Multimodal Semantic Navigation for Video and Podcast Segments
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Solution Overview
Problem
Users of temporally sequenced media like videos and podcasts often struggle to identify relevant segments efficiently, leading to the consumption of non-relevant content before accessing relevant information.
Innovation Solution
A processor-based system that infers a semantic understanding of media content and generates adaptive recommendations for efficient navigation within and across temporally sequenced media.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If users watch or listen to temporally sequenced media from beginning to end, then they can access all content, but they waste time consuming non-relevant content before reaching relevant segments
Solution Approach 1:
The system performs preliminary analysis of the media content by generating semantic representations and identifying relevant segments before the user actually consumes the content. This allows the system to prepare navigation guidance (such as chapter markers or highlighted segments) in advance, enabling users to jump directly to relevant portions without having to consume all preceding content.
Solution Approach 2:
The patent introduces semantic representations as an intermediary layer between the raw media content and the user's information needs. This intermediary layer processes the media into structured semantic data that can be searched and navigated efficiently, allowing users to access relevant information without processing the entire temporal sequence of the original media.
2Productivity
If users manually search for relevant segments in temporally sequenced media, then they can find what they need, but the process is time-consuming and inefficient
Solution Approach 1:
The patent replaces the mechanical manual search process with an automated semantic processing system. Instead of users manually scanning through temporal sequences, the system uses computational methods to generate semantic representations, index relevant segments, and provide automated navigation guidance, thereby substituting human manual labor with intelligent automation.
Solution Approach 2:
The system performs self-service by automatically analyzing media content, generating semantic representations, identifying relevant segments, and creating navigation structures without requiring user intervention. This automated self-service approach eliminates the need for users to manually search through content, significantly improving productivity and reducing time loss.
3Measurement precision
If the system provides detailed semantic understanding of media content, then navigation accuracy improves, but system complexity increases
Solution Approach 1:
The patent segments the complex media content into discrete relevant segments based on semantic analysis. By dividing the continuous temporal sequence into identifiable segments (such as chapters, scenes, or thematic portions), the system achieves precise identification of relevant content while managing complexity through structured organization. Each segment can be independently processed and navigated.
Solution Approach 2:
The system transforms the media content into different parameter representations (semantic representations) that are more suitable for analysis and navigation. By changing the representation parameters from raw temporal data to structured semantic data, the system achieves accurate identification while the transformation process itself manages complexity through standardized parameter conversions.
Data Source
AI summary
A method and system for semantic-based navigation of temporally sequenced content such as videos interprets the image and audio-based content by applying computer-implemented neural networks and performs multi-modal inferences of temporally aligned content. The multi-modal inferences may be performed by means of the application of vectorized embeddings and/or by application of semantic chaining techniques. The multi-modal inferences are applied to generate navigational indicators and/or responses to user inputs that comprise natural language or images. The navigational indicators and responses to user inputs may be personalized based upon user behaviors.