Abstract: Systems and methods for managing resources across a global and/or cloud network. In particular, systems and methods for mitigating issues related to providing services while resources are off-line (or may potentially go off-line). For example, the systems and methods may mitigate issues related to providing services while resources are off-line (or may potentially go off-line) by monitoring network services at an aggregate level.

Abstract: Presented herein are systems and methods for the employment of machine learning models for image processing. A method may include a capture of a video feed including image data of a document at a client device. The client device can provide the video feed to another computing device. The method can include, by the client device or the other computing device object recognition for recognizing a type of document and capturing an image exceeding a quality threshold of the document amongst the frames within the video feed. The method may further include the execution of other image processing operations on the image data to improve the quality of the image or features extracted therefrom. The method may further include anti-fraud detection or scoring operations to determine an amount of risk associated with the image data.

Abstract: The systems and methods disclosed herein receive a dataset including an observed set of values for a set of variables. The system can use a first set of AI models to identify a set of anomalies in the observed set of values by comparing an observed set of patterns against multiple reference patterns. The system can use a second set of AI models to evaluate the identified anomalies by comparing an observed set of association rules with an expected set of association rules. The system can use a third set of AI models to generate reconfiguration commands to remove the identified anomalies. The reconfiguration commands can be automatically executed to modify the observed association rules to align with the expected association rules.

Abstract: Methods and systems for routing execution requests to autonomous artificial intelligence agents using performance-based selection are disclosed herein. An agent routing system may receive a prompt for completing an action using artificial intelligence agents from a plurality of agents, where each agent may be associated with computer-executable operations configured for autonomous execution on software applications. The system may determine a set of agents enabled to complete the requested action and may access action completion datasets for these agents. Each dataset may include action parameters for completed actions, including status parameters indicating successful completion and timestamps of completion. The system may select an artificial intelligence agent based on a frequency composite parameter generated from successful completion status parameters and a time composite parameter indicating completion timing.

Abstract: Systems and methods for uses and/or improvements to data transfers that mitigate and/or avoid the metadata overwrites. For example, systems and methods for data transfer operations such as those involving, but not limited to, performing multicomputer data transferring across computer networks without staging servers, synthetically generating metadata subject to multicomputer data transferring across computer networks, or performing multicomputer data transfer across computer networks for real-time metadata ingestion. The systems and methods first load data into a buffer and then stream it directly to the target location. By performing data transfers by first loading data into a buffer and then streaming it directly to the target location, the system avoids many metadata-preservation challenges because the data is treated as a raw byte stream rather than as an object being rewritten by the storage system.

Abstract: Systems and methods for executing multi-party validations for complex communications across multiple computer networks using bifurcated digital signing processes. For example, the system may receive a first request, at a bifurcated digital signing process, to perform a first multi-party validation action, wherein the first request has a first processing characteristic. The system may, in response to receiving the first request, process the first request with a first signing routine of the bifurcated digital signing process. The system may determine, based on the first signing routine, whether the first processing characteristic corresponds to a predetermined processing characteristic. The system may, in response to determining that the first processing characteristic corresponds to the predetermined processing characteristic, process the first request with a second signing routine of the bifurcated digital signing process to perform the first multi-party validation action.

Abstract: A system for handling interrupts in a cloud computing environment is disclosed. The system may include a controller process that receives an interrupt indicator from a worker process, signaling a potential interruption. The controller may determine if the worker process is expected to be halted and removed within a predetermined time period. If so, the controller may transmit a status request to the worker process. In response, the controller may receive completed task results and determine uncompleted tasks. The controller may generate a checkpoint data structure indicating completed and uncompleted tasks. A computational payload comprising the completed results and checkpoint data structure may be transmitted for recording within the cloud computing system. This approach may enable efficient management of computational resources and task continuity in cloud environments subject to periodic process halting and removal.

