Singularly Adaptive Digital Content Layers Using Engagement Feedback
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Solution Overview
Problem
Existing digital content generation techniques fail to adapt dynamically to individual user interests, resulting in content that is not optimally interesting or useful for diverse audiences, requiring significant human intervention and lacking in efficiency.
Innovation Solution
A computer-implemented method using an interest model and machine learning to dynamically adapt digital content by selecting layers based on user engagement metrics, employing a generative model to generate multiple content components with varying attributes, and implementing a feedback loop for continuous improvement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional static content generation techniques are used, then content can be delivered to users, but the content is not optimally interesting or useful for diverse audiences and requires significant human intervention
Solution Approach 1:
The patent implements dynamic content generation by continuously monitoring user engagement metrics (scroll position, reading speed, time spent) and adapting content in real-time. The system transitions from static pre-defined content to dynamically generated content that responds to user behavior, making the content adaptive rather than fixed.
Solution Approach 2:
The system employs machine learning models that automatically analyze user engagement data and generate optimized content without human intervention. The AI models self-adjust content parameters based on measured user responses, eliminating the need for manual content creation and optimization for different user segments.
2Adaptability or versatility
If multiple unique versions of content are produced for different user types, then content optimality for specific users improves, but the process becomes time-consuming and difficult or impossible to scale
Solution Approach 1:
The patent creates a universal content generation system that serves multiple user types simultaneously through a single AI model. Rather than creating separate content versions for different user segments, the system uses one multi-functional model that adapts to any user type in real-time based on engagement metrics, enabling scalable personalization.
Solution Approach 2:
The system optimizes content by dynamically changing parameters such as text length, complexity, tone, and format based on real-time user engagement data. The AI model adjusts these parameters continuously rather than creating entirely different content versions, improving efficiency while maintaining personalization.
3Measurement precision
If traditional content delivery methods are used, then content can be presented to users, but the system cannot adapt to determine what type of content would be most useful or interesting to each user
Solution Approach 1:
The patent implements a feedback loop where user engagement metrics (scroll position, reading speed, time spent on content) are continuously measured and fed back to the AI model. The system uses this feedback to real-time adjust content generation, creating a closed-loop system that learns from user responses and improves content delivery iteratively.
Data Source
AI summary
Certain aspects of the present disclosure provide techniques for delivering singularly adaptive digital content that includes content components which are adapted in real time based on user interest metrics and using a generative model. Multi-layered content is generated, such that the content may be divided into content components which may each be adapted to be of a selected content type for a content class. The content components include one or more classes having one or more types. To adapt the content, subsequent content components may be adapted by changing which layer of the content component is selected or presented. The content component layers may have been previously generated using a generative model by using a base content component and a selection of content types for content classes. Changing layers for content components may occur when an attention score for the content falls below a threshold to improve interest in the content. Additional metrics may be recorded while the adapted content components are presented to create a feedback loop further optimizing layer selection and increasing interest in the content.