Method and system for character and space control in metaverse environment based on emotional rhythm

Abstract

A character and space control system in an emotion rhythm-based metaverse environment may include memory and a processor for storing instructions. When executed by the processor, the instructions may control the system to collect emotion-related data from a user through text, voice, facial expression recognition, or biosignal measurement; analyze and store an emotion rhythm, which is a pattern of emotional change over time, from the collected data; dynamically control the facial expressions, gestures, and movement patterns of a user avatar within the metaverse environment based on the analyzed emotion data; adjust environmental elements including background, lighting, color, and sound in the metaverse space in real time according to the user's emotional state; automatically generate and control the facial expressions, dialogue, and actions of a Non-Player Character (NPC) based on the user's emotion rhythm; convert the emotion data and avatar characteristics into a standardized format for interlinking between multiple metaverse platforms; and issue and manage the user's emotion data, emotion character, or emotion-based creation as a Non-Fungible Token (NFT).

Description

Technology Field

[0001] The present invention relates to an emotion processing system in a metaverse and virtual reality (VR) environment, and more specifically, to a method and system for improving user experience by analyzing a user's emotional rhythm and reflecting it in real-time in the behavior and expressions of characters, environments, and Non-Player Characters (NPCs) within a metaverse platform. The present invention can be applied to technical fields such as emotion computing, metaverse interfaces, virtual avatar control, Web3 technology, NFT assetization, and multi-platform integration. Background Technology

[0003] Existing metaverse and VR environments have been implemented by relying on direct user manipulation to control avatars or virtual environments. These systems have had limited capabilities for recognizing a user's real-time emotional state and naturally reflecting it in the virtual environment; in particular, they may be limited in their ability to reflect emotional rhythms, which are patterns of emotional change over time. Most current metaverse platforms support only fixed facial expressions or limited emotional expressions, which can make it difficult to express the diverse and subtle emotional states of users.

[0004] Furthermore, existing systems lack mechanisms to naturally control the emotional responses of NPCs within the metaverse environment by linking them to the user's emotional state, which can lead to mechanical interactions between users and NPCs and a lack of emotional connection. In particular, the lack of compatibility of emotional data between various metaverse platforms, such as Zepeto and Roblox, can result in a disconnect where an emotional profile formed on one platform cannot be transferred to another.

[0005] Another limitation of current technology is that spatial elements of the virtual environment, such as backgrounds, lighting, and sound, fail to dynamically respond to the user's emotional state, which can reduce immersion. Additionally, there is a limitation in that the technical framework for utilizing user emotional data as digital assets by integrating it with Web3 technologies, such as NFTs, has not yet been sufficiently developed. The problem to be solved

[0007] The method and system for controlling characters and spaces in an emotion rhythm-based metaverse environment according to the present invention aims to provide a framework that processes a user's emotions not as a simple one-time input, but as an emotion rhythm that changes according to time and situation, and reflects this in real-time in the metaverse and VR environment. Through this, the invention seeks to realize a metaverse experience in which the avatar's facial expressions, gestures, and tone of voice change naturally according to the user's emotional state.

[0008] The present invention aims to provide a technical structure that visually and audibly amplifies a user's emotional state by changing environmental elements such as background, lighting, sound, and color in the metaverse space in real time according to the user's emotional state. This can enable a new form of metaverse experience where emotions and space interact.

[0009] The present invention aims to provide a technical structure that enhances emotional connection with virtual characters by automatically generating and controlling the facial expressions, dialogue, and actions of NPCs based on the user's emotional rhythm. This can significantly improve user immersion in various metaverse application areas, such as education, games, psychotherapy, and social media.

[0010] The present invention aims to provide a standardized interface that enables seamless linking of user emotion data and emotion characters between various metaverse platforms. means of solving the problem

[0012] A character and space control system in an emotion rhythm-based metaverse environment may include memory and a processor for storing instructions. When executed by the processor, the instructions may control the system to collect emotion-related data from a user through text, voice, facial expression recognition, or biosignal measurement; analyze and store an emotion rhythm, which is a pattern of emotional change over time, from the collected data; dynamically control the facial expressions, gestures, and movement patterns of a user avatar within the metaverse environment based on the analyzed emotion data; adjust environmental elements including background, lighting, color, and sound in the metaverse space in real time according to the user's emotional state; automatically generate and control the facial expressions, dialogue, and actions of a Non-Player Character (NPC) based on the user's emotion rhythm; convert the emotion data and avatar characteristics into a standardized format for interlinking between multiple metaverse platforms; and issue and manage the user's emotion data, emotion character, or emotion-based creation as a Non-Fungible Token (NFT). Effects of the invention

[0014] The method and system for controlling characters and spaces in an emotion rhythm-based metaverse environment according to the present invention have the effect of enhancing expressiveness and immersion by naturally reflecting the user's emotional state in the metaverse environment. By going beyond existing simple and limited emotional expressions and reflecting even the emotional rhythm, which is the change in emotion over time, more natural and rich emotional expressions can be achieved.

[0015] The present invention has the effect of maximizing emotional immersion by dynamically changing spatial elements such as background, lighting, and sound in the metaverse environment according to the user's emotional state. For example, it can amplify the user's emotional experience by automatically switching to a bright and lively environment in a state of joy and to a calm and subtle environment in a state of sadness.

[0016] The present invention has the effect of enhancing emotional connection in interactions with NPCs. By having NPCs recognize the user's emotional state and respond with appropriate facial expressions, dialogue, and actions, it can provide a natural conversational experience that is emotionally connected, rather than the existing mechanical interactions. This can significantly improve the quality of social interactions within the metaverse.

[0017] The present invention has the effect of enhancing the mobility of emotion data across platforms. Through a standardized interface, users can consistently maintain and utilize their emotion profiles and characters across various metaverse platforms, such as Zepeto and Roblox, thereby providing a continuous experience without interruption between platforms.

[0018] This invention has the effect of creating a new form of digital economic value through the assetization of emotion data into NFTs. Users can generate economic and social value within the metaverse by issuing, trading, or sharing their unique emotional expressions, emotion avatars, and emotion space designs as NFTs. This can contribute to the formation of a new emotion-based content ecosystem.

[0019] This invention has the effect of promoting the development of emotion-based metaverse services in various application fields such as education, psychotherapy, and entertainment. In particular, it can enhance therapeutic and learning effects by providing an immersive virtual environment in the fields of psychotherapy or emotional education, where the expression and recognition of emotions are important. Brief explanation of the drawing

[0021] FIG. 1 is a block diagram showing the configuration of a character and space control system in an emotion rhythm-based metaverse environment according to one embodiment. Figure 2 is a flowchart illustrating a method for controlling characters and space in an emotion rhythm-based metaverse environment. Figure 3 is a flowchart illustrating a method for controlling characters and space in an emotion rhythm-based metaverse environment. Figure 4 is a flowchart illustrating a method for controlling characters and space in an emotion rhythm-based metaverse environment. Specific details for implementing the invention

[0022] FIG. 1 is a block diagram showing the configuration of a character and space control system in an emotion rhythm-based metaverse environment according to one embodiment.

[0023] A system (100) according to one embodiment may include a processor (120) and memory (130), and some of the illustrated components may be omitted or substituted. In a modern computing environment, various input / output interfaces, network modules, security chips, power management units, etc., may be added in addition to these basic components, but for the sake of brevity in the drawings, only the core components are shown. A system (100) according to one embodiment may be a server or a terminal, and if implemented as a server, it may be equipped with high-performance enterprise-grade hardware and deployed in a cloud environment or an on-premises data center. If implemented as a terminal, it may be realized in various forms such as a smartphone, tablet, IoT device, or wearable device, and may have a hardware configuration optimized for each form.

[0024] According to one embodiment, the processor (120) is configured to perform operations or data processing regarding the control and / or communication of each component of the system (100) and may be composed of one or more processors. In modern systems, heterogeneous computing structures are widely adopted in which various types of processors, such as a central processing unit (CPU), a graphics processing unit (GPU), a neural network processing unit (NPU), and a digital signal processing unit (DSP), are used together and optimized for different workloads. The CPU is based on architectures such as x86-64, ARM, and RISC-V, and various configurations are possible from a single core to 64 cores or more. The memory (130) may store information related to the method described above or store a program in which the method described above is implemented. Memory (130) may be volatile or non-volatile memory; volatile memory mainly uses DRAM and SRAM, while non-volatile memory may utilize various next-generation memory technologies such as NAND flash (SSD), eMMC, UFS, NOR flash, PRAM, MRAM, and ReRAM. In modern systems, L1 / L2 / L3 caches, main memory, and swap space are organically linked through a memory hierarchy to optimize the balance between performance and capacity. Memory (130) can store various file data, and the stored file data can be updated according to the operation of the processor (120). Modern storage technology can improve data integrity and performance through file system-level transaction support, journaling, data deduplication, real-time compression, and snapshot functions.

[0025] According to one embodiment, the processor (120) can execute a program and control the device (100). The code of the program executed by the processor (120) can be stored in memory (130), and such code can be stored and executed in various forms, such as machine language, bytecode, or interpreter language. Modern processors optimize execution performance through advanced techniques such as instruction pipelining, branch prediction, speculative execution, and instruction-level parallelism. The operations of the processor (120) can be performed by loading instructions stored in memory (130), and in this process, steps such as instruction fetch, decode, execute, memory access, and write-back are performed. The system (100) 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. Input / output devices may include various wired and wireless interfaces such as USB, Thunderbolt, HDMI, DisplayPort, PCIe, Ethernet, Wi-Fi, Bluetooth, NFC, 5G, and infrared communication, which enable interaction with other devices.

[0026] According to one embodiment, the processor (120) may have a form capable of executing software to control various components of an electronic device, namely hardware or software, and performing various data processing and operations. At the operating system level, various hardware is managed through a consistent interface via a Hardware Abstraction Layer (HAL), and the functions of specific hardware can be utilized through device drivers. For example, the processor may have a form capable of storing commands or data received from a sensor module or a communication module in volatile memory, processing them, and storing the resulting data in non-volatile memory. When processing sensor data, it is processed into meaningful information through processes such as noise filtering, calibration, feature extraction, and pattern recognition. Such a processor may include a main processor and an auxiliary processor, and the auxiliary processor may have a form capable of consuming less power than the main processor or being optimized for specific functions. The low-power auxiliary processor can efficiently perform background tasks such as motion detection, voice recognition, and location tracking while minimizing battery consumption in standby mode. The auxiliary processor may have a form capable of operating on behalf of the main processor when it is inactive, or operating together with it when it is active to control functions related to the display module, sensor module, or communication module. This asymmetric multiprocessing structure is effective for optimizing the balance between power efficiency and performance.

[0027] According to one embodiment, the auxiliary processor may be implemented as a functionally related component, such as an image signal processor (ISP) or a communication processor. The ISP processes raw data received from a camera sensor to perform tasks such as noise reduction, color correction, HDR synthesis, and sharpness enhancement. Additionally, it may have a hardware structure specialized for processing artificial intelligence models and may include functions such as tensor operation acceleration, matrix multiplication optimization, and quantization operation support. Such artificial intelligence models are generated through machine learning and may be trained either within the electronic device or via a separate server as mathematical models for complex pattern recognition and decision-making. On-device learning has the advantage of being able to build personalized models while protecting user privacy, while server-based learning has the advantage of utilizing large-scale data and high-performance computing resources. Learning algorithms may include supervised, unsupervised, semi-supervised, and reinforcement learning. Supervised learning learns the mapping between input and output based on labeled data, unsupervised learning discovers the structure or pattern of data without labels, and reinforcement learning learns a policy to maximize rewards through interaction with the environment. Artificial intelligence models can take the form of multiple artificial neural network layers and can be implemented based on various architectures such as Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Transformers, and Graph Neural Networks (GNN).

[0028] A system (100) according to one embodiment includes a processor (120) and memory (130). The system architecture is based on a von Neumann architecture or a Harvard architecture, and recently, consistent communication between memory and the processor is becoming possible through new interconnection standards such as CXL (Compute Express Link). A system (100) according to one embodiment may be the server or terminal described above. The memory (130) may store information related to the method described above or store a program in which the method described above is implemented. In modern memory systems, Error Correction Code (ECC) technology is applied to ensure data integrity, bandwidth can be maximized through memory interleaving, and capacity can be expanded through memory rank technology. The memory (130) may be volatile memory or non-volatile memory. The memory stores data used in various components of an electronic device and may have a form that includes input and output data related to software. Memory management technologies such as virtual memory, paging, segmentation, and garbage collection are utilized to overcome physical memory limitations and enable efficient memory usage. Memory can be classified as either volatile or non-volatile; the latest memory technology, Storage Class Memory (SCM), blurs the boundaries between the two types, providing performance close to DRAM while maintaining non-volatility.

