View source for 17944022. NEUROERGONOMIC API SERVICE FOR SOFTWARE APPLICATIONS simplified abstract (MICROSOFT TECHNOLOGY LICENSING, LLC)

=NEUROERGONOMIC API SERVICE FOR SOFTWARE APPLICATIONS=


==Organization Name==

[[:Category:MICROSOFT TECHNOLOGY LICENSING, LLC|MICROSOFT TECHNOLOGY LICENSING, LLC]]


[[Category:MICROSOFT TECHNOLOGY LICENSING, LLC]]

==Inventor(s)==

[[:Category:Aashish Patel of San Diego CA (US)|Aashish Patel of San Diego CA (US)]][[Category:Aashish Patel of San Diego CA (US)]]

[[:Category:Weiwei Yang of Seattle WA (US)|Weiwei Yang of Seattle WA (US)]][[Category:Weiwei Yang of Seattle WA (US)]]

[[:Category:Hayden Helm of San Francisco CA (US)|Hayden Helm of San Francisco CA (US)]][[Category:Hayden Helm of San Francisco CA (US)]]

[[:Category:Daniel J. Mcduff of Seattle WA (US)|Daniel J. Mcduff of Seattle WA (US)]][[Category:Daniel J. Mcduff of Seattle WA (US)]]

[[:Category:Siddharth Siddharth of Redmond WA (US)|Siddharth Siddharth of Redmond WA (US)]][[Category:Siddharth Siddharth of Redmond WA (US)]]

[[:Category:Jen-Tse Dong of Bellevue WA (US)|Jen-Tse Dong of Bellevue WA (US)]][[Category:Jen-Tse Dong of Bellevue WA (US)]]

==NEUROERGONOMIC API SERVICE FOR SOFTWARE APPLICATIONS - A simplified explanation of the abstract==


This abstract first appeared for US patent application 17944022 titled 'NEUROERGONOMIC API SERVICE FOR SOFTWARE APPLICATIONS

==Simplified Explanation==

The present concepts include a neuroergonomic service that processes multimodal physiological, digital, and/or environmental inputs from a user and predicts cognitive states of the user. Thus, the neuroergonomic service provides personalized feedback to the user about her current mental and physiological wellbeing to enable modulation of mood, stress, attention, and other cognitive measures for improved productivity and satisfaction. The neuroergonomic service utilizes machine learning models that are trained offline using sensor inputs taken from participants in a controlled environment that purposefully induce an array of cognitive states upon the participants.

* Neuroergonomic service processes inputs from user to predict cognitive states
* Provides personalized feedback on mental and physiological wellbeing
* Modulates mood, stress, attention, and other cognitive measures
* Uses machine learning models trained offline with sensor inputs from participants
* Controlled environment induces cognitive states for training

== Potential Applications ==
The technology can be applied in various fields such as healthcare, education, and workplace environments to enhance productivity and overall wellbeing.

== Problems Solved ==
This technology helps individuals better understand and manage their cognitive states, leading to improved mental health and performance.

== Benefits ==
Users can receive personalized feedback and guidance to optimize their cognitive functions and overall wellbeing.

== Potential Commercial Applications ==
This technology can be utilized in mental health clinics, educational institutions, and corporate settings to improve employee performance and satisfaction.

== Possible Prior Art ==
There may be existing technologies that track physiological data for health and wellness purposes, but the specific combination of multimodal inputs and cognitive state prediction may be novel.

=== Unanswered Questions ===

=== How does the neuroergonomic service ensure data privacy and security for users? ===
The article does not address the specific measures taken to protect user data and ensure privacy in the neuroergonomic service.

=== What are the potential limitations or drawbacks of relying on machine learning models for cognitive state prediction? ===
The article does not discuss any potential challenges or limitations associated with using machine learning models in this context.


==Original Abstract Submitted==

The present concepts include a neuroergonomic service that processes multimodal physiological, digital, and/or environmental inputs from a user and predicts cognitive states of the user. Thus, the neuroergonomic service provides personalized feedback to the user about her current mental and physiological wellbeing to enable modulation of mood, stress, attention, and other cognitive measures for improved productivity and satisfaction. The neuroergonomic service utilizes machine learning models that are trained offline using sensor inputs taken from participants in a controlled environment that purposefully induce an array of cognitive states upon the participants.



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