NLP Audio Tagging for Memory Resource Management
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Solution Overview
Problem
Computer systems face challenges in managing memory resources due to large audio files, leading to performance degradation as they require constant storage capacity expansion and lack automated methods to determine 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 optimally routes files to storage devices, using user-defined, AI-defined, and context tags to prioritize and manage memory resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual process is used to determine whether to store or remove audio files, then system can control memory resources, but processing time increases and system speed decreases
Solution Approach 1:
The system employs automated NLP processing to analyze audio file content and make independent decisions about storage, eliminating the need for manual operator intervention. The system processes audio files automatically by extracting features, generating tags, and applying storage policies without human involvement, thus resolving the time loss while maintaining operational control.
Solution Approach 2:
The patent replaces the manual mechanical process of listening to and deciding on audio files with an automated electronic NLP system. The system uses speech recognition, feature extraction, and algorithmic decision-making to automatically determine storage requirements, substituting human manual operations with automated computational processes that are much faster and more efficient.
2Quantity of substance
If audio files are stored in memory, then system has sufficient storage capacity, but memory resources for other operations are reduced
Solution Approach 1:
The system applies different storage policies to different audio files based on their content characteristics. Important audio files with high priority tags are stored in main memory, while less important files are routed to secondary storage or discarded. This localized differentiation of storage quality allows the system to maintain sufficient capacity for critical operations without wasting resources on all audio files uniformly.
Solution Approach 2:
The system dynamically changes storage parameters based on audio file characteristics such as duration, complexity, and content importance. By adjusting storage decisions based on these parameters, the system optimizes memory utilization and maintains system performance while ensuring adequate storage capacity for essential audio files.
3Quantity of substance
If system constantly expands storage capacity, then sufficient storage capacity is provided, but system complexity increases
Solution Approach 1:
The system implements dynamic storage management that automatically adjusts to varying audio file characteristics and system conditions. Rather than static expansion, the system dynamically decides which files to store, how long to retain them, and where to place them based on real-time analysis of audio content and current system state, reducing the need for constant capacity expansion and simplifying management.
Solution Approach 2:
The system incorporates feedback mechanisms that monitor storage usage patterns, audio file characteristics, and system performance. This feedback enables the system to learn from past decisions and optimize future storage allocations, reducing the need for manual capacity expansion and simplifying storage management through automated adaptive control.
4Measurement precision
If operator listens to several minutes of audio for each file, then accurate determination can be made, but time intensive process creates bottleneck
Solution Approach 1:
The system extracts key features from audio files such as speech content, background noise, and temporal patterns using NLP and signal processing. By extracting and analyzing only these critical features rather than processing the entire audio file, the system achieves accurate determination of storage requirements while dramatically reducing processing time and eliminating the bottleneck.
Solution Approach 2:
The system performs partial analysis of audio files by focusing on the most informative characteristics rather than complete listening. This selective processing approach provides sufficient accuracy for storage decisions while significantly reducing the time required compared to full audio review, thus resolving the productivity constraint.
Data Source
AI summary
A natural language processing system that includes an artificial intelligence (AI) engine and a tag management 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 tag management engine is configured to receive an audio file, identify tags linked with the audio file, to determine an access frequency for the audio file within a predetermined time period, and to adjust the activity level of the tags based on the access frequency. The tag management engine is further configured to remove tags from the set of tags with an activity level that is less than a purge threshold.