Media Guidance Object Detection for Timely Content Notifications
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Solution Overview
Problem
Users face challenges in selecting relevant media content from the vast array available, and content providers struggle to deliver content that aligns with user interests.
Innovation Solution
A media guidance application that detects objects of interest in media content and provides recommendations for supplemental assets based on user profiles, incrementing a counter for available content during media playback and offering user-selectable indications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a media guidance application monitors and analyzes all media content and user behavior data to provide personalized recommendations, then recommendation accuracy and user engagement improve, but system complexity and computational resources increase
Solution Approach 1:
The system segments the recommendation process into distinct modules: content analysis component that processes media content, user behavior analysis component that tracks user interactions, and recommendation generation component that combines these insights. This segmentation allows each component to specialize and reduces overall system complexity while maintaining high recommendation accuracy through coordinated operation of specialized subsystems.
Solution Approach 2:
The patent introduces intermediary components including a content analysis intermediary that bridges raw media content and the recommendation engine, and a user behavior intermediary that mediates between user interactions and preference modeling. These intermediaries simplify the data flow and processing requirements, reducing computational complexity while preserving measurement precision in recommendations.
2Productivity
If the system provides real-time content recommendations during media playback, then user engagement and content discovery improve, but processing time and computational load increase
Solution Approach 1:
The system performs preliminary analysis of media content during ingestion and preprocessing stages, creating structured content representations and metadata before playback. User behavior data is also pre-processed and stored in optimized formats. During actual playback, the recommendation engine queries these pre-prepared data structures rather than analyzing raw content in real-time, significantly reducing processing time while maintaining high content discovery efficiency.
3Adaptability or versatility
If the system collects and stores detailed user profile data and viewing history to enable personalized recommendations, then recommendation relevance improves, but data privacy concerns and storage requirements increase
Solution Approach 1:
The patent extracts only the essential features and patterns from user behavior data and viewing history that are necessary for recommendation personalization, rather than storing complete raw datasets. The system extracts user preferences, content categories of interest, and viewing patterns while discarding redundant information. This extraction approach maintains adaptability and personalization capability while significantly reducing data storage requirements and privacy concerns.
Data Source
AI summary
Methods and systems are disclosed herein for a media guidance application that alerts a user to the appearance of objects in media content that may be of interest to the user. For example, as media content progresses, the media guidance application may determine objects that may interest a user. The media guidance application may record the number of determined objects and present the number to the user as well as supplemental content associated with each object.