View source for 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.)

=MACHINE LEARNING METHOD FOR ASSESSING A CONFIDENCE LEVEL OF VERBAL COMMUNICATIONS OF A PERSON USING VIDEO AND AUDIO ANALYTICS=


==Organization Name==

[[:Category:Insight Direct USA, Inc.|Insight Direct USA, Inc.]]


[[Category:Insight Direct USA, Inc.]]

==Inventor(s)==

[[:Category:Michael Griffin of Wayland MA (US)|Michael Griffin of Wayland MA (US)]][[Category:Michael Griffin of Wayland MA (US)]]

[[:Category:Hailey Kotvis of Wauwatosa WI (US)|Hailey Kotvis of Wauwatosa WI (US)]][[Category:Hailey Kotvis of Wauwatosa WI (US)]]

[[:Category:Josephine Miner of Hope RI (US)|Josephine Miner of Hope RI (US)]][[Category:Josephine Miner of Hope RI (US)]]

[[:Category:Porter Moody of Wayland MA (US)|Porter Moody of Wayland MA (US)]][[Category:Porter Moody of Wayland MA (US)]]

[[:Category:Kayla Poulsen of Natick MA (US)|Kayla Poulsen of Natick MA (US)]][[Category:Kayla Poulsen of Natick MA (US)]]

[[:Category:Austin Malmin of Gilbert AZ (US)|Austin Malmin of Gilbert AZ (US)]][[Category:Austin Malmin of Gilbert AZ (US)]]

[[:Category:Sarah Onstad-hawes of Seattle WA (US)|Sarah Onstad-hawes of Seattle WA (US)]][[Category:Sarah Onstad-hawes of Seattle WA (US)]]

[[:Category:Gloria Solovey of Arlington MA (US)|Gloria Solovey of Arlington MA (US)]][[Category:Gloria Solovey of Arlington MA (US)]]

[[:Category:Austin Streitmatter of Palm Harbor FL (US)|Austin Streitmatter of Palm Harbor FL (US)]][[Category: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.



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