Microsoft technology licensing, llc (20240114106). MACHINE LEARNING DRIVEN TELEPROMPTER simplified abstract

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 20240114106 titled 'MACHINE LEARNING DRIVEN TELEPROMPTER

Simplified Explanation

The patent application describes techniques for a machine learning driven teleprompter system that displays a transcript associated with a presentation, analyzes audio content of the presentation to obtain real-time textual translation, and automatically scrolls the teleprompter transcript based on the analysis.

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

Potential Applications

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

Problems Solved

This technology solves the problem of ensuring presenters stay on track with their script by providing real-time feedback and assistance through the teleprompter system.

Benefits

The benefits of this technology include improved presentation delivery, reduced errors in speech, enhanced audience engagement, and increased efficiency in content creation.

Potential Commercial Applications

Potential commercial applications of this technology include teleprompter software for media production companies, public speaking training tools, and video conferencing platforms.

Possible Prior Art

One possible prior art could be traditional teleprompter systems that display text for presenters to read during speeches or presentations. However, the use of machine learning models for real-time translation and analysis is a novel aspect of this technology.

How does the system handle accents or speech variations in the presenter's speech?

The patent application does not provide specific details on how the system handles accents or speech variations in the presenter's speech. Further information on the robustness of the machine learning models in understanding different speech patterns would be helpful.

What is the accuracy rate of the real-time textual translation provided by the first machine learning model?

The patent application does not mention the accuracy rate of the real-time textual translation obtained by the first machine learning model. Understanding the level of accuracy and any potential limitations of the translation process would be important for assessing the overall effectiveness of the system.

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.