[0029] According to one embodiment, a character and space control system in an emotion rhythm-based metaverse environment may have a core structure that analyzes a user's emotions and reflects them in the metaverse environment in real time. The system may collect emotion data from the user in various ways. The user's current emotional state can be detected through various input channels, such as text input, voice analysis, facial expression recognition cameras, and biosignal measurement devices, and the collected data can be processed through an emotion analysis engine.

[0030] According to one embodiment, the emotion analysis engine can analyze the type and intensity of a user's emotions (joy, sadness, anger, surprise, etc.) by utilizing the latest natural language processing technology, such as HuggingFace Transformers, and can track and record emotional rhythms, which are patterns of emotional change over time. The analyzed emotion data can be transmitted to a metaverse interface module and utilized for avatar control, environmental changes, NPC reaction generation, etc.

[0031] According to one embodiment, the avatar control module can dynamically adjust the user's virtual character's facial expressions, gestures, and movement patterns based on analyzed emotion data. For example, when the emotion of joy is detected, the avatar can automatically change to a smiling expression and display brighter, more energetic gestures. These changes are not merely one-time reactions but can naturally evolve according to the user's emotional rhythm, thereby providing a more realistic and expressive avatar experience.

[0032] According to one embodiment, an environment control module can adjust visual and auditory elements of the metaverse space to match the user's emotional state. In a state of joy, the environment may be composed of bright colors and lively background music, while in a state of sadness, it may be switched to calm lighting and soothing music. These environmental changes can be implemented by adjusting environment parameters of a game engine, such as Unity or Unreal Engine, in real time, thereby amplifying the user's emotional state visually and auditorily to enhance immersion.

[0033] According to one embodiment, an NPC control module can generate behaviors and dialogue of a virtual character that respond appropriately to the user's emotional state. If the user expresses sadness, the NPC responds with words of comfort and facial expressions, and if the user expresses joy, it can provide messages of congratulations or empathy. These emotion-based NPC responses can be implemented by combining predefined response templates with a natural language generation model, thereby enhancing emotional connection between the user and the NPC.

[0034] According to one embodiment, the platform integration module provides an interface with various metaverse platforms, such as Zepeto and Roblox, to transmit emotion data and avatar characteristics in a standardized manner. It can be implemented using a REST API structure utilizing Web3.js and SDKs specific to each platform, thereby enabling users to maintain a consistent emotion-based experience across multiple metaverse platforms.

[0035] According to one embodiment, the NFT assetization module can provide a function to issue and manage a user's emotion data, emotion characters, emotion-based creations, etc., as NFTs. This can be implemented as a distributed storage system utilizing Solidity-based smart contracts and IPFS, and the user can convert their unique emotion expressions and related content into digital assets to trade or share.

[0036] According to one embodiment, an emotion rhythm-based metaverse system can be utilized in various scenarios. In educational environments, the atmosphere of a virtual classroom and educational content can be adjusted according to the learner's emotional state. An emotion-based adaptive learning experience can be provided, such as switching to a more active learning method when the learner feels bored or confused, and providing in-depth learning content when they are focused. In the field of psychotherapy, it can be utilized as a tool to aid in emotion recognition and expression by projecting and visualizing the client's emotional state into the metaverse environment. Even complex emotions that are difficult for the client to express can be materialized into colors, shapes, and movements within the metaverse space, facilitating communication with the therapist and enhancing self-understanding. In a social metaverse environment, emotional exchanges between users can be enriched. Subtle emotional nuances that are difficult to convey through text or voice conversation alone can be naturally shared through avatar facial expressions, gestures, and changes in the surrounding environment, enabling deeper social connections and the formation of empathy. In the entertainment field, an interactive storytelling experience that responds to the user's emotions can be provided. Emotion-responsive content, in which the story progression, background music, and visual effects change according to the user's emotional state, can enable a personalized and immersive entertainment experience.

[0037] In an emotion NFT marketplace, users can issue and trade their unique emotional expressions, emotion-based avatar designs, and emotion-responsive virtual spaces as NFTs. This can create a new digital economic model that assigns economic value to the abstract element of emotion. During the process of collecting and analyzing emotion data, text input can be processed through BERT or GPT-based emotion analysis models, while voice input can be analyzed to infer emotional states by analyzing both the acoustic characteristics (pitch, speed, energy, etc.) and the content of the speech. Facial expression analysis utilizes computer vision technology to track the movements of the eyes, mouth, eyebrows, etc., and map them to emotional states.

[0038] Avatar control in the metaverse environment can be implemented by combining predefined sets of emotion-specific animations with real-time blending technology. Facial and body language animations for each emotional state (joy, sadness, anger, etc.) are prepared, and these animations can be naturally blended according to analyzed emotional intensity to enable continuous emotional expression. Environmental control can be implemented based on mapping tables between emotional states and environmental parameters. For example, joy can be mapped to bright lighting (increased illumination), warm colors (increased color temperature), and lively background music (increased tempo), and such mapping can be optimized based on psychological research.

[0039] According to one embodiment, NPC response generation can be implemented using a hybrid approach that combines a State Machine and a Natural Language Generation (NLP) model. An appropriate response type (e.g., comfort, empathy, encouragement) is determined via the State Machine based on the user's emotional state and conversational context, while specific dialogue content can be generated via the NLP model. Interoperability between platforms can be implemented by converting emotion data and avatar characteristics into a standardized JSON format and transmitting them via APIs specific to each platform. In this process, an OAuth-based authentication system can be utilized to ensure the security and privacy of user data. NFT assetization can be implemented through smart contracts conforming to ERC-721 or ERC-1155 standards, and emotion data and related metadata can be stored in IPFS for permanent and decentralized preservation. Users can manage and trade their emotion NFTs through a Web3.js-based interface. Through these technical implementations, the emotion rhythm-based metaverse system can naturally integrate users' emotions into the virtual environment to provide a more immersive and expressive metaverse experience, and enable the development of innovative services across various application areas.

[0041] Figure 2 is a flowchart illustrating a method for controlling characters and space in an emotion rhythm-based metaverse environment.

[0042] Although processes, methods, algorithms, etc. are described sequentially in the flowchart of FIG. 2, they may be configured to operate in any suitable order. In other words, the steps of the processes, methods, and algorithms described in various embodiments of the present invention do not need to be performed in the order described in the present invention. Even if some steps are described as being performed asynchronously, in other embodiments, such steps may be performed simultaneously. The processes illustrated in the drawings are not excluded from other changes or modifications, and none of the illustrated processes or steps are essential to one or more of the various embodiments of the present invention.

[0043] In operation 210, the system (e.g., the system (100) of FIG. 1) can collect emotion-related data through text, voice, facial expression recognition, or biosignal measurement under the control of a processor (e.g., the processor (120) of FIG. 1). The system (100) can analyze and store an emotion rhythm, which is a pattern of change in emotion over time, from the collected data. In this process, the system (100) can analyze the emotion data by utilizing, for example, a natural language processing algorithm based on HuggingFace Transformers, and the analyzed emotion data can be stored in a time-series form to form an emotion rhythm pattern for each user.

[0044] In operation 220, the system (100) can dynamically control the facial expressions, gestures, and movement patterns of a user avatar within the metaverse environment based on analyzed emotion data. Additionally, the system (100) can adjust environmental elements, including background, lighting, color, and sound in the metaverse space, in real time according to the user's emotional state. In this process, animation blending technology can be applied to achieve continuous and natural emotional expressions, and the user's immersion can be enhanced through a close connection between emotions and environmental elements.

[0045] In operation 230, the system (100) can automatically generate and control the facial expressions, dialogue, and actions of a Non-Player Character (NPC) based on the user's emotional rhythm. The system (100) can generate NPC responses suitable for the user's emotional state and conversational context through a hybrid structure that combines a state machine and a natural language generation model. This enables emotionally empathetic interactions within the metaverse environment and allows for the provision of appropriate social support according to the user's emotional state.

[0046] In operation 240, the system (100) can convert emotion data and avatar characteristics into a standardized format to enable interoperability between multiple metaverse platforms. Additionally, the system (100) can issue and manage users' emotion data, emotion characters, or emotion-based creations as Non-Fungible Tokens (NFTs). In this process, emotion data can be transmitted to external metaverse platforms via REST APIs utilizing Web3.js and SDKs specific to each platform, and NFTs conforming to ERC-721 or ERC-1155 standards can be issued through smart contracts.

[0047] According to one embodiment, the system (100) collects emotion-related data from a user through text, voice, facial expression recognition, or biosignal measurement, analyzes and stores an emotion rhythm, which is a pattern of change in emotion over time, from the collected data, dynamically controls the facial expressions, gestures, and movement patterns of a user avatar in a metaverse environment based on the analyzed emotion data, adjusts environmental elements including background, lighting, color, and sound in the metaverse space in real time according to the user's emotional state, automatically generates and controls the facial expressions, dialogue, and actions of a Non-Player Character (NPC) based on the user's emotion rhythm, converts the emotion data and avatar characteristics into a standardized format to link between multiple metaverse platforms, and issues and manages the user's emotion data, emotion character, or emotion-based creation as a Non-Fungible Token (NFT).

[0048] According to one embodiment, the system (100) can collect emotion-related data from the user in various ways. In the case of text input, the emotional nuances of the sentence can be analyzed through a natural language processing algorithm, and in the case of voice input, the emotional state can be inferred by analyzing acoustic characteristics (e.g., pitch, speed, energy) and content together. Facial expression recognition captures emotional signals from facial expressions, and biosignal measurement can objectively measure the user's emotional state through changes in heart rate, skin conductivity, etc. The system (100) can derive a more accurate emotional evaluation by integrally analyzing these multimodal inputs, and can build an emotional rhythm, which is a pattern of emotional change over time, by storing this as time-series data. This emotional rhythm can serve as a basis for understanding the user's emotional flow, rather than a simple one-time emotional state.

[0049] According to one embodiment, the system (100) can control a user avatar in a metaverse environment by utilizing analyzed emotion data. A set of facial expression and gesture animations corresponding to each emotional state (joy, sadness, anger, surprise, etc.) can be built in advance, and an appropriate animation can be selected and applied according to the detected emotion type and intensity. By applying animation blending technology according to the emotion intensity (a numeric value between 0 and 1), even subtle emotional changes can be naturally reflected in the avatar. Through this, the avatar in the metaverse environment can more accurately express the user's actual emotional state and improve non-verbal communication.

[0050] According to one embodiment, the system (100) can adjust various elements of the metaverse environment in real time according to the user's emotional state. By constructing a mapping table between the emotional state and environmental parameters, the system can apply increased illumination, increased color temperature, and fast-tempo background music to positive emotions such as joy, and apply decreased illumination, decreased color temperature, and slow-tempo background music to negative emotions such as sadness. The adjustment of these environmental elements can serve to amplify or alleviate the user's emotional experience and enable the user to experience their emotions more three-dimensionally in the metaverse space.

[0051] According to one embodiment, the system (100) can control the response of a Non-Player Character (NPC) based on the user's emotional rhythm. Through a hybrid structure combining a state machine and a natural language generation model, the system (100) can select an appropriate response type and generate specific dialogue content according to the user's emotional state and conversational context. The system (100) can provide customized support or responses based on the user's emotional state. For example, when the user expresses sadness, the NPC can provide empathetic facial expressions and words of comfort, and when the user expresses joy, it can show a response that celebrates and amplifies it together.

[0052] According to one embodiment, the system (100) can convert emotion data and avatar characteristics into a standardized format (e.g., JSON) and transmit them to various metaverse platforms. Emotion data can be transmitted to external metaverse environments via REST APIs utilizing Web3.js and SDKs specific to each platform, thereby maintaining consistency in emotion-based interactions between platforms. This standardization and integration feature enables users to utilize a single emotion profile across multiple metaverse platforms and maintain consistent emotional expressions even when moving between platforms.

[0053] According to one embodiment, the system (100) can issue and manage a user's emotion data, emotion character, or emotion-based creation as a Non-Fungible Token (NFT). An NFT conforming to the ERC-721 or ERC-1155 standard can be created through a Solidity-based smart contract, and the emotion data and related metadata can be permanently stored in a distributed storage such as IPFS. Through this, the emotion data can have value as a digital asset, and the user can prove ownership of their unique emotion expression or emotion-based creation on the blockchain.

[0054] According to one embodiment, the system (100) can generate an emotion label and an emotion intensity and reflect them in a user interface of a VR or metaverse environment. By stepwise adjusting environmental parameters, including color values, lighting intensity, music tempo, and volume of UI elements, according to a predefined numerical range of emotion intensity, the user's emotional state can be expressed visually and audibly. These changes in UI elements allow the user to intuitively recognize their emotional state and can promote emotional self-awareness.

