Chatbot Intelligence System Using Vertical Classification
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Solution Overview
Problem
Chatbots struggle to effectively understand user requests, leading to diminished effectiveness and utility due to their inability to accurately interpret and respond to user input.
Innovation Solution
A chatbot improvement system (CIS) that enhances chatbot intelligence by using a classifier to identify user verticals and keywords for natural language processing, allowing for dynamic selection of verticals based on user history and role, and employing a GULP system for pattern recognition and learning to improve interpretation and response accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a chatbot uses extensive keyword searches to interpret user input, then interpretation accuracy may improve, but processing time increases
Solution Approach 1:
The system performs preliminary actions by pre-processing user input to extract key features and characteristics before the main interpretation process. The classifier analyzes the input message to identify user vertical, role, and intent categories in advance, preparing structured data that accelerates the subsequent keyword matching and response generation processes.
Solution Approach 2:
The interpretation process is segmented into distinct modular components: (1) classifier for extracting user attributes and input characteristics, (2) pattern identifier for recognizing conversation patterns, (3) vertical/role identification, and (4) response generation. This segmentation allows each component to specialize in specific tasks, improving overall efficiency without sacrificing accuracy.
2Device complexity
If a chatbot uses a single vertical for processing all user input, then device complexity is reduced, but adaptability to different user contexts deteriorates
Solution Approach 1:
The system dynamically adapts its processing approach based on the user's vertical and role characteristics. Instead of using a fixed single-vertical approach, the classifier identifies the appropriate vertical and role for each user session, and the system adjusts its keyword sets, interpretation strategies, and response templates accordingly. This dynamic adaptation maintains low complexity by selecting from predefined configurations rather than maintaining all possibilities simultaneously.
Solution Approach 2:
The system changes parameters such as keyword sets, interpretation thresholds, and response templates based on the identified user vertical and role. For example, a technical support vertical may use different keywords and interpretation rules compared to a sales vertical, allowing the same underlying system to adapt to different contexts through parameter adjustment rather than structural complexity.
3Measurement precision
If a chatbot performs comprehensive pattern recognition and learning, then response accuracy improves, but processing requirements increase
Solution Approach 1:
The system applies partial pattern recognition by focusing on the most relevant patterns and features for each specific user context rather than analyzing all possible patterns. The classifier identifies key characteristics of the user input and selectively applies pattern matching rules that are most relevant to the identified vertical and role, avoiding unnecessary processing of irrelevant patterns.
Data Source
AI summary
Various embodiments for a chatbot improvement system are described herein. An embodiment operates by receiving input from a user via a chatbot interface. A first vertical corresponding to the user is identified. The input from the user is interpreted based on a first set of keywords corresponding to the first vertical. A first confidence score is calculated for the first vertical based on the interpretation of the input using the first set of keywords. It is determined whether the first confidence score exceeds a threshold. If the threshold is exceeded, a response to the input is generated based on the first set of keywords. If the threshold is not exceeded, the response to the input based on the second set of keywords. The generated response is provided for display via the chatbot interface.