Patent
System and method for artificial intelligence (AI) assisted activity training
| العنوان: | System and method for artificial intelligence (AI) assisted activity training |
|---|---|
| Patent Number: | 11996,090 |
| تاريخ النشر: | May 28, 2024 |
| Appl. No: | 17/467386 |
| Application Filed: | September 06, 2021 |
| مستخلص: | The disclosure relates to system and method for Artificial Intelligence (AI) assisted activity training. The method includes presenting a plurality of activity categories to a user and receiving a voice-based input from the user. The method uses a Natural Language Processing (NLP) model to process the received voice-based input to extract the selection of at least one activity and at least one activity attribute. Contemporaneous to receiving voice-based input, the method presents a multimedia content in conformance with one activity and one activity attribute. In response to initiation of the multimedia content, the method further detects initiation of a user activity performance. The method captures a video of the user activity and process the video using an AI model to extract a set of user performance parameters. Further, a feedback may be generated based on differential between the set of user performance parameters and a target set of performance parameters. |
| Inventors: | Trehan, Rajiv (Bangkok, TH) |
| Assignees: | Trehan, Rajiv (Bangkok, TH) |
| Claim: | 1. A method for Artificial Intelligence (AI) assisted activity training, the method comprising: presenting, by a rendering device, a plurality of activity categories to a user, wherein each of the plurality of activity categories comprises a plurality of activities, and wherein the plurality of activity categories are presented as multimedia content; receiving a voice-based input from the user, wherein the voice-based input comprises an activity training plan comprising a selection of at least one activity from at least one of the plurality of activity categories and at least one activity attribute associated with each of the at least one activity, and wherein the voice-based input is in a source language; processing, by a Natural Language Processing (NLP) model, the received voice-based input to extract the selection of at least one activity and the at least one activity attribute, wherein the NLP model is configured using a single language, and wherein the single language is an intermediate language; initiating, contemporaneous to receiving the voice-based input, presentation of a multimedia content in conformance with the at least one activity and the at least one activity attribute, wherein the multimedia content comprises a plurality of guidance steps performed by a virtual assistant corresponding to the at least one activity; detecting, via at least one camera, initiation of a user activity performance of the user in response to initiation of the multimedia content, wherein the user activity performance of the user at a given time comprises imitation of one of the at least one activity; capturing, via the at least one camera, a video of the user activity performance of the user, wherein the at least one camera is placed at distributed locations; processing, in-real time, by an Artificial Intelligence (AI) model, the video to extract a set of user performance parameters of the user based on the user activity performance, wherein the set of user performance parameters comprises speed of a current activity performance, number of repetitions completed, overall completion of an activity circuit, third-party smart device information, pulse rate of the user, blood pressure of the user, and motion of the user; generating, by the AI model, a feedback based on differential between the set of user performance parameters and a target set of performance parameters, wherein the target set of performance parameters comprises speed of the target activity performance, blood pressure, target number of repetitions, target pulse rate of the user, and target motion of the user; and rendering, contemporaneous to the user activity performance, the feedback to the user in at least one of an aural form, a visual form, or as a haptic feedback. |
| Claim: | 2. The method of claim 1 , wherein the at least one activity attribute comprises at least one of sequence of execution of the at least activity, a number of sets for performing each of the at least one activity, a count for each of the at least one activity in each of the sets, duration of performing each of the at least one activity, rest period between each of the sets, intensity of performing each of the at least one activity, difficulty level of performing each of the at least one activity, or pace of performing each of the at least one activity. |
| Claim: | 3. The method of claim 2 , wherein the sequence of execution of the at least one activity corresponds to an activity training circuit. |
| Claim: | 4. The method of claim 3 , further comprising sharing the activity training circuit with one or more remote users. |
| Claim: | 5. The method of claim 3 , further comprising: saving the activity training circuit as a playlist; and incorporating, based on voice-based inputs, at least one metadata to the playlist, wherein the metadata corresponds to an intensity, count, enjoyment factor, projected calorie burn, target muscle group, target body part, age group, weight, gender, time taken, or expected heart rate. |
| Claim: | 6. The method of claim 1 , wherein the feedback comprises at least one of: calories burnt, maximum count of the at least one activity performed, maximum time spent for the at least one activity during a previous activity session of the user, incorrect posture or pace of the user while performing the at least one activity, correct posture or pace to perform the at least one activity, absolute activity performance proficiency of the user, relative activity performance proficiency of the user, best time taken to perform the at least one activity, or warnings associated with biometric parameters of the user. |
