Media Session Perception Adjustment via User Feedback
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Solution Overview
Problem
Users experience difficulties in perceiving audio and video during media conferences due to various factors such as device performance, network connections, and server issues, making it challenging to identify and address the cause of degraded quality.
Innovation Solution
An input device processes user feedback through automatic speech recognition and natural language understanding to collect metadata and content samples, which are then analyzed to determine adjustments for improving content perception, including changing connection networks, devices, or encoding audio as text for better quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If automatic speech recognition and natural language understanding are used to process user feedback, then content perception quality can be determined and improved, but device complexity increases
Solution Approach 1:
The system processes user feedback through automatic speech recognition and natural language understanding to determine content perception quality. This feedback loop enables the system to continuously monitor and adjust media session parameters based on actual user experience, resolving the contradiction by implementing intelligent feedback processing that improves measurement precision while managing device complexity through automated analysis.
Solution Approach 2:
The input device automatically collects metadata, processes feedback, and determines adjustments without requiring manual user intervention. The system self-diagnoses perception quality issues and implements corrections autonomously, reducing the perceived complexity for users while maintaining high measurement precision through automated speech and language processing.
2Reliability
If metadata and content samples are collected and analyzed in real-time, then media session adjustments can be made promptly, but loss of time occurs due to data processing requirements
Solution Approach 1:
The system collects metadata and content samples continuously during the media session, preparing data for analysis before quality issues manifest. This preliminary data collection enables faster processing and quicker implementation of adjustments when perception quality deteriorates, reducing the time loss associated with reactive problem-solving.
Solution Approach 2:
The system prioritizes and expedites the processing of critical metadata and feedback data when content perception quality degrades. By focusing computational resources on the most relevant data during quality events, the system minimizes processing time while maintaining reliable quality determination and rapid adjustment implementation.
3Ease of operation
If multiple adjustments are implemented to improve content perception, then user experience is enhanced, but device complexity increases
Solution Approach 1:
The system implements adjustments by changing media session parameters such as encoding settings, bitrate, resolution, or format based on analyzed feedback. These parameter changes improve content perception quality and user experience while maintaining manageable device complexity by focusing on software-level adjustments rather than hardware modifications.
Solution Approach 2:
The system dynamically selects and implements adjustments based on real-time feedback analysis and current media session conditions. This dynamic approach optimizes user experience by applying the most appropriate adjustments for each situation while managing device complexity through adaptive decision-making algorithms that prioritize effective interventions.
Data Source
AI summary
Implementations for determining content perception by a participant in a media session by analyzing content are described. A content signal, such as an audio signal or a video signal, is received during a media session. A transcript of the content can be determined. Feedback, such as a pre-determined phrase, related to content perception by a participant in the media session is determined using a learning algorithm at the device. Metadata regarding the media session is collected in response to the feedback related to the content perception. The indication of the feedback related to the content perception and the metadata regarding the media session is then sent. In response, one or more adjustments to the media session are received. The one or more adjustments to the media session can be determined based on the indication of the feedback related to the content perception, the metadata regarding the media session, and network performance information.