System for securely translating volatile assets in blockchain network using ai

Abstract

To provide a method of securely and real-time converting a volatile asset to another asset using an artificial intelligence (AI) model.SOLUTION: The method includes: receiving, by a user device, a volatile asset conversion request and user preferences from a user; for personalizing an AI model, identifying a pattern and a correlation between the user preferences and real-time behavior patterns and historical data of the user; predicting a value of each volatile asset over time by using a personalized AI model; determining an optimal time to transform each volatile asset based on a predicted value of the volatile asset over time; converting each volatile asset into another asset preferred by the user at the determined optimal time; and generating a smart contract on an anti-quantum block chain network for safe conversion from each volatile asset to another asset.SELECTED DRAWING: Figure 5B

Description

[Technical Field]

[0001] TECHNICAL FIELD Embodiments herein relate generally to blockchain and artificial intelligence (AI), and more particularly to systems and methods for secure, real-time conversion of a volatile asset into another asset in a quantum-resistant blockchain network using an artificial intelligence (AI) model. [Background technology]

[0002] The rapid growth of the cryptocurrency market and blockchain technology has created a growing need for efficient, secure, and user-friendly systems for managing and converting digital assets. As the variety of cryptocurrencies continues to expand, so too does the complexity of managing them across different blockchain protocols. Traditional methods of cryptocurrency conversion are cumbersome and require manual intervention, often resulting in delays, higher transaction fees, and an increased risk of error.

[0003] Existing custodial cryptocurrency systems are designed to automate the conversion of cryptocurrencies into a single blockchain token. The systems utilize a combination of hot and cold storage to manage custodial private keys. They handle blockchain transactions by determining network fees, signing transactions with the appropriate custodial private key, and broadcasting them as part of a batch, reducing block confirmation monitoring resources. The systems do not adapt to users' individual preferences or behavior, meaning their conversion process is generalized. The conversion system is limited by its inability to adapt to the volatility of the assets being converted. The systems predict network fees but do not consider the fluctuating value of the converted cryptocurrency. Another drawback of the systems is their security approach. While using hot and cold storage to protect private keys, the systems are unable to efficiently and securely perform cryptocurrency conversions.

[0004] A method for managing cryptocurrency transactions within a cryptocurrency transfer service system includes converting an amount of assets in a user 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 an initial user group, and a reserve in the system ensures that the transfer is backed by assets or virtual currency associated with the user. The 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 certain conditions.

[0005] Existing methods primarily focus on cryptocurrency asset management, converting traditional assets (e.g., fiat currency, securities, or commodities) into virtual cryptocurrency assets and facilitating their transfer. One significant drawback is that these methods are related to decision support for securities investments, which inherently involve high volatility and market risk. Fluctuations in cryptocurrency prices can introduce additional uncertainty and complexity into the management of reserves and user transactions. Furthermore, these methods rely on cryptocurrency reserve systems, and the need for periodic rebalancing can result in increased operational overhead, especially if reserves are not effectively managed to account for changes in asset values. Existing methods require the management of private keys and ensure the security of cryptocurrency transactions, which can lead to vulnerabilities, especially if the cryptocurrency system is not sufficiently robust against hacking or fraud. These methods rely on reserve systems that may lack flexibility or adaptability when managing a diversified portfolio of assets. The rebalancing mechanism of these methods reacts based on predefined time periods or specific conditions. This reactive nature can hinder the ability to quickly respond to market changes, particularly in volatile cryptocurrency markets.

[0006] An electronic payment processing system allows users to pay for goods or services using securities from their securities account. The user selects a securities account and chooses securities, and the system checks their value to determine whether they are sufficient for the payment. If they are, the securities are sold or transferred to settle the payment. The system operates on static valuation checks and does not predict future security values. The system lacks real-time conversion optimization. The system does not use AI to analyze user behavior or predict asset values over time, resulting in inefficient and inadequate management of stocks. It also has issues with remote locations and does not support secure smart contracts for transactions.

[0007] An existing peer-to-peer (P2P) payment processing platform allows merchants to receive payments in crypto and fiat. Merchants use a point-of-sale (POS) application to split payments between crypto and fiat. The system securely manages the private keys of merchant wallets and transfers funds accordingly. This solution focuses on crypto and fiat payments but does not support the valuation or conversion of other asset types, such as stocks. It lacks AI-based prediction and optimization for asset conversion. This system cannot manage a variety of assets and its versatility is low.

[0008] Existing blockchain-based resource transaction systems record transactions on the blockchain and predict future resource prices (e.g., electricity or manufactured goods) based on demand. They use cryptocurrencies to complete transactions and securely track payment and transaction data. While this 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] Existing decentralized bond trading systems facilitate secure bond trading at mid-market prices. They match buy and sell orders using encrypted transaction details, timestamps, and matching algorithms. Trades are executed based on third-party pricing and confirmation. The system is specific to bond trading and lacks the ability to convert volatile assets, such as stocks, into other asset types. It does not utilize AI for real-time predictions or optimization, or offer a quantum-resistant blockchain for enhanced security. The system cannot analyze user preferences or behavior to personalize conversions.

