Chatbot Intention Prediction via Multi-Layer ML Segmentation
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Solution Overview
Problem
Current chatbot systems struggle to accurately predict user intentions due to complexities arising from multiple input sources, multiple optimization goals, and varying human user behavior.
Innovation Solution
A method and system that utilize multiple machine learning layers to classify and model relationships between feature data groups, enabling the prediction of user intentions and displaying them as suggestions to the user.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional decision tree approaches are used to generate chatbot responses, then the system structure is simple and easy to implement, but the system cannot accurately predict user intentions due to multiple input sources and varying user behaviors
Solution Approach 1:
The patent segments the chatbot system into multiple independent components: a decision tree module for structured response generation, and a separate machine learning module for intention prediction. This segmentation allows each module to specialize in its strength while working together to overcome the limitations of either approach alone.
Solution Approach 2:
The patent introduces an intermediary intention prediction layer between user input and the decision tree response generation. This intermediary uses machine learning models to predict user intentions from multiple input sources before the decision tree processes the request, thereby improving overall prediction accuracy without compromising the simplicity of the decision tree structure.
2Measurement precision
If multiple machine learning layers are introduced to model complex relationships in feature data, then the prediction accuracy improves, but the system complexity and computational requirements increase
Solution Approach 1:
The patent divides the machine learning processing into two distinct layers: a first machine learning layer for extracting features from raw input data, and a second machine learning layer for modeling relationships between extracted features to predict intentions. This segmentation allows each layer to focus on specific tasks, improving accuracy while keeping individual layer complexities manageable.
Solution Approach 2:
The first machine learning layer performs preliminary feature extraction and processing before the second layer attempts to model complex relationships. This preliminary action simplifies the input for the second layer, reducing its computational burden and complexity while maintaining high prediction accuracy.
3Reliability
If the system processes data from multiple feature sources and models multiple relationships, then the robustness to varying user behaviors improves, but the processing time and computational resources increase
Solution Approach 1:
The system performs preliminary feature extraction and data preprocessing in the first machine learning layer before the main intention prediction processing. This preliminary action organizes and prepares data from multiple feature sources in advance, making the subsequent relationship modeling more efficient and reducing overall processing time.
Solution Approach 2:
The patent segments the processing pipeline into distinct stages with specialized functions: feature extraction, feature processing, and relationship modeling. This segmentation allows parallel processing of different feature sources and optimizes resource allocation, improving system robustness while managing processing time effectively.
Data Source
AI summary
Aspects of the disclosed technology provide solutions for predicting an intention of a user of a chatbot system. The method can include steps for: receiving or obtaining feature data from a plurality of feature sources; classifying the feature data into a plurality of feature groups; extracting the feature data using a first machine learning layer; and modelling at least one relationship between one group of feature data and another group of feature data in the plurality of feature groups using a second machine learning layer. Systems and machine-readable media are also provided.