DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
This is in response to Applicant’s Arguments/Remarks filed on September 15th, 2025, which has been entered and made of record.
Response to Arguments
Claim Rejections - 35 USC § 102/103
Applicant’s arguments regarding the current claim(s) have been fully considered. But, the arguments/remarks are directed to the claims as amended, and so are believed to be answered by and therefore moot in view of the new grounds of rejection presented below.
Status of Claims
Claims 1-20 are pending. Claim(s) 1, 3-4, 6, 8-10, 14, 16-18, and 20 were amended. No claim(s) were canceled. No new claim(s) were added. Claims 1-20 are considered below.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-2, 4-6, 10, 14-15, and 17-19 are rejected under 35 U.S.C. 102(a)(2) as being unpatentable by Trehan (US 20230071274 A1).
Regarding Claim 1, representative of Claim 14, Trehan teaches a computing system, comprising:
a posture assessment machine learning model configured to:
receive a first input of one or more posture assessment signals from one or more posture assessment sensors ([0007]: method may capture, via a multimedia input device, at least one activity performed by a plurality of users…method is configured to process in real-time, via at least one Artificial Intelligence (AI) model from a plurality of AI models, the captured activity for each of the plurality of users), and
generate a posture assessment output of a human subject's posture based at least on the one or more posture assessment signals ([0007]: method may comprise comparing the set of user performance parameters with a set of target activity performance parameters obtained via at least one AI model), the one or more posture assessment sensors including a camera, and the one or more posture assessment signals including one or more images of a human subject captured by the camera ([0060]: the smart mirrors 206 and 210 may use an AI model to process the video of the user activity performance captured by the one or more cameras 204 and 208 to extract a set of user performance parameters. In general, the AI model may process the video to determine the posture, the pose, and the body movements of the users); and
a posture correction machine learning model ([0007]: method may generate for each of the plurality of users, by the at least one AI model, a feedback based on the comparison) configured to:
receive a second input, including:
the first input of the one or more posture assessment signals including the one or more images of the human subject ([0059]: one or more cameras (that may be placed at distributed locations) may also capture a video of the user activity performance. In an embodiment, during the user activity performance, the smart mirrors 206 and 210 may overlay a pose skeletal model corresponding to the user activity performance over a reflection of the user on the smart mirrors 206 and 210, while performing the activity); and
the posture assessment output of the human subject's posture generated by the posture assessment machine learning model ([0007]: method may generate for each of the plurality of users, by the at least one AI model, a feedback based on the comparison of the set of user performance parameters with the set of target activity performance parameters. [0063]: the AI model may compare the set of user performance parameters with a set of target performance parameters); and
generate, based on the second input, a posture correction output ([0063]: the AI model may generate feedback for the user, [0007]: feedback may comprise at least one of visual feedback), including:
a virtual clone of the human subject having an improved posture relative to the human subject's posture ([0071] In some embodiment, the visual feedback may be in the form of skeletal pose model or skeletal points overlayed on the reflection of the user 102 in the smart mirror 100…the feedback generation may be replaced by augmentation or the video streaming or augmentation of an avatar), and
a composite image including the virtual clone admixed with an image of the human subject ([0071] In some embodiment, the visual feedback may be in the form of skeletal pose model or skeletal points overlayed on the reflection of the user 102 in the smart mirror 100 [0071]: in case of the display device 116, the skeletal pose model or the skeletal points may be overlayed on a video stream captured for the user 102 while performing the activity. In some other embodiments, a multidimensional (3D or 2D) model of the user 102 or the virtual assistant may also be displayed via the GUI to provide feedback to the user).
