ML Intent and Signal Extraction for Proactive Interaction Data
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Solution Overview
Problem
Existing data extraction methods are reactive, delayed, prone to selection bias, and subjective, leading to inefficiencies and incomplete insights, particularly in complex interaction data scenarios.
Innovation Solution
A system and method using machine learning and rule-based techniques to extract granular information from pre-service interaction data, including intent and subject classification models, to generate signal data objects for real-time analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If traditional reactive methods are used for data extraction, then the process can be simpler to implement, but the extraction is delayed and cannot enable preemptive action
Solution Approach 1:
The system performs preliminary actions by extracting and analyzing data before service interactions occur. The machine learning models process interaction data in advance to identify patterns, intents, and signals, enabling the system to take preemptive actions before the actual service delivery, thus eliminating the reactive delay inherent in traditional methods
2Measurement precision
If manual interpretation of raw data is used, then the process can be more flexible, but selection bias and subjectivity introduce inaccuracies
Solution Approach 1:
The patent replaces manual mechanical interpretation with automated machine learning models that objectively analyze interaction data. The intent classification model, subject classification model, and signal extraction models process data algorithmically, eliminating human subjectivity and selection bias while maintaining high measurement precision through trained neural networks
3Measurement precision
If comprehensive analysis of all interaction data is performed, then the accuracy of insights improves, but the processing time and resource consumption increase
Solution Approach 1:
The system segments the data analysis process into specialized functional components: intent classification, subject classification, and signal extraction. Each component focuses on specific aspects of the data, allowing comprehensive analysis to be divided into manageable tasks that can be processed in parallel, improving overall productivity while maintaining high accuracy through specialized model attention
4Measurement precision
If historical data is required for accurate analysis, then the model can be more accurate, but new or unique scenarios cannot be assessed due to the cold start problem
Solution Approach 1:
The machine learning models are designed to adapt to changing data characteristics and can learn from new patterns as they emerge. The models continuously update their parameters based on incoming interaction data, allowing them to accurately assess new and unique scenarios without requiring extensive historical data, thus solving the cold start problem while maintaining high measurement precision
Data Source
AI summary
Systems and methods are disclosed for data extraction. One or more processors may receive an interaction data object containing text data and generate an intent data object with high-level and granular intent indicators using a trained intent classification machine-learning model. The processors may also generate a subject data object using a trained subject classification machine-learning model. The processors may select a target model bundle from multiple bundles based on the granular intent indicator, and the target bundle contains machine-learning models trained to extract signals from the text data. By applying the interaction data object to the target model bundle, the processors may generate a signal data object with signal indicators. The processors may modify a curated data object by changing data entries based on the generated intent, subject, or signal data objects.