Video Summary Generation Using Crowd Annotation Segmentation
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Solution Overview
Problem
Existing video annotation summary solutions are inadequate in generating summaries based on crowd-based comments associated with video direct accesses, failing to effectively communicate commercial product or service reviews to consumers, leading to incomplete information and reduced consumer awareness.
Innovation Solution
The system utilizes natural language processing, Word2vec, and cognitive AI processing to analyze crowd-based comments and video direct accesses, generating video segments and summaries that highlight relevant product reviews, enabling consumers to make informed purchasing decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If video annotation summaries are generated using traditional methods, then the process is simple, but the summaries are incomplete and fail to effectively communicate crowd-based feedback
Solution Approach 1:
The patent segments videos into multiple clips based on crowd-based annotations and timestamps, then selectively processes only relevant segments rather than entire videos. This reduces information loss while maintaining manageable processing complexity through targeted analysis of annotated portions.
Solution Approach 2:
The patent introduces crowd-based comments and annotations as intermediary elements that bridge raw video content and final summaries. These intermediaries guide the processing system to extract only relevant information, improving completeness without proportionally increasing system complexity.
2Loss of information
If the system analyzes all video content to generate comprehensive summaries, then information completeness improves, but processing time increases significantly
Solution Approach 1:
The patent performs preliminary segmentation and annotation of videos before summary generation. Crowd-based comments and timestamps are pre-associated with specific video segments, enabling rapid retrieval and synthesis during summary creation without analyzing entire video content, thus reducing processing time while maintaining information completeness.
Solution Approach 2:
The patent extracts only the most relevant video segments based on crowd-based annotations and timestamps, excluding irrelevant portions from processing. This extraction approach ensures complete product review information is captured while significantly reducing the time required to generate summaries by focusing only on annotated segments.
3Loss of information
If video summaries include detailed crowd-based feedback, then consumer awareness improves, but the complexity of processing and organizing comments increases
Solution Approach 1:
The patent enables crowd-based comments to self-organize around specific video segments through timestamps and annotations. The system leverages the inherent structure created by user annotations rather than imposing complex organizational frameworks, improving consumer awareness while minimizing processing complexity through natural data organization.
Solution Approach 2:
The patent segments crowd-based feedback and associates it with specific video clips through timestamps. This segmentation allows the system to process and present detailed consumer awareness information in manageable, organized units rather than as undifferentiated text, reducing processing complexity while maintaining feedback completeness.
4Productivity
If the system processes and organizes crowd-based annotations, then video summary relevance improves, but processing complexity increases
Solution Approach 1:
The patent extracts and processes only the essential elements of crowd-based annotations (timestamps, key comments, segment identifiers) rather than analyzing entire comment texts. This extraction approach improves video summary relevance for marketing effectiveness while keeping processing complexity manageable by focusing on critical annotation data points.
Solution Approach 2:
The patent uses structured annotation data as an intermediary layer between raw crowd comments and video summary generation. This intermediary structure organizes feedback into processable formats with timestamps and segment associations, improving marketing effectiveness through relevant summaries while reducing complexity compared to processing unstructured comment data.
Data Source
AI summary
A system for executing a video summary is provided. One or more video segments for a video based on one or more digital media is generated. A video summary is generated based on a user request.