Regarding Claim 2, representative of Claim 15, Trehan teaches the computing system of claim 1. In addition, Trehan teaches wherein the composite image includes posture adjustment feedback indicating whether the human subject's posture approaches the improved posture of the virtual clone ([0123]: Additionally, specific skeletal points 1704 overlayed over the user 102's reflection/video may be distinctly highlighted, for example, by changing the color of these skeletal points 1704 or rendering specific graphical elements over these skeletal points 1704. Examiner interpreting the highlighted keypoints to be the indicator of deviating from vs approaching the improved/ideal posture).
Regarding Claim 4, representative of Claims 17 and 18, Trehan teaches the computing system of claim 1. In addition, Trehan teaches wherein the posture assessment machine learning model is configured to:
receive, as the first input, a plurality of posture assessment signals from the one or more posture assessment sensors over a posture tracking duration ([0059]: one or more cameras (that may be placed at distributed locations) may also capture a video of the user activity performance), and
progressively generate updated posture assessment outputs of the human subject's posture over the posture tracking duration based at least on the plurality of posture assessment signals ([0060]: may use an AI model to process the video of the user activity performance captured by the one or more cameras 204 and 208 to extract a set of user performance parameters. In general, the AI model may process the video to determine the posture, the pose, and the body movements of the users 102 and 104), and
wherein the posture correction machine learning model ([0063]: the AI model may generate feedback for the user) is configured to:
generate the posture correction output to include a posture assessment notification ([0063]: Upon observing a difference or deviation between the two set of parameters (i.e., user vs target), the AI model may generate feedback for the user) based at least on the updated posture assessment outputs of the human subject's posture over the posture tracking duration ([0067]: the feedback may be provided after the user has completed the given activity), the posture assessment notification visually summarizing how the human subject's posture changed over the posture tracking duration ([0067]: feedback may include…incorrect posture or pace of the user 102 while performing the activity, correct posture or pace to perform the activity, [0071]: visual feedback may be in the form of skeletal pose model or skeletal points overlayed, [0072]: model may be overlayed on the reflection of the user 102 or the video of the user 102 while performing a given activity).
Regarding Claim 5, representative of Claim 19, Trehan teaches the computing system of claim 4. In addition, Trehan teaches wherein the plurality of posture assessment signals includes a plurality of images of the human subject captured by the camera over the posture tracking duration ([0060]: may use an AI model to process the video of the user activity performance captured by the one or more cameras 204 and 208 to extract a set of user performance parameters. In general, the AI model may process the video to determine the posture, the pose, and the body movements of the users 102 and 104), and wherein the posture assessment notification is derived from the plurality of images of the human subject captured over the posture tracking duration ([0067]: feedback may include…incorrect posture or pace of the user 102 while performing the activity, correct posture or pace to perform the activity, [0071]: visual feedback may be in the form of skeletal pose model or skeletal points overlayed, [0072]: model may be overlayed on the reflection of the user 102 or the video of the user 102 while performing a given activity).
Regarding Claim 6, Trehan teaches the computing system of claim 4. In addition, Trehan teaches wherein the posture assessment notification includes a visual representation of different time intervals during the posture tracking duration where the posture assessment machine learning model outputs assessments of the human subject's posture ([0060]: may use an AI model to process the video of the user activity performance captured by the one or more cameras 204 and 208 to extract a set of user performance parameters. In general, the AI model may process the video to determine the posture, the pose, and the body movements of the users 102 and 104, [0067]: feedback may include…incorrect posture or pace of the user 102 while performing the activity, correct posture or pace to perform the activity, [0071]: visual feedback may be in the form of skeletal pose model or skeletal points overlayed, [0072]: model may be overlayed on the reflection of the user 102 or the video of the user 102 while performing a given activity).
Regarding Claim 10, Trehan teaches the computing system of claim 1. In addition, Trehan teaches wherein the one or more posture assessment signals includes one or more user-state metrics output from one or more trained user-state machine-learning models ([0008]: The AI model may be configured based on the target activity performance of an activity expert, and by a plurality of correct and/or incorrect movements, as well as the tolerance metrics associated with the current activity of the users. Furthermore, each of the plurality of AI models are trained and configured for a given set of activity. [0049]: The NLP model may process the voice-based inputs to extract user selection of one or more activities and the associated activity attributes. Examiner interpreting user state metric to be the activity selected by the user).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 3 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Trehan (US-20230071274-A1) in view of Rao (US 20230069758 A1) and Zelenin (US 10489957 B2).
