Real-Time Audio Directive System for VoIP Calls
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Solution Overview
Problem
Current VoIP and computerized telephony systems lack the ability to automatically detect specific triggers during audio calls and respond with real-time directives, limiting the integration of advanced features and analytics in real-time communication processes.
Innovation Solution
A real-time directive providing system that monitors audio calls for speech recognition and sound characteristics, automatically detects triggers, and outputs corresponding directives to parties on computing devices, allowing for multiple trigger and directive definitions, contextual analysis, and machine learning-based optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If real-time monitoring and automatic trigger detection is implemented during audio calls, then communication efficiency and responsiveness are improved, but system complexity and processing requirements increase
Solution Approach 1:
The system segments the audio call monitoring process into distinct functional modules: speech recognition module, trigger detection module, and directive output module. Each module operates independently and processes specific aspects of the audio stream, allowing real-time analysis without overwhelming system complexity. The segmentation enables specialized processing of speech content, sound characteristics, and contextual factors separately before integrating results for trigger detection.
Solution Approach 2:
The patent introduces an intermediary processing layer between audio capture and response generation. This intermediary layer includes the speech recognition system and trigger detection algorithms that mediate between raw audio input and the directives output to parties. The intermediary processes audio through multiple analysis stages (speech-to-text, sound characteristic analysis, contextual analysis) before generating appropriate responses, thereby managing system complexity through structured intermediate processing steps.
2Adaptability or versatility
If multiple triggers and directives are defined and monitored in real-time, then the system becomes more versatile and adaptable, but the difficulty of detecting and measuring triggers increases
Solution Approach 1:
The system implements a universal trigger detection framework that can identify multiple types of triggers through a single integrated platform. The same speech recognition and analysis infrastructure supports detection of various trigger types including specific words, sound characteristics (pitch, timbre, intonation), contextual events (objections, questions, pauses), and speech patterns. This multi-functional approach allows the system to adapt to different monitoring requirements without requiring separate specialized systems for each trigger type.
Solution Approach 2:
The trigger detection system employs dynamic analysis that adapts to varying speech patterns, contexts, and temporal patterns. The system can detect triggers based on real-time changes in audio characteristics, contextual shifts during conversation, and temporal patterns (such as pause durations). This dynamic detection capability allows the system to handle diverse trigger types using adaptable algorithms that adjust their detection criteria based on ongoing conversation context and detected patterns.
3Loss of time
If speech recognition and audio analysis are performed in real-time during calls, then real-time recommendations can be provided, but energy consumption and processing requirements increase
Solution Approach 1:
The system implements periodic analysis of audio streams rather than continuous processing at maximum capacity. The speech recognition and trigger detection operate in periodic cycles, analyzing audio segments as they become available and comparing them against defined triggers. This periodic action allows real-time responsiveness while reducing peak processing demands and energy consumption compared to continuous high-intensity analysis, as the system can leverage the periodic nature of speech to optimize processing timing and intensity.
Data Source
AI summary
The content and/or sound characteristics of audio calls are monitored in real-time. The occurrence of triggers during audio calls is automatically detected, and in response directives are displayed to parties in real-time while calls are occurring. Multiple triggers and directives can be defined and edited, either by human users and/or automatically (e.g., in response to results obtained and tracked over time). Each given directive corresponds to one or more given triggers, such that detection of an occurrence of a given trigger during a monitored audio call results in the automatic outputting of a corresponding directive.