Conversation Loop Detection via Visual Representation Dashboard
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Solution Overview
Problem
Conventional meeting technologies fail to effectively detect and resolve conversation loops, leading to inefficient meetings and significant time wastage due to misunderstandings and repetitive discussions among participants.
Innovation Solution
A computer-implemented system that monitors conversations during meetings, generates real-time visual representations (concept maps or mind maps) within a graphical user interface dashboard, detects conversation loops, and provides support materials to help participants reach a consensus, thereby resolving loops and optimizing meeting progress.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional meeting technologies are used without conversation loop detection, then meeting participants can discuss freely, but meetings become inefficient and time-consuming due to repetitive discussions and misunderstandings
Solution Approach 1:
The system continuously monitors conversation content and provides real-time feedback to participants by detecting conversation loops through trigger-based analysis. When repetitive patterns or misunderstandings are identified, the system alerts participants, enabling them to adjust their discussion and avoid unnecessary time consumption.
Solution Approach 2:
The patent replaces manual monitoring of conversation efficiency with an automated computer-based system that uses natural language processing and pattern recognition algorithms to detect conversation loops, thereby eliminating the need for human participants to manually track and manage discussion efficiency.
2Reliability
If real-time conversation monitoring and visual representation are implemented, then conversation loops can be detected and resolved, but system complexity increases
Solution Approach 1:
The system segments conversation monitoring into distinct functional modules: trigger detection module, pattern recognition module, visual representation module, and resolution assistance module. Each module handles a specific aspect of conversation analysis, making the overall system more manageable and maintainable despite its complexity.
Solution Approach 2:
The patent introduces a dashboard as an intermediary interface between the complex detection system and meeting participants. The dashboard visualizes conversation loops and provides resolution suggestions in an intuitive manner, shielding users from the underlying system complexity while maintaining high detection accuracy.
3Productivity
If support materials are provided for loop resolution, then participants can resolve misunderstandings faster, but information processing requirements increase
Solution Approach 1:
The system performs preliminary analysis of conversation content to identify potential loops before they fully develop. By detecting triggers and patterns early, the system can retrieve relevant support materials in advance, reducing the computational burden during active resolution and speeding up the overall process.
Solution Approach 2:
The system selectively retrieves and discards support materials based on the specific conversation loop detected. Only relevant materials are fetched and presented to participants, while irrelevant information is discarded, optimizing computational resource usage while providing timely resolution assistance.
Data Source
AI summary
Detecting and resolving conversation loops during a meeting is provided. A conversation between a set of participating entities is monitored during the meeting. A visual representation of the conversation is updated within a dashboard when one or more of a first predefined set of triggers are activated based on monitoring the conversation. A conversation loop is detected in the conversation when one or more of a second predefined set of triggers are activated based on monitoring the conversation and updating the visual representation of the conversation within the dashboard. Support materials that provide support for resolving the conversation loop are retrieved from at least one of local sources including a knowledgebase and remote sources including websites. The support materials are displayed in the dashboard along with an input section for the set of participating entities to indicate a consensus for resolving the conversation loop.


