Abstract: Systems and methods for generating and testing on-chain programs are disclosed herein. A system for generating on-chain programs may receive a request to generate on-chain program code for an on-chain program and determine a model required for the program. The system may retrieve functions associated with the model and determine blockchain operations to be added to the program. The system may determine parameters for blockchain operations and generate on-chain program code. A system for testing on-chain programs may install an on-chain program onto a test blockchain and retrieve functions associated with a model generated for testing. The system may generate blockchain operations for testing and input each operation into a cryptography-based storage application. The system may cause execution of the operations and receive results. Based on determining that a number of failed results exceed a threshold, the system may generate a notification.

Abstract: Systems and methods for real-time mapping and visualization generation of system components. The system may receive a first user request to generate a first visualization of a first configuration of a first subset of components in a first software system. The system may, in response to the first user request, retrieve a first software applications lineage log, wherein the first software applications lineage log comprises a log of event data of current processes being performed in the first software system. The system may generate a first feature input based on the first software applications lineage log. The system may input the first feature input into a first artificial intelligence model to generate a first output.

Abstract: Systems and methods for a bifurcated self-executing program that wraps a first self-executing program (e.g., a first smart contract) on a blockchain within a second self-executing program (e.g., a second smart contract), in which the second self-executing program enforces the digital signature requirement. The bifurcated self-executing program comprises a single compiled self-executing program that combines the first self-executing program and the second self-executing program.

Abstract: Systems and methods are described herein for novel uses and/or improvements to data aggregation related to artificial intelligence applications, specifically applications related to aggregating time-series data. As one example, systems and methods are described herein for predicting effects when aggregating time-series data and modifying the one or more data streams used to populate a model profile and/or feed an artificial intelligence application with the time-series data.

Abstract: Methods and systems are described herein for tracking function completion using electronic artifacts to address the need for systematic management and utilization of completion data in various job management fields such as software, banking, insurance, etc. A function completion data structure may be generated based on user input. The data structure may include a plurality of electronic artifacts and dependency data indicating relationships between these artifacts. For each electronic artifact, a corresponding set of progress tracking templates may be generated, each indicating the completion of a function associated with the artifact. Progress data for a first electronic artifact may be received and updated. Appropriate progress tracking templates may be selected to be completed by associated operators. Once that completion data is received, artifacts may be updated, and other artifacts may be selected for completion.

Abstract: Systems and methods for cross-platform authorization are disclosed herein. A system may receive, at a server, a request to generate an authorization token for a service. The request may include an indication of a service public key and an encrypted service account token. The system may decrypt, using a second service key, the encrypted service account token into a service account token. The system may then generate a service identity for the service and a key identifier and an access key pair that includes a private and public access key. The system may generate a service identity token and a server token that includes access permissions for the service. The system may then encrypt the server token and the service identity token using the second service key into an encrypted usage token and transmit the encrypted usage token.

Abstract: Systems and methods are described herein for novel uses and/or improvements for determining malfunctions within undeployed artificial intelligence agents. A plurality of parameter combinations may be generated from parameters used to train an artificial intelligence agent. The plurality of parameter combinations may be used to generate a plurality of test requests. The plurality of test requests may be input into the undeployed artificial intelligence agent and one or more deployed artificial intelligence agents. The outputs of the agents may be compared to determine a context differential. The context differential may be used to determine one or more categories where the undeployed artificial intelligence agent's performance is deficient.

Abstract: Systems, methods, and devices for monitoring and controlling communications between autonomous agents are disclosed. The system monitors real-time communications between autonomous agents, intercepting and recording each communication. Communications are translated to a standardized language and processed through communication protocol filters that evaluate compliance with predefined operational policies. The system parses each translated communication to identify policy violations. When violations are detected, the system modifies communications to ensure compliance with operational policies. All communications and modifications are recorded via distributed ledger technology for audit and accountability purposes. This approach enables comprehensive oversight of autonomous agent interactions while maintaining tamper-proof records of all monitoring and control activities.

Abstract: Systems and methods for monitoring emerging faults at a network system are disclosed herein. The system may receive, such as from tool-based monitoring systems, data including measurements and testing results. The system may input an operational metric dataset into a fault detection machine learning model to obtain an indication of and degree of the fault condition at the network system. Responsive to determining that the degree of the fault condition indicates uncertainty of whether fault has occurred, the system may identify second commands. Based on transmitting the second commands, additional parameters may be obtained for generating an augmented dataset. The system may input the augmented dataset into the fault detection machine learning model to obtain an updated indication of the fault condition and updated degree of the fault condition and transmit an alert to an operator device.

