Inventor

KOLAR VINAY KUMAR

US103 patents

⚠️ This page may combine multiple inventors who share the name “KOLAR VINAY KUMAR”. Patents are grouped by organization below to help distinguish them — per-person disambiguation is on the roadmap.

KOLAR VINAY KUMAR

CISCO TECH INC

Forecasting network KPIs

Congestion detection using machine learning on arbitrary end-to-end paths

Preempting time-based SLA violations in computer networks

Scoring policies for predictive routing suggestions

Adaptive oscillation control for network paths using machine learning

Network configuration change analysis using machine learning

Predicting network states for answering what-if scenario outcomes

Detecting bug patterns across evolving network software versions

Anomaly detection of model performance in an MLOps platform

Selection of SaaS endpoint instances based on local service provider connectivity statistics

Assessing the true impact of predictive application-driven routing on end user experience

Actively learning PoPs to probe and probing frequency to maximize application experience predictions

Predictive network routing with dynamic smoothing envelope creation for noisy network timeseries

Globally avoiding simultaneous reroutes in a network

Root-causing user experience anomalies to coordinate reactive policies in application-aware routing

Peer-to-peer feature exchange for edge inference of forecasting models

Detecting a lack of correlation between application quality of experience and network conditions

Application-aware routing based on path KPI dynamics

Estimating the efficacy of predictive routing for prospective deployments

Probe fusion for application-driven routing

Path metric oscillation analysis with SLA threshold adjustment

Extraction of prototypical trajectories for automatic classification of network KPI predictions

On-the-fly SD-WAN tunnel creation for application-driven routing

Reverting routing decisions made based on incorrect network predictions

Anomaly detection triggered proactive rerouting for software as a service (SaaS) application traffic

Using a flappiness metric to limit traffic disruption in wide area networks

Machine learning driven data collection of high-frequency network telemetry for failure prediction

Event-triggered machine learning for rare event forecasting in a software defined wide area Network (SD-WAN)

Predictive routing using machine learning in SD-WANs

Predicting wireless access point radio failures using machine learning

Intelligent tunnel assignment and dynamic SLA threshold configuration to increase availability and utilization of SD-WAN tunnels

Per-flow call admission control using a predictive model to estimate tunnel QoS in SD-WAN networks

Active probe construction using machine learning for measuring SD-WAN tunnel metrics

Machine learning-based approach to network planning using observed patterns

Root cause analysis of seasonal service level agreement (SLA) violations in SD-WAN tunnels

Dynamic rerouting of wireless traffic based on input from machine learning-based mobility path analysis

Compressed transmission of network data for networking machine learning systems

Proactive routing using predicted path seasonality and trends for enhanced application experience

Capturing network dynamics and forecasting across multiple timescales

Detecting application performance breaking points based on uncertainty and active learning

Adaptive stress testing of SD-WAN tunnels for what-if scenario model training

User-assisted training data denoising for predictive systems

IBM

Software testing

Detecting and diagnosing call quality degradation issues using data session record data

Network load estimation and prediction for cellular networks

Detecting cellular connectivity issues in a wireless communication network

Detecting cellular connectivity issues in a wireless communication network

Correlating road network information and user mobility information for wireless communication network planning

Network load estimation and prediction for cellular networks

PAL KALLOL

Software testing by groups