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

[0011] Thus, there remains a need for more efficient systems and methods to mitigate and / or overcome the drawbacks associated with current methods. Summary of the Invention [Problem to be solved by the invention]

[0012] (Means for solving the problem) In view of the above, one embodiment of the present disclosure provides a method for securely converting a volatile asset into another asset in real time using an artificial intelligence (AI) model in a quantum-resistant blockchain network. The method includes receiving, by a quantum computing volatile pay payment gateway (VP-PG) server, a volatile asset conversion request and user preferences from a user through a user device. The volatile asset conversion request includes details of the volatile asset to be exchanged. The user preferences include a conversion threshold and an asset preference. The method includes analyzing, by the quantum-resistant blockchain network, the user preferences, as well as real-time behavioral patterns and historical data of the user, and further personalizing the AI model by 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. The method includes predicting, by the quantum-resistant blockchain network, the value of each volatile asset over time using the personalized AI model. The behavioral value of the volatile asset is predicted by analyzing real-time volatile asset data received from at least one volatile asset server based on user preferences and the volatile asset being exchanged. The real-time volatile asset data analyzed using quantum computing principles. The method includes determining, via a quantum-resistant blockchain network, an optimal time to transform each volatile asset based on the predicted value of the volatile asset over the optimal time. The method includes transforming, via 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 securely negotiate each volatile asset into another asset. The smart contract includes conditions for transforming the volatile asset.

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

[0014] In some embodiments, the method includes using ZKP methods to access historical data of the user when personalizing the AI model.

[0015] In some embodiments, quantum computing principles refer to the use of quantum mechanics to improve the computational power of personalized AI models in predicting the value of each volatile asset by utilizing quantum parallelism and quantum entanglement to analyze multiple scenarios simultaneously.

[0016] In some embodiments, the method includes enabling volatile asset negotiation, thereby enabling high-speed volatile asset-based payment processing at a remote location, through a satellite IoT linked to a quantum computing VP-PG server associated with a quantum-resistant blockchain network.

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

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

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

[0020] In some embodiments, the method further includes 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 securely converting a volatile asset into another asset in real time on a quantum-resistant blockchain network using an artificial intelligence (AI) model is provided. The system includes a quantum computing volatile pay payment gateway (VP-PG) server. The quantum computing VP-PG server receives a volatile asset conversion request and user preferences from a user through a user device. The volatile asset conversion request includes details of the volatile asset to be exchanged. The user preferences include a conversion threshold and an asset preference. The quantum computing VP-PG server is communicatively connected to a quantum-resistant blockchain network. The quantum-resistant blockchain network includes a memory including a set of instructions and a processor. The processor is configured to analyze the user preferences, as well as real-time behavioral patterns and historical data of the user, and further personalize the AI model by 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. The processor is configured to predict the value of each volatile asset over time using the personalized AI model. The value of the volatile asset is predicted by analyzing real-time volatile asset data received from at least one volatile asset server based on user preferences and the volatile asset to be exchanged. The real-time volatile asset data analyzed using quantum computing principles. The processor is configured to determine an optimal time to convert each volatile asset based on the predicted value of the 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 a quantum-resistant blockchain network to securely negotiate each volatile asset into another asset. The smart contract includes conditions for converting the volatile asset.

[0022] These and other aspects of the embodiments herein will be better appreciated and understood when considered in conjunction with the following description and the accompanying drawings. It should be understood, however, that the following description, while indicating preferred embodiments and many specific details thereof, is given by way of illustration and not limitation. Many changes and modifications may be made within the scope of the embodiments herein without departing from the spirit thereof, and the embodiments herein include all such modifications. [Brief explanation of the drawings]

[0023] The embodiments herein will be best understood from the following detailed description taken in conjunction with the drawings, in which:

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

[0025] [Figure 2] FIG. 1 is a block diagram of a quantum-resistant blockchain network, according to some embodiments of the present disclosure.

[0026] [Figure 3] FIG. 2 is an exploded view of the system of FIG. 1, according to some embodiments herein.

[0027] [Figure 4A] FIG. 1 is a diagram of an exemplary user interface for a secure login system, according to some embodiments herein.

[0028] [Figure 4B] 1A-1C are diagrams of exemplary user interfaces for initiating various actions within the system, according to some embodiments herein.

[0029] [Figure 4C]FIG. 10 is an illustration of an exemplary user interface for selecting a particular volatile asset to be converted, according to some embodiments herein.

