System for securely translating variable assets in blockchain network using AI

Abstract

Provided herein is a method of securely and real-time converting a variable asset to another asset using an artificial intelligence (AI) model in an anti-quantum blockchain network. The method includes: receiving, by a user device, a variable asset conversion request and user preferences from a user; personalizing the AI model by identifying a pattern and a correlation between the user preferences and real-time behavior patterns and historical data of the user to personalize the AI model; predicting a value of each variable asset over time by using a personalized AI model; determining an optimal time to transform each variable asset based on a predicted value of the variable asset over time; converting each variable asset into another asset preferred by the user at the determined optimal time; and generating a smart contract on the anti-quantum block chain network to protect conversion from each variable asset to another asset.

Description

Technical Field

[0001] Embodiments herein relate generally to blockchain and artificial intelligence (AI), and more particularly, to a system and method for securely and in real time converting a volatile asset into another asset using an artificial intelligence (AI) model in a quantum-resistant blockchain network. Background Art

[0002] The rapid growth of the cryptocurrency market and blockchain technology has created an increasing need for efficient, secure, and user-friendly systems to manage and convert digital assets. As the variety of cryptocurrencies continues to expand, so does the complexity of managing cryptocurrencies across different blockchain protocols. Traditional methods of converting cryptocurrencies can be cumbersome and require manual intervention, which often results in delays, higher transaction fees, and an increased risk of errors.

[0003] Existing custodial cryptocurrency systems are designed to automatically convert cryptocurrencies into a single blockchain token. The system utilizes a combination of hot and cold storage to manage custodial private keys. The system processes blockchain transactions by determining network fees, signing the transactions with the appropriate custodial private keys, and broadcasting the transactions as part of a batch, thereby reducing block confirmation monitoring resources. The system does not cater to the individual preferences or behaviors of users, which means that its conversion process is more general. The system's handling of conversions is limited by its inability to adapt to the volatility of the assets being converted. The system predicts network fees but does not take into account the fluctuating value of the converted cryptocurrency. Another shortcoming of the system is its security approach. Although it uses hot and cold storage to protect private keys, the system cannot effectively protect the conversion of cryptocurrencies.

[0004] Existing methods for managing cryptocurrency transactions within a cryptocurrency transfer service system. The process involves converting a certain amount of assets in a user's account into a virtual cryptocurrency asset backed by an equivalent value of the original asset. The system facilitates the transfer of virtual cryptocurrency from a first user to a second user not included in the initial user group, wherein the system's reserve ensures that the transfer is backed by assets or virtual currency associated with the user. This reserve is managed by a cryptocurrency account server, which also updates and debits the user's account as needed. The method also includes a rebalancing process that adjusts the reserve periodically or based on specific conditions.

[0005] Existing methods mainly focus on cryptocurrency asset management, converting traditional assets (such as legal tender, securities or commodities) into virtual cryptocurrency assets and facilitating their transfer. A significant disadvantage of this method is securities investment decision support, which inherently brings higher volatility and market risk. The volatility of cryptocurrency prices may bring additional uncertainty and complexity to the management of reserves and user transactions. In addition, this method relies on the cryptocurrency reserve system, and the need for periodic rebalancing may lead to increased operational overhead, especially when the reserves are not effectively managed to take into account changes in asset values. Existing methods need to manage private keys and ensure the security of virtual currency transactions, which may introduce vulnerabilities, especially if the cryptocurrency system is not robust enough to hacker attacks or fraud. This method relies on a reserve system that may not be flexible or adaptable when managing a diversified portfolio of assets. The rebalancing mechanism of this method is reactive and depends on predefined cycles or specific conditions. This reactive nature may hinder its ability to respond quickly to market changes in the volatile cryptocurrency market, especially.

[0006] The electronic payment processing system enables users to pay for goods or services using securities from their brokerage accounts. The user selects a brokerage account and picks securities, and the system checks the value of the securities to determine if it is sufficient for payment. If sufficient, the securities are sold or transferred to settle the payment. This system operates using static valuation checks and does not predict future security values. This system lacks real-time conversion optimization. This system does not use AI to analyze user behavior or predict asset values ​​over time, making it less efficient and unsuitable for managing stocks. It is also limited in remote areas and does not support secure smart contracts for transactions.

[0007] Existing peer-to-peer (P2P) payment processing platforms enable merchants to receive payments in both cryptocurrencies and fiat currencies. Merchants use point-of-sale (POS) applications to split payments between cryptocurrencies and fiat currencies. The system securely manages the private keys of merchant wallets and transfers funds accordingly. This solution focuses on cryptocurrencies and fiat payments, but does not support the valuation or conversion of other asset types, such as stocks. This solution lacks AI-based prediction and optimization of asset conversions. This system cannot manage different assets, which reduces its versatility.

[0008] Existing blockchain-based resource trading systems record transactions on the blockchain and predict future resource prices (e.g., electricity or products) based on demand. It uses cryptocurrency to complete transactions and securely track payments and transaction data. While the system provides basic transaction security and price prediction for resources, it does not handle volatile assets like stocks or securities. It lacks AI-driven personalization or real-time prediction of asset values.

[0009] The existing distributed bond trading system facilitates secure bond trading at mid-market prices. The system uses encrypted trade details, timestamps, and matching algorithms to match buy and sell orders. Trades are enriched with third-party prices and executed based on confirmations. This system is specific to bond trading and lacks functionality for converting volatile assets such as stocks into other asset types. It does not utilize AI for real-time prediction or optimization, nor does it provide a quantum-resistant blockchain for enhanced security. The system cannot analyze user preferences or behaviors to personalize conversions.

[0010] Existing payment systems often rely on cloud-based data processing, which can introduce latency, security risks, and privacy issues. In addition, reliance on an internet connection can limit functionality and hinder the user experience.

