Intent Prediction ML System for Routing User Requests
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Solution Overview
Problem
Software platforms face challenges in providing intuitive experiences for users due to non-intuitive products and services, leading to inefficient customer service workflows and increased user request handling time.
Innovation Solution
A machine learning system generates intent predictions for user inputs, such as questions and search queries, to classify user intents and route requests to specialized queues, improving user experience and reducing handling time by personalizing answers and routing requests to appropriate experts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a machine learning system with word and sentence features is implemented for intent prediction, then user request routing accuracy and handling efficiency are improved, but system complexity and processing resources increase
Solution Approach 1:
The system segments the intent prediction task into multiple independent feature extraction components: word-level features (TF-IDF, word embeddings) and sentence-level features (syntax, semantics). This segmentation allows each component to be processed separately and combined, improving routing accuracy while managing system complexity through modular architecture.
Solution Approach 2:
The machine learning model serves multiple functions: it performs intent classification, routes requests to appropriate queues, and provides predictions for both word and sentence levels. This multi-functionality consolidates what would otherwise require separate systems into a single unified platform, improving efficiency without proportionally increasing complexity.
2Measurement precision
If multiple feature extraction methods are used for intent prediction, then prediction accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary feature extraction by pre-computing word embeddings and TF-IDF scores during idle periods or in parallel with other processing tasks. This preliminary action ensures that when intent prediction is needed, the feature extraction is already complete or nearly complete, reducing real-time processing time while maintaining high accuracy.
Solution Approach 2:
The system extracts more features than strictly necessary (excessive action) but selectively uses only the most relevant features for each prediction task. Word-level features are extracted for all inputs, but sentence-level features are only processed when they add significant value, balancing accuracy with processing efficiency.
3Productivity
If intent predictions are used to route user requests to specialized queues, then customer service workflow efficiency is improved, but system complexity increases
Solution Approach 1:
The machine learning model acts as an intermediary between user requests and the customer service routing system. It translates unstructured user input into structured intent predictions that the routing system can easily process. This intermediary layer simplifies the overall system architecture by handling the complex analysis in one dedicated component rather than distributing complexity across multiple routing decisions.
Data Source
AI summary
Systems and methods may be used to generate and use intent predictions to enhance user experience. The intent predictions may describe the data required to resolve a user request included in a user input (e.g., question, search query, and the like) submitted by a user. The intent predictions may be generated using a machine learning model that comprises a model framework for extracting features and classifying user inputs into intent classes based on the extracted features. The intent predictions may be integrated into an information service to improve business metrics including contact rate, transfer rate, helpful rate, and net total promoter score.