Abstract: Systems, computer program products, and methods are described herein for intelligent exception handling within a distributed network environment. The present disclosure is configured to receive an incidence of a first exception associated with a network interaction; extract information associated with the first exception; determine, via a machine learning (ML) subsystem, a first exception category associated with the first exception based on at least the information associated with the exception; retrieve, from a repository, a first set of actions associated with the first exception category, wherein the first set of actions are configured to resolve the first exception; and in response, execute the first set of actions.

Abstract: Aspects of the disclosure relate to a distributed evaluation platform. The distributed evaluation platform may train a machine learning model based on historical nonfungible tokens. The distributed evaluation platform may receive client information from a client device. The distributed evaluation platform may generate NFTs corresponding to the client information. The distributed evaluation platform may generate soft tokens corresponding to each NFT. The distributed evaluation platform may apply test cases to the soft tokens. The distributed evaluation platform may generate remedial tokens based on the soft tokens and remediation actions. The distributed evaluation platform may apply the test cases to the remedial tokens. The distributed evaluation platform may overwrite the NFTs using the remedial tokens. The distributed evaluation platform may send an event processing request to an event processing system. The distributed evaluation platform may refine the machine learning model based on the NFTs.

Abstract: Methods and systems described herein for addressing issues associated with varying graph analytics tools that require different tool-specific coding languages. An artificial intelligence (AI) sub-system of various modules extracts metadata from a dataset and identifies nodes and relationships in the dataset using the metadata. The dataset is matched with a corresponding graph-analytics template in a data store, and a dynamic template modifier modifies the corresponding graph-analytics template. In some examples, the AI system generates smart guided videos with logical breakpoints that are embedded along with templates for quick learning and to build faster graphical analytics. The AI system includes a dynamic template modifier and a cognitive smart AI engine that includes a graph.

Abstract: Methods and systems described herein for addressing issues associated with varying graph analytics tools that require different tool-specific coding languages. An artificial intelligence (AI) sub-system of various modules extracts metadata from a dataset and identifies nodes and relationships in the dataset using the metadata. The dataset is matched with a corresponding graph-analytics template in a data store, and a dynamic template modifier modifies the corresponding graph-analytics template. In some examples, the AI system generates smart guided videos with logical breakpoints that are embedded along with templates for quick learning and to build faster graphical analytics. The AI system includes a dynamic template modifier and a cognitive smart AI engine that includes a graph.

Abstract: Aspects of the disclosure relate to upgrading an application within a simulated version of an enterprise system to detect and correct potential errors as a result of the upgrade. A computing platform may create a simulated version of the enterprise system by receiving metadata associated with the enterprise system, and converting the metadata into system parameters. Virtual parameters may be created by the computing system based on upgrading an application within the simulated version of the enterprise system. The computing system may create system nodes and virtual nodes. The system nodes and virtual nodes may be dynamically linked in order to determine errors caused by the application upgrade within the simulated version of the enterprise system. The computing platform may determine actions to correct the errors and input the results and feedback into an AI engine to further refine the accuracy and reliability of the computing platform over time.

Abstract: Aspects of the disclosure relate to upgrading an application within a simulated version of an enterprise system to detect and correct potential errors as a result of the upgrade. A computing platform may create a simulated version of the enterprise system by receiving metadata associated with the enterprise system, and converting the metadata into system parameters. Virtual parameters may be created by the computing system based on upgrading an application within the simulated version of the enterprise system. The computing system may determine errors caused by the application upgrade within the simulated version of the enterprise system based on differences between the system parameters and the virtual parameters. The computing platform may determine actions to correct the errors and input the results and feedback into an AI engine to further refine the accuracy and reliability of the computing platform over time.

Abstract: Systems, computer program products, and methods are described herein for implementing parametric optimization analysis for resource selection. The present invention is configured to determine a first set of requirements associated with a resource exchange agreement; identify one or more non-fungible tokens (NFTs) for one or more categories of past resource exchange agreements based on at least the first set of requirements; extract, from the one or more NFTs, one or more resource descriptors associated with one or more past resource exchange agreements in the one or more categories; predict, using a machine learning subsystem, an optimal resource valuation model for one or more resources that meet the first set of requirements using the one or more resource descriptors and the first set of requirements; and transmit control signals configured to cause a first end-point device to display the optimal resource valuation model.

Abstract: Aspects of the disclosure relate to generating synthetic invisible fingerprints for metadata security and document verification using generative artificial intelligence (AI). A computing platform may capture first fingerprint information via a computing device. The first fingerprint information may include one or more physical fingerprint images of a user. The computing platform may encode the first fingerprint information in accordance with a quantum key distribution scheme. The computing platform may store the encoded first fingerprint information in a data store. Based on the encoded first fingerprint information and using a generative artificial intelligence algorithm, the computing platform may generate second fingerprint information. The second fingerprint information may include one or more synthetic fingerprint images associated with the one or more physical fingerprint images of a user. The computing platform may transmit the second fingerprint information for smart contract generation.

Abstract: Aspects of the disclosure relate to generating smart contracts for electronic notarization using synthetically generated invisible fingerprint metadata. An apparatus may receive synthetic fingerprint information. In addition, the synthetic fingerprint information may be generated based on one or more physical fingerprint images of a user and using a generative artificial intelligence algorithm. The apparatus may receive one or more digital documents associated with an electronic notarization. The apparatus may receive rule information corresponding to location specific protocols associated with the electronic notarization. The apparatus may generate a smart contract on a blockchain based on the synthetic fingerprint information, the one or more digital documents, and the rule information.