[0011] Therefore, there remains a need for a more efficient system and method for alleviating and / or overcoming the disadvantages associated with current approaches. Summary of the invention

[0012] In view of the foregoing, an embodiment of the present invention provides a method for converting a volatile asset into another asset securely and in real time using an artificial intelligence (AI) model in a quantum-resistant blockchain network. The method includes receiving a volatile asset conversion request and a user preference from a user through a user device by a quantum computing volatile payment (volatilepay) payment gateway (VP-PG) server. The volatile asset conversion request includes details of the volatile asset to be traded. The user preferences include a conversion threshold and an asset preference. The method includes personalizing the AI ​​model by the quantum-resistant blockchain network by analyzing the user preferences and the user's real-time behavior pattern and historical data and identifying patterns and correlations between the user preferences and the user's real-time behavior pattern and historical data to personalize the AI ​​model. The method includes predicting the value of each volatile asset over time using a personalized AI model by the quantum-resistant blockchain network. Based on the user preferences and the volatile asset to be traded, the behavior value of the volatile asset is predicted by analyzing real-time volatile asset data received from at least one volatile asset server. The real-time volatile asset data is analyzed using quantum computing principles. The method includes determining, by the quantum-resistant blockchain network, an optimal time to convert the volatile asset based on a predicted value of each volatile asset over time. The method includes converting, by the quantum-resistant blockchain network, each volatile asset into another asset preferred by the user at the determined optimal time. The method includes generating a smart contract on the quantum-resistant blockchain network to protect the conversion of each volatile asset to another asset. The smart contract includes conditions for converting the volatile asset.

[0013] In some embodiments, the volatile asset conversion request is initiated when the identity (ID) of the user has been verified through the quantum-resistant blockchain network using biometric authentication. The identity (ID) of the user is verified using a zero-knowledge proof (ZKP) method.

[0014] In some embodiments, the method includes accessing historical data of the user using a ZKP approach when personalizing the AI ​​model.

[0015] In some embodiments, the quantum computing principles refer to using quantum mechanics to simultaneously analyze multiple scenarios when predicting the value of each volatile asset by employing quantum parallelism and quantum entanglement, thereby improving the computing power of the personalized AI model.

[0016] In some embodiments, the method includes enabling volatile asset conversion via satellite IoT, thereby enabling high-speed volatile asset-based payment processing in remote areas. The satellite IoT is linked to a quantum computing VP-PG server associated with the quantum-resistant blockchain network.

[0017] In some embodiments, the personalized AI model utilizes at least one of option pricing, derivatives trading, or over-the-counter (OTC) derivatives methodologies to predict the value of each volatile asset over time.

[0018] In some embodiments, when the predicted value of a volatile asset meets a collateral threshold, the volatile asset is used as collateral by generating a smart contract.

[0019] In some embodiments, the method further comprises: (i) receiving at the quantum computing VP-PG server a mutable asset transfer request from the user device that has scanned a quick response (QR) code linked to an entity identity (ID); (ii) processing the mutable asset transfer request at the VP-PG server using at least one of the quantum computing methods to verify transaction data; and (iii) securely transferring the asset from the user's converted mutable asset to the entity ID by generating the smart contract. The mutable asset transfer request comprises at least one of a digital signature, an asset to be transferred, the transaction data, and the user's converted mutable asset.

[0020] In some embodiments, the method further comprises generating an invoice between the user and the entity using robotic process automation (RPA) when the asset is transferred to the entity ID.

[0021] In one aspect, a system for converting a volatile asset into another asset securely and in real time using an artificial intelligence (AI) model in a quantum-resistant blockchain network. The system includes a quantum computing volatile payment gateway (VP-PG) server. The quantum computing VP-PG server receives a volatile asset conversion request and a user preference from a user through a user device. The volatile asset conversion request includes details of the volatile asset to be traded. The user preferences include a conversion threshold and an asset preference. The quantum computing VP-PG server is communicatively connected to the quantum-resistant blockchain network. The quantum-resistant blockchain network includes: a memory including an instruction set; and a processor. The processor is configured to personalize the AI ​​model by analyzing user preferences and the user's real-time behavior patterns and historical data and identifying patterns and correlations between the user preferences and the user's real-time behavior patterns and historical data to personalize the AI ​​model. The processor is configured to predict the value of each volatile asset over time using the personalized AI model. Based on the user preferences and the volatile assets to be traded, the value of the volatile asset is predicted by analyzing real-time volatile asset data received from at least one volatile asset server. The real-time volatile asset data is analyzed using quantum computing principles. The processor is configured to determine an optimal time to convert each volatile asset based on a predicted value of each volatile asset over time. The processor is configured to convert each volatile asset into another asset preferred by the user at the determined optimal time. The processor is configured to generate a smart contract on the quantum-resistant blockchain network to protect the conversion of each volatile asset to another asset. The smart contract includes conditions for converting the volatile asset.

[0022] These and other aspects of the embodiments herein will be better understood and appreciated when considered in conjunction with the following description and accompanying drawings. However, it should be understood that the following description is provided as an illustration rather than as a limitation when indicating the preferred embodiments and many of their specific details. Many changes and modifications may be made within the scope of the embodiments herein without departing from the spirit of the embodiments herein, and the embodiments herein include all such modifications. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The embodiments herein will be better understood from the following detailed description with reference to the accompanying drawings, in which:

[0024] Figure 1 A block diagram illustrating a system for converting a volatile asset into another asset securely and in real time using an artificial intelligence (AI) model in a quantum-resistant blockchain network according to some embodiments herein;

[0025] Figure 2 A block diagram illustrating a quantum-resistant blockchain network according to some embodiments herein;

[0026] Figure 3 According to some embodiments of the present invention, Figure 1 Exploded view of the system;

[0027] Figure 4A shows an exemplary user interface view for a secure login system according to some embodiments herein;

[0028] Figure 4B illustrates an exemplary user interface view for initiating various actions in the system according to some embodiments herein;

