Context-Aware Virtual Assistant Response Generation
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Solution Overview
Problem
Conventional virtual-assistant software agents fail to generate relevant responses to ambiguous user queries as they process inputs in isolation, lacking the ability to consider prior conversational context.
Innovation Solution
A system and method that determine conversational context by analyzing immediate prior conversational inputs, using a deep learning-based natural language understanding module with layers such as embedding, attention, intent classification, and entity recognition to generate responses based on context, intent, and entities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional virtual-assistant software agents process user queries in isolation, then the system complexity is low, but the response relevance to ambiguous queries deteriorates
Solution Approach 1:
The system performs preliminary action by analyzing and storing conversational context from prior inputs before processing the current query. The context analysis module examines previous user inputs and assistant responses to build a contextual understanding that is then applied when interpreting ambiguous queries, allowing the system to resolve ambiguity without requiring users to rephrase or clarify their intent.
Solution Approach 2:
The context analysis module serves as an intermediary between the query processing module and the response generation module. It receives the current query along with prior conversational inputs, analyzes the contextual relationships, and passes enriched query representations to the response generation module, thereby enabling relevant responses to ambiguous queries while maintaining modular system architecture.
2Measurement precision
If the system analyzes immediate prior conversational inputs to determine context, then the understanding of user intent improves, but the processing time increases
Solution Approach 1:
The system extracts only the necessary contextual elements from prior conversational inputs that are relevant to resolving the current query's ambiguity. Rather than analyzing the entire conversation history, the context analysis module identifies and extracts specific contextual features such as referenced entities, previously discussed topics, and implicit meanings that directly relate to the current query, thereby reducing processing time while maintaining intent understanding accuracy.
3Reliability
If conventional systems rely solely on current input analysis, then the processing speed is high, but the ability to handle ambiguous queries deteriorates
Solution Approach 1:
The system applies partial action by selectively analyzing only the portion of prior conversational context that is necessary to resolve the current query's ambiguity. When queries are clear and unambiguous, the system processes them with minimal context analysis, maintaining high processing efficiency. When queries are ambiguous, the system performs additional context analysis to the extent needed to resolve the ambiguity, thereby improving reliable handling without consistently sacrificing processing efficiency.
Data Source
AI summary
A method can include upon receiving, from a computer network, a conversational input from a user device for a user, determining a context based on one or more contextual units. The one or more contextual units can be associated with immediate prior one or more conversational inputs relative to the conversational input. The method further can include determining an intent associated with the conversational input based on the context. Moreover, the method can include determining one or more entities associated with the conversational input based on the context and one or more expected entities determined based on one or more predefined conversation flows. The method additionally can include determining an output based on the intent and the one or more entities. The method also can include transmitting, via the computer network, the output to be displayed on the user device. Other embodiments are disclosed.