Meeting Importance Scoring via Graph Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Professional workers face productivity losses due to information overload from scattered data sources, and existing methods for evaluating the importance of meetings and attendees rely heavily on manual input and title hierarchies, lacking objectivity.
Innovation Solution
The system employs graph theoretical principles and automated software agents to analyze meeting histories, attendee data, and metadata to derive the importance of meetings and attendees, reducing the need for manual evaluation and providing an objective scoring algorithm.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual evaluation and title hierarchy are used to assess meeting importance, then the assessment process is simple to implement, but the objectivity and accuracy of the assessment deteriorates
Solution Approach 1:
The system enables automatic self-assessment of meeting importance by analyzing meeting data, attendee information, and organizational context without requiring manual human evaluation. The automated scoring algorithm processes multiple data sources to generate objective importance scores, eliminating the need for manual assessment while maintaining simplicity through automation.
Solution Approach 2:
The patent replaces manual mechanical assessment processes with an automated computational system. Instead of human evaluators manually assessing meeting importance based on titles and hierarchies, the system uses software agents and algorithms to automatically analyze meeting data, attendee profiles, and organizational context to generate objective importance scores.
2Measurement precision
If scattered data from multiple sources is collected to improve assessment accuracy, then the assessment quality improves, but information overload and processing complexity increases
Solution Approach 1:
The system extracts only the most relevant features and data elements from scattered sources across the enterprise IT infrastructure. Instead of processing all available data, the system identifies and extracts key attributes such as meeting attendee roles, meeting outcomes, agenda items, and organizational hierarchy information that are most predictive of meeting importance, thereby reducing information overload while maintaining assessment accuracy.
Solution Approach 2:
The patent segments the assessment process into distinct modules that handle different data sources independently. The system divides the complex assessment task into separate components: meeting data collection, attendee profile analysis, organizational context evaluation, and importance scoring. This segmentation allows each module to process specific types of data efficiently without being overwhelmed by the total information volume.
3Productivity
If automated software agents and graph theoretical principles are used, then manual evaluation burden is reduced, but the computational complexity and resource requirements increase
Solution Approach 1:
The automated software agents operate autonomously to assess meeting importance without requiring human intervention. The system self-manages the entire evaluation process including data collection from multiple sources, graph construction representing organizational relationships, algorithm execution, and score generation. This self-service capability dramatically reduces manual evaluation burden while the system handles computational complexity internally.
Solution Approach 2:
The patent transforms the assessment problem by changing parameters from manual evaluation metrics to computable algorithmic parameters. The system converts qualitative assessment criteria into quantifiable parameters that can be processed by graph theoretical algorithms and scoring functions. This parameter transformation enables automated processing while managing computational complexity through mathematical modeling.
Data Source
AI summary
Systems and methods are provided for analyzing a history of meetings, the attendees, date of occurrence, and other content to determine the value of the meetings and the attendees. The importance of people and the meetings they attend can be derived based on patterns of attendees. In one embodiment, the meta-data of meetings and the attendees can be used to determine value without requiring time-consuming manual steps or manual evaluation of people and their titles. A graph of meetings and its attendees can be generated and used by one or more automated software agents to place value to the content of the meeting, its agenda, and other meeting collateral such as meeting briefs/attachments of meetings. Accordingly, embodiments dramatically reduce the need for human examination of meeting history.