. The system (400) can extract this by utilizing mathematical techniques such as Fourier transform, wavelet analysis, and Markov chain modeling. The emotional DNA fingerprint thus generated can serve a role similar to unique biometric information that can identify an individual's emotional expression style.

[0139] According to one embodiment, the system (400) can encode an emotion DNA fingerprint into a vector of a preset bit length and utilize it as an authenticity verification code embedded in each NFT. During this encoding process, the system (400) can apply dimensionality reduction techniques, hash functions, quantization algorithms, etc., to convert emotion characteristics into a compressed digital signature form. The code thus generated can be included in the NFT metadata and serve as the basis for a verification system that distinguishes between fake emotions generated by AI and actual human emotions. The system (400) can determine authenticity by applying a statistical verification algorithm that evaluates consistency between the newly generated emotion expression and the existing emotion DNA fingerprint.

[0140] According to one embodiment, the system (400) can detect a collective resonance phenomenon that occurs when more than a preset number of users simultaneously share a similar emotional state. This phenomenon can be viewed as a collective emotional response to a specific event, news, social movement, etc., and the system (400) can calculate a collective resonance index by multiplying the number of participating users by the emotional intensity of each user. This index is an indicator that quantifies the scale and intensity of the collective emotional response and can be used to analyze social trends or collective psychological states.

[0141] According to one embodiment, the system (400) can generate a single root hash by combining the participants' emotional DNA fingerprints into a Merkle tree structure when the collective resonance index exceeds a preset threshold. A Merkle tree is a binary tree structure capable of efficiently verifying a large amount of data, through which the system (400) can compress data from multiple participants into a single cryptographic proof. Based on the generated root hash, the system (400) can permanently record the collective emotional experience as a digital asset by automatically generating a community emotional NFT.

[0142] According to one embodiment, the system (400) can divide ownership of a generated community sentiment NFT in proportion to each participant's contribution score. The contribution score can be calculated by multiplying each participant's sentiment intensity and duration, which can reflect the individual's degree of participation in the collective resonance phenomenon. The system (400) can execute a fractional NFT mechanism that automatically distributes the divided ownership to each person's wallet using the ERC-1155 standard. ERC-1155 is a token standard of the Ethereum blockchain that enables the efficient management of various types of tokens within a single smart contract.

[0143] According to one embodiment, the system (400) can permanently record the time of occurrence, duration, number of participants, and average emotional intensity of a collective resonance event in the event log of a blockchain and store them in a searchable form. This data can be preserved as a reliable historical record due to the transparency and immutability characteristics of the blockchain and can be utilized as material for future social and cultural research. The system (400) can structure the stored data to enable efficient searching and analysis by applying indexing and metadata tagging.

[0144] According to one embodiment, the system (400) can learn collective emotion patterns that occur repeatedly in a specific region or community using a machine learning algorithm. In this learning process, the system (400) can execute an algorithm that extracts patterns by time of day, day of the week, and season and predicts the likelihood of future collective emotion events occurring based on them. This prediction function can be implemented by combining time series prediction models, deep learning networks, ensemble learning techniques, etc., and can be utilized in various fields such as social trend prediction, marketing strategy formulation, and public policy decision-making. The system (400) can expand the social dimension of the emotion economy by converting the emotional experiences of individuals and groups into unique and verifiable digital assets, and by effectively capturing collective emotion phenomena and distributing them fairly.

[0146] According to one embodiment, the system (400) enables a user to create an emotional time capsule NFT to be disclosed at a specific future time along with a current emotional message, and deploys a smart contract that sets disclosure conditions for the emotional time capsule NFT, including a block height-based disclosure time, conditions for the occurrence of a real-world event via an external oracle, and a designated recipient address; the smart contract implements conditional execution logic that is automatically executed upon reaching a set block height or receiving a specific event signal transmitted by the oracle; the system applies a time-delay encryption mechanism in which the content of the time capsule NFT is encrypted with the AES-256 algorithm and stored in IPFS, and the decryption key is stored in a time-lock smart contract so that the key is released only when a set condition is satisfied; and provides a digital emotional inheritance system capable of analyzing the emotional NFT collection held by the user to construct an emotional legacy portfolio including the distribution by emotion type, changes over time, and major resonance targets, and transmitting it to a pre-designated heir address; and at the time of inheritance, the continuity of emotional bonds between family members is permanently preserved on the blockchain by creating a unique intergenerational emotional connection NFT by combining the original owner's emotional DNA fingerprint and the heir's emotional DNA fingerprint using an XOR operation. Whenever an inherited Emotion NFT is viewed, control can be exercised to execute an intergenerational Emotion mining mechanism that measures resonance by calculating the cosine similarity between the inheritor's current Emotion state and the original owner's past Emotion state, and generates additional Emotion coins in proportion to the measured resonance.