[0029] Figure 4C illustrates an exemplary user interface view for selecting a particular volatile asset to convert according to some embodiments herein;

[0030] Figure 4D showing an exemplary user interface view for configuring preferences related to conversion of a volatile asset selected in a previous step according to some embodiments herein;

[0031] Figure 4E shows an exemplary user interface view for summarizing details of a volatile asset conversion request according to some embodiments herein;

[0032] Figure 4F An exemplary user interface view illustrating smart contract terms and conditions associated with a volatile asset conversion according to some embodiments herein;

[0033] Figure 4G showing an exemplary user interface view according to some embodiments herein requiring a user to verify their permission and agreement to terms and conditions before a transaction is processed;

[0034] Figure 4H showing an exemplary user interface view displaying blockchain transaction details for a selected and converted asset according to some embodiments herein;

[0035] Figure 5A and 5B is a flow chart illustrating a method for converting a volatile asset securely and in real time into another asset using an artificial intelligence (AI) model in a quantum-resistant blockchain network according to some embodiments herein; and

[0036] Figure 6 is a schematic diagram of a computer architecture according to an embodiment of the present invention. DETAILED DESCRIPTION

[0037] The embodiments and various features herein and their advantageous details are explained more fully with reference to non-limiting embodiments, which are shown in the accompanying drawings and described in detail in the following description. Descriptions of well-known components and processing techniques are omitted so as not to unnecessarily obscure the embodiments herein. The examples used herein are only to promote an understanding of the manner in which the embodiments herein can be practiced, and to further enable those skilled in the art to practice the embodiments herein. Therefore, the examples should not be understood to limit the scope of the embodiments herein.

[0038] As mentioned, according to some embodiments herein, there is a method and system for converting a volatile asset into another asset securely and in real time using an artificial intelligence (AI) model in a quantum-resistant blockchain network. Now referring to the drawings, and more particularly to Figures 1 to 6 , wherein like reference numerals indicate corresponding features throughout the drawings, there is shown a preferred embodiment.

[0039] Figure 1 A block diagram of a system 100 for converting a volatile asset into another asset securely and in real time using an artificial intelligence (AI) model 114 in a quantum-resistant blockchain network 108 according to some embodiments herein is shown. The system 100 includes a user device 102, a quantum computing volatile payment gateway (VP-PG) server 104, and a quantum-resistant blockchain network 108. The user device 102 is communicatively connected to the quantum computing VP-PG server 104 via a network 106. The user uses the user device 102 to provide a volatile asset conversion request and user preferences to the quantum computing VP-PG server 104. The volatile asset conversion request includes details of the volatile asset to be traded.

[0040] In some embodiments, the system 100 classifies volatile assets based on the type of volatile asset (e.g., the stock's industry, market capitalization, or other relevant investment factors). This classification enables efficient handling of stocks and investment vehicles during transactions. For example, when a user uses a digital wallet to pay for goods or services with stocks, the system 100 identifies the category of assets being used. This identification can be performed by, for example, analyzing the stock's stock symbol, referencing a database of listed stocks with corresponding asset classifications, or using a machine learning algorithm to classify stocks based on their inherent characteristics.

[0041] After determining the volatile asset class, the digital wallet utilizes this information to process the payment. For example, if the payment involves large-cap technology stocks, the system 100 can automatically convert the stocks into cash or cryptocurrency using a conversion rate customized for that asset class. Similarly, if the payment involves mid-cap pharmaceutical stocks, the system 100 applies an appropriate conversion rate to ensure an accurate and fair valuation of the transaction.

[0042] The user preferences include conversion thresholds and asset preferences. The user device 102 can be a handheld, mobile phone, e-reader (Kindle), personal digital assistant (PDA), tablet computer, laptop, computer, electronic notebook or smart phone. In some embodiments, the quantum computing VP-PG server 104 receives a volatile asset transfer request from a user device 102 that has scanned a quick response (QR) code linked to an entity identity (ID). The AI ​​model 114 is trained using stock market data, currency data, news, social media data, and derivative and option market data. Stock data includes stock prices, trading volume, market capitalization, and historical records of other financial metrics associated with stocks traded on various exchanges. Currency data includes income, earnings, profit margins, GDP growth, inflation rates, interest rates, and financial ratios of entities. News and social media data include news articles and social media posts related to stocks.

[0043] The quantum computing VP-PG server 104 includes a processor and a non-transitory computer-readable storage medium (or memory) storing a database. The database may store one or more instruction sequences that, when executed by the processor, generate a smart contract on the quantum-resistant blockchain network 108 to protect the conversion of each volatile asset to another asset. The included quantum computing VP-PG server 104 may be a handheld device, a mobile phone, an e-reader, a personal digital assistant (PDA), a tablet, a laptop, a computer, an electronic notebook, or a smart phone. The network 106 may be a wired or wireless network based on at least one of a 2G protocol, a 3G protocol, a 4G protocol, or a 5G protocol, Bluetooth low energy (BLE), near field communication (NFC), Bluetooth, Wi-Fi, and a narrowband Internet of Things protocol (NBIoT), or a combination of wired and wireless networks or the Internet. The network 106 may be the Internet. The quantum computing VP-PG server 104 is hosted on a cloud platform. The quantum-resistant blockchain network 108 is hosted on a cloud platform. The quantum-resistant blockchain network 108 includes an AI model 114.

[0044] The quantum-resistant blockchain network 108 is communicatively connected to the quantum computing VP-PG server 104 to receive volatile asset conversion requests and user preferences. When the identity (ID) of the user is verified by the quantum-resistant blockchain network 108 using biometric authentication, the quantum-resistant blockchain network 108 initiates a volatile asset conversion request. The volatile asset transfer request includes at least one digital signature, the asset to be transferred, transaction data, and the user's converted volatile asset. The quantum-resistant blockchain network 108 verifies the identity (ID) of the user using a zero-knowledge proof (ZKP) method. ZKP is a cryptographic method that allows one party (prover or user) to prove to another party (verifier) ​​that a statement is true without revealing any additional information other than the fact that the statement is indeed true.

