Video Event Overlays Using Participant Context and Speech Input
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing digital assistants struggle to efficiently and intelligently augment video event displays with relevant graphical overlays in response to natural language inputs, requiring additional user interaction and consuming excessive power.
Innovation Solution
A digital assistant system that identifies participant locations in a video event based on context information and augments the display with graphical overlays without additional user input, utilizing natural language processing, computer vision, and machine learning to generate and update overlays in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If digital assistants use natural language processing and computer vision to identify participants and generate graphical overlays in real-time, then user interaction efficiency is improved and relevant information is provided automatically, but device power consumption increases and battery life is reduced
Solution Approach 1:
The system performs preliminary actions by continuously tracking participants and pre-processing video frames before user queries occur. Context information about participants is prepared in advance, allowing the digital assistant to respond quickly when users ask questions without requiring real-time processing during the actual query moment.
Solution Approach 2:
Instead of continuous processing, the system uses periodic action by analyzing video frames at specific intervals and updating participant information only when necessary. The digital assistant processes video content periodically rather than continuously, reducing power consumption while maintaining the ability to provide timely responses to user queries.
2Reliability
If the digital assistant continuously processes video content to identify participants and update graphical overlays, then the system provides timely and relevant responses, but device complexity and computational requirements increase
Solution Approach 1:
The system applies partial action by processing only the necessary portions of video content rather than analyzing every frame completely. The digital assistant focuses computational resources on identifying participants and extracting relevant context information only when needed, rather than continuously processing the entire video stream at full computational capacity.
Solution Approach 2:
The system extracts only the essential information needed for participant identification and graphical overlay generation from the video content. By taking out and processing only the critical elements (participant locations, identities, and relevant context) rather than the entire video stream, the system reduces computational requirements while maintaining timely and accurate responses.
Data Source
AI summary
An example process includes while displaying, on a display, a video event: receiving, by a digital assistant, a natural language speech input corresponding to a participant of the video event; in accordance with receiving the natural language speech input, identifying, by the digital assistant, based on context information associated with the video event, a first location of the participant; and in accordance with identifying the first location of the participant, augmenting, by the digital assistant, the display of the video event with a graphical overlay displayed at a first display location corresponding to the first location of the participant.