[0055] According to one embodiment, the system (100) can control the facial expressions and gestures of an NPC through an animation control system based on a finite state machine model. After selecting response templates classified by emotion type in a multi-layered response generation system, specific conversational content appropriate to the situation can be generated through a natural language generation model. This structural approach ensures consistency and naturalness in NPC responses and enables appropriate interaction based on the user's emotional state.

[0056] According to one embodiment, the system (100) can detect real-time emotional changes of a user and dynamically update the response parameters of an NPC accordingly. By analyzing text or voice input and facial expression data in real time, changes in the emotional state can be detected, and the behavior and response of the NPC can be adjusted according to these changes. Through this, interaction between the user and the NPC can be more natural and organic, and sensitive responses to the user's emotional needs can be provided.

[0057] According to one embodiment, the system (100) records emotional change data in a structured format in a time-series database and can generate a report visualizing the user's emotional change pattern through a trend analysis algorithm. This emotional analysis report can help in understanding the user's long-term emotional flow and can be used to identify the correlation between activities and emotional states in the metaverse environment.

[0059] Figure 3 is a flowchart illustrating a method for controlling characters and space in an emotion rhythm-based metaverse environment.

[0060] In the flowchart of FIG. 3, processes, methods, algorithms, etc. are described sequentially, but they may be configured to operate in any suitable order. In other words, the steps of the processes, methods, and algorithms described in various embodiments of the present invention do not need to be performed in the order described in the present invention. Even if some steps are described as being performed asynchronously, in other embodiments, such steps may be performed simultaneously. The processes illustrated in the drawings are not to exclude other changes or modifications, and none of the illustrated processes or steps are essential to one or more of the various embodiments of the present invention.

[0061] In operation 310, a system (e.g., system (100) of FIG. 1) can analyze a user's text input through a natural language processing algorithm (e.g., HuggingFace Transformers) under the control of a processor (e.g., processor (120) of FIG. 1). Through this analysis, the system (100) can extract emotional types, including joy, sadness, anger, and surprise, and emotional intensity, expressed as a numerical value between 0 and 1. Additionally, the system (100) can infer an emotional state by analyzing the content and acoustic characteristics, including pitch, speed, and energy, from the user's voice input, and can construct an emotional rhythm pattern by storing this as time-series data.

[0062] In action 320, the system (100) can build a set of predefined facial expressions and gesture animations for each emotional state. The system (100) can control the user avatar by applying animation blending technology according to the analyzed emotional intensity to implement continuous and natural emotional expressions. The system (100) can reflect even the user's subtle emotional changes in the avatar through stepwise animation changes according to emotional intensity, thereby enabling more natural emotional expressions in the metaverse environment.

[0063] In operation 330, the system (100) may utilize a mapping table between emotional states and environmental parameters. The system (100) may apply background music with increased illumination, increased color temperature, and increased tempo to the emotion of joy, and background music with decreased illumination, decreased color temperature, and decreased tempo to the emotion of sadness. By adjusting environmental factors, the system (100) can harmonize the user's emotional state with the atmosphere of the metaverse space, which can further enhance the user's emotional immersion.

[0064] In operation 340, the system (100) can select an appropriate response type based on the user's emotional state and conversation context through a hybrid structure that combines a state machine and a natural language generation model. Through this, the system (100) can control the NPC by generating specific dialogue content.

[0065] In operation 350, the system (100) can convert emotion data and avatar characteristics into a standardized JSON format. The system (100) can transmit this data to external metaverse platforms (e.g., Zepeto, Roblox) via REST APIs utilizing Web3.js and platform-specific SDKs. Additionally, the system (100) can issue NFTs conforming to ERC-721 or ERC-1155 standards through Solidity-based smart contracts, and can permanently preserve emotion data and related metadata in a distributed manner by storing them in IPFS. This allows for the provision of a function to convert emotion currency exchange rates based on the emotion resonance index.

[0066] According to one embodiment, the system (100) analyzes a user's text input through a natural language processing algorithm to extract emotion types including joy, sadness, anger, and surprise, and emotion intensity expressed as a numeric value between 0 and 1; analyzes acoustic characteristics including pitch, speed, and energy along with content from the user's voice input to infer an emotional state; stores the inferred emotional state as time-series data to construct an emotional rhythm pattern; constructs a predefined set of facial expressions and gesture animations for each emotional state; controls a user avatar by implementing continuous and natural emotional expressions by applying animation blending technology according to the analyzed emotional intensity; utilizes a mapping table between emotional states and environmental parameters to apply background music with increased illumination, increased color temperature, and increased tempo to the emotion of joy, and background music with decreased illumination, decreased color temperature, and decreased tempo to the emotion of sadness; controls an NPC by selecting an appropriate response type according to the user's emotional state and conversation context and generating specific dialogue content through a hybrid structure combining a state machine and a natural language generation model; converts emotional data and avatar characteristics into a standardized JSON format; and uses a REST API utilizing Web3.js and SDKs for each platform It can transmit to external metaverse platforms including Zepeto and Roblox, issue NFTs conforming to ERC-721 or ERC-1155 standards through Solidity-based smart contracts, permanently preserve sentiment data and related metadata in a decentralized manner by storing them in IPFS, and provide a function to convert sentiment currency exchange rates based on the sentiment resonance index.

[0067] According to one embodiment, the system (100) can analyze a user's text input through a natural language processing algorithm. The natural language processing algorithm can identify types of emotions such as joy, sadness, anger, and surprise in the text by utilizing a pre-trained language model, and can quantify the intensity of each emotion as a numerical value between 0 and 1. This quantified emotion analysis can capture even the subtle nuances of the text content and enables fine distinction of emotional states.

[0068] According to one embodiment, the system (100) can analyze a user's voice input to infer an emotional state. Acoustic characteristics such as pitch, speed, and energy of the voice can be extracted and combined with semantic analysis of the voice content to perform a multidimensional emotional evaluation. For example, a voice combining high pitch, fast speed, and strong energy may indicate emotions of excitement or joy, while a voice combining low pitch, slow speed, and weak energy may indicate sadness or depression.

[0069] According to one embodiment, the system (100) can construct an emotional rhythm pattern by storing collected emotional data as time-series data. This time-series approach enables the capture of the flow of emotional changes over time, rather than fragmentary emotional states. The emotional rhythm pattern can be utilized to understand the user's tendency to respond emotionally to specific stimuli or situations, thereby providing a more personalized metaverse experience.

[0070] According to one embodiment, the system (100) can construct a set of predefined facial expressions and gesture animations for each emotional state. Facial movements, body postures, and motion patterns corresponding to basic emotional states (e.g., joy, sadness, anger, surprise, fear, disgust, neutrality, etc.) can be defined and applied to an avatar control system. These sets of animations can be designed to mimic natural human emotional expressions and can be adjusted according to cultural context.

[0071] According to one embodiment, the system (100) can implement continuous and natural emotional expression by applying animation blending technology according to the analyzed emotional intensity. Animation blending is a technology that smoothly transitions between different animations or mixes two or more animations at a specific ratio, and can adjust the degree of expression according to the emotional intensity. For example, joy of medium intensity (0.5) can be expressed with subtle facial expressions such as a smile, and joy of high intensity (0.9) can be expressed with more dynamic expressions such as raising arms accompanied by laughter. The types and values ​​of emotions are merely examples and may vary depending on the settings.

[0072] According to one embodiment, the system (100) can dynamically adjust the metaverse environment by utilizing a mapping table between emotional states and environmental parameters. The mapping table can define appropriate environmental settings according to each emotion type and intensity.

[0073] According to one embodiment, the system (100) can control a Non-Player Character (NPC) through a hybrid structure that combines a state machine and a natural language generation model. The state machine is a rule-based machine that defines the behavior logic and transition conditions of the NPC, and can select a response type (e.g., comfort, congratulations, empathy, question, etc.) based on the user's emotional state. The natural language generation model generates specific dialogue content based on the selected response type and can use an appropriate tone and expression by considering the conversation context and the user's emotional state. This hybrid approach can simultaneously ensure consistency and diversity in NPC responses.

[0074] According to one embodiment, the system (100) can convert emotion data and avatar characteristics into a standardized JSON format. JSON (JavaScript Object Notation) is a lightweight yet structured data format that has the advantage of being easily processed across various platforms and languages. This standardized format ensures data compatibility between different metaverse platforms and allows the user's emotion data and avatar characteristics to be maintained consistently.

[0075] According to one embodiment, the system (100) can transmit data to an external metaverse platform via a REST API utilizing Web3.js and a platform-specific SDK. Web3.js is a JavaScript library capable of interacting with the Ethereum blockchain and is widely used in the development of decentralized applications (DApps). Each metaverse platform (e.g., Zepeto, Roblox, etc.) provides a unique Software Development Kit (SDK), and a REST API utilizing this can transmit emotion data and avatar characteristics to the external platform in a standardized manner. Through this, users can enjoy a consistent emotion-based experience across various metaverse environments.

[0076] According to one embodiment, the system (100) can issue a Non-Fungible Token (NFT) through a Solidity-based smart contract. Solidity is a programming language for developing smart contracts on the Ethereum blockchain and can be used to implement NFT standards such as ERC-721 or ERC-1155. ERC-721 is a standard in which each token has completely unique characteristics, while ERC-1155 is a more efficient standard that allows various types of tokens to be managed in a single contract. Through these NFT standards, users' emotion data, emotion characters, emotion-based creations, etc., can be issued as unique digital assets. Smart contract

[0077] According to one embodiment, the system (100) can store emotion data and related metadata in the InterPlanetary File System (IPFS). IPFS is a distributed file system that can increase durability and availability by distributing and storing data across network nodes rather than on a central server. The metadata includes attributes, characteristics, image URLs, etc. of the NFT, and by storing it in IPFS, the permanent preservation and integrity of the data can be guaranteed. This distributed storage method can reduce the risk of central server failure or data loss and ensure long-term accessibility of the data.

[0078] According to one embodiment, the system (100) may provide a function for converting emotional currency exchange rates based on an emotional resonance index. The emotional resonance index is a value calculated by analyzing the frequency, intensity, and duration of expression of a specific emotion in a specific region or cultural sphere, and based on this, the regional value of an emotional NFT can be differentiated.

[0080] Figure 4 is a flowchart illustrating a method for controlling characters and space in an emotion rhythm-based metaverse environment.

[0081] In the flowchart of FIG. 4, processes, methods, algorithms, etc. are described sequentially, but they may be configured to operate in any suitable order. In other words, the steps of the processes, methods, and algorithms described in various embodiments of the present invention do not need to be performed in the order described in the present invention. Even if some steps are described as being performed asynchronously, in other embodiments, such steps may be performed simultaneously. The processes illustrated in the drawings are not to exclude other changes or modifications, and none of the illustrated processes or steps are essential to one or more of the various embodiments of the present invention.

[0082] In operation 410, a system (e.g., system (100) of FIG. 1) can generate an emotion label and an emotion intensity expressed as a numeric value between 0 and 1 through the analysis of the user's emotions, under the control of a processor (e.g., processor (120) of FIG. 1). The system (100) can modify user interface elements, including backgrounds, colors, images, and graphic elements, by linking them with a VR or metaverse engine. Additionally, the system (100) can stepwise adjust environmental parameters, including color values, lighting intensity, music tempo, and volume, according to a predefined numerical range of emotion intensity. The system (100) can increase immersion in the metaverse environment by visually and audibly enhancing the user's emotional state by changing UI elements.

[0083] In action 420, the system (100) can control the facial expressions and gestures of the NPC through an animation control system in response to the user's emotional state based on emotional rhythms. The system (100) can generate specific conversational content appropriate to the situation through a natural language generation model after selecting response templates classified by emotion type in a multi-layered response generation system. The system (100) can enrich the social experience in the metaverse environment by enabling customized interactions based on the user's emotional state through an NPC control mechanism.

[0084] In operation 430, the system (100) can detect changes in the emotional state by processing the user's text or voice input and facial expression data through a real-time emotion analysis engine. The system (100) can update the NPC's response parameters accordingly. This real-time analysis and response system enables natural and organic interaction between the user and the NPC, and can enhance immersion by responding immediately to changes in the user's emotions.

[0085] In operation 440, the system (100) can record emotional change data in a structured format in a time-series database. The system (100) can generate a report visualizing the user's emotional change pattern through a trend analysis algorithm.

