AI Dialog Generation for Broader Chatbot Response Coverage
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Solution Overview
Problem
Chatbot responses often lack sufficient dialogue for various questions, leading to repetitive queries and inefficient use of data processing resources.
Innovation Solution
Automatically generate and modify problem-solution data sets using artificial intelligence to enhance chatbot dialogues by identifying intents and entities from knowledge sources, thereby improving response accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If dialogs are manually created and maintained for chatbot responses, then response quality can be controlled, but the system lacks sufficient responses for various questions and requires repetitive manual updates
Solution Approach 1:
The system automatically generates and updates dialog responses by extracting information from knowledge sources and generating intents, questions, and answers without requiring manual intervention. This self-service approach expands response coverage while eliminating manual maintenance burden
Solution Approach 2:
The system pre-generates multiple potential responses and stores them in the problem-solution data set before they are needed. When questions are asked during chatbot conversations, these pre-generated responses are readily available, eliminating the need for real-time generation and reducing repetitive queries
2Reliability
If chatbot dialogs are expanded to cover more questions, then response thoroughness improves, but data processing resources are consumed
Solution Approach 1:
The system pre-generates and stores comprehensive responses in the problem-solution data set before actual chatbot interactions. This allows thorough responses to be delivered during conversations without consuming excessive processing resources in real-time, as the heavy lifting of response generation occurs beforehand
Solution Approach 2:
The system creates multiple copies of responses for different questioning styles and formats. Instead of generating unique responses for each question variant, pre-generated responses are copied and matched to similar questions, reducing processing resource consumption while maintaining response thoroughness
3Measurement precision
If the problem-solution data set is manually updated with new dialogs, then response accuracy can be maintained, but the process is time-consuming and inefficient
Solution Approach 1:
The system automatically extracts information from knowledge sources, generates intents, creates questions and answers, and updates the problem-solution data set without manual intervention. This self-service update process maintains response accuracy through structured extraction while dramatically improving update efficiency compared to manual methods
Solution Approach 2:
The system uses metadata generated from knowledge sources to automatically update and refine the problem-solution data set. This feedback loop ensures that new information is systematically integrated while maintaining accuracy, eliminating the need for time-consuming manual verification and updates
Data Source
AI summary
Metadata can be generated for documentation accessed from at least one knowledge source. Intents for main topics of the documentation can be generated using content structure information for the documentation. Dialogs for the intents can be generated. Sub-dialogs can be created for each of the dialogs based, at least in part, on the metadata for the documentation. The dialogs can be configured to be used to modify existing dialogs of a problem-solution data set.