3021 results about "Telemetry" patented technology

A method for conditioning and maintaining power with predictive load compensation using an uninterruptible power supply system is disclosed. The method includes measuring an electrical input from a primary power supply using analog sensors and transmitting the input through an impedance to introduce a controlled delay. During the delay, input parameters and downstream system effects are determined based on telemetry sensor data. Digital identities are generated for the electrical input and the downstream load and compared to a stored digital identity representing historical conditions. Using the comparison, a system simulation is performed with analog twinning to predict downstream system responses. The predicted response is used to adjust a power converter parameter, modifying the electrical input to an intermediate output that compensates for voltage fluctuations and harmonic distortions. A secondary power supply with a bidirectional high-discharge battery supplements or stores energy as needed, ensuring stable power delivery to the downstream load.

The present invention discloses a system and method for performing artificial intelligence (AI) safety red-teaming with integrated policy fuzzing and adversarial prompting to systematically identify, characterize, and mitigate unsafe or non-compliant behaviors in AI models. The disclosed invention automates the process of generating, executing, and analyzing adversarial test cases through coordinated functional units comprising a policy fuzzing unit, an adversarial prompting unit, an execution sandbox, a telemetry processing unit, a scoring and triage processor, and a cryptographic provenance processor. The system applies grammar-driven and reinforcement-based fuzzing techniques to vary policy descriptors, model configuration parameters, and instruction hierarchies, while a learned adversarial prompt generator synthesizes contextually coherent adversarial prompts optimized for maximum policy violation likelihood. The generated prompts and policy vectors are executed in an isolated, instrumented sandbox that records input-output interactions, timing characteristics, and intermediate representations.

The present invention discloses an artificial intelligence-based adaptive big data storage and retrieval optimization system and method designed to intelligently manage and optimize large-scale distributed data environments. The system integrates data acquisition, distributed storage, metadata processing, adaptive learning, and retrieval optimization units configured to work collaboratively for continuous self-optimization. The invention employs deep reinforcement learning and predictive neural network techniques to dynamically analyze system telemetry, workload behavior, and data access patterns in real time, enabling proactive adjustment of data placement, caching, replication, and compression parameters across distributed nodes. The metadata processing framework utilizes graph-based dependency modeling to maintain semantic and contextual relationships among datasets, facilitating intelligent and context-aware data retrieval. The retrieval optimization unit interprets user queries semantically and computes the optimal retrieval route using latency prediction models and dynamic routing techniques.

Techniques for configurable telemetry data processing via observability pipelines are described. A user may configure an observability pipeline for particular sources of telemetry data such as a containerized application in a provider network. The observability pipeline configuration can identify telemetry data sources, types of telemetry data to be processed, as well as zero, one, or more processing functions to act upon the telemetry data before being sent to one or more destination systems specified in the configuration. Telemetry data collection agents can be automatically configured for use and subsequently halted when no longer needed.

A system for adaptively caching network communication protocols enhances efficiency across heterogeneous device environments through a multi-level cache architecture with device-capability-based tiers. The system collects endpoint telemetry data including device capabilities and operational constraints to classify endpoints and generate context-aware protocol variants optimized for specific device types. Protocol optimization opportunities are determined through structural analysis of message patterns and state transitions. The system performs protocol deduplication by identifying functionally equivalent variants and maintaining canonical representations to reduce cache redundancy. Cache synchronization across distributed nodes uses enhanced Merkle tree structures with protocol normalization processing. The system predicts communication needs based on historical patterns, network context, and endpoint constraints, enabling proactive cache management tailored to device capabilities. Integration with event-driven data communication systems enables seamless protocol selection and translation while maintaining compatibility between diverse endpoint types, from high-performance servers to resource-constrained IoT devices.

Described herein are devices, systems, methods, and processes for managing the collection and synchronization of telemetry data in a network overseen by a cloud-based network controller. This can be achieved by representing telemetry data as doubly-indexed state blocks within a shared meta-schema. Each type within the schema may be associated with a temporal list of objects of that type, providing ordered indexing by name and by time of last change. Cursors representing data witnesses may be threaded in place within these lists, enabling synchronization of telemetry data between devices without buffering. The system can dynamically adjust telemetry collection cadence in real time across devices in the fabric as users navigate the user interface. This approach can provide an effective mechanism to manage the load created by the telemetry, particularly in the context of network switches and telemetry collection.