[0086] According to one embodiment, the system (100) generates an emotion label and an emotion intensity expressed as a numeric value between 0 and 1 through the analysis of the user's emotions, and changes user interface elements including background, color, image, and graphic elements by linking them with a VR or metaverse engine, and adjusts environmental parameters including color values, lighting intensity, music tempo, and volume in stages according to a predefined numerical range of emotion intensity, controls facial expressions and gestures through an animation control system based on a finite state machine model in response to the user's emotional state based on an emotion rhythm, selects a response template classified by emotion type in a multi-layered response generation system, and generates specific conversation content appropriate to the situation through a natural language generation model, processes the user's text or voice input and facial expression data through a real-time emotion analysis engine to detect changes in the emotional state and update the NPC's response parameters accordingly, and records the emotion change data in a structured format in a time-series database and generates a report visualizing the user's emotion change pattern through a trend analysis algorithm.

[0087] According to one embodiment, the system (100) can analyze a user's emotions to generate emotion labels and emotion intensities. Emotion labels may be categorized, for example, into categories such as joy, sadness, anger, surprise, fear, disgust, and neutrality. Emotion intensity may be expressed as a continuous numerical value from, for example, 0 (no emotion) to 1 (maximum intensity). The system (100) can objectively measure the user's emotional state through quantified emotion analysis and reflect this in the metaverse environment.

[0088] According to one embodiment, the system (100) can dynamically change user interface elements by linking analyzed emotion data with a VR or metaverse engine. Background images, color palettes, graphic elements, etc., can be adjusted to match the user's emotional state, thereby providing a visual experience that harmonizes with the emotion. For example, if a happy emotion is detected, UI elements with bright and warm tones can be applied, and if a sad emotion is detected, UI elements with calm and dark tones can be applied.

[0089] According to one embodiment, the system (100) can adjust environmental parameters in steps according to a predefined numerical range of emotional intensity. When the emotional intensity is low (e.g., 0.2 or less), only subtle environmental changes may be applied, when the intensity is medium (e.g., 0.2 to 0.6), more distinct changes may be applied, and when the intensity is high (e.g., 0.6 or more), more dramatic changes may be applied.

[0090] According to one embodiment, the system (100) can control the NPC to respond to the user's emotional state based on an emotional rhythm. A finite state machine model is a structure that defines the behavioral state and transition conditions of the NPC, and can transition to an appropriate state according to the user's emotional state and play an animation corresponding to that state. Through a state-based approach, the system (100) can ensure consistency and predictability of the NPC's behavior while enabling flexible response to changes in the user's emotions.

[0091] According to one embodiment, the system (100) can generate conversation content for an NPC through a multi-layered response generation system. The system can select response templates classified by emotion type (comfort, congratulations, empathy, question, etc.) at a higher level and generate specific conversation content corresponding to the template through a natural language generation model at a lower level. Through a hierarchical approach, the system (100) can generate diverse and natural conversations appropriate to the situation while maintaining structural consistency of the responses.

[0092] According to one embodiment, the system (100) can record emotional change data in a structured format in a time-series database. This data may include timestamps, emotional labels, emotional intensity, context information, etc., and can be used to track emotional changes over time. The system (100) can identify patterns, periodicity, trends, etc. of emotional changes by applying trend analysis algorithms (e.g., moving average, exponential smoothing, ARIMA, etc.) and can generate a report visualizing this.

[0093] According to one embodiment, the system (100) can monitor the emotional well-being of a user and provide insights through a generated emotional change pattern report. This report may include a graph of emotional changes over time, a distribution of major emotional types, and patterns of emotional responses to specific situations or stimuli, and may help the user understand their own emotional tendencies. Additionally, by analyzing the impact of specific activities or interactions in the metaverse environment on the user's emotional state, personalized recommendations that promote a more positive experience can be provided.

[0094] According to one embodiment, the system (100) can provide a customized metaverse experience that comprehensively considers the user's current emotional state and long-term emotional patterns by combining real-time emotion analysis and time-series data analysis. The current emotional state can be reflected in immediate environmental adjustments and NPC reactions, and long-term emotional patterns can be utilized to understand the user's tendencies and preferences.

[0096] According to one embodiment, the system (100) generates an emotion character or message and issues it as an NFT, and generates a unique digital identifier by applying an encryption algorithm including SHA-256 or Keccak-256 to a string containing a user identifier, an emotion label, and time information, and ensures data integrity and makes the content addressable by redundantly storing the generated emotion-related data on network nodes using IPFS or a distributed storage protocol compatible therewith, and requests the stored emotion NFT information through an API call mechanism via an interface with an external metaverse platform to transmit emotion state parameters and expression data to the avatar control system of the platform, and vectorizes the user's emotion data processing record to identify content with similar emotion characteristics through a pattern matching technique including cosine similarity calculation or collaborative filtering, sorts it according to a relevance score, and proposes it to the user.

[0097] According to one embodiment, the system (100) can generate an emotion character or message and then issue it as a Non-Fungible Token (NFT). In this process, the system (100) can generate a string containing a user identifier, an emotion label, and time information, and apply a cryptographic hash algorithm such as SHA-256 or Keccak-256 to it to generate a unique digital identifier. SHA-256 is a hash algorithm standardized by the National Institute of Standards and Technology (NIST) and has the characteristic of being able to convert any length of data into a fixed-length hash value of 256 bits. Keccak-256 is a hash algorithm mainly used in the Ethereum blockchain and may be suitable for ensuring the uniqueness and integrity of an NFT.

[0098] According to one embodiment, the system (100) may store generated emotion-related data using the InterPlanetary File System (IPFS) or a distributed storage protocol compatible therewith. IPFS is a distributed file system that uses Content Addressing, which allows data to be identified and accessed through a unique hash value based on the content of the file. The system (100) can increase the availability and durability of the data by redundantly storing the emotion data across multiple network nodes. This distributed storage method can minimize the risk of central server failure or data loss and ensure the integrity of the data.

[0099] According to one embodiment, the system (100) can efficiently access sentiment data by utilizing the IPFS content addressing method. Since this method generates a unique identifier (CID, Content Identifier) ​​based on the content rather than the location of the data, it can be accessed in a consistent manner regardless of which node the data is stored on. Additionally, since the CID changes when the content changes, it can also be used as a mechanism to verify the integrity of the data.

[0100] According to one embodiment, the system (100) can link stored emotion NFT information through an interface with an external metaverse platform. By requesting NFT information through an API call mechanism and transmitting it to an avatar control system of the metaverse platform, emotion state parameters and expression data can be applied. This API-based linkage can be implemented using standard protocols such as REST (Representational State Transfer) or GraphQL, which ensures compatibility with various metaverse platforms.

[0101] According to one embodiment, the system (100) can implement emotion-based experiences on external metaverse platforms through emotion data transmitted to the avatar control system. For example, on platforms such as Zepeto and Roblox, a user's avatar can express facial expressions, gestures, and movements according to an emotion state stored in an NFT, and the surrounding environment can also be adjusted to match the corresponding emotion. This enables consistent emotional expression across platforms, thereby ensuring continuity of the user experience.

[0102] According to one embodiment, the system (100) can vectorize the user's emotional data processing records and utilize them in a recommendation system. Vectorization is a process of converting multidimensional characteristics such as emotional type, intensity, duration, and change pattern into numerical vectors, thereby enabling mathematical analysis of emotional data. Cosine similarity calculation can be applied to measure the similarity between emotional vectors, which can quantify similarity as a value between 0 and 1 based on the angle between the two vectors.

[0103] According to one embodiment, the system (100) can analyze the preferences of users with similar emotional patterns using collaborative filtering and recommend content based thereon. Collaborative filtering is a technique that recommends items preferred by users with similar tastes by analyzing user-item interaction data, and in the context of emotional data, recommendations can be made based on the content consumption patterns of users who exhibit similar emotional responses.

[0104] According to one embodiment, the system (100) can identify content with similar emotional characteristics through a pattern matching technique and suggest it to a user by sorting it according to a relevance score. This emotion-based recommendation system can provide a personalized content experience by comprehensively considering the user's current emotional state, past emotional patterns, content characteristics, etc. For example, it can recommend content that can be comforting to a user who feels sadness, and content that can maintain or amplify that joy to a user who feels joy.

[0105] According to one embodiment, the system (100) can continuously improve the recommendation algorithm by monitoring the user's reaction to the recommended content and subsequent emotional changes. This feedback loop can progressively improve the accuracy and appropriateness of the recommendation and optimize the content experience in a way that promotes the user's emotional well-being.

[0107] According to one embodiment, the system (100) collects and analyzes emotional data of users by region to calculate an indicator that quantifies the degree of regional reaction to a specific emotion, wherein the indicator is calculated by a summation method that applies statistical weighting coefficients to the frequency of specific emotional expressions, the average value of emotional expression intensity, and the duration of specific emotional expressions by users within the region, and implements an algorithm that assigns a differentiated value by region by multiplying the coefficient calculated according to the ratio of the calculated indicator value and the reference value to the exchange value of the emotional NFT, and operates a value exchange network that reflects regional cultural characteristics and emotional patterns, and may apply a data anonymization process that converts user identification information into alternative identifiers and individual data into aggregate statistics during the process of collecting and analyzing emotional data, and a security protocol using public key encryption.

[0108] According to one embodiment, the system (100) can collect and analyze emotional data of users by region to calculate a regional emotional index. According to one embodiment, the system (100) can calculate an index that quantifies the degree of regional reaction to a specific emotion. This index may comprehensively consider factors such as the frequency of specific emotional expressions by users within the region (how often the emotion occurs), the average value of the intensity of emotional expression (how strongly it is expressed), and the duration (how long it is maintained). The system (100) can convert this multidimensional data into a single numerical index by calculating it using a summation method that applies statistical weighting coefficients.

[0109] According to one embodiment, the system (100) can adjust the importance of each element through weighting factors. For example, an indicator can be calculated by applying weights such as 0.5 for emotional intensity, 0.3 for frequency, and 0.2 for duration, and these weights can be set differently depending on the type of emotion or culture. This is because in a certain culture, there may be emotions where duration is more important than intensity, and in another culture, frequency may have a more significant meaning.

[0110] According to one embodiment, the system (100) can implement an algorithm that calculates a coefficient based on the ratio of the calculated indicator value and the reference value, and multiplies this by the exchange value of the appraisal NFT to assign a regionally differentiated value. The reference value can be a global average or a reference value of a specific region, thereby enabling a relative value comparison.

[0111] According to one embodiment, the system (100) can establish an inter-regional exchange economy for emotion NFTs through this differentiated value system. In regions where a specific emotion is highly valued, the demand for the corresponding emotion NFT may increase, which can become a new economic model that links the cultural value of the emotion with the value of digital assets. This system reflects the recognition that the economic value of emotions is not universal and can be evaluated differently depending on culture and region.

[0112] According to one embodiment, the system (100) can operate a value exchange network that reflects regional cultural characteristics and emotional patterns. This network can promote the exchange of emotional values ​​between different cultural spheres and build a digital economic ecosystem that respects cultural diversity.

[0113] According to one embodiment, the system (100) may apply a data anonymization process to protect user personal information during the process of collecting and analyzing sentiment data. By converting user identification information into alternative identifiers (pseudonyms or hashed IDs), individuals cannot be directly identified, and by converting individual detailed data into aggregated statistics, individual privacy can be protected. This anonymization process enables the use of local sentiment data while complying with privacy protection regulations such as the General Data Protection Regulation (GDPR) or the California Consumer Privacy Act (CCPA).

[0114] According to one embodiment, the system (100) can ensure the safe transmission and storage of emotional data by applying a security protocol of public key encryption. Public key encryption is an asymmetric encryption method, and when transmitting data, it can be encrypted with the recipient's public key and decrypted with a private key possessed only by the recipient, so even if the data is intercepted in transit, the content cannot be verified.

[0115] According to one embodiment, the system (100) can ensure the integrity and transparency of emotion data by utilizing blockchain technology. Anonymized emotion indicators and value conversion coefficients are recorded on the blockchain and can be verified by anyone, but personal identification information or detailed data can be encrypted and protected. This can be a balanced approach that protects privacy while maintaining the reliability of the data.

[0116] According to one embodiment, the system (100) can implement a stabilization mechanism for monitoring and managing the volatility of the emotional currency exchange rate. It can maintain market stability through a control algorithm that operates automatically when a sudden change in value occurs, and support users in exchanging emotional NFTs in a predictable environment.

