Image Analysis for Automatic Audio Endpoint Switching
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Solution Overview
Problem
Selecting an audio endpoint from multiple available options on computing devices is inefficient, cumbersome, and often unclear, as existing methods require specialized hardware and data handling, making it confusing and time-consuming for users.
Innovation Solution
Implementing image analysis using machine learning to detect user gestures and recognize audio endpoints, eliminating the need for dedicated sensors by leveraging integrated cameras and reducing specialized data handling, allowing automatic switching between audio endpoints during videoconferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If specialized sensors and dedicated data handling are used to detect user activity for audio endpoint selection, then the detection capability is improved, but the device complexity and cost increase
Solution Approach 1:
The patent applies multi-functionality by using the integrated camera for dual purposes: its primary function for capturing images/videos and a secondary function for detecting user activity and gestures to determine audio endpoint selection. This eliminates the need for dedicated sensors while maintaining detection capability through creative reuse of existing hardware.
Solution Approach 2:
The system performs self-service by utilizing the camera's existing processing capabilities and the operating system's image processing frameworks to extract user activity information without requiring separate specialized sensors or dedicated data handling infrastructure. The same hardware that captures images also analyzes user behavior for audio selection.
2Adaptability or versatility
If multiple different ways to select audio endpoint are provided by operating system and applications, then the user has more options, but the precedence among methods becomes unclear and selection becomes confusing
Solution Approach 1:
The patent implements feedback by continuously monitoring user activity through camera analysis and automatically responding by switching the audio endpoint. This creates a closed-loop system where user actions are detected, processed, and immediately reflected in audio output selection, eliminating confusion about which method is active and providing intuitive, automatic control.
Solution Approach 2:
The system performs self-service by automatically determining and executing audio endpoint selection based on detected user activity, eliminating the need for users to manually navigate through multiple selection methods. The system autonomously interprets user gestures and actions to automatically switch audio endpoints, simplifying the interaction.
3Ease of operation
If manual selection of audio endpoint is required, then the user has control over the choice, but the process is time-consuming and inefficient
Solution Approach 1:
The patent applies preliminary action by continuously analyzing user activity and camera input in advance to predict and prepare for audio endpoint switching. The system detects user gestures and actions before the user explicitly selects an audio endpoint, automatically preparing the switch in advance to execute when needed, thereby reducing response time while maintaining user intent control.
Solution Approach 2:
The system performs self-service by automatically executing audio endpoint selection based on detected user activity patterns, eliminating the need for manual selection while preserving user control through intelligent automation. The system autonomously interprets user actions and executes appropriate audio endpoint switches without requiring explicit user input.
4Measurement precision
If dedicated sensor and specialized data handling are implemented, then the audio endpoint selection accuracy is improved, but the manufacturing cost and device complexity increase
Solution Approach 1:
The patent applies multi-functionality by making the integrated camera serve dual purposes: its primary imaging function and secondary audio endpoint detection function. This approach maintains selection accuracy through sophisticated image processing while eliminating the need for additional dedicated sensors, thereby simplifying manufacturing and reducing costs.
Solution Approach 2:
The system uses a computational copy or virtual representation of user activity extracted from camera images to control audio endpoint selection, rather than requiring physical dedicated sensors. This virtual modeling approach achieves equivalent or superior detection accuracy while avoiding the manufacturing complexity of specialized hardware.
Data Source
AI summary
An example non-transitory machine-readable medium includes instructions that, when executed by a processor, cause the processor to analyze a video captured by a computing device to detect a sequence of motion in the video, and select an audio endpoint of the computing device based on the sequence of motion.