In some implementations, a cybersecurity threat detection and mitigation system is provided. The system refines an artificial intelligence (AI) model with a corpus of historical data that represents security events that occurred, queries that were submitted by security analysts in response to the security events, and actions that were performed for mitigating the security events. Telemetry data that corresponds to behavior and performance of a computer network is collected and provided to the AI model. Based on the telemetry data, the AI model predicts a potential security threat to the computer network and performs an assessment of risk to the computer network. When the assessment of risk to the computer network indicates that the potential security threat is an actual security threat, a security alert that corresponds to the actual security threat is triggered. Other embodiments are described and claimed.

A system and method for facilitating collection of Road User Charges (RUC) using a digital currency within a Distributed Ledger Technology (DLT) network are disclosed. The system generates secure digital wallets for Road Users and Facility Owners, receives trip, position, distance, and cost data from vehicles, sensors, and facility systems, and encodes settlement logic into smart contracts. These contracts automatically execute jurisdiction-specific disbursements across blockchain, directed acyclic graph (DAG), or hashgraph frameworks, ensuring scalability and auditability without reliance on centralized tolling infrastructure. An AI Activity Module processes multi-source telemetry—including vehicle sensors, GNSS / PNT data, roadside infrastructure, and third-party traffic feeds—to detect congestion, forecast conditions, optimize routing, and dynamically adjust segment-level RUC pricing in real time. The integration of AI-driven adaptive pricing with distributed ledger settlement provides a decentralized, infrastructure-agnostic framework for secure, transparent, and verifiable road use fee collection across multiple jurisdictions, distinct from conventional tolling systems.

The present invention relates to an intelligent, context-aware battery management system embedded within a mobile device that dynamically allocates power resources based on real-time user behavior, system state, and environmental context. It incorporates a smart recognition engine that analyzes sensor-derived telemetry data to compute behavioral deviation scores, enabling the system to anticipate abnormal or emergency-prone conditions. A continuous activity classification module contextualizes user motion and geolocation to inform power policy decisions. Upon detecting significant behavioral anomalies or critically low battery conditions, an emergency mode subsystem is triggered, restricting device operations to essential functionalities while preserving energy for critical communication and navigation tasks. The system also establishes a secure, lightweight emergency communication tunnel for relaying essential metadata, including GPS and behavioral indicators, to predefined response servers.

Systems and methods support collection of telemetry by an Information Handling System (IHS). A policy decision point of a zero-trust computing environment controls access to protected resources and receives an indication of attack related to the IHS. A telemetry definition is identified that specifies telemetry being collected by the IHS and it is updated to specify a security delay for telemetry related to the indication of attack. The updated telemetry definition is transmitted to the IHS. Upon identifying telemetry that is ready for transmission, the IHS determines whether the telemetry is subject to a security delay specified in the updated telemetry definition. When the telemetry is subject to a security delay, the telemetry that is ready for transmission is queued and transmitted to one or more destinations specified in the updated telemetry definition upon expiration of the security delay.

In one implementation, a device obtains a natural language-based description of a network via a user interface. The device generates, based on the natural language-based description, network configuration parameters for the network using a generative model. The device conducts a simulation of traffic in the network using the network configuration parameters, to obtain telemetry data. The device uses the telemetry data to train a machine learning model to perform network analytics.

The invention discloses a hierarchical optimization scheduling method for deep peak regulation of thermal power generating units, and the method comprises the following steps: system layering: dividing a power system into a plurality of subsystems, each subsystem comprising a thermal power generating unit cluster and differentiated load demands; subsystem-level prediction: generating a basic scheduling plan; dynamic preference-driven multi-target collaborative optimization: receiving multi-target data from each subsystem, carrying out multi-target collaborative optimization, modeling three targets of a power grid company, an environmental protection department and a terminal user as game participants, quantifying the priority of each party by adopting a fuzzy membership function, generating a dynamic game solution through a Nash equilibrium solver, and carrying out multi-target collaborative optimization; screening out a Pareto optimal solution giving consideration to interests of multiple parties; performing online verification on the dynamic security domain; virtual synchronous machine cooperative support: simulating operation characteristics of a synchronous generator; and global coordination and iterative optimization: uploading the optimal scheduling scheme of each subsystem to a scheduling center through a message queue telemetry transmission protocol, and performing global constraint verification.

An organization-specific cybersecurity dialogue system (“system”) intelligently generates responses to user utterances for cybersecurity by employing documentation and playbook agents for an associated cybersecurity organization. The system extracts an intent and an intent category from a user utterance, extracts relevant entities from the intent and uses them to determine relevant user telemetry data, and matches the intent, intent category, and user telemetry data to documents of the cybersecurity organization. The system then determines whether to generate a response based on matching document, matching playbook agents, or to prompt a user to create a ticket according to logic applied to the intent, intent category, user telemetry data, and matching documents and generates a corresponding response with large language models.

