Multi-party Communication Engagement Analysis Framework
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Solution Overview
Problem
Current methods for gauging participant engagement in multi-party communications (MPCs) are inadequate, relying on barebones statistics that are open to differing interpretations, which hinders effective streamlining of enterprise processes and reduces efficiency.
Innovation Solution
An MPC analysis framework that determines participant engagement by applying trained engagement score models to general and participant-specific data, generating engagement scores, action items, and visual representations to illustrate core concepts, using a system comprising a processor, communication module, data module, prediction module, and training module.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If trained engagement score models are applied to determine participant engagement, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent introduces trained engagement score models as intermediary components that process communication data and transform it into interpretable engagement metrics. These models act as mediators between raw communication data and meaningful engagement assessments, improving measurement precision while managing system complexity through modular model deployment.
Solution Approach 2:
The patent replaces traditional manual or simple statistical engagement assessment methods with machine learning-based engagement score models. This substitution enables automated, data-driven engagement measurement that is more precise and scalable, transitioning from mechanical/manual analysis to intelligent automated analysis.
2Adaptability or versatility
If multiple modules (communication module, data module, prediction module, training module) are integrated into the system, then functionality is improved, but device complexity increases
Solution Approach 1:
The patent divides the engagement assessment system into distinct functional modules: communication module for data collection, data module for preprocessing, prediction module for engagement scoring, and training module for model optimization. This segmentation allows each module to perform its specific function independently, improving overall system adaptability while managing complexity through clear separation of concerns.
Solution Approach 2:
The integrated system is designed to perform multiple functions: collecting communication data, preprocessing and storing data, training engagement models, predicting engagement scores, and generating visual representations. This multi-functionality approach allows a single system to handle the entire engagement assessment workflow, improving versatility while consolidating complexity into one unified platform.
3Loss of information
If engagement scores and visual representations are generated for multiple participants, then information completeness is improved, but loss of time increases
Solution Approach 1:
The patent implements a training module that pre-trains engagement score models using historical communication data before actual engagement assessment. This preliminary action prepares the models in advance, enabling them to quickly generate accurate engagement scores and visual representations for multiple participants during actual use, reducing processing time while maintaining information completeness.
Solution Approach 2:
The system continuously processes communication data and generates engagement metrics for all participants throughout the communication event. By maintaining continuous processing rather than batch processing, the system ensures complete engagement information is captured for all participants while minimizing delays and time loss through efficient real-time analysis.
Data Source
AI summary
A multi-party communications (MPC) analysis framework allows for the determination of participant engagement and MPC insights. In some embodiments, the MPC analysis framework allows for the determination of an engagement score indicating a level of engagement of participants to the MPC. In some embodiments, the MPC analysis framework allows for the identification of relevant participant metrics with respect to the MPC. In some embodiments, the MPC analysis framework allows for the generation of visual representations illustrating core concepts discussed during the MPC.