Abstract: The present disclosure is directed to a novel system for using unique device and user identifiers to perform authentication of a user, device, and/or transaction. In particular, the system may use a heterogeneous blockchain system comprising a series of non-uniform blockchain nodes that may differ in functionality and/or positions of hierarchy. Said non-uniform nodes may further be organized into groups and/or subgroups with custom weight inputs/outputs which alter the degree to which the nodes, groups, and/or subgroups influence the blockchain consensus. In this way, the system increases the security of device authentication by helping to prevent the use of device hijacking methods that circumvent conventional authentication practices.

Abstract: A system is provided which uses unique device and user identifiers to perform authentication of a user, device, and/or transaction. In particular, the system may use a heterogeneous blockchain system comprising a series of non-uniform blockchain nodes that may differ in functionality and/or positions of hierarchy. Said non-uniform nodes may further be organized into groups and/or subgroups with custom weight inputs/outputs which alter the degree to which the nodes, groups, and/or subgroups influence the blockchain consensus. In this way, the system increases the security of device authentication by helping to prevent unauthorized access to computing devices.

Abstract: A system for event-based peer-to-peer resource transfers. The system comprising: a controller configured for tracking and confirming resource transfers, the controller being further configured to: generate a resource transfer for transferring a resource from a first user device to a second user device, wherein the resource transfer comprises a conditional event for triggering a transfer of the resource, and wherein the conditional event is accepted by the first user device and the second user device; receive the resource from the first user device, wherein the resource is held separate from the first user device and the second user device; determine that the conditional event has been executed by at least one of the first user device and the second user device; and based on the determining that the conditional event has been executed, trigger the transfer of the resource to the second user device.

Abstract: A system and method for providing an alternate treatment for an interaction with unauthorized users is provided. The system receives a request from a user to access information and further receives an authentication credential from the user, wherein the authentication credential is received from one or more user computer systems. The system determines that the user is an unauthorized user based on the authentication credential, and in response, provides an alternate treatment to the unauthorized user. Unauthorized user information is captured from the unauthorized user based on the alternate treatment; and used to generate a unique user identification profile for the unauthorized user. User profiles are further categorized for efficient application of additional alternate treatments.

Abstract: Embodiments of the invention are directed to a block chain architecture for optimized system performance and data storage. The invention provides a unique structure comprising levels of interconnected chains that allow for dynamically configurable connections to be formed between the various nodes. Connections between the different of the hierarchy create a continuum of regions allowing for data to be moved across the spiral construction over time depending on the requirements of the data. In this way, storage and management of the data is optimized through a trade-off of data requirements as data moves between different levels of the spiral architecture based on user preference and, ultimately, to permanent data retirement and long-term storage. Further, the present invention provides a method of decentralized data storage across optimized storage regions within the block chain hierarchy.

Abstract: The present disclosure is directed to a novel system for using unique device and user identifiers to perform authentication of a user, device, and/or transaction. In particular, the system may use device biometric profiles and/or user identifiers to generate a unique identifiable signature for each user and/or device. The unique signature may then be used to authenticate devices as well as transactions submitted by said devices. In this way, the system increases the security of device authentication by helping to prevent the use of device hijacking methods that circumvent conventional authentication practices.

Abstract: An electronic communication security system is typically configured for tracking and monitoring user activity of a user, identifying a trigger based on monitoring and tracking the user activity, communicating with back-end system to extract information associated with a resource entity that is associated with the trigger, communicating with the back-end systems to identify user agreement associated with the user and the resource entity, identifying one or more supplemental resources provided by the resource entity, based on the user agreement, prompting the user to authorize transfer of anonymized user data to the resource entity to receive the one or more supplemental resources, anonymizing the user data and transmit the anonymized user data to the resource entity, in response to transmitting the anonymized user data to the resource entity, receiving the one or more supplemental resources from the resource entity, and transmitting the one or more supplemental resources to the user device.

Abstract: An electronic communication security system is typically configured for continuously tracking and monitoring user activity associated with a user, identifying a trigger based on continuously tracking and monitoring the user activity, wherein identifying the trigger is based on identifying that the user activity meets one or more conditions, determining initiation of an interaction between the user and a resource entity based on identifying the trigger, communicating with back-end systems to extract information associated with the resource entity associated with the interaction, wherein the information comprises an output that is generated by one or more machine learning models, generating exposure characteristics for the interaction based on the output associated with the resource entity and user data associated with the user, wherein the exposure characteristics are unique to the interaction and the user, and in response to generating the exposure characteristics, transmitting the exposure characteristics to

Abstract: An electronic communication security system is typically configured for receiving historical data from one or more data sources, wherein the historical data comprises at least one of exposure data associated with one or more exposures, user data associated with one or more users, and resource entity data associated with one or more resource entities, storing the historical data in a historical database, analyzing, using one or more machine learning models, the historical data associated with the one or more exposures, the one or more users and the one or more resource entities, and generating, using the one or more machine learning models, an output associated with each of the one or more resource entities based on analyzing the historical data associated with the one or more resource entities, wherein the output comprises an exposure rating associated with the one or more resource entities.