[0118] According to one embodiment, the system (100) stores the user's emotional state data and virtual space coordinate information at the corresponding time point in a metaverse environment in a database, generates a three-dimensional data visualization model in which emotional intensity is expressed as color intensity through a visualization algorithm, analyzes space-emotion data collected from multiple users using a clustering and pattern recognition algorithm to identify common emotional response patterns caused by a specific virtual space and quantifies them as a space-emotion influence index, provides the emotional characteristics of the space through a notification system when the user accesses a virtual space with a specific emotional influence index, analyzes the difference between the user's current emotional state and the target emotional state to calculate and recommend an optimal path to reach the target emotional state through a path search algorithm, and operates a dynamic environment optimization engine that changes the location, size, and accessibility of space components within the virtual environment to optimize the emotional well-being of a user group.

[0119] According to one embodiment, the system (100) can store in a database the user's emotional state data and virtual space coordinate information at that time in a metaverse environment. This can serve as basic data for understanding the emotional response experienced by the user at a specific location within the metaverse in a spatial context. The system (100) can construct spatial-emotion mapping data by recording the user's location as three-dimensional coordinates (x, y, z) and storing the type and intensity of the emotion detected at that location together.

[0120] According to one embodiment, the system (100) can convert stored spatial-emotion data into a three-dimensional data visualization model through a visualization algorithm. In this model, emotional intensity can be expressed as color intensity, for example, joy can be visualized as a change in intensity of yellow, sadness as blue, and anger as red. Color intensity can be expressed as darker or lighter in proportion to emotional intensity, thereby allowing for an intuitive understanding of the distribution of emotions within the metaverse environment. Such visualization can be implemented through heat map or volume rendering techniques. The types of colors and emotions are merely examples and may vary depending on the settings.

[0121] According to one embodiment, the system (100) can analyze spatial-emotion data collected from multiple users using clustering and pattern recognition algorithms. Spatial regions exhibiting similar emotional responses can be grouped by applying algorithms such as K-means clustering, hierarchical clustering, and DBSCAN (Density-Based Spatial Clustering of Applications with Noise). Additionally, the system (100) can recognize more complex spatial-emotion patterns by utilizing deep learning models such as recurrent neural networks (RNN) or convolutional neural networks (CNN).

[0122] According to one embodiment, the system (100) can identify common emotional response patterns triggered by a specific virtual space and quantify them as a spatial emotional influence index. This index can be calculated by comprehensively considering the consistency, intensity, and duration of emotional responses experienced by multiple users in a specific space. For example, if 90% of users felt joy with an average intensity of 0.8 or higher in a specific space, that space may have a high influence index for joy. This quantification can be utilized to identify emotional landmarks within the metaverse environment.

[0123] According to one embodiment, the system (100) can provide the emotional characteristics of a space through a notification system when a user accesses a virtual space having a specific emotional influence index. This notification can be delivered in various forms, such as visual displays (e.g., icons, color changes), sound effects, haptic feedback, or text messages, and the user can recognize the emotional influence of a specific space in advance through this.

[0124] According to one embodiment, the system (100) can analyze the difference between the user's current emotional state and the target emotional state. If the user wishes to transition from a stressful state to a comfortable state, the system (100) can calculate the difference between the current high stress level and the target low stress level and suggest an optimal path to reduce this emotional distance. This goal-based approach can provide a tool for the user to actively manage and regulate their emotional state.

[0125] According to one embodiment, the system (100) can calculate and recommend an optimal path to reach a target emotional state through a path search algorithm. By utilizing Dijkstra's algorithm or a reinforcement learning-based path optimization technique, an optimal match between the emotional influence index of each space and the user's emotional needs can be found. In this process, the system (100) can propose a sequential path that includes intermediate steps for a gradual transition to the target emotional state. For example, when transitioning from a state of high anxiety to calmness, a path may be presented that first passes through a space where one can feel a medium intensity of vitality and then gradually moves to a calm space.

[0126] According to one embodiment, the system (100) may operate a dynamic environment optimization engine to optimize the emotional well-being of a user group. This engine may automatically adjust the location, size, and accessibility of spatial components within a virtual environment based on collected emotional data and user feedback.

[0127] According to one embodiment, the system (100) can progressively improve the configuration of a virtual environment by utilizing techniques such as a genetic algorithm or Bayesian optimization. By testing various environment configurations and measuring the emotional responses of users, an environment configuration that maximizes a positive emotional state can be found. This approach combines data-driven optimization with traditional environment design, enabling the implementation of a user-centered emotional space design paradigm.

[0128] According to one embodiment, the system (100) can statistically analyze collective emotional data to identify patterns of emotional change by time period and by event, and preemptively adjust the environment accordingly. For example, if a tendency for the stress index to increase during a specific time period is detected, the environment can be pre-adjusted to be more calming before entering that time period. This predictive environment optimization can maintain the quality of the user experience by taking preventive measures before emotional problems occur.

[0130] According to one embodiment, the system (100) automatically identifies, through an event detection algorithm, the moment when the emotional intensity in the user's emotional data exceeds a threshold or shows a rapid change, vectorizes multidimensional data including avatar facial expressions, gestures, and surrounding environment elements at that moment to structure it into a unique emotional expression data set, converts the structured emotional expression data set into a digital art form by applying a visualization algorithm, issues it as a limited quantity of digital collectibles through a blockchain-based token issuance protocol, includes a smart contract function in the digital collectibles to provide an event function that allows the owner to activate the emotional expression data within the metaverse environment, calculates an interaction index through an algorithm that measures the impact of the activated emotional expression data on the avatar emotional state of other nearby users, constructs an emotional influence profile for each user based on this index, and can link it with a social network system within the metaverse environment.

[0131] According to one embodiment, the system (100) can automatically identify particularly meaningful moments in the user's emotional data. An event detection algorithm can capture moments when emotional intensity exceeds a predefined threshold (e.g., 0.8 or higher) or shows a rapid change within a short period of time (e.g., a change in emotional intensity of 0.3 or higher). This algorithm can identify statistically significant emotional changes by utilizing deviations from a moving average, calculation of the rate of change, peak detection techniques, etc. This approach enables capturing and preserving emotional moments that have special meaning within the flow of daily emotions.

[0132] According to one embodiment, the system (100) can vectorize multidimensional data of identified emotional moments to structure them into a unique emotional expression data set. This data set may include facial expressions of an avatar (facial muscle activation patterns, speed of facial expression change), gestures (posture, movement patterns, gesture intensity), surrounding environment elements (lighting conditions, color composition, acoustic characteristics), etc. The system (100) can convert these various characteristics into numerical vectors and apply dimensionality reduction techniques such as principal component analysis (PCA) or t-SNE to generate efficient representations while preserving the core characteristics of the high-dimensional data.

[0133] According to one embodiment, the system (100) can convert a structured emotion expression data set into a digital art form by applying a visualization algorithm. In this process, data-based generative art techniques may be utilized, and emotion types may be expressed as a color palette, emotion intensity as complexity of form, and emotion change patterns as dynamic elements or directionality.

[0134] According to one embodiment, the system (100) can issue the generated digital art as a limited quantity of digital collectibles through a blockchain-based token issuance protocol. In this process, NFT standards such as ERC-721 or ERC-1155 are utilized, and each token may include a unique identifier and metadata (creation time, emotion context, creation parameters, etc.). By limiting the issuance quantity, scarcity can be imparted, which can increase the value as a digital collectible. Additionally, the system (100) can implement a scarcity algorithm that issues a smaller quantity of tokens for particularly intense or rare emotional moments.

[0135] According to one embodiment, the system (100) may provide additional utility by including smart contract functions in digital collectibles. These smart contracts may include event functions that allow the owner to activate the corresponding emotional expression data within the metaverse environment. The activated emotional expression may be implemented as visual effects (e.g., auras, particle effects), environmental changes (e.g., temporary changes in lighting, sound, gravity, etc.), or special animations. These functions may give the digital collectibles practical value beyond simple ownership and may create new forms of social interaction within the metaverse.

[0136] According to one embodiment, the system (100) can measure the effect of activated emotional expression data on the avatar emotional state of other nearby users. It can track emotional changes of other users within the radius of influence and statistically analyze them to calculate an interaction index. The interaction index can be determined by considering factors including the range of influence (e.g., how many people were affected), intensity (e.g., how large an emotional change was caused), and duration. The system (100) can utilize a Bayesian network or a causal inference model to more accurately identify the causal relationship between the emotional activation event and the emotional changes of surrounding users.

[0137] According to one embodiment, the system (100) can construct a user-specific emotional influence profile based on a calculated interaction index. This profile may include the main types of emotions the user conveys to others, the intensity of the influence, the duration of the influence, etc.

[0138] According to one embodiment, the system (100) can link the constructed emotional influence profile with a social network system within the metaverse environment. Through this, users can recognize each other's emotional influence and form new forms of social connections and interactions based on this. For example, a user who wants to experience a specific emotional state can find and interact with users who have strong influence over that emotion. Additionally, the system (100) can implement a recommendation system based on the emotional influence profile to suggest appropriate social interactions or events according to the user's current emotional state.

[0140] According to one embodiment, the system (100) can collect emotion data from multiple users located in the same virtual space within the metaverse environment in real time. By recording each user's emotion data (type, intensity, duration) and the exact location coordinates within the virtual space together, emotion patterns can be analyzed in a spatial context. Based on the collected data, the system (100) can calculate the distribution of emotion density by location coordinates within the space, which can be implemented through spatial statistical techniques such as Kernel Density Estimation or Voronoi diagrams. This emotion density map can serve as a basis for identifying hotspots where specific emotions are concentrated and understanding the dynamics of emotion flow within the space.

[0141] According to one embodiment, the system (100) may implement a collective emotion amplification mechanism that generates an emotion resonance phenomenon at a point where the emotion density exceeds a predefined threshold. This threshold may be set by considering the number of users expressing the same emotion within a specific radius and the average intensity of the emotion, and a collective amplification effect may be triggered when the threshold is exceeded. For example, if 10 or more users express joy with an intensity of 0.7 or higher within a radius of 5 meters, a resonance phenomenon of the emotion of joy may occur at that point. This resonance mechanism enables the reinforcement and sharing of collective emotional experiences and can create new forms of social interaction in a metaverse environment.

[0142] According to one embodiment, the system (100) can implement an emotional resonance phenomenon through a dominant emotion vector calculated based on the frequency and average intensity of major emotion types of multiple users. The dominant emotion vector is a multidimensional vector representing the collective intensity of each emotion dimension (e.g., joy, sadness, anger, calmness), and the dimension with the highest value can determine the dominant emotion of the space. The system (100) can apply a non-linear influence function that dynamically transforms the physical characteristics of the space according to this dominant emotion vector. For example, if joy is dominant, gravity may be reduced to increase the jump height of the avatar, and if sadness is dominant, gravity may be increased to make movement feel heavier.

[0143] According to one embodiment, the system (100) can dynamically modify not only the gravity of space but also various physical laws and the speed of time flow. For example, in a space where excitement is the dominant emotion, time flow can be accelerated to enable faster-paced interactions, and in a space where calmness is the dominant emotion, time flow can be decelerated to provide a more relaxed experience. Additionally, physical properties (e.g., friction, elasticity, fluid resistance) can be adjusted according to the type of emotion, which can provide a new way of physically embodying emotional experiences in a virtual environment.

[0144] According to one embodiment, the system (100) can operate an ecosystem feedback system in which a collective emotional state shared by multiple users controls the weather, seasons, and natural phenomena of the metaverse environment. The system can continuously monitor the overall emotional state of the user group and dynamically adjust environmental factors accordingly.

[0145] According to one embodiment, the system (100) can be designed so that these environmental changes have a meaningful impact on the user experience beyond simple visual effects. For example, clear weather induced by positive emotions can increase visibility to facilitate navigation, while a storm induced by negative emotions can reduce movement speed or temporarily restrict access to specific areas. Such environmental feedback can function as a natural regulatory mechanism for collective emotional dynamics and can enable users to recognize the impact of their emotional expressions on the shared environment.

[0147] According to one embodiment, the system (100) can analyze the user's text input through a natural language understanding model, which can comprehensively interpret keywords, context, and grammatical structures indicating emotions in the input text to identify types of emotions such as joy, sadness, anger, and surprise, and quantify the intensity of each emotion as a numerical value between 0 and 1. At the same time, the system (100) can extract acoustic characteristics such as pitch, speech rate, and voice energy from the user's voice input through digital signal processing techniques and utilize them for inferring the emotional state, and can build an emotional rhythm pattern that can identify the user's emotional change pattern by storing the results of such inference in a database in chronological order.

[0148] According to one embodiment, the system (100) can build an animation library that predefines facial expressions and body movements corresponding to each emotional state, and can control the expression of the user avatar more finely and naturally by applying a blending technique that naturally mixes different animations according to the analyzed emotional intensity. In addition, the system (100) can create an immersive environment that matches the user's emotional state by utilizing a mapping table that defines the correspondence between emotional states and environmental elements, thereby automatically applying bright lighting and energetic music to positive emotions such as joy, and dim lighting and calm music to negative emotions such as sadness.