[0030] [Figure 4D] FIG. 10 is a diagram of an exemplary user interface for configuring preferences related to the conversion of the volatile asset selected in the previous step, according to some embodiments herein.

[0031] [Figure 4E] FIG. 10 is an illustration of an exemplary user interface for summarizing details of a volatile asset conversion request, according to some embodiments herein.

[0032] [Figure 4F] FIG. 10 is an illustration of an example user interface of smart contract terms of use associated with volatile asset transformation, according to some embodiments herein.

[0033] [Figure 4G] FIG. 10 is a diagram of an exemplary user interface that requests confirmation that a user has accepted and agreed to terms of use before a transaction is processed, according to some embodiments herein.

[0034] [Figure 4H] FIG. 10 is an illustration of an exemplary user interface displaying blockchain transaction details of a selected and converted asset, according to some embodiments herein.

[0035] [Figure 5A] 1 is a flow diagram illustrating a method for securely converting a volatile asset into another asset in real time in a quantum-resistant blockchain network using an artificial intelligence (AI) model, according to some embodiments herein. [Figure 5B]1 is a flow diagram illustrating a method for securely converting a volatile asset into another asset in real time in a quantum-resistant blockchain network using an artificial intelligence (AI) model, according to some embodiments herein.

[0036] [Figure 6] FIG. 1 is a schematic diagram of a computer architecture according to embodiments herein. DETAILED DESCRIPTION OF THE INVENTION

[0037] The embodiments herein, as well as various features and advantageous details thereof, will be more fully described with reference to the non-limiting embodiments shown in the accompanying drawings and detailed in the following description. Descriptions of well-known components and processing techniques will be omitted so as not to unnecessarily obscure the embodiments herein. The examples used herein are intended only to facilitate understanding of how the embodiments herein can be implemented and to further enable those skilled in the art to implement the embodiments herein. Therefore, the examples should not be construed as limiting the scope of the embodiments herein.

[0038] As noted above, there are methods and systems for securely converting a volatile asset into another asset in a quantum-resistant blockchain network in real time using an artificial intelligence (AI) model according to some embodiments of the present disclosure. Referring now to the drawings, and more particularly to Figures 1-6, like reference numerals indicate corresponding features consistently throughout the figures and indicate preferred embodiments.

[0039] 1 shows a block diagram of a system 100 for securely converting a volatile asset into another asset in real time on a quantum-resistant blockchain network 108 using an artificial intelligence (AI) model 114, according to some embodiments herein. The system 100 includes a user device 102, a quantum computing volatile pay 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 through a network 106. The user 102 provides a volatile asset conversion request and user preferences to the quantum computing VP-PG server 104 using the user device 102. The volatile asset conversion request includes details of the volatile asset to be exchanged.

[0040] In some embodiments, system 100 categorizes volatile assets based on type, such as the stock's industry, market capitalization, or other relevant investment factors. This categorization enables efficient handling of stocks and investment products during transactions. For example, when a user uses a digital wallet to pay for goods or services with stocks, system 100 identifies the class of asset being used. This identification can be performed through methods such as analyzing the stock's ticker symbol, referencing a database of listed stocks with corresponding asset classifications, or employing machine learning algorithms to classify stocks based on their inherent characteristics.

[0041] After determining the volatile asset class, the digital wallet uses this information to process the payment. For example, if the payment involves large-cap technology stocks, the system 100 may automatically convert the stocks into cash or cryptocurrency using a conversion rate tailored to that asset class. Similarly, if the payment involves mid-cap pharmaceutical stocks, the system 100 applies the 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 may be a handheld, mobile phone, kindle, personal digital assistant (PDA), tablet, laptop, computer, electronic notebook, or smartphone. In some embodiments, the quantum computing VP-PG server 104 receives a volatile asset transfer request from a user device 102 that scans a quick response (QR) code linked to an entity's identification (ID). The AI model 114 is trained using stock market data, currency data, news, social media data, and derivative and options market data. The stock data includes historical records of stock prices, trading volume, market capitalization, and other financial indicators associated with stocks traded on various exchanges. The currency data includes an entity's revenue, earnings, profit margins, GDP growth rates, inflation rates, interest rates, and financial ratios. The news and social media data includes 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) that stores a database. The database may store one or more instruction sequences that, when executed by the processor, generate smart contracts on the quantum-resistant blockchain network 108 to securely negotiate each volatile asset to another asset. The quantum computing VP-PG server 104 may be a handheld device, a mobile phone, a kindle, a personal digital assistant (PDA), a tablet, a laptop, a computer, an electronic notebook, or a smartphone. 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 Narrowband Internet of Things Protocol (NBIoT), or a combination of a wired and wireless network 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. The quantum-resistant blockchain network 108 initiates volatile asset conversion requests when a user's identity (ID) is verified through the quantum-resistant blockchain network 108 using biometric authentication. 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 user's identity (ID) using a zero-knowledge proof (ZKP) method. A ZKP is a cryptographic method that allows one party (the provider or the user) to prove to another party (the verifier) that a statement is true without revealing any additional information aside from the fact that the statement is in fact true.

