Abstract: A predictive AI model generation and execution system with a plurality of engines is provided. A data exchange computing engine may receive and process one or more data streams to generate processed data, and send the processed data to a model generation and execution computing engine. The model generation and execution computing engine may receive the processed data and update a first predictive artificial intelligence model using the processed data. The client interface computing engine may receive a model execution request, generate and send a first graphical user interface, receive model execution data from the external client computing system, and send the model execution data to the workflow management computing engine. The workflow management computing engine may generate one or more model execution instructions based on the model execution data, and send the one or more model execution instructions to the model generation and execution computing engine.

Abstract: Systems, computer program products, and methods are described herein for network event monitoring and network event remediation using artificial intelligence (AI). The present disclosure is directed to receiving and extracting data from one or more data sources and using an AI engine to identify at least one network event trigger attribute set. In addition, the AI engine determines and assigns weights to the at least one network event trigger attribute set. The AI engine generates a forecast network event based on the weights. The AI engine also generates network event remediations based on the forecast network event. The present disclosure is also directed to generating and transmitting a notification based on the network event remediations.

Abstract: Embodiments of the present invention provide a system for analyzing and predicting impact of resources over a lifetime of the resources. The system is configured for receiving a request to analyze and predict impact of a resource associated with an entity, determining one or more parameters associated with the resource, analyzing the one or more parameters, via a generative artificial intelligence engine, and predicting the impact of the resource, via the generative artificial intelligence engine, based on analyzing the one or more parameters.

Abstract: A method for enabling continuity of access to a primary dataset stored in a computer network is provided. The method utilizes a node, a computer processor, and non-transitory computer-readable media storing computer-executable instructions. The node is connected to the computer network. The method includes configuring the mobile device for wireless connection to the computer network. The method includes configuring the mobile device to receive indication that the node is disconnected from the computer network and transmit a copy of the primary dataset or a portion thereof from the computer network to the node. The method includes configuring the node to enable operations on the dataset copy and keep a record of them. The method includes configuring the node to receive indication that the node is connected to the computer network and transmit the record to the computer network.

Abstract: A method for determining whether an existing customer is eligible for a zero-knowledge proof (“ZKP”) application is provided. The method may include determining that the existing customer possesses a private key linked to an account profile associated with the existing customer absent provision of the private key. The determining may also include executing a behavior-scan on the account profile to confirm that a score of the behavior-scan is equal to or greater than a pre-determined score. In response to the determining that the existing customer is eligible for the ZKP application, the method may include generating the ZKP application by executing an encryption algorithm on the sensitive data of the account profile to output a digital zero-knowledge token for each of the sensitive data and auto-filling each input field in the ZKP application that is associated with sensitive data with the digital zero-knowledge token.

Abstract: A method for enabling continuity of access to a primary dataset stored in a computer network is provided. The method utilizes a node, a computer processor, and non-transitory computer-readable media storing computer-executable instructions. The node is connected to the computer network. The method includes configuring the mobile device for wireless connection to the computer network. The method includes configuring the mobile device to receive indication that the node is disconnected from the computer network and transmit a copy of the primary dataset or a portion thereof from the computer network to the node. The method includes configuring the node to enable operations on the dataset copy and keep a record of them. The method includes configuring the node to receive indication that the node is connected to the computer network and transmit the record to the computer network.

Abstract: A predictive AI model generation and execution system with a plurality of engines is provided. A data exchange computing engine may receive and process one or more data streams to generate processed data, and send the processed data to a model generation and execution computing engine. The model generation and execution computing engine may receive the processed data and update a first predictive artificial intelligence model using the processed data. The client interface computing engine may receive a model execution request, generate and send a first graphical user interface, receive model execution data from the external client computing system, and send the model execution data to the workflow management computing engine. The workflow management computing engine may generate one or more model execution instructions based on the model execution data, and send the one or more model execution instructions to the model generation and execution computing engine.

Abstract: Provided herein is a method of storing an incoming dataset in a data mesh. The method may include a plurality of steps. The steps may include associating a metadata tag with a classifying feature and a storage instruction in a (first) relational database. The steps may include scanning incoming datasets to identify datasets characterized by the classifying feature. The steps may include tagging an incoming dataset to generate a tagged dataset. The steps may include storing the tagged dataset in the data mesh, according to the storage instruction. The steps may include associating, in a second relational database, the metadata tag with the initial storage location. The steps may include modifying the storage instruction. The steps may include storing the incoming dataset an additional time in the data mesh, according to the modified storage instruction.

Abstract: Provided herein is a method for enabling continuity of access to a primary dataset stored in a computer network, the method utilizing a node, a computer processor, and non-transitory computer-readable media storing computer-executable instructions, the node being connected to the computer network; the method including the steps of: configuring the mobile device for wireless connection to the computer network; configuring the mobile device to receive indication that the node is disconnected from the computer network, and transmit a copy of the primary dataset or a portion thereof from the computer network to the node; configuring the node to enable operations on the dataset copy and keep a record of them; and configuring the node to receive indication that the node is connected to the computer network and transmit the record to the computer network.

Abstract: Provided herein is a method of storing an incoming dataset in a data mesh. The method may include a plurality of steps. The steps may include associating a metadata tag with a classifying feature and a storage instruction in a (first) relational database. The steps may include scanning incoming datasets to identify datasets characterized by the classifying feature. The steps may include tagging an incoming dataset to generate a tagged dataset. The steps may include storing the tagged dataset in the data mesh, according to the storage instruction. The steps may include associating, in a second relational database, the metadata tag with the initial storage location. The steps may include modifying the storage instruction. The steps may include storing the incoming dataset an additional time in the data mesh, according to the modified storage instruction.

Abstract: A method for determining whether an existing customer is eligible for a zero-knowledge proof (“ZKP”) application is provided. The method may include determining that the existing customer possesses a private key linked to an account profile associated with the existing customer absent provision of the private key. The determining may also include executing a behavior-scan on the account profile to confirm that a score of the behavior-scan is equal to or greater than a pre-determined score. In response to the determining that the existing customer is eligible for the ZKP application, the method may include generating the ZKP application by executing an encryption algorithm on the sensitive data of the account profile to output a digital zero-knowledge token for each of the sensitive data and auto-filling each input field in the ZKP application that is associated with sensitive data with the digital zero-knowledge token.

Abstract: There is provided a method for increasing available memory in an edge layer of a network, where the network stores data and has a default retention period for datasets. The method may include the steps of: (a) receiving the dataset by the edge layer of the network; (b) analyzing the dataset by the front-end filter to identify disposable data points in the dataset, (c) instructing a computer processor to remove the disposable data points from the dataset, upon passage of a specified time interval.