AI Content Delivery Framework for Dynamic Contextual Mapping
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Solution Overview
Problem
Conventional mechanisms for determining content to provide with rendered electronic resources are static and rely on predefined rules, failing to capture the dynamic nature of user interactions and resource context.
Innovation Solution
A decision intelligence (DI)-based computerized framework that uses AI/ML and NLP techniques to dynamically interpret and render electronic resources, performing contextual mapping and content curation for optimized content delivery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If predefined rules are used for content selection, then device complexity is reduced, but adaptability to dynamic user interactions deteriorates
Solution Approach 1:
The patent implements dynamic content selection by transitioning from static predefined rules to a machine learning model that continuously adapts to user interactions. The system dynamically generates content recommendations based on real-time user behavior patterns, resource context, and interaction history, making the content selection mechanism flexible and responsive to changing conditions.
Solution Approach 2:
The system changes the parameters of content selection by incorporating multiple dynamic variables including user interaction patterns, resource context features, and sentiment analysis results. These parameters are processed by the ML model to generate optimized content recommendations, replacing the fixed parameter sets used in traditional rule-based systems.
2Speed
If static content selection methods are used, then processing speed is maintained, but measurement precision of user intent deteriorates
Solution Approach 1:
The patent replaces mechanical rule-based processing with machine learning-based cognitive processing. The ML model substitutes traditional keyword matching and rule evaluation with neural network-based sentiment analysis and user intent recognition, enabling more precise measurement of user intent while maintaining processing speed through optimized model inference.
Solution Approach 2:
The system performs self-service by automatically analyzing user interactions and generating content recommendations without manual intervention. The ML model continuously learns from user behavior patterns and autonomously optimizes content selection, eliminating the need for manual rule updates while improving measurement precision of user intent.
3Adaptability or versatility
If AI/ML techniques are implemented for dynamic content interpretation, then adaptability to context improves, but device complexity increases
Solution Approach 1:
The patent segments the content delivery system into distinct functional modules: user interaction analysis component, resource context analysis component, ML-based content recommendation component, and A/B testing component. This segmentation allows each module to specialize in specific tasks, improving contextual interpretation while managing overall system complexity through modular architecture.
Solution Approach 2:
The system introduces an intermediary ML model that bridges user interactions and content selection. This intermediary layer processes raw interaction data and resource context through sentiment analysis and pattern recognition, translating complex inputs into optimized content recommendations, thereby improving adaptability while encapsulating complexity within the intermediary component.
4Manufacturing precision
If real-time contextual analysis is performed, then content accuracy improves, but processing time increases
Solution Approach 1:
The system performs preliminary actions by pre-processing user interaction data and resource context into structured features before ML inference. Sentiment analysis and context extraction are conducted in advance, and the ML model is trained offline on historical data, enabling fast real-time content recommendations without sacrificing accuracy during actual content delivery.
Solution Approach 2:
The patent implements continuous learning and optimization through A/B testing and feedback loops. The system continuously refines the ML model using real-world performance data, maintaining high content selection accuracy over time. This continuous improvement reduces the computational burden required for accurate real-time analysis, balancing precision and processing time.
Data Source
AI summary
Disclosed are systems and methods that provide a decision-intelligence (DI)-based, computerized framework for performing contextual mapping between hosted and provided content, from which curated digital content and/or associated content campaigns can be implemented. The disclosed framework can be implemented by supply-side platforms (SSPs), demand-side platforms (DSPs) and/or content delivery platforms (CDPs), which can leverage the contextual mapping and content curation tools provided by the disclosed framework to effectively plan, launch, optimize and monitor the performance of content campaigns implemented over a network on network resources. The disclosed strategic and data-driven processes rendered capably by the disclosed framework for SSP and/or DSP initiatives can define campaign parameters, and in real-time, monitor the effectiveness of campaigns such that their modifications and/or alterations can be dynamically performed so as to adapt to the changing landscapes of how the campaign is being disseminated over a network and received by users.