Question Answering Prioritization for High-Volume Query Handling
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Solution Overview
Problem
In multi-user online intelligent question answering scenarios, computing devices struggle to efficiently process a large volume of questions, particularly when faced with meaningless or repetitive queries, leading to paralysis of the entire question answering procedure due to insufficient processing capacity.
Innovation Solution
An intelligent question answering method that evaluates the importance of questions based on various influencing factors, determines the answering importance degree, and selectively provides answers with varying degrees of detail, utilizing semantic clustering to identify fundamental questions and incorporating network answers for enriched responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If all questions are answered indiscriminately in retrieval-based intelligent question answering, then user coverage is improved, but computing device processing capacity is exhausted and important questions cannot be processed
Solution Approach 1:
The patent applies local quality by differentiating question processing based on importance evaluation. Questions are classified into different importance levels (e.g., high, medium, low priority) and processed accordingly - high importance questions receive detailed processing while low importance questions receive simplified or aggregated processing. This allows the system to allocate computing resources dynamically based on question characteristics rather than treating all questions uniformly.
Solution Approach 2:
The patent segments the question answering process into multiple stages: question reception, importance evaluation, and selective answering. By introducing an importance evaluation module that analyzes questions before processing, the system divides the workflow to filter and prioritize questions, preventing unnecessary processing of low-value questions from exhausting system capacity.
2Measurement precision
If retrieval-based question answering is used with fixed corpus database, then answer accuracy for known questions is improved, but adaptability to new and varied questions deteriorates
Solution Approach 1:
The patent introduces dynamic adaptability by allowing the system to adjust its answering strategy based on real-time question characteristics. The importance evaluation module dynamically assesses each question and modifies processing depth accordingly. For novel or complex questions not found in the corpus, the system can generate answers based on evaluated importance rather than failing completely, thus adapting to varied question types while maintaining accuracy for known questions.
Data Source
AI summary
An intelligent question answering method includes receiving an original question; determining an importance evaluation result of the original question based on the original question, where the importance evaluation result indicates an answering importance degree of the original question, and the importance evaluation result is determined based on at least one evaluation influencing factor; determining, based on the importance evaluation result, an answer corresponding to the original question in at least one candidate answer, where the at least one candidate answer has a different degree of detail; and outputting the answer corresponding to the original question. That is, importance of the original question is evaluated, and a degree of detail for answering the original question is determined based on the importance evaluation result.