US 12,246,241 B2
Method and system of capturing and coordinating physical activities of multiple users
Rajiv Trehan, Bangkok (TH)
Assigned to Rajiv Trehan, Bangkik (TH)
Filed by Rajiv Trehan, Bangkok (TH)
Filed on Apr. 15, 2022, as Appl. No. 17/721,395.
Application 17/721,395 is a continuation in part of application No. 17/467,374, filed on Sep. 6, 2021, granted, now 12,131,731.
Application 17/721,395 is a continuation in part of application No. 17/467,381, filed on Sep. 6, 2021.
Application 17/721,395 is a continuation in part of application No. 17/467,386, filed on Sep. 6, 2021, granted, now 11,996,090.
Prior Publication US 2023/0071274 A1, Mar. 9, 2023
Int. Cl. A63B 71/06 (2006.01); A63B 24/00 (2006.01); G06F 3/16 (2006.01); G06T 13/40 (2011.01); G06T 13/80 (2011.01)
CPC A63B 71/0622 (2013.01) [A63B 24/0062 (2013.01); A63B 71/0616 (2013.01); G06F 3/16 (2013.01); G06T 13/40 (2013.01); G06T 13/80 (2013.01); A63B 2024/0068 (2013.01); A63B 2071/0625 (2013.01); A63B 2071/0655 (2013.01); A63B 2071/0694 (2013.01); G06T 2200/24 (2013.01)] 18 Claims
OG exemplary drawing
 
1. A method for capturing and coordinating physical activities of multiple users, the method comprising:
capturing, via at least one multimedia input device, at least one activity performed by a plurality of users, wherein a first set of users from the plurality of users perform the at least one activity in one or more distributed locations and a second set of users from the plurality of users perform the at least one activity in said one or more distributed locations;
processing in real-time, by at least one Artificial Intelligence (AI) model from a plurality of AI models, the captured at least one activity for each of the plurality of users to determine, for each of the plurality of users:
a set of user performance parameters based on current activity performance, wherein the at least one AI model is configured based on target activity performance of an activity expert, a plurality of correct and incorrect movements, and tolerance metrics associated with the current activity, and wherein each of the plurality of AI models are trained and configured for a given activity;
comparing for each of the plurality of users, by the at least one AI model, the set of user performance parameters with a set of target activity performance parameters, wherein the set of target activity performance parameters is determined based on inputs provided by a set of activity experts;
assigning, by the at least one AI model, at least one conditional priority to each of a human instructor and an AI instructor associated with the AI model, wherein the at least one conditional priority is assigned based on a plurality of priority rules;
scheduling, by the at least one AI model, rendering of human feedback provided by the human instructor and AI feedback provided by the AI instructor to each of the plurality of users, based on the assigned at least one conditional priority and context determined for a current activity being performed each of the plurality of users;
generating for each of the plurality of users, by the at least one AI model, feedback based on comparison of the set of user performance parameters with the set of target activity performance parameters, and wherein the feedback comprises at least one of visual feedback, aural feedback, or haptic feedback; and
sharing with each of the plurality of users, by the at least one AI model, the feedback, wherein sharing the feedback comprises:
rendering the feedback on a multimedia output device associated with the corresponding user; and
sending the feedback to external portals via corresponding Application Programming Interfaces (APIs).