Message Enhancement Module for Intent Classification
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Solution Overview
Problem
Automated agents, such as chatbots, face challenges in interpreting uniquely phrased user inquiries, leading to reduced accuracy in intent detection and increased resource consumption due to users providing vaguer and shorter messages, as they assume the automated agent may not understand complicated statements.
Innovation Solution
A method that enhances user messages by predicting how the same request would be phrased for a human agent and combining it with the original message, using a different language modality, to improve intent classification, without requiring retraining or restructuring of the automated agent, utilizing a module outside the bot architecture that leverages lookup tables, machine learning, and natural language processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If automated agents use standard intent detection methods, then the system architecture remains simple, but intent detection accuracy decreases due to uniquely phrased user inquiries
Solution Approach 1:
The patent introduces an intermediary message enhancement module that sits between the user input and the automated agent. This module transforms user messages by predicting alternative phrasings and combining them with the original message, thereby improving intent detection accuracy without modifying the core automated agent architecture. The intermediary handles the complexity of language variation while preserving the simplicity of the existing bot system.
2Measurement precision
If users provide more detailed messages to ensure understanding, then intent detection accuracy improves, but message length and processing time increase
Solution Approach 1:
The system performs preliminary action by pre-generating multiple alternative phrasings for user messages using prediction models trained on conversation data. These alternative phrasings are prepared in advance and then combined with the original message before being processed by the automated agent. This preliminary preparation of enhanced messages improves intent detection without requiring users to manually provide longer messages, thereby reducing the time loss associated with extended user input.
3Measurement precision
If the automated agent is retrained with more data to handle varied phrasing, then intent detection accuracy improves, but system maintenance complexity increases
Solution Approach 1:
Rather than retraining the automated agent itself, the patent introduces an intermediary message enhancement module that handles the complexity of varied phrasing. This module uses prediction models to generate alternative phrasings and combine them with original messages, thereby improving intent detection accuracy without requiring retraining of the core automated agent. This approach maintains the simplicity of the existing bot architecture while still addressing the challenge of language variation.
4Reliability
If human agents handle all complex inquiries, then user satisfaction improves, but resource consumption and cost increase
Solution Approach 1:
The system performs preliminary enhancement of user messages by predicting and combining alternative phrasings before the message reaches the automated agent. This preliminary action improves the quality of input to the automated agent, enabling it to handle more inquiries successfully without human intervention. By enhancing messages in advance, the system reduces the need for human agent involvement in complex cases, thereby lowering resource consumption and costs while maintaining high user satisfaction.
Data Source
AI summary
A method, computer system, and a computer program product for automated agent intent detection enhancement are provided. A first message from a first user is received. The first message is generated during a first conversation between the first user and a first automated agent. A computer produces a second message that includes a same request as the first message but a different language modality than the first message. The second message and the first message are combined to form a combined message. The combined message is input into the first automated agent such that the first automated agent produces an intent classification for the first message.