[0045] The quantum-resistant blockchain network 108 processes the volatile asset transfer request at the VP-PG server 104 using at least one of the quantum computing methods to verify the transaction data. The quantum computing method can be a quantum gate model, quantum annealing, quantum parallelism, quantum algorithm, topological quantum computing, adiabatic quantum computing, quantum error correction, quantum simulation, or quantum machine learning. For example, using the mobile application of the system 100 on the user device 102, user A initiates a request to convert the volatile asset B into USD. The request includes the volatile asset (stock) to be converted, the user's minimum conversion rate preference (e.g., $30,000 / share), and the target asset (USD). The system uses biometric verification (e.g., fingerprint scanning) to authenticate user A. Verification is securely processed using zero-knowledge proof (ZKP) on the quantum-resistant blockchain network 108 to ensure the privacy and identity security of user A.

[0046] The quantum-resistant blockchain network 108 personalizes the AI ​​model 114 by analyzing the user preferences and the user's real-time behavior patterns and historical data and identifying patterns and correlations between the user preferences and the user's real-time behavior patterns and historical data to personalize the AI ​​model 114. The quantum-resistant blockchain network 108 accesses the user's historical data using a ZKP approach when personalizing the AI ​​model 114.

[0047] The quantum computing VP-PG server 104 analyzes the real-time behavior, preferences, and historical data of user A to personalize the AI ​​model 114. The quantum-resistant blockchain network 108 uses the personalized AI model 114 to predict the value of each volatile asset over time. The AI ​​model 114 predicts the value of the volatile asset by analyzing the real-time volatile asset data received from at least one volatile asset server based on the user preferences and the volatile assets to be traded. The real-time volatile asset data is analyzed using quantum computing principles. The quantum computing principles refer to the use of quantum mechanics to simultaneously analyze multiple scenarios when predicting the value of each volatile asset by adopting quantum parallelism and quantum entanglement, thereby improving the computing power of the personalized AI model. The personalized AI model uses at least one option pricing, derivative trading, or over-the-counter (OTC) derivative method to predict the value of each volatile asset over time.

[0048] In the system 100, the AI ​​model 114 is used to evaluate the value of available stocks with enhanced accuracy by utilizing advanced machine learning and data analysis techniques. The AI ​​model 114 processes large amounts of data from various sources including stock exchanges, news platforms, and social media to extract key insights. By identifying relevant information such as stock prices, company financial metrics, and market trends, the system 100 generates actionable data to aid in stock evaluation and trading decisions.

[0049] The system 100 identifies complex patterns in stock data. This pattern recognition capability enables trends to be identified and helps predict future stock prices with greater accuracy. In addition, the system 100 employs predictive analytics to predict stock values ​​based on historical data and emerging market trends. The system 100 incorporates natural language processing (NLP) techniques to analyze unstructured text data from financial news articles and social media platforms. This functionality allows the system 100 to assess market sentiment, thereby providing a deeper understanding of factors that may affect stock prices. In addition, the use of deep learning algorithms facilitates the development of AI models 114 that are capable of identifying complex interdependencies between various factors that affect stock value, thereby improving the overall accuracy and reliability of stock analysis and predictions.

[0050] The personalized AI model 114 analyzes the historical transactions of user A (e.g., user A typically converts virtual currency when the price of Bitcoin rises above a certain threshold). The personalized AI model 114 evaluates real-time market data from a cryptocurrency server and uses quantum computing principles such as quantum parallelism (to analyze multiple scenarios simultaneously) and quantum entanglement (to find correlations in large data sets) to predict trends. The personalized AI model 114 predicts that the stock price of volatile asset B may fluctuate between $29,800 and $31,500 in the next 3 hours, with the highest predicted price of $31,200 occurring within 90 minutes.

[0051] The quantum-resistant blockchain network 108 determines the optimal time to convert each volatile asset based on its predicted value over time. The VP-PG server 104 calculates the optimal time for conversion based on user A's preference for a minimum rate of $30,000 / share B. The AI ​​model 114 predicts that stock B will reach $31,200 in 90 minutes. When the stock B price reaches or exceeds $31,000, the server 104 schedules the conversion to occur automatically to maximize the user's profit while minimizing risk.

[0052] The quantum-resistant blockchain network 108 converts each volatile asset into another asset preferred by the user at the determined optimal time. The quantum-resistant blockchain network 108 generates a smart contract on the quantum-resistant blockchain network to protect the conversion of each volatile asset to another asset. The smart contract includes conditions for converting volatile assets. Once the stock value is evaluated, a smart contract is created between the seller and the buyer of the stock on the quantum-resistant blockchain network 108. A smart contract is a self-executing contract that contains the terms and conditions of the transaction and is stored on the blockchain, thereby ensuring its immutability and transparency. For example, at the predicted optimal time, the price of stock B reaches $31,100. The system 100 automatically performs the conversion. A smart contract is generated on the quantum-resistant blockchain network 108 to protect the transaction. The details in the smart contract include the conversion rate ($31,100 / share B), the timestamp of the conversion, and the amount of the conversion (stock B = $62,200). The system 100 ensures that the conversion process is immutable, tamper-proof, and completely transparent on the blockchain. After the conversion, the resulting USD ($62,200) is deposited into the digital wallet module of user A, which is securely linked to the blockchain network. The quantum-resistant blockchain network 108 sends a notification to the user device 102 confirming the successful conversion.

[0053] The quantum-resistant blockchain network 108 enables volatile asset conversion through satellite Internet of Things (IoT), thereby enabling high-speed volatile asset-based payment processing in remote areas. The satellite IoT is linked to the quantum computing VP-PG server 104.