[0147] According to one embodiment, the system (400) may enable a user to create an emotion time capsule NFT that will be disclosed at a specific future time along with a current emotion message. The emotion time capsule NFT is a digital asset that contains a current emotional state or message, remains locked until a specific future time or condition is met, and is disclosed at that time, thereby allowing the user to experience emotional communication that transcends time. The system (400) may provide an intuitive UI / UX through a web interface or mobile application that allows the user to write an emotion message and set disclosure conditions.

[0148] According to one embodiment, the system (400) may deploy a smart contract that sets various disclosure conditions for an emotional time capsule NFT. These disclosure conditions may include a block height-based disclosure time, a condition for the occurrence of a real-world event via an external oracle, a designated recipient address, etc. A block height-based disclosure time can ensure temporal accuracy by causing the time capsule to be opened when a specific block number of the blockchain is reached. An external oracle condition can cause the time capsule to be opened based on the occurrence of real-world events, such as weddings, graduations, and birthdays, received on the blockchain via an oracle service such as Chainlink.

[0149] According to one embodiment, a smart contract deployed by the system (400) can implement conditional execution logic that is automatically executed when a set condition is met. This logic can be written in a smart contract language such as Solidity running on the Ethereum Virtual Machine (EVM) and can be composed of elements such as a condition check function, an event listener, and an execution trigger. When the condition is met, the smart contract can automatically perform tasks such as changing the state of the NFT's metadata or granting access rights, thereby enabling reliable time-based execution without centralized management.

[0150] According to one embodiment, the system (400) can encrypt the content of a time capsule NFT using the AES-256 algorithm and store it in IPFS. AES-256 is currently one of the most secure symmetric key encryption algorithms and can provide strong security certified by the National Institute of Standards and Technology (NIST). The system (400) can apply a time-delayed encryption mechanism in which the decryption key of the encrypted content is stored in a time-lock smart contract and the key is released only when a set condition is met. This dual security structure can effectively prevent the content of the time capsule NFT from being exposed prior to a specified point in time.

[0151] According to one embodiment, the system (400) can construct an emotional legacy portfolio by analyzing a collection of emotional NFTs held by a user. This portfolio can comprehensively represent an individual's emotional history and values ​​by including information such as the distribution by emotion type, trends of change over time, and major resonance targets. By providing a digital emotional inheritance system capable of transmitting this portfolio to a pre-designated heir address, the system (400) can establish a foundation for an individual's emotional legacy to be passed down across generations. This can be an innovative approach that extends the traditional concept of legacy inheritance into the realm of digital emotions.

[0152] According to one embodiment, the system (400) can generate a unique intergenerational emotional connection NFT by combining the original owner's emotional DNA fingerprint and the heir's emotional DNA fingerprint using an XOR operation during the inheritance process. The XOR (exclusive OR) operation is a logical operation that outputs 1 only when two input bits are different, thereby generating a new digital signature that reflects both the similarities and differences between the two emotional DNA fingerprints. This intergenerational emotional connection NFT can function as a symbolic digital asset that permanently preserves the continuity of emotional bonds between family members on the blockchain and can present a new form of family history and emotional connection.

[0153] According to one embodiment, the system (400) can measure resonance by calculating the cosine similarity between the heir's current emotional state and the original owner's past emotional state whenever the inherited emotional NFT is viewed. Cosine similarity is a method of measuring the angle cosine value between two vectors, which can quantify the directional similarity between emotional vectors as a value between -1 and 1. The system (400) can provide incentives for the continuation and development of emotional bonds by executing an intergenerational emotional mining mechanism that generates additional emotional coins in proportion to the measured resonance. This mechanism can establish an active participation model that promotes intergenerational emotional communion and understanding beyond the simple transfer of digital assets. The system (400) can present a new paradigm of emotional exchange transcending time and generations, preserve an individual's emotional legacy, and provide an innovative platform that strengthens emotional connections among family members.

