Meeting Analytics Engine for Participant Time Distribution
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Solution Overview
Problem
Current digital communication tools lack analytics and metrics for time distribution of participants across topic segments in remote communication sessions, leading to inefficient use of meeting time and potential need for follow-up meetings.
Innovation Solution
A system that connects to a communication session, receives a conversation transcript with timestamps, determines the meeting type, generates topic segments, analyzes time spent by participants on each segment, and presents data on time distribution across topic segments, using AI and NLP techniques for accurate analysis and visualization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional communication tools are used without analytics, then device complexity is reduced, but productivity is worsened due to inefficient meeting time allocation
Solution Approach 1:
The patent introduces an intermediary analytics system that sits between the communication session and the participants. This intermediary automatically transcribes conversations, generates topic segments, analyzes time distributions, and provides actionable insights without requiring participants to manually track or analyze meeting content, thus improving productivity without significantly increasing user-side complexity
Solution Approach 2:
The patent replaces manual mechanical processes (participants manually tracking time, topics, and their own contributions) with automated computational processes (AI transcription, NLP topic segmentation, automatic time distribution analysis). This substitution eliminates the need for participants to manually monitor and record meeting dynamics, freeing them to focus on productive discussion while the system handles the analytical overhead
2Measurement precision
If manual tracking of meeting topics and time is implemented, then measurement precision is improved, but ease of operation is worsened
Solution Approach 1:
The system performs self-service by automatically transcribing the entire conversation, autonomously identifying topic segments through NLP, and computing time distributions without requiring any user intervention. Participants simply engage in their natural discussion while the system independently handles all measurement and analysis functions, maintaining ease of operation while achieving high measurement precision
Solution Approach 2:
The system performs preliminary actions by pre-processing the conversation transcript to identify topic segments and their boundaries before analyzing time distributions. This preliminary segmentation automates the complex task of topic identification and timing, delivering precise measurements without requiring participants to manually track or categorize discussion topics during the meeting
3Loss of information
If detailed analytics and topic segmentation are generated, then loss of information is reduced, but device complexity is increased
Solution Approach 1:
The patent applies segmentation by automatically dividing the continuous conversation transcript into discrete topic segments based on NLP analysis of topic transitions. This segmentation preserves all original information while organizing it into manageable, analyzable units that can be easily reviewed and acted upon, reducing information loss without requiring the system to process the entire transcript as a single complex block
Solution Approach 2:
The system extracts key information elements (topic segments, time stamps, participant contributions) from the full conversation transcript and presents them as structured analytics. This extraction preserves essential meeting information while filtering out redundant details, allowing comprehensive information retention in organized form without overwhelming complexity in the presentation layer
Data Source
AI summary
Methods and systems provide for presenting time distributions of participants across topic segments in a communication session. In one embodiment, the system connects to a communication session with a number of participants; receives a transcript of a conversation between the participants produced during the communication session, the transcript including timestamps for each utterance of a speaking participant; determines, based on analysis of the transcript, a meeting type for the communication session; generates a number of topic segments for the conversation and respective timestamps for the topic segments; for each participant, analyzes the time spent by the participant on each of the generated topic segments in the meeting; and presents, to one or more users, data on the time distribution of participants for each topic segment and across topic segments within the conversation.