Screenshot Analysis for Personalized Content Delivery
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Solution Overview
Problem
Existing methods fail to effectively personalize content delivery when users share screenshots, as they miss opportunities to analyze the content and sharing patterns, limiting the ability to tailor content to user interests.
Innovation Solution
An electronic device analyzes screenshots for text, images, and metadata, computes an interest score based on sharing information, and delivers personalized content such as ads, video recommendations, or audio recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If screenshot analysis is not performed, then device complexity and processing requirements are reduced, but content personalization and user experience are worsened
Solution Approach 1:
The patent segments the screenshot analysis into distinct components: extracting text content, identifying images, and gathering metadata separately. Each component is processed independently through dedicated analysis modules, allowing the system to handle complex personalization tasks through manageable, modular operations that reduce overall processing complexity.
Solution Approach 2:
The patent introduces an intermediary processing layer that analyzes screenshot content and generates structured data representations. This intermediary layer acts as a bridge between raw screenshot data and the content delivery system, transforming unstructured visual information into actionable insights that drive personalized content selection without overwhelming the core system.
2Measurement precision
If screenshot content is analyzed in detail, then content relevance and user engagement are improved, but processing time and computational resources are increased
Solution Approach 1:
The patent applies partial action by selectively analyzing only the most relevant portions of screenshot content based on predefined criteria. Instead of processing every pixel and data point equally, the system focuses computational resources on key elements such as prominent text, significant images, and critical metadata, achieving sufficient personalization accuracy without exhaustive processing.
Solution Approach 2:
The patent performs preliminary analysis of screenshot content to identify and prioritize key elements before generating personalized content recommendations. By pre-processing and categorizing screenshot data in advance, the system prepares structured information that accelerates subsequent personalization operations and reduces real-time processing requirements.
3Adaptability or versatility
If sharing information is recorded and analyzed, then personalized content delivery is improved, but data privacy concerns and system complexity are increased
Solution Approach 1:
The patent extracts only the essential sharing information needed for personalization while discarding unnecessary data. By selectively extracting key parameters such as sharing frequency, content categories, and user preferences from comprehensive sharing records, the system achieves effective personalization with minimal data management overhead and reduced privacy concerns.
Solution Approach 2:
The patent applies different levels of analysis and data retention to different types of sharing information based on their relevance to personalization. High-value sharing data that directly indicates user interests receives detailed analysis and is retained for personalized content delivery, while less relevant sharing information is processed minimally or discarded, optimizing the balance between personalization quality and system complexity.
Data Source
AI summary
A method provides techniques for personalized content delivery based on screenshot analysis. Image data of a screenshot is obtained by a processor of a communication device that comprises a display. A screenshot analysis record (SAR) is created by performing an analysis of the screenshot. The SAR is transmitted to a remote computing device that supports a personalization engine and a recommendation engine. At least one personalized content asset is received, based at least in part, on the SAR. The display is modified by rendering the at least one personalized content asset on the display.