Dynamic Feature Abstraction for Audio Correlation Accuracy
Solution Overview
Problem
Current systems for analyzing and categorizing audio content face challenges in accurately deriving correlations due to context-dependent and user-dependent word meanings, often requiring large training datasets and frequent retraining to maintain accuracy, especially when dealing with variable and ambiguous data such as audio recordings of conversations.
Innovation Solution
The system employs a feature abstraction module that replaces specific words with surrogate words, allowing for context-dependent correlative meanings, enabling the correlation module to derive correlations across different users and contexts without the need for extensive retraining, using a database to manage associations between actual words, surrogate words, and their meanings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a very large number of training data files are collected to cover multiple vendors and contexts, then the correlation machine can be trained for multiple car dealers, but the accuracy of the correlation machine decreases due to ambiguity from brand- and model-specific words having different correlations for different dealers
Solution Approach 1:
The patent introduces an intermediary layer between the raw text data and the correlation analysis. This layer involves processing transcripts to identify and handle context-dependent words (brand names, model names) by creating separate correlation machines for different contexts or by using metadata to disambiguate meanings. The intermediary processing step resolves the contradiction by structuring the data to preserve accuracy while enabling multi-vendor adaptability.
Solution Approach 2:
The patent segments the training data and correlation analysis by vendor, brand, or context. Instead of creating one large correlation machine that suffers from ambiguity, the system divides the problem into multiple smaller correlation machines, each trained on specific vendor-brand contexts. This segmentation maintains accuracy within each segment while collectively providing broad adaptability across multiple dealers.
2Measurement precision
If training data is collected from nearly identical or highly-related contexts, then positive and negative correlations of specific words are preserved, but highly-valuable words are erroneously disregarded as insignificant
Solution Approach 1:
The patent applies local quality by allowing different correlation weights and interpretations for different contexts. Words that have negative correlation in one context (e.g., brand names for dealers not carrying that brand) are not uniformly treated as insignificant across all contexts. The system adjusts the local quality of correlation analysis based on the specific vendor and context, preserving valuable words by recognizing their context-dependent value.
Solution Approach 2:
The patent introduces dynamic adjustment of correlation weights based on context. Rather than static correlation values, the system dynamically adjusts the significance of words based on metadata, vendor identity, and context information. This allows highly-valuable words to be recognized as significant in appropriate contexts while maintaining the preservation of positive and negative correlations within each specific context.
3Measurement precision
If brand and model names change over time, then the correlation machine must be retrained to preserve accuracy, but ongoing replacement and retraining increases time and resource requirements
Solution Approach 1:
The patent prepares for future changes by structuring the correlation system to accommodate new brands and models without requiring complete retraining. Metadata schemas and context frameworks are established in advance, allowing new brand-name associations to be integrated by adding new training data in the existing format rather than retraining entire correlation machines. This preliminary structuring reduces the time required for ongoing updates.
Solution Approach 2:
The patent creates a universal correlation framework that can handle multiple vendors, brands, and models through a common structure. The metadata-driven approach and segmented architecture allow the system to accommodate new brands and models by simply adding new data in the existing framework, rather than requiring separate correlation machines or extensive retraining. This universality reduces ongoing time requirements for adapting to brand and model changes.
Data Source
AI summary
Systems for analyzing and categorizing audio content that has been transcribed into text are provided. The systems include at least one machine that has a central processing unit, random access memory, a correlation module, a feature abstraction module, and at least one database. The correlation module is configured to receive written transcripts (each of which has been generated from audio content) and derive a correlation between each written transcript and one or more attributes. The feature abstraction module is configured to receive instructions that identify specific words within the written transcripts; replace the specific words with surrogate words; and associate correlative meanings with each of the surrogate words. The database is configured to receive, record, and make accessible to the feature abstraction module a table of specific words, each of which is associated with corresponding surrogate words and correlative meanings associated with each of the surrogate words.