Abstract: A system for monitoring optical computing components is disclosed. The system may include processors and non-transitory computer-readable media comprising instructions. The system may receive optical measurements from optical interconnects internal to a distributed computing system, where the optical interconnects couple distributed computing components each having an associated optical communication device enabling optical communication at a package level. The system may receive performance metrics of the distributed computing components comprising workload activity, synchronization behavior, or thermal conditions. The system may input the optical measurements and performance metrics into an artificial intelligence model trained to predict potential faults. The system may determine a fault domain comprising a chiplet interface, an optical engine, a laser source, a coupler, or a reconfigurable optical path.

Abstract: Computer-automated tools to assist machine learning model development may be suitable for users without domain expertise or specialized expertise in particular model types. An initial grid search via gradient boosting is applied to a source data set and target to generate a reduced search space including prioritized set of features ranked by importance value. A genetic algorithm searches the supervised learning classification problem space with various model choices and feature engineering. AI model training techniques may include machine learning test models, test hyperparameters, and network architectures. Feature engineering generates new features, and model development promotes features with fitness scores satisfying a defined threshold. Feature engineering may employ feature combinations, scaling, normalization, and feature convolutions. ML model development may apply a genetic operator including one or more of crossover, mutation, and selection.

Abstract: Systems and methods for assessing vulnerability of virtual resources are disclosed herein. A system may receive a request and parameterized rules from a database of parameterized rules for assessing potential vulnerability of user resources caused by a virtual resource transmission. The system may extract user vulnerability tolerance parameters indicative of user propensity for vulnerability and virtual resource vulnerability tolerance parameters indicative of vulnerability of the virtual resource. The system may input, into a parameterized rule, a user vulnerability tolerance parameter and a virtual resource vulnerability tolerance parameter to trigger execution of each parameterized rule. The system may then generate a match indicator indicating whether the virtual resource matches the user transmissions profile and, responsive to determining that the virtual resource does not match the user transmissions profile, trigger a temporary rejection on the transmission request requiring an override authorization.

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: The systems and methods disclosed herein enable evaluation of machine learning model outputs within a virtual environment. The disclosed model validation platform enables testing of code generated for detection of malicious or anomalous outputs. For example, the model validation platform can construct a virtual machine isolated from the system and test model-generated code for validation of LLM-generated outputs. In some implementations, the model validation platform determines parameters of the virtual machine and/or associated validation test based on an evaluation of the machine learning model's output and/or the associated underlying prompt. For example, the parameters of the validation test depend on an evaluation of the user or the provided input (e.g., depending on the presence of sensitive data within the prompt). By doing so, the system enables dynamic evaluation of machine learning model outputs to improve the security and robustness of associated generated code.

Abstract: Systems and methods for encrypting data are described herein. For example, the system may receive, from an application associated with a provider system, a request for a delivery token. The request may include an address and an identifier of a distribution system. The system may determine a distributor key for decrypting delivery tokens for the distribution system. The system may also identify an encryption key for encrypts delivery tokens, where the encryption key corresponds to the distributor key. The system may generate the delivery token, including the address, using the encryption key. The system may then transmit the delivery token to the application associated with the provider system.

Abstract: An identity verification system enables peer-to-peer authentication in a potentially insecure channel by leveraging a secure channel communication. The system authenticates a user via an identity verification application. The system provides a validation code to the user. The user communicates the validation code to a counterparty of the peer-to-peer communication. The system receives a request to authenticate the counterparty with the validation code and counterparty authentication data. The system authenticates the counterparty and sends the user the authentication of the counterparty. Alternatively, the user device communicates a request to generate a secure code for participants in a first insecure group application session. The user device selects an authenticated counterparty to receive the secure code from a list of authenticated counterparties. The user creates a second application session using the secure code as a password.

Abstract: The systems and methods disclosed herein receives artifacts generated using a first set of models within a multi-model superstructure. The multi-model superstructure includes a second set of models to test the first set of models. The multi-model superstructure dynamically routes the artifacts of the first set of models to one or more models of the second set of models by (i) determining a set of dimensions of the artifacts against which to evaluate the artifacts and (ii) identifying the models in the second set used to test the particular dimension. The second set of models then assesses each artifact against a set of assessment metrics. If an artifact fails to meet one or more assessment metrics, the second set of models generates actions to align the artifact with the set of assessment metrics.

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 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.

Abstract: Disclosed herein are systems, methods, and computer-readable media for generating and transmitting communication signals based on update signals that are detected using artificial intelligence (AI) agents. One or more AI agents can monitor a plurality of sources to detect update signals associated with available operations targeting one or more entities. In response to detecting an update signal, the AI agents can identify operations based on relevance and adherence to policy rulesets. The AI agents can generate a multimodal communication artifact that causes presentation of the operations, describes factors supporting the operations, and identifies a degree of adherence of the operations to the policy rulesets. The AI agents can automatically capture indications of the operations and adherence to the policy rulesets, allowing for provable compliance of the AI agents with the policy rulesets.