[0149] According to one embodiment, the system (100) can control the NPC to select a response suitable for the user's emotional state and conversation flow and to generate specific conversation content through a hybrid structure that combines a state machine that switches states according to predefined rules and a natural language generation model that understands the context of the conversation. The system (100) can also convert emotional data and avatar characteristic information into a standardized data format such as JSON and transmit them to an external metaverse platform via a REST API utilizing the Web3.js library and the software development kit of each metaverse platform, thereby ensuring compatibility and interoperability between different platforms.

[0150] According to one embodiment, the system (100) can issue an NFT compliant with the ERC-721 or ERC-1155 standard to a blockchain network by calling a function of a smart contract, and can ensure the permanent preservation and integrity of the data without a centralized server by storing the sentiment data and related metadata in a distributed file system such as IPFS. The system (100) can provide a function to dynamically convert the exchange rate of sentiment currency by utilizing an sentiment resonance index that reflects the sentiment response characteristics by region or user group, thereby enabling the implementation of a sentiment value exchange system that takes cultural diversity into account.

[0151] According to one embodiment, the system (100) can change visual elements such as backgrounds, colors, and images of a user interface in real time by linking emotion labels and intensity information generated through emotion analysis with a VR or metaverse engine, and can provide an optimized visual experience tailored to the user's emotional state. The system (100) can gradually adjust environmental parameters such as color values, lighting intensity, music tempo, and volume according to the numerical range to which the generated emotion intensity belongs, thereby enabling a gradual change in the environment according to the change in emotion.

[0152] According to one embodiment, the system (100) can control facial expressions and gestures through an animation control system utilizing a finite state machine model so that an NPC can respond to a user's emotional state based on emotional rhythms, which can enable consistent animation control by clearly defining transition conditions between each state. The system (100) can generate specific conversational content suitable for the situation by selecting response templates classified by emotion type and utilizing a natural language generation model through a response generation system having a multi-layered structure of input emotion analysis, response type selection, and final response generation.

[0153] According to one embodiment, the system (100) can immediately detect changes in the emotional state through an emotion analysis engine that processes the user's text, voice input, and facial expression data in real time, and dynamically update the NPC's response parameters accordingly, which can enable natural interaction between the user and the NPC. The system (100) can record the emotional change data in a structured format in a time-series database and generate a visual report of the user's emotional change pattern by applying analysis algorithms such as moving averages, time-series decomposition, and machine learning-based prediction models, thereby effectively identifying trends and patterns of the emotional state.

[0154] According to one embodiment, the system (100) can generate a unique and tamper-proof digital identifier by applying a cryptographic hash function, such as SHA-256 or Keccak-256, to a string containing a user identifier, an emotion label, and time information, which can guarantee the integrity and uniqueness of the emotion data. The system (100) can ensure the permanence and accessibility of the data without a single point of failure by redundantly storing the generated digital identifier on multiple network nodes through a distributed storage network such as IPFS, and can verify data integrity by generating an address from the data content itself through a content addressing method.

[0155] According to one embodiment, the system (100) may request emotional NFT information through a predefined API endpoint via an interface with an external metaverse platform and transmit emotional state parameters and expression data to the platform. The system (100) may convert and store the user's emotional data processing records into a multidimensional vector, identify content with similar emotional characteristics through pattern matching techniques such as cosine similarity calculation or collaborative filtering, and provide personalized content recommendations by sorting them according to relevance scores.

[0156] According to one embodiment, the system (100) can collect and analyze emotional data of users by region to calculate an indicator that quantifies the degree of regional reaction to a specific emotion. This indicator can be calculated by a summation method that applies weights based on a pre-set standard or a learned model by synthesizing multiple elements such as the frequency, average value of intensity, and duration of a specific emotional expression. The system (100) can implement an algorithm that assigns differentiated values ​​by region by applying a coefficient calculated based on the ratio of the calculated indicator value to the standard value to the exchange value of the emotion NFT, thereby enabling the operation of a fair value exchange network that reflects the cultural characteristics and emotional patterns of each region.

[0157] According to one embodiment, the system (100) can pseudonymize user identification information or convert it into an alternative identifier using a hash value during the process of collecting and analyzing emotion data, apply a data anonymization process that converts individual data into aggregate statistics, and apply a security protocol of a public key encryption method such as RSA or elliptic curve cryptography to enhance the protection of personal information and security during the data transmission and storage process.

[0158] According to one embodiment, the system (100) can store the user's emotional state data and virtual space coordinate information at the corresponding point in time in a database in a metaverse environment, and can generate a visualization model representing the distribution of emotions in a three-dimensional space through a visualization algorithm such as a heatmap or volume rendering of the stored information, which can help intuitive understanding by expressing emotional intensity as color intensity or other visual attributes. The system (100) can analyze spatial-emotion data collected from multiple users using clustering and pattern recognition algorithms such as k-means clustering, DBSCAN, and association rule mining to identify common emotional response patterns triggered by a specific virtual space and quantify them into a spatial emotion influence index that integrates multiple emotion-related indicators.

[0159] According to one embodiment, the system (100) can provide the emotional characteristics of a space through various methods, such as visual pop-up messages, voice guidance, and haptic feedback, when a user accesses a virtual space having a specific emotional influence index, thereby helping the user to recognize the emotional characteristics of the space in advance and respond appropriately. The system (100) can analyze the difference between the user's current emotional state and the target emotional state and calculate the optimal path to reach the target emotional state using a pathfinding algorithm such as Dijkstra's algorithm, and can dynamically change the location, size, and accessibility of space components within the virtual environment based on a predefined optimization objective function and constraints to optimize the emotional well-being of the user group.

[0160] According to one embodiment, the system (100) can automatically identify moments in the user's emotion data where the emotion intensity exceeds a preset threshold value or shows a sudden change through a change point detection algorithm or an outlier detection algorithm, which can capture special moments of emotion and convert them into meaningful digital assets. The system (100) can structure multidimensional data including avatar facial expressions, gestures, and surrounding environment elements of the moment into a unique emotion expression data set by vectorizing it through a feature extraction technique using an autoencoder or a pre-trained embedding model, and can convert it into a unique digital art form by applying a generative art technique using a generative adversarial network or a neural network style transformation algorithm.

[0161] According to one embodiment, the system (100) can guarantee scarcity and uniqueness by issuing digital collectibles in a limited quantity according to a predetermined issuance quantity or unique creation rules through a blockchain-based token issuance protocol that follows the ERC-721 or ERC-1155 standards. The system (100) can provide an event function that includes a smart contract function in the digital collectibles to activate the corresponding emotional expression data when the owner satisfies a predefined condition within the metaverse environment, and can calculate an interaction index by measuring the impact of the activated emotional expression data on the avatar emotional state of other nearby users through indicators such as the emotional synchronization rate between users or the transfer intensity of a specific emotion. Based on the calculated interaction index, the system (100) can build an emotional influence profile for each user and visualize and manage emotional connections and influence between users by linking with a social network system within the metaverse environment.

[0163] According to one embodiment, the system (100) implements a spatiotemporal emotional strata system in which user emotional data accumulated by coordinates in a virtual space is recorded in chronological order and emotional history is stored in a layered structure at each point in the space, and through this, provides an emotional archaeology interface that allows searching for past emotional data of a specific location by time period, and generates a reverberation effect in the current metaverse environment according to the intensity and frequency of past emotional data stored in the emotional strata, and if strong emotions were frequently expressed in the past at a specific location, the emotion is conveyed to the current user through a visual translucent effect, spatial audio, and haptic feedback, and calculates the resonance between the current user's emotional state and the past accumulated emotions at that location, and if the emotional match is high, grants a visual aura effect to the user avatar and temporarily amplifies movement speed, interaction range, and emotional expression intensity, and provides an emotional time capsule function that can reproduce the collective emotional state of a specific point in the past at the present time, and when the user selects emotional data from a specific point in the past, the overall environmental state, background music, lighting effects, and NPC reactions of that time are temporarily superimposed and displayed in the current environment, and if emotional data of multiple users is accumulated in a specific space for a long period, an autonomous emotional consciousness is generated in that space and independent A spatial entity system implemented as NPCs is constructed, and the appearance, personality, and behavioral patterns of these spatial entities are determined by the dominant emotional characteristics of the location, and they can evolve through interaction with the user.

[0164] According to one embodiment, an entity may refer to a virtual object that is independently identifiable and operates autonomously within a metaverse environment. More specifically, in the system (100), a spatial entity may be formed based on emotional data accumulated at specific virtual coordinates and may refer to a software construct that has a unique ID and attributes, acts autonomously according to programmed rules, and interacts with users and the environment. Such an entity may exist as an object within a database while being implemented through visual representations and behavioral patterns in the metaverse environment, and may possess the ability to adapt to and learn from environmental changes and user interactions through artificial intelligence characteristics based on accumulated emotional data.

[0165] According to one embodiment, the system (100) can implement a spatiotemporal emotion strata system that records user emotion data accumulated by coordinates in virtual space in chronological order. The system can store emotion history at each 3D coordinate point in the metaverse environment in a manner similar to a geological layer structure. The most recent emotion data can be structured so that it is located in the upper layer and older data in the lower layer, and each layer may include metadata such as timestamps, emotion type, intensity, and duration. This strata structure can preserve changes in emotion patterns over time and emotion history in a spatial context.

[0166] According to one embodiment, the system (100) can generate a reverberation effect in the current metaverse environment based on the intensity and frequency of past emotion data stored in the emotion layer. This reverberation effect is a mechanism that conveys the emotion to the current user when strong emotions were frequently expressed in the past at a specific location, and can be implemented through various sensory channels. Visually, a translucent effect (e.g., aura, particle effect, light wave) with a hue and pattern according to the emotion type can be expressed, and auditorily, background music or ambient sound reflecting the emotion can be played three-dimensionally through spatial audio technology. Additionally, in a VR environment, the texture of the emotion can be physically conveyed through haptic feedback. For example, vibrations below a certain intensity may correspond to joy, and vibrations exceeding a certain intensity may correspond to anger.

[0167] According to one embodiment, the system (100) can calculate the resonance between the current user's emotional state and the past accumulated emotions at the corresponding location. This resonance can be measured by mathematical methods such as cosine similarity or Euclidean distance between the current emotion vector and the past accumulated emotion vector, and a high degree of agreement may indicate that the current user's emotions strongly resonate with past emotion patterns. The system (100) may apply special effects to the user avatar when the degree of emotional agreement is high. Visually, an aura effect with unique colors and patterns according to the emotion type may surround the avatar, and functionally, temporary ability enhancements such as increased movement speed, expanded interaction range, and amplified emotional expression intensity may be applied.

[0168] According to one embodiment, the system (100) may provide an emotional time capsule function capable of reproducing a collective emotional state at a specific point in the past at the present time. This function is a mechanism that preserves the emotional state of a specific event or important moment and allows it to be experienced again later. When a user selects emotional data from a specific point in the past, the overall environmental state at that time can be reproduced. This may include background music, lighting effects, and environmental settings at that time, as well as the reaction patterns of NPCs that existed at that time, and these elements may be temporarily superimposed and displayed in the current environment.

[0169] According to one embodiment, the system (100) can build a system in which an autonomous emotional consciousness is generated in a specific space when emotional data of multiple users is accumulated in that space for a long period of time. This system operates when a specific threshold (e.g., emotional data expressed by more than 1,000 users for more than 3 months) is exceeded, and can form a unique emotional identity of the space by analyzing the pattern of the accumulated emotional data. The emotional identity can be implemented as a space entity, which is an independent NPC entity, and its appearance, personality, and behavioral patterns can be determined according to the dominant emotional characteristics of the location.

[0170] According to one embodiment, the system (100) can generate the appearance of a spatial entity as a visual representation suitable for the dominant emotion of the location. For example, in a space where joy and excitement are primarily expressed, an entity in the form of energy with bright colors and lively movements may be generated, and in a space where calmness and meditation are predominant, an entity with blue hues and fluid, soft forms may appear. Personality and behavioral patterns may also reflect accumulated emotional data and exhibit characteristics that match the type of emotion primarily expressed in the space.

[0171] According to one embodiment, the system (100) can implement a mechanism that allows a spatial entity to interact with and evolve with a user. The entity can recognize the emotional state of a user who has entered its area and show a corresponding reaction. For example, it may show an intimate reaction when the user is in a state that matches the entity's primary emotion, or express curiosity or careful observation when the user is in a contrasting emotional state. Additionally, the system (100) can implement a learning mechanism that allows the entity to gradually evolve and develop more complex emotional characteristics through continuous interaction with the user.

