Call Context Determination Using Machine Learning Feature Weighting
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Solution Overview
Problem
Customer service representatives face difficulties in determining the motivation and context of calls, making it challenging to provide appropriate assistance, as various features need to be considered and weighted to accurately assess the caller's intentions.
Innovation Solution
A system and method utilizing machine learning models to analyze call data, incorporating features from sensors, internet, social networks, and user profiles, to determine the context and motivation of calls, with feedback loops to improve model accuracy, applicable to various communication devices and environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple features from sensors, internet, social networks, and user profiles are collected and weighted to determine call context, then the accuracy of call context determination is improved, but the device complexity and data processing requirements increase
Solution Approach 1:
The system segments the call context determination process into distinct modules: data collection from multiple sources (sensors, internet, social networks, user profiles), feature extraction, model application, and context determination. This segmentation allows each module to handle specific tasks independently, managing complexity while maintaining high accuracy through comprehensive feature analysis.
Solution Approach 2:
The patent introduces machine learning models as intermediaries between the collected call data/features and the final context determination. These models process and weigh multiple features automatically, reducing the complexity burden on the overall system while improving determination accuracy through learned patterns from training data.
2Measurement precision
If comprehensive call data is collected from multiple sources to improve context determination, then the quality of call analysis is improved, but the loss of time for data collection and processing increases
Solution Approach 1:
The system performs preliminary actions by pre-collecting and storing user profile data, sensor calibrations, and historical call information before actual call analysis is needed. This pre-prepared data is readily available during call processing, reducing real-time data collection time while maintaining comprehensive analysis quality.
Solution Approach 2:
The patent replaces manual or rule-based feature weighting and context determination with machine learning models that automatically process comprehensive call data. These models efficiently handle large volumes of data from multiple sources, reducing processing time while improving accuracy through learned patterns rather than exhaustive rule evaluation.
3Productivity
If machine learning models are applied to extract features and determine call context, then the productivity of customer service representatives is improved, but the loss of information during feature extraction and model processing may increase
Solution Approach 1:
The system implements feedback mechanisms where model predictions and determination results are fed back into the system for continuous improvement. This feedback loop allows the model to learn from actual call outcomes and refine its feature extraction and weighting, maintaining information integrity while improving productivity through increasingly accurate automated analysis.
Solution Approach 2:
The patent employs parameter changes by adjusting model hyperparameters, feature weighting schemes, and data preprocessing parameters based on performance feedback. This optimization ensures the model extracts the most relevant information from call data while minimizing information loss, balancing productivity gains with information preservation.
Data Source
AI summary
The exemplary embodiments disclose a system and method, a computer program product, and a computer system for determining the context of calls and providing a user interface to a user. The exemplary embodiments may include collecting data from the call, extracting one or more features from the collected data, determining a context of the call based on applying one or more models to the extracted one or more features, and providing a user with a user interface.