202 results about "Remedial action" patented technology

A system detects changes in model context protocol (“MCP”) processes, and performs a remedial action. A server-sent events (“SSE”) bridge sends a request to an MCP server. A first resource profile is received from the MCP server. This is stored and compared against a second updated version of the resource profile. When a difference is detected, the SSE bridge determines whether to block a resource command from reaching the SSE bridge. The decision is based on comparing the difference to security rules, which can be defined as part of a management profile.

The structures and algorithms described herein employ look ahead motion planning, in which motion planning for a least two goals is performed before a robot executes the resulting motion plans, and the ability to transition between the preceding one of the motion plans to a subsequent (e.g. following) one of the motions plans is assessed. Thus, the system can determine whether a robot will get trapped (e.g., blocked by another robot) at the end of a first motion plan, preventing, limiting or delaying execution of a second motion plan. Detection of such a condition can cause one or more remedial actions can be taken, for example generating a new, revised or replacement first motion plan. Other remedial action can moving another robot, performing motion planning for the other robot, to alleviate a blocking condition and / or determining a new order for the goals.

An illustrative method includes monitoring operations performed with respect to a storage system by an entity using an identity associated with a particular role, the particular role providing the entity with a set of permissions associated with the storage system; determining, based on the monitoring, that one or more operations of the operations deviate from an expected activity profile associated with the role by more than a threshold; and performing, based on the determining that the one or more operations deviate from the expected activity by more than the threshold, a remedial action with respect to the entity.

One or more computing devices, systems, and / or methods for mobile device security profiling are provided. A device connected to a communication network is detected. Device profile information associated with the device is used to select a device behavior security model from available device behavior security models. The device behavior security model is provided to the device. The device utilizes the device behavior security model to determine whether the device is exhibiting normal operating behavior or abnormal operating behavior (e.g., an application on the device performing a denial of service attack). If abnormal operating behavior is detected by the device, then the device can perform a remedial action.

A processor implemented method including monitoring an event in a system, analyzing a log of the event to determine whether the event is a system incident, searching, responsive to the event being determined to be a system incident event, an internal knowledge base for causes of the system incident event and remedial actions for the system incident event, based on retrieval-augmented generation, prompting a first inquiry, the first inquiry including the log and a search result from the searching to a first LLM, and generating a first response including remedial actions for the system incident event by the first LLM, based on the first inquiry and the search result.

The invention provides a rules engine that applies customizable security rules and artificial intelligence (AI) model routing to queries. The rules are evaluated with respect to the query, an anticipate result of the query, and the actual result. Remedial actions are taken when the rule evaluation exceeds a threshold. The remedial actions include transforming the query by replacing sensitive information with a reversible placeholder. The actual result can be modified by reversing the placeholder and redacting additional sensitive information. The system can notify the user, an administrator, and a supervisor regarding the security evaluation and remedial actions. The evaluations can be logged for auditing purposes.

Aspects relate to monitoring timing margin of logic paths and its degradation through an integrated circuit. In one example an integrated circuit has a logic path formed in the integrated circuit. A monitor circuit is formed in the integrated circuit near the logic path and configured to monitor a condition of the logic path and to generate a diagnostic code sequence to indicate the condition of the logic path over time. A monitor controller is configured to receive diagnostic codes of the diagnostic code sequence, to determine a condition of the integrated circuit based on the diagnostic code sequence, and to initiate a remedial action in response to the condition of the integrated circuit.

Techniques are disclosed for sending downloadable software packages that may be executed to perform tests of a network device without requiring the network device to update firmware. In one example, the network device obtains a software package comprising instructions for simulating a network instance and stores the software package in a volatile memory of the network device. The network device simulates, based on the instructions, the network instance to obtain data indicative of a performance of the network instance. The network device sends, via a communication channel established with a network management system (NMS) based on the software package, the data indicative of the performance of the network instance. In some examples, the NMS uses the data indicative of the performance of the network instance to identify a root cause of a failure of the network device or a remedial action to be performed upon the network device.

A system detects changes in model context protocol (“MCP”) processes, and performs a remedial action. A server-sent events (“SSE”) bridge sends a request to an MCP server. A first list of resource profiles, including tools and instructions, is received from the MCP server. This is stored and compared against a later list of tools and instructions. When a difference is detected, a user interface can display the difference to an administrator. Security rules cause the SSE bridge to be blocked from sending commands to the impacts tool or MCP server until approved by the administrator.

Systems and methods are disclosed relating to preventative maintenance. In an example, equipment data for equipment that can include data points relating to physical attributes of the equipment or environmental conditions for the equipment can be received. A machine learning (ML) model can be used to analyze the equipment data to determine whether the equipment needs maintenance. A correlator can be used to analyze the equipment data to determine whether the equipment data contains an anomaly indicative of a negative correlation between two more different attributes of the physical attributes and two or more different environmental conditions of the environmental conditions. A remedial action can be issued in response to the ML model determining that the equipment needs maintenance and / or the correlator determining that the equipment data contains the anomaly.

