381results about "Error avoidance" patented technology

As described herein, various embodiments of the present invention improve computational efficiency of performing reliable persistent monitoring of a computer system. Persistent monitoring of the operations of a computer system has various operational reliability benefits for various computer systems and is an important part of service level objectives for highly maintenance-critical computer systems. However, doing the noted persistent monitoring operations in a reliable manner requires access to large amounts of labeled training data that are not always available for more customized computer systems with unique operational / behaviorial pattern signatures. In response, various embodiments of the present invention address the noted challenges by generating training data for an anomalous operational state detection machine learning model whose training data may be generated using a ground-truth validation criterion that is defined based at least in part on an inferred outlier score for a decomposed residual component of a given operational monitoring timeseries trend.

A method for quantum error correction includes: trapping and / or manipulating an array of qubits that include encoded neutral atoms or ions and forms a computational space; encoding the array of qubits to a state characterized in that an error during at least one of an idling, gate, or other operation results in a respective qubit transition to a disjointed state where that error can be detected without disturbing the qubits remaining in the computational space; performing a syndrome measurement of a quantum error correcting code; identifying the presence and location of an error by detecting the corresponding neutral atom or ion has left the computational space after performing the operation; identifying a logical error in the quantum error correcting code associated with a loss of qubit information based on the identified error location and results of the syndrome measurement; and adjusting subsequent computational steps based on the identified logical error.

Techniques for managing resource constraints of a cloud environment are disclosed. A system receives a request to initiate a provisioning process for provisioning a first service in the cloud environment. The system determines a resource constraint associated with a resource that the first service utilizes. Based on the resource constraint, the system determines a set of candidate services that also utilize the resource as candidates for deprovisioning from the cloud environment. The system identifies respective service features of the set of candidate services and generates a ranking of the set of candidate services based on weighting metrics associated with the respective service features. Based on the ranking, the system selects a second service of the set of candidate services for deprovisioning from the cloud environment. The system deprovisions the second service to alleviate the resource constraint and then provisions the first service by executing the provisioning process.

Techniques for identifying faults in a collaborative computing system including a plurality of disparate, geographically separated computing systems are described herein. An intelligent monitoring (IM) server computing system may receive data from the plurality of computing devices and may monitor the health of the collaborative computing system. The IM server computing system may analyze the data and identify one or more faults associated with a portion of the collaborative system (e.g., an associated computing device, platform, network, etc.). In some examples, the IM server computing system may be configured to identify potential future faults associated with the portion of the collaborative system. Based on the fault, the IM server computing device may determine an action to take to remedy the fault and / or prevent the potential future fault. The IM server computing device may either automatically perform the action or send a notification to the associated computing system to perform the action.

Systems and methods for diagnosing accidents in Information Handling Systems (IHSs) are described. In some embodiments, an IHS may include a processor and a memory coupled to the processor, the memory having program instructions stored thereon that, upon execution, cause an Operating System (OS) to: in response an accident, collect fan data and, based at least in part upon a comparison between collected fan data and stored fan data, perform an accident remediation or mitigation operation.

Conventional error detection for integration processes in an integration platform are inefficient and require significant expertise. Accordingly, an error prediction model is disclosed. The error prediction model may be operated to produce error predictions, based on the current design (e.g., lineage) of an integration process, during construction of that integration process (e.g., on a virtual canvas). A generative language model may also be used to provide the error predictions in natural language. This enables the efficient troubleshooting and resolution of errors in an integration process, prior to that integration process being deployed and executed, and without requiring significant expertise.

The invention discloses a multi-mode induction display screen response method fusing voice interaction, and relates to the technical field of man-machine interaction, and the method comprises the following steps: in a user interaction process, calling a built-in sensor through an interface component real-time state monitoring mechanism, continuously monitoring a rendering process of a target interface component at a millisecond-level time granularity, and displaying the rendering process of the target interface component; collecting multi-dimensional availability state parameter information of the interface component; the method comprises the following steps: preprocessing collected interface component multi-dimensional availability state parameter information, and extracting key indexes reflecting potential asynchronous triggering risks from preprocessed data through a feature engineering method; by sensing the state of the interface component in real time, extracting the asynchronous risk index and combining machine learning recognition and TCN prediction modeling, dynamic regulation and control of the induction triggering window and the animation rhythm are achieved, the problem of mispointing or dislocation of the induction component is avoided, the accuracy and safety of interaction response are improved, and the user experience is improved. The method is suitable for high-precision scenes such as vehicle-mounted, medical and industrial control.