[0156] According to one embodiment, the system (400) may incorporate Zero-Knowledge Proofs technology to enable a user to prove that a specific resonance score was derived from an emotional message without disclosing the specific content of the emotional message. Through this, the system (400) can present an advanced security model that balances privacy and economic incentives by supporting users to participate in emotional economy activities and receive rewards while protecting their sensitive emotional information. This can establish a core foundation of trust that is differentiated from existing blockchain systems, considering the sensitivity of emotional data.

[0157] According to one embodiment, the system (400) may provide an 'emotion staking' mechanism in which a user stakes an emotion coin or a specific emotion NFT held by the user into a specific smart contract and obtains additional rewards in return. In this case, the rewards may not be simply proportional to the staking period or quantity, but may be paid differentially depending on whether the user participated in positive emotional exchanges or performed activities that contributed to the emotional well-being of the community during the staking period. This can create a unique incentive structure that incorporates the concept of DeFi (decentralized finance) into the emotion economy to induce positive user participation and strengthen the soundness of the ecosystem.

[0158] According to one embodiment, the system (400) can generate an 'Emotion Journey NFT' that expresses the continuity of emotional interactions between users over time, beyond a single emotional moment. This can be configured by connecting multiple emotion NFTs generated in a specific relationship in chronological order to form a single 'chain,' and this chain itself can be issued as a new form of NFT having unique value. It can be designed so that the value or scarcity of this Emotion Journey NFT increases as the relationship deepens and positive interactions accumulate, which can provide an unprecedented way to record and value the history of the relationship on the blockchain.

[0159] According to one embodiment, the system (400) can implement 'dynamic emotion NFT art' in which the visual representation of the issued emotion NFT is not static but changes dynamically according to the owner's current emotional state or the collective emotion index of the relevant community. For example, if the owner feels joy, the color of the NFT may brighten, or if the community shares sadness, a visual effect of rain falling may appear on the NFT. This can be implemented by receiving real-time data from an external oracle or an emotion analysis engine within the system and updating the metadata or visual rendering of the NFT, thereby making the NFT a living emotional symbol rather than a simple record, and maximizing the user experience.

[0160] According to one embodiment, the system (400) may provide a function to create a Fused Emotion NFT with a new meaning or value by combining different emotion NFTs according to specific combination rules. For example, an NFT corresponding to 'joy' and an NFT corresponding to 'expectation' can be combined to create an NFT corresponding to the emotion of 'excitement'. This process can be transparently managed through a smart contract and can be differentiated from existing NFT systems by building a participatory economic model in which users creatively reconfigure emotion assets and create new value.

[0161] According to one embodiment, the system (400) may open an emotion prediction market by utilizing emotion coins or emotion NFTs to bet on or predict whether a specific future emotional performance or event will occur. For example, a market may be formed in which people participate via coins to predict what emotion a specific celebrity AI will convey most to fans next month, or whether a specific community's positive emotion index will achieve a specific goal.

[0163] According to one embodiment, the system (400) can significantly improve the accuracy and reliability of the emotion resonance score by collecting and integrating complex biosignals in real time, such as voice waveforms, facial muscle micro-movements, skin conductivity, and heart rate variability, as well as text-based emotion messages from the user. In this process, the system (400) can implement a metacognitive layer that uses a hybrid emotion inference engine combining an artificial neural network and a Bayesian probability model to detect discrepancies between biosignals and linguistic expressions, thereby evaluating the authenticity of the emotion. This is an innovative approach that is differentiated from existing text-based emotion analysis systems and can more precisely capture the multi-layered characteristics of human emotion.

[0164] According to one embodiment, the system (400) can integrate a quantum-resistant cryptographic algorithm and Zero-Knowledge Proof technology in the process of generating emotion coins to protect the privacy of the user's emotion data while enabling verification of the accuracy and authenticity of the resonance score. The system (400) can maintain the security of emotion assets against future quantum computing attacks by introducing cryptographic methods that are secure even in the post-quantum era, such as lattice-based cryptography and hash-based signatures. Additionally, the system (400) can achieve an ideal balance between the transparency of the emotion economy and personal privacy by implementing a SNARK (Succinct Non-interactive Arguments of Knowledge) protocol that can prove the validity of issuing emotion coins without exposing personal information.

[0165] According to one embodiment, the system (400) can implement a sophisticated emotion expression model that includes various axes such as joy-sadness, activation-deactivation, and dominance-compliance by mapping emotions into a Dimensional Emotion Space beyond simple basic emotion classification. The system (400) can visualize this Dimensional Emotion data to provide an Emotion Landscape that allows the user to intuitively explore and interact with their own emotion trajectory in a 3D metaverse space. This Emotion Landscape can provide the user with a new understanding and insight into emotions by spatially representing individual and group emotion patterns, changes over time, and social influences.

