Expertise Evaluation in Group Calls Using Dynamic Floor Priority
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Solution Overview
Problem
In group calls, especially those critical for emergency situations, participants with relevant expertise may not have the opportunity to contribute their knowledge, leading to inaccurate decision-making due to technical limitations and human discomfort in expressing opinions, particularly when there are technical constraints like half-duplex calls where participants can only speak after being granted a floor based on priority.
Innovation Solution
A method and system that evaluates the expertise of participants during a group call using natural language processing to analyze speech content, assign expected and demonstrated expertise scores, and provide visual or audio outputs to indicate expertise levels, ensuring that participants with relevant knowledge are given the opportunity to contribute, thereby improving decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If floor is granted on priority basis to participants based on rank or role, then communication efficiency is improved, but expertise diversity is reduced
Solution Approach 1:
The system dynamically adjusts floor grant priority during the group call based on real-time analysis of demonstrated expertise. Participants who show relevant expertise through their speech content receive increased priority for obtaining the floor, while those with less relevant contributions see their priority reduced. This dynamic adjustment resolves the contradiction by maintaining communication efficiency through automated priority management while simultaneously ensuring expertise diversity is preserved and promoted.
Solution Approach 2:
The system implements continuous feedback loops where speech content is analyzed to determine demonstrated expertise, which then feeds back into floor grant priority assignments. The natural language processing engine monitors participant contributions in real-time, evaluates their expertise level relative to the call context, and adjusts floor grant decisions accordingly. This feedback mechanism ensures that both communication efficiency and expertise diversity are maintained through data-driven priority adjustments.
2Measurement precision
If natural language processing is used to analyze speech content and evaluate expertise, then decision-making quality is improved, but device complexity is increased
Solution Approach 1:
The system introduces a natural language processing engine as an intermediary component that bridges the gap between speech content analysis and expertise evaluation. This NLP intermediary processes participant contributions, extracts meaningful information about demonstrated expertise, and provides structured output that can be used for floor grant decisions. By using this specialized intermediary, the system achieves high measurement precision in expertise evaluation while managing complexity through modular architecture.
Data Source
AI summary
A process of evaluating the expertise of participants during a group call. In operation, an electronic computing device analyzes speech content of participants on a group call to determine a call context associated with the group call. The electronic computing device then assigns an expected expertise score for the participant by correlating the call context with a participant profile and further determines a demonstrated expertise score for the participant as a function of the expected expertise score and a call participation score indicating a duration of time that the participant has spoken during the group call. When the electronic computing device detects that a decision is being made with respect to the call context, the electronic computing device provides a visual or audio output on a corresponding visual or audio output device indicating the demonstrated expertise score of at least one of the participants.