[0172] According to one embodiment, the system (100) can intuitively convey the emotional history and characteristics of a place through a spatial entity. The entity can tell stories about significant emotional events or patterns that occurred at the place, or guide users on how to fully experience the space. Additionally, as the emotional characteristics of the place change over time, the appearance and personality of the entity can also gradually change accordingly. This imbues the metaverse environment with a vivid emotional history and identity, enabling users to form deeper and more meaningful connections with the virtual space.

[0173] According to one embodiment, the system (100) can apply thermodynamic principles to emotional dynamics within a metaverse environment by introducing an emotional entropy balance mechanism. This mechanism models emotional states using the concepts of energy and entropy, implementing the principle that within a closed metaverse space, the total amount of emotional energy is conserved, but entropy (the disorder of emotions) increases over time. Through this, when a strong single emotion is dominant in a specific space, the system can naturally activate a balancing process that disperses it into various emotional states.

[0174] According to one embodiment, the system (100) can implement a method of expressing emotions beyond the existing range of perception through a supersensory feedback loop. This system utilizes sensory experiences beyond the five human senses (e.g., electromagnetic field detection, ultrasound, infrared, etc.) as a medium for expressing emotions, thereby enabling a new dimension of emotional communication that was impossible with existing interfaces.

[0175] According to one embodiment, the system (100) can implement a multiverse emotion synchronization protocol to enable consistent emotion expression and transmission between different metaverse platforms. This protocol provides the function of translating and synchronizing the unique emotion expression systems of each metaverse platform into a common meta-emotion language, thereby enabling users to maintain emotional continuity and consistency even when moving from one metaverse to another. This goes beyond simple API integration and utilizes an advanced translation algorithm that deeply analyzes and maps the emotion expression grammar and semantics of each platform, thereby enabling an integrated emotion experience that transcends the boundaries between platforms.

[0177] According to one embodiment, the system (100) can implement an emotion-linked reward system that generates a unique digital item or visual effect for an avatar based on the user's achievement of a specific emotional state or a meaningful emotional rhythm pattern and issues it as an NFT. The system (100) can continuously monitor the user's emotional data to determine whether a predefined emotional goal or milestone has been achieved, such as, for example, maintaining an average joy index of 0.8 or higher for a week, achieving an extreme sense of accomplishment (emotional intensity of 0.95 or higher) upon completing a specific high-difficulty quest, or repeating a successful emotional transition pattern from sadness to calmness.

[0178] When these goals are achieved, the system (100) can automatically generate avatar accessories (e.g., emotion aura, emotion pattern badge, clothing that changes color in a specific emotional state) or special effects (e.g., an effect where the symbol of the corresponding emotion appears with every step) that visually embody the characteristics of the corresponding emotion (e.g., joy as bright, sparkling particles, tranquility as a gently flowing wave shape). The generated item or effect can be issued as an NFT including metadata such as the user's unique identification information, the emotional condition achieved, and the time of creation, and the user can wear it on their avatar or display it in their personal space. In particular, this NFT item can trigger additional interactions under specific conditions, for example, when the wearer experiences an emotional state similar to the time of NFT creation, the visual effect of the item may be amplified more vividly, or it may be designed to produce a minor effect that weakly conveys the corresponding emotion to surrounding users.

[0179] According to one embodiment, the system (100) may include an emotion-responsive space curation system that automatically generates or recommends an environment theme for a personal virtual space by analyzing the user's recent emotional rhythm and preferences. The system (100) identifies the user's current main emotional state or mood by analyzing the user's emotional data (e.g., major emotion types, distribution of emotional intensity, periodicity and amplitude of emotional changes) collected over a certain period (e.g., the last 24 hours, the last 1 week). Based on this, the system (100) may generate or suggest one or more customized space themes by combining elements that best match the current emotional state from a database of various pre-built space theme elements (e.g., color palettes, lighting presets, background music genres, furniture styles, images of the view outside the window, etc.).

[0180] For example, if a user has recently shown a creative and energetic emotional rhythm, a modern furniture arrangement with bright and colorful hues can be recommended; conversely, if they have shown a calm and meditative emotional rhythm, a theme featuring minimal furniture and soothing music can be suggested. Users can select a suggested theme to apply immediately or customize their own space by modifying certain elements.

[0181] According to one embodiment, the system (100) may provide a function to issue a specific emotion-based space theme configuration that a user is satisfied with as a space preset NFT, which can then be sold or shared with other users. Other users who purchase this NFT can easily apply the theme to their own spaces, and the original creator can receive compensation for this, thereby contributing to the activation of the user-created content ecosystem.

[0182] According to one embodiment, the system (100) can embody an emotion-sensitive NPC interaction system that dynamically adjusts the depth and content of interaction with an NPC in the metaverse according to the user's real-time emotional changes. The system (100) detects subtle changes in the emotional state (e.g., transition to positive emotion, detection of discomfort regarding a specific topic, increase in confusion) by analyzing the user's text, voice, and facial expression changes in real time during a conversation between the user and the NPC. Based on these detected changes, the NPC can actively modify its conversation strategy, going beyond simply outputting a predefined response.

[0183] For example, if a user shows positive emotion toward a specific suggestion, the NPC can expand the conversation by actively providing related additional information or linked quests. Conversely, if a user shows subtle stress or resistance toward a specific topic, the NPC can act to reduce the user's emotional burden by smoothly changing the topic, showing an attitude of listening to the user's opinion first, or briefly slowing down the tempo of the conversation. Additionally, the NPC can record the history of past emotional interactions with the user in internal memory (e.g., memories of the user enjoying a specific joke, the intensity of the emotion the user expressed when a specific request was granted) and combine this with the current emotional state to generate a more personalized and continuous conversation.

[0184] According to one embodiment, the system (100) may include a space-emotion analysis and visualization system that analyzes collective emotion data of multiple users to generate a collective emotion heatmap and an emotion biorhythm of a specific virtual space or event, and provides the results for visualization. The system (100) may collect in real-time anonymized emotion data of multiple users participating in a specific metaverse space (e.g., a plaza, a performance venue, a shop) or an event taking place at a specific time, and may map the distribution and average intensity of each emotion type (e.g., joy, excitement, calmness, boredom) according to coordinates within the space or the flow of time. The collective emotion heatmap may visualize, using color or intensity, which emotions are primarily expressed at which location within the space at a specific point in time. For example, the system (100) may display red, indicating excitement, in front of the stage where a performance is taking place, and green, indicating calmness, in the resting area. The system (100) can express the trend of change in major group emotions over time in a specific space or event as a graph or dynamic visual effect, so that the operator or other users can identify which part of the event users showed the most positive reaction, or at which point they started to feel bored, etc.

[0185] According to one embodiment, the system (100) can provide data-driven insights to the metaverse operator regarding space design improvement, event flow adjustment, content placement optimization, etc., based on this analysis information. Additionally, general users can utilize this information to plan a more satisfying metaverse experience, such as deciding whether to visit a specific space or participating in an event at a specific time. The collected collective sentiment data is statistically processed so that individuals cannot be identified, and data visualization can be provided so that users can selectively view information by specific sentiment types or time periods through filters.

[0187] In the present invention, Artificial Intelligence (AI) refers to a technology that implements human learning ability, reasoning ability, perception ability, and decision-making ability in a computer by imitating them, and may include concepts such as machine learning, deep learning, symbolic logic, knowledge representation, reasoning systems, natural language processing, and computer vision.

[0188] According to one embodiment, Machine Learning (ML) is an algorithmic technology that classifies or learns the characteristics of input data on its own without explicit programming. Machine learning is based on statistics, optimization theory, probability theory, and information theory, and possesses the ability to discover patterns from data and generalize. Artificial intelligence technology can perform judgments or predictions by analyzing input data and learning the results through machine learning algorithms. Machine learning algorithms prevent overfitting and underfitting by optimizing the balance between statistical bias and variance, and improve the generalization performance of models through techniques such as cross-validation, regularization, and ensemble methods. Furthermore, technology that mimics the cognitive and judgment functions of the human brain using machine learning algorithms can also be understood within the category of artificial intelligence, and this is evolving in the direction of implementing more biologically valid models by incorporating research results from neuroscience and cognitive science.

[0189] According to one embodiment, machine learning refers to the process of training a neural network model based on experience in processing data, through which computer software can improve its own data processing capabilities. The learning process can generally be divided into preprocessing, feature extraction, model selection, training, validation, evaluation, and deployment stages, and each stage includes various algorithms and methodologies to solve specific technical tasks. A neural network model is constructed by modeling the correlations between data, and these correlations can be expressed by various parameters such as weights, biases, activation functions, loss functions, and regularization parameters. These parameters are adjusted through optimization algorithms such as Stochastic Gradient Descent (SGD), Adam, RMSprop, and AdaGrad, and the learning speed and stability can be controlled through hyperparameters such as the learning rate, momentum, and batch size.

[0190] According to one embodiment, the core of machine learning is to optimize the parameters of a neural network model by extracting and analyzing features from given data to derive correlations between the data and repeating this process. In this process, the backpropagation algorithm calculates the gradient of the loss function for each parameter, which is accomplished through differential calculations utilizing the chain rule. In complex neural network models, problems such as vanishing gradients or exploding gradients may occur, and to resolve these, batch normalization, residual connections, and weight initialization techniques (e.g., Xavier, He initialization) are used. Additionally, hyperparameter tuning can be performed using various techniques such as grid search, random search, Bayesian optimization, and evolutionary algorithms.

[0191] For example, neural network models can learn the mapping (correlation) between inputs and outputs for data provided as input-output pairs, which is the fundamental principle of supervised learning. Even when only input data is given, they can learn relationships by deriving regularities between the data; this approach is the fundamental principle of unsupervised learning. Representative techniques of unsupervised learning include autoencoders, Restricted Boltzmann Machines (RBMs), Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and self-supervised learning, which are utilized to learn useful representations from large-scale, unlabeled datasets.

[0192] According to one embodiment, an artificial intelligence learning model or neural network model is designed to implement the structure of the human brain on a computer and may include multiple network nodes with weights that mimic neurons of biological neural networks. Each artificial neuron receives multiple input signals, calculates a weighted sum, and passes this value through an activation function to generate an output signal. Activation functions such as sigmoid, hyperbolic tangent (tanh), ReLU, Leaky ReLU, GELU, Swish, and Maxout may be used, and each function has different characteristics and advantages and disadvantages depending on the specific problem domain and network layer. Recently, self-parameterized activation functions and learnable activation functions are also being studied, which enable the model to automatically learn an activation function optimized for the task.

[0193] According to one embodiment, multiple network nodes can be interconnected to mimic the synaptic activity of neurons exchanging signals through synapses. This connection structure can be implemented in various forms, such as fully connected, sparsely connected, convolutional connections, and attention-based connections, each structure being optimized for specific types of data and tasks. The depth of a network refers to the number of layers; deeper networks can learn more complex and abstract features, but simultaneously increase the difficulty of learning and computational cost. Multiple network nodes of an artificial intelligence learning model are located in layers of different depths and can exchange data through connection relationships and operations such as convolution, pooling, normalization, dropout, and non-linear transformation.

[0194] According to one embodiment, modern neural network architectures have evolved from existing feedforward structures and include various special structures. Representative examples include Recurrent Neural Networks (RNNs) utilizing memory cells, Long Short-Term Memory (LSTMs) solving long-term dependency problems, Gated Recurrent Units (GRUs), Temporal Convolutional Networks (TCNs), and Transformer architectures utilizing self-attention mechanisms. In particular, Transformers demonstrate excellent performance in various fields such as natural language processing, visual recognition, and time series forecasting due to their parallel processing efficiency and long-range dependency modeling capabilities, and have evolved into models such as GPT (Generative Pre-trained Transformer), BERT (Bidirectional Encoder Representations from Transformers), and ViT (Vision Transformer).

[0195] According to one embodiment, examples of artificial intelligence learning models include Artificial Neural Networks (NNs), Deep Neural Networks (DNNs), Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Graph Neural Networks (GNNs), Capsule Networks, and Neuro-Symbolic Networks. Recently developed architectures include efficient CNNs such as EfficientNet and MobileNet, Transformer-based language models such as BERT, GPT, and T5, segmentation networks such as U-Net and SegNet, and generative models such as VAEs, GANs, and Diffusion Models. These various architectures are optimized for specific problem domains and can apply knowledge from pre-trained models to new tasks through transfer learning.