[0054] When the predicted value of the volatile asset meets the collateral threshold, the volatile asset is used as collateral by generating a smart contract. The quantum-resistant blockchain network 108 securely transfers the volatile asset from the user's converted volatile asset to the entity ID through the smart contract. In some embodiments, the system includes (i) a brokerage host for executing stock transactions and changing the status of transactions in the smart contract, and (ii) an investment host for executing funding transactions and changing the status of transactions in the smart contract, and (iii) an intermediary bank host coupled to the quantum-resistant blockchain network 108 and used to use the smart contract as collateral for financing according to the established funding transaction.

[0055] In some embodiments, the system 100 is used to simplify the payment process between buyers and sellers, including entities or individuals. This is achieved by utilizing invoices stored within a hybrid cloud architecture integrated with the Internet of Things (IoT). The system 100 also includes robotic process automation (RPA) to facilitate the transaction process, enabling customers to utilize available stock to settle payments for goods and services in real time. This process improves the efficiency and automation of payment processing and reduces the manual intervention required for transaction management.

[0056] When an asset is transferred to an entity ID, the quantum-resistant blockchain network 108 generates an invoice between the user and the entity using RPA. RPA interacts directly with the system's applications to perform tasks such as data entry, processing transactions, and generating reports with high speed and accuracy.

[0057] Figure 2 A block diagram of a quantum-resistant blockchain network 108 according to some embodiments of the present invention is shown. The quantum-resistant blockchain network 108 includes an artificial intelligence (AI) model 114, a volatile asset value prediction module 202, an optimal time determination module 204, a volatile asset conversion module 206, a smart contract generation module 208, and a database 200 including an instruction set. The quantum-resistant blockchain network 108 receives a volatile asset conversion request and user preferences from a user through a quantum computing volatile payment gateway (VP-PG) server. The volatile asset conversion request includes details of the volatile asset to be traded. The user preferences include conversion thresholds and asset preferences. The quantum-resistant blockchain network 108 personalizes the AI ​​model 114 by analyzing user preferences, the user's real-time behavior patterns and historical data and identifying patterns and correlations between user preferences and the user's real-time behavior patterns and historical data. The volatile asset value prediction module 202 uses the personalized AI model to predict the value of each volatile asset over time. Based on the user preferences and the volatile asset to be traded, the value of the volatile asset is predicted by analyzing real-time volatile asset data received from at least one volatile asset server. The real-time volatile asset data is analyzed using quantum computing principles.

[0058] The optimal time determination module 204 determines the optimal time to convert the volatile asset based on the predicted value of each volatile asset over time. The volatile asset conversion module 206 converts each volatile asset into another asset preferred by the user at the determined optimal time. The smart contract generation module 208 generates a smart contract on the quantum-resistant blockchain network to protect the conversion of each volatile asset to another asset. The smart contract includes conditions for converting volatile assets. Once the terms of the smart contract are met, such as making a payment and transferring the stock to the buyer, the smart contract automatically executes the transaction. This eliminates the need for intermediaries such as banks or brokers and reduces the risk of fraud or errors in transactions. The conditions for conversion, such as the exchange rate and the currency to be received. Once the terms of the smart contract are met, the conversion is automatically performed and the buyer receives the agreed currency. The use of smart contracts in volatile assets ensures that transactions are automatically and transparently executed without the need for intermediaries and reduces the risk of fraud or errors in transactions.

[0059] Figure 3 A method of converting volatile assets into other assets securely and in real time using quantum-resistant blockchain networks, artificial intelligence (AI), and quantum computing principles according to some embodiments of the present invention is shown. Figure 1 Exploded view of system 100. System 100 includes quantum-resistant blockchain network 108, quantum computing VP-PG server 104, database 200, user device 102, and robotic process automation module 318. Quantum-resistant blockchain network 108 is linked to stock processing module 302, encryption processing module 304, and another currency processing module 306. Quantum computing VP-PG server 104 is linked to satellite Internet Internet (IoT) 308. Another currency processing module 306 includes wallet module 312, card module 314, and client location module 316. Satellite IoT 308 is linked to user device 310. User device 310 is in a remote area. System 100 ensures secure, efficient, and personalized asset conversion by leveraging advanced AI-driven predictions, biometric authentication, satellite IoT, and smart contract generation. The system is designed to operate seamlessly across a variety of asset types including cryptocurrencies, fiat currencies, and other financial instruments.

[0060] The satellite IoT 308 network facilitates communication between the digital wallet module 312 and the quantum computer VP-PG server 104, especially in remote locations. This network utilizes low-power wide area network (LPWAN) technologies, such as LoRaWAN and Sigfox, which support long-range communications with minimal power consumption.

[0061] The operation of the satellite IoT 308 network involves sending LPWAN signals from the digital wallet module 312 to the satellite. The satellite IoT 308 relays these signals to the ground station connected to the VP-PG server 104. This configuration enables seamless data exchange between the components of the system 100, thereby ensuring uninterrupted functionality in areas with limited or no Internet connectivity. By utilizing the satellite IoT 308, the system 100 can provide payment processing to users in geographically isolated areas. In addition, the satellite IoT 308 network provides a secure and reliable communication infrastructure.

[0062] The quantum computing VP-PG server 104 receives a volatile asset conversion request and user preferences from the user device 102. The conversion request includes details of the volatile asset to be traded, while the user preferences specify thresholds and preferred assets. The VP-PG server 104 is communicatively connected to a quantum-resistant blockchain network 108, which protects the conversion process by generating a smart contract including predefined conditions. The VP-PG server 104 uses quantum computing principles, such as quantum parallelism and entanglement, to predict asset values ​​over time by analyzing real-time data, user preferences, and historical data from the volatile asset server. The system 100 not only supports real-time volatile asset conversion, but also facilitates secure asset transfer. The VP-PG server 104 processes the transfer request by verifying the transaction data using quantum computing methods. The transfer data includes a digital signature, transaction details, and the converted asset balance. Once verified, the asset is securely transferred to the recipient's entity ID, and a smart contract is generated to record the transaction.