Abstract: The technology may be utilized in network environments that use fiber optic cables to connect components. Components of the network perform functions as transceivers that transmit and receive light signals via the fiber cable. When optic power data received from the transceivers is recognized as a fault, an event is identified. The system logs each received event and converts all measurements into a common unit of measurement. The system analyzes aggregated data from the transceivers to detect patterns and trends, monitor the received data and provide real time fault predictions. For example, a machine learning process may recognize subtle trends or patterns in the data, and use the recognition to predict potential failures. The system uses the received data to create a graphical user interface (“GUI”) that represents the health of the network.

Abstract: Systems and methods for uses and/or improvements to data transfers that mitigate and/or avoid the metadata overwrites. For example, systems and methods for data transfer operations such as those involving, but not limited to, performing multicomputer data transferring across computer networks without staging servers, synthetically generating metadata subject to multicomputer data transferring across computer networks, or performing multicomputer data transfer across computer networks for real-time metadata ingestion. The systems and methods first load data into a buffer and then stream it directly to the target location. By performing data transfers by first loading data into a buffer and then streaming it directly to the target location, the system avoids many metadata-preservation challenges because the data is treated as a raw byte stream rather than as an object being rewritten by the storage system.

Abstract: A distributed ledger technology application can establish a first connection with a computing application executed by a banking computing infrastructure and a second connection with one or more nodes hosting a distributed ledger via a second communication protocol. The application can receive, from a client device through the through the first connection using the first communication protocol, a receive instruction to complete an asset transfer to transfer an asset from a source account hosted in the banking computing infrastructure to a DLT-based client account. The application can execute an asset conversion agent to determine whether any asset conversion is required. The application can execute an instruction generation agent to generate a new receive instruction. The application can execute a ledger agent to generate an instruction to cause the one or more nodes to add a record of the asset transfer to the distributed ledger.

Abstract: Systems and methods for uses and/or improvements to data transfers that mitigate and/or avoid the metadata overwrites. For example, systems and methods for data transfer operations such as those involving, but not limited to, performing multicomputer data transferring across computer networks without staging servers, synthetically generating metadata subject to multicomputer data transferring across computer networks, or performing multicomputer data transfer across computer networks for real-time metadata ingestion. The systems and methods first load data into a buffer and then stream it directly to the target location. By performing data transfers by first loading data into a buffer and then streaming it directly to the target location, the system avoids many metadata-preservation challenges because the data is treated as a raw byte stream rather than as an object being rewritten by the storage system.

Abstract: A computer-implemented method comprising receiving a transaction request from a first node within the plurality of nodes, the transaction request corresponding to a pending transaction between the first node and a second node; identifying a distributed ledger associated with the pending transaction, the distributed ledger including a first record having a hierarchy file indicating a hierarchy among the first node, the second node, and the third node; identifying a second record having an executable file to approve the transaction request received from the first node; executing the executable file, wherein the executable file is configured to retrieve data from a data source associated with the third node and analyze data to approve or deny the transaction request; and appending a third record comprising the set of transaction attributes to the distributed ledger.

Abstract: Systems and methods are described herein for novel uses and/or improvements for autonomous configuration of network applications on a network. When a request is received for adding a new application to the network, the system may analyze the request and identify which network devices need a change in configuration. The system may also determine, based on the request, what type of changes are needed. The system may then generate one or more commands for each identified network device and transmit those commands to each device for performing automatic network configuration for the application.

Abstract: Systems and methods are described herein for novel uses and/or improvements for autonomous configuration of network applications on a network. When a request is received for adding a new application to the network, the system may analyze the request and identify which network devices need a change in configuration. The system may also determine, based on the request, what type of changes are needed. The system may then generate one or more commands for each identified network device and transmit those commands to each device for performing automatic network configuration for the application.