The invention provides an equipment telemetry data fault analysis method and system based on machine learning, and relates to the technical field of fault analysis, and the method comprises the steps: carrying out the standardization processing of multi-dimensional telemetry data, extracting time sequence features and statistical features, screening a core feature set, and constructing a lightweight feature extraction rule; according to the method, feature component contribution degree scores are calculated, adaptive weight coefficients are distributed, fault feature representation is generated, and a fault diagnosis rule base is matched, so that the fault type and probability are determined, edge end data processing lightweight and timely generation of fault early warning are realized, and the accuracy and efficiency of fault diagnosis are improved.

Systems and methods provide collection of telemetry by an Information Handling System (IHS). A policy decision point (PDP) of a zero-trust computing environment controls access to protected resources and receives an indicator of attack related to the IHS. The PDP identifies a telemetry definition specifying telemetry being collected by the IHS and updates the telemetry definition to specify a subsystem telemetry chain for configuring telemetry by hardware subsystems of the IHS that are related to the indicator of attack. The updated telemetry definition is transmitted to the IHS. Upon identifying the subsystem telemetry chain in the updated telemetry definition, the IHS adjusts telemetry generation by one or more of the hardware subsystems of the IHS based on their position in the subsystem telemetry chain.

An electronic circuit system implementing and executing machine learning inference engines. While ML inference engines are based on (architectures and parameters defined by) configured, trained and tuned machine learning models, our design has the novel ability to support data driven, on-the-fly-reconfigured model runs. Reconfiguration and tuning operations include dynamic computational graph modifications, define-by-run alterations, changes to network depth (number of layers) and width (neurons per layer), and adjustments to weights, biases, plus activation function parameters. Neural networks supported include Feed-Forward, RNN, CNN, and Hopfield architectures, plus Ensemble, Federated, Cooperating, Adversarial, and Swarm collections. Decision Trees and Forests are also supported, as are more esoteric approaches such as ART and KAN. Our invention is capable of running both standalone and cooperatively, the cooperative processing being local and / or remote / cloud based, interfacing with telemetry applications to feed data, and machine learning software to feed new or updated models.

Systems and methods provide collection of telemetry by an Information Handling System (IHS). A policy decision point (PDP), of a zero-trust computing environment that controls access to a plurality of protected resources, receives an indication of resource piracy related to hardware components of the IHS. The PDP identifies a telemetry definition specifying telemetry being collected by the IHS and updates the telemetry definition to specify an updated telemetry chain for configuring telemetry collection by the hardware components of the IHS. The updated telemetry definition is transmitted to the IHS. Upon identifying the updated telemetry chain in the telemetry definition received from the PDP, the IHS adjusts telemetry generation by one or more of the hardware components of the IHS based on their position in the telemetry chain.

Systems and methods provide adaptive collection of telemetry by an Information Handling System (IHS). Telemetry is identified that is ready for transmission by the IHS. A position of the telemetry is determined within a telemetry chain of telemetry generated by the IHS. A signaling pathway is selected for transmission of the telemetry based on the position of the telemetry within the telemetry chain.

A predictive incident management system, consisting of: a sensor input module configured to receive heterogeneous telemetry data streams from mechanical, thermal, electrical and cyber sources; a temporal correlation control unit operationally coupled to the sensor input module, wherein the temporal correlation control unit is configured to normalize received data into a uniform time series envelope that includes identifiers, microsecond-precision timestamps, metric names, values, and context markers, and is further configured to compute sliding window-cross-sensor correlation matrices, event motifs, and lead-lag dependencies across multiple time granularities; a scalable rule processor that is communicatively linked to the control unit for temporal correlation, wherein the rule engine includes an in-memory runtime environment for processing complex events and a domain-specific declarative language, and is configured to apply rules that reference primitive sensor metrics, derived correlation features, and motive-based early warning vectors to classify, escalate, or resolve predicted incidents; A historical repository that is communicatively connected to both the temporal correlation control unit and the rule engine. The repository is configured to store tagged event histories, correlation motif dictionaries, rule versions, and rule origin metadata to ensure the verifiability and explainability of predictions; and An incident response interface is operationally connected to the rule engine. The incident response interface is configured to trigger automated workflows, including the generation of tickets for IT service management, chat ops notifications, the execution of orchestration playbooks, and direct machine control via industrial protocols. the system is configured to perform predictive analyses based on temporal correlations between sensors and to execute context-aware, rule-based incident management in real time.

