Digital Assistant for Off-Topic Detection in Group Calls
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Solution Overview
Problem
Existing voice-based group communication sessions lack effective mechanisms to detect and address off-topic discussions, leading to potential embarrassment and inefficiencies.
Innovation Solution
Implementing real-time network-hosted artificial intelligence and machine learning to monitor audio content, identify off-topic participants, and apply remedial actions such as muting or notifications, using topic detection models and user profiles to manage contextual relevance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real-time AI monitoring is implemented to detect off-topic participants, then discussion focus and professionalism are improved, but system complexity and computational resource requirements increase
Solution Approach 1:
A digital assistant acts as an intermediary between the communication session and the off-topic detection system. The assistant receives audio data, processes it through topic detection models, and applies remedial actions without requiring direct complex integration into the main communication system. This mediator approach reduces system complexity while maintaining reliable off-topic detection.
Solution Approach 2:
The patent replaces manual monitoring mechanisms with automated AI-based topic detection models. Instead of requiring human moderators to identify off-topic discussions, the system uses machine learning models that continuously analyze audio content and automatically apply remedial actions, reducing operational complexity while improving detection reliability.
2Productivity
If automated remedial actions are applied to off-topic users, then meeting efficiency is improved, but user experience and professionalism may deteriorate due to lack of context understanding
Solution Approach 1:
The system dynamically adjusts its monitoring and intervention behavior based on contextual factors. It can modify detection sensitivity, select appropriate remedial actions, and adapt to different meeting scenarios in real-time. This dynamic approach allows the system to maintain high productivity by acting only when necessary while preserving good user experience through context-aware intervention.
Solution Approach 2:
The topic detection model uses multiple parameters including audio features, semantic analysis, and contextual information to determine off-topic status. By changing and weighing these parameters flexibly, the system can distinguish between genuine off-topic deviations and acceptable variations in discussion, thereby improving meeting efficiency without compromising user experience.
3Reliability
If continuous audio monitoring is performed to detect off-topic content, then topic adherence is improved, but energy consumption and processing requirements increase
Solution Approach 1:
Instead of continuously processing all audio data without interruption, the system employs periodic sampling and monitoring. The digital assistant analyzes audio content at strategic intervals and only when anomalies are detected, maintaining reliable topic adherence while significantly reducing processing energy consumption compared to continuous monitoring.
Solution Approach 2:
The topic detection model performs self-optimization by learning from meeting patterns and automatically adjusting its monitoring intensity. When discussions remain on-topic, the system reduces processing activity; when off-topic behavior is detected, it increases monitoring and intervention. This self-service approach maintains topic adherence reliability while optimizing energy consumption based on actual meeting needs.
Data Source
AI summary
Method, computer-readable media, and apparatuses for applying at least one remedial action within a communication session via at least one network in response to detecting via at least one detection model that the audio content of a first user is off-topic are described. For example, a processing system including at least one processor may establish a communication session between at least a first communication system of a first user and a plurality of communication systems of a plurality of additional users via at least one network. The processing system may then determine at least one topic for the communication session, detect, via at least one topic detection model, an audio content of the first user that is off-topic, and apply at least one remedial action within the communication session in response to the detecting that the audio content of the first user is off-topic.