Instead of incurring interface errors caused by power drops, continuously track all the channels between the controller and the storage device (both in-band and side-band channels). The tracking will include extracting relevant indications and use a prediction model that correlates between the tracked indications to later occurrence of power drop events. In response to the prediction results, the system may perform different rehabilitation operations (countermeasures). The controller will monitor different indications from the storage element and power supply to predict a signal integrity degradation marginality event on the interface. The monitoring and mitigation strategy relies on an open-ended system. The controller does not employ a real-time continuous return channel from the storage device that may signal pass / fail conditions. Given the nature of an open-ended system and tolerance of calibration for the data gathering, processing and inference system, the controller will have to cope also with false positive events.

According to one aspect, a method includes: receiving, by a node of a computer cluster, a command for a health status report, the node having a plurality applications and being one of a plurality of nodes of the cluster, the command indicating a class of a health checks to be executed and a scope; identifying, from a configuration, one or more health checks to be executed based on the indicated class; executing the one or more health checks on one or more of the applications and on one or more of the nodes of the cluster according to the indicated scope, where different ones of the health checks are configured to determine state of different ones of the applications; aggregating, by the node, results of the executed health checks; and outputting the aggregated results.

Techniques are provided for maintaining and recomputing reference counts in a persistent memory file system of a node. Primary reference counts are maintained for pages within persistent memory of the node. In response to receiving a first operation to link a page into a persistent memory file system of the persistent memory, a primary reference count of the page is incremented before linking the page into the persistent memory file system. In response to receiving a second operation to unlink the page from the persistent memory file system, the page is unlinked from the persistent memory file system before the primary reference count is decremented. Upon the node recovering from a crash, the persistent memory file system is traversed in order to update shadow reference counts for the pages with correct reference count values, which are used to overwrite the primary reference counts with the correct reference count values.

Systems and methods of loopback control in multi-active database synchronization are provided. A first database instance and a second database instance are configured with two-way synchronization. A synchronization tool detects a first log change in a first operation log associated with the first database instance. The first log change includes a first operation entry associated with a first data operation on a first data record in the first database instance. The first data record includes a first synchronization tag field comprising a first source tag representing a first data source associated with the first data operation on the first data record. If the source tag contains the identification of the second database instance, the synchronization tool skips the first operation entry, without synchronizing the first data operation to the second database instance to avoid data loopback.

Processing systems and associated methods are provided for managing tasks and handling errors based on criticality levels assigned to individual processing circuitry blocks. The system includes a plurality of processing circuitry blocks, each assigned a level of criticality by a critical domain manager, such that errors arising in relation to these blocks are handled in accordance with their assigned level of criticality. The system features timers assigned to tasks initiated by the processing circuitry blocks, with timeout durations determined by the criticality levels respectively assigned to the processing circuitry blocks and tasks initiated by them. Upon timeout expiration without task completion, various error handling procedures are selected and executed based on the assigned criticality levels.

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.

A system and method for assessing, broadcasting and remediating a health of a cloud computing infrastructure. A system includes a health assessment platform interfaced with components of the cloud computing infrastructure, and configured to perform an evaluation loop of the cloud computing infrastructure to produce a health assessment, the evaluation loop assessing one or more of a drift between desired and actual states, resource utilization, cost or spend controls, policy compliance, and planning or refactoring of the components of the cloud computing infrastructure. The health assessment platform is further configured to report the health assessment via notifications to a user interface associated with the cloud computing infrastructure. The system further includes a remediation engine that is interfaced with the user interface, the remediation engine configured to provide a user of the user interface with remediation recommendations and optimizations based on the reported health assessment.

