Online Meeting Analysis Using Emotion Data and Meeting Attributes
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Solution Overview
Problem
Existing technologies for evaluating online meetings focus on speech content and emotion, but fail to account for varying attributes of different types of meetings, leading to inconsistent evaluation criteria.
Innovation Solution
An analysis apparatus and method that acquires emotion data from face images, generates analysis data based on meeting attributes, and selects messages for effective meeting management, incorporating emotion data, meeting data, and attribute-specific analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If evaluation is based on speech content and emotion only, then meeting analysis can be performed, but the evaluation criteria become inconsistent across different meeting types
Solution Approach 1:
The patent implements dynamic evaluation criteria that automatically adjust based on meeting attributes. The system determines meeting type (e.g., brainstorming, decision-making, presentation) and selects appropriate evaluation standards for each type, making the evaluation system adaptable while maintaining consistency within each meeting category.
Solution Approach 2:
The patent changes evaluation parameters according to meeting attributes. Different weightings and criteria are applied based on the determined meeting type, allowing the evaluation system to adapt to different contexts while maintaining precise measurement within each context through standardized parameter sets.
2Ease of operation
If generic meeting evaluation is used, then simplicity is maintained, but effectiveness for specific meeting types is reduced
Solution Approach 1:
The patent segments the evaluation process into distinct phases: meeting attribute determination, type classification, and targeted evaluation. This segmentation allows the system to maintain operational simplicity through automated flow control while achieving reliability through specialized evaluation criteria for each meeting type.
Solution Approach 2:
The system performs self-service by automatically determining meeting attributes and selecting appropriate evaluation criteria without manual intervention. This maintains ease of operation while ensuring reliability through consistent application of type-specific evaluation standards.
Data Source
AI summary
An analysis apparatus comprises at least one memory storing instructions, and at least one processor configured to execute the instructions to comprises at least one memory storing instructions, and at least one processor configured to execute the instructions to acquire emotion data from an emotion data generation apparatus that generates emotion data from face image data of a meeting participant in an online meeting, generate analysis data for the meeting on the basis of the emotion data, acquire meeting data including attribute data of the meeting, store message data in which a pattern of a message to be presented to a user is associated with the meeting data, select the message on the basis of the analysis data and the message data, and store an analysis result including the selected message in a storage unit in an outputtable manner.