Abstract: A system for resolving exceptions in requests determines that a request comprises an exception. The exception impeded processing the request. The system generates a block in a blockchain network. Based on the stored details in the block, the type of exception is identified. Accordingly, the block is segregated for exception processing based on its exception type. The system stores the exception in the block. The system compares the request with user information previously provided by the user. The system determines a similarity score between the request and the user information. The system compares the similarity score with a threshold percentage. In response to determining that the similarity score exceeds the threshold percentage, the system determines that the exception can be resolved and resolves the exception.

Abstract: Arrangements for using a cognitive convergence engine for resource optimization are provided. Requests for service, such as a loan, an account, or the like, may be received via different communication channels. The requests for service may be aggregated based on type of request and transferred to a cloud environment for evaluation. A request may be evaluated to determine whether it is eligible for bot processing. If so, the request may be transferred to a bot server for processing. If not, the request may be evaluated to identify a best fit resource for processing. Identifying the best fit resource may include scores computed by a plurality of computational instances or virtual machines configured to process the computations. A number of computational instances may be determined based on a volume of requests. The computational instances may then be deployed to determine a best fit resource for the first request.

Abstract: Systems, computer program products, and methods are described herein for secure document retrieval and sharing using multistage encryption and authorization. The method includes receiving a user location verification request for an account associated with a user. The user location verification request is for one or more documents relating a user residence. The method includes receiving at least one encryption key for document(s) associated with the user. The document(s) associated with the user relate to the user residence of the user. The method also includes causing a transmission of at least one encryption key used to access document(s) associated with the account. The method further includes determining the user residence of the user based on the document(s) obtained via the at least one encryption key. The method still further includes determining an account access level for the account associated with the user based on the user residence.

Abstract: Communication, authentication, and/or validation may be required in various types of device interactions. For example, for some types of device functions, user or device authentication may be required to confirm that the function can and should be performed. Authentication information may be communicated and authentication and/or validation may be performed using LoRaWAN signaling. LoRaWAN signaling provides low power and long-range communication capabilities to efficiently and effectively perform authentication processes. In some arrangements, LoRaWAN signals may be generated by both a first device (e.g., a user device) and a second device (e.g., a function-performing device) and subsequently compared to determine whether they correspond. This correspondence may provide the requisite authentication or validation of the first device and/or an associated user.

Abstract: Aspects of the disclosure relate to NFT exchanges. A computing platform may generate a plurality of F-NFTs including F-NFT identifiers that configure the F-NFTs for end-to-end tracking. The computing platform may extract, from a voice communication, information of a request to transfer an F-NFT. The computing platform may generate, based on the information of the request, a text file. The computing platform may input, into a GPT-4 algorithm, the text file to produce a F-NFT contract identifying the F-NFT, the transferor of the F-NFT, and a transferee of the F-NFT. The computing platform may send, to an NFT exchange platform, the F-NFT contract, which may cause the NFT exchange platform to transfer custody of the F-NFT from an account of the transferor at a first institution to an account of the transferee at a second institution.

Abstract: A system and method for voice processing using photonic quantum computing. A method includes receiving voice data represented by classical binary bits. The voice data is converted to a first photon beam including converted voice data represented by photonic quantum bits. The first photon beam is split into split photon beams. The split photon beams are received by a quantum neural network, which includes quantum neural network clusters. The converted voice data is processed by processing the split photon beams. Each of the split photon beams is processed by a respective neural network cluster in parallel. Processing the converted voice data includes extracting voice features. Word embeddings are created based on the voice features. Sentences are determined based on the word embeddings.

Abstract: Communication, authentication, and/or validation may be required in various types of device interactions. For example, for some types of device functions, user or device authentication may be required to confirm that the function can and should be performed. Authentication information may be communicated and authentication and/or validation may be performed using LoRaWAN signaling. LoRaWAN signaling provides low power and long-range communication capabilities to efficiently and effectively perform authentication processes. In some arrangements, LoRaWAN signals may be generated by both a first device (e.g., a user device) and a second device (e.g., a function-performing device) and subsequently compared to determine whether they correspond. This correspondence may provide the requisite authentication or validation of the first device and/or an associated user.

Abstract: A system for validating a response based on context information receives a first message that indicates that a data object is removed from a memory resource via a third party device without authorization by a user. The system communicates a second response that indicates whether a third party confirms the removal of the data object without the authorization by the user to the third party device. The system receives a response from the third party device. The system extracts context information from the response. The system determines whether the response is valid based on the context information. In response to determining that the response is valid, the system recommends one or more actions to be performed with respect to the memory resource.

Abstract: Device information may be obtained using LoRaWAN communications. A device information request may be generated and transmitted by a user device and include request parameters such as a device type for which information is desired, a type of information requested, a geographic range for the request, and the like and/or combinations thereof. Device information requests may also be propagated or repeated by intermediary devices to achieve the desired request range. Accordingly, a recipient device of the original request may propagate or repeat, using LoRaWAN, a request signal if it determines that the request range has not been met. If the request range has been met, the recipient device might not repeat the request.

Abstract: Aspects of the disclosure relate to a cloud-based parallel processing and cognitive learning computing platform. A computing platform may receive query data comprising a plurality of requested metrics. The computing platform may select a secondary node to process the query request from a plurality of secondary nodes of the computing platform. The computing platform may send the query request to the secondary node. The computing platform may send a request to generate a metric mapping table for a first requested metric to a cognitive learning module of the computing platform. The cognitive learning module may generate the metric mapping table for the first requested metric using neuron clusters that comprise a plurality of unique programmable quadratic function neurons. The computing platform may calculate a value for the first requested metric based on the metric mapping table.

