Objection Message Identification via Parent Context Analysis
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Solution Overview
Problem
Current communication systems are siloed, preventing comprehensive analysis of multi-layered communication threads and requiring significant processing resources, leading to inaccurate analysis and high computational overhead in high communication volume scenarios.
Innovation Solution
A cloud-based system that utilizes data stream connections, pre-processing techniques, and machine learning models to identify objection messages within communication threads by analyzing new messages and their parent messages, reducing the load on resource-intensive models and improving classification accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If comprehensive analysis of multi-layered communication threads is performed, then measurement precision is improved, but use of energy increases
Solution Approach 1:
The system segments communication threads into hierarchical layers (parent messages and child messages), analyzing only relevant portions at each level. This segmentation allows comprehensive analysis of multi-layered threads while reducing overall processing resources by avoiding redundant analysis of entire thread histories.
Solution Approach 2:
The system performs preliminary analysis of parent messages before analyzing child messages. By pre-processing and indexing parent message content, the system prepares data structures in advance that enable faster, more efficient analysis of subsequent child messages, improving accuracy while reducing computational overhead.
2Measurement precision
If comprehensive analysis of multi-layered communication threads is performed, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system divides the communication analysis task into distinct modular components: parent message processing, child message processing, and synthesis. Each module handles specific aspects of the analysis independently, making the overall system more manageable and easier to implement despite the comprehensive nature of the analysis.
3Productivity
If significant processing resources are allocated, then productivity is improved, but use of energy increases
Solution Approach 1:
The system applies different processing qualities to different parts of communication data. Parent messages receive intensive processing to extract key contextual information, while child messages receive streamlined processing that leverages the pre-extracted context. This local quality differentiation maintains high productivity while reducing overall computational overhead.
Data Source
AI summary
The system described herein may utilize a data stream connection to detect that a new message is transmitted between users. The system may perform various pre-processing techniques on the new message to identify that the new message is an objection message candidate. The system may retrieve one or parent messages of the new message. The new message and the parent messages may be input into a model trained to classify objection messages. The model may identify that the new message is classified as an objection message based at least in part on processing the new message and the parent messages. An objection classification identifier may be stored in association with the new message based at least in part on the result of the processing by the model.