Meeting Summary Generator Using Featural Script Analysis
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Solution Overview
Problem
Conventional systems for generating meeting minutes are cumbersome due to the need for advance preparation using pre-defined minutes blueprints, which requires significant time and effort.
Innovation Solution
A summary generating device that includes a featural script extracting unit, a segment candidate generating unit, and a structuring estimating unit, which extracts featural script information from voice data, generates segment candidates, and estimates structure information to automatically create a summary document in a format-structured text format based on voice recognition and morphological analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If a minutes blueprint is prepared in advance to create meeting minutes, then the structure and organization of minutes can be ensured, but the advance preparation becomes a cumbersome task requiring significant time and effort
Solution Approach 1:
The system performs preliminary actions by automatically generating a minutes blueprint before the meeting based on the meeting topic and participants. This pre-generated blueprint includes anticipated discussion points and structure, eliminating the need for manual advance preparation while ensuring proper organization of minutes
Solution Approach 2:
The system enables self-service by automatically creating and updating the minutes blueprint during the meeting based on real-time speech recognition and analysis. The blueprint adapts itself to the actual discussion flow without requiring manual intervention, thus reducing preparation time while maintaining structural organization
2Manufacturing precision
If manual creation of meeting minutes is performed to ensure accuracy and completeness, then the quality of minutes can be maintained, but the process requires significant time and effort
Solution Approach 1:
The system replaces the mechanical manual process of creating minutes with an automated speech recognition and natural language processing system. The featural script extracting unit, segment candidate generating unit, and structuring estimating unit work together to automatically generate accurate and complete minutes, maintaining quality while dramatically improving productivity
Solution Approach 2:
The system introduces an intermediary processing layer between the meeting speech and the final minutes. This intermediary layer includes multiple processing units that analyze speech patterns, extract key information, and generate structured minutes, ensuring both accuracy and efficiency in the minutes creation process
3Extent of automation
If conventional speech recognition technology is used to document meeting remarks, then voice-to-text conversion can be achieved, but the output lacks proper structure and organization
Solution Approach 1:
The system segments the continuous speech output into structured components using the featural script extracting unit to identify key information elements, the segment candidate generating unit to create potential structure units, and the structuring estimating unit to organize these segments into a coherent hierarchical structure with proper headings, subheadings, and content organization
Solution Approach 2:
The system dynamically adjusts the structure of the minutes based on the actual content and flow of the meeting discussion. The structuring estimating unit adapts the blueprint structure in real-time to match the discussed topics, ensuring the output maintains proper organization while reflecting the dynamic nature of the meeting content
Data Source
AI summary
A summary generating device includes a featural script extracting unit, a segment candidate generating unit, and a structuring estimating unit. The featural script extracting unit extracts featural script information of the words included in text information. Based on the extracted feature script information, the segment candidate generating unit generates candidates of segments that represent the constitutional units for the display purpose. Based on the generated candidates of segments and based on an estimation model for structuring, the structuring estimating unit estimates structure information containing information ranging from information of a comprehensive structure level to information of a local structure level.