Abstract: The systems and methods disclosed herein relate to querying data using artificial intelligence models. A generalized model receives an output generation request and partitions it into segments mapped to specific domains, where each domain indicates associated databases and guidelines. The segments are routed to domain-specific models trained on domain-specific data, which generate query fragments by comparing performance metrics and system resource usage metrics. The query fragments are aggregated into an overall query that satisfies guidelines across domains. The systems and methods can include a feedback loop to adjust the domain-specific models using user interactions and performance metrics to dynamically adapt to a skill level or experience of the user.

Abstract: The systems and methods disclosed herein load a monitoring program into a kernel or other monitoring component of an operating system external to a user space. The monitoring program attaches to at least one hook point associated with execution of an artificial intelligence agent and captures a plurality of event records (a process identifier, an operation identifier, a resource identifier, a timestamp) associated with the agent. The system generates a cryptographic value by applying one or more functions to a representation of at least a subset of the event records and maintains the cryptographic value on a distributed database within a record associated with the agent. The distributed database maintains a sequence of records that links each record to one or more previous records. The system can detect anomalous agent operations and execute enforcement actions within the monitoring component that restrict agent execution.

Abstract: The systems and methods disclosed herein provide a lightweight artificial intelligence agent router that dynamically allocates tasks across a plurality of stateless AI agents. The router receives a task request and classifies it into one or more task types by comparing the request against historical task-agent pairings stored in a datastore. Each agent is registered in an agent registry that includes a metadata profile with performance metrics derived from prior executions. The router scores each agent by evaluating these performance metrics against the classified task type to rank the agents and select a subset of the agents whose scores satisfy a threshold value. The router determines an execution order by evaluating dependencies between sequential portions of the task request, thereby ensuring that outputs from earlier agents are available as inputs to subsequent agents. After task completion, the router updates the agent registry with execution results to adjust future routing decisions.

Abstract: Systems, methods, and devices that relate to assessing uncertainty associated with entities are disclosed. In one example aspect, the method receives artifacts relating to an entity and categories for assessing uncertainty. For each category, a generative model retrieves and standardizes data points from the artifacts. A rule-based model inputs the standardized data points to output a rating. The generative model then generates an assessment of the rating and data points according to a predefined structure. The method outputs a summary, rating, and standardized data points for each category. These outputs can be used by other systems for assessing the uncertainty of the entity and taking action based on the assessment.

Abstract: Systems and methods disclosed herein enforce agent compliance using kernel-level eBPF programs. The system obtains a machine-readable data structure defining operative boundaries for a target AI agent set and generates a reference value by applying a transformation operation on the data structure. The system loads eBPF programs and policy maps into a kernel, attaching them to hook points to intercept agent operations. Kernel-captured events are received via a kernel event stream, and an observer component generates a kernel-attested observed value by canonically serializing the events and applying a second transformation operation. A compliance comparator determines verification status by comparing the reference value with the observed value. When non-compliance is detected, the system loads an enforcement program into the kernel to restrict agent operations.

Abstract: Systems and methods for an active-active mirrored artificial intelligence architecture are disclosed. In some implementations, the system identifies, for an inference request from a user, at least two geographically separate compute clusters configured to handle the inference request, each compute cluster storing a version of an artificial intelligence model for processing the inference request. The system determines, from the inference request, a data consistency requirement mapping the inference request to one or more data consistency types. The system determines, for each compute cluster, a composite metric based on, among other things, a consistency freshness score of the compute cluster. The system routes the inference request to a selected compute cluster having a lowest composite metric subject to a constraint that the consistency freshness score for the compute cluster satisfies the data consistency requirement for the inference request.

Abstract: Systems and methods for analyzing operations to determine factors contributing to a malicious classification and providing recommendations to prevent future misclassifications are disclosed herein. A feature modification system receives operation data associated with monitored operations, where each operation is characterized by a set of feature values. A machine learning-based detection model processes the operation data to generate a prediction indicating whether each operation is malicious. Based on a prediction indicating that an operation has been classified as malicious, the system determines the impact of each feature on the prediction as well as the variability of each feature to identify impactful and modifiable features. The system generates an input dataset including entries with modified features and processes the dataset to obtain a new set of predictions.

Abstract: Presented herein are systems and methods for determining root cause of errors during execution of multiple applications. Systems include at least one processor to detect a cause analysis instruction identifying one or more systems; receive error metadata associated with a first operation error; determine a first debug operation set based on the error metadata; determine a first result associated with a first debug operation of the first debug operation set; and determine a second result associated with a second debug operation based on the first result for the first debug operation. The one or more processors can generate an error report based on the first result and the second result, the error report indicating each result and a next debug operation set of one or more next debug operations.

Abstract: Presented herein are systems and methods for generating suspicious activity reports using large language models. A system may include one or more processors that obtain event data associated with an event from a client device and from one or more databases, apply a prompt generator on the event data to generate a large language model (LLM) prompt, and generate a machine-readable suspicious activity (SAR) report in accordance with an LLM prompt. The one or more processors may also apply the prompt generator on the event data based on determining that a fraud risk score associated with the event satisfies a reporting threshold score. Computer program products are also presented.