Techniques are disclosed for a network management system (NMS) that performs quality of service (QoS) monitoring and troubleshooting of user experience issues occurring outside of a network managed by the NMS using data obtained from third-party sources. For example, an NMS obtains third-party data of a third-party application server or third-party service provider server from a third-party monitoring vendor. The NMS identifies a user experience issue indicated by the third-party data and stitches the third-party data to network data received from network devices. The NMS determines a root cause or a remedial action of the user experience issue based at least on the network data received from the one or more network devices. The NMS generates a notification for presentation to an administrator device which identifies the root cause or the remedial action of the user experience issue.

A dynamic, infrastructure-aware incident response and automated problem resolution system (100) for cloud environments, comprising: (a) an infrastructure and topology mapping module configured to continuously scan, discover, and map virtualized and containerized cloud resources and their interdependencies in real time; (b) an incident detection and correlation module configured to ingest telemetry data from distributed monitoring sources and apply rule-based and machine learning models to detect, correlate and classify system incidents; (c) a context analysis and impact assessment module configured to analyse the scope, severity and business impact of incidents based on real-time infrastructure context and service dependency diagrams; (d) a policy-driven decision engine configured to dynamically select response actions based on predefined rules, compliance policies, service level agreements and historical incident data; e) an Automated Remediation Orchestrator configured to execute predefined or dynamic remediation playbooks through integrations with cloud orchestration and configuration tools; and (f) a feedback loop and learning module configured to collect post-incident data, evaluate the effectiveness of remedial actions, and continuously refine the detection and response logic using machine learning algorithms.

A storage management system that provides persistent storage to containerized applications running in a cluster of nodes may be configured to identify an anomalous pattern of storage operations associated with a container instance deployed in the cluster of nodes, determine, based on the anomalous pattern of storage operations, a potential security threat associated with the container instance, and perform a remedial action based on the potential security threat.

Method, computer-readable media, and apparatuses for applying at least one remedial action within a communication session via at least one network in response to detecting via at least one detection model that the audio content of a first user is off-topic are described. For example, a processing system including at least one processor may establish a communication session between at least a first communication system of a first user and a plurality of communication systems of a plurality of additional users via at least one network. The processing system may then determine at least one topic for the communication session, detect, via at least one topic detection model, an audio content of the first user that is off-topic, and apply at least one remedial action within the communication session in response to the detecting that the audio content of the first user is off-topic.

Techniques for implementing a fuzzy logic expert system / service to control sending of a requested media file and / or control triggering of a remedial action (e.g., not sending the media file) are described. According to some examples, a computer-implemented method includes receiving a request at a content delivery service from a user for a media file, generating a piracy risk score for the media file by a fuzzy logic expert service of the content delivery service, sending the media file to the user based on the piracy risk score not exceeding a threshold, and blocking the sending of the media file to the user based on the piracy risk score exceeding the threshold.

Methods and systems for controlling an industrial system are disclosed. The industrial system includes a plurality of sub-components. The method includes receiving time-series data representative of operations of at least one of the plurality of sub-components, applying a machine learning algorithm (MLA) configured to process the time-series data to generate: a root cause of an anomaly in operation of the at least one of the plurality of sub-components; and a remedial action parameter to bring operation of the at least one of the plurality of sub- components to specification. The method also includes transmitting instructions for triggering the remedial action.

Methods for database recovery for encrypted indexes are performed by systems and devices. A query with a decryption key is received from a client device, where the query modifies an encrypted index of a database using a secure enclave. When events requiring remedial actions for the database occur during the querying, some transactions of the query and later queries are deferred, and a remedial action is initiated that includes restarting the database. A determination of the remedial action being unsuccessful in recovering the encrypted index causes the action to be re-performed until another query having the decryption key is received whereupon the action is performed again to recover the encrypted index utilizing the decryption key. Deferred transactions are then performed with the decryption key. When a database restarts for access without secure enclaves, the encrypted index for the database is invalidated, and the remedial actions are otherwise completed or discarded.

A system detects changes in model context protocol (“MCP”) processes, and performs a remedial action. A user interface (“UI”) is generated that comprises a selections for identifying restricted actions, permissions for those restricted actions, triggers based on the restricted actions, and remedial actions that correspond to the triggers. The restricted actions can be based on a change to a resource profile of the MCP server. They can also apply to resource commands. The triggers can be specific keywords or change thresholds. The UI selections can be used to generate a management profile. Using the management profile, the system can automate remedial actions for MCP servers, protecting enterprises from data loss and other security issues.

The invention includes a UI that allows addition of functional placeholders to an AI pipeline, and a rules engine that chooses pipeline objects during or before AI pipeline execution. The rules engine can select pipeline objects meeting the same function type and object rules of the functional placeholder. The rules engine also applies customizable security rules and artificial intelligence (AI) model routing to queries. Remedial actions are taken when the rule evaluation exceeds a threshold. The remedial actions include transforming the query by replacing sensitive information with a reversible placeholder. The actual result can be modified by reversing the placeholder and redacting additional sensitive information. The system can notify the user, an administrator, and a supervisor regarding the security evaluation and remedial actions. The evaluations can be logged for auditing purposes.

