US 12,333,465 B2
Detection, aggregation, and distribution of user and/or team effectiveness utilizing fatigue detection from online collaboration
Daniel L. Hamlin, Round Rock, TX (US); and Juan C. Agudelo, Round Rock, TX (US)
Assigned to Dell Products, LLP, Round Rock, TX (US)
Filed by Dell Products, L.P., Round Rock, TX (US)
Filed on Apr. 20, 2022, as Appl. No. 17/659,906.
Prior Publication US 2023/0342682 A1, Oct. 26, 2023
Int. Cl. G06Q 10/0631 (2023.01); G06Q 10/0639 (2023.01)
CPC G06Q 10/063114 (2013.01) [G06Q 10/063118 (2013.01); G06Q 10/06398 (2013.01)] 17 Claims
OG exemplary drawing
 
1. A method to detect, aggregate, and distribute user and team effectiveness, comprising:
detecting, using one or more machine learning algorithms, fatigue levels of a user that is a member of a team, based, at least in part, on detected attributes comprising one or more of information handling system use telemetry, real time information handling system use, data from a calendaring application program and collaborative meeting services reporting data collected by a collaborative meeting services vendor;
evaluating and/or classifying, using one or more second machine learning algorithms, the fatigue levels of the user, based, at least in part, on the detected attributes;
implementing, using a plurality of artificial intelligence algorithms, a facial recognition inference model, based at least in part on resultingly detected and evaluated and/or classified visual fatigue levels of the user;
generating a plurality of effectiveness weights for performance of the user at different intervals and for performance of each member of the team at the different intervals, wherein the plurality of effectiveness weights are generated based on a combination of the facial recognition inference model, historical data for the user and for each member of the team, and user-specific collaboration factors; and
providing, by a machine learning predictive services algorithm, effectiveness weights for each of two or more requested collaboration sessions that include the user and other members of the team, wherein the effectiveness weights for each of the two or more requested collaboration sessions are calculated based on a combination of user-specific factors, historical performance data, and real-time collaboration metrics.