Network Anomaly Resolution for Real-Time Media Transmission
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Solution Overview
Problem
Existing network communication systems face challenges in identifying and resolving anomalies such as bandwidth decrease, packet loss, increased traffic, and hardware failures during media data transmission, leading to distortions and comprehension issues among participants in real-time communications.
Innovation Solution
A system utilizing machine learning models and natural language processing to automatically identify anomalies by analyzing media characteristics and contextual information, determining operational scores, and executing operations like encoding, decoding, and load balancing to resolve these issues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual monitoring and identification of anomalies is performed, then anomalies can be detected, but the process becomes labor-intensive and prone to human error and delay
Solution Approach 1:
The system enables self-service by implementing automated anomaly detection through machine learning models that continuously monitor media data transmission without human intervention. The system autonomously identifies anomalies, determines operational scores, and executes resolution operations, eliminating the need for manual monitoring while improving detection accuracy and reducing identification time.
Solution Approach 2:
The patent replaces the mechanical manual monitoring system with an automated computational system using machine learning models and natural language processing. This substitution transforms the anomaly detection process from human-based mechanical observation to automated algorithmic analysis, significantly improving reliability and reducing time loss.
2Productivity
If network bandwidth is increased to handle more media data, then communication quality improves, but network congestion and traffic management challenges increase
Solution Approach 1:
The system implements dynamic network management by continuously monitoring media characteristics and contextual information in real-time. Based on the determined operational scores, the system dynamically adjusts network resource allocation and traffic management strategies, allowing the network to adapt to changing conditions without requiring permanent increases in bandwidth capacity.
Solution Approach 2:
The patent changes network operation parameters dynamically by adjusting media characteristics (such as encoding formats, resolution, or bitrate) based on detected anomalies and operational scores. This allows the system to optimize transmission capacity utilization without permanently increasing network bandwidth, thereby managing traffic complexity more effectively.
3Productivity
If automated anomaly detection and resolution systems are implemented, then communication efficiency improves, but system complexity and computational requirements increase
Solution Approach 1:
The automated system is segmented into distinct functional modules: media characteristic analysis components, contextual information processing components, operational score determination modules, anomaly identification engines, and resolution operation executors. This segmentation allows each component to specialize in specific tasks, improving overall communication efficiency while managing system complexity through modular architecture.
Solution Approach 2:
The system implements multi-functional components that perform multiple tasks. For example, the machine learning models simultaneously analyze media characteristics, process contextual information, and determine operational scores. This universality reduces the number of separate systems needed, thereby improving communication efficiency without proportionally increasing overall system complexity.
Data Source
AI summary
Identification and resolution of anomalies over a network include obtaining at least a set of media characteristics associated with media data transmitted from a first entity to a second entity over the network and contextual information associated with at least one of the first entity or the second entity. A first operational score associated with the first entity is determined based on the obtained set of media characteristics and the obtained contextual information. The first operational score is indicative of operating conditions associated with the first entity for the transmission of the media data over the network. Based on a comparison of the first operational score with a threshold, a set of anomalies associated with the first entity is identified. A set of operations to resolve the set of anomalies is determined. The first entity is controlled to execute the determined set of operations on the media data.