Digital Data Pattern Recognition via 3D Fingerprinting
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Solution Overview
Problem
Current computer systems face challenges in isolating and processing specific parts of digital data, such as sound or video, due to data loss during cleanup and manipulation, and difficulty in identifying patterns for filtering and masking.
Innovation Solution
The method involves analyzing digital data by segmenting it into additional dimensions, determining resemblance values, and generating three-dimensional fingerprints to filter and mask the data non-destructively, allowing for the isolation and manipulation of specific elements within the data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If digital data is cleaned up and manipulated using conventional methods, then processing can be performed, but data is lost during the process
Solution Approach 1:
The patent applies preliminary action by creating a comprehensive fingerprint of the entire audio source before any segmentation or isolation operations. This fingerprint captures the complete spectral and temporal characteristics of all elements in the audio, allowing for non-destructive analysis and isolation of specific components without losing original data quality.
Solution Approach 2:
The patent transforms the audio data from traditional time-domain representation into a multi-dimensional frequency-time-spectral space using Fourier transforms and spectrogram analysis. This dimensional transformation enables precise identification and isolation of specific audio elements (such as individual instruments or voices) without affecting or losing the original data, as operations can be performed independently in this expanded dimensional space.
2Ease of operation
If conventional audio processing tools are used to isolate particular parts of digital data, then some isolation can be achieved, but it is very often difficult to isolate particular parts effectively
Solution Approach 1:
The patent segments the audio spectrum into distinct frequency bands and temporal windows, creating a granular representation of the audio source. By dividing the audio into manageable segments across multiple dimensions (time, frequency, spectral density), the system can precisely identify and isolate specific elements such as individual instruments or voices with high accuracy, making the isolation process both easier to operate and more precise.
Solution Approach 2:
The patent employs spectral analysis that visualizes different audio elements with distinct spectral signatures, analogous to color differentiation. Each audio element (instrument, voice, noise) has a unique spectral fingerprint that can be identified and targeted for isolation. This spectral differentiation enables precise isolation of particular parts by targeting their unique spectral characteristics rather than relying on simple frequency filtering.
3Measurement precision
If pattern identification tools are implemented to identify patterns within audio source, then filtering and masking can be improved, but device complexity increases
Solution Approach 1:
The patent creates a fingerprint copy of the audio source that contains all the necessary information for pattern recognition and analysis. This fingerprint is a compressed representation that captures the essential spectral and temporal characteristics without requiring the full complexity of the original audio data. By working with this simplified copy for pattern identification and matching, the system achieves high recognition accuracy while reducing computational complexity compared to analyzing the complete high-resolution audio data.
Data Source
AI summary
Embodiments disclosed herein extend to methods, systems, and computer program products for analyzing digital data. A source of digital data is analyzed and separated into segments, each segment having an identifiable characteristic. The separated segments are copied into planes of a higher dimension. The separated segments are compared to determine a resemblance factor. A fingerprint is generated for segments having a resemblance factor above a particular threshold. Based upon the generated fingerprint, a data source may be filtered to block or to pass data corresponding to the generated fingerprint. The digital data may be audio data, video data, or other data.