Automated Response Generation Using Semantic Clause Parsing
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Solution Overview
Problem
Current methods for responding to customer text-based natural language messages are labor-intensive and inefficient, as they often require human agents to analyze and respond to each message individually, lacking the ability to leverage previous responses and promoting inconsistencies across agents.
Innovation Solution
An automated system that identifies and parses sentences and clauses in incoming messages, correlates them with historical messages, and generates responses based on previously generated responses stored in a learned knowledge repository, using syntactic and semantic analysis to ensure accurate and efficient response generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If human agents manually analyze and prepare individual responses to customer messages, then response quality and consistency can be maintained, but labor intensity and time consumption increase significantly
Solution Approach 1:
The system creates a knowledge base by copying and storing previous customer messages and their corresponding agent responses. When a new message arrives, the system searches for and retrieves similar historical messages and their responses, adapting them to the current context. This allows automated generation of consistent responses based on proven historical interactions, reducing reliance on manual agent analysis while maintaining response quality through established patterns.
Solution Approach 2:
The system performs preliminary analysis and response generation automatically before human agents need to intervene. By pre-processing incoming messages through syntactic and semantic analysis, and generating draft responses using the knowledge base, the system prepares work in advance, reducing the time and effort required for final response creation and improving overall productivity.
2Productivity
If pre-formed boilerplate response templates are used, then response generation speed increases, but labor intensity remains high and inconsistencies persist across different agents
Solution Approach 1:
Instead of using static boilerplate templates, the system dynamically copies and adapts responses from actual historical customer interactions. The knowledge base stores real message-response pairs that have been proven effective, and the system retrieves and modifies these based on the specific context of the current message, ensuring both speed and consistency.
Solution Approach 2:
The system changes the parameters of response generation by moving from fixed template variables to dynamic content selection based on semantic similarity. The syntactic and semantic analysis allows the system to identify the appropriate historical responses by comparing message parameters and characteristics, then adapts those responses to fit the current context, maintaining consistency across different agents.
3Productivity
If automated systems are introduced to reduce labor costs, then productivity increases, but the ability to handle natural language messages effectively decreases
Solution Approach 1:
The patent replaces manual mechanical analysis by human agents with automated computational systems. The system uses computer-based syntactic analysis to parse message structure and semantic analysis to understand meaning, substituting human cognitive processes with algorithmic ones. This enables automated handling of natural language messages while maintaining effectiveness through sophisticated language processing techniques.
Solution Approach 2:
The system introduces an intermediary layer between the customer message and the response generation process. This intermediary performs syntactic and semantic analysis, bridges the gap between natural language input and structured response generation, and enables the automated system to effectively understand and process human language without requiring direct human intervention.
Data Source
AI summary
A system to generate a response to a text-based natural language message includes a user interface, processing device, and a computer-readable storage medium storing executable instructions to generate the response to the text-based natural language message. The instructions and a method for generating the response include identifying a sentence in the text-based natural language message, identifying an input clause in the sentence, and parsing the input clause, thereby defining a relationship between words in the input clause. The instructions and method also include assigning a semantic tag to the parsed input clause, comparing the input clause to a previously received clause, the previously received clause being correlated with a previously generated response clause, and generating an output response message derived from the previously generated response clause.