DETAILED ACTION
Applicant's Submission of a Response (RCE)
Applicant’s submission of a response on 9/1/2026 has been received and fully considered. In the response, claims 1 and 11 have been amended. Therefore, claims 1-5, 7-15 and 17-20 are pending.
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.
Claims 1-5, 7-15 and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Publication No. 2022/0203168 to Calderon (Figs. 1 and 6 shown below for convenience, but entire document is relevant) in view of U.S. Patent Application Publication No. 2022/0147818 to Zhang.
PNG
media_image1.png
496
735
media_image1.png
Greyscale
PNG
media_image2.png
495
500
media_image2.png
Greyscale
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).
Regarding claim 1, Calderon discloses the use of machine learning (e.g., see at least paragraphs 19, 26, and 26 that disclose machine learning and predictive neural networks) but is silent regarding the specific model now recited in claim 1, which appears to be variational autoencoder, VAE, type of neural network (e.g., see current application, paragraphs 77 and 78).
Reasonably pertinent to the problem face, Zhang teaches a neural network using a variational autoencoder (e.g., see at least paragraphs 142-165; see also Figs. 4A-4D) in an online education setting (e.g., see at least paragraph 123). Zhang teaches the use of a VAE in paragraph 149, “The requirement to learn to encode to Z and back again amount to a constraint placed on the overall neural network 208 of the VAE formed from the constituent neural networks of the encoder and decoder 208q, 208p” and “variational autoencoder, the latent vector Z is subject to an additional constraint that it follows a predetermined form of probabilistic distribution such as a multidimensional Gaussian distribution or gamma distribution.” (emphasis added by Examiner) A variational autoencoder is a generative artificial neural network that learns a smooth, probabilistic latent space to compress data and create new, realistic samples. This appears to equate to the newly added features of claim 1.
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the current invention to modify ABC with VAE as taught by XYZ in order to use a known technique to improve similar devices (methods, or products) in the same way. In this case, the use of VAE offers several advantages including good generative modeling, anomaly detection, and reconstructing data with missing/noisy parts.
Claims 11-15 and 17-20 are made obvious by Calderon in view of Zhang as set above for claims 1-5 and 7-10, which are similar in claim scope.
Response to Arguments
Applicant’s arguments with respect to claims 1-5, 7-15, and 17-20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
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.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/James S. McClellan/Primary Examiner, Art Unit 3715
Prosecution Insights