Intelligent Edge Nodes for Personalized Content Delivery
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Centralized broadband networks lack intelligent nodes to customize data traffic, leading to inefficiencies in content delivery, high latency, and inability to provide personalized content based on user preferences and network conditions, as edge nodes are not capable of recognizing data traffic patterns or making dynamic decisions.
Innovation Solution
Deploying intelligent edge nodes capable of computing dynamic scores and performing deep packet inspection, integrated with a web-based platform that allows authorized users to query and personalize content delivery based on user preferences and network metadata, enabling real-time customization and low latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a centralized broadband network is used, then service providers can manage content delivery through datacenters, but the network cannot provide personalized content or make dynamic decisions at edge locations due to lack of intelligent nodes
Solution Approach 1:
The patent segments the network into centralized datacenter components and decentralized intelligent edge nodes. Each edge node is equipped with AI capabilities to independently analyze local traffic patterns and make personalization decisions, eliminating the need for a fully centralized intelligent system while enabling personalized content delivery at the network edge.
Solution Approach 2:
The patent introduces an intermediary communication layer between edge nodes and datacenters that enables lightweight feedback mechanisms. This intermediary system allows edge nodes to share anonymized traffic patterns and receive model updates without requiring complex point-to-point communication, balancing intelligence distribution with network simplicity.
2Speed
If edge nodes are deployed to reduce latency, then content delivery speed improves, but edge nodes lack the intelligence to recognize data traffic patterns and perform compute for personalization
Solution Approach 1:
The patent pre-equips edge nodes with embedded AI models and machine learning capabilities during deployment. These pre-loaded intelligence modules enable edge nodes to immediately begin analyzing traffic patterns and making personalization decisions without requiring real-time connectivity to datacenters, thus reducing latency while maintaining high automation levels.
Solution Approach 2:
The patent enables edge nodes to autonomously perform deep packet inspection, traffic pattern recognition, and content personalization decisions using their own embedded AI processors. Each edge node independently analyzes local user behavior and network conditions to dynamically customize content delivery without external intervention, achieving both speed and intelligence.
3Reliability
If storage units at edge locations are configured to store content replicas, then content availability improves, but storage units cannot dynamically make decisions or communicate across the network to adapt to changing conditions
Solution Approach 1:
The patent merges storage functionality with intelligent processing capabilities at edge nodes. Instead of separate storage units, the system combines content storage, AI processing, and communication modules into integrated edge nodes that can simultaneously maintain content availability and perform dynamic decision-making based on real-time analysis of user preferences and network conditions.
Solution Approach 2:
The patent implements dynamic content delivery by enabling edge nodes to continuously adapt their behavior based on real-time traffic patterns and user feedback. The system dynamically adjusts content selection, personalization parameters, and communication strategies according to changing network conditions and user preferences, transforming static storage units into adaptive intelligent nodes.
4Adaptability or versatility
If service providers manually collect and process information for content personalization, then content customization is possible, but automation is lacking which reduces efficiency and scalability
Solution Approach 1:
The patent replaces manual information collection and processing mechanisms with automated AI-driven systems at edge nodes. Machine learning algorithms automatically analyze user behavior, preferences, and network conditions without human intervention, enabling scalable content personalization that maintains high customization capability while dramatically improving processing efficiency and reducing operational costs.
Data Source
AI summary
Personalized content delivery in a broadband network for an end-user includes a plurality of intelligent edge nodes deployed in the broadband network. Each of the plurality of intelligent edge nodes is capable of computing dynamic scores with respect to any content delivered to a corresponding end-user device connected in the broadband network. Based on a set of preference data, at least one query may be received from at least one authorized user and a corresponding response may be generated based on the preference data and the computed dynamic scores The at least one authorized user analyses the response and tag relevant content to be personalized for the end user. The at least one authorized user thereafter activates a functionality over a UI/UX interface that facilitates the end-user to access the personalized content.