[0196] In one embodiment, an artificial intelligence learning model can be machine learned according to methods such as supervised learning, unsupervised learning, semi-supervised learning, self-supervised learning, multi-task learning, meta-learning, reinforcement learning, federated learning, and continual learning. In supervised learning, input data and their corresponding labels (correct answers) are provided as pairs, and the model learns a mapping function from input to label. In unsupervised learning, only input data is provided without labels, and the model discovers the inherent structure or patterns of the data. Semi-supervised learning is an approach that utilizes a small amount of labeled data along with a large amount of unlabeled data. Self-supervised learning learns representations by utilizing supervisory signals automatically generated from the data itself. Reinforcement learning is a method in which an agent learns a policy to maximize rewards by interacting with the environment, and is based on a Markov Decision Process (MDP).

[0197] Algorithms for machine learning that can be used include Decision Trees, Random Forests, Bayesian Networks, Support Vector Machines (SVM), Artificial Neural Networks, AdaBoost, Gradient Boosting, XGBoost, LightGBM, CatBoost, Perceptrons, Genetic Programming, K-means Clustering, Hierarchical Clustering, DBSCAN, Principal Component Analysis (PCA), t-SNE, UMAP, and Independent Component Analysis (ICA). Each algorithm has strengths and weaknesses for specific problem types, and an appropriate algorithm must be selected based on the characteristics of the problem, the quantity and quality of data, constraints on computational resources, and interpretability requirements.

[0198] Recently, in the field of deep learning, techniques such as fine-tuning, knowledge distillation, pruning, quantization, Neural Architecture Search (NAS), AutoML, and prompt engineering are being actively researched, contributing to improvements in the efficiency, accuracy, and generality of models. In particular, the pre-training and fine-tuning paradigms enable effective learning even in situations where data is limited by learning general representations from large datasets and then fine-tuning the model to suit a specific task.

[0199] A Convolutional Neural Network (CNN) is a type of multilayer perceptron designed to use minimal preprocessing, serving as an architecture capable of directly analyzing visual patterns. A CNN consists of one or more convolutional layers and general artificial neural network layers placed on top of them, additionally utilizing weights and pooling layers. In the convolutional layers, trainable filters (or kernels) scan the input data to generate feature maps, which provide translation invariance. Pooling layers reduce spatial dimensions and computational complexity through downsampling, while providing robustness against small positional changes. Key techniques used in CNNs include padding, stride, dilated convolution, grouped convolution, channel attention, and spatial attention.

[0200] Thanks to this structure, CNNs can effectively process input data with a two-dimensional structure and can be applied to various forms of grid-structured data, such as visual data like images and videos, audio spectrograms, and time-series data. Compared to other deep learning architectures, CNNs demonstrate superior performance in the fields of video and speech and can be trained through standard backpropagation. The main advantages of CNNs are efficient learning through parameter sharing, spatial information preservation through local connectivity, and hierarchical feature extraction. Due to these characteristics, CNNs are widely applied to various computer vision tasks, such as image classification, object detection, segmentation, face recognition, and pose estimation.

[0201] Convolutional networks are neural networks composed of a set of nodes with tied parameters, which enables the learning of filters that detect the same features at all locations. Modern CNN architectures include ResNet, DenseNet, EfficientNet, MobileNet, and ShuffleNet, which have improved performance and efficiency by utilizing techniques such as skip connections, bottleneck structures, and depthwise separable convolution. As the amount of available training data has increased and computational power has improved, the combined advancement of algorithms such as piecewise linear units and dropout training has significantly improved many computer vision tasks. Furthermore, normalization techniques such as batch normalization, layer normalization, and group normalization have contributed to improving training stability and generalization performance.

[0202] Artificial intelligence models are implemented through software frameworks (such as TensorFlow, PyTorch, JAX, and ONNX), which provide features such as automatic differentiation, GPU / TPU acceleration, distributed training, and model serialization. On the hardware side, various computing devices such as CPUs, GPUs, TPUs, FPGAs, and ASICs are utilized for the training and inference of AI models, and each device has advantages and disadvantages for specific task types and deployment scenarios. In particular, lightweight inference engines and optimization tools such as TensorFlow Lite, ONNX Runtime, CoreML, and PyTorch Mobile are being developed for the execution of AI in edge computing and on mobile devices.

[0203] Model optimization techniques include quantization, pruning, knowledge distillation, model compression, kernel optimization, operation fusion, and memory optimization. Quantization is a technique that reduces model size and computational requirements by converting 32-bit floating-point representations to lower precision, such as 8-bit integers. Pruning is a technique that sparsifies a model by removing unimportant weights or neurons. Knowledge distillation is a technique that transfers knowledge from a large teacher model to a small student model. These optimization techniques aim to maintain accuracy or allow only minimal loss of accuracy while reducing model size, latency, and energy consumption.

[0204] As such, the present invention can be implemented by utilizing the various artificial intelligence technologies, algorithms, architectures, optimization methods, etc. described above, and appropriate technologies and methods can be selectively applied according to the requirements and constraints of a specific application field.

[0206] 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.

[0207] 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.

[0208] 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 A character and space control system in an emotion rhythm-based metaverse environment comprises a memory and a processor for storing instructions, wherein, when the instructions are executed by the processor, the system collects emotion-related data from a user through text, voice, facial expression recognition, or biosignal measurement, analyzes and stores an emotion rhythm—which is a pattern of emotional change over time—from the collected data, dynamically controls the facial expressions, gestures, and movement patterns of a user avatar within the metaverse environment based on the analyzed emotion data, adjusts environmental elements including background, lighting, color, and sound of the metaverse space according to the user's emotional state, automatically generates and controls the facial expressions, dialogue, and actions of a Non-Player Character (NPC) based on the user's emotion rhythm, converts the emotion data and avatar characteristics into a standardized format to link between multiple metaverse platforms, controls the issuance and management of the user's emotion data, emotion character, or emotion-based creation as a Non-Fungible Token (NFT), and analyzes the user's text input through a natural language processing algorithm including a Natural Language Understanding (NLU) model to identify joy, sadness, anger, Extracts multiple emotion types, including surprise, and emotion intensity expressed as a numerical value within a preset range; infers an emotional state by analyzing acoustic characteristics from the user's voice input, including pitch, velocity, energy, and voice feature parameters; constructs an emotional rhythm pattern by storing the inferred emotional state as time-series data; constructs an animation set corresponding to predefined facial expressions and gestures for each emotional state; controls the user avatar by applying weight-based or state-based animation blending techniques according to the analyzed emotion intensity; and adjusts environmental elements, including illuminance, color temperature, and background music tempo, by utilizing a mapping table between the emotional state and environmental parameters that can be preset or dynamically updated based on user interaction.A system that controls NPCs by selecting appropriate response types and generating specific dialogue content based on the user's emotional state and conversation context through a hybrid structure combining a state machine that follows predefined state transition rules and a natural language generation model that generates conversations using contextual information; converts emotional data and avatar characteristics into a standardized format including JSON (JavaScript Object Notation) and transmits them to external metaverse platforms via REST (Representational State Transfer) APIs (Application Programming Interfaces) utilizing Web3.js libraries and software development kits (SDKs) specific to each metaverse platform; issues NFTs conforming to ERC-721 or ERC-1155 standards through function calls of predefined smart contracts; stores the said emotional data and related metadata in IPFS (InterPlanetary File System) or a similar distributed file system; and controls the provision of an emotional currency exchange rate conversion function based on predefined regional or user group-specific emotional resonance indices. Claim 2 delete Claim 3 In claim 1, when the instructions are executed by the processor, the system generates an emotion label and an emotion intensity expressed as a numeric value within a preset range through the analysis of the user's emotions; modifies user interface elements including backgrounds, colors, images, and graphic elements by linking the generated emotion intensity with a VR (Virtual Reality) or metaverse engine through a preset API or SDK; stepwise adjusts environmental parameters including color values, lighting intensity, music tempo, and volume as the generated emotion intensity corresponds to any one of a plurality of preset numerical ranges; controls facial expressions and gestures through an animation control system based on a finite state machine model having a plurality of preset states and transition conditions between each state, in response to the user's emotional state based on emotion rhythms, and generates specific conversational content appropriate to the situation through a natural language generation model after selecting a response template classified by emotion type in a multi-layered response generation system including an input emotion analysis layer, a response type selection layer, and a final response generation layer; and detects changes in the emotional state by processing the user's text or voice input and facial expression data through a real-time emotion analysis engine operating periodically or based on event occurrences, and accordingly A system that updates the response parameters of an NPC and controls the generation of a report visualizing the user's emotional change patterns by recording emotional change data in a structured format in a time-series database and using trend analysis algorithms including moving averages, time-series decomposition, or machine learning-based prediction models. Claim 4 In claim 1, the above instructions are a system that, when executed by the processor, generates a unique digital identifier by applying an encryption algorithm including a cryptographic hash function corresponding to SHA-256 or Keccak-256 to a string containing a user identifier, an emotion label, and time information, ensures data integrity and enables access via content addressing by redundantly storing the generated digital identifier on at least one of the network nodes using a distributed storage protocol including a distributed storage network that supports IPFS or content addressing, requests stored emotion NFT information via a predefined API endpoint through an interface with an external metaverse platform to transmit emotion state parameters and expression data to the platform, converts and stores the user's emotion data processing record into a vector representing the multidimensional characteristics of the emotion, identifies content having emotion characteristics similar to the user's current emotion through a pattern matching technique including cosine similarity calculation or collaborative filtering, and controls the system to suggest to the user by sorting according to the calculated relevance score. Claim 5 In claim 1, the above instructions are characterized in that, when executed by the processor, the system collects and analyzes regional users' emotional data to calculate an indicator that quantifies the degree of regional reaction to a specific emotion, and the indicator is calculated by a summation method that applies statistical weighting coefficients based on a preset standard or a learned model to quantify multiple emotional response elements, including the frequency of specific emotional expressions, the average value of emotional expression intensity, and the duration of the users within the region; the system implements an algorithm that assigns regionally differentiated values ​​by multiplying the exchange value of an emotional NFT by a coefficient calculated according to the ratio of the calculated indicator value and the preset standard value; and operates a value exchange network that reflects regional cultural characteristics and emotional patterns, wherein during the process of collecting and analyzing emotional data, user identification information is processed as a pseudonym or converted into an alternative identifier using a hash value, and individual data is converted into aggregate statistics, and a system controls the application of a security protocol using a public key cryptography method including RSA (Rivest-Shamir-Adleman) or ECC (Elliptic Curve Cryptography). Claim 6 In claim 1, the instructions, when executed by the processor, cause the system to link user emotional state data and virtual space coordinate information at a corresponding point in time in a metaverse environment and store them in a database; generate a 3D data visualization model in which emotional intensity is expressed as color density or other specified visual attributes through visualization algorithms such as a heatmap or volume rendering representing the emotional distribution in 3D space for the stored information; analyze spatial-emotion data collected from multiple users using clustering and pattern recognition algorithms including k-Means Clustering, DBSCAN (Density-Based Spatial Clustering of Applications with Noise), or Association Rule Mining to identify common emotional response patterns triggered by a specific virtual space; quantify this into a spatial emotional influence index by integrating multiple emotion-related indicators according to a set formula; and when a user accesses a virtual space having a specific emotional influence index, the system [represents] the emotional characteristics of the space using one or more methods selected from a plurality of methods including visual pop-up messages, voice guidance, or haptic feedback. A system that provides, analyzes the difference between the user's current emotional state and the target emotional state, and controls the calculation of the optimal path to reach the target emotional state through a path search algorithm including the Dijkstra algorithm. Claim 7 In claim 1, the instructions, when executed by the processor, automatically identify, through an event detection algorithm including a Change Point Detection algorithm or an Anomaly Detection algorithm, the moment when the emotional intensity in the user's emotional data exceeds a preset threshold value or shows a sudden change; structure the multidimensional data including avatar facial expressions, gestures, and surrounding environment elements at that moment into a unique emotional expression dataset by vectorizing it using a feature extraction technique utilizing an autoencoder or a pre-trained embedding model; convert the structured emotional expression dataset into a digital art form by applying a visualization algorithm including a Generative Art technique utilizing a Generative Adversarial Network (GAN) or a Neural Style Transfer algorithm; issue it as a digital collectible in a predetermined issuance quantity or a limited quantity according to unique generation rules through a blockchain-based token issuance protocol conforming to the ERC-721 or ERC-1155 standard; and include smart contract functions in the digital collectible so that the owner satisfies a preset condition within the metaverse environment A system that provides an event function to activate the corresponding emotional expression data, calculates an interaction index through an algorithm that measures the impact of the activated emotional expression data on the avatar emotional state of other nearby users through indicators including the emotional synchronization rate between users or the transfer intensity of a specific emotion, constructs an emotional influence profile for each user based on the calculated interaction index, and controls the interaction to link with social networks within the metaverse environment.

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