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

[0046] The quantum-resistant blockchain network 108 analyzes user preferences, as well as real-time behavioral patterns and the user's historical data, and further personalizes the AI model 114 by identifying patterns and correlations between the user preferences and the real-time behavioral patterns and the user's historical data to personalize the AI model 114. The quantum-resistant blockchain network 108 uses ZKP methods to access the user's historical data when personalizing the AI model 114.

[0047] The quantum computing VP-PG server 104 analyzes User A's real-time behavior, preferences, and historical data 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 real-time volatile asset data received from at least one of the volatile asset servers based on user preferences and the volatile asset being exchanged. The real-time volatile asset data analyzed using quantum computing principles. Quantum computing principles refer to the use of quantum mechanics to improve the computational power of the personalized AI model in predicting the value of each volatile asset by utilizing quantum parallelism and quantum entanglement to simultaneously analyze multiple scenarios. The personalized AI model utilizes at least one of options pricing, derivatives trading, or over-the-counter (OTC) derivatives methods to predict the value of each volatile asset over time.

[0048] The system 100 utilizes an AI model 114 to accurately assess the value of available stocks by leveraging advanced machine learning and data analytics techniques. The AI model 114 processes vast 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 indicators, and market trends, the system 100 generates actionable data to assist in stock valuation and trading decisions.

[0049] The system 100 recognizes complex patterns within stock data. This pattern recognition capability enables the identification of trends and contributes to predicting future stock prices with a high degree of accuracy. Furthermore, the system 100 employs predictive analytics to forecast 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 enables the system 100 to assess market sentiment and provide a deeper understanding of factors that may affect stock prices. Furthermore, the use of deep learning algorithms facilitates the development of AI models 114 that can identify complex interdependencies between various factors that affect stock values, enhancing the overall accuracy and reliability of stock analysis and forecasting.

[0050] The personalized AI model 114 analyzes User A's historical transactions (e.g., User A typically converts Bitcoin when its price rises above a certain threshold). The personalized AI model 114 evaluates real-time market data from cryptocurrency servers and predicts trends using quantum computing principles such as quantum parallelism (to analyze multiple scenarios simultaneously) and quantum entanglement (to find correlations in large data sets). The personalized AI model 114 predicts that the stock price of volatile asset B is likely to fluctuate between $29,800 and $31,500 over the next three hours, with a peak 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 the volatile asset's predicted value over that optimal 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 / Stock B. The AI model 114 predicts that Stock B will reach $31,200 in 90 minutes. The server 104 schedules the conversion to occur automatically when the price of Stock B reaches or exceeds $31,000 to maximize the user's profit while minimizing risk.

[0052] The quantum-resistant blockchain network 108 converts each volatile asset into another asset of the user's choice at the determined optimal time. The quantum-resistant blockchain network 108 generates a smart contract on the quantum-resistant blockchain network to securely negotiate each volatile asset into another asset. The smart contract includes the terms for converting the volatile asset. Once the stock value is evaluated, a smart contract is created between the seller and buyer of the stock on the quantum-resistant blockchain network 108. The smart contract is a self-executing contract that includes the terms of use of the transaction, is stored on the blockchain, and ensures its immutability and transparency. For example, at the predicted optimal time, the price of Stock B will reach $31,100. The system 100 automatically executes the conversion. The smart contract is generated on the quantum-resistant blockchain network 108 to securely execute the transaction. The smart contract details include the conversion rate ($31,100 / Stock B), a timestamp of the conversion, and the converted amount (Stock B = $62,200). The system 100 ensures that the conversion process is immutable on the blockchain, tamper-proof, and fully transparent. After the conversion, the resulting USD (62,200) is deposited into User A's digital wallet module, which is securely linked to the blockchain network. The quantum-resistant blockchain network 108 sends a notification to the user device 102 confirming the success of the conversion.

[0053] The quantum-resistant blockchain network 108 enables volatile asset negotiation through a satellite Internet of Things (IoT), thereby enabling high-speed volatile asset-based payment processing in remote locations. The satellite IoT is linked to a 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 via the smart contract. In some embodiments, the system includes: (i) an intermediary host is used to execute the equity transaction and change the status of the transaction in the smart contract; (ii) an investment host is used to execute the funds transaction and change the status of the transaction in the smart contract; and (iii) an intermediate bank host is coupled to the quantum-resistant blockchain network 108 and used to use the smart contract as collateral for funding according to the established funds transaction.