Abstract: Aspects of the disclosure relate to NFT exchanges. A computing platform may generate a plurality of F-NFTs including F-NFT identifiers that configure the F-NFTs for end-to-end tracking. The computing platform may extract, from a voice communication, information of a request to transfer an F-NFT. The computing platform may generate, based on the information of the request, a text file. The computing platform may input, into a GPT-4 algorithm, the text file to produce a F-NFT contract identifying the F-NFT, the transferor of the F-NFT, and a transferee of the F-NFT. The computing platform may send, to an NFT exchange platform, the F-NFT contract, which may cause the NFT exchange platform to transfer custody of the F-NFT from an account of the transferor at a first institution to an account of the transferee at a second institution.

Abstract: Device information may be obtained using LoRaWAN communications. A device information request may be generated and transmitted by a user device and include request parameters such as a device type for which information is desired, a type of information requested, a geographic range for the request, and the like and/or combinations thereof. Device information requests may also be propagated or repeated by intermediary devices to achieve the desired request range. Accordingly, a recipient device of the original request may propagate or repeat, using LoRaWAN, a request signal if it determines that the request range has not been met. If the request range has been met, the recipient device might not repeat the request.

Abstract: A system receives a speech of a user that indicates a request. The system extracts a plurality of voice features from the speech. The system converts the speech into a plurality of binary digits. The system determines a first voice feature constant value associated with a first voice feature, where the first voice feature constant value is an average of the first voice feature. The system determines a second voice feature constant value associated with the second voice feature, where the second voice feature constant value is an average of the second voice feature. The system encrypts the plurality of binary digits using the first and second voice feature constant values, where the encrypted plurality of binary digits corresponds to a voice-based hash value. The system generates a new block in a blockchain network using the voice-based hash value.

Abstract: Transactions may be performed using a mobile device such as a user-wearable device using LoRaWAN protocol to minimize power consumption while maximizing communication range. Transaction information may be stored in the user-wearable device and transmitted to a transaction processing device using LoRaWAN communications. Transaction information may include user-specific information, payment information, merchant information, product information, user preference information and the like. In some instances, the only communication interface (or wireless communication interface) provided in the mobile device is a LoRaWAN communication interface.

Abstract: The systems and methods described include the ability to store such information in an immutable and distributed, verifiable fashion. Furthermore, the system includes enhanced means for retrieving, interacting with, and visualizing such information via user interface on a variety of user device types. The system is further configured to intelligently determine data forwarding decisions based on one or more configurable data guidelines as determined by one or more entities or third party systems, and provide access to verified data to these entities and systems.

Abstract: Systems, computer program products, and methods are described herein for monitoring and removing drift in machine learning models. The present disclosure is configured to receive, from one or more control automation modules, a data stream, wherein the data stream comprises data associated with the one or more control automation modules; transmit the data stream to a gauging and monitoring module; determine whether the data stream matches a declarative mapping protocol; determine one or more deviation instances in an instance in which the data stream does not match the declarative mapping protocol; determine one or more prescriptive actions for the one or more deviation instances; and implement, using an intelligence restoration module, the one or more prescriptive actions on the one or more control automation modules.

Abstract: A system communicates a request to a plurality of network nodes to provide user information associated with a document. The system receives a plurality of log files that comprises data communications among the network nodes and responses to the request. The system determines a first number of network nodes that are associated with a first anomaly from the plurality of log files. The system determines a second number of network nodes that are associated with a second anomaly from the plurality of log files. The system determines the number of malicious network nodes by combining the first number and the second number of network nodes. If the system determines that the number of malicious network nodes exceeds a threshold percentage, it assigns one or more network nodes to store the user information until the number of malicious network nodes is less than the threshold percentage.

Abstract: A system for resolving exceptions in requests determines that a request comprises an exception. The exception impeded processing the request. The system generates a block in a blockchain network. Based on the stored details in the block, the type of exception is identified. Accordingly, the block is segregated for exception processing based on its exception type. The system stores the exception in the block. The system compares the request with user information previously provided by the user. The system determines a similarity score between the request and the user information. The system compares the similarity score with a threshold percentage. In response to determining that the similarity score exceeds the threshold percentage, the system determines that the exception can be resolved and resolves the exception.

Abstract: A system includes a production computing environment including a plurality of components, a centralized data repository that receives and stores data feeds relating to one or more components as a data log and at least one processor configured to obtain the data log of each component, generate a current state vector for the component based on the data log, compare the current state vector to a normal state vector of the component, determine that the current state vector deviates from the normal state vector, and in response, predict an anomaly associated with the component using an iterative machine learning method. The at least one processor may be configured to correct the predicted anomaly by taking at least one pre-configured action corresponding to the predicted anomaly.

Abstract: Systems, computer program products, and methods are described herein for secure document retrieval and sharing using multistage encryption and authorization. The method includes receiving a user location verification request for an account associated with a user. The user location verification request is for one or more documents relating a user residence. The method includes receiving at least one encryption key for document(s) associated with the user. The document(s) associated with the user relate to the user residence of the user. The method also includes causing a transmission of at least one encryption key used to access document(s) associated with the account. The method further includes determining the user residence of the user based on the document(s) obtained via the at least one encryption key. The method still further includes determining an account access level for the account associated with the user based on the user residence.