A system and method for implementing artificial intelligence using deterministic wave interference and collapse logic derived from the Total Wave Modified Schrdinger Equation (TWMSE). An artificial agent is modeled as a system wavefunction interacting with one or more observer wavefunctions representing electromagnetic, gravitational, weak, or strong fields. A collapse function determines when interference exceeds a threshold, producing deterministic action or comprehension. Field parameters adapt through feedback to enable learning, residual interference forms resonant memory, and computation is performed directly on optical, electromagnetic, or neuromorphic hardware. The invention provides a unified frameworkTotal Wave Artificial Intelligence (TWAI)that integrates action, understanding, learning, memory, and physical embodiment through field-based collapse rather than probabilistic inference.

The invention relates to computer-implemented systems and methods for implementing an Intangibles Analytics Tool that may be used to benchmark purchase price allocation (PPA) data from public filings. The Intangibles Analytics Tool may include the ability to: (i) automatically retrieve acquisition disclosure information and provide initial PPA mapping, replacing the need to perform these steps manually and (ii) serve as a database of verified public PPA data that may be leveraged across offices, applications, industries, etc. With the Intangibles Analytics Tool, users may perform transaction searches and automatically retrieve filings associated with the buyers of those transactions.

A device of providing an investment value evaluation service according to one embodiment of the present disclosure may include a preprocessing module that processes financial data for each company or asset collected from a market server to generate training data including figures for each preset financial indicator; an analysis module that is constructed through the training of a preset machine learning model using the training data to predict a value evaluation index for each company or asset, and output analysis data based on the predicted value evaluation index; and a service providing module that processes analysis data for a specific company or asset output from the analysis module according to an analysis request from a user terminal to provide investment value evaluation data to the user terminal, wherein the value evaluation index is a fair price representing an upside potential in the value of a company or asset according to the figures for each financial indicator compared to a current price.

The method includes: receiving a communication that relates to a request for a price quote for at least one equity derivative product; extracting from the communication, attributes of the requested price quote for the at least one equity derivative product; generating based on the extracted attributes, a template request that has a predetermined format for each of the at least one equity derivative product; displaying, via a graphical user interface (GUI), each template request for review by a user, wherein the GUI includes an input mechanism for at least one of accepting and modifying each template request; transmitting each reviewed template request to a pricing system; receiving, from the pricing system, a quote for each of the at least one equity derivative product; and displaying, by the GUI, the price quote.

A method of facilitating trading non-negotiable financial assets includes receiving an issue data from at least one issuer device associated with at least one issuer, issuing a certificate of deposit based on the issue data, receiving a purchase request data from at least one investor device associated with at least one investor, identifying the CD based on the CD identifier, processing a transaction for the CD using one or more investor data, generating one or more transaction attributes associated with the transaction based on the processing, storing the one or more transaction attributes in a distributed ledger using a smart contract, and transmitting the CD to the at least one investor device.

Disclosed herein are systems, methods, and computer-readable media for an exchange management platform that obtains, using artificial intelligence (AI) orchestrators, a set of available exchanges based on a set of exchange protocols. In some implementations, a first command set directs one or more AI orchestrators to generate, as output, a set of resources satisfying a first protocol from the set of exchange protocols. A second command set and a second protocol from the set of exchange protocols can then be provided, as input, to the one or more AI orchestrators, causing the one or more AI orchestrators to determine a set of available exchanges for transferring a resource between two entities. The set of available exchanges can be obtained from the one or more AI orchestrators and display of a graphical layout based on the set of available exchanges can be caused.

A system for creating a financial instrument selection, sharing the selection, and executing trade instructions thereof: a selector engine for creating selection parameters according to a statistical model for weighting desirability of financial instruments combined with entered user selection preferences or objectives for financial instruments, creating investment portfolios of financial instruments based on the selection parameters, and converting the investment portfolios data to output data representing trade instructions of financial instruments; a deep learning decision engine providing predicative input data and an investment sharing and democratization and portfolio monitoring module providing peer investment portfolio shared data input to the selector engine; and an execution platform for executing the trade instructions, the execution platform including electronic selection limiters to prevent execution of specific trades based on electronic flags computed from electronic checks relating to the amounts and types of the trades.

A network-based computing system can implement a transaction service in which digital wallets of client transaction entities are dynamically monitored to maintain a balance within a liquidity tranche unique to each transaction entity. The computing system can further execute a smart liquidity model that parses transaction requests into smaller clips and execution timing intervals such that risk of transaction failure is minimized or eliminated.

A system for managing and carrying out trading and swaps of spot cryptoasset index tracking weighted asset benchmark prices (SCAITWABPs). The system makes use of a plurality of edge artificial intelligence computing devices (EAICDs) for powering peer-to-peer (P2P) marketplaces. The EAICDs facilitate processor computer driven decentralized exchanges (DEX) of SCAITWABPs whose spot index cryptoasset values track market prices and algorithm-driven performance analytics of referenced weighted average benchmark (WAB) sets of tradable assets, whereby each asset specified in the WAB set qualifies for transactions physically settled over counterpart marketplaces. A plurality of dongles are used with each being independently operably associated with a specific one of said EAICDs. Each one of the EAICDs includes: artificial intelligence (AI); machine learning (ML) and dongle authentication processes. These components advance carrying out decentralized finance with controls over access to price discovery and analytics data streamed to and from a remote hybrid paradigm price discovery database (HPPDD), as well as an electronic, self-custodial wallet and operations pertaining to the electronic, self-custodial wallet.