[0055] In some embodiments, a system 100 is provided for streamlining the payment process between buyers and sellers, including entities or individuals. This is accomplished by utilizing invoices stored within a hybrid cloud architecture integrated with the Internet of Things (IoT). The system 100 further incorporates robotic process automation (RPA) to facilitate the transaction process, allowing customers to utilize available stock to settle payments for goods and services in real time. This process enhances the efficiency and automation of payment processing and reduces the manual intervention required to manage transactions.

[0056] The quantum-resistant blockchain network 108 uses RPA to generate invoices between users and entities when assets are transferred to the entity ID. The RPA interacts directly with the system's applications to perform tasks such as data entry, processing transactions, and generating fast, accurate reports.

[0057] 2 illustrates a block diagram of a quantum-resistant blockchain network 108 according to some embodiments of the present disclosure. The quantum-resistant blockchain network 108 includes an artificial intelligence (AI) model 114, a volatile asset 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 Pay Payment Gateway (VP-PG) server. The volatile asset conversion request includes details of the volatile asset to be exchanged. The user preferences include a conversion threshold and asset preferences. The quantum-resistant blockchain network 108 analyzes the user preferences, real-time behavioral patterns, and the user's historical data, and further personalizes the AI model 114 by identifying patterns and correlations between the user preferences, the real-time behavioral patterns, and the user's historical data to personalize the AI model 114. The volatile asset prediction module 202 uses a personalized AI model to predict the value of each volatile asset over time. The value of the volatile asset is predicted based on user preferences and the volatile asset being exchanged by analyzing real-time volatile asset data received from at least one of the volatile asset servers. The real-time volatile asset data analyzed using quantum computing principles.

[0058] The optimal time determination module 204 determines an optimal time to convert each volatile asset based on the predicted value of the volatile asset over the optimal 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 a quantum-resistant blockchain network to securely negotiate each volatile asset into another asset. The smart contract includes conditions for converting the volatile asset. Once the smart contract's terms are met, such as payment being made and shares being transferred 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 the transaction. The terms of the conversion, such as the exchange rate and the currency to be received, are also included. Once the smart contract's terms are met, the conversion is automatically executed and the buyer receives the agreed-upon currency. The use of smart contracts in volatile assets ensures that transactions are executed automatically and transparently, without the need for intermediaries, reducing the risk of fraud or errors in transactions.

[0059] 3 is an exploded view of the system 100 of FIG. 1 illustrating the secure, real-time conversion of volatile assets into other assets using a quantum-resistant blockchain network, artificial intelligence (AI), and quantum computing principles, according to some embodiments herein. The system 100 includes a quantum-resistant blockchain network 108, a quantum computing VP-PG server 104, a database 200, a user device 102, and a robotic process automation module 318. The quantum-resistant blockchain network 108 is linked to a stock processing module 302, a cryptographic processing module 304, and an other currency processing module 306. The quantum computing VP-PG server 104 is linked to a satellite Internet of Things (IoT) 308. The other currency processing module 306 includes a wallet module 312, a card module 314, and a client location module 316. The satellite IoT 308 is linked to a user device 310. The user device 310 is located in a remote location. System 100 ensures secure, efficient, and personalized asset transformation by leveraging advanced AI-driven forecasting, biometric authentication, satellite IoT, and smart contract generation. It is designed to work seamlessly across various 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 distance communication with minimal power consumption.

[0061] The operation of the Satellite IoT 308 network involves transmitting LPWAN signals from the digital wallet module 312 to a satellite. The Satellite IoT 308 relays these signals to a ground station connected to the VP-PG server 104. This configuration enables seamless data exchange between components of the system 100 and ensures uninterrupted functionality in areas with limited or no internet connectivity. By leveraging the Satellite IoT 308, the system 100 can provide payment processing to users in geographically isolated areas. Furthermore, the Satellite IoT 308 network provides a secure and reliable communications 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 exchanged, while the user preferences specify a threshold and a preferred asset. The VP-PG server 104 is communicatively connected to a quantum-resistant blockchain network 108, which securely implements the conversion process by generating a smart contract containing 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 from the volatile asset server, user preferences, and historical data. The system 100 not only supports real-time volatile asset conversions but also facilitates secure asset transfers. 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 assets are securely transferred to the recipient's entity ID, and a smart contract is generated to document the transaction.

[0063] The quantum-resistant blockchain network 108 ensures the security and transparency of all transactions. The quantum-resistant blockchain network 108 uses biometric authentication and zero-knowledge proofs (ZKPs) to verify user identity (ID) while maintaining privacy. ZKP methods also enable 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 further identify patterns and correlations, thereby optimizing predictions of volatile asset conversions.

