141 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.

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.

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.

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 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.

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.

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.

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.

Systems and methods are disclosed herein for performing a milling operation within a wellbore in an oil-and-gas setting. An example system can include a milling tool, which can include a cutting head, a linear actuator configured to advance the cutting head, and a linear displacement measurement sensor associated with the linear actuator. The sensor can be configured to measure linear displacement of the linear actuator. The system can also include a cable to lower the milling tool, a control unit that receives data from the sensor, and a display device that displays a visualization of the data from the sensor. The control unit can analyze the data to determine a location of a milling target, a status of the milling operation, and undesirable milling conditions down hole. The control unit can perform a remedial action, such as by turning on a pump to remove debris from the wellbore.

Discussed herein are devices, systems, machine-readable media, and methods for assessing a software build for a vulnerability, generating release recommendations, and implementing a remedial action to mitigate security risks. A method includes receiving a Software Bill of Materials (SBOM) that lists one or more libraries used in a software build, receiving a user-specified administration policy, generating an over overall provenance bundle from a metadata of the one or more libraries used in the software build, implementing a gradient boosted tree algorithm using both the overall provenance bundle and the user-specified administration policy to generate a software releasability recommendation, receiving the software releasability recommendation into a Large Language Model (LLM) to generate a recommendation report detailing one or more software vulnerabilities, and implementing the software releasability recommendation by releasing the software build or blocking the release of the software build based on the software releasability recommendation.

A control system deploys a virtual safety bubble for autonomous operation of a construction vehicle in a worksite. The control system obtains a work schedule for the construction vehicle to perform one construction action. The control system identifies one or more objects to be interacted with by the construction vehicle while performing the one construction action. The control system deploys a virtual safety bubble for the construction vehicle based on the one construction action. Breach of the virtual safety bubble triggers remedial actions; however, the virtual safety bubble permits breach by the one or more identified objects during the construction action. The control system detects breach of the virtual safety bubble by the one or more identified objects during the construction action. Responsive to the breach of the virtual safety bubble by the one or more identified objects during the construction action, the control system withholds the remedial actions.

A method and system for controlling a vehicle are provided. The method comprises: in parallel to a first processing pipeline for controlling the vehicle, executing a risk management processing pipeline comprising: acquiring first monitoring data from a first monitoring source used in first processing pipeline; acquiring second monitoring data from a second monitoring source used in the first processing pipeline; generating a cumulative risk assessment value based on a combination of the first monitoring data and the second monitoring data; in response to the cumulative risk assessment value being above a pre-determined main threshold, generating a warning notification; and in response to a frequency of occurrence of the generating the warning notification being above a pre-determined frequency threshold, triggering, independently from the first processing pipeline, a remedial action to be performed by the vehicle. The present technology may allow increasing safety of operation of the vehicle.

An online system receives a request from a client device associated with a user to place an order for pickup from a source location during a timeframe and identifies candidate remedial actions associated with the order based on the timeframe and a current time. The system retrieves user data for the user and accesses a machine-learning model. For each candidate remedial action, the system applies the model to predict, based on the user data and order data for the order, a likelihood the user will pick up the order if the candidate remedial action is taken and computes an associated value based on the likelihood. The system selects a remedial action from the candidate remedial actions based on the values, generates, based on the selected remedial action, a message associated with the order that includes a set of selectable options, and sends the message to the client device.

A request to add or replace a storage system resource is received by one or more controllers of a storage system. A compatibility validation process is executed that evaluates the storage system resource and one or more existing resources of the storage system across multiple system layers. In response to determining an incompatibility of the storage system resource, an alert identifying the incompatibility is generated and a remedial action is recommended.

The application discloses a kind of microservice fault prediction method and terminal, collect the history fault of each instance on microservice, according to the classification type and historical running information of history fault constructs fault type library. According to the cycle time, the real-time running information of each instance on microservice is collected in real time, if real-time running information and historical running information in fault type library match successfully, then the fault type corresponding to real-time running information is obtained. In this way, possible fault conditions can be predicted before the real fault occurs, facilitating subsequent execution of related remedial action, and the probability of system failure can also be reduced through prediction, improving user experience.

Aspects described herein may use machine learning models to predict one or more remediation actions to mitigate reoccurrence of an incident that has become restored based upon previous incidents of an entity. Historical incident data is compiled into two incident datasets: one representative of incidents that were assigned a remediation action to mitigate reoccurrence of the incident, and a second representative of incidents that were not assigned a remediation action. A machine learning model matches relationships between data in the two datasets and outputs scores representative of similarities. Based on the scores, one or more remediation actions are mapped to an incident in the second dataset and the remediation action is performed for the incident.

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.

Methods, communications device, infrastructure equipment and circuitry for defining UE behaviour when requesting on-demand SIB1, in particular when the on-demand SIB1 cannot be acquired by the UE. Based on determining that the UE is unable to acquire the on-demand SIB1, the UE performs one or more remedial actions, which may include retransmission of a RACH preamble, performing a new RACH procedure, performing cell reselection, and / or performing an essential system information missing procedure.

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.

A method of identifying blade penetration that is ineffectual by a blade-based machine at a work site, including: attempting to cut an area of ground by the blade-based machine at the work site; determining that the cut is ineffectual based on a determination of a threshold cut volume achieved by the blade-based machine, the threshold cut volume being based on input from one or more position sensors on the blade-based machine; and initiating one or more remedial actions based on the determination of ineffectual cutting by the blade-based machine.

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.