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.

Abstract: A system splits the text into at least a first and a second portions. The system extracts a first context information from the first portion, and a second context information from a second portion in response to feeding the plurality of portions to a first plurality of neuron logic gates. The system compares the first context information with the second context information. If it is determined that the first context information is different from the second context information, the system dynamically activates at least one of a second plurality of neuron logic gates. The system determines an additional information from at least one of the first portion and second portions. The system updates at least one of the first context information and the second context information to include the additional information. The system generates a first output that comprises the updated first context information and the updated second context information.

Abstract: A system accesses a scan of a document that shows first user information. The system extracts the first user information from the scan of the document. The system identifies an entity associated with the document, where an identifier associated with the entity is shown on the scan of the document. The system communicates a request to provide second user information to a computing device associated with the entity. The system receives a response comprising the second user information and compares each component in the first user information with a counterpart component in the second user information. The determines whether the document is valid based on the comparison between the first user information and the second user information.

Abstract: Aspects of the disclosure relate to processing asset transfers. A computing platform may receive an asset transfer request, and may modify a distributed ledger to include a new block corresponding to the asset transfer request. The computing platform may identify, for each of a plurality of consensus methods, a subset of existing blocks that, when used to perform the corresponding consensus method, uses a minimum number of the existing blocks to perform the corresponding consensus method. The computing platform may store, in the distributed ledger, block combinations having the minimum number for each of the plurality of consensus methods, and may execute, using each corresponding identified subset of the existing blocks, each of the plurality of consensus methods. Based on establishing the consensus, the computing platform may direct an event processing platform to process the asset transfer request, which may cause the event processing platform to process the request.

Abstract: A system for resolving exceptions in requests determines that a request comprises an exception. The exception impeded processing the request to be granted or denied. The system determines a type of exception that indicates whether the exception is incomplete information, incorrect information, or previously-unknown information. 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 and method for parallel data processing using photonic quantum computing. A method includes receiving tasks represented by classical binary bits. The tasks are converted to a first photon beam including converted tasks, which are represented by photonic quantum bits. The first photon beam is split into split photon beams. Each split photon beam corresponds to a subset of the converted tasks that have a same work type. The split photon beams are received by a quantum neural network, which includes quantum neural network clusters. Each split photon beam is received by a respective quantum neural network cluster. Each subset of the converted tasks is processed by a respective quantum neural network cluster in parallel. Processing the converted tasks includes identifying and removing one or more duplicate tasks in each subset of the converted tasks. Outputs are generated based on respective converted tasks as a second photon beam.

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: A system and method for image processing using photonic quantum computing. A method includes receiving image data represented by classical binary bits. The image data is converted to a photon beam including converted image data represented by photonic quantum bits. The photon beam is split into red-, green-, and blue-channel photon beams, which are received by a quantum neural network including quantum neural network clusters. The converted image data is processed by processing the red-, green-, and the blue-channel photon beams. Each beam is processed by a respective neural network cluster in parallel. Processing the red-channel photon beam includes converting converted red-channel image data into first grayscale image data. First filters are applied to the first grayscale image data to extract first features. The first features are compared to pre-trained features. In response to the first features matching respective pre-trained features, identified first features are identified.

Abstract: Transactions between entities may be validated using LoRaWAN communications to more quickly process those transactions. In the example of check depositing, a first entity associated with a check payee may transmit a check clearance request to a second entity associated with a check payor using LoRaWAN communications. The LoRaWAN signaling and communications may be performed between devices within LoRaWAN range. For example, a check depositing device may receive a check deposit where the payor is associated with a different financial institution or entity. Accordingly, the check depositing device may communicate with another device associated with the payor institution or entity using LoRaWAN signals to confirm check clearance.