Systems and techniques for intelligent detection of abnormal conditions associated with a shipping container. A system includes an on-site system configured to capture sensor data associated with a condition of a container carried by a train car of a train, analyze the sensor data to determine whether the condition of the container indicates an abnormal condition, and identify one or more of the train car, the container, and the train. The system also includes a backend system configured to receive a detection message from the on-site system and generate an alert signal in response to a determination that the condition of the container indicates an abnormal condition. The alert includes the abnormal condition and identification information, and causes one or more remedial actions to respond to the abnormal condition of the container.

An illustrative method includes a data protection system detecting an anomaly associated with a managed run-time environment; determining, based on the detecting the anomaly, that the managed run-time environment is possibly being targeted by a security threat; and performing, based on the determining that the managed run-time environment is possibly being targeted by the security threat, a remedial action associated with the managed run-time environment. The performing the remedial action may include identifying a recovery dataset generated prior to the detecting the anomaly, the recovery dataset for data associated with the managed run-time environment and stored by a storage system, and converting the recovery dataset into a protected recovery dataset that cannot be deleted without a specific instruction provided by an authorized entity.

Techniques for monitoring a computer-assisted device include a plurality of manipulators, each manipulator being associated with at least one control point of a plurality of control points; and a control unit. The control unit is configured to: determine a first spatial configuration of the plurality of control points, the first spatial configuration being prior to a movement of a table being operated concurrently with the computer-assisted device; determine the movement of the table causing motion of at least one control point; determine, based on the movement of the table and the first spatial configuration, an expected spatial configuration of the plurality of control points; compare the expected spatial configuration with an actual spatial configuration of the plurality of control points to determine whether to perform a remedial action; and perform the remedial action in response to a determination to perform the remedial action.

A system for signaling intent of an autonomous vehicles. The system is comprised of a memory storing executable instructions representing a signaling display module on a trailer of an autonomous vehicle. The system also comprises a processor coupled to the memory and configured to execute the signaling display module. The processor is configured to receive data from an autonomy computing system descriptive of a condition near the autonomous vehicle, process the data to generate a message relating to the condition indicating a remedial action of the autonomous vehicle in response to the condition, and transmit the message to a signaling display on the trailer prior to executing the remedial action.

Embodiments described herein are directed to determining whether an application executing on a compute instance has been corrupted or compromised by malicious code. This may achieved by statically analyzing an image file from which the application is based to determine characteristics thereof. Such characteristics are representative of the behavior that is expected to be performed by the application during execution. During execution of the application, runtime characteristics of the application are determined, which are determined based on an analysis of the address space in memory allocated for a computing process of the application. The statically-determined characteristics are compared to the determined runtime characteristics to determine discrepancies therebetween. In the event that a discrepancy is found, a determination is made that the application has been compromised or corrupted and an appropriate remedial action is automatically performed.

Examples described herein relate to a network interface device that includes: a host interface; a direct memory access (DMA) circuitry; a network interface to receive, in at least one packet, time data associated with at least one of multiple layers, wherein the multiple layers provide inputs to a collective operation associated with a large language model (LLM); and circuitry. The circuitry is to based, at least in part, on the time data associated with the multiple layers, identify a first operation of a first layer of the multiple layers as a late completing process relative to times to completion of multiple first operations of other layers and based on the first operation being identified as a late completing process, perform a remedial action to adjust at least one configuration of a first device to execute a second operation of the first layer.

A vehicle including a sensor unit and a processor is disclosed. The sensor unit may be configured to capture inputs associated with a charging station when the vehicle may be located at the charging station. The processor may be configured to obtain the inputs from the sensor unit, and determine that the charging station is in a suboptimal condition based on the inputs. The processor may further classify the suboptimal condition into a predefined type, of a plurality of predefined types, based on the inputs, and perform a remedial action based on the predefined type.

An online system predicts whether a user will be at a delivery location at a delivery time for an attended delivery of an order using a machine-learned model. The online system receives the order from a client device of a user and a request by the user for an attended delivery of the order where the user will be at the delivery location at the delivery time of the order. The machine-learned model predicts that the user will not be at the delivery location at the delivery time based on user attributes of the user and order attributes of the order that are input into the machine-learned model. The online system performs a remedial action including transmitting a notification to the client device of the user to provide additional instructions for the attended delivery responsive to the determination that the user is not likely to be at the delivery location.

Systems, computer program products, and methods are provided for automated detection of source code discrepancies. The method includes receiving a data transmission including a text file and a source code file; processing the source code file via a machine learning engine, where an output of the machine learning engine includes a plurality of identified updates; processing the text file via a natural language processing engine, where an output of the natural language processing engine includes a plurality of expected updates; identifying a difference between the plurality of identified updates and the plurality of expected updates; and performing a remedial action.

The invention provides a rules engine that evaluates, modifies, and dynamically routes inputs to AI models based on provider restrictions and management rules. Provider restrictions are stored. Based on management rules and the detected provider restrictions, the input can be modified with contextual placeholders and prompts can be injected for input into the destination model. Reversal of the modifications can occur to the outputs. The system can notify the user, an administrator, and a supervisor regarding the security evaluation and remedial actions. The evaluations can be logged for auditing purposes.