Dynamic Question Prioritization in Cognitive QA Systems
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional cognitive question answering systems face inefficiencies in resource allocation and prioritization when handling multiple natural language questions simultaneously, as they treat all questions with equal priority, leading to poor response processing for questions of higher importance due to the non-deterministic nature of natural language queries.
Innovation Solution
A system that dynamically prioritizes questions by performing natural language processing (NLP) analysis to identify named entities and question context, using feature values to assign a target question priority value, incorporating AI and rule-based logic to determine question urgency and sort processing accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional QA systems treat all questions with equal priority, then the system processing is simple and fair, but resource allocation becomes inefficient and high-importance questions receive poor response processing
Solution Approach 1:
The patent implements dynamic question prioritization by continuously monitoring system state (queue depth, processing time, resource availability) and adjusting question priorities in real-time. The priority assignment module dynamically re-evaluates questions based on current system conditions, allowing high-importance questions to be elevated when resources are available and deprioritized when the system is overloaded, thus resolving the contradiction between simple processing and efficient resource allocation.
Solution Approach 2:
The system changes the priority parameter of questions based on extracted features (urgency, importance, context) and system state. By transforming static equal-priority treatment into dynamic priority adjustment based on multiple parameters (question features, system load, resource availability), the patent achieves efficient resource allocation without requiring complex manual intervention, balancing productivity improvement with acceptable system complexity.
2Ease of operation
If ad-hoc static priority fields are used for question prioritization, then implementation is simple, but the non-deterministic nature of natural language questions makes effective prioritization extremely difficult
Solution Approach 1:
The patent enables the QA system to automatically analyze and prioritize questions using its own NLP capabilities and domain knowledge. The priority assignment module extracts features from the natural language questions themselves (urgency, importance, context) and uses the system's cognitive processing to determine priorities, making the system self-sufficient and eliminating the need for external static priority fields or manual configuration, thus achieving both ease of operation and reliable prioritization.
Solution Approach 2:
The patent replaces mechanical static priority fields with a cognitive NLP-based analysis system. Instead of relying on pre-defined static priorities that cannot understand natural language nuance, the system uses NLP techniques to extract semantic features, context, and urgency from the questions themselves, substituting a rigid mechanical prioritization mechanism with an adaptive intelligent system that effectively handles the non-deterministic nature of natural language queries.
3Productivity
If questions are processed in chronological order, then processing is straightforward and fair, but single noisy neighbor users asking multiple narrowly focused questions cause poor response processing for other important questions
Solution Approach 1:
The patent applies local quality by treating different questions differently based on their individual characteristics (urgency, importance, context) rather than treating all questions uniformly. The priority assignment module analyzes each question's specific features and assigns appropriate priority levels, ensuring that high-importance questions from any user receive adequate processing attention while allowing lower-priority questions to be handled appropriately, thus improving response processing quality without requiring complete redesign of the processing architecture.
Data Source
AI summary
An approach is provided dynamically prioritizing question requests based on extracted question data. In the approach, performed by an information handling system, a number of question requests to a question and answering (QA) system are received from a computer network, and a plurality of question priority parameters are identified, including one or more question topics and a plurality question context parameters, by performing natural language processing (NLP) analysis of each question request. The approach determines a target priority value for each question request based on the plurality of question priority parameters identified for said question request. By evaluating the target priority values for the plurality of question requests, processing of the question requests is prioritized, such as by applying an artificial intelligence (AI) learned models and rule-based logic at the information handling system to evaluate the target priority values for the plurality of question requests.