4118 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 disclosure relates to a method and system for dynamically mitigating threats of generative Artificial Intelligence (AI) models. Conventional systems often suffer from inefficiencies due to sequentially applying threat detection checks leading to unnecessary preprocessing and increased computational demands. Additionally, such systems typically focus only on input data, neglecting potential threats in outputs. The disclosed system and method addresses these drawbacks by employing a hierarchical structure of macro and nano classifiers. The system utilizes macro classifiers for broad initial threat categorization followed by specialized nano classifiers for detailed analysis of specific threat subtypes, thereby optimizing processing time and computational resources. The system operates in real time, applying predefined moderation rules to both input and output data to ensure comprehensive threat mitigation. Additionally, continuous telemetry data updates refine nano classifiers and threat identification mechanisms, maintaining high accuracy and adaptability. The disclosed method enhances safety efficiency and reliability of generative AI models.

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.

Data acquired from at least one source is provided for observability on a cloud network. The data is placed in a common language for observability on the network so that data can be targeted based on a telemetry characteristic. Data having a first telemetry characteristic is acquired and routed to a destination for observability, whereas data having a second telemetry characteristic may be routed to another destination to be stored for observability.

Aspects of the disclosure include methods and systems for performing automated fault scenario generation for chaos engineering. Aspects include obtaining a configuration of a service under test, obtaining a first plurality of fault scenarios, and applying each of the first plurality of fault scenarios to the service under test. Aspects also include recording telemetry data regarding an operation of the service under test under each of the fault scenarios, selecting, based on the telemetry data, a first fault scenario from the fault scenarios, and generating a second plurality of fault scenarios. Aspects further include applying each of the second plurality of fault scenarios to the service under test, recording telemetry data regarding the operation of the service under test under each of the second plurality of fault scenarios, and identifying a vulnerability of the service under test based on the recorded telemetry data.

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 geo-converged telemetry (GCT) system is described that provides an exchange of geographically-relevant telemetry data amongst various vehicles, devices, and systems for providing a holistic view of a geographic area. Telemetry data from various sources may be continually collected and transmitted to a nearby node of a GCT system, where the data may be converged based on geolocation and processed. The same and / or other vehicles, devices, and systems may operate as telemetry consumers that receive feeds of geo-converged telemetry data that may be tailored to functionalities of the consumer. As a vehicle moves, data that are determined to be geographically-relevant changes. The location of the processing may move from one processing node to another processing node in the edge network such that the processing is located proximate to the vehicle to minimize latency and provide real time or near-real time feedback based on a holistic view of the geographic area.

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.

The present disclosure relates to cyber-attack mitigation, information theoretic security in-motion, at-rest and storage intrusion mitigation with exclusive-path, content routing across an overlay network. Overlay network built upon Data Transport Controllers (DTC), SG nodes and SV nodes. System modules are deployed across various geo locations in a Wide Area Network, operating at the control of Universal Security Controller (USC). USC extracts system, security and storage activity telemetry data from Secure Vaults, Storage Gateways and Data Transport Controllers to orchestrate Autonomous Security Operations. SG nodes create SP fragments and store in SV nodes upon a data input operation. USC initiates an Exclusive-path content forwarding from the first SG node, across the DTC overlay through an exclusive list of DTC nodes and move it to second SG node. USC keeps the security control of SG nodes, DTC nodes and SV nodes and realize multi-vectored storage security at-rest and in-motion. SG nodes are connected to SV nodes which are micro-segmented, data vaults with restricted network reachability.

Methods, systems, and devices for leveraging automotive and wearable-based data are described. For example, a system may include a wearable device, a user device associated with the wearable device, and a vehicle. The system may determine when a user is within a proximity of the vehicle based on communications between the vehicle and the wearable device, between the vehicle and the user device, or both. The system may retrieve physiological data measurement from the user via the wearable device based on determining the user is positioned in a proximity of the vehicle, such that one or more operational parameters of the vehicle may be adjusted based on the physiological data. Additionally, or alternatively, the system may receive telemetry data from one more sensors of the vehicle and may provide feedback to the user based on the telemetry data satisfying one or more trigger conditions.

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.

A system and method are provided for routing traffic through a network to ensure load balancing and avoid untrustworthy nodes. Based on network data (e.g., telemetry data), a machine learning model generates trust scores, which are used for routing decisions by determining preferred routes from a source to a destination. The trust scores for nodes along a potential route can be combined into a cumulative trust score. The potential route with the lowest cumulative trust score (i.e., most trustworthy) is preferred, when all other factors are equal. Traffic is routed along the preferred routes, until their capacity is exceeded. Then to achieve load balancing, traffic flows are extended to the next most preferred routes (e.g., the next lowest cumulative trust score), and so forth. When traffic flows include a mix of sensitive and non-sensitive data, the sensitive data is preferentially directed along the most preferred routes.

The invention discloses a water and soil loss telemetry data analysis method and system based on deep learning, and relates to the technical field of data analysis, and the method comprises the steps: extracting soil loss data, surface runoff data and topographic data; fusing the soil loss data, the surface runoff data and the topographic data, and obtaining water and soil loss risk index data and soil erosion rate data through a feature extraction technology; calculating weight values of the water and soil loss risk index data and the soil erosion rate data by using a feature evaluation technology; and based on the water and soil loss risk index data, the soil erosion rate data and the weight value, constructing a water and soil loss prediction model by using a deep learning framework and a space-time analysis algorithm. According to the invention, through telemetry data collection and feature extraction, key water and soil loss information can be rapidly obtained, the data processing flow is simplified, and the accuracy and reliability of data are ensured.

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.

Examples described herein relate to a switch, when operational, that is configured to receive in a packet an indicator of number of remaining bytes in a flow and to selectively send a congestion message based on a fullness level of a buffer and indication of remainder of the flow. In some examples, the indicator is received in an Internet Protocol version 4 consistent Options header field or Internet Protocol version 6 consistent Flow label field. In some examples, the congestion message comprises one or more of: an Explicit Congestion Control Notification (ECN), priority-based flow control (PFC), and / or in-band telemetry (INT). In some examples, to selectively send a congestion message to a transmitter based on a fullness level of a buffer that stored the packet and the number of remaining bytes in flow, the switch is to determine whether the buffer is large enough to store the remaining bytes in the flow.

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.