[0064] To enable remote access, system 100 incorporates a satellite IoT module 308, which facilitates high-speed asset conversion and payment processing in areas with limited connectivity. Satellite IoT module 308 communicates with VP-PG server 104 and blockchain network 108, ensuring uninterrupted service even in remote locations. System 100 also includes a crypto processing module 304 for managing cryptocurrency transactions and an other currency processing module 306 for handling fiat currency and other traditional financial instruments.

[0065] User devices 102 and 310 serve as interfaces for initiating volatile asset conversion requests and transfers. Users can scan quick response (QR) codes linked to entity identification information to enable secure transfers. A Quantum Computing Volatile Pay Payment Gateway (VP-PG) server 104 is implemented at merchant locations. Once integrated, the VP-PG server 104 facilitates transaction processing by generating a QR code for each user-initiated payment. The user scans the QR code using a mobile device to initiate the payment process. The VP-PG server 104 then processes the transaction and updates its status on the blockchain network 108 upon completion, ensuring transparency and traceability.

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

[0067] The system 100 also integrates a robotic process automation (RPA) module 318 that automates workflows related to asset transformation and transaction processing. For example, the RPA module 318 automatically generates invoices when assets are transferred to an entity ID. Additionally, the client location module 316 verifies the user's geographic location, 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 predicted values. A personalized AI model 114 in the server 104 employs techniques such as options pricing, derivatives trading, and over-the-counter (OTC) derivatives to enhance prediction accuracy. Additionally, the system 100 allows assets such as stocks to be used as collateral by generating a smart contract when the predicted asset value meets a predefined threshold.

[0069] 4A shows a diagram of an example user interface 400A for a secure login system, according to some embodiments herein. User interface 400A shows fields for "Username," "Password," and "Login." This input field allows a user to enter a username. This input field allows a user to securely enter a password. Clicking "Login" causes user interface 400A to display a "Welcome Screen."

[0070] FIG. 4B shows a diagram of an exemplary user interface 400B for initiating various actions within the system, according to some embodiments of the present disclosure. The user interface 400B shows a user profile including a profile picture and user name (TAN) for user identification and personalization. Notification icons provide access to system notifications, including alerts or updates relevant to the user, and action buttons. The action buttons include a New Negotiation Request button, which allows the user to initiate a new stock negotiation request; a Transaction History button, which allows the user to view a historical record of transactions or actions the user has performed within the system; and a Volatile Asset Portfolio Summary button, which provides access to a detailed summary of the user's volatile asset portfolio. When the user selects New Negotiation Request and then clicks Submit, the button confirms the selected action and proceeds to the next step.

[0071] FIG. 4C shows a diagram of an exemplary user interface 400C for selecting a particular volatile asset to be converted, according to some embodiments herein. User interface 400C shows a volatile asset selection drop-down menu. The drop-down menu is labeled "Select Volatile Asset to Convert" and provides a list of available volatile assets for user selection. The menu includes options such as: "Volatile Asset A," "Volatile Asset B," "Volatile Asset C," and "Volatile Asset D." For example, the user selects "Volatile Asset B" and clicks "Submit." User interface 400C ensures that users can easily specify the asset they wish to transact and supports accurate and efficient management of volatile assets.

[0072] FIG. 4D shows a diagram of an exemplary user interface 400D for configuring preferences related to the conversion of the volatile asset selected in the previous step, according to some embodiments herein. User interface 400D displays the volatile asset previously selected by the user and allows for review or modification before proceeding. User interface 400D displays a preference configuration drop-down menu labeled "Enter Your Preferences," which allows the user to configure specific parameters for the conversion process. Options include: "Negotiation Threshold" refers to a threshold parameter that defines the specific conditions for asset conversion; "Asset Type to be Converted" allows the user to specify the asset type for the conversion process; the user enters the "Negotiation Threshold," confirms the configured preferences, and "Asset Type to be Converted," and then initiates the processing of the volatile asset by clicking "Submit."

[0073] 4E shows a diagram of an example user interface 400E for summarizing details of a volatile asset conversion request, according to some embodiments herein. User interface 400E shows parameters related to the requested conversion, including "Asset Conversion Details," which specify the assets involved in the conversion process, i.e., stocks as the source asset and Ethereum (ETH) as the target asset; "Amount to Convert," which is the specific amount of the source volatile asset to be converted, which is volatile asset B; "Optimal Conversion Time," determined by the system using an AI-based prediction, displayed as 14:30 UTC on January 6, 2025; and "Predicted Conversion Rate," which refers to the expected exchange rate, displayed as volatile asset B = 15 ETH.

[0074] 4F shows an example user interface 400F diagram of smart contract terms of use associated with volatile asset conversion, according to some embodiments herein. User interface 400F illustrates terms of use including "AI-Driven Optimal Timing," meaning the conversion will be performed at the optimal time determined by a personalized AI model on a quantum-resistant blockchain network; "Transaction Fee," which applies a fee of 0.1% of the transaction amount; "Irreversible Conversion," which states that the conversion process is final and cannot be reversed once initiated and processed; "Users must ensure the destination wallet address is accurate as the system is not responsible for errors in the address provided"; and "The smart contract will comply with applicable regulations governing blockchain-based asset conversion."

