Contextual Awareness Engine for Home Network Policy Optimization
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Solution Overview
Problem
Current systems fail to effectively leverage contextual awareness data from various sources to optimize network operations and user experiences in home environments, particularly in managing Quality of Service (QoS) and security policies based on user presence or absence.
Innovation Solution
A contextual awareness architecture that utilizes a contextual awareness engine to collect and manage data from diverse sources, including user devices and appliances, to drive dynamic policy decisions and optimize network operations by distinguishing between user and machine traffic, enabling tailored QoS and security policies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a modem or home routing gateway captures and processes contextual awareness data from multiple sources, then network operations and user experiences can be optimized through dynamic policy decisions, but system complexity and data processing requirements increase
Solution Approach 1:
The patent introduces a contextual awareness engine as an intermediary component that sits between the data collection layer (multiple data sources) and the policy decision layer. This engine aggregates, processes, and contextualizes data from diverse sources (appliances, user devices, network elements) before presenting processed contextual information to policy decision makers, thereby simplifying the overall system architecture while enabling comprehensive contextual awareness for optimized network operations
Solution Approach 2:
The system architecture is segmented into distinct functional layers: data collection from multiple sources, contextual awareness data processing through a dedicated engine, and policy decision implementation. This segmentation allows each component to be optimized independently and reduces the complexity burden on any single element while maintaining the ability to leverage contextual information across the entire system
2Reliability
If contextual awareness data is collected from diverse sources including user devices and appliances, then Quality of Service and security policies can be dynamically optimized, but user privacy concerns increase
Solution Approach 1:
The patent implements local quality by processing and contextualizing data locally within the home network environment through the contextual awareness engine, rather than centrally collecting raw data from all sources. This allows QoS and security policies to be optimized based on contextual patterns (such as user presence, device activity levels, network conditions) without requiring centralized access to sensitive user information, thereby maintaining privacy while achieving reliable policy optimization
3Ease of operation
If the system distinguishes between user and machine traffic to enable tailored QoS policies, then service delivery is improved, but measurement and detection difficulty increases
Solution Approach 1:
The contextual awareness engine continuously monitors network traffic patterns, device behaviors, and contextual signals to distinguish between user-initiated and machine-initiated traffic flows. By analyzing feedback from multiple data sources (device metadata, traffic patterns, appliance communication patterns), the system automatically classifies traffic and applies appropriate QoS policies, improving service delivery while reducing the manual measurement and detection burden through automated contextual analysis
Data Source
AI summary
An architecture for collecting and managing contextual awareness data is contemplated. The architecture may be used to implement various policies as a function of the contextual awareness data, such as but not limited to implementing dwelling specific policies depending on the contextual awareness data indicating whether one or more users are presence within a dwelling.