18463645. MACHINE LEARNING METHOD FOR ASSESSING A CONFIDENCE LEVEL OF VERBAL COMMUNICATIONS OF A PERSON USING VIDEO AND AUDIO ANALYTICS simplified abstract (Insight Direct USA, Inc.)
Contents
- 1 MACHINE LEARNING METHOD FOR ASSESSING A CONFIDENCE LEVEL OF VERBAL COMMUNICATIONS OF A PERSON USING VIDEO AND AUDIO ANALYTICS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 MACHINE LEARNING METHOD FOR ASSESSING A CONFIDENCE LEVEL OF VERBAL COMMUNICATIONS OF A PERSON USING VIDEO AND AUDIO ANALYTICS - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
MACHINE LEARNING METHOD FOR ASSESSING A CONFIDENCE LEVEL OF VERBAL COMMUNICATIONS OF A PERSON USING VIDEO AND AUDIO ANALYTICS
Organization Name
Inventor(s)
Michael Griffin of Wayland MA (US)
Hailey Kotvis of Wauwatosa WI (US)
Josephine Miner of Hope RI (US)
Porter Moody of Wayland MA (US)
Kayla Poulsen of Natick MA (US)
Austin Malmin of Gilbert AZ (US)
Sarah Onstad-hawes of Seattle WA (US)
Gloria Solovey of Arlington MA (US)
Austin Streitmatter of Palm Harbor FL (US)
MACHINE LEARNING METHOD FOR ASSESSING A CONFIDENCE LEVEL OF VERBAL COMMUNICATIONS OF A PERSON USING VIDEO AND AUDIO ANALYTICS - A simplified explanation of the abstract
This abstract first appeared for US patent application 18463645 titled 'MACHINE LEARNING METHOD FOR ASSESSING A CONFIDENCE LEVEL OF VERBAL COMMUNICATIONS OF A PERSON USING VIDEO AND AUDIO ANALYTICS
Simplified Explanation
The patent application describes an apparatus and methods for assessing the confidence level of verbal communication of a person using a machine learning model operating on a video stream of the person.
- Video data, audio data, and semantic text data are extracted from the video stream.
- The video data are analyzed to identify a first feature set.
- The audio data are analyzed to identify a second feature set.
- The semantic text data are analyzed to identify a third feature set.
- A machine-learning model is used to assess the confidence level of the verbal communication.
- The confidence level is associated with a time of the video stream and reported.
Potential Applications
This technology could be applied in various fields such as:
- Education
- Customer service
- Speech therapy
Problems Solved
This technology helps in:
- Assessing the confidence level of verbal communication
- Providing real-time feedback
- Improving communication skills
Benefits
The benefits of this technology include:
- Enhanced communication analysis
- Personalized feedback
- Improved understanding of verbal communication patterns
Potential Commercial Applications
"Assessing Confidence Levels in Verbal Communication Using Machine Learning" could be used in industries such as:
- Market research
- Human resources
- Communication training programs
Possible Prior Art
One possible prior art could be the use of machine learning models in analyzing verbal communication patterns, but the specific application of assessing confidence levels in real-time video streams may be novel.
Unanswered Questions
How does this technology handle different languages and accents?
The patent application does not provide information on how the machine learning model adapts to different languages and accents in assessing confidence levels of verbal communication.
What is the accuracy rate of the confidence level assessment?
The patent application does not mention the accuracy rate of the machine learning model in assessing the confidence level of verbal communication.
Original Abstract Submitted
Apparatus and associated methods relate to assessing a confidence level of a verbal communication of a person as determined by a machine learning model operating on a video stream of the person. Video data, audio data, and semantic text data are extracted from a video stream of the person. The video data are analyzed to identify a first feature set. The audio data are analyzed to identify a second feature set. The semantic text data are analyzed to identify a third feature set. Using a computer-implemented machine-learning model, a confidence level of the verbal communication of the person is assessed. The confidence level is then associated with a time of the video stream to which the confidence level pertains. The confidence level and the associated time of the video stream are then reported.
- Insight Direct USA, Inc.
- Michael Griffin of Wayland MA (US)
- Hailey Kotvis of Wauwatosa WI (US)
- Josephine Miner of Hope RI (US)
- Porter Moody of Wayland MA (US)
- Kayla Poulsen of Natick MA (US)
- Austin Malmin of Gilbert AZ (US)
- Sarah Onstad-hawes of Seattle WA (US)
- Gloria Solovey of Arlington MA (US)
- Austin Streitmatter of Palm Harbor FL (US)
- G16H15/00
- G06V10/774
- G06V20/40
- G06V40/10
- G10L15/02
- G10L15/06
- G10L15/18
- G10L25/57
- G16H10/60