Regarding Claim 3, representative of Claim 16, Trehan teaches the computing system of claim 1. Although Trehan teaches the visual feedback may be presented as a model of the user, Trehan does not explicitly teach the remaining limitations of claim 3.
Rao teaches wherein the posture correction machine learning model is configured to: receive, via user input from the human subject, a selection of a best-fit clone and customize a posture of the virtual clone based at least on the best-fit clone ([0052]: parameters that the first NN model 106 may require to generate the first avatar, [0055] In an embodiment, the first avatar 120 may be a virtual and animated three-dimensional (3D) graphical representation of the first user 118 that may be customized to a persona of the first user 118).
However, Neither Trehan nor Rao explicitly teaches a plurality of training clones visually presented to the human subject.
Zelenin teaches a plurality of training clones visually presented to the human subject ([abstract]: human interactor can select facial expressions, poses, and behaviors of the virtual character using an input device, [0046]: each of the buttons 112a-112d can be used to select a different avatar when multiple avatars are available for selection).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present invention to have modified Trehan to include the teachings of Rao. Trehan teaches an AI model for generating feedback of a user performing an exercise including visual feedback of a multidimensional model of a user. Rao teaches an NN model for generating feedback of a user performing an exercise through a customizable avatar. Modifying Trehan by Rao would improve the accuracy of the feedback generated by enabling a feedback avatar to be customized, thereby being better tailored to the user. Further, it would have been obvious to have modified the Trehan and Rao combination to include the teachings of Zelenin. Zelenin teaches a user’s ability to select a desired avatar from a plurality of avatars. Substituting the Trehan and Rao combination’s general teaching of an avatar representing a user with Zelenin’s avatar selection from multiple avatars would provide the predictable result of selecting an avatar representing a user.
Claim(s) 7 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Trehan (US-20230071274-A1) in view of Jaimes (JAIMES, et al., "Sit Straight (and tell me what I did today): A Human Posture Alarm and Activity Summarization System", In Proceedings of the 2nd ACM Workshop on Continuous Archival and Retrieval of Personal Experiences, November 11, 2005, pp. 23-34).
Regarding Claim 7, representative of Claim 20, Trehan teaches the computing system of claim 6. However, Trehan does not explicitly teach the remaining limitations of Claim 7. Jaimes teaches wherein the posture assessment notification includes context tags indicating different activities ([Section 6.2, paragraph 1]: system to indicate different activities. Examples include: typing, reading, stretching, talking to someone, filing, speaking on the phone, etc) the human subject was involved in during the different time intervals ([Section 6.2, paragraph 3]: user can view a summary of his activities at any time… this will help him quickly determine how much time he is spending on different activities and adjust his work for the rest of the day).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the present invention to have modified the teachings of Trehan to include the teachings of Jaimes. Trehan generally teaches assessing posture or pose of a user based on a video of the user and displaying posture feedback overlaid on the video. Jaimes teaches classifying different user activities and enabling a user to view a summary of the different activities a user participated in during a given time. Modifying Trehan to include the teachings of Jaimes would improve posture correction by allowing a user to view what activities they participated in throughout a time interval along with their posture tracking information.
Allowable Subject Matter
Claims 8-9 and 11-13 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JANICE VAZ whose telephone number is (703)756-4685. The examiner can normally be reached Monday-Friday 9:00-5:00pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Matthew Bella can be reached at (571) 272-7778. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JANICE E. VAZ/Examiner, Art Unit 2667
/MATTHEW C BELLA/Supervisory Patent Examiner, Art Unit 2667