Associative Memory Audio Analysis for Call Prioritization
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Solution Overview
Problem
Call centers face challenges in extracting actionable information from large volumes of voice communications due to the difficulty in identifying critical calls, incomplete call specifics, and the inability to rapidly analyze voice patterns or language related to management priorities.
Innovation Solution
An apparatus utilizing associative memory technology, which includes a data store, an associative memory unit, a query unit, a parsing unit, and an assessing unit, to categorize and prioritize audio segments by analyzing historical intelligence data and identifying patterns in real-time audio and non-audio inputs, enabling efficient call handling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional call center methods are used to handle voice communications, then employees can manually analyze calls, but the analysis speed is too slow to keep up with the high volume of calls
Solution Approach 1:
The patent replaces manual mechanical analysis by human employees with an automated computer system that uses associative memory technology. The system automatically analyzes audio communications, identifies critical calls, and prioritizes them for manager review, eliminating the bottleneck of human analysis speed while maintaining comprehensive evaluation capabilities.
Solution Approach 2:
The system performs self-service analysis by automatically processing calls through associative memory patterns without requiring continuous human intervention. The computer system independently categorizes calls, identifies patterns, and presents prioritized lists to managers, allowing the system to serve itself in the analysis process while humans focus on high-value decisions.
2Measurement precision
If detailed analysis of voice patterns and language is performed, then call categorization accuracy improves, but the time required for analysis increases
Solution Approach 1:
The system performs preliminary action by pre-establishing associative memory patterns and categories before actual call analysis occurs. Managers can provide training data and define critical patterns in advance, and the system stores these patterns in associative memory for rapid retrieval and matching during actual call processing, enabling accurate categorization without real-time complex analysis.
Solution Approach 2:
The patent replaces slow manual linguistic and tonal analysis with automated pattern recognition systems that use associative memory. The computer system rapidly matches voice patterns against stored patterns to categorize calls, achieving high accuracy much faster than human analysts could manually process the same detailed analysis.
3Loss of information
If all call data is collected and analyzed, then complete information is available, but the complexity of processing and understanding the data increases
Solution Approach 1:
The system extracts and isolates only the critical information and patterns from the vast amount of call data. By using associative memory, the system identifies and separates important calls based on predefined patterns and categories, presenting only the essential information that requires manager attention while filtering out routine calls that can be handled automatically or deferred.
Solution Approach 2:
The patent segments the complex data processing task into distinct functional components: audio capture, pattern recognition, associative memory matching, categorization, and prioritization. This segmentation allows each component to handle specific aspects of data processing independently, reducing overall system complexity while maintaining comprehensive analysis capability.
4Speed
If voice patterns and tone are analyzed in real-time, then timely manager notification is possible, but the computational resources required increase
Solution Approach 1:
The system uses preliminary action by pre-loading voice patterns, categories, and analysis criteria into associative memory before real-time call processing occurs. This allows the system to perform rapid pattern matching during actual calls without requiring complex computational resources in real-time, as the heavy lifting of pattern definition and memory organization is completed beforehand.
Data Source
AI summary
An apparatus for evaluating an audio communication comprises a data store for storing a plurality of digital units representing a plurality of characterized aspects of historical audio communications. The characterized aspects include words and sets of words. The apparatus further comprises an associative memory unit coupled with the data store to create associations between entities representing the characterized aspects, and relate the entities and the frequency of occurrence of the entities to identify relationships with call handling categories and priorities requiring intervention, and an assessing unit coupled with the associative memory unit to indicate whether the audio communication contains any associative memory associations related to a call handling category and call handling priority requiring intervention.