[0166] According to one embodiment, the system (400) can ensure the sustainability and value stability of the Emotion Coin economy by implementing a dynamic burning mechanism for managing the token circulation. This mechanism may include an algorithm that automatically adjusts the burning rate based on a composite indicator that considers various factors such as the issuance volume, the number of users, transaction frequency, and market value fluctuations. In particular, the system (400) can prevent excessive dilution of Emotion assets and preserve long-term value by executing an anti-hyperinflation protocol that is automatically activated when the circulation of Emotion Coins exceeds a threshold.

[0167] According to one embodiment, the system (400) can ensure the reliability and authenticity of an emotion NFT by implementing an emotion authentication system based on neuroscience research results. This system can verify the biological basis of emotional expressions by utilizing the latest neuroscience research data, such as brainwave patterns, neurotransmitter activity models, and cognitive psychological response predictions. Through this, the system (400) can distinguish between artificially manipulated emotional expressions and natural emotional expressions with high accuracy, and further clarify the value of the emotional asset by including the verified authenticity score in the NFT metadata.

[0168] According to one embodiment, the system (400) can implement a Carbon-Neutral Emotion Mining protocol for the sustainability of the emotion token economy. This protocol may include a mechanism that optimizes the use of computing resources in the emotion coin generation process, calculates the required energy consumption, and automatically contributes to a corresponding carbon offset project. The system (400) can also promote the long-term development and balance of the ecosystem by providing a proposal and voting system through a decentralized governance structure that allows coin holders to democratically adjust key parameters of the emotion economy (resonance threshold, scarcity coefficient, burning rate, etc.).

[0169] According to one embodiment, the system (400) can implement an Emotional Resonance Contribution system that links the emotional economy with social impact. The system can execute a smart contract that automatically provides donations or support in proportion to the intensity and scale when collective emotional resonance regarding a specific social issue or humanitarian goal occurs.

[0170] According to one embodiment, the system (400) can provide a Hybrid Emotional Experience that visualizes and interacts with emotional messages and NFTs in a physical space by extending emotional interaction between a celebrity and a fan using Augmented Reality (AR) and Extended Reality (XR) technologies. In particular, the system (400) can significantly enhance the experiential value of emotional assets by implementing a Multi-sensory Emotion Transfer system that allows emotions contained in emotional NFTs to be experienced tactilely and physically, going beyond simply visual representation, by integrating neural interface technology and a haptic feedback system.

[0172] The embodiments described above may be implemented as hardware components, software components, and / or combinations of hardware and software components. For example, the devices, methods, and components described in the embodiments may be implemented using one or more general-purpose or special-purpose computers, such as, for example, a processor, a controller, an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, a field programmable gate array (FPGA), a programmable logic unit (PLU), a microprocessor, or any other device capable of executing and responding to instructions. The processing unit may execute an operating system (OS) and one or more software applications executed on said operating system. Additionally, the processing unit may access, store, manipulate, process, and generate data in response to the execution of the software. For ease of understanding, the processing unit may be described as being used as a single unit, but those skilled in the art will understand that the processing unit may include multiple processing elements and / or multiple types of processing elements. For example, the processing unit may include multiple processors or one processor and one controller. Additionally, other processing configurations, such as parallel processors, are also possible.

[0173] The embodiments described above may be implemented as hardware components, software components, and / or combinations of hardware and software components. For example, the devices, methods, and components described in the embodiments may be implemented using one or more general-purpose or special-purpose computers, such as, for example, a processor, a controller, an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, a field programmable gate array (FPGA), a programmable logic unit (PLU), a microprocessor, or any other device capable of executing and responding to instructions. The processing unit may execute an operating system (OS) and one or more software applications executed on said operating system. Additionally, the processing unit may access, store, manipulate, process, and generate data in response to the execution of the software. For ease of understanding, the processing unit may be described as being used as a single unit, but those skilled in the art will understand that the processing unit may include multiple processing elements and / or multiple types of processing elements. For example, the processing unit may include multiple processors or one processor and one controller. Additionally, other processing configurations, such as parallel processors, are also possible.