Abstract: Presented herein are system and methods for detecting anomalies in microservices. A server may receive a first plurality of metrics from a microservice of a plurality of microservices. Each of the microservices may provide a respective function for an application independently from other microservices. The server may apply the first plurality of metrics to an ensemble of anomaly detection models to generate classifications. Each of the classifications may indicate the first plurality of metrics as one of anomalous or normal from a respective model of the ensemble. The ensemble may be trained using a second plurality of metrics from the microservice. The server may identify a majority of the plurality of classifications as indicating the first plurality of metrics as anomalous. The server may determine that at least one of the first plurality of metrics satisfies a threshold. The server may generate an alert to indicate an anomaly event.

Abstract: Systems and methods for using on-chain programs for message transfer are disclosed herein. A system may receive a request to obtain control of second on-chain data structures of a second type. The request may comprise identifiers for first on-chain data structures of a first type. The system may determine that the first type is stored on a first blockchain and the second type is stored on a second blockchain and execute an operation that transfers control of first on-chain data structures of the first type to be controlled by a second cryptography-based storage application associated with an agent on the first blockchain. In response to a confirmation that the operation was successful, the system may cause a message to be transmitted to a second on-chain program instructing it to transfer the second on-chain data structures to be controlled by the user.

Abstract: A computer-implemented method comprising receiving a request from a computer of a first of a first computing environment to execute a network operation transferring network operation data from the first data structure to a second data structure associated with a second computing device of a second computing environment; identifying, from a hierarchical model indicating relationships between data structures maintained through a blockchain, a third data structure of a third computing device in communication with the plurality of nodes maintaining the blockchain; retrieving at least one condition corresponding to the third data structure from the blockchain; determining the network operation data satisfies the at least one condition; and appending a first record to a second record of the blockchain.

Abstract: Systems, methods, and devices for generating constrained outputs for large language models are disclosed. The system receives an input query relating to a request from a user via a user interface associated with an entity. The system retrieves knowledge elements from a proprietary knowledge corpus, including frameworks, methodologies, or constraints specific to the entity. For each knowledge element, the system inputs the query and knowledge element into a large language model to generate a preliminary output. The system applies user-specific rules to generate a constrained output by parsing the preliminary output, evaluating response components against user-specific constraints as logical predicates, and modifying components that violate rules. The system generates citations linking outputs to source identifiers, aggregates the constrained outputs and citations to generate a composite response, and transmits it to the user interface.

Abstract: Methods and systems for accessing customer account information involve, for example, binding a computing device with a customer's profile via one or more attributes of the computing device and an encrypted token stored on the computing device and thereafter receiving an authentication request consisting at least in part of the computing device attributes and the encrypted token stored on the computing device. A determination is made as to whether or not entry of a customer credential was received within a pre-determined preceding interval of time, and predefined customer account information is displayed on the computing device without requiring entry of the customer credential when a determination is made that entry of the customer credential was received within the pre-determined preceding interval of time.

Abstract: The systems and methods disclosed herein receive alphanumeric characters defining operative boundaries for expected model use cases, along with operational data. The expected model use cases share common attributes, which are used by a first AI model to construct observed model use cases from the operational data. Each observed model use case includes features such as a text-based description, expected input and output, AI model(s) generating the expected output from the input, and/or data supporting the AI models. For each observed model use case, a second AI model maps the alphanumeric characters and features to a risk category, selecting from multiple risk categories based on the level of risk associated with the features. The system identifies criteria for the observed model use case within the alphanumeric characters and generates gaps by comparing the criteria with the features of the observed model use case.

Abstract: The systems and methods disclosed herein detect indicators of undesirable intent during interactions between users and digital avatars by evaluating emotional signals in user speech. The system generates emotion feature vectors from user input streams that capture characteristics such as pitch variation, speech rate, vocal intensity, spectral features, and so forth. The system uses a two-level evaluation that compares the emotion feature vector against a population baseline derived from historical user interactions to generate a population anomaly score and further compares the emotion feature vector against an allow list database and a deny list database to generate a threat match score based on known protected categories (e.g., threat categories, allowed categories). The system aggregates these scores to produce a risk score, which is evaluated against threshold values to determine a response action (e.g.

Abstract: The systems and methods disclosed herein receives, from a computing device, operational data indicating software or hardware assets used on informational assets, and obtains set of alphanumeric characters defining operative boundaries for expected system assets, which include a set of common attributes. Using the set of attributes, a first set of AI models determines observed system assets from the operational data, each with specific features. A second set of AI models associates each information asset with the corresponding observed system assets. For each observed system asset, a third set of AI models identifies criteria within the alphanumeric characters, compares the criteria with the asset's features to identify gaps, and generates actions to ensure the observed system asset meets the identified criteria.

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: 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: 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 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: 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: 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: 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.

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.