Decision Element Extraction from Unstructured Communications
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Solution Overview
Problem
Current computing systems are inefficient in extracting and summarizing decision elements from unstructured communication discussions, making it time-consuming and labor-intensive to gather structured talking points, especially in collaborative environments where decisions need to be made.
Innovation Solution
A method for extracting and summarizing decision elements from communications using a processor, involving automatic transcription, identification of speakers and segments, grouping of decision elements, and enrichment with domain knowledge, displayed via an interactive GUI on IoT devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual extraction and summarization of decision elements from communications is performed, then accuracy and understanding of decision contexts are improved, but time consumption and labor intensity increase significantly
Solution Approach 1:
The system performs automatic transcription and extraction of decision elements from communications without requiring manual human intervention. The processor autonomously identifies speakers, segments communications, extracts decision elements, and generates summaries, making the system self-sufficient in processing unstructured communication data.
Solution Approach 2:
The patent replaces manual mechanical processes (human reading, analyzing, and summarizing communications) with automated computational processes. The processor uses algorithms to transcribe audio/video communications, identify decision elements, and generate structured summaries, substituting human cognitive labor with machine-based information processing.
2Loss of information
If comprehensive decision elements are extracted from all communication segments, then completeness of decision information is improved, but system complexity and processing requirements increase
Solution Approach 1:
The communication is divided into distinct segments with identified speakers and topics. The system segments the communication data by speaker, time, and subject matter, allowing systematic processing of each segment to extract relevant decision elements while maintaining overall information completeness.
Solution Approach 2:
The system organizes extracted decision elements into a multi-dimensional structured format with hierarchies of decisions, alternatives, criteria, and supporting evidence. This dimensional organization transforms unstructured communication data into a structured knowledge representation that is easier to process and query.
3Productivity
If structured summaries of decision elements are generated automatically, then productivity and decision-making efficiency are improved, but reliance on automated processing increases system dependency
Solution Approach 1:
The system provides structured summaries and visualizations of decision elements that can be reviewed and validated by users. The automated processing generates organized information that serves as feedback for decision-makers, allowing them to verify accuracy and make informed adjustments while maintaining efficient automated support.
Data Source
AI summary
Embodiments for extraction and summarization of decision discussions of a communication by a processor. The decision elements may be grouped together according to similar characteristics. The decision elements may be linked, and sentiments of the discussion participants towards each of the decision elements may be analyzed. A summary of the plurality of the decision elements may be provided via an interactive graphical user interface (GUI) on one or more Internet of Things (IoT) devices. The summary of the decision elements may be linked to domain knowledge. The summary may be enhanced using a domain knowledge.