[0174] Software may include computer programs, code, instructions, or a combination of one or more of these, and may configure a processing unit to operate as desired or command the processing unit independently or collectively. Software and / or data may be permanently or temporarily embodied in any type of machine, component, physical device, virtual equipment, computer storage medium or device, or transmitted signal wave in order to be interpreted by the processing unit or to provide instructions or data to the processing unit. Software may be distributed over networked computer systems and may be stored or executed in a distributed manner. Software and data may be stored on one or more computer-readable recording media.

[0175] Although the embodiments have been described above with reference to the limited drawings, those skilled in the art can apply various technical modifications and variations based on the above. For example, suitable results may be achieved even if the described techniques are performed in a different order than described, and / or if the components of the described system, structure, device, circuit, etc. are combined or assembled in a form different from described, or replaced or substituted by other components or equivalents.

Claims

Claim 1 An emotional economy operating system utilizing Web3-based emotional coins and NFTs includes memory for storing instructions; The system includes a processor, and when the instructions are executed by the processor, the system evaluates the recipient's resonance score for the user's emotional message or interaction, generates a unique emotional DNA fingerprint including the periodicity, amplitude, and transition probability between major emotions of emotional changes by time-series analysis of each user's emotional expression pattern, generates an emotional coin based on the evaluated resonance score and scarcity coefficient, issues an ERC-721-based NFT (non-fungible token) and records it in the IPFS (Inter Planetary File System) when a preset condition is satisfied by linking the generated emotional coin with a specific emotional moment, controls the token circulation supply through the choice of consumption or burning of the issued NFT, dynamically updates the resonance ranking and scarcity index by analyzing the user's emotional consumption history, creates an AI character by learning the speech patterns and emotional patterns of celebrities or fans, and builds a fandom-based emotional economy model by converting emotional messages into NFTs through emotional interactions with fans, and the emotional messages generated by the AI ​​character are verified by an ethical judgment layer before issuance, and if inappropriate content is included, automatically It is filtered and controls the operation of a circular economy model that promotes the balance and value stability of the token economy based on the circulating supply obtained by subtracting the burned amount from the total token issuance amount; it uses a natural language processing model to extract emotion labels and intensity from the user's emotion messages, compares and analyzes the extracted emotion data with the recipient's past emotion patterns to calculate a resonance score, including an emotion matching rate, authenticity index, and response pattern, as a normalized value between 0 and 1, and calculates the amount of emotion coins generated by applying a formula that multiplies the said resonance score by a scarcity coefficient and a preset constant.If the conditions are satisfied that the Resonance Score is above a preset first threshold and the elapsed time after an emotional exchange is above a preset second threshold, it is determined that the system is in a state where NFT issuance is possible. Upon NFT issuance, the system connects to the Ethereum blockchain via the Web3.py library and a blockchain node connection service, configures the metadata of the emotional content in JSON format and uploads it to IPFS, and sets the returned hash value as the tokenURI of the ERC-721 smart contract. If a user chooses to burn the NFT, a designated function is called to permanently remove the corresponding token from the blockchain and deduct it from the total circulating supply. The system stores each user's emotional coin issuance history, NFT creation and burning records, and changes in the Resonance Score along with timestamps in the database. The stored history is analyzed periodically; if a user's average Resonance Score falls within a preset upper percentile, the Resonance Ranking is raised by a preset step. If the issuance frequency of a specific emotional type NFT is below a preset ratio relative to the overall average, the scarcity index of that emotional type is increased by a preset increment value and reflected in future coin creation volume. Finally, the total amount of burned coins is calculated by subtracting the total amount of coins burned from the total amount of coins issued by the entire system. A system that calculates the net circulating supply in real time and controls the dynamic adjustment of the scarcity coefficient upon new issuance based on this. Claim 2 delete Claim 3 In claim 1, the above instructions, when executed by the processor, the system learns the speech patterns, emotional rhythms, and message patterns of a celebrity or a specific user using a BERT-based emotional label sequencing technique to generate an emotional rhythm vector; generates a celebrity AI or fan AI character that mimics the emotional characteristics of the person based on the generated emotional rhythm vector; provides a subscription system so that fans can acquire the right to interact with the AI ​​character in the form of a monthly subscription or gift; generates a unique identifier using the SHA256 hash algorithm along with a timestamp for the emotional message generated by the AI ​​character and converts it into an NFT-capable form; issues the generated emotional message NFT on a blockchain according to the ERC-721 standard so that fans can own the unique emotional message as a digital asset; verifies the content of the emotional message at an ethical judgment layer before issuing the message and automatically filters it if inappropriate