A system and method for processing multimodal data using jointly trained neural compression and enhancement networks. The system efficiently handles temporal, textual, sentiment, and structured data through specialized encoding modules. A fusion module integrates these encodings, capturing cross-modal relationships via attention mechanisms. The fused representation is compressed into a latent space by a trained compression network, then reconstructed and enhanced by a trained reconstruction network and neural enhancement network. Joint training optimizes all components simultaneously using a comprehensive loss function that balances reconstruction quality across modalities with enhancement performance. This approach enables superior data compression, reconstruction, and analysis by leveraging inter-modal correlations across various application domains. The system's latent space exploration capability facilitates generation of synthetic data scenarios for model testing and development.

Tokenization, social distribution, and redemption of digital entitlements, including: a digital entitlement ingestion and tokenization engine configured to receive and escrow value-bearing digital entitlements from an entity; a token engine configured to generate transferable tokens from the entitlements with embedded redemption rules; a distribution module enabling third-party entities to purchase and airdrop the tokens to end users based on location or contextual triggers; a user wallet interface enabling the end users to collect, store, gift, and redeem the tokens; a social gifting module configured to allow the end users to transmit the tokens to other users subject to transfer rules; and an optional redemption bridge configured to un-tokenize the tokens that have been redeemed and initiate value transfer to accounts of the end users within the originating system.

The present disclosure provides a method of operating an idea trading platform using artificial intelligence (AI) models. Further, the method may include validating first user credentials of a first user received through a first user interface to create an idea generator account. Further, the method includes evaluating the disclosure using an artificial intelligence (AI) model. Further, the method includes generating a patentability score using a first AI model based on the evaluating. Further, the method includes operating an ingestion engine to extract an idea title, an idea summary, and an idea specification and encoding the idea title, the idea summary, the idea specification, and the patentability score into an encoded package for storage. Further, the method includes generating an idea listing for display on a second user interface through operation of a listings generator. Further, the method includes notifying two or more idea purchaser accounts through operation of a reporter.

Provided herein a method for secure and real-time converting a volatile asset into another asset in a quantum-resistant blockchain network using an Artificial Intelligence (AI) model. The method includes receiving volatile asset conversion request and user preferences from a user through a user device, personalizing the AI model by identifying patterns and correlations between the user preference, and the real-time behavioral patterns and the historic data of the user to personalize the AI model, predicting value of each volatile asset over time using the personalized AI model, determining an optimal time to convert each volatile asset based on the predicted value of the volatile assets over time, converting each volatile asset into another asset preferred by the user, at the determined optimal time and generating a smart contract on the quantum-resistant blockchain network to secure each volatile asset's conversation into another asset.

A system and method for trading and risk management are disclosed. The invention introduces a Q-Score framework derived from the Total Wave Modified Schrdinger Equation (TWMSE), using five quantum physics indicators: price curvature, phase interference, amplitude-amplitude interaction, amplitude-charge interaction, and volatility-price correlation. A genetic algorithm optimizer adapts indicator weights by asset and regime, generating buy, sell, or neutral signals. Collapse-based logic enables abstain states, reducing false positives. Integrated risk protocols include adaptive sizing, pyramiding, and turnover controls. Tests on equities and FX in August 2025 confirm robustness, supporting institutional use in adaptive, explainable platforms. This Continuation-in-Part extends prior wave-collapse inventions into the domain of financial markets, providing a physics-inspired, adaptive, and transparent system that bridges theoretical innovation with practical trading execution across diverse asset classes.

Embodiments of the present disclosure herein provide a method and system of a non-linear single level optimization model for estimating DER responses in MAMS environment. Existing methods do not have a framework for volume allocation optimization and price-volume optimization especially in multi-aggregator multi-subscriber systems. Moreover, the price-volume optimization methods in existing literature consider a system having multiple subscribers and single aggregator only and using these methods it is difficult to assess how multiple subscribers will behave with multiple aggregators. Further, in a multi-aggregator system, determining the prices offered by each for an amount of energy resource allocated by the subscribers is another challenge, since the prices offered are dependent on several factors such as day ahead market prices, market risks and the like. The disclosed non-linear single level optimization model enables to estimate equilibrium optimal values between an amount of energy resources allocated for each aggregator among the one or more aggregators and the amount of energy resource traded by each aggregator in one or more energy markets.

Provided is a device supporting financial services, and an integrated system that incorporates technologies such as QR codes, blockchain, distributed finance (DeFi), non-fungible tokens (NFT), and metaverse into financial services. Furthermore, the present disclosure relates to a method and a device for providing blockchain-based financial services and, more particularly, to a method and a device for providing services of making a payment or exchanging money through blockchain-based cryptocurrency that is performed in real time without a delay in processing time. Further, the present invention relates to: an ATM device that determines forgery/falsification of an ID card in an account opening process and authenticates the identity of an ID card holder by comparing the face of the ID card holder with the face of a photo of the ID card; and an operating method therefor. Moreover, the present disclosure relates to an electronic access management system using QR codes.

The present application generally relates to electronic trading systems, and more specifically to systems and methods for electronic trade order routing. A routing algorithm may generate an execution style recommendation for incoming IG corporate orders. For example, the style recommendation may be shown as a new column in the trading application dashboard that suggests a course of action to traders for each order. In one implementation, automatic decision making may occur based on the style recommendation.

Systems and methods for price discovery offering are disclosed. A method may include: receiving, by a computer program executed by an electronic device, an identification of an initial public offering (IPO); receiving, by the computer program, price discovery trading data comprising order placements and trades during a price discovery trading period; predicting, by the computer program, a demand expectation for the IPO based on the price discovery trading data, a sentiment for the IPO, and a momentum analysis for the IPO; pricing, by the computer program and using a machine learning model that is trained with historical data, the IPO based on the demand expectation and an IPO supply; receiving, by the computer program, public trading information for the IPO; and settling, by the computer program, the order placements and the trades during a price discovery training period.

A market explanation event extraction method includes a step of acquiring, by a market explanation event extraction system, a stock price index data and unstructured data of a text type for predicting a stock price, a step of extracting, by the market explanation event extraction system, an event that influences a stock market based on the acquired unstructured data, a step of predicting, by the market explanation event extraction system, a future direction of a stock price by analyzing the extracted event, and a step of selecting, by the market explanation event extraction system, an event that matches a result of predicting the direction by comparing the result of predicting the direction and an actual stock price index, and extracting a market explanation event that explains a cause of a rise or fall of a stock price based on the selected event.