According to one embodiment of the present invention, a computer-implemented method for dynamically altering a frequency at which data scrubbing is performed on a memory device is disclosed. The computer-implemented method includes monitoring at least one of a temperature and a magnetic field of the memory device. The computer-implemented method further includes, responsive to determining that at least one of the temperature and the magnetic field of the memory device reaches and / or exceeds a predetermined threshold, respectively, increasing the frequency at which data scrubbing is performed on the memory device.

A system and method for detecting code intent of code objects in a computing environment for assessing cybersecurity risk is presented. The method includes detecting a plurality of code objects in a computing environment; statically analyzing each code object of the plurality of code objects to determine a plurality of potential intents, each potential intent corresponding to an action; generating a cybersecurity signal in the computing environment based on a plurality of event records; detecting in the cybersecurity signal a first event corresponding to a potential intent; determining that the potential intent is a code intent based on the detection; detecting in the cybersecurity signal a second event which does not correspond to any potential intent; and initiating a remediation action in the computing environment based on the detected second event.

Systems, methods, and other embodiments associated with automatic configuration of a circular buffer for ingesting a stream and generating ML estimates in real-time are described. In one embodiment, an example method includes loading a stream of multivariate time series observations into a circular buffer at a real-time pace of input from a target asset. The circular buffer is configured with a buffer configuration that specifies buffer length and choice of arrangement as a single-buffer or dual-buffer. The method then adjusts the buffer configuration until generation of machine learning estimates of the multivariate time series observations that are in the circular buffer satisfies a threshold test for generation at the real-time pace. And, at the real time pace, the method loads additional multivariate time series observations into the circular buffer that is in the adjusted configuration and generates additional machine learning estimates of the additional multivariate time series observations.

Systems and methods for accident detection and handling are described. In some embodiments, an Information Handling System (IHS) may include an Embedded Controller (EC), and a memory coupled to, or integrated into, the EC, where the memory comprises program instructions that, upon execution by the EC, cause the EC to receive data from a sensor and, in response to the data being indicative of an accident, perform an action.

Methods and articles of manufacture for hosting a safety critical application on an uncontrolled data processing device are provided. Various checks and combinations of checks including installation, functional, host integrity, coexistence, interoperability, power management, and environment checks are performed at various times to determine if the safety critical application operates properly on the device. The operation of the SCA on the UDPD may be controlled accordingly.

A system and method for fail-operational mission continuity in a multi-agent autonomous system. Each autonomous agent includes an onboard Prognostic Health Management (PHM) module that monitors health using sensor data. Upon detecting an incipient fault, the PHM module calculates a prognostic Remaining Useful Life (RUL). This RUL is transformed into a quantitative Operational Risk Cost (Ω) and communicated to a multi-agent control system. The control system's dynamic task allocation algorithm uses the Ω values as key inputs in a multi-objective optimization process. This enables the system to proactively and autonomously re-allocate a task from a degrading agent to a healthy agent before a failure occurs. The degrading agent is simultaneously commanded to perform a safe contingency maneuver. This integration of real-time prognostics and multi-agent control creates a resilient, self-healing system capable of completing missions despite hardware degradation.

The present invention introduces an advanced system and method for predictive maintenance and fault detection, leveraging the synergistic potential of quantum computing and graph transformer networks. This innovation collects and preprocesses data from diverse sources through edge computing, enriching this data with supplemental information to construct a comprehensive operational dataset. Utilizing an ontology-based framework, the system organizes the data into a knowledge graph, which is then analyzed using quantum computing techniques to uncover complex, correlated relationships. The extracted relationships are further analyzed by a Graph Transformer Network (GTN) equipped with a multi-head attention mechanism, enabling the identification of spatio-temporal patterns indicative of potential system faults. The system classifies these patterns to distinguish between normal operation, potential faults, and outliers, facilitating proactive maintenance actions. This invention represents a significant advancement in the field of predictive maintenance, offering improved reliability, efficiency, and operational insight for complex systems.

