Natural Language Interface Intent Classifier Training
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Solution Overview
Problem
Companies face challenges in implementing and improving natural language interfaces due to the variety of customer expressions and types of requests, leading to high costs and inefficiencies in customer support.
Innovation Solution
The use of semantic processing and clustering of usage data to identify effective and ineffective areas of the natural language interface, allowing for efficient improvement and configuration, including the deployment of intent classifiers and hierarchical intent graphs to disambiguate customer requests.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If traditional customer support methods (website navigation, phone calls, social media) are used, then customers can receive support, but interaction time and costs increase
Solution Approach 1:
The system enables customers to obtain support through natural language interactions with an automated interface that processes their requests independently. The interface uses semantic processing to understand customer intent and automatically retrieves relevant information or performs actions without requiring human agent intervention, thus reducing interaction time while maintaining support effectiveness
Solution Approach 2:
The patent replaces traditional mechanical customer support systems (phone calls, website navigation, social media monitoring) with an automated natural language processing system. This substitution eliminates the need for manual handling of customer requests, significantly reducing interaction time and improving support efficiency through automated intent classification and information retrieval
2Reliability
If manual customer service representatives are hired to respond to requests, then customer support quality improves, but implementation costs increase
Solution Approach 1:
The system replaces human customer service representatives with an automated natural language interface that independently processes customer requests. The interface uses trained intent classifiers and semantic processing to understand and respond to customer needs, eliminating the need for hiring and training human agents while maintaining consistent support quality across all customer interactions
Solution Approach 2:
The patent transforms the customer support system from a human-based manual process to an automated digital system by changing key parameters: replacing human judgment with machine learning models, converting voice/text inputs into structured intent classifications, and automating information retrieval and response generation. This parameter transformation reduces implementation costs while preserving support quality through consistent automated processing
3Ease of manufacture
If natural language interface configuration is performed manually without usage data, then initial setup is simpler, but interface accuracy decreases
Solution Approach 1:
The system implements a feedback mechanism where usage data from customer interactions is collected and used to retrain intent classifiers. The trained classifiers identify patterns in actual customer language, and this feedback loop continuously improves the interface's accuracy in understanding customer intent. Configuration remains simple initially, but accuracy improves over time through automated learning from real usage patterns
Solution Approach 2:
The patent performs preliminary configuration of the natural language interface with basic intent classifiers before deployment. After deployment, usage data is collected and used to retrain and refine the classifiers, improving accuracy for future interactions. This preliminary action allows quick initial setup while enabling subsequent accuracy improvements through data-driven retraining
Data Source
AI summary
A third-party company may assist companies in providing natural language interfaces for their customers. To implement a natural language interface for a company, a configuration may be received that includes information, such as a list intents, seed messages for the intents, and hierarchical information of the intents. An intent classifier may be trained using the configuration, and the natural language interface may be deployed for use with customers. Usage data of the natural language classifier may be collected and used to improve the natural language interface. Messages corresponding to an intent may be clustered into clusters of similar messages, and a prototype message may be obtained for each cluster to provide a human understandable description of the cluster. The information about the clusters may be used to improve the natural language interface, such as by creating a new intent with a cluster or moving a cluster to a different intent.