Chatbot Response Selection Using Domain Index Filtering
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Solution Overview
Problem
Current AI chatbots lack the ability to provide relevant and engaging responses in event-related sessions, failing to maintain consistency and relevance, which limits their participation and interaction with users in discussions centered around specific events or domains.
Innovation Solution
The proposed solution involves a chatbot system that detects messages in an event-related session, retrieves candidate responses from domain-specific indexes, optimizes these responses based on predetermined criteria such as consistency and relevance, and provides real-time event content, ensuring freshness and alignment with user opinions, thereby enhancing engagement and activeness in the session.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If AI chatbots use general machine learning-based chatting models to generate responses, then they can handle diverse user inputs, but they fail to maintain consistency and relevance in event-related sessions
Solution Approach 1:
The patent segments the response generation process into multiple stages: retrieving candidate responses from domain-specific indexes, filtering candidates based on event-related criteria, and selecting the final response. This segmentation allows the system to maintain both versatility in handling diverse inputs and reliability in maintaining event-related consistency.
Solution Approach 2:
The patent implements preliminary action by pre-establishing domain-specific indexes and event-related criteria before actual chat interactions. The system pre-retrieves candidate responses and pre-filters them based on event relevance, ensuring that when user inputs arrive, the chatbot can quickly select appropriate responses that maintain consistency with the event context.
2Reliability
If chatbots retrieve responses from domain-specific indexes, then they can provide relevant and professional responses, but the process requires multiple steps including filtering and optimization
Solution Approach 1:
The patent creates a universal filtering framework that handles multiple criteria (event relevance, user opinion alignment, timeliness) through a single integrated process. The filtering module serves multiple functions simultaneously: evaluating candidate responses against event context, optimizing for user engagement, and ensuring response quality, thereby managing complexity through multi-functionality rather than separate dedicated modules for each criterion.
3Reliability
If chatbots filter and optimize candidate responses based on predetermined criteria, then they can maintain consistency and relevance, but this increases processing time and computational resources
Solution Approach 1:
The patent applies partial action by implementing multi-stage filtering where not all candidate responses undergo the complete filtering and optimization process. The system retrieves a set of candidates, applies initial filtering to eliminate clearly inappropriate options, and then performs more intensive optimization only on the remaining viable candidates. This partial processing approach maintains response quality while reducing overall computational time and resource consumption.
Data Source
AI summary
The present disclosure provides a method and an apparatus for providing responses in an event-related session. The event is associated with a predefined domain, and the session comprises an electronic conversational agent and at least one participant. At least one message from the at least one participant may be detected. A set of candidate responses may be retrieved, from an index set being based on the domain, according to the at least one message. The set of candidate responses may be optimized through filtering the set of candidate responses according to predetermined criteria. A response to the at least one message may be selected from the filtered set of candidate responses. The selected response may be provided in the session.