NLP Audio Tagging for Dynamic Memory Allocation
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Solution Overview
Problem
Computer systems face challenges in managing memory resources due to large audio files, which require constant storage expansion and manual operator intervention to determine whether to store or remove audio files, leading to performance degradation and bottlenecks.
Innovation Solution
A natural language processing system dynamically tags audio files based on content, generates new tags, selectively stores or removes files, and optimizes storage location using user-defined, AI-defined, and context tags, allowing for efficient resource management and prioritization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual operator review is used to determine whether to store or remove audio files, then storage decisions can be made with human judgment, but the process becomes time intensive and creates a bottleneck that limits system throughput
Solution Approach 1:
The patent introduces an automated analysis system that acts as an intermediary between audio files and storage decisions. This system uses machine learning models to analyze audio content, extract features, and generate recommendations for storage or removal, eliminating the need for manual operator review while maintaining reliable decision-making through algorithmic analysis
Solution Approach 2:
The patent replaces the mechanical system of manual operator review with an automated computational system. Machine learning models and algorithms substitute human operators, enabling high-speed automated analysis and decision-making that maintains reliability while dramatically increasing productivity and system throughput
2Reliability
If audio files are stored to preserve important content, then data retention is improved, but memory resources are consumed and system performance degrades when capacity is approached
Solution Approach 1:
The patent changes the parameters used for storage decisions from simple binary keep/delete rules to multi-dimensional analysis including audio content features, importance scoring, and resource availability. This enables dynamic adjustment of storage decisions based on current system state, preserving important data while managing memory resources effectively to maintain system performance
Solution Approach 2:
The patent applies different storage policies to different audio files based on their individual characteristics. Important audio files are preserved in memory while less critical files are removed or archived, creating a differentiated storage strategy that maintains data retention for valuable content while freeing memory resources to sustain system performance
3Quantity of substance
If memory capacity is expanded to accommodate more audio files, then storage capacity is increased, but the cost and complexity of the system increases
Solution Approach 1:
The patent implements dynamic storage capacity management through automated analysis and selective retention. The system continuously evaluates audio files, adjusts storage decisions based on current needs, and optimizes memory utilization. This dynamic approach allows the system to maintain adequate storage capacity for important files without requiring permanent expansion of memory resources, thereby reducing system complexity and cost
Data Source
AI summary
A natural language processing system that includes an artificial intelligence (AI) engine, a tagging engine, and a resource allocation 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 modify metadata for the audio file to include AI-defined tags. The resource allocation engine is configured to identify a storage location from among the plurality of storage devices based on tags associated with the audio file and send the audio file to the identified storage location.