Abstract: Systems and methods for secure data-retrieval are disclosed herein. A system for secure data retrieval initializes a session between a user-specific model executing locally at a user device and an entity-specific model at an entity device. The user-specific model has access to a locally stored ontology comprising interaction data. The system can receive a request from the entity device, where resolving the request requires access to the interaction data from the ontology. The system inputs the request into the user-specific model to generate a query for obtaining interaction data from one or more classes of the ontology associated with the request. The system can generate a set of filtering operations, which can be applied to the interaction data to obtain filtered data. The system can use the filtered data to generate and transmit a response.

Abstract: Systems, methods, and devices for error detection and remediation in software applications are disclosed. The system receives functional specifications and user requirement specifications for a software application. A prompt is generated for a generative machine learning model to output dynamic parameters that enable error detection within software elements during application generation phases. These dynamic parameters comprise application-specific rules based on the specifications. The prompt is input into the model to output dynamic parameters. Quality metrics are generated for software elements using these parameters, indicating conformity to specifications. Errors are detected when quality metrics show software elements fail to conform to requirements. Modifications are generated to remediate errors and then automatically implemented in the software elements.

Abstract: Disclosed herein are systems, methods, and computer-readable media for generating and transmitting event artifacts based on events that are detected using artificial intelligence (AI) agents. One or more AI agents can monitor a plurality of sources to detect events associated with available interactions with one or more public entities. In response to detecting an occurrence of a first event, the one or more AI agents can assign a first priority metric to the first event. The first priority metric can be based on particular properties of a primary entity. In response to the first priority metric falling within a first predetermined metric range, the one or more AI agents can generate a first event artifact based on the first event and the properties of the primary entity. The first event artifact can be transmitted to the primary entity via one or more engagement channels of a multimodal interface.

Abstract: The present disclosure provides a system for securing communications between services in different computing environments. A pair-wise secret key may be generated at an inspection layer for a first service in a first environment and a second service in a second environment. Each service may be associated with an inspection layer component, and the pair-wise secret key is provided to both components. When a communication with encrypted payload is received from the first service, an initial decryption vector may be determined using the communication, creating a unique vector for each communication. A unique decryption key may be generated combining the decryption vector and pair-wise secret key. The encrypted payload may then be decrypted using this unique key, and the decrypted communication may be transmitted to the second service for processing.

Abstract: Systems and methods for generating and testing on-chain programs are disclosed herein. A system for generating on-chain programs may receive a request to generate on-chain program code for an on-chain program and determine a model required for the program. The system may retrieve functions associated with the model and determine blockchain operations to be added to the program. The system may determine parameters for blockchain operations and generate on-chain program code. A system for testing on-chain programs may install an on-chain program onto a test blockchain and retrieve functions associated with a model generated for testing. The system may generate blockchain operations for testing and input each operation into a cryptography-based storage application. The system may cause execution of the operations and receive results. Based on determining that a number of failed results exceed a threshold, the system may generate a notification.

Abstract: Presented herein are systems and methods for the employment of machine learning models for image processing. A mobile application for client-side image processing and validation, which interacts with and leverages native image processing software of the client device, where the image processing software and the mobile application include any number of machine-learning models for identifying a document and attributes of the document for recognition and validation. This mobile application uses the image processing software from a client operating system to control the camera. The image processing software generates various types of information about a video frame and the document, and the mobile application invokes APIs or software libraries of the image processing software to access the information and validate the frame and document.

Abstract: Presented herein are systems and methods for the employment of machine learning models for image processing. A mobile application for client-side image processing and validation, which interacts with and leverages native image processing software of the client device, where the image processing software and the mobile application include any number of machine-learning models for identifying a document and attributes of the document for recognition and validation. This mobile application uses the image processing software from a client operating system to control the camera. The image processing software generates various types of information about a video frame and the document, and the mobile application invokes APIs or software libraries of the image processing software to access the information and validate the frame and document.

Abstract: Presented herein are system and methods for integrating application programming interfaces (APIs) for use in network environments. A service of an API management platform may receive, from an administrator device, a request to deploy an API for use in a network environment among one or more applications. The request may include a specification defining the API according to a template for a domain of a plurality of domains. The service may identify, from a plurality of policies corresponding to the plurality of domains, a policy based on the domain for the template with which the specification of the request is defined. The service may determine that the API is validated in accordance with the policy for the domain. The service may generate an indication of approval of the API for use in the network environment among the one or more applications, responsive to determining that the API is validated.

Abstract: Presented herein are systems and methods for the employment of machine learning models for image processing as may be performed by computing devices associated with an end user. A method may include obtaining video data comprising a plurality of frames including a document of a document type. The method may include executing an object recognition engine of a machine-learning architecture using image data of the plurality of frames, the object recognition engine trained to detect edges of documents. The method may include identifying, based on the edge detection, a plurality of boundaries for the document. The method may include validating, based on the plurality of boundaries, the document as the document type. The method may include transmitting via one or more networks, to a computer remote from the computing device, responsive to the validation of the type of document, the image data for the plurality of frames depicting the document.

