Abstract: Aspects of the disclosure relate to cognitive automation-based engine processing to propagate data across multiple systems via a private network to overcome technical system, resource consumption, and architecture limitations. Data to be propagated can be manually input or extracted from a digital file. The data can be parsed by analyzing for correct syntax, normalized into first through sixth normal forms, segmented into packets for efficient data transmission, validated to ensure that the data satisfies defined formats and input criteria, and distributed into a plurality of data stores coupled to the private network, thereby propagating data without repetitive manual entry. The data may also be enriched by, for example, correcting for any errors or linking with other potentially related data. Based on data enrichment, recommendations of additional target(s) for propagation of data can be identified. Reports may also be generated. The cognitive automation may be performed in real-time to expedite processing.

Abstract: Systems and methods for self-referencing utilization calculation and dynamic resource allocation for digital transmissions are provided. Systems may include a target resource module configured to derive and store a target resource allocation for each sender from a plurality of senders. Systems may include a cost-calculation engine configured to generate a resource cost estimate for a transmission based on metadata associated with the transmission, and embed the resource cost estimate in the metadata. Systems may also include a resource allocation module configured to allocate digital resources for the transmission based on the metadata. Allocating digital resources for the transmission may reserve the digital resources for the transmission and prevent overloading of the digital resources.

Abstract: Aspects of the disclosure relate to transforming event data associated with an entity using a multicomputer platform and directing a client computing device to perform a determined action based on a detected event characteristic of the event data and a detected current event. With some embodiments, an event tracking server detects the current event from sensed data provided by a sensed data server and identifies a correlation measure between the detected event characteristic and the detected current event. The event tracking server may direct one or more client computing devices to perform one or more actions related to an entity based on the correlation measure.

Abstract: Intelligent delivery/transmission of data within a secure transmission path of a distributed computing network. A plurality of logical switches are disposed throughout a secure transmission path between a source and target. A controller is configured to control a timing for delivery and/or routing of the data packets to the target apparatus by activating and deactivating the logical switches. In addition, activation of two or more switches provides for isolating data, such that, inline processing (e.g., security checks or the like) can be performed on the data.

Abstract: Aspects of the disclosure relate to providing cloud computing resources from one or more cloud service providers for a client computing device through a computing platform. The client computing device may benefit from an economy of scale while being able to obtain different types of cloud services over a plurality of cloud providers. The client computing device may request an initial amount of cloud services and subsequently may request cloud services that utilize a requested amount of cloud resources. The requested amount of cloud resources may be apportioned among the plurality of cloud service providers, to provide the requested cloud service. The computing platform may also support a cloud abstraction layer interacting between client computing device and one or more cloud providers so that the client computing device can obtain cloud service in a transparent manner.

Abstract: Embodiments of the present invention provide a system for identifying, splicing, and formatting code via machine learning for application analysis. In particular, the system may generate a repository that links multiple historical codes with associated analysis parameters. When a request for a new set of analysis codes is requested for analyzing a first application, the system can determine a set of analysis parameters associated with that first application and determine the set of analysis codes for analyzing the first application through machine learning analysis of the repository. The system may then generate the set of analysis codes by splicing together the determined set of analysis codes and formatting the codes into a format that is compatible with the first application. The system may then automatically execute the code on the first application within a testing environment and display the results on a computing device of an associated user.

Abstract: Distributed tiered data exchanges within a distributed trust/blockchain network are provided for. A continual and ongoing data asset transfer being conducted via a distributed ledger of the distributed trust/blockchain network is leveraged for the purpose of establishing and conducting other continuous and ongoing data asset transfers using the same distributed ledger of the distributed trust/blockchain network. The tiered nature of the asset transfers/distributions means that one distributed ledger can be established and used to conduct multiple different continuous and ongoing data assets transfers between the initial data asset transfer entities and/or other entities. As such, through the use of one distributed ledger of a distributed trust/blockchain network, a single data asset request made by a first entity (i.e., data asset transferee) may be used to make multiple different data asset distributions/transfers to one or many different entities.

Abstract: An intelligent hub for protocol-agnostic file transfer. The hub may include a transceiver, an interrogator processor, an intelligence engine processor, a data translation processor and a hashing processor. The hub may utilize artificial intelligence processes to perform real-time networking or file transfer protocol conversion.

Abstract: Aspects of the disclosure relate to cognitive automation-based engine processing to propagate data across multiple systems via a private network to overcome technical system, resource consumption, and architecture limitations. Data to be propagated can be manually input or extracted from a digital file. The data can be parsed by analyzing for correct syntax, normalized into first through sixth normal forms, segmented into packets for efficient data transmission, validated to ensure that the data satisfies defined formats and input criteria, and distributed into a plurality of data stores coupled to the private network, thereby propagating data without repetitive manual entry. The data may also be enriched by, for example, correcting for any errors or linking with other potentially related data. Based on data enrichment, recommendations of additional target(s) for propagation of data can be identified. Reports may also be generated. The cognitive automation may be performed in real-time to expedite processing.

Abstract: Systems for dynamic authentication are provided. In some examples, a system may receive a request to process an event. In some examples, the request to process the event may include additional details associated with the event. The system may initiate dynamic authentication functions and may retrieve data from a plurality of sources. In some examples, the data from the plurality of sources may be analyzed using machine learning to dynamically generate authentication data, such as one or more authentication questions. The system may also generate one or more corresponding responses or answers to the one or more authentication questions. In some examples, the one or more authentication questions may be transmitted to a user device or other device and may be displayed to the user. The user may provide authentication response data that may be analyzed by the system to determine whether it matches the generated response or answer. If so, the user may be authenticated and/or the event may be processed.