Abstract: The systems and methods disclosed herein monitor application (e.g., artificial intelligence (AI) model) operations using interactions between the application and a kernel. The systems and methods disclosed herein intercept, using a kernel interface, one or more function invocations transmitted from the application (e.g., an AI model without model modification). Event record(s) are generated for one or more functions to define process identifiers, resource interaction types, timestamps, and/or resource identifiers. Observed pattern(s) for the application are identified by comparing current event record(s) with previous record(s), and the identified observed pattern(s) are evaluated against reference pattern(s) to generate score(s). Data packet(s) that indicate observed pattern(s), corresponding score(s), and/or cryptographic digital fingerprint(s) of the one or more functions are generated. The data packet(s) are transmitted to distributed ledgers for immutable storage.

Abstract: Systems and methods disclosed herein perform privacy-preserving evaluations of artificial intelligence (AI) agents. The system identifies an auditing AI agent from a set of auditing AI agents for assessing target AI agent sets. The system obtains a data structure that defines operative boundaries for a target AI agent set and generates a reference value by applying a first transformation operation set on the data structure. The system transmits the reference value to a multi-agent storage and receives, via the multi-agent storage, a verification artifact from the target AI agent set that indicates an observed value generated by applying a second transformation operation set on an artifact set generated by the target AI agent set. The system determines, via the auditing AI agent, a verification status and responsive to a particular artifact failing to satisfy one or more assessment metrics, generates an action set to modify the target AI agent set.

Abstract: The systems and methods disclosed herein monitor application (e.g., artificial intelligence (AI) model) operations using interactions between the application and a kernel. The systems and methods disclosed herein intercept, using a kernel interface, one or more function invocations transmitted from the application (e.g., an AI model without model modification). Event record(s) are generated for one or more functions to define process identifiers, resource interaction types, timestamps, and/or resource identifiers. Observed pattern(s) for the application are identified by comparing current event record(s) with previous record(s), and the identified observed pattern(s) are evaluated against reference pattern(s) to generate score(s). Data packet(s) that indicate observed pattern(s), corresponding score(s), and/or cryptographic digital fingerprint(s) of the one or more functions are generated. The data packet(s) are transmitted to distributed ledgers for immutable storage.

Abstract: The systems and methods disclosed herein orchestrate memory sharing among autonomous (or semi-autonomous) AI agentic models (“agents”) by dynamically organizing agent memories based on operational characteristics, taxonomic classifications, and/or other factors. Received operational data (e.g., token sets) are mapped to predefined taxonomic nodes (e.g., taxonomic node subsets) based on a degree of similarity between the operational data and predefined taxonomic nodes. An ontological reference pointer is determined for the operational data based on a position of the taxonomic node subset within the ontological data structure. The operational data is compressed into a memory capsule data structure using the ontological reference pointer. A blockchain transaction identifier is generated for the memory capsule data structure and stored in a distributed network accessible to multiple agents.

Abstract: Methods and systems for cloud-based resource transmission are disclosed. A system may receive a resource allocation message from a computing device in a source encoding. The message may be transformed into a destination encoding using a transformation map to generate an encoded message. The encoded message may be transmitted to a message processing system. An error message with an error code may be received in response. The system may determine which message caused the error based on the error code. It may be determined that the first computing device caused the error. An indication of the error code may be transmitted to the first computing device. This approach may enable efficient handling and error resolution for resource allocation messages across multiple encodings and devices.

Abstract: A computer-implemented method comprising receiving a first event request corresponding to a pending event between a first computing device and a second computing device, the first event request comprising a first set of event attributes corresponding to the pending event; transmitting, each of a plurality of first subsets of event attributes to a different set of nodes; receiving a second event request corresponding to the pending event between the first computing device and the second computing device; and transmitting each of the plurality of second subsets of event attributes to a different set of the plurality of sets of nodes, wherein each of the plurality of sets of nodes compares the first subset of event attributes that the set of nodes receives with the second subset of event attributes that the set of nodes receives; and a block instance corresponding to the pending event to a blockchain.