Frequency Domain Feature Vector Generation for Content Search
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Solution Overview
Problem
Traditional methods for matching content, such as text and metadata, in large databases are inefficient due to the sheer volume of data, lacking techniques for efficient processing and comparison.
Innovation Solution
The method involves generating a feature vector by converting content into a signal, then a spectrogram, and extracting features from it, which allows for efficient searching, comparison, and grouping of content by matching these vectors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional methods of matching content are used in large databases, then content matching can be performed, but processing efficiency and speed deteriorate due to the sheer volume of data
Solution Approach 1:
The patent extracts essential features from content by generating feature vectors that capture salient characteristics. This extraction process transforms large volumes of content data into compact representations that retain meaningful information while eliminating redundant data, enabling efficient matching without processing the entire original content.
Solution Approach 2:
The patent creates simplified copies of content in the form of feature vectors. These vectors are condensed representations that preserve the essential identity and characteristics of the original content, allowing for rapid comparison and matching operations without handling the full complexity of the source material.
2Loss of time
If traditional content matching methods are used, then content can be searched and compared, but processing time and computational resources increase significantly
Solution Approach 1:
The patent segments content into discrete feature vectors that can be independently processed and compared. This segmentation allows the matching system to operate on small, manageable units rather than entire content items, significantly reducing processing time and computational complexity while maintaining matching accuracy.
Solution Approach 2:
The patent transforms content into feature vectors with specific mathematical properties and dimensions. By changing the representation parameters from raw content to normalized feature vectors, the system enables faster computational operations and more efficient similarity calculations, directly reducing processing time and resource requirements.
3Productivity
If feature vectors are generated from content, then content matching efficiency improves, but the process of generating feature vectors requires computational resources
Solution Approach 1:
The patent performs feature vector generation as a preliminary action during content ingestion or indexing. By pre-computing and storing feature vectors when content is first received, the system avoids the need to generate vectors during matching operations, thereby reducing real-time computational resource consumption while maintaining high matching speed.
Data Source
AI summary
A method and a system are provided for searching content (e.g., text, metadata and/or a fingerprint, etc.). In one example, the system receives content and a query for matching the content. The content includes computer readable data. The system generates a feature vector for the content. Generating the feature vector comprises generating a signal from the content, generating a spectrogram from the signal, and generating the feature vector from the spectrogram. The system searches for at least one feature vector that matches the feature vector for the content.