Search Result Coverage Analysis via Topical Gap Visualization
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Solution Overview
Problem
Conventional information retrieval systems fail to assist users in assessing and improving the quality of selected documents from search results, leaving users to manually browse through numerous documents without clear guidance on relevance or completeness.
Innovation Solution
An information retrieval system that analyzes selected documents to identify key subjects and presents a graphical interface, such as a pie chart, showing the relevance to the query and highlighting gaps in topical scope, allowing users to initiate targeted searches to fill coverage gaps.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If conventional search engines return all matching documents, then search completeness is improved, but user assessment burden increases
Solution Approach 1:
The patent introduces a coverage analysis tool as an intermediary between the search engine and the user. This tool automatically analyzes selected documents against the original query to compute topical coverage metrics, serving as a mediator that translates raw search results into actionable coverage assessments without requiring manual evaluation by the user.
Solution Approach 2:
The patent replaces the manual mechanical process of browsing and evaluating documents with an automated computational system. The coverage analysis tool uses algorithms to automatically compare selected documents against the query, compute coverage scores, and generate visual representations, substituting human manual assessment with automated mechanical processing.
2Adaptability or versatility
If users manually browse search results, then document selection flexibility is improved, but time consumption increases
Solution Approach 1:
The patent performs preliminary analysis of the selected documents against the original query before the user needs to make final selections. The coverage analysis tool proactively computes topical coverage metrics and identifies gaps in advance, allowing users to make informed decisions faster without needing to manually review each document in detail.
Solution Approach 2:
The patent implements a feedback mechanism where the coverage analysis tool provides immediate feedback to users about the quality and completeness of their document selections. By displaying coverage scores and visual representations of topical coverage, the system feedbacks to users enabling them to quickly assess whether their selections are sufficient or if additional documents are needed.
3Device complexity
If search results are presented without quality indicators, then system simplicity is improved, but user trust in results decreases
Solution Approach 1:
The patent uses visual representations with color coding to indicate coverage quality levels. Different colors or visual intensities represent different coverage scores, providing an intuitive visual feedback mechanism that helps users quickly assess the quality of search results without adding complex textual explanations or indicators.
4Speed
If users select documents without coverage analysis, then selection speed is improved, but information completeness deteriorates
Solution Approach 1:
The coverage analysis tool provides feedback to users about topical coverage gaps in their selected documents. By identifying and displaying which topics are underrepresented or missing, the system enables users to quickly assess information completeness and make targeted selections to fill gaps without requiring comprehensive manual review of all possible documents.
Data Source
AI summary
The present inventor devised, among other things, information retrieval systems, methods, software, and related interfaces that help users assess and if necessary bolster the quality of their manual selections from search results. One exemplary system receives a set of documents selected from search results for an input query, identifies key subjects in the selected documents, and outputs a graphic, such as a pie chart, that shows not only how well these selected documents relate to the query, but also whether there are gaps in the topical scope of the selected documents related to the input query.