[0075] 4G shows a diagram of an example user interface 400G that requires a user to confirm that they have accepted and agreed to the terms and conditions before the transaction can be processed, according to some embodiments herein. User interface 400G shows a confirmation checkbox, i.e., the user must explicitly agree to the terms and conditions of the conversion process and confirm that the provided destination wallet address is accurate and error-free. The user can proceed to complete the transaction by selecting the "Next" button.

[0076] 4H shows a diagram of an example user interface 400H displaying blockchain transaction details for a selected converted asset, according to some embodiments herein. User interface 400H shows the following blockchain transaction details: Transaction Hash: 0xabc1234def5678...xyz789, Block Number: 12345678, Timestamp: 2025-01-06 14:30:01 (UTC), and Status: Success.

[0077] 5A and 5B are flow diagrams illustrating a method for securely converting a volatile asset into another asset in real time on a quantum-resistant blockchain network using an artificial intelligence (AI) model, according to some embodiments herein. In step 502, a quantum computing volatile pay payment gateway (VP-PG) server receives a volatile asset conversion request and user preferences from a user through a user device. The volatile asset conversion request includes details of the volatile asset to be exchanged. The user preferences include a conversion threshold and asset preferences. In step 504, the quantum-resistant blockchain network analyzes the user preferences, as well as real-time behavioral patterns and user historical data, and further personalizes the AI model by identifying patterns and correlations between the user preferences and the real-time behavioral patterns and user 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 the personalized AI model. The value of the volatile asset is predicted by analyzing real-time volatile asset data received from at least one volatile asset server based on user preferences and the volatile asset being exchanged. The real-time volatile asset data is analyzed using quantum computing principles. In step 508, the quantum-resistant blockchain network determines an optimal time to convert each volatile asset based on the predicted value of the 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 securely negotiate each volatile asset into another asset. The smart contract includes conditions for converting the volatile asset.

[0079] AI model processing occurs directly on the user's device. This on-device processing eliminates the need to send data to an external cloud server, 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 capabilities to operate offline, ensuring user data, including photos, voice recordings, text input, and activity patterns, remains private and secure on the device. The system integrates on-device AI with hybrid cloud, IoT, and RPA technologies to optimize payment processing. In-device AI processing capabilities enable real-time handling of invoicing, payment tracking, and transaction verification without the need for continuous internet access. This localized processing results in a fast, secure, and efficient user payment experience. Furthermore, hybrid cloud and IoT integration enables seamless synchronization across devices, ensuring payment data remains accurate and up-to-date across systems.

[0080] Incorporating on-device AI models into payment platforms not only enhances privacy, safety, and performance, but also enables a personalized, context-aware experience that adapts to each user's individual needs. The use of on-device AI models offers several notable advantages: (i) user data is processed locally on the device, maintaining confidentiality and thus reducing exposure to third-party cloud services; and (ii) sensitive user data is stored on the device, minimizing the risk of hacking or data breaches associated with cloud storage and transmission. AI models do not require an active internet connection to function, enabling consistent and reliable intelligent functionality even in offline scenarios. On-device AI models enable near-instant 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, tailored notifications, and a personalized payment experience for each user.

[0081] AI models are integrated into user devices 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 times; (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 documents; (iii) AI models run locally on the device to generate word predictions and text recommendations, streamlining the user experience during data entry; (iv) AI models embedded within the device process messages, searches, and other text-based inputs and use natural language processing (NLP) to improve response and transaction accuracy; and (v) on-device AI models use augmented reality (AR) and virtual reality (VR) to support motion tracking, environment analysis, and point-of-interest identification, enabling a more interactive and immersive user experience during payment processing.

[0082] By analyzing and processing a wide range of data sets from these sources, AI models are able to identify patterns and correlations, enabling them to make informed and accurate predictions about future stock prices and market trends. The integration of diverse data sources ensures a comprehensive approach to stock valuation within the system.