[0063] The quantum-resistant blockchain network 108 ensures security and transparency of all transactions. The quantum-resistant blockchain network 108 uses biometric authentication and zero-knowledge proofs (ZKP) to verify user identities (IDs) while maintaining privacy. The ZKP approach also enables secure access to users' historical data to personalize the AI ​​models used by the VP-PG server. The AI ​​models analyze user preferences, real-time behavioral patterns, and historical data to identify patterns and correlations, thereby optimizing predictions of volatile asset conversions.

[0064] To enable remote accessibility, the system 100 includes a satellite IoT module 308 that facilitates high-speed asset conversion and payment processing in areas with limited connectivity. The satellite IoT module 308 communicates with the VP-PG server 104 and the blockchain network 108, thereby ensuring uninterrupted service even in remote locations. The system 100 also includes a cryptographic processing module 304 for managing cryptocurrency transactions and another currency processing module 306 for processing fiat currencies and other traditional financial instruments.

[0065] The user devices 102 and 310 serve as interfaces for initiating mutable asset conversion requests and transfers. Users can scan a quick response (QR) code linked to an entity's identity to enable secure transfers. The Quantum Computing Mutable Payment Gateway Platform (VP-PG) server 104 is implemented at merchant distribution points. Once integrated, the VP-PG server 104 facilitates transaction processing by generating a QR code for each payment initiated by the user. The user scans the QR code using his mobile device to start the payment process. The VP-PG server 104 then processes the transaction and updates its status on the blockchain network 108 when completed, thereby ensuring transparency and traceability.

[0066] The digital wallet module 312 maintains the converted asset balance, allowing the user to securely store and manage digital assets. The card module 314 ensures compatibility with traditional payment methods such as credit or debit cards for greater versatility.

[0067] The system 100 also integrates a robotic process automation (RPA) module 318 that automates workflows associated with asset conversion and transaction processing. For example, when an asset is transferred to an entity ID, the RPA module 318 automatically generates an invoice. Additionally, the client location module 316 verifies the user's geographic location, thereby adding a layer of fraud prevention and enabling geographic location-specific services.

[0068] The conversion process is driven by a quantum-resistant blockchain network 108, which determines the optimal time to convert volatile assets by analyzing the predicted values. The personalized AI model 114 within the server 104 uses techniques such as option pricing, derivative trading, or over-the-counter (OTC) derivatives to enhance the accuracy of predictions. In addition, the system 100 allows assets such as stocks to be used as collateral by generating smart contracts when the predicted asset value meets a predefined threshold.

[0069] Figure 4A An exemplary user interface 400A view for a secure login system according to some embodiments herein is shown. The user interface 400A depicts a "Username" field, a "Password" and a "Login" field. This input field allows the user to enter their username. This input field allows secure entry of the user's password. Upon clicking "Login", the user interface 400A displays a "Welcome Screen".

[0070] Figure 4BAn exemplary user interface 400B view for initiating various actions in the system according to some embodiments of the present invention is shown. The user interface 400B depicts a "User Profile, including a 'Profile Picture' and a 'User Name' ('TAN'), for user identification and personalization". A "Notification Icon" provides access to system notifications including reminders or updates relevant to the user, and "Action Buttons". The action buttons include a "New Conversion Request" button that enables a user to initiate a new stock conversion request, a "Transaction History Button" that allows a user to view the history of transactions or actions performed in the system, and a "Volatile Portfolio Overview" button that provides access to a detailed overview of the user's volatile asset portfolio. When the user selects "New Conversion Request" and then clicks "Submit", this button confirms the selected action and proceeds to the next step.

[0071] Figure 4C An exemplary user interface 400C view for selecting a specific volatile asset to be converted according to some embodiments of the present invention is shown. User interface 400C depicts a volatile asset selection drop-down menu. The drop-down menu is labeled "Select a volatile asset to be converted" and provides a list of available volatile assets for the user to select. The menu includes, for example, the following options: "Volatile Asset A", "Volatile Asset B", "Volatile Asset C", and "Volatile Asset D". For example, the user selects "Volatile Asset 2" and clicks "Submit". User interface 400C ensures that users can easily specify the assets they intend to process, thereby supporting accurate and efficient management of volatile assets.

[0072] Figure 4D An exemplary user interface 400D view is shown for configuring preferences related to the conversion of a volatile asset selected in a previous step according to some embodiments of the present invention. The user interface 400D depicts the volatile assets previously selected by the user, allowing for review or modification before proceeding. The user interface 400D depicts a preference configuration drop-down menu labeled "Enter Your Preferences," which allows the user to configure specific parameters for the conversion process. Options include: "Conversion Threshold," which means a threshold parameter that defines specific conditions for asset conversion; and "Asset Type to be Converted," which allows the user to specify the type of asset to be used for the conversion process. The user enters the "Conversion Threshold," confirms the configured preferences and "Asset Type to be Converted," and then initiates processing of the volatile asset by clicking "Submit."

[0073] Figure 4EAn exemplary user interface 400E view for summarizing the details of a volatile asset conversion request according to some embodiments of the present invention is shown. The user interface 400E depicts parameters related to the requested conversion, including: "Asset Conversion Details", which specifies the assets involved in the conversion process, i.e., stocks as source assets and Ethereum (ETH) as target assets; "Amount to be converted", i.e., the specific amount of the source volatile asset to be converted, i.e., volatile asset B; "Optimal Conversion Time", which is determined by the system for the conversion using AI-based predictions, and is displayed as 14:30 UTC on January 6, 2025; "Forecast Conversion Rate", which means the expected exchange rate, is displayed as volatile asset B = 15 ETH.

