ML-Based Presentation Transition Automation
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Solution Overview
Problem
Existing presentation programs require manual user inputs for transitions, which can be cumbersome, distracting, and burdensome, especially for presenters with impaired mobility, and often necessitate assistance from another person, disrupting the presentation flow.
Innovation Solution
A machine learning model is trained during rehearsals to associate transitional triggers (such as spoken phrases, gestures, and bodily movements) with specified transitions, enabling automatic enactment of transitions during performances.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual user inputs are used for transitions, then the presenter can control the presentation, but the operation becomes cumbersome and distracting
Solution Approach 1:
The system automatically detects transitions and enacts them without requiring manual user input. The presenter simply speaks or gestures naturally, and the system self-services by interpreting these cues and performing the appropriate transitions, eliminating the need for cumbersome manual controls.
Solution Approach 2:
The patent replaces manual mechanical input devices (clickers, keyboard shortcuts) with voice recognition and gesture detection systems. This substitution allows presenters to control transitions through natural speech or body movements rather than physical devices, reducing distraction and improving ease of operation.
2Productivity
If manual control is required for transitions, then the presenter maintains control, but the presentation flow is disrupted and assistance is needed
Solution Approach 1:
The system performs preliminary analysis during the rehearsal phase, learning the presenter's speech patterns, gestures, and intended transitions. This preliminary action enables the system to automatically execute transitions during the actual presentation without requiring real-time manual input or assistance, improving productivity and eliminating time loss.
Solution Approach 2:
The system continuously monitors the presenter's speech and gestures, providing real-time feedback by detecting transition cues and automatically enacting them. This feedback loop eliminates the need for manual control and external assistance, allowing seamless presentation delivery and improving overall efficiency.
3Extent of automation
If automatic transition detection is implemented, then manual control is freed, but the system complexity increases
Solution Approach 1:
The system uses multi-functional technology that can detect both speech patterns and gestures using the same hardware infrastructure (microphones, cameras, and processing algorithms). This universal approach achieves high automation while minimizing additional system complexity by consolidating detection capabilities into a unified framework.
Data Source
AI summary
Examples are disclosed that relate to providing transition-related assistance during a presentation. One example provides a method comprising, during a rehearsal of a presentation, receiving content of the presentation. Based on the content received, a transition within the presentation is determined via a machine learning model. During a performance of the presentation, the transition is automatically enacted.