Two-Tier Machine Learning Model for Intent Classification
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Solution Overview
Problem
Current systems face challenges in accurately and timely determining user intent for dynamic conversational responses, especially with sparse training data and similar feature inputs for different intents, leading to difficulties in generating precise and relevant responses.
Innovation Solution
A two-tier machine learning model approach, where a first machine learning model, such as a factorization machine model, determines an intent cluster, and a second model, like an artificial neural network, refines the specific intent based on the output from the first model, using aggregated user and context information to generate dynamic conversational responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single machine learning model is used to determine user intent, then the system complexity is low, but the accuracy and precision of intent determination deteriorates due to sparse training data and similar feature inputs for different intents
Solution Approach 1:
The patent divides the intent determination task into two separate machine learning models: a first model that handles initial intent classification and a second model that refines the determination. This segmentation allows each model to specialize in specific aspects of intent recognition, improving overall accuracy while managing complexity through modular architecture.
Solution Approach 2:
The first machine learning model acts as an intermediary between the input features and the second model. It processes sparse training data and similar feature inputs initially, then passes its output to the second model for refined intent determination, effectively mediating the information flow to enhance precision.
2Measurement precision
If more training data is collected to improve model accuracy, then the intent determination precision improves, but the time required for data collection and model training increases
Solution Approach 1:
The patent uses two models where the first model handles the bulk of initial processing with available sparse data, and the second model applies refined processing only where needed. This partial action approach achieves high precision without requiring exhaustive data collection and training for a single comprehensive model.
Solution Approach 2:
The first machine learning model performs preliminary intent classification before the second model refines the determination. This preliminary action allows the system to quickly narrow down potential intents using sparse data, reducing the subsequent processing burden and time requirements.
3Productivity
If the system generates responses quickly in real-time, then the responsiveness improves, but the accuracy of intent determination may deteriorate due to limited processing time
Solution Approach 1:
The two-model architecture segments the processing into rapid initial classification by the first model and faster refinement by the second model. This segmentation enables real-time response generation while maintaining accuracy, as each model performs its specialized function efficiently within the time constraints.
Solution Approach 2:
The system changes processing parameters by using two specialized models rather than one general model. The first model is optimized for quick initial assessment and the second for precise refinement, allowing the system to maintain both speed and accuracy through parameter optimization in each stage.
Data Source
AI summary
Methods and systems are described for generating dynamic conversational responses using two-tier machine learning models. The dynamic conversational responses may be generated in real time and reflect the likely goals and/or intents of a user. The two-tier machine learning model may include a first tier that determines an intent cluster based on a feature input, and a second tier that determines a specific intent from the cluster.