[0074] Figure 4F An exemplary user interface 400F view showing smart contract terms and conditions associated with volatile asset conversions according to some embodiments of the present invention. The user interface 400F depicts the terms and conditions, including: "AI-driven optimal timing", meaning that the conversion is performed at the optimal time determined by a personalized AI model on a quantum-resistant blockchain network; "Transaction fee", a fee of 0.1% of the transaction volume is applied; "Irreversibility of conversion", the conversion process is final and cannot be reversed once initiated and processed; "The user needs to verify the accuracy of the destination wallet address, as the system does not assume liability for errors in the provided address"; and "The smart contract complies with applicable regulations governing blockchain-based asset conversions".

[0075] Figure 4G An exemplary user interface 400G view is shown that requires a user to verify that they approve and agree to the terms and conditions before processing a transaction according to some embodiments herein. The user interface 400G depicts a verification checkbox: the user must explicitly agree to the terms and conditions of the conversion process and confirm that the provided destination wallet address is accurate. The user can proceed to complete the transaction by selecting a "Next" button.

[0076] Figure 4H An exemplary user interface 400H view showing blockchain transaction details of a selected and converted asset according to some embodiments of the present invention is shown. The user interface 400H depicts blockchain transaction details: "Transaction Hash: 0xabc1234def5678...xyz789", "Block Number: 12345678", "Timestamp: 2025-01-06 14:30:01 (UTC)", and "Status: Success".

[0077] Figure 5A and 5Bis a flow chart illustrating a method for converting a volatile asset into another asset securely and in real time using an artificial intelligence (AI) model in a quantum-resistant blockchain network according to some embodiments of the present invention. In step 502, a volatile asset conversion request and user preferences are received from a user via a user device by a quantum computing volatile payment gateway (VP-PG) server. The volatile asset conversion request includes details of the volatile asset to be traded. The user preferences include conversion thresholds and asset preferences. In step 504, the quantum-resistant blockchain network personalizes the AI ​​model by analyzing the user preferences and the user's real-time behavior patterns and historical data and identifying patterns and correlations between the user preferences and the user's real-time behavior patterns and historical data to personalize the AI ​​model.

[0078] In step 506, the quantum-resistant blockchain network predicts the value of each volatile asset over time using a personalized AI model. Based on the user preferences and the volatile assets to be traded, the value of the volatile assets is predicted by analyzing real-time volatile asset data received from at least one volatile asset server. The real-time volatile asset data is analyzed using quantum computing principles. In step 508, the quantum-resistant blockchain network determines the optimal time to convert the volatile assets based on the predicted value of each volatile asset over time. In step 510, the quantum-resistant blockchain network converts each volatile asset into another asset preferred by the user at the determined optimal time. In step 512, a smart contract is generated on the quantum-resistant blockchain network to protect the conversion of each volatile asset to another asset. The smart contract includes conditions for converting the volatile assets.

[0079] AI model processing occurs directly on the user's device. This on-device processing eliminates the need to send data to external cloud servers, thereby enhancing performance and privacy. AI models such as neural networks are initially trained in the cloud, but are then optimized to run locally on the device's processor. This allows AI features to operate offline, ensuring that user data, including photos, voice recordings, text input, and activity patterns, remains private and secure within the device. The system integrates on-device AI with hybrid cloud, IoT, and RPA technologies to optimize payment processing. AI processing capabilities within the device enable real-time processing of invoicing, payment tracking, and transaction verification without the need for continuous internet access. This localized processing enables a faster, safer, and more efficient user payment experience. In addition, hybrid cloud and IoT integration allows seamless synchronization between devices, ensuring that payment data remains accurate and up-to-date throughout the system.

[0080] Incorporating on-device AI models into payment platforms not only enhances privacy, security, and performance, but also enables a personalized, context-aware experience that adapts to the individual needs of each user. The use of on-device AI models provides several significant advantages: (i) user data remains confidential because user data is processed locally on the device, thereby reducing exposure to third-party cloud services; (ii) sensitive user data is stored on the device, thereby minimizing the risk of hacking or data breaches associated with cloud storage and transmission. The AI ​​model does not require an active Internet connection to function, and can achieve consistent and reliable intelligent features even in offline scenarios. On-device AI models allow for near-instantaneous processing, eliminating network latency and improving the overall responsiveness of the payment system. By analyzing user data locally, the system can provide context-aware AI recommendations, customized notifications, and personalized payment experiences for each user.

[0081] AI models are integrated into the user's device to optimize various functions related to payment processing and user interaction. Specific applications include: (i) voice assistants process certain queries locally, reducing the need to send voice data to the cloud, thereby enhancing privacy and response time; (ii) on-device AI models process camera input, including scene recognition, face detection and object identification, to enhance the quality of photos and videos associated with transaction materials; (iii) AI models run locally on the device to generate word predictions and text suggestions, making the user experience smooth during data entry; (iv) AI models embedded in the device process messages, searches and other text-based inputs to improve the accuracy of responses and transactions using natural language processing (NLP); (v) on-device AI models use augmented reality (AR) and virtual reality (VR) to support motion tracking, environmental analysis and point of interest identification, thereby enabling user interaction and immersion during payment processing.

[0082] By analyzing and processing large data sets from these sources, AI models can identify patterns and correlations, enabling them to make informed and accurate predictions about future stock prices and market trends. The integration of different data sources ensures that stocks are fully evaluated within the system.

[0083] Figure 6 is a schematic diagram of a system according to an embodiment of the present invention. Figures 1 to 5A and 5B in Figure 6A representative hardware environment for practicing the embodiments herein is depicted in . This schematic diagram shows the hardware configuration of the quantum-resistant blockchain network / quantum computing VP-PG server / computer system / computing device according to the embodiments herein. The system includes at least one processing device CPU 10, which can be interconnected to various devices such as random access memory (RAM) 12, read-only memory (ROM) 16 and input / output (I / O) adapter 18 via a system bus 14. The I / O adapter 18 can be connected to peripheral devices such as a disk unit 38 and a program storage device 40 that can be read by the system. The system can read the instructions of the present invention on the program storage device 40 and follow these instructions to execute the method of the embodiments herein. The system also includes a subject interface adapter 22, which connects a keyboard 28, a mouse 30, a speaker 32, a microphone 34 and / or other subject interface devices such as a touch screen device (not shown) to the bus 14 to collect subject input. Additionally, communications adapter 20 connects bus 14 to a data processing network 42, and display adapter 24 connects bus 14 to a display device 26 which provides a graphical user interface (GUI) 36 for outputting data according to embodiments herein, or which may be embodied as an output device such as a monitor screen, printer, or transmitter.

