Abstract: Security measures are provided for resource exchange events occurring within a virtual environment, such as metaverse or the like. Intelligent resource exchange event authentication is realized by leveraging Artificial Intelligence (AI) and, more specifically, Machine Learning (ML) techniques to identify user behavioral patterns associated with previous resource exchange events conducted within the virtual environment and, in some instances, non-virtual environment. Current resource exchange event characteristics are compared to the user behavior patterns to ensure that the resource exchange event is authentic/legitimate. Additionally, intelligent user authentication occurs by leveraging the use of a Non-Fungible Token (NFT) that is presented by the user at the onset of the resource exchange event and is verified within a distributed trust computing network.

Abstract: Systems, computer program products, and methods are described herein for parsing and tokenization of designated electronic resource segments via a machine learning engine. The present invention is configured to electronically receive a request for an electronic resource into a machine learning engine, identify and parse one or more segments of the electronic resource using the machine learning engine, predictively determine a number of tokens required by analyzing the electronic resource using the machine learning engine, convert the one or more segments into corresponding one or more tokens, transmit the electronic resource to at least one user for approval, receive approval for the electronic resource from the at least one user, and designate token expiration.

Abstract: Security measures are provided for resource exchange events occurring within a virtual environment, such as metaverse or the like. Intelligent resource exchange event authentication is realized by leveraging Artificial Intelligence (AI) and, more specifically, Machine Learning (ML) techniques to identify user behavioral patterns associated with previous resource exchange events conducted within the virtual environment and, in some instances, non-virtual environment. Current resource exchange event characteristics are compared to the user behavior patterns to ensure that the resource exchange event is authentic/legitimate. Additionally, intelligent user authentication occurs by leveraging the use of a Non-Fungible Token (NFT) that is presented by the user at the onset of the resource exchange event and is verified within a distributed trust computing network.

Abstract: Aspects of the disclosure relate to real-time dynamic securitization of blockchain records. A computing platform may receive record retrieval data comprising record data identifying one or more requested records. The computing platform may decrypt the first requested record to generate a decrypted requested record. The computing platform may parse the decrypted requested record to generate parsed record data. The computing platform may determine that the parsed record data comprises a subset of predetermined textual content. The computing platform may mark one or more predetermined textual content of the subset of predetermined textual content for securitization. The computing platform may generate a securitized record by redacting, from the decrypted requested record, each of the one or more predetermined textual content marked for securitization.

Abstract: Systems, computer program products, and methods are described herein for dynamic data aggregation and prediction for assessment of electronic non-fungible resources. The present invention is configured to receive, from a user input device, a request to predict an assessment of an NFT for a resource at a first time; capture, using a ML subsystem, one or more attributes associated with the NFT; trigger a vectorization engine to map the one or more attributes represented in the one or more data formats into a vector array; train, using the ML subsystem, an ML model using the vector array of the one or more attributes; generate, using the ML subsystem, a trained ML model based on at least the training; predict, using the trained ML model, the assessment of the NFT at the first time; and store the predicted assessment of the NFT at the first time in an assessment repository.

Abstract: A system for automated and intelligent error correction within an electronic blockchain ledger is provided. The system may analyze unformatted/unstructured blockchain event logs using machine learning algorithms in order to identify and label the errors within the event logs. Based on the identified errors, the system may use predictive analysis in conjunction with error or rule repositories and/or machine learning to identify potential solutions to the identified errors. Once the potential solutions have been identified, the system may automatically attempt to rectify the blockchain transaction errors using the potential solutions. The system may further comprise trend/correlation analyses and reporting functions regarding various metrics and may output said metrics in various accessible formats.

Abstract: Aspects of the disclosure relate to real-time dynamic securitization of blockchain records. A computing platform may receive record retrieval data comprising record data identifying one or more requested records. The computing platform may decrypt the first requested record to generate a decrypted requested record. The computing platform may parse the decrypted requested record to generate parsed record data. The computing platform may determine that the parsed record data comprises a subset of predetermined textual content. The computing platform may mark one or more predetermined textual content of the subset of predetermined textual content for securitization. The computing platform may generate a securitized record by redacting, from the decrypted requested record, each of the one or more predetermined textual content marked for securitization.

Abstract: A system for optimizing data requests in an electronic data storage environment may be configured to receive and identify data requests to perform operations on data stored in a data storage environment. The system may further to implement tuning algorithms on the data requests upon identifying that the data requests are causing the data storage environment to perform below optimal performance. The present invention may be implemented as a system, a computer program product, or a computer-implemented method.

Abstract: Aspects of this disclosure relate to artificial intelligence (AI)-assisted creation of a blockchain by a decentralized network. Various aspects of this disclosure relate to AI-based determination of blockchain parameters for use in workflow management processes. However, the AI-based approaches described herein are applicable for any blockchain-based procedures. Blockchain parameters, such as a block size, may be determined based on one or more parameters associated with the decentralized network (e.g., input/output load, network bandwidth, etc.).

Abstract: Aspects of this disclosure relate to artificial intelligence (AI)-assisted creation of a blockchain by a decentralized network. Various aspects of this disclosure relate to AI-based determination of blockchain parameters for use in workflow management processes. However, the AI-based approaches described herein are applicable for any blockchain-based procedures. Blockchain parameters, such as a block size, may be determined based on one or more parameters associated with the decentralized network (e.g., input/output load, network bandwidth, etc.).