| Claim: | 7. The method of claim 1 , further comprising augmenting, by the user, at least one of the plurality of activity categories, wherein augmenting comprises at least one of: creating a new activity category; downloading a new activity from a remote server and adding the new activity under the new activity category or at least one of the plurality of activity categories; recording a new activity, via the at least one camera and adding the new activity under the new activity category or at least one of the plurality of activity categories; and modifying at least one attribute associated with the plurality of activity categories, wherein the at least one attribute comprises name, priority, number of activities with an activity category, or display position. |
| Claim: | 8. The method of claim 7 , further comprising: sharing, by the user, the recorded new activity with a plurality of users, wherein the recorded new activity is shared for a predetermined fees; and presenting on a leader board the at least one attribute associated with each of the plurality of activity categories for each of the plurality of users for training and feedback. |
| Claim: | 9. The method of claim 1 , further comprising: receiving an intermediate voice-based input from the user to control and process progress of the multimedia content and the user activity performance, wherein the intermediate voice-based input comprises at least one of: pausing the multimedia content; adding comments to an activity during the user activity performance; instructing the at least one camera to alter focus on a specific body part or muscle group of the user; instructing the at least one camera to switch to an infrared mode in order to detect activation of a specific muscle group during the user activity performance; instructing the at least one camera to switch to an infrared mode in order to detect activation of a specific muscle group during the user activity performance; and requesting instant side by side video based comparison of a current performance of a user for a given activity with a past performance of the user or a current performance of a remote user for the given activity. |
| Claim: | 10. The method of claim 1 , wherein generating the feedback in the aural form comprises: assigning a priority to each of a plurality of audio messages based on satisfaction of associated criterion from a plurality of criteria; analyzing the set of user performance parameters to determine meeting of at least one criterion from the plurality of criteria; and rendering at least one audio message from a plurality of audio messages to the user, in response to meeting of the at least one criterion, wherein the at least one audio message corresponds to the at least one criterion, and wherein the at least one audio message is rendered in the source language. |
| Claim: | 11. The method of claim 1 , further comprising: detecting, via the at least one camera, an initial position of the user to initiate presentation of the multimedia content; determining, by the AI model, whether the detected initial position of the user matches an initial position mapped to the at least one activity; instructing, by the AI model, the user to correct the initial position, when the detected initial position fails to match the initial position; and initiating presentation of the multimedia content, when the detected initial position matches the initial position. |
| Claim: | 12. The method of claim 1 , further comprising: storing a multimedia data received from the at least one camera in a database; and editing the multimedia data based on one or more user commands of the user, wherein the user command is at least one of a text command, voice command, touch command, or a visual gesture, wherein the one or more user commands comprise at least one of: setting a start point of the multimedia data; setting an end point of the multimedia data; removing background from the multimedia data; assigning one or more tags to the multimedia data; and sharing the multimedia data with a set of other users. |
| Claim: | 13. A system for Artificial Intelligence (AI) assisted activity training, the system comprising: a processor; and a memory communicatively coupled to the processor, wherein the memory stores processor-executable instructions, which, on execution, causes the processor to: present, by a rendering device, a plurality of activity categories to a user, wherein each of the plurality of activity categories comprises a plurality of activities, and wherein the plurality of activity categories are presented as multimedia content; receive a voice-based input from the user, wherein the voice-based input comprises an activity training plan comprising a selection of at least one activity from at least one of the plurality of activity categories and at least one activity attribute associated with each of the at least one activity, and wherein the voice-based input is in a source language; process, by a Natural Language Processing (NLP) model, the received voice-based input to extract the selection of at least one activity and the at least one activity attribute, wherein the NLP model is configured using a single language, and wherein the single language is an intermediate language; initiate, contemporaneous to receiving the voice-based input, presentation of a multimedia content in conformance with the at least one activity and the at least one activity attribute, wherein the multimedia content comprises a plurality of guidance steps performed by a virtual assistant corresponding to the at least one activity; detect, via at least one camera, initiation of a user activity performance of the user in response to initiation of the multimedia content, wherein the user activity performance of the user at a given time comprises imitation of one of the at least one activity; capture, via the at least one camera, a video of the user activity performance of the user, wherein the at least one camera is placed at distributed locations; process, in-real time, by an Artificial Intelligence (AI) model, the video to extract a set of user performance parameters of the user based on the user activity performance, wherein the set of user performance parameters comprises speed of a current activity performance, number of repetitions completed, overall completion of an activity circuit, third-party smart device information, pulse rate of the user, blood pressure of the user, and motion of the user; generate, by the AI model, a feedback based on differential between the set of user performance parameters and a target set of performance parameters, wherein the target set of performance parameters comprises speed of the target activity performance, blood pressure, target number of repetitions, target pulse rate of the user, and target motion of the user; and render, contemporaneous to the user activity performance, the feedback to the user in at least one of an aural form, a visual form, or as haptic feedback. |
