User Communication Classification Using Contextual Action Routing
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Solution Overview
Problem
Current data management systems face challenges in efficiently classifying and responding to diverse and complex user communications in real-time, particularly in processing large volumes of data to ensure timely and relevant responses.
Innovation Solution
An apparatus and method that includes a processor and a computer-readable storage medium configured to receive user communications, preprocess them, extract features using a feature extraction algorithm, train a contact classifier with historical data, and classify the communication to a contact profile, enabling accurate and timely responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional data management systems are used to classify user communications, then the system structure is simple, but the classification accuracy and response timeliness deteriorate when handling large volumes of diverse communications
Solution Approach 1:
The system segments the classification process into distinct modules: preprocessing module for data cleaning and normalization, feature extraction module for identifying key attributes, and classification module for routing to contact profiles. This segmentation enables each module to specialize in specific tasks, improving overall classification accuracy while maintaining manageable system complexity through modular architecture.
Solution Approach 2:
The system performs preliminary actions by pre-processing user communications before classification, including text normalization, tokenization, and extraction of contextual features. Historical communications are pre-trained into contact classifiers, establishing ready-to-use classification models that enable rapid and accurate real-time classification without recalibration during operation.
2Loss of time
If real-time processing of large volumes of user communications is implemented, then response timeliness improves, but the processing capacity and computational resources required worsen
Solution Approach 1:
The system performs preliminary processing of communications during off-peak periods, pre-training contact classifiers with historical data and pre-extracting features from frequently occurring communication patterns. This preliminary action reduces the computational burden during real-time operation, enabling rapid classification without requiring excessive processing capacity during peak communication volumes.
Solution Approach 2:
The system dynamically adjusts processing parameters based on communication volume and complexity, modifying feature extraction depth, classification model selection, and processing priority queues. This parameter adaptation enables the system to maintain responsive classification across varying loads, optimizing the balance between response timeliness and processing capacity requirements.
Data Source
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AI summary
An apparatus and method for answering a user communication, wherein the apparatus includes at least a processor, a computer-readable storage medium communicatively connected to the at least a processor, wherein the computer-readable storage medium contains instructions configuring the at least processor to receive a user communication comprising contextual data, classify the user communication to a contact profile, classify the contextual data to an action table within a repository, and answer the user communication.