Abstract: Systems and methods are described herein for novel uses and/or improvements for determining malfunctions within undeployed artificial intelligence agents. A plurality of parameter combinations may be generated from parameters used to train an artificial intelligence agent. The plurality of parameter combinations may be used to generate a plurality of test requests. The plurality of test requests may be input into the undeployed artificial intelligence agent and one or more deployed artificial intelligence agents. The outputs of the agents may be compared to determine a context differential. The context differential may be used to determine one or more categories where the undeployed artificial intelligence agent's performance is deficient.

Abstract: Systems and methods for dynamically managing application programming interface (API) resource allocation and rate limits are disclosed herein. An API monitoring system receives usage monitoring data for APIs, where each API is associated with a corresponding rate limit. A machine learning model processes the usage monitoring data to predict time periods when API usage is expected to exceed rate limits. Based on determining that a matching time period has been predicted for a subset of oversubscribed APIs, the system retrieves resource allocation data and identifies undersubscribed APIs from which resources may be reallocated. The system dynamically adjusts memory, processing power, or network bandwidth allocations and updates corresponding rate limits to balance API demand. By proactively reallocating resources and adjusting rate limits, the system optimizes API performance, prevents failures, and ensures efficient utilization of computing resources.

Abstract: The model generation platform enables generation of machine learning models based on modular storage of user-specified training data. The platform can obtain a dataset from a different heterogenous source (e.g., a structured database, an unstructured database, a semi-structured file system, manual upload of a comma-separated value file, a spreadsheet, and/or big data) through an associated application programming interface and store this data in a first storage medium. The model generation platform can obtain an indication of a portion of the dataset from a user via a user interface and determine a second storage medium for this portion of the dataset based on an associated estimated performance metric. In response to a request for generation of a machine learning model, the model generation platform can generate a machine learning model using training data comprising a subset of the portion of the dataset.

Abstract: The present disclosure provides a malfunction interdiction system with monitoring, detection, and response capabilities specifically designed for distributed AI environments. The system may first detect any AI agents that are malfunctioning and then remediate any malfunctions via quarantine and/or AI agent termination. Upon detection of a malfunction, the system may automatically isolate the compromised agent through a quarantine process. The system may alternatively or additionally, initiate graduated deactivation procedures ranging from partial restrictions to full deactivation.

Abstract: Presented herein are system and methods for establishing data provenance by generating one-time signatures. A system may include one or more processors that receive, via an application programming interface (API) request, a request for a one-time signature and data associated with the request, provide a seed identifier and the data associated with the request to an HSM in a set of HSMs, and receive a response message from the HSM, the response message including a one-time signature. In examples, the response message and the one-time signature are provided to the device that transmitted the request for the one-time signature and the data associated with the request. Methods and non-transitory computer-readable mediums are also presented.

Abstract: Systems and methods for enabling operation tracking using graphical interfaces are disclosed. The system receives operational files from multiple operation management systems and transforms operations from native to standard encoding. A first graphical interface displays selectable operational file indicators. Upon user selection of a file, the system identifies the corresponding operation management system and generates a second interface with selectable operational indicators. When a user selects an operation indicator, the system determines available functions based on the operation management system. A third interface displays operational details and distinguishes between available and unavailable functions through selectable and non-selectable indicators. This approach enables users to efficiently manage operations across different systems through a unified interface that adapts based on operational context.

Abstract: Systems and methods are disclosed comprising techniques for agentic cross-verification in distributed systems, such as obtaining a request to validate a decision state for a first memory structure of the first multi-agent storage that is accessible to a first AI agent set, transmitting a verification artifact representing the decision state of the first multi-agent storage to a second multi-agent storage that stores the verification artifact, receiving a verification status of the verification artifact that is determined by the second AI agent set, generating a first programmatic workflow set configured to trigger a first sequence of computer-executable commands at the first memory structure, generating a second programmatic workflow set configured to trigger a second sequence of computer-executable commands at the second memory structure, and automatically executing the first programmatic workflow set using the first AI agent set and the second programmatic workflow set using the second AI agent set.

Abstract: Methods and systems for performing database modifications based on event parameters are disclosed herein. The system may receive a resource event comprising one or more event parameters. The system may determine a set of entities associated with the resource event. The system may validate the resource event using the one or more event parameters. The system may determine a plurality of user accounts associated with the resource event. The system may modify resource distributions for each user account of the plurality of user accounts to generate modified resource distributions. The system may transmit, to a central database, one or more commands to update a central record of the resource distributions.

