Inbound Call Analysis Pipeline With Model Routing and Response Generation
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Solution Overview
Problem
Conventional machine learning models for processing voice interactions face challenges due to variability in speech patterns, background noise, and differences in language or accent, leading to resource-intensive processing and inconsistent results, making it difficult to analyze large volumes of voice data effectively.
Innovation Solution
A multi-stage processing pipeline that includes a computer model routing system using a configurable mapping to route questions to appropriate analysis engines, combining rule-based, machine learning, and agentic-AI subagents, along with a short-term and long-term memory cache for real-time and post-call analysis, to optimize computational resources and improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional machine learning models are used to process voice interactions, then voice data analysis can be performed, but computational resources and processing time are excessively consumed
Solution Approach 1:
The patent segments the monolithic machine learning model into multiple specialized analysis engines (acoustic feature extraction, speech-to-text transcription, sentiment analysis, topic modeling) that process different aspects of voice data independently. This segmentation allows parallel processing and optimizes resource allocation for each specific task, improving throughput while reducing overall computational burden.
Solution Approach 2:
The patent extracts and separates specific processing functions from the conventional unified model. Acoustic feature extraction, transcription, and sentiment analysis are pulled out as distinct engines that can be selectively applied based on the specific analysis needs, eliminating unnecessary computational overhead for each individual processing task.
2Reliability
If conventional machine learning models process voice data with variability in speech patterns and background noise, then voice interactions can be analyzed, but measurement precision and reliability deteriorate
Solution Approach 1:
The patent applies local quality by deploying different specialized analysis engines for different aspects of voice processing. Acoustic feature extraction engines handle noise robustness, transcription engines handle speech-to-text conversion, and sentiment analysis engines handle emotional context. Each engine is optimized for its specific local task, improving overall reliability and precision despite speech variability and background noise.
3Speed
If a single machine learning model is used for all voice processing tasks, then implementation is simple, but processing speed and response time are slow
Solution Approach 1:
The patent segments the processing system into multiple specialized engines that can operate in parallel. Acoustic feature extraction, transcription, and sentiment analysis are performed simultaneously by separate engines rather than sequentially by a single model, dramatically improving processing speed despite the increased architectural complexity of coordinating multiple components.
Data Source
AI summary
Systems and methods for automated machine learning-based inbound call monitoring and structured response generation are disclosed. A server receives audio data of an inbound call and executes an acoustic feature extraction model to generate acoustic features for frames of the audio data. An audio transcription model generates a transcript segmented by speaker. The system identifies a plurality of question strings from a data repository, queries a mapping data structure to select a machine learning classification model or large language model for each question, and segments acoustic features and transcript text for each question string. The selected model is executed for each question string using the segmented data to generate a response. The system generates, in memory, a structured response object comprising the generated responses to the question strings. This approach enables efficient, transparent, and auditable quality assessment of voice interactions, supporting both deterministic and probabilistic analysis engines.