Chatbot Intent Classification via Fallout Utterance Analysis
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Solution Overview
Problem
Existing chatbots face limitations such as limited comprehension of user queries, repetitive responses, and irrelevance, leading to unsatisfactory user experiences and increased manual intervention, which hinder their efficiency and usage.
Innovation Solution
A system comprising a processor with a fallout utterance analyzer, response identifier, deviation identifier, flow generator and enhancer, and self-learning engine that analyzes chat logs to classify user intents, generate auto-generated responses, and optimize conversational flows, thereby upgrading the chatbot's performance and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If automated learning is implemented to improve chatbot comprehension, then the chatbot's ability to understand user intents improves, but the system complexity increases
Solution Approach 1:
The system segments the chatbot improvement process into distinct functional modules: a fallout utterance analyzer that processes chat logs and identifies classification opportunities, a response identifier that generates candidate responses, and a self-learning engine that implements automated learning. Each module handles specific aspects of intent classification, reducing overall system complexity while improving comprehension through specialized processing.
Solution Approach 2:
The patent introduces intermediary components that facilitate automated learning without requiring complete system redesign. The fallout utterance analyzer acts as an intermediary that processes existing chat logs and identifies classification opportunities, while the self-learning engine serves as an intermediary layer that implements learning algorithms between the chatbot and user interactions, thereby improving comprehension while managing complexity.
2Productivity
If manual intervention is reduced through automated learning, then productivity improves, but the difficulty of detecting and measuring intent classification accuracy increases
Solution Approach 1:
The system implements feedback mechanisms where the self-learning engine continuously monitors chatbot performance by analyzing chat logs and evaluating intent classification accuracy. The fallout utterance analyzer provides feedback on classification opportunities, and the system uses this feedback to automatically adjust and improve its performance, enabling reduced manual intervention while maintaining measurable accuracy through automated evaluation loops.
3Adaptability or versatility
If the chatbot responds to new intents automatically, then adaptability improves, but the loss of information through incorrect classifications increases
Solution Approach 1:
The system performs preliminary actions by analyzing chat logs before the chatbot needs to respond to new intents. The fallout utterance analyzer proactively identifies classification opportunities and evaluates chat logs to detect patterns of new intents in advance. This preliminary analysis allows the self-learning engine to prepare and validate response classifications before deployment, improving adaptability while reducing information loss through pre-verification of classification accuracy.
Data Source
AI summary
A system and method for upgrading an executable chatbot is disclosed. The system may include a processor including a fallout utterance analyzer, a response identifier, a deviation identifier, a flow generator and enhancer. The fallout utterance analyzer may receive chats logs comprising a plurality of utterances and corresponding bot responses. The fallout utterance analyzer may classify the plurality of utterances into multiple buckets pertaining to at least one of an out-of-scope intent, a newly identified intent, and a new variation of an existing intent. The response identifier may generate auto-generated responses corresponding to new intents for upgrading the executable chatbot. The deviation identifier may overlay corresponding intent in the chat logs with the prestored flow dialog network to designate an extent of deviation with respect to flow prediction performance by the executable chatbot. The flow generator and enhancer may generate an auto-generated conversational dialog flow for upgrading the executable chatbot.