Abstract: Embodiments of the present invention provide a system for optimizing configuration of resource upgrades. The system is configured for identifying entity resources that meet conditions associated with upgrading the entity resources, identifying rules associated with upgrading the entity resources, determining change windows for upgrading the entity resources based at least on the rules, generating a schedule for upgrading the entity resources based on the rules and the change windows, displaying the schedule for upgrading the entity resources to a user, via a dynamic editable user interface, prompting the user to provide an input associated with the schedule via the dynamic editable user interface, receiving the response from the user via the dynamic editable user interface, and automatically upgrading the entity resources based on the schedule and the response received from the user.

Abstract: Systems, computer program products, and methods are described herein for preventing synthetic misappropriation events over an electronic network.

Abstract: Systems, computer program products, and methods are described herein for preventing synthetic misappropriation events over an electronic network.

Abstract: Systems, computer program products, and methods are described herein for secure training of machine learning models. The present disclosure is configured to receive a training dataset, wherein the training dataset comprises personally identifiable information (PII); implement a data sanitization algorithm using adjustable data sanitization parameters on the training dataset to generate a corresponding sanitized training dataset; train an ML model using the sanitized training dataset; deploy the trained ML model on a live dataset; evaluate a performance accuracy of the ML model against an accuracy threshold; and iteratively adjust the data sanitization parameters for subsequent iterative re-training of the ML model until the performance accuracy of the ML model meets the accuracy threshold.

Abstract: A system is provided for computing and tracking resource state changes using a multichain distributed register technology. In particular, the system may comprise an index distributed register that may be stored across a plurality of distributed register nodes. The index distributed register may comprise one or more resource-specific data records that may in turn make up a resource-specific distributed register originating from the index distributed register. The resource-specific distributed register may comprise an initial data record that defines the attributes of the resource, and one or more subsequent data records that may be linked with the initial data record to reflect changes in the state of the resource to be tracked. Based on the data within each of the resource-specific distributed register, the system may compute resource analysis scores and/or trigger real-time user alerts.

Abstract: The present disclosure details systems, computer program products, and methods for machine engine analysis of network interaction data to identify conflicts. This involves the activation of an intelligence engine that connects to a backend data engine to access a database of conflict of interest (COI) datasets. The intelligence engine analyzes incoming applicant data and the COI datasets to identify potential conflicts. The identified conflicts are validated using stochastic metrics via a rules engine. A quantifiable probability metric is calculated, representing the probability of conflict from both organizational and individual perspectives. Based on the potential conflict of interest, a recommendation is generated that aligns with the calculated probability. The system then provides recommendations via API or on a user interface that is communicatively coupled to the processor.

Abstract: Big data searches, statistical computation and artificial intelligence is leveraged to determine the likelihood that a user will renounce an object post-resource event. Specifically, the present invention relies on object-identifying data and user data to key a plurality of data mining searches of big data sources. In response to extracting responsive data from the big data sources, the present invention implements statistical computing to determine a go/no-go indicator that indicates either that (i) the user is unlikely to renounce (i.e., abandon, fail to use and/or return) the object post-resource event, or (ii) the user is likely to renounce (i.e., abandon, fail to use and/or return) the object post-resource event. Artificial Intelligence (AI) is used to analyze previous likelihood of renunciation determinations to determine a confidence level which is used in the statistical computation of the go/no-go indicator.

Abstract: A system is provided for an artificial intelligence-based engine for generating recommendations for resource allocation. In particular, the system may comprise an artificial intelligence module configured to access one or more data sources containing resource data associated with one or more users. Based on the resource data, the system may identify a group of analogous users with respect to a specific user. The system may then generate one or more recommendations for a resource allocation associated with the user. The one or more recommendations may then be presented on a graphical interface of a user device associated with the user. In this way, the system may provide an AI-driven method for efficient allocation of resources.

Abstract: Big data searches, statistical computation and artificial intelligence is leveraged to determine the likelihood that a user will renounce an object post-resource event. Specifically, the present invention relies on object-identifying data and user data to key a plurality of data mining searches of big data sources. In response to extracting responsive data from the big data sources, the present invention implements statistical computing to determine a go/no-go indicator that indicates either that (i) the user is unlikely to renounce (i.e., abandon, fail to use and/or return) the object post-resource event, or (ii) the user is likely to renounce (i.e., abandon, fail to use and/or return) the object post-resource event. Artificial Intelligence (AI) is used to analyze previous likelihood of renunciation determinations to determine a confidence level which is used in the statistical computation of the go/no-go indicator.

Abstract: Big data searches, statistical computation and artificial intelligence are leveraged to determine the likelihood that a user will renounce a facility or service post-acceptance. Specifically, the present invention relies on facility/service data and/or user data to key a plurality of data mining searches of big data sources. In response to extracted responsive data from the big data sources, the present invention implements statistical computing along with machine learning/Artificial Intelligence techniques to determine a go/no-go indicator that indicates either (i) the user is unlikely to renounce (i.e., abandon, fail to use and/or return) the facility or service post-acceptance/acquisition, or (ii) the user is likely to renounce the facility/service.

Abstract: A method of automatically administering servers is disclosed. The method includes automatically transmitting commands to at least one of the servers. Administrator level access is non-invasively obtained on the at least one of the servers utilizing the automatically transmitted commands. The at least one of the servers is administered using the administrator level access and the automatically transmitted commands.

Abstract: A method of automatically administering servers is disclosed. The method includes automatically transmitting commands to at least one of the servers. Administrator level access is non-invasively obtained on the at least one of the servers utilizing the automatically transmitted commands. The at least one of the servers is administered using the administrator level access and the automatically transmitted commands.

Abstract: According to one embodiment of the invention, method of automatically administering a plurality of servers includes automatically transmitting commands to at least one of the plurality of servers. Administrator level access is non-invasively obtained on the at least one of the plurality of the servers utilizing the automatically transmitted commands. The at least one of the plurality of the servers is administered using the administrator level access and the automatically transmitted commands.