Context-Aware Call Coaching Using Real-Time Linguistic Event Detection
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Solution Overview
Problem
Conventional event notification systems fail to account for contextual data when providing notifications, leading to isolated and ineffective coaching for remote workers.
Innovation Solution
A cloud-based real-time messaging system that ingests events from multiple sources, determines context, and provides contextual event notifications to support agents by analyzing linguistic and application events during interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional event notification systems use simple if-then rules to provide notifications, then the system complexity is low and ease of operation is high, but the notifications lack contextual awareness and effectiveness
Solution Approach 1:
The patent combines multiple data streams (audio data, linguistic events, acoustic events, application events) into a unified contextual framework. The system merges these diverse inputs to create enriched notifications that incorporate contextual information, resolving the contradiction by integrating multiple information sources while managing system complexity through structured processing.
Solution Approach 2:
The system introduces an intermediary processing layer that receives events from multiple sources, determines context, and generates enriched notifications. This intermediary component (the real-time messaging system with context determination logic) bridges the gap between simple event detection and complex contextual awareness, allowing the system to provide context-rich notifications without requiring complete system redesign.
2Reliability
If the system analyzes multiple data streams to determine context, then notification effectiveness is improved, but the processing time and loss of time increase
Solution Approach 1:
The system performs preliminary actions by continuously monitoring and pre-processing data streams in real-time, maintaining a ready state for context determination. When an event occurs, the contextual information is already available or can be quickly assembled from pre-processed data, reducing the actual processing time required at the moment of notification generation while maintaining high reliability.
Solution Approach 2:
The system maintains continuous analysis of audio and application data streams, ensuring that contextual information is always available or near-available. This continuous processing allows the system to generate reliable contextual notifications without significant delays, as the useful action of data analysis is ongoing rather than initiated only when needed.
3Loss of information
If the system provides detailed contextual notifications, then coaching quality for remote workers is improved, but the quantity of information processed and device complexity increase
Solution Approach 1:
The system segments the complex task of contextual analysis into distinct components: audio event detection, linguistic event detection, acoustic event detection, and application event detection. Each component processes specific types of data independently, then results are integrated to form comprehensive contextual notifications. This segmentation reduces processing complexity by dividing the overall task into manageable, specialized sub-tasks.
Solution Approach 2:
The real-time messaging system serves multiple functions: it receives events from various sources, determines context across different data types, generates notifications, and provides coaching information. This multi-functional approach consolidates what could be separate complex systems into a single universal platform, reducing overall device complexity while maintaining comprehensive contextual awareness.
Data Source
AI summary
A real-time contextual event notification system ingests events as streams from any authorized entity, applies rules to the event streams, determines a context of an end-user who is a recipient of a targeted notification, and provides notifications to the end-user in accordance with the context. The event streams may come from multiple sources, and rules may be applied to provide real-time contextual information associated with the end user. One such event stream includes detected linguistic and/or acoustic events during a phone call between two or more persons.