Abstract: A system for optimizing data acceptance and data storage in distributed computing network may be configured to implement tuning algorithms in order to improve the performance of a data requests in the distributed computing network. The tuning algorithms may be applied based on identifying components of the distributed computer network that are performing below an optimal level. Tuning algorithms may comprise implementing a parallel validation process; restructuring a memory hierarchy for fast data access; increasing a number of endorsing peers; increasing a number of transactions per node; reducing a number of node commits; implementing an event handling strategy; or other similar mechanisms.

Abstract: Aspects of the disclosure relate to training and using machine-learning models to determine an optimal number of robotic process automation (RPA) bot instances to process a work queue. In some embodiments, a computing platform train a machine learning model based on RPA using process step information and historical execution log data. Thereafter, the computing platform may receive a current work queue of items to be processed, and determine, using the machine learning model, an optimal number of RPA bot instances to process a current work queue. The computing platform may send the determined optimal number of RPA bot instances to a RPA bot orchestrator on a virtual bot host server, causing the orchestrator to instantiate at least one bot instance corresponding to the determined optimal number of RPA bot instances to process items from the current work queue using the at least one bot instance.

Abstract: A system for optimizing data acceptance and data storage in distributed computing network may be configured to implement tuning algorithms in order to improve the performance of a data requests in the distributed computing network. The tuning algorithms may be applied based on identifying components of the distributed computer network that are performing below an optimal level. Tuning algorithms may comprise implementing a parallel validation process; restructuring a memory hierarchy for fast data access; increasing a number of endorsing peers; increasing a number of transactions per node; reducing a number of node commits; implementing an event handling strategy; or other similar mechanisms.

Abstract: A system for optimizing data requests in an electronic data storage environment may be configured to receive and identify data requests to perform operations on data stored in a data storage environment. The system may further to implement tuning algorithms on the data requests upon identifying that the data requests are causing the data storage environment to perform below optimal performance. The present invention may be implemented as a system, a computer program product, or a computer-implemented method.

Abstract: Systems, computer program products, and methods are described herein for intelligent code update for a test automation engine. The present invention is configured to receive, from a test automation engine, one or more exceptions associated with one or more automated test scripts; initiate an execution one or more machine learning algorithms on the one or more exceptions; classify the one or more exceptions into one or more exception types; generate an exception handling routine based on at least classifying the one or more exceptions into the one or more exception types; initiate an execution of the exception handling routine on the automated test script, wherein the exception handling routine comprises computer instructions for updating a source code of the one or more automated test scripts; and initiate an execution of the one or more updated automated test scripts on the test automation engine.

Abstract: A system for automated and intelligent error correction within an electronic blockchain ledger is provided. The system may analyze unformatted/unstructured blockchain event logs using machine learning algorithms in order to identify and label the errors within the event logs. Based on the identified errors, the system may use predictive analysis in conjunction with error or rule repositories and/or machine learning to identify potential solutions to the identified errors. Once the potential solutions have been identified, the system may automatically attempt to rectify the blockchain transaction errors using the potential solutions. The system may further comprise trend/correlation analyses and reporting functions regarding various metrics and may output said metrics in various accessible formats.

Abstract: A system for automated and intelligent error correction within an electronic blockchain ledger is provided. The system may analyze unformatted/unstructured blockchain event logs using machine learning algorithms in order to identify and label the errors within the event logs. Based on the identified errors, the system may use predictive analysis in conjunction with error or rule repositories and/or machine learning to identify potential solutions to the identified errors. Once the potential solutions have been identified, the system may automatically attempt to rectify the blockchain transaction errors using the potential solutions. The system may further comprise trend/correlation analyses and reporting functions regarding various metrics and may output said metrics in various accessible formats.

Abstract: Systems, computer program products, and methods are described herein for intelligent code update for a test automation engine. The present invention is configured to receive, from a test automation engine, one or more exceptions associated with one or more automated test scripts; initiate an execution one or more machine learning algorithms on the one or more exceptions; classify the one or more exceptions into one or more exception types; generate an exception handling routine based on at least classifying the one or more exceptions into the one or more exception types; initiate an execution of the exception handling routine on the automated test script, wherein the exception handling routine comprises computer instructions for updating a source code of the one or more automated test scripts; and initiate an execution of the one or more updated automated test scripts on the test automation engine.

Abstract: A system for automated and intelligent error correction within an electronic blockchain ledger is provided. The system may analyze unformatted/unstructured blockchain event logs using machine learning algorithms in order to identify and label the errors within the event logs. Based on the identified errors, the system may use predictive analysis in conjunction with error or rule repositories and/or machine learning to identify potential solutions to the identified errors. Once the potential solutions have been identified, the system may automatically attempt to rectify the blockchain transaction errors using the potential solutions. The system may further comprise trend/correlation analyses and reporting functions regarding various metrics and may output said metrics in various accessible formats.

Abstract: Systems, computer program products, and methods are described herein for intelligent control code update for robotic process automation. The present invention is configured to retrieve execution logs associated with robotic process automation (RPA) sessions, wherein the execution logs comprises exceptions. Next, the present invention is configured to initiate machine learning algorithms configured to process the one or more execution logs and classify the exceptions into predetermined classes. Next, the present invention is configured to deploy automated exception handling subroutines to address the exceptions based on at least classifying the exceptions into the predetermined classes.