Dynamic NLP Tagging for Audio Storage Optimization
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Solution Overview
Problem
Computer systems face challenges in managing memory resources due to the large size of audio files, leading to performance degradation as they require constant storage capacity expansion and lack automated processes for determining whether to store or remove audio files, resulting in a time-intensive manual process that limits system speed and efficiency.
Innovation Solution
A natural language processing system dynamically tags audio files based on content, generates new tags, selectively stores or removes files, periodically purges unused tags, and routes files to optimal storage locations, using user-defined, AI-defined, and context tags to prioritize and manage memory resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If audio files are stored in memory, then storage capacity is provided, but memory resources are consumed and system performance degrades
Solution Approach 1:
The patent changes the state of audio files by generating compressed text representations that capture essential semantic information. This parameter change from raw audio to compressed text reduces storage requirements while maintaining accessibility of key information, thereby resolving the contradiction between storage capacity and system performance.
Solution Approach 2:
The patent extracts essential semantic content from audio files and stores only this extracted information in text form. By separating the essential information from the full audio data, the system provides storage capacity for critical information without consuming excessive memory resources, thus resolving the performance degradation issue.
2Ease of operation
If manual process is used to determine whether to store or remove audio files, then storage decisions are made, but the process is time intensive and creates a bottleneck
Solution Approach 1:
The system performs self-service by automatically analyzing audio files, generating text representations, and making storage decisions without human intervention. The natural language processing system independently determines which audio files to store based on their semantic content, eliminating the time-intensive manual review process while maintaining intelligent storage management.
Solution Approach 2:
The patent replaces the mechanical manual process of listening to and evaluating audio files with an automated natural language processing system. This substitution transforms the manual operational process into an automated computational process, dramatically reducing processing time while maintaining the ability to make informed storage decisions.
3Reliability
If audio files are processed to determine storage value, then storage decisions are improved, but the processing speed of the system is limited
Solution Approach 1:
The patent extracts only the essential semantic information from audio files rather than processing the entire audio file. By taking out only the critical textual representation needed for storage decisions, the system improves decision accuracy while significantly reducing processing time and increasing system throughput.
Solution Approach 2:
The patent segments the audio processing task into two distinct stages: first, generating compressed text representations of audio content; second, making storage decisions based on these representations. This segmentation allows rapid processing of audio files through text generation while maintaining reliable storage decisions through subsequent analysis, thereby resolving the contradiction between accuracy and speed.
Data Source
AI summary
A natural language processing system that includes an artificial intelligence (AI) engine and a tagging engine. The AI engine is configured to receive a set of audio files and to identify concepts within the set of audio files. The AI engine is further configured to determine a usage frequency for each of the identified concepts and to generate an AI-defined tag for concepts with a usage frequency that is greater than a usage frequency threshold. The tagging engine is configured to receive an audio file and to identify observed concepts within the audio file. The tagging engine is further configured to compare the observed concepts to the first set of concepts, to determine one or more observed concepts matches concepts linked with AI-defined tags, and to modify metadata for the audio file to include AI-defined tags.