Video Conference Chat Filtering Using Topic-Aware Neural Networks
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Solution Overview
Problem
Existing video conference chat systems struggle with off-topic or inappropriate comments, leading to a diminished user experience and missed on-topic comments due to ineffective filtering methods that rely on word filters or user restrictions, failing to consider the context of the discussion.
Innovation Solution
Implementing a neural network-based system that determines the relevance of chat comments by analyzing the context of the discussion, using a neural network trained on specific topics, to filter and prioritize comments based on their relevance to the discussion topic.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If traditional word filters are used to scan and remove comments, then some inappropriate comments can be blocked, but off-topic or harassing comments that are not identified by the word filter still get entered into the chat box
Solution Approach 1:
The patent replaces the mechanical word-filtering system with a neural network-based machine learning system. Instead of relying on predefined keyword lists and simple string matching, the system uses trained neural networks to analyze the semantic meaning and context of comments, enabling more accurate identification of off-topic and inappropriate content while reducing false positives.
Solution Approach 2:
The system changes the filtering parameters from simple keyword matching to complex semantic analysis. By training neural networks on topic-specific data, the system adapts its filtering criteria dynamically based on the current discussion topic, allowing it to distinguish between relevant and irrelevant comments with higher precision.
2Object-affected harmful factors
If hosts disable chat boxes or block all comments to avoid off-topic content, then the chat box becomes clean, but on-topic comments from attendees are also blocked
Solution Approach 1:
The patent segments the comment filtering process into topic-specific neural networks. Each neural network is trained on a particular discussion topic, allowing the system to evaluate comments based on their relevance to the specific topic being discussed. This segmentation enables the system to allow on-topic comments while blocking off-topic ones, rather than applying a blanket filter.
Solution Approach 2:
The system implements feedback mechanisms where the neural network analyzes the current discussion context and adjusts its filtering decisions accordingly. The system continuously learns from the discussion flow and participant interactions, refining its ability to distinguish between valuable on-topic contributions and distracting off-topic comments.
3Ease of operation
If a simple word filter is used to scan comments, then the system remains simple to operate, but it removes or overwrites comments regardless of context
Solution Approach 1:
The patent implements a self-service filtering system where the neural networks automatically analyze and evaluate comments based on their learned understanding of the discussion topic. The system autonomously determines comment relevance without requiring manual configuration or intervention, maintaining ease of operation while achieving sophisticated context-aware filtering.
Data Source
AI summary
In various examples, as a user is speaking or presenting content during an online video conference, the data stream may be processed to generate a textual representation (e.g., transcript) of the audio and/or information relating to the video. The textual representation and/or video related information may then be processed to determine a context or one or more topic(s) of discussion. Based on the determined context/topic(s), a corresponding neural network(s) may be selected. Once a neural network has been selected, comments may be retrieved from a chat feature of the application and applied to the neural network. The neural network may then output data to indicate the relevance of the comments to the determined discussion topic. Based on the relevance of the comment, the comment may be allowed, prioritized, deleted, de-emphasized, or otherwise filtered in the chat feature.