Digital Data Stream Structuring via Automated Descriptor Accumulation
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Solution Overview
Problem
Existing methods for extracting repetitive sequences from digital data streams, such as audiovisual content, are imprecise, incomplete, and require prior knowledge or a learning period, limiting their applicability and efficiency.
Innovation Solution
A method that segments and describes digital data streams using colorimetric similarity measures and key image extraction, allowing for the detection of repetitive sequences without prior knowledge or a learning period, and automatically creates a catalog of these sequences for on-the-fly processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If programming information (EITs or program guides) is used to extract sequences from a stream, then the extraction process is simple and direct, but the results are imprecise and incomplete due to inaccurate or missing programming data
Solution Approach 1:
The patent introduces an intermediary automated analysis system that processes the digital stream to extract programming information independently of external program guides. This mediator validates and corrects programming data by analyzing actual stream content, resolving the contradiction between simple implementation and extraction precision.
Solution Approach 2:
The system performs self-service by automatically analyzing the digital stream to generate and validate its own programming information without relying on external sources. The stream itself provides the data needed for extraction through automated pattern recognition and sequence detection.
2Measurement precision
If a learning period with corrected programming information is used to build a hidden Markov model, then predictive accuracy improves, but the implementation becomes very cumbersome due to the volume of data collection and model building requirements
Solution Approach 1:
The patent extracts only the essential features and patterns needed for sequence detection without building complex predictive models. It takes out the core functionality of pattern recognition from the cumbersome model-building process, achieving accuracy through direct stream analysis rather than extensive learning periods.
Solution Approach 2:
The system performs partial action by focusing only on detecting repetitive sequences rather than building a complete predictive model of the entire stream. This selective approach achieves the necessary accuracy without the excessive complexity of comprehensive model building.
3Productivity
If a set of references is manually constituted to index stream segments, then indexing can be performed, but the performance degrades when new programs are inserted and the cost multiplies for each stream
Solution Approach 1:
The patent implements a dynamic reference system that automatically adapts to new programs and content types in the stream. Rather than static manually-created references, the system continuously learns from and adjusts to new content patterns, maintaining high indexing capability while adapting to changing content.
Solution Approach 2:
The system creates a universal indexing mechanism that works across different stream types and content formats without requiring separate manual reference sets for each stream. The automated pattern recognition approach provides multi-functional capability that handles diverse content types with a single system.
4Measurement precision
If exhaustive search for similar shots is performed to detect jingles, then all repetitive sequences can be identified, but the processing time and computational cost become very high
Solution Approach 1:
The patent segments the stream into manageable units and uses hierarchical processing to detect repetitive sequences. By dividing the exhaustive search into structured stages with intermediate results, it maintains detection completeness while reducing overall processing time through efficient organization of the search process.
Solution Approach 2:
The system implements periodic action by processing stream segments in regular intervals and using incremental updates rather than continuous exhaustive search. This approach maintains complete detection capability while reducing processing time through structured, periodic analysis of stream content.
Data Source
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AI summary
The invention relates to a method of structuring a digital data stream comprising steps of: describing the stream by means of detailed descriptors and summary descriptors, accumulating, in the course of a given period, detailed descriptors and summary descriptors, grouping together accumulated detailed descriptors, forming repetitive sequences as a function of grouped detailed descriptors, extending repetitive sequences, as a function of summary descriptors accumulated during the given period, so as to form extended sequences, deleting the detailed descriptors and the summary descriptors that allowed the formation of the extended sequences.