Draft Document Generation via Interactive Component Selection
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Solution Overview
Problem
Conventional methods fail to effectively support the selection of important component elements for document creation, leading to inefficiencies in conveying information without excess or deficiency, particularly in drafting documents that require precise representation of features or trends.
Innovation Solution
A document creation support apparatus that uses processors and storage devices to generate draft documents by presenting questions to users about selected component elements, acquiring answers, and generating content based on answer history and statistical analysis to emphasize relevant information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional grammar checking methods are used to support document writing, then basic language errors can be detected, but the ability to determine which component elements are important and should be described is not supported
Solution Approach 1:
The system performs preliminary action by automatically selecting and presenting candidate component elements before the user needs to write the document. The apparatus pre-processes the data to identify potential important elements and presents them in a structured manner, allowing the user to quickly review and confirm selections without having to analyze all possible elements from scratch.
Solution Approach 2:
The system implements feedback by allowing the user to provide input on the presented component elements and using this feedback to refine the selection. The user can indicate which elements are indeed important and which are not, and the system adjusts its recommendations accordingly, creating an iterative process that improves accuracy while reducing manual effort.
2Loss of information
If all component elements are described in detail, then complete information is provided, but the document becomes too long and difficult to read
Solution Approach 1:
The system extracts and separates the most important component elements from the complete set of available data. By using the learned model to identify and extract only the essential elements that convey the core meaning, the system enables selective inclusion in the document, removing unnecessary details while preserving critical information.
Solution Approach 2:
The system applies local quality by differentiating the treatment of different component elements based on their importance. Important elements receive detailed description and emphasis, while less critical elements are summarized or omitted entirely. This selective approach ensures that reading time and attention are allocated proportionally to the significance of each element.
3Measurement precision
If the number of feature items is increased to improve comparison validity, then comparison accuracy improves, but the time and effort to organize characteristics increases
Solution Approach 1:
The system performs preliminary action by pre-organizing and pre-analyzing the characteristics of multiple contractors before the comparison process begins. The apparatus automatically structures the data, identifies relevant comparison dimensions, and prepares organized presentations of contractor features, eliminating the need for manual organization during the comparison phase.
Solution Approach 2:
The system implements self-service by enabling the comparison process to organize and structure the characteristics automatically without requiring significant human intervention. The learned model autonomously analyzes the data, identifies patterns, and presents organized comparisons, allowing the system to serve itself in the data organization task while reducing human time investment.
Data Source
AI summary
The draft document generating method repeats question processing for component elements selected from the case database to the user. The question processing presents, by an output device, one or more of the component elements selected from the case database to the user, presents a question as to whether the component elements are applicable to contents of a draft document to the user, acquires, via an input device, an answer of the user to the question; and adds the answer in an answer history. The selecting of the one or more component elements for which the question processing is to be executed next from unprocessed component elements in the plurality of cases is based on statistics of part of the component elements in the plurality of cases. The draft document is generated based on a component element indicating that the answer history is applicable to contents of the draft document.