Semantic Analysis for Complete and Relevant Question Answers
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Solution Overview
Problem
Current computer systems face challenges in efficiently analyzing and providing relevant, complete, and timely answers to user questions, especially in fields like medicine, where users need to sift through vast amounts of information to find concise and current data.
Innovation Solution
A computer-based method that identifies semantic elements in user questions and compares them to structured and unstructured information sources to select candidate responses based on completeness, relevance, conciseness, and timeliness, tailoring answers to the user's knowledge level and providing feedback on user-generated responses to assess knowledge gaps.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If computer systems provide comprehensive information from vast sources, then information completeness is improved, but information retrieval time and complexity increase
Solution Approach 1:
The system performs preliminary actions by pre-processing and indexing information from multiple sources, organizing it into structured formats with identified semantic elements before user queries are submitted. This allows rapid retrieval and comparison of candidate responses without sacrificing completeness.
Solution Approach 2:
The patent replaces manual information search and analysis with automated computer-based semantic analysis. The system automatically identifies semantic elements in questions, retrieves candidate responses from multiple sources, analyzes their completeness and relevance, and ranks them - substituting human cognitive processing with computational algorithms.
2Measurement precision
If computer systems analyze detailed semantic elements in questions and answers, then answer relevance and accuracy are improved, but system complexity increases
Solution Approach 1:
The system segments both questions and candidate responses into discrete semantic elements (keywords, concepts, entities). This segmentation allows for systematic comparison of individual semantic components rather than attempting to analyze entire texts as monolithic units, making the complexity manageable.
Solution Approach 2:
The patent transforms qualitative assessments of answer quality into quantitative parameters by counting semantic element matches, calculating completeness scores, and generating relevance rankings. This parameterization converts complex semantic analysis into measurable metrics that can be systematically processed.
3Loss of information
If computer systems provide comprehensive candidate responses from multiple sources, then information completeness is improved, but conciseness of final answer decreases
Solution Approach 1:
The system extracts only the essential semantic elements and key information from comprehensive candidate responses. By identifying and extracting the most relevant semantic components that directly answer the question, the system maintains information completeness while presenting condensed, concise final answers to users.
Solution Approach 2:
The patent performs partial analysis by focusing on the most critical semantic elements and candidate responses rather than exhaustively analyzing every detail. The system identifies a sufficient subset of semantic matches needed to determine answer quality, avoiding excessive processing while maintaining adequate completeness.
Data Source
AI summary
A computer-implemented method includes receiving, at a computer system, a question; identifying one or more first semantic elements in the question; selecting, from one or more electronic documents, a plurality of candidate responses to the question based on comparison of the one or more first semantic elements to second semantic elements; determining completeness scores for the plurality of candidate responses, wherein each of the completeness scores indicates how completely a corresponding candidate response from the plurality of candidate responses answers the question; determining relevance scores for the plurality of candidate responses, wherein each of the relevance scores indicates how relevant a corresponding candidate response from the plurality of candidate responses is to the question; and providing, by the computer system, at least a portion of the plurality of candidate responses based, at least in part, on the completeness scores and the relevance scores.