Distributed Personalization Pods for Real-Time Content Delivery
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Solution Overview
Problem
Current systems struggle to provide real-time personalized content to users, often limiting customization to website appearance while failing to deliver targeted content effectively.
Innovation Solution
A distributed, modular network architecture with local and enterprise layers that analyze user interactions in real-time, generate personalized content, and deliver it on-the-fly, utilizing a pod-based infrastructure for improved performance and resilience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a centralized system is used to deliver personalized content, then content personalization capability is improved, but system performance and responsiveness deteriorate due to processing delays
Solution Approach 1:
The patent divides the centralized personalization system into distributed pod-based units, each capable of independent real-time content personalization. This segmentation allows local processing near users, eliminating centralized bottlenecks and improving responsiveness while maintaining personalization capabilities.
Solution Approach 2:
The patent introduces a spatial dimension by distributing processing units across multiple locations rather than relying on a single centralized system. This dimensional shift enables parallel processing and reduces latency by placing computation closer to end users.
2Measurement precision
If real-time content analysis is performed, then content relevance and personalization quality are improved, but processing time and computational load increase
Solution Approach 1:
The patent performs preliminary content analysis and user profiling in advance, storing processed data in the pod-based distributed system. This pre-processing reduces the computational burden during real-time delivery, allowing rapid retrieval and personalization without sacrificing analysis depth.
Solution Approach 2:
The patent enables each pod to perform localized real-time analysis specific to its user base and content cache, rather than requiring centralized analysis of all data. This local processing reduces overall processing time while maintaining high relevance accuracy for local contexts.
3Reliability
If a distributed pod-based architecture is implemented, then system resilience and performance are improved, but system complexity increases
Solution Approach 1:
The patent designs each pod as a universal, multi-functional unit that can handle various content types, user interactions, and personalization tasks independently. This standardization reduces complexity by using identical modular components rather than specialized systems, while still achieving distributed resilience.
Solution Approach 2:
The patent creates multiple copies of the same pod-based processing unit distributed across different locations. This replication strategy simplifies the overall architecture by using identical templates rather than designing unique systems for each node, while improving resilience through redundancy.
4Measurement precision
If extensive user interaction data is collected and processed, then personalization accuracy is improved, but data processing overhead and system resource consumption increase
Solution Approach 1:
The patent extracts only the most relevant user interaction features and data points needed for personalization, rather than processing all collected data. This selective extraction reduces computational overhead and resource consumption while maintaining high personalization accuracy by focusing on critical signals.
Data Source
AI summary
According to an embodiment of the present invention, an automated computer implemented method and system for providing targeted content in real-time comprises a web and application server that identifies one or more user interactions from a user on a website hosted by an entity; a processor that receives the one or more user interactions and converts the one or more user interactions into one or more real-time attributes; and an engine that identifies customized content for the user from a remote processor, adjusts the customized content based on the one or more real-time attributes, and generates an output comprising the customized content for display on the website for the user in real-time.