Coaching System for Document Refinement via Answer Similarity
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Solution Overview
Problem
Existing methods for creating high-quality documents requiring specialized knowledge, such as research papers and patents, are inefficient due to reliance on expert schedules and lack of systematic question selection to refine document content.
Innovation Solution
A coaching system that includes a question presenting unit, an answer receiving unit, a data management unit, and an information amount estimating unit to select and adapt questions based on user responses, ensuring appropriate questioning to deepen or change topics according to the similarity of user answers, thereby improving document refinement without expert schedule constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If an expert checks and refines a document manually, then the document quality is improved, but the process takes time and depends on the expert's schedule
Solution Approach 1:
The system enables self-service by having the user independently refine their own document through interactive questioning. The coaching system asks targeted questions based on answer similarity analysis, allowing users to improve document quality without waiting for expert availability, thus resolving the contradiction between quality improvement and time consumption
Solution Approach 2:
The system implements feedback mechanisms by analyzing user answers, calculating similarity with past answers, and dynamically selecting follow-up questions. This automated feedback loop provides continuous guidance for document refinement, maintaining quality improvement while eliminating dependence on expert schedules and reducing time loss
2Productivity
If a system asks questions to refine documents, then document quality can be improved without expert schedule constraints, but the system must select appropriate questions based on user knowledge level
Solution Approach 1:
The system replaces the mechanical expert questioning process with an automated information amount estimating unit. This unit calculates similarity between past and current answers using algorithmic processing instead of human judgment, thereby improving refinement efficiency while managing selection complexity through systematic computational methods rather than manual expert analysis
3Loss of information
If the system asks detailed follow-up questions, then new information can be extracted from users, but the questioning process becomes more complex and time-consuming
Solution Approach 1:
The system applies partial action by selectively asking follow-up questions only when answer similarity indicates insufficient information extraction. Rather than asking all possible questions, the system judiciously selects necessary questions based on similarity thresholds, thereby extracting maximum new information while minimizing questioning time and avoiding excessive complexity
Data Source
AI summary
In coaching with the purpose of creating a document in mind, data containing question group related to components of the document, a question of details, and a question of another topic is included, an increase/decrease of information amount of the answers of the writer is estimated from a writer's past answers and a current answer, and a next question is selected based on the estimation result.