Abstract: Embodiments of the present invention provide a system for identifying, splicing, and formatting code via machine learning for application analysis. In particular, the system may generate a repository that links multiple historical codes with associated analysis parameters. When a request for a new set of analysis codes is requested for analyzing a first application, the system can determine a set of analysis parameters associated with that first application and determine the set of analysis codes for analyzing the first application through machine learning analysis of the repository. The system may then generate the set of analysis codes by splicing together the determined set of analysis codes and formatting the codes into a format that is compatible with the first application. The system may then automatically execute the code on the first application within a testing environment and display the results on a computing device of an associated user.

Abstract: Embodiments of the present invention provide a system and platform for virtual resource management. The system may receive, from a user, an amount of resources in a medium of exchange that is foreign to the user. The system stores the received resources and associates them with a virtual account without converting or exchanging the resources for a local medium of exchange. The system then monitors information associated with the user to determine when a triggering event associated with the received resources is occurring or has occurred. In response to determining that the triggering event is occurring or has occurred, the system automatically executes a triggering action associated with the received resources.

Abstract: Intelligent delivery/transmission of data within a secure transmission path of a distributed computing network. A plurality of logical switches are disposed throughout a secure transmission path between a source and target. A controller is configured to control a timing for delivery and/or routing of the data packets to the target apparatus by activating and deactivating the logical switches. In addition, activation of two or more switches provides for isolating data, such that, inline processing (e.g., security checks or the like) can be performed on the data.

Abstract: A file transfer system that includes a data source, a destination device, and a transfer server. The transfer server is configured to receive a file from the data source and determine a file size of the file. The transfer server is further configured to determine an available disk space for the destination device, to compare the available disk space to the file size of the file, and to determine that the available disk space is less than the file size of the file. In response to the determination, the transfer server is further configured to determine a file type for the file based on content within the file and to identify metadata linked with the determined file type. The transfer server is further configured to extract data from the file corresponding with the identified metadata and to send the data to the destination device.

Abstract: Real-time resource distribution/availability for a resource event that typically would otherwise incur a standard delay period in resource availability. The invention generates a resource depository and provides the resource depository with resources in an amount at least equal to the output of the resource event incurring the delay period. The resources in the resource depository are immediately available as an input another pending resource event. In addition, the present invention provides for implementing machine learning and/or artificial intelligence to determine which resources to use as the input for the resource event that incurs a delay period and/or the timing for conducting the resource event that incurs the delay period and/or the pending resource event.

Abstract: Distributed tiered data exchanges within a distributed trust/blockchain network are provided for. A continual and ongoing data asset transfer being conducted via a distributed ledger of the distributed trust/blockchain network is leveraged for the purpose of establishing and conducting other continuous and ongoing data asset transfers using the same distributed ledger of the distributed trust/blockchain network. The tiered nature of the asset transfers/distributions means that one distributed ledger can be established and used to conduct multiple different continuous and ongoing data assets transfers between the initial data asset transfer entities and/or other entities. As such, through the use of one distributed ledger of a distributed trust/blockchain network, a single data asset request made by a first entity (i.e., data asset transferee) may be used to make multiple different data asset distributions/transfers to one or many different entities.

Abstract: Aspects of the disclosure relate to electronically detecting and responding to a security risk associated with a non-electronic transaction. These aspects include recording and storing a video of a customer entering a financial institution (FI). Upon review of the video, the method confirms that the customer is a legacy customer and then deletes the video, or confirms that the customer is a non-legacy customer. If the customer is a non-legacy customer, the method stores the video for an amount of time sufficient to quantify a security risk associated with the customer. The method may then use an input device including at least one customer-imperceptible biometric sensor to retrieve information about the customer. The method compares the stored video to the biometric information to adjust a level of the security risk associated with the customer or transaction. If the security risk is lower than a threshold, the method may delete the video.

Abstract: Aspects of the disclosure relate to electronically detecting and responding to a security risk associated with a non-electronic transaction. These aspects include recording and storing a video of a customer entering a financial institution (FI). Upon review of the video, the method confirms that the customer is a legacy customer and then deletes the video, or confirms that the customer is a non-legacy customer. If the customer is a non-legacy customer, the method stores the video for an amount of time sufficient to quantify a security risk associated with the customer. The method may then use an input device including at least one customer-imperceptible biometric sensor to retrieve information about the customer. The method compares the stored video to the biometric information to adjust a level of the security risk associated with the customer or transaction. If the security risk is lower than a threshold, the method may delete the video.

Abstract: Systems and methods for self-referencing utilization calculation and dynamic resource allocation for digital transmissions are provided. Systems may include a target resource module configured to derive and store a target resource allocation for each sender from a plurality of senders. Systems may include a cost-calculation engine configured to generate a resource cost estimate for a transmission based on metadata associated with the transmission, and embed the resource cost estimate in the metadata. Systems may also include a resource allocation module configured to allocate digital resources for the transmission based on the metadata. Allocating digital resources for the transmission may reserve the digital resources for the transmission and prevent overloading of the digital resources.

Abstract: A method for extracting, correlating, consolidating and presenting metadata from transmissions is provided. The method may include receiving a TCP/IP transmission. The transmission may include a header and a body. The method may include extracting an originating IP address from a location of the transmission. The location may be in the header or in the body. The IP address may be extracted in binary form. The method may include determining an accuracy and validity metric of the transmission using an artificial intelligence module. The method may include converting the extracted IP address from binary form into hexadecimal form. The method may include embedding the hexadecimal form of the IP address into one or more unused options of the header. The method may include processing the transmission. The processing may be completed upon determination that the transmission is a valid transmission.