Automated Question Answering System Visualizing Answer Confusion

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Solution Overview

Problem

Automated question answering systems face challenges in identifying and addressing confusion between answers to different queries, leading to decreased customer satisfaction and inefficient use of computer resources.

Innovation Solution

A method and system for visualizing data in automated question answering systems, which involves identifying answers to queries, determining semantic relationships between queries, and visually representing potential confusions using a network graph with directed edges indicating confusion directions.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If automated question answering systems process and store answers for multiple queries, then the system's knowledge base expands and coverage improves, but confusion between similar answers increases and system accuracy decreases

Engineering Contradiction:

Improveknowledge base coverageVSAvoidanswer accuracy

Core Design Contradiction:

Adaptability or versatilityVSReliability

Solution Approach 1:

The system segments the knowledge base by creating distinct answer groups with unique identifiers, separating potentially confusing answers into organized clusters. Each answer group contains answers that are semantically related but distinct, allowing the system to manage and differentiate between similar responses efficiently.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system implements feedback mechanisms by analyzing query-answer interactions to identify confusion patterns. When users indicate confusion or when semantic analysis detects overlapping answers, the system adjusts its routing logic and provides feedback to improve future answer selection, thereby maintaining accuracy while expanding knowledge coverage.

Inventive Principle:

Principle #23Feedback

2Reliability

If the system analyzes semantic relationships between all queries to identify confusion, then answer accuracy improves, but computational resource usage increases

Engineering Contradiction:

Improveanswer accuracyVSAvoidcomputational resource usage

Core Design Contradiction:

ReliabilityVSUse of energy by moving object

Solution Approach 1:

The system performs preliminary semantic analysis during the answer grouping phase, creating pre-segmented answer clusters before actual query processing. This preliminary organization reduces the computational burden during runtime by eliminating the need for exhaustive semantic comparisons of all answers for every incoming query.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system applies semantic analysis selectively rather than universally - performing detailed semantic comparisons only for answer groups that are potentially relevant to the incoming query based on initial keyword matching or vector similarity, rather than analyzing all answers in the knowledge base.

Inventive Principle:

Principle #16Partial or excessive action

3Reliability

If administrators manually review and organize answers to eliminate confusion, then system accuracy improves, but maintenance time and complexity increase

Engineering Contradiction:

Improveanswer accuracyVSAvoidmaintenance time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system performs self-service by automatically analyzing and grouping answers based on semantic relationships without requiring manual administrator intervention. The automated semantic analysis and clustering algorithms continuously organize the knowledge base, reducing maintenance time while maintaining high answer accuracy.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system uses feedback from user interactions and performance metrics to automatically adjust answer groupings and identify confusion areas that require attention. This feedback-driven approach allows the system to self-optimize over time, reducing the need for manual review while maintaining accuracy.

Inventive Principle:

Principle #23Feedback

4Measurement precision

If the system uses detailed semantic analysis to differentiate between similar answers, then answer precision improves, but processing speed decreases

Engineering Contradiction:

Improveanswer differentiation precisionVSAvoidquery processing speed

Core Design Contradiction:

Measurement precisionVSSpeed

Solution Approach 1:

The system segments the answer space into distinct groups based on semantic categories, allowing for efficient retrieval and comparison. By organizing answers into predefined semantic clusters, the system reduces the search space and enables faster processing while maintaining precise differentiation between similar answers within each segment.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system performs preliminary semantic indexing and vector representation of all answers during the knowledge base setup phase. This preliminary processing creates optimized data structures that enable rapid similarity comparisons during query processing, achieving both high precision and fast speed.

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS12266037B2Systems and methods for visualizing problem areas of an automated question answering system

Publication Date: 2025.04.01 ADA SUPPORT INC

AI summary

Systems and method for visualizing data for an automated question answering system are disclosed. The method includes identifying a first answer associated with a first query; identifying a second answer associated with a second query; determining a criterion of the first answer with respect to the second answer; and visually representing the first answer and the second answer based on the criterion.