David Tedaldi
25Patents
5h-index
12Co-inventors
55Inventor score
Filing activity: Jan 29, 2018 → Oct 29, 2021
Most-cited inventions
| Patent | Title | Area | Cited by | Status |
|---|---|---|---|---|
| US10574512B1 | Deep learning architecture for collaborative anomaly detection and explanation | Electricity | 18 | Active |
| US10771313B2 | Using random forests to generate rules for causation analysis of network anomalies | Electricity | 10 | Active |
| US10944641B1 | Systems and methods for application traffic simulation using captured flows | Electricity | 7 | Active |
| US11146463B2 | Predicting network states for answering what-if scenario outcomes | Electricity | 6 | Active |
| US10999146B1 | Learning when to reuse existing rules in active labeling for device classification | Electricity | 5 | Active |
| US11018943B1 | Learning packet capture policies to enrich context for device classification systems | Electricity | 4 | Active |
| US11451456B2 | Learning stable representations of devices for clustering-based device classification systems | Electricity | 2 | Active |
| US11438240B2 | Compressed transmission of network data for networking machine learning systems | Electricity | 2 | Active |
| US11196629B2 | Learning robust and accurate rules for device classification from clusters of devices | Electricity | 1 | Active |
| US11805003B2 | Anomaly detection with root cause learning in a network assurance service | Electricity | 1 | Active |
| US11290331B2 | Detection and resolution of rule conflicts in device classification systems | Electricity | 1 | Active |
| US11283830B2 | Protecting device classification systems from adversarial endpoints | Electricity | 1 | Active |
| US11297079B2 | Continuous validation of active labeling for device type classification | Physics | 1 | Active |
| US10917302B2 | Learning robust and accurate rules for device classification from clusters of devices | Electricity | 0 | Active |
| US11483207B2 | Learning robust and accurate rules for device classification from clusters of devices | Electricity | 0 | Active |
| US11425048B2 | Using throughput mode distribution as a proxy for quality of experience and path selection in the internet | Electricity | 0 | Active |
| US11349716B2 | Flash classification using machine learning for device classification systems | Electricity | 0 | Active |
| US11893456B2 | Device type classification using metric learning in weakly supervised settings | Electricity | 0 | Active |
| US11399023B2 | Revisiting device classification rules upon observation of new endpoint attributes | Electricity | 0 | Active |
| US11438406B2 | Adaptive training of machine learning models based on live performance metrics | Electricity | 0 | Active |
| US11971962B2 | Learning and assessing device classification rules | Electricity | 0 | Active |
| US11729210B2 | Detecting spoofing in device classification systems | Physics | 0 | Active |
| US11416522B2 | Unsupervised learning of local-aware attribute relevance for device classification and clustering | Electricity | 0 | Active |
| US11100364B2 | Active learning for interactive labeling of new device types based on limited feedback | Electricity | 0 | Active |
| US11916777B2 | Learning SLA violation probability from intelligent fine grained probing | Electricity | 0 | Active |
Source: USPTO / EPO open patent data. Inventor disambiguation is heuristic; counts are objective bibliographic measures.