A method may include obtaining an event log of one or more event logs of telemetry data. The event log may include one or more event log key-value pairs. The method may include identifying, from among one or more predefined fields and by using an artificial intelligence (AI) model, a predefined field for an event log key of an event log key-value pair of the one or more event log key-value pairs. The method may include generating, using the AI model, a confidence score associated with the predefined field. The method may include, responsive to the confidence score satisfying a threshold confidence score criterion, mapping a value of the event log key-value pair to the predefined field.

A system for detecting service interruptions in a cable broadband network using telemetry-based anomaly detection, wherein the system comprises the following: a telemetry acquisition unit configured to acquire multi-parameter telemetry data from heterogeneous broadband infrastructure elements, including cable modems, amplifiers, optical nodes and cable modem termination systems (CMTS), wherein the telemetry data includes the signal-to-noise ratio, modulation error ratio, forward error correction counter, power levels and latency statistics; a preprocessing and harmonization module that is operationally coupled with the telemetry acquisition unit, wherein the module is configured to normalize heterogeneous telemetry streams by adjusting sampling rates, synchronizing timestamps, interpolating missing data, and filtering out false outliers; an anomaly detection unit that is communicatively connected to the preprocessing and harmonization module, wherein the unit comprises a hybrid detection framework with statistical prediction models and machine learning models, wherein the statistical prediction models include ARIMA or Holt-Winters models to predict the expected telemetry behavior and the machine learning models include recurrent neural networks and autoencoders trained on historical telemetry; an ensemble evaluation subsystem within the anomaly detection unit, configured to combine the outputs of the statistical prediction models and the machine learning models to generate anomaly probability evaluations with adaptive confidence intervals; an interruption classification module configured to receive anomaly probability values ​​and correlate anomalies across multiple devices, geographic clusters, and time windows, wherein the interruption classification module differentiates between transient anomalies and service-impairing interruptions based on a multidimensional correlation; and an alerting interface configured to transmit outage alerts with severity, root cause metadata, and geolocation to a network management system so that the operator can intervene.

The invention discloses a vehicle-mounted holder laser telemetering scanning type gas leakage positioning system, and relates to the technical field of gas leakage monitoring. The vehicle-mounted pan-tilt laser telemetering scanning type gas leakage positioning system comprises a data acquisition unit for acquiring laser absorption second harmonic signal intensity of an inspection vehicle on a set route; the concentration inversion processing unit is used for inverting the gas concentration on the basis of the signal; the concentration analysis unit is used for identifying an abnormal inspection point by comparing the concentration change rate; the leakage direction analysis unit is used for predicting the leakage direction by combining wind direction concentration data; according to the invention, a signal saturation upper limit value and an optical path length value are introduced to carry out normalization processing, concentration inversion calibration is carried out in combination with a gas absorption coefficient, independent calculation is carried out at each inspection position point, and the accuracy of the inspection is greatly improved by combining remote measurement concentration distribution data, image data and a leakage position analysis unit for accurately positioning a leakage point in combination with a gas diffusion model. And continuous, dynamic and high-precision concentration distribution reconstruction in space is realized.

A system and method for a hierarchical thought supervision network with adaptive processing capabilities. The system processes data through a base graph layer of interconnected computational nodes, a telemetry layer for real-time monitoring, and one or more supervision layers composed of supervisory nodes. The base layer handles thought processing and management, while the telemetry layer continuously tracks operational metrics to evaluate processing efficiency. Supervisory nodes adapt network operations by optimizing thought encodings, generating new nodes when needed, and pruning inefficient nodes based on performance objectives. A telemetry layer continuously tracks processing efficiency using adaptive kernel functions and topology-aware distance metrics. The system maintains effective processing while dynamically adjusting to computational demands through coordinated supervision across multiple layers. This approach enables real-time network adaptation while optimizing performance and efficiency across the system.

Systems and methods that operate an Information Handling System (IHS) support secure telemetry for use in a zero-trust environment. Upon being initialized, the IHS retrieves a factory-provisioned locator of a service that provides the IHS with a network location of a policy decision point of the zero-trust environment and that provides an encryption key. The IHS identifies telemetry generated by the sensors that is ready for transmission. The IHS transmits the telemetry to a policy information point of the zero-trust environment, where the telemetry includes the factory-provisioned encryption key and also includes the provided network location of the policy decision point. The policy information point uses the network location included in the telemetry and the factory-provisioned encryption key included in the telemetry to establish an encrypted session with the policy decision point. Via the encrypted session, the telemetry is transmitted to the policy decision point.