Abstract: Embodiments of the present invention provide an improvement to conventional machine model training techniques by providing an innovative system, method and computer program product for the generation of synthetic data using an iterative process that incorporates multiple machine learning models and neural network approaches. A collaborative system for receiving data and continuously analyzing the data to determine emerging patterns is provided. The proposed invention involves generating synthetic data clusters to be stored and used for retraining the main model as well as other models. In addition, the invention includes using one or more (subset) of the synthetic data clusters to train or retrain machine learning models, developing and training machine learning models that are trained with emerging synthetic data clusters, and ensembling machine learning models trained with emerging synthetic data clusters.

Abstract: Embodiments of the present invention provide an improvement to convention machine model training techniques by providing an innovative system, method and computer program product for the generation of synthetic data using an iterative process that incorporates multiple machine learning models and neural network approaches. A collaborative system for receiving data and continuously analyzing the data to determine emerging patterns is provided. Common characteristics of data from the identified emerging patterns are broadened in scope and used to generate a synthetic data set using a generative neural network approach. The resulting synthetic data set is narrowed based on analysis of the synthetic data as compared to the detected emerging patterns, and can then be used to further train one or more machine learning models for further pattern detection.

Abstract: A system for generating a hybrid token architecture for chaining authenticated interactions is provided.

Abstract: Systems, computer program products, and methods are described herein for a centralized resource transfer engine for facilitating resource transfers between distributed IoT devices. The present invention is configured to receive, from a first autonomous IoT device, a transaction authorization request to execute a transaction with a second autonomous IoT device; receive information associated with the first autonomous IoT device, information associated with the second autonomous IoT device, and information associated with the transaction; determine one or more constraints associated with the transaction; determine that the first autonomous IoT device is authorized to execute the transaction with the second autonomous IoT device within the one or more constraints; transmit a transaction authorization to the first autonomous IoT device to execute the transaction within the one or more constraints; and receive, from the first autonomous IoT device, an indication that the transaction has been executed.

Abstract: Systems, computer program products, and methods are described herein for a centralized resource transfer engine for facilitating resource transfers between distributed IoT devices.

Abstract: A system for modifying electronic records for tracking use is provided. The system comprises a controller configured for transforming an electronic record, the controller comprising a memory device with computer-readable program code stored thereon, a communication device connected to a network, and a processing device, wherein the processing device is configured to execute the computer-readable program code to: receive a request from a user device to access an electronic record during an interaction session; extract a session identifier signature for the interaction session; generate, with a transformation function, one or more modifications for the electronic record based on the session identifier signature; transform the electronic record to a modified record by applying the one or more modifications to the electronic record; and provide the modified record to the user device.

Abstract: A system for integrated natural language programming (“NLP”) and event analysis provides threat detection in computing systems. In particular, the system may use an NLP unit to analyze threat logs from various sources according to multiple different metrics and/or analysis paradigms. Upon completing the analysis, the system may extract, via machine learning, event and/or threat patterns which may be integrated into the system's threat detection processes.

Abstract: Systems, computer program products, and methods are described herein for machine learning based system for authorization of autonomous resource transfers between distributed IoT components. The present invention is configured to receive, from a first autonomous IoT device, a transaction authorization request to execute a transaction with a second autonomous IoT device; receive information associated with the first autonomous IoT device, information associated with the second autonomous IoT device, and information associated with the transaction; initiate an execution of one or more machine learning algorithms; determine that the first autonomous IoT device is authorized to execute the transaction with the second autonomous IoT device; transmit a transaction authorization to the first autonomous IoT device to execute the transaction; and receive, from the first autonomous IoT device, an indication that the transaction has been executed.

Abstract: Systems, computer program products, and methods are described herein for a centralized resource transfer engine for facilitating resource transfers between distributed IoT devices. The present invention is configured to receive, from a first autonomous IoT device, a transaction authorization request to execute a transaction with a second autonomous IoT device; receive information associated with the first autonomous IoT device, information associated with the second autonomous IoT device, and information associated with the transaction; determine one or more constraints associated with the transaction; determine that the first autonomous IoT device is authorized to execute the transaction with the second autonomous IoT device within the one or more constraints; transmit a transaction authorization to the first autonomous IoT device to execute the transaction within the one or more constraints; and receive, from the first autonomous IoT device, an indication that the transaction has been executed.

Abstract: A system is typically configured for extracting interaction data from one or more data sources, analyzing the interaction data to identify one or more exposure event sequences, storing the one or more exposure event sequences in look-up libraries, modelling a game by mapping one or more interactions associated with the interaction data, continuously monitoring real-time interaction streams, identifying at least one real-time interaction request based on continuously monitoring the real-time interaction streams, mapping the at least one real-time interaction request onto the game, and playing the game, via a neural network, to generate an output associated with the at least one real-time interaction request.

Abstract: Embodiments of the system, as described herein leverage artificial intelligence, machine-learning, and/or other complex, specific-use computer systems to provide a novel approach for identifying patterns in input data and determine and implement necessary changes to a regulated ML model within the bounds of a regulatory control structure. The system utilizes a collection of machine learning models, either individually or clustered, to process incoming data to determine if specific data should be flagged as irregular or part of the formation of an emerging pattern. The system may intelligently analyze such patterns to determine any regulatory implications that may arise from acting on or adapting to the perceived patterns.