Abstract: Systems and methods are disclosed comprising techniques for agentic cross-verification in distributed systems, such as obtaining a request to validate a decision state for a first memory structure of the first multi-agent storage that is accessible to a first AI agent set, transmitting a verification artifact representing the decision state of the first multi-agent storage to a second multi-agent storage that stores the verification artifact, receiving a verification status of the verification artifact that is determined by the second AI agent set, generating a first programmatic workflow set configured to trigger a first sequence of computer-executable commands at the first memory structure, generating a second programmatic workflow set configured to trigger a second sequence of computer-executable commands at the second memory structure, and automatically executing the first programmatic workflow set using the first AI agent set and the second programmatic workflow set using the second AI agent set.

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: Metadata corresponding to a set of conditions applicable to an exchange to be performed among a plurality of entities is obtained, the set of conditions determined using one or more trained artificial intelligence models. A set of application programming interfaces (APIs) are generated based, at least in part, on the set of conditions. One or more points to insert the set of APIs to an executable application code to be executed for the exchange are determined. The set of APIs to be called during runtime of the executable application code to implement the set of conditions.

Abstract: Methods and systems are described for utilizing machine learning models to generate a response. The system receives an operator input requesting actions to prompt a desired user interaction. The system selects user-specific machine learning models trained on interactions associated with each user of a plurality of users. Based on the operator input, the system determines a model subset and selects a candidate operator action to test. The system inputs the candidate action into each model of the subset to obtain a predicted user interaction. The system then assesses whether the candidate action causes the models to predict the desired interaction. If the candidate action is effective, the system executes the candidate action in relation to the user subset.

Abstract: Systems, methods, and devices that relate to monitoring and managing autonomous agents are disclosed. In one example aspect, the method includes receiving activity data from autonomous agents in an operational environment, deploying static and dynamic observing agents to monitor expected behavior and deviations, detecting a deviation by an autonomous agent, determining the cause through analysis, performing a mitigative action based on the cause, and executing a preventative action to block similar future deviations. The method may also involve configuring observing agents with different observation modalities, periodically modifying observation parameters unpredictably, facilitating direct communication between observing agents, resolving conflicts in observations, and updating observation policies. Mitigative actions can include disabling credentials, rerouting communications, and logging actions.

Abstract: The systems and methods disclosed herein orchestrate task execution among autonomous (or semi-autonomous) AI agentic models (“agents”) responsive to a received query by using a hierarchical semantic fingerprinting framework to generate semantic-aware fingerprints for the query and agents. Queries and descriptions of agent capabilities are processed by a series of hierarchical levels using locality-sensitive hash (LSH) functions, where subsequent layers encode more complex semantic information and generate longer hash values. The hash values are aggregated into a semantic fingerprint. A bloom filter cascade uses a series of increasingly accurate hierarchical bloom filters to reject agent fingerprints that differ from the query fingerprint. The remaining agent fingerprints are compared bitwise to the query fingerprint, and those closest to the query fingerprint are selected to generate a routing path for the query. Responses from the selected agents are aggregated into an output that is responsive to the input.

Abstract: Presented herein are systems and methods for aggregating data from disparate sources using generative models. A computing system may retrieve, from a plurality of data sources, a first plurality of datasets for at least one of a plurality of features for a network environment over a first time period. The computing system may execute a first generative model using the first plurality of datasets to create a second plurality of datasets. The computing system may identify, from the second plurality of datasets, at least one dataset corresponding to a feature of the plurality of features. The one or more processors may execute select at least one machine learning model based on the feature. The computing system may execute the at least one ML model using the dataset to determine an output. The computing system may cause displaying of a visualization of the output via a graphical user interface.

Abstract: A computer can monitor network traffic on a blockchain computing network. The computer can determine a current level of network congestion on the blockchain computing network. The computer can execute a first machine learning model that predicts a timeseries of future transaction costs based on historical data and the current level network congestion level of the blockchain computing network.  The computer can also execute a second machine learning model to predict a timeseries of future transaction sizes and UTXO types for the distributed ledger-based account based on historical transaction data.  The computer can select one or more UTXOs to use to complete the transaction of the transaction request.  The computer can append a block instance containing an identification of the selected one or more UTXOs to the blockchain to complete the transaction.

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 for uses and/or improvements to blockchain and blockchain technology, particularly to provide a scalable solution to the aforementioned security and privacy concerns. As one example, systems and methods are described herein for a double-layer restriction subnet architecture that overcomes the technical limitations of conventional blockchains, whether public or permissioned, and subnets thereof. The double-layer restriction subnet architecture comprises a series of permissioned subnets, which provides both the security/privacy benefits of permissioned blockchains but also the scalability of subnet efficiency.

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.