Video Capture Parameter Adjustment via Machine Learning Analysis
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Solution Overview
Problem
Conventional client devices require manual user input to configure and adjust video capture settings during video messaging, limiting users' ability to perform other tasks and failing to adapt to changing conditions without continuous user intervention.
Innovation Solution
Incorporating a controller that applies machine learning models to video data to modify capture parameters, such as focal point and magnification, based on previously captured data, while enforcing privacy settings to prioritize user identification and data management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual user input is required to configure video capture settings, then user control over video data is improved, but user convenience and ability to multitask deteriorate
Solution Approach 1:
The system automatically analyzes video data to identify users and determines optimal capture parameters without requiring manual user input. The controller continuously monitors video content and self-adjusts focal point, magnification, and other parameters based on detected user positions and identified content, allowing users to multitask while maintaining control through automated decision-making.
Solution Approach 2:
The system uses feedback from continuous analysis of video data to dynamically adjust capture settings. By monitoring previously captured video data and real-time video content, the controller receives feedback about user positions and important elements, then automatically modifies capture parameters to optimize the video feed, eliminating the need for manual reconfiguration.
2Manufacturing precision
If manual repositioning of image capture device is required, then precise framing is improved, but user productivity and multitasking capability deteriorate
Solution Approach 1:
The patent replaces manual mechanical repositioning of the image capture device with an automated computer vision system. The controller applies machine learning models to analyze video data, automatically determines optimal framing and focal points, and adjusts capture parameters accordingly, substituting mechanical user intervention with automated digital processing and control.
Solution Approach 2:
The system dynamically adjusts capture parameters based on real-time analysis of video content. Instead of static manual positioning, the controller continuously monitors video data, identifies users and important elements, and dynamically repositions the focal point and adjusts magnification to maintain optimal framing as conditions change during the video session.
3Ease of operation
If automatic adjustment of video capture is implemented, then user convenience and productivity are improved, but system complexity and privacy management requirements increase
Solution Approach 1:
The controller serves as an intermediary between the image capture device and the user, automatically managing the complexity of video analysis and parameter adjustment. The controller applies machine learning models to analyze video data and determines optimal capture settings, shielding users from system complexity while maintaining privacy control through automated enforcement of privacy settings and user consent management.
4Adaptability or versatility
If machine learning models are applied to video data for automatic adjustment, then adaptability to changing conditions is improved, but processing requirements and energy consumption increase
Solution Approach 1:
The system applies machine learning models selectively to analyze video data and identify users and important elements. Rather than processing all video data at full complexity continuously, the controller uses partial action by applying models only when needed to detect changes in conditions, identify new users, or adjust framing, reducing overall processing energy while maintaining adaptability to changing conditions.
Data Source
AI summary
Various client devices include displays and one or more image capture devices configured to capture video data. Different users of an online system may authorize client devices to exchange information captured by their respective image capture devices. Additionally, a client device modifies captured video data based on users identified in the video data. For example, the client device changes parameters of the image capture device to more prominently display a user identified in the video data and may further change parameters of the image capture device based on gestures or movement of the user identified in the video data. The client device may apply multiple models to captured video data to modify the captured video data or subsequent capturing of video data by the image capture device.