Personalized Video Frame Selection Using Semantic Analysis
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Solution Overview
Problem
Existing video player technologies require manual selection or random selection of frames as representative still images, which may not accurately reflect the content or user preferences, leading to suboptimal user experiences.
Innovation Solution
A method using semantic and sentiment analysis to derive user-specific parameters from video frames and social media information, identifying and displaying frames that best match these parameters to personalize the representative still image for each user.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual selection or random selection of frames is used, then the process is simple, but the representative still image does not accurately reflect user preferences
Solution Approach 1:
The patent transforms the frame selection process from manual or random methods to an automated system that analyzes multiple parameters including semantic content, sentiment, and user-specific cognitive profiles. By changing the selection criteria from simple randomization to multi-parameter matching, the system achieves higher accuracy in selecting representative still images that reflect user preferences.
Solution Approach 2:
The system enables automatic frame selection without requiring manual user input. The cognitive analysis module autonomously processes video frames, compares them against user profiles, and selects the most representative frame based on computed similarity metrics, making the selection process self-service and eliminating manual intervention.
2Adaptability or versatility
If a single representative frame is selected for all users, then the process is efficient, but it cannot adapt to individual user preferences
Solution Approach 1:
The patent applies local quality by creating user-specific representative still images tailored to individual preferences. Instead of using a single generic frame for all users, the system generates customized selections for each user based on their unique cognitive profiles, ensuring that each user receives a personalized representation of the video content.
Solution Approach 2:
The system performs preliminary action by pre-computing and storing cognitive profiles for multiple users before actual video playback. These profiles are created in advance and can be quickly matched against video frames during playback, reducing real-time processing requirements and maintaining efficiency while enabling personalization.
3Loss of information
If cognitive analysis of user information is performed, then user-specific parameters are derived, but the computational complexity increases
Solution Approach 1:
The patent extracts only the essential cognitive parameters from user information that are most relevant to video frame selection. Rather than analyzing all possible user data, the system identifies and extracts key features such as preferred content types, emotional responses, and viewing patterns, reducing computational complexity while maintaining effective personalization.
Data Source
AI summary
In determining a representative still image of a video, a first set of parameters for each frame in a video is derived, and a second set of parameters for a specific user is derived. A frame with first set of parameters that best matches the second set of parameters is identified and selected as the representative still image of the video for the specific user. Different parameters may be derived for different specific users, and different frames are identified as best matches for different specific users. Different parameters may be derived for the specific user at different times, and different frames are identified as best matching at the different times. Different values for the parameters for the same specific user may be calculated at different times, and different frames may be identified as best matching the parameters at the different time.