[0083] FIG. 6 is a schematic diagram of a system according to an embodiment of the present disclosure. A representative hardware environment for implementing an embodiment of the present disclosure is illustrated in FIG. 6 with reference to FIGS. 1-5A and 5B. This schematic diagram illustrates a hardware configuration of a quantum-resistant blockchain network / quantum computing SP-PG server / computer system / computing device according to an embodiment of the present disclosure. The system includes at least one processing device (CPU) 10, which may be interconnected to various devices, such as a random access memory (RAM) 14, a read-only memory (ROM) 16, and an input / output (I / O) adapter 18, via a system bus 12. The I / O adapter 18 may be connected to peripheral devices, such as a disk unit 38 and a program storage device 40, that are readable by the system. The system can read instructions of the present invention on the program storage device 40 and execute the methodology of an embodiment of the present disclosure in accordance with these instructions. The system further includes a subject interface adapter 19 that connects other subject interface devices, such as a keyboard 15, a mouse 17, a speaker 24, a microphone 22, and / or a touchscreen device (not shown), to the bus 12 to collect subject input. Additionally, a communications adapter 20 connects the bus 12 to a data processing network 42, and a display adapter 21 connects the bus 12 to a display device 23, which provides a graphical object interface (GUI) 36 for output data according to embodiments herein or may be embodied as an output device such as a monitor, printer, or transmitter, for example.

[0084] The foregoing description of specific embodiments sufficiently reveals the general nature of the embodiments herein, so that others, by applying their current knowledge, can easily modify and / or adapt such specific embodiments to various uses without departing from the general concept; therefore, 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 purposes of description and not of limitation. Thus, while the embodiments herein have been described with reference to preferred embodiments, those skilled in the art will recognize that the embodiments herein can be practiced with modification within the spirit and scope thereof.

Claims

1. 1. A processor-implemented method for securely converting a volatile asset into another asset in a quantum-resistant blockchain network in real time using an artificial intelligence (AI) model, comprising: receiving, by a Quantum Computing Volatile Pay Payment Gateway (VP-PG) server, a volatile asset conversion request from a user through a user device and user preferences, wherein the volatile asset conversion request includes details of a volatile asset to be exchanged, and the user preferences include a conversion threshold and an asset preference; personalizing the AI model by analyzing the user's preferences, as well as real-time behavioral patterns and historical data of the user, through the quantum-resistant blockchain network, and further identifying patterns and correlations between the user's preferences, the real-time behavioral patterns, and the historical data of the user to personalize the AI model; predicting, by the quantum-resistant blockchain network, the value of each volatile asset over time using the personalized AI model; the value of the volatile asset is predicted based on the user preferences and the volatile asset to be exchanged 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 and transforming each volatile asset based on the predicted value of the volatile asset over the optimal time; converting each volatile asset into another asset preferred by the user at the determined optimal time via the quantum-resistant blockchain network; generating a smart contract on the quantum-resistant blockchain network to securely negotiate each volatile asset into another asset, the smart contract including conditions for converting the volatile asset; 2. A processor implementation method comprising:

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

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

4. 2. The processor-implemented method of claim 1, wherein the quantum computing principles refer to the use of quantum mechanics to improve the computational power of the personalized AI model in predicting the value of each volatile asset by utilizing quantum parallelism and quantum entanglement to analyze multiple scenarios simultaneously.

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

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

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

8. The processor-implemented method comprises: receiving, at the Quantum Computing VP-PG server, a volatile asset transfer request from the user device scanning a Quick Response (QR) code linked to an entity's identification (ID); processing the volatile asset transfer request at the VP-PG server using at least one of quantum computing methods to verify transaction data, the volatile asset transfer request including at least one of a digital signature, the asset to be transferred, the transaction data, and the converted volatile asset of the user; securely transferring the assets from the converted volatile assets of the user to the entity ID by generating the smart contract; The processor implementation method of claim 1 further comprising:

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

10. 1. A system for securely converting a volatile asset into another asset in real time on a quantum-resistant blockchain network using an artificial intelligence (AI) model, comprising: a quantum computing Volatile Pay Payment Gateway (VP-PG) server that receives a volatile asset conversion request and user preferences from a user through a user device, the volatile asset conversion request including details of a volatile asset to be exchanged, and the user preferences including a conversion threshold and an asset preference, the quantum computing VP-PG server being communicatively connected to the quantum resistant blockchain network, the quantum resistant blockchain network comprising: a memory having a set of instructions; Executing said set of instructions; personalizing the AI model by analyzing the user preferences, as well as real-time behavioral patterns and historical data of the user, and further identifying patterns and correlations between the user preferences, the real-time behavioral patterns and the historical data of the user to personalize the AI model; predicting, using the personalized AI model, the value of each volatile asset over time, wherein the value of the volatile asset is predicted based on the user preferences and the exchanged volatile asset by analyzing real-time volatile asset data received from at least one volatile asset server, and wherein the real-time volatile asset data is analyzed using quantum computing principles; determining an optimal time to transform each volatile asset based on the predicted value of the volatile asset over the optimal time; converting each volatile asset into another asset preferred by the user at the determined optimal time; generating a smart contract on the quantum-resistant blockchain network to securely negotiate each volatile asset into another asset, the smart contract including conditions for converting the volatile asset; a processor configured to implement A quantum computing Volatile Pay payment gateway (VP-PG) server comprising: A system comprising:

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