Abstract: Aspects of the disclosure relate to automatic tagging of smart contracts for electronic notarization in a decentralized finance system. A computing platform may retrieve, from a node in a decentralized distributed ledger system, a smart contract associated with a document for remote online notarization. The computing platform may extract one or more metadata attributes from the document. The computing platform may tag the smart contract based on the extracted one or more metadata attributes. The computing platform may request and receive approval of the tagging from one or more individuals associated with the document for remote online notarization. The computing platform may digitally notarize the document. The computing platform may transmit the tagged smart contract to the node in the decentralized distributed ledger system and cause the tagged smart contract to be saved in the node.

Abstract: A system includes a plurality of computing nodes that form a blockchain network, wherein one or more of the computing nodes is a metaverse computing node configured to generate a mixed reality environment. A processor of at least one computing node is configured to record a plurality of data interactions performed by a user using a metaverse computing node, determine, based on the plurality of data interactions, a data interaction behavior of the user in relation to the data file, detect that a data interaction performed in relation to the data file of the user is not in accordance with the data interaction behavior of the user, determine that the data interaction is a suspicious data interaction, assign an alert level to the suspicious data interaction, determine at least one action to be performed based on the alert level and perform the at least one action.

Abstract: A system includes a plurality of computing nodes that form a blockchain network, wherein one or more of the computing nodes is a metaverse computing node configured to generate a mixed reality environment. A processor of at least one computing node is configured to receive information relating to a suspicious data interaction associated with a data file of a user, simulate, based on the received information, the suspicious data interaction in a synthetic mixed reality environment that is substantially identical to the mixed reality environment, verify the suspicious data interaction while the simulated data interaction is being performed, when the suspicious data interaction cannot be verified, disable one or more future data interactions processed using the same smart contract used to process the suspicious data interaction, and when the suspicious data interaction is successfully verified, terminate the simulated data interaction and process the suspicious data interaction.

Abstract: A security system environment that uses artificial intelligence (AI) and machine learning (ML) (collectively “AI/ML”) to provide dynamic detection of potential security breaches and a mixed realty interface to decision the potential security breaches.

Abstract: A system for determining transactions eligible for currency transaction report (CTR) exemption using a quantum computing algorithm is described. The system may further leverage a distributed computing architecture for manual review and filing of CTRs and/or approving a CTR exemption as determined by the quantum computing algorithm.

Abstract: A system for determining transactions eligible for currency transaction report (CTR) exemption using a quantum computing algorithm is described. The system may further leverage a distributed computing architecture for manual review and filing of CTRs and/or approving a CTR exemption as determined by the quantum computing algorithm.

Abstract: A system for classifying tasks based on workflow patterns detected on workflows through a real time video feed that shows steps being performed to accomplish a plurality of tasks. Each task is associated with a different set of steps. The system accesses a first set of steps known to be performed to accomplish a first task on the webpages. The first set of steps is represented by a first set of metadata. The system extracts a second set of metadata from the video feed. The second set of metadata represents a second set of steps to perform a second task. The system determines whether the second set of metadata corresponds to the first set of metadata. If it is determined that the second set of metadata corresponds to the first set of metadata, the system classifies the second task in a class to which the first task belongs.

Abstract: Various systems, methods, and computer program products are provided for dynamically generating an API. The method includes extracting metadata from a command recording of a system API. The command recording of the system includes a starting breakpoint and an ending breakpoint for one or more repetitive processes. The method also includes determining one or more API parameters for an API to be generated based on the extracted metadata. The method further includes dynamically generating the API based on the API parameters. The method still further includes causing an authentication of the credentials relating to the API via robotic process automation.

Abstract: Systems, methods, and apparatus are provided for modification of RPA controls during a workflow without impacting bot performance. A background scan may be initiated in parallel to an RPA application workflow. The scan may identify values for an application control parameter and a corresponding bot control parameter. Hashes of the values may be validated against a key value pair stored as a block in a distributed ledger. If any changes have been made to the application control values, the bot control value will not match the application control value and validation will fail. If validation fails, an override may be generated for the bot value. An updated bot value may be stored in a temporary cache until the workflow is complete. Following completion of the workflow, a new block may be added to the blockchain storing a new key value pair that includes the application control value and the updated bot control value.

Abstract: A system for detecting errors in task workflows from a real time video feed records. The video feed that shows a plurality of steps being performed to accomplish a plurality of tasks through an automation process system. The system splits the video feed into a plurality of video recordings which are valid breakpoints determined through cognitive Machine Learning Engine, where each video recording shows a single task. For each task from among the plurality of tasks, the system determines whether the task fails and the exact point of failure for that task. If the system determines that the task fails, the system determines a particular step where the task fails. The system flags the particular step as a failed step. The system reports the flagged step for troubleshooting.

Abstract: Systems, methods, and apparatus are provided for a dynamic contract payment term (“payterm”) generator. A machine learning algorithm may generate a replacement payment term for a contract based on market-based parameters and blockchain metadata for the contract. The blockchain metadata may encode hierarchical interdependencies between contracts using blockchain encryption. The blockchain metadata may be applied to auto-generate machine learning inputs for related contracts having interdependent payment terms. The machine learning inputs may include contract parameters that have been extracted and encrypted as blockchain metadata, as well as market-based parameters extracted from enterprise sources.