content is included; and controls the system to collect fan reaction data to periodically update the emotional rhythm of the AI ​​character and recommend customized content for each fan. Claim 4 In claim 1, the above instructions, when executed by the processor, the system executes a smart contract that automatically converts a preset quantity of appraisal coins into a physical product voucher by establishing a payment API that links the appraisal NFT with the physical product, and the smart contract implements a direct value exchange between the appraisal economy and the physical economy by including a mapping table that defines the exchange rate between the appraisal coin and the physical product, and operates a protection system that records the hash value of the content stored in IPFS on the blockchain when issuing the NFT and detects in real time whether content with the same hash value is duplicated based on this, and automatically manages usage rights by specifying license types including non-commercial use, limited use, and permanent retention for each NFT in the license field of the smart contract, and controls to protect the value and rights of the appraisal asset by executing a function that disables the transfer function of the NFT or automatically returns ownership to the original owner when a license violation is detected. Claim 5 In claim 1, the above instructions, when executed by the processor, execute an algorithm that analyzes the user's emotional consumption history to extract content selected by other users with similar emotional patterns, sorts it based on emotional scores, and recommends a preset number of top content; construct a global emotional currency system that analyzes the emotional resonance patterns of users by country to calculate the average resonance index by country, calculates the ratio between the calculated average resonance indices by country, and dynamically determines the emotional currency exchange rate between countries; apply an emotional analysis model that reflects multilingual emotional differences to refer to a language-specific emotional expression weighting table to correct differences in emotional expression according to cultural context; construct a global emotional flow database to collect the emotional coin issuance amount, burning amount, and average resonance index of each country in real time, and control the system to periodically recalculate and update the international value of the emotional coin based thereon. Claim 6 In claim 1, the instructions, when executed by the processor, establish a verification system that distinguishes between AI-generated fake emotions and actual human emotions by the system performing time-series analysis of each user's emotional expression pattern to generate a unique emotional DNA fingerprint including the periodicity, amplitude, and transition probability between major emotions of emotional changes, encoding the emotional DNA fingerprint into a vector of a preset bit length and utilizing it as an authenticity verification code embedded in each NFT, detecting a collective resonance phenomenon that occurs when more than a preset number of users simultaneously share a similar emotional state, calculating a collective resonance index by multiplying the number of participating users by each user's emotional intensity, and if the collective resonance index exceeds a preset threshold, generating a single root hash by combining the participants' emotional DNA fingerprints into a Merkle tree structure and automatically generating a community emotion NFT based thereon, and executing a fractional NFT mechanism that divides ownership of the generated community emotion NFT in proportion to a contribution score obtained by multiplying each participant's emotional intensity and duration, and automatically distributes them to their respective wallets using the ERC-1155 standard, and the time of occurrence and duration of the collective resonance event A system that permanently records time, number of participants, and average emotional intensity in the event log of a blockchain and stores them in a searchable format, and controls the execution of an algorithm that learns collective emotional patterns that repeatedly occur in a specific region or community using a machine learning algorithm to extract patterns by time of day, day of the week, and season, and predicts the probability of future collective emotional events based on these patterns. Claim 7 In claim 1, the instructions, when executed by the processor, enable the system to create an Emotion Time Capsule NFT to be disclosed at a specific future time along with a current emotion message, deploy a smart contract that sets disclosure conditions for the Emotion Time Capsule NFT, including a block height-based disclosure time, conditions for the occurrence of a real-world event via an external oracle, and a designated recipient address; the smart contract implements conditional execution logic that is automatically executed upon reaching a set block height or receiving a specific event signal transmitted by the oracle; the content of the Time Capsule NFT is encrypted using the AES-256 algorithm and stored in IPFS, and the decryption key is stored in a time-lock smart contract to control that the key is released only when the set conditions are met; the system analyzes the Emotion NFT collection held by the user to construct an Emotion Legacy Portfolio including the distribution by emotion type, trends of change over time, and major resonance targets, and enables the transfer of this to a pre-designated heir address; and upon inheritance, the system creates an intergenerational Emotion Link NFT by combining the original owner's emotion DNA fingerprint and the heir's emotion DNA fingerprint using an XOR operation, thereby preserving the continuity of emotional bonds between family members on the blockchain. A system that stores and controls the generation of additional emotion coins in proportion to the measured resonance by calculating the cosine similarity between the heir's current emotion state and the original owner's past emotion state whenever an inherited emotion NFT is viewed.

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