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: 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: 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 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.