[0084] The foregoing description of specific embodiments will so fully reveal the general nature of the embodiments herein that by applying current knowledge, others may readily modify such specific embodiments and / or adapt them to various applications without departing from the general concepts, and such adaptations and modifications should and are intended to be understood within the meaning and range of equivalents of the disclosed embodiments. It should be understood that the phraseology or terminology employed herein is for the purpose of description and not limitation. Therefore, although the embodiments herein have been described in terms of preferred embodiments, those skilled in the art will recognize that the embodiments herein may be practiced by modifications within the spirit and scope.

Claims

1. A processor-implemented method for securely and in real-time converting a volatile asset into another asset using an artificial intelligence (AI) model in a quantum-resistant blockchain network, comprising: Receiving, by a quantum computing volatile payment gateway (VP-PG) server from a user via a user device, a volatile asset conversion request and user preferences, wherein the volatile asset conversion request includes details of a volatile asset to be traded, and wherein the user preferences include a conversion threshold and asset preferences; personalizing the AI ​​model by the quantum-resistant blockchain network by analyzing the user preferences and the user's real-time behavioral patterns and historical data and identifying patterns and correlations between the user preferences and the user's real-time behavioral patterns and historical data to personalize the AI ​​model; The quantum-resistant blockchain network uses a personalized AI model to predict the value of each volatile asset over time. wherein, based on the user preferences and the volatile asset to be traded, the value of the volatile asset is predicted by analyzing real-time volatile asset data received from at least one volatile asset server, wherein the real-time volatile asset data is analyzed using quantum computing principles; Determining, by the quantum-resistant blockchain network, an optimal time to convert each volatile asset based on a predicted value of the volatile asset over time; The quantum-resistant blockchain network converts each volatile asset into another asset preferred by the user at a determined optimal time; as well as A smart contract is generated on the quantum-resistant blockchain network to protect the conversion of each volatile asset to another asset, wherein the smart contract includes conditions for converting the volatile asset.

2. The processor-implemented method of claim 1 , wherein the volatile asset conversion request is initiated when the identity (ID) of the user has been verified through the quantum-resistant blockchain network using biometric authentication, wherein the identity (ID) of the user is verified using a zero-knowledge proof (ZKP) method.

3. The processor-implemented method of claim 1 , wherein the method comprises accessing the historical data of the user using the ZKP method when personalizing the AI ​​model.

4. The processor-implemented method of claim 1 , wherein the quantum computing principle refers to using quantum mechanics to simultaneously analyze multiple scenarios when predicting the value of each volatile asset by employing quantum parallelism and quantum entanglement to improve the computing power of the personalized AI model.

5. The processor-implemented method of claim 1, wherein the method comprises enabling the volatile asset conversion via satellite IoT, thereby enabling high-speed volatile asset-based payment processing in remote areas, wherein the satellite IoT is linked to a quantum computing VP-PG server associated with the quantum-resistant blockchain network.

6. The processor-implemented method of claim 1, wherein the personalized AI model utilizes at least one of option pricing, derivatives trading, or over-the-counter (OTC) derivatives methodologies to predict the value of each volatile asset over time.

7. The processor-implemented method of claim 1 , wherein when the predicted value of the volatile asset satisfies a collateral threshold, the volatile asset is used as collateral by generating the smart contract.

8. The processor-implemented method of claim 1 , wherein the method further comprises: receiving, at the quantum computing VP-PG server, a volatile asset transfer request from the user device that has scanned a quick response (QR) code linked to an entity identity (ID); processing the volatile asset transfer request at the VP-PG server using at least one of quantum computing methods to verify transaction data, wherein the volatile asset transfer request includes at least one of a digital signature, an asset to be transferred, the transaction data, and a converted volatile asset of the user; and The asset is securely transferred from the converted volatile asset of the user to the entity ID by generating the smart contract.

9. The processor-implemented method of claim 8, wherein the method further comprises generating an invoice between the user and the entity using robotic process automation (RPA) when the asset is transferred to the entity ID.

10. A system for converting a volatile asset into another asset securely and in real time using an artificial intelligence (AI) model in a quantum-resistant blockchain network, comprising: A quantum computing volatile payment gateway (VP-PG) server that receives a volatile asset conversion request and user preferences from a user via a user device, wherein the volatile asset conversion request includes details of a volatile asset to be traded, wherein the user preferences include a conversion threshold and asset preferences, wherein the quantum computing VP-PG server is communicatively connected to the quantum-resistant blockchain network, wherein the quantum-resistant blockchain network includes a memory including an instruction set; A processor that executes the instruction set and is configured to: personalizing the AI ​​model by analyzing user preferences and real-time behavioral patterns and historical data of the user and identifying patterns and correlations between the user preferences and the real-time behavioral patterns and historical data of the user to personalize the AI ​​model; Use personalized AI models to predict the value of each volatile asset over time, wherein, based on the user preferences and the volatile asset to be traded, the value of the volatile asset is predicted by analyzing real-time volatile asset data received from at least one volatile asset server, wherein the real-time volatile asset data is analyzed using quantum computing principles; determining an optimal time to convert each volatile asset based on the predicted value of the volatile asset over time; At the determined optimal time, converting each volatile asset into another asset preferred by the user; and A smart contract is generated on the quantum-resistant blockchain network to protect the conversion of each volatile asset to another asset, wherein the smart contract includes conditions for converting the volatile asset.

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