| Claim: | 14. The system of claim 13 , wherein the at least one activity attribute comprises at least one of sequence of execution of the at least activity, a number of sets for performing each of the at least one activity, a count for each of the at least one activity in each of the sets, duration of performing each of the at least one activity, rest period between each of the sets, intensity of performing each of the at least one activity, difficulty level of performing each of the at least one activity, or pace of performing each of the at least one activity. |
| Claim: | 15. The system of claim 14 , wherein the sequence of execution of the at least activity corresponds to an activity training circuit. |
| Claim: | 16. The system of claim 15 , wherein the processor-executable instructions further cause the processor to share the activity training circuit with one or more remote users. |
| Claim: | 17. The system of claim 13 , wherein the feedback comprises at least one of: calories burnt, maximum count of the at least one activity performed, maximum time spent for the at least one activity during a previous activity session of the user, incorrect posture or pace of the user while performing the at least one activity, correct posture or pace to perform the at least one activity, absolute activity performance proficiency of the user, relative activity performance proficiency of the user, best time taken to perform the at least one activity, or warnings associated with biometric parameters of the user. |
| Claim: | 18. The system of claim 13 , wherein the processor-executable instructions further cause the processor to augment, in response to an action by the user, at least one of the plurality of activity categories, wherein augmenting comprises at least one of: creating a new activity category; downloading a new activity from a remote server and adding the new activity under the new activity category or at least one of the plurality of activity categories; recording a new activity, via the at least one camera and adding the new activity under the new activity category or at least one of the plurality of activity categories; and modifying at least one attribute associated with the plurality of activity categories, wherein the at least one attribute comprises name, priority, number of activities with an activity category, or display position. |
| Claim: | 19. A computer program product being embodied in a non-transitory computer readable storage medium of a computing device and comprising computer instructions for Artificial Intelligence (AI) assisted activity training, the computer program product comprising: presenting, by a rendering device, a plurality of activity categories to a user, wherein each of the plurality of activity categories comprises a plurality of activities, and wherein the plurality of activity categories are presented as multimedia content; receiving a voice-based input from the user, wherein the voice-based input comprises an activity training plan comprising a selection of at least one activity from at least one of the plurality of activity categories and at least one activity attribute associated with each of the at least one activity, and wherein the voice-based input is in a source language; processing, by a Natural Language Processing (NLP) model, the received voice-based input to extract the selection of at least one activity and the at least one activity attribute, wherein the NLP model is configured using a single language, and wherein the single language is an intermediate language; initiating, contemporaneous to receiving the voice-based input, presentation of a multimedia content in conformance with the at least one activity and the at least one activity attribute, wherein the multimedia content comprises a plurality of guidance steps performed by a virtual assistant corresponding to the at least one activity; detecting, via at least one camera, initiation of a user activity performance of the user in response to initiation of the multimedia content, wherein the user activity performance of the user at a given time comprises imitation of one of the at least one activity; capturing, via the at least one camera, a video of the user activity performance of the user, wherein the at least one camera is placed at distributed locations; processing, in-real time, by an Artificial Intelligence (AI) model, the video to extract a set of user performance parameters of the user based on the user activity performance, wherein the set of user performance parameters comprises speed of a current activity performance, number of repetitions completed, overall completion of an activity circuit, third-party smart device information, pulse rate of the user, blood pressure of the user, and motion of the user; generating, by the AI model, a feedback based on differential between the set of user performance parameters and a target set of performance parameters, wherein the target set of performance parameters comprises speed of the target activity performance, blood pressure, target number of repetitions, target pulse rate of the user, and target motion of the user; rendering, contemporaneous to the user activity performance, the feedback to the user in at least one of an aural form, a visual form, or as haptic feedback. |
| Patent References Cited: | 11202579 December 2021 Freeman 11738237 August 2023 Shavit 20100222179 September 2010 Temple 20140315610 October 2014 Shachar 20170310664 October 2017 Satkunarajah 20180165854 June 2018 Du 20210110737 April 2021 Kamath 20210342952 November 2021 Putnam |
| Primary Examiner: | Augustin, Marcellus J |
| رقم الانضمام: | edspgr.11996090 |
| قاعدة البيانات: | USPTO Patent Grants |
| الوصف غير متاح. |