Disclosed is an inter-channel interference attenuation circuit system, which includes a plurality of channels provided between a memory and a processor and that transmits data received from the memory to the processor, and an interference attenuation circuit module connected one by one to each of the channels, and each of the channels is, a victim channel with respect to the connected interference attenuation circuit module, and an aggressive channel with respect to an adjacent interference attenuation circuit module, which is an interference attenuation circuit module connected to an adjacent channel which is interfered with by its own data transmission, and each of the interference attenuation circuit modules, adjusts an output signal of the connected victim channel based on the interference attenuation circuit module and the number of adjacent channels.

A data processing method includes starting, based on a state of a cache line in a memory changing from a non-modified state to a modified state, a timer of the cache line; and writing the cache line back to a next-level memory when the timer of the cache line expires such that the cache line does not remain in the modified state for a long time. When the timer of the cache line expires, the cache line is written back to the next-level memory, and data of the cache line is backed up.

Methods, systems, and products for health signature based load shifting for part longevity may include monitoring one or more variables associated with each of multiple redundant parts of a computing system, where one or more of the redundant parts are experiencing a load, generating, based on the monitored variables, a health signature for each of the redundant parts, and shifting, based on the health signatures, the load between the redundant parts.

Methods, systems and computer program products for bringing-up a computing cluster on a public cloud infrastructure with techniques utilizing expressed intents (high level descriptions of desired configuration) and asynchronously receiving configuration status messages from the public cloud infrastructure. The method includes a cloud management computing system transmitting to the public cloud infrastructure a first expressed intent for bringing-up a computing cluster. The cloud management computing system asynchronously receiving periodic status messages comprising cluster status data from the public cloud infrastructure reflecting a current configuration state of the computing cluster. The system determines, based on the cluster status data, whether the first expressed intent for the computing cluster has been achieved.

Aspects of the disclosure relate to s computing system that is configured to use heuristic and / or metaheuristic algorithms based on swarm learning (SL) intelligence frameworks and combine SL with blockchain and edge computing frameworks to provide a technologically efficient, responsive, and / or adaptable solution to detecting and preventing defects in software applications.

A system and method for anomaly prediction in a plurality of applications to maintain operability of the system. Real-time data from the plurality of applications can be received, and for each desired variable in the data an entropy that can indicate an anomaly in the respective data. The data can be divided into batches of fixes size and a plurality of machine learning (ML) models applied to each of the batches to determine whether the data contains an anomaly or is about to contain an anomaly. The plurality of ML models can be trained based on historical data from the plurality of applications. A large language model (LLM) can be applied to the output of the plurality of ML models to evaluate performance / fine tune the plurality of ML models, and an alert can be transmitted to a system that automatically executes script(s) to take action to avoid the anomaly.

The invention discloses a firmware switching method and device, equipment and a storage medium, and relates to the technical field of computers, after an upgrading command of an operation and maintenance end is received, integrity verification is firstly carried out on target firmware, it is ensured that the target firmware to be upgraded is legal and then stored in a preset address of a memory, and then the target firmware is upgraded. After the execution of the stored to-be-processed command taken out from the command queue is finished, the firmware initial address is remapped through the memory management unit, so that the service execution of the target equipment directly jumps to the execution of the target firmware without switching to a bootstrap program, and the time for carrying out secondary firmware loading and bootstrap from a bootstrap loading program is saved.

An artificial-intelligence-assisted (AI-assisted) certification system includes an argumentation processor and an assurance case processor. The argumentation processor is configured to generate an argumentation pattern. The assurance case processor is configured to obtain the argumentation pattern from the argumentation processor, to automatically generate an assurance case based on one or more argumentation patterns, to determine evidence indicative of premises in the argumentation pattern, and to automatically assess the assurance case based on the evidence.

A plurality of computing devices are communicatively coupled to each other via a network, and each of the plurality of computing devices is operably coupled to one or more of a plurality of storage devices. A plurality of failure resilient stripes is distributed across the plurality of storage devices such that each of the plurality of failure resilient stripes spans a plurality of the storage devices. A graphics processing unit is operable to access data files from the failure resilient stripes, while bypassing a kernel page cache. Furthermore, these data files may be accessed in parallel by the graphics processing unit.