18532396. MACHINE LEARNING DRIVEN TELEPROMPTER simplified abstract (Microsoft Technology Licensing, LLC)

MACHINE LEARNING DRIVEN TELEPROMPTER

Organization Name

Microsoft Technology Licensing, LLC

Inventor(s)

Chakkaradeep Chinnakonda Chandran of Woodinville WA (US)

Stephanie Lorraine Horn of Bellevue WA (US)

Michael Jay Gilmore of Bothell WA (US)

Tarun Malik of Gurgaon (IN)

Sarah Zaki of New Delhi (IN)

Tiffany Michelle Smith of Seattle WA (US)

Shivani Gupta of Greater Noida (IN)

Pranjal Saxena of Hyderabad (IN)

Ridhima Gupta of Gurgaon (IN)

MACHINE LEARNING DRIVEN TELEPROMPTER - A simplified explanation of the abstract

This abstract first appeared for US patent application 18532396 titled 'MACHINE LEARNING DRIVEN TELEPROMPTER

Simplified Explanation

The patent application describes a data processing system for a machine learning driven teleprompter that assists presenters during presentations by displaying a teleprompter transcript, analyzing audio content of the presentation, and automatically scrolling the teleprompter transcript based on the presenter's speech.

• Displaying teleprompter transcript on a computing device display
• Receiving audio content of the presentation
• Analyzing audio content using a natural language processing model to obtain real-time textual translation
• Analyzing real-time textual representation and teleprompter transcript with a second machine learning model to obtain transcript position information
• Automatically scrolling the teleprompter transcript on the display based on the transcript position information

Potential Applications

This technology can be applied in various fields such as public speaking, broadcasting, video production, and online content creation.

Problems Solved

- Helps presenters stay on track with their presentations - Reduces the need for manual teleprompter operation - Improves the overall presentation delivery and accuracy

Benefits

- Enhances presenter performance and confidence - Increases efficiency in delivering presentations - Provides real-time support for presenters

Potential Commercial Applications

Optimizing Presentations: Revolutionizing the way presentations are delivered with AI-driven teleprompter technology

Possible Prior Art

One possible prior art could be traditional Fame by usinging thising thising thising thising thising thising thising thising thising thising thisiginging thisesing thisesing thising thising thising thising thising thisinginginging thising thising thising thising thising thising thising thising thisinging thisinging thisinginging thisinging thisinging thising thising thising thising this the== applications for a ainginginginginging this theable you the foring thising this theable us abig ares aing thisesting the the theed a. This technology is a novel approach to teleprompter systems, utilizing machine learning models to enhance the presenter's experience and improve presentation delivery.

b. How does this technology handle different accents and speech patterns of presenters? This article does not provide specific details on how the system accommodates various accents and speech patterns. Further information on the system's ability to adapt to different presenter styles would be beneficial for a comprehensive understanding of the technology.

Original Abstract Submitted

Techniques performed by a data processing system for a machine learning driven teleprompter include displaying a teleprompter transcript associated with a presentation on a display of a computing device associated with a presenter; receiving audio content of the presentation including speech of the presenter in which the presenter is reading the teleprompter transcript; analyzing the audio content of the presentation using a first machine learning model to obtain a real-time textual translation of the audio content, the first machine learning model being a natural language processing model trained to receive audio content including speech and to translate the audio content into a textual representation of the speech; analyzing the real-time textual representation and the teleprompter transcript with a second machine learning model to obtain transcript position information; and automatically scrolling the teleprompter transcript on the display of the computing device based on the transcript position information.