Autonomous Agent Utterance Classification via Parse Tree Analysis
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Solution Overview
Problem
Current solutions for autonomous agents fail to accurately distinguish between user utterances that are questions and those that are requests for actions, leading to user frustration and reduced adoption due to misinterpretation.
Innovation Solution
A system utilizing a linguistic engine and a rule engine to classify utterances as questions or requests through parse tree generation, keyword identification, and rule-based classification, including the use of imperative verbs, predefined keywords, and linguistic templates, with the ability to adjust configurations or send messages based on the classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If current solutions classify all user utterances as questions, then the system can provide information answers, but it fails to recognize requests for actions leading to user frustration
Solution Approach 1:
The patent segments user utterances into distinct categories (questions vs. requests) using multiple classification rules. The system applies linguistic analysis to identify imperative verbs, request keywords, and question keywords, thereby dividing the classification task into separate detectable patterns that improve both accuracy and user satisfaction.
Solution Approach 2:
The patent introduces an intermediary classification layer between user input and system response. This intermediary system uses linguistic engines and rule-based analysis to determine utterance type before routing to appropriate handling, preventing direct misclassification while maintaining ease of use through automated discrimination.
2Speed
If the system uses simple keyword matching, then the classification process is fast, but it cannot accurately distinguish implicit requests from questions
Solution Approach 1:
The patent performs preliminary linguistic analysis on user utterances by generating parse trees and identifying linguistic patterns before final classification. This preliminary processing of syntactic structure and linguistic templates enables accurate distinction between implicit requests and questions while maintaining efficient classification throughput.
Solution Approach 2:
The patent changes the parameters of analysis from simple keyword presence to comprehensive linguistic feature extraction including parse tree structure, verb type identification, and template matching. This parameter expansion improves discrimination accuracy while the rule-based approach maintains processing speed through efficient pattern recognition.
3Ease of manufacture
If the system misinterprets requests as questions, then it can provide information responses, but it ignores user action desires causing frustration and reduced adoption
Solution Approach 1:
The patent segments the response generation process into distinct branches based on utterance classification. Requests are routed to action-handling routines while questions are routed to information-provision routines, ensuring appropriate responses that improve user adoption without significantly complicating the overall system architecture through clear conditional logic.
Solution Approach 2:
The patent implements dynamic response selection based on real-time utterance analysis. The system adapts its behavior by changing response type (information vs. action execution) based on classified utterance intent, thereby improving user adoption through appropriate responsiveness while maintaining a unified system structure that does not require separate manufacturing complexities.
Data Source
AI summary
Systems, devices, and methods of the present invention relate to text classification. A text classification system accesses an utterance of text. The utterance includes at least one word. The text classification system generates a parse tree for the utterance. The parse tree includes at least one terminal node with a word type. The terminal node represents a word of the utterance. The text classification system applies one or more rules to the text. The text classification system then classifies the utterance as a question or a request for an autonomous agent to perform an action.