A cloud deployment automation system consisting of: a deployment automation device housed in a rack-mountable enclosure, the device comprising: a multi-core orchestration processor configured to execute deployment logic as compiled execution graphs; a storage module operatively coupled to the orchestration processor, the storage storing a set of deployment templates, real-time execution states, telemetry logs, and policy configurations; a secure credential management processing unit embedded in the device, configured to generate, store, and rotate cloud access tokens, API keys, and user-specific credentials, and to provide encrypted access to those credentials during deployment execution; an in-memory workflow execution engine executed by the orchestration processor, configured to analyze a user-defined deployment configuration that includes a declarative specification of infrastructure resources and compile that configuration into a directed acyclic graph (DAG) that represents the resource deployment order, dependency mapping, and rollback relationships, a cloud provider interface subsystem communicatively connected to multiple heterogeneous cloud platforms via appropriate API adapters, the subsystem enabling the orchestration processor to send provisioning requests and receive status events from the platforms; a customizable workflow compiler unit configured to convert graphical workflow definitions or domain-specific language (DSL) scripts into execution sequences that can be used by the workflow execution engine, where the workflow compiler unit supports conditional branching, asynchronous operations, and runtime variable resolution; and A policy enforcement control unit integrated into the deployment automation device, with the policy engine configured to apply organization-specific compliance rules, tagging conventions, security group configurations, and runtime resource limits to all deployment actions in a context-aware manner prior to execution.

The present invention relates to a digital accompanying flight system for a launch vehicle based on real-time telemetry data, and a method. The system comprises a ballistic simulation model, a propulsion simulation model, a mass simulation model, an aerodynamics simulation model, an environment simulation model, a timing model, and a trigger correction model. The ballistic simulation model, the propulsion simulation model, the mass simulation model, the aerodynamics simulation model, and the environment simulation model synchronously perform simulation. On the basis of a preset flight sequence and real-time telemetry data, the timing model determines, at each simulation step length, whether a key timing action has occurred, and simulates the key timing action by adopting a discrete event message. On the basis of telemetry data of a launch vehicle which is obtained in real time, the trigger correction model determines whether the ballistic simulation model needs to be corrected, and when correction is needed, sends a correction message to the ballistic simulation model.

Systems and methods that operate an Information Handling System (IHS) support secure telemetry for use in a zero-trust environment. Upon being initialized, the IHS retrieves a factory-provisioned resource locator of a service that provides the location of a policy decision point of the zero-trust environment. The IHS establishes an encrypted session with the policy decision point that is located using the factory-provisioned resource locator. Via the encrypted session, the IHS receives a symmetric key from the policy decision point, where the key may be fleet-wide key for encryption of a customer's telemetry. Telemetry that is generated by the sensors is identified when ready for transmission and encrypted using the symmetric key received from the policy decision point. The customer's encrypted telemetry can then be securely transmitted.

Systems and methods provided adaptive collection of telemetry. A policy decision point of a zero-trust computing environment receives an indication of a change in risk posture within the environment. The policy decision point identifies a telemetry definition specifying telemetry being collected by one or more IHSs that are currently accessing a protected resource of the zero-trust computing environment. The telemetry definition is updated to specify adjusted telemetry to be collected by an IHS that is currently accessing the protected resource and the updated telemetry definition is transmitted to the IHS. Based on the updated telemetry definition received from the policy decision point, the IHS adjust measurements by one or more of the sensors of the IHS. Telemetry generated based on the adjusted measurements is transmitted by the IHS to one or more destinations specified in the updated telemetry definition.

Various implementations include systems and methods to recommend inventory, such as hardware inventory, software inventory, and default configurations, for conference rooms based on telemetry data. Telemetry data may include any data regarding use of hardware and software in the conference room. A cloud-based agent may analyze the telemetry data to make recommendations to add or remove hardware, renew or not renew applications, and to change default settings if appropriate.

Systems and methods provide adaptive collection of telemetry. A telemetry orchestrator of a IHS (Information Handling System) collects telemetry related to a session used by the IHS to access a protected resource of a zero-trust environment, where the telemetry is collected based on a telemetry definition received from a policy decision point of the zero-trust environment. The telemetry orchestrator of the IHS monitors for updates to the telemetry definition, where the updates are generated by the policy decision point of the zero-trust environment. The telemetry orchestrator adjusts measurements by one or more of the sensors of the IHS based on updates to the telemetry definition received from the policy decision point. Telemetry that is generated based on the adjusted measurements is transmitted by the telemetry orchestrator to one or more destinations specified in the update telemetry definition.