DETAILED ACTION
Applicant's Submission of a Response
Applicant’s submission of a response on 5/18/2026 has been received and fully considered. In the response, claims 1, 5, 11, 15, and 18 have been amended; and claims 6 and 16 have been canceled. Therefore, claims 1-5, 7-15 and 17-20 are pending.
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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-5, 7-15 and 17-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by U.S. Patent Application Publication No. 2022/0203168 to Calderon (Figs. 1 and 6 shown below for convenience, but entire document is relevant)
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With regard to claim 1, Calderon discloses an apparatus for class administration (e.g., see at least paragraph 36 that discusses use in workout classes), the apparatus comprising:
a first input device (e.g., see at least paragraph 33 that discusses a video camera can record a fitness instructor; see also Fig. 4 of video of fitness instructor), the first input device configured to receive at least audio-visual data of an instructor (e.g., see at least paragraph 33 that discusses video; see at least paragraph 46 for audio or video affirmation…are sent as property of a user);
at least a processor (e.g., see at least Fig. 1, processor 109, neural network processor 108, and image processor 104; see more detailed discussion of system processors in paragraph 19);
a memory communicatively connected to the at least a processor (e.g., see at least Fig. 1, storage medium 106 connected to processor 109; see at least paragraph 19 that states “One or more processors (104, 108, 109) and a computer readable storage medium (106) may be coupled to the device”), wherein the memory contains instructions configuring the at least a processor to:
receive instructor data from the first input device (e.g., see at least paragraph 33 that discusses a video camera can record a fitness instructor);
generate instructions data (e.g., see at least paragraph 20 that discussion instruction data including, for example, “a simulated representation of a user (604) can demonstrate to a user, correct form”) as a function of instructor data (e.g., see at least paragraphs 28, 31 and 42 that discuss providing instructor feedback, including in paragraph 42, “the present invention can modify future instructions…based on user performance”), wherein generating the instructions data further comprises determining an instructions data modifier as a function of an analysis of the instructions data (e.g., see at least paragraph 28 that discusses analyzing attributes and ranking errors from 1 to 10, which impacts “how to order feedback to give to a user”), wherein instructions data comprises advice data, wherein the advice data comprises data related to a degree of match (e.g., see at least paragraph 42 that discusses “goal is set to achieve a 100% efficacy”, wherein percent efficacy equates to a degree of match), wherein the degree of match indicates an achievability of at least a participant pose (e.g., see at least paragraph 42 that discusses “a user had poor form”, “form score”, wherein form equates to a pose), and modifying the instructions data a function of the instruction data modifier, wherein generating the instructions data as a function of the instructor data comprises:
receiving instruction training data comprising a plurality of instructor data correlated to a plurality of the instruction data (e.g., see at least paragraphs 28, 31, and 42 for discussion of instructor data; see also paragraphs 20, 30, and 31 for correlation of instructor data and instruction data);
training an instruction machine learning model as a function of the instruction training data (e.g., see at least paragraphs 31 and 42 for discussion of machine learning; see also paragraph 34 for discussion of a machine learning model for generating instructions); and
generating the instruction data as a function of the instruction machine learning model (e.g., see at least paragraphs 31 and 42 for discussion of machine learning; see also paragraph 35 for discussion of recommending instructions; see also paragraph 39 for instructions in combination with machine learning)
create a user interface data structure (e.g., see at least paragraph 28 that discusses data collection), wherein the user interface data structure comprises the instructor data and the instructions data (e.g., see at least paragraphs 20 and 33 as discussed above for instructor and instruction data); and
transmit the instructor data, the instructions data, and the user interface data structure (e.g., see at least paragraphs 19 and 20 for discussion of communicating data); and
a graphical user interface (GUI) communicatively connected to the at least a processor (e.g., see Fig. 1, display 102; see also paragraph 19 for discussion of the display), the GUI configured to:
receive the user interface data structure (e.g., see at least paragraph 19 that discuses receiving information from a cloud); and
display the instructions data and the instructor data as a function of the user interface data structure (e.g., see at least paragraph 20 that discusses overlaying instructions (604) onto a visual simulation (603) showing an animation of where a user’s legs should be placed);
[claim 2] the instructor data further comprising previous class data and current class data (e.g., see at least paragraph 41 for discussion of adjusting instructions based on performance during an exercise or past performance);
[claim 3] the apparatus further comprising a second input device, the second input device configured to receive second view data (e.g., see at least paragraph 39 that discusses the use of a stream of camera images for a user’s pose estimation, wherein the user camera is a second input device);
[claim 4] the apparatus further comprising a second input device, the second input device configured to receive participant data (e.g., see at least paragraph 39 that discusses the use of a stream of camera images for a user’s pose estimation, wherein the user camera is a second input device);
[claim 5] wherein generating the instructions data comprises generating the instructions data as a function of the instructor data (e.g., see at least paragraphs 30 and 31 that discuss comparing user movements to fitness instructor movements; see also paragraph 20 for generating instruction data);
[claim 7] wherein generating the plurality of instructor data as a function of the instructor data comprises: determining instructor pose data as a function of the instructor data (e.g., see at least paragraphs 30 and 31 that discuss comparing user movements to fitness instructor movements; see also paragraph 20 for generating instruction data); and generating the instructions data as a function of the instructor pose data (e.g., see at least paragraph 19 that discusses comparing instructor pose data with user pose data);
[claim 8] wherein the GUI further comprises an interaction feature, the interaction feature configured to allow a user to interact with the GUI (e.g., as shown in Fig. 6, the user can observe their actions and the associated instruction to modify/interact with their movements; see paragraph 11 that states “Fig. 6 shows an exemplary third person view of a user interacting with a virtual environment projected on a television with a computer vision device”);
[claim 9] wherein the previous class data is displayed on a first device display and the current class data is displayed on a second device display (e.g., Fig. 1 shows a display 102, a previous class data will be displayed on the device that is used for that class and if the user switches devices, the current class would be displayed on a second device); and
[claim 10] wherein the instructions data is generated as a function of a participant input (e.g., see at least paragraph 20 that provides an example of a user performing a squat and be corrected with feedback based on the user input).
Claims 11-15 and 17-20 are anticipated by Calderon as set above for claims 1-5 and 7-10, which are similar in claim scope.
Response to Arguments
Applicant's arguments filed 5/18/2026 have been fully considered but they are not persuasive.
Beginning on page 1 of Applicant’s remarks, Applicant’s amendments and corresponding arguments regarding 101 are persuasive. The 101 rejections are withdrawn.
Beginning on page 7, Applicant argues that Calderon does not disclose the specific limitations relating to “degree of match and achievability.” The Examiner respectfully disagrees. As set forth above in the 102 analysis, Calderon discloses degree of match and achievability (e.g., see at least paragraph 42 that discusses “goal is set to achieve a 100% efficacy”, wherein percent efficacy equates to a degree of match). Calderon discloses comparing user pose data with instructor data for percentage match and then outputs updated feedback based on the match (e.g., see at least paragraphs 21-42). For at least these reasons, claims 1-5, 7-15, and 17-20 remained rejected by Calderon.
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 JAMES S MCCLELLAN whose telephone number is (571)272-7167. The examiner can normally be reached Monday-Friday (8:30AM-5:00PM).
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kang Hu can be reached at 571-270-1344. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/James S. McClellan/Primary Examiner, Art Unit 3715
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