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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 4/30/2026 has been entered.
Status of Claims
This action is in response to the RCE filed 4/30/2026.
Claims 1, 8, 15 were amended 3/31/2026.
Claims 1-20 are pending and have been examined.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claims 1-20 are drawn to a method, a system, a computer-readable storage medium comprising a non-transitory storage medium which are statutory categories of invention (Step 1: YES).
Independent claims 1, 8, 15 recite receiving an image of a body of a user; determining a body position based on the image, wherein the body position includes a movement, a pose, a posture, or a stance of the body, and determining the body position involves: outputting, a probability distribution of identifications of the body position of the user based on the image; upon determining that the probability distribution of identifications of the body position of the user exceeds an identification threshold, transferring the image, wherein the identification threshold represents a configurable accuracy confidence value of at least 65%; and upon determining that the probability distribution of identifications of the body position of the user does not exceed the identification threshold, disregarding the probability distribution of identifications of the body position of the user and awaiting a new image of the body of the user; mapping markers to positions of the body of the user; determining a body position angle; generating a correction angle of the body of the user based on a comparison between the body position angle and a standard body position angle; and displaying a correction message based on the correction angle of the body of the user by outputting the correction message as feedback to the user.
The recited limitations, as drafted, under their broadest reasonable interpretation, cover certain methods of organizing human activity between a user and expert, as reflected in the specification, which states that “In one embodiment, the standard body position angle is identified from a predetermined mapping between body positions and corresponding standard body position angles, as determined by a subject matter expert. The predetermined mappings may be stored in a database of the storage 210, or on another computer connected to the network 230…. Continuing the previous example, upon determining the correction angle of 10-20 degrees for the hind leg, the correction module can generate a message such as, "Good kick; try bending the knee of your hind leg by 10-20 degrees; there are no adjustments needed for your front leg." The method 300 ends at block 316.” (see: specification paragraphs 36-39). If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or relationships or interactions between people, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, the claims recite an abstract idea(s) (Step 2A Prong One: YES).”
The judicial exception is not integrated into a practical application. The claims are abstract but for the inclusion of the additional elements including “computer system”, “camera system”, “first machine learning model”, “second machine learning model”, “display”, “system”, “processor”, and “memory or storage”, “computer-readable storage medium”, “non-transitory storage medium”, “one or more computer processors”, are recited at a high level of generality (e.g., that the determining and displaying is performed using generic computer components with instructions are executed to perform the claimed limitations). Such that they amount to no more than mere instructions to apply the exception using generic computer components. See: MPEP 2106.05(f).
Hence, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Accordingly, the claims are directed to an abstract idea (Step 2A Prong Two: NO).
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, using the additional elements to perform the abstract idea amounts to no more than mere instructions to apply the exception using generic components. Mere instructions to apply an exception using a generic component cannot provide an inventive concept. See MPEP 2106.05(f).
Further, the claimed additional elements, identified above, are not sufficient to amount to significantly more than the judicial exception because they are generic components that are configured to perform well-understood, routine, and conventional activities previously known to the industry. See MPEP 2106.05(d). Said additional elements are recited at a high level of generality and provide conventional functions that do not add meaningful limits to practicing the abstract idea. The originally filed specification supports this conclusion at Figure 1, Figure 2, Figure 3 and
Paragraph 13, where “In one embodiment, the camera system I 02 captures an image I 04 of a body position of a user. The body position can represent a movement, a pose, a posture, a stance, or the like, of the body included in the image. The camera system I 02 can also record a video, and capture the image 104 (e.g., an image frame, or a picture) from the video. Although the image 104 is generally referenced as a single image, the image 104 may also include multiple images or image frames. “
Paragraph 15, where “In one embodiment, the image 104 is processed by the BPI module 108 (a first machine learning model) to identify the body position of the body included in the image 104. For example, the BPI module 108 may identify the body position as a pose used in a ballet dance move, such as an arabesque, a passe, a sous-sous, a leap, or the like. This process is described further in Fig. 3 below.”
Paragraph 16, where “Afterwards, the MM module 110 (a second machine learning model) can map markers to various positions of the body included in the image 104. In one embodiment, the markers are placed at the joints of the body. This process is described further in Fig. 3 below.”
Paragraph 19, where “In one embodiment, the computer 202 includes a processor 204 that obtains instructions and data via a bus 222 from a memory 206 or storage 210. Not all components of the computer 202 are shown. The computer 202 can be under the control of an operating system (OS) suitable to perform or support the functions or processes disclosed herein. The processor 204 is a programmable logic device that performs instruction, logic, and mathematical processing, and may be representative of one or more CPUs. The processor may execute one or more algorithms, instruction sets, or applications in the memory 206 or storage 210 to perform the functions or processes described herein.”
Paragraph 20, where “The memory 206 and storage 210 can be representative of hard-disk drives, solid state drives, flash memory devices, optical media, and the like. The storage 210 can also include structured storage (e.g., a database). In addition, the memory 206 and storage 210 may be considered to include memory physically located elsewhere. For example, the memory 206 and storage 210 may be physically located on another computer communicatively coupled to the computer 202 via the bus 222 or the network 230.”
Paragraph 21, where “The computer 202 can be connected to other computers (e.g., controllers, distributed databases, servers, or webhosts), or the user computer 240 (which includes the camera system 102 and the display 116), via a network interface 220 and the network 230. Examples of the network 230 include electrical busses, physical transmission cables, optical transmission fibers, wireless transmissions mediums, routers, firewalls, switches, gateway computers, edge servers, a local area network, a wide area network, a wireless network, or the like. The network interface 220 may be any type of network communications device allowing the computer 202 to communicate with computers and other components of the computing environment 200 via the network 230.”
Paragraph 26, where “At block 306, the body position identifying module I 08 determines a body position based on the image 104, where the body position includes a movement, a pose, a posture, or a stance of the body. The BPI module 108 can include a machine learning model trained via a supervised learning process using images of standard and non-standard body positions.”
Paragraph 31, where “At block 308, the marker mapping module 110 maps markers to positions of the body. The MM module 110 can include a machine learning model trained to perform a pose estimation process. In one embodiment, the pose estimation process involves determining a position or orientation of the body shown in the image 104 in a 2-dimensional or 3-dimensional coordinate system.”
Viewing the limitations as an ordered combination, the claims simply instruct the additional elements to implement the concept described above in the identification of abstract idea with route, conventional activity specified at a high level of generality in a particular technological environment.
Hence, the claims as a whole, considering the additional elements individually and as an ordered combination, do not amount to significantly more than the abstract idea (Step 2B: NO).
Dependent claims 2-7 and 9-14 when analyzed as a whole, considering the additional elements individually and/or as an ordered combination, are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitations fail to establish that the claims are directed to an abstract idea without significantly more. Claim 3-4, 6-7, 10-11, and 13-14 recite generating, determining, and outputting positional data from mapping image data on the generically recited computing device as shown in the parent claims above. Claims 2, 5, 9 and 12 further recite “a Convolutional Neural Network”, “user computer”, and “network”, which are generically recited as being performed on a generic computing device in the specification (paragraphs 21, 29 and Figures 3-4) and do not provide significantly more to the abstract idea.
These claims fail to remedy the deficiencies of their parent claims above, and therefore rejected for at least the same rationale as applied to their parent claims above, and incorporated herein.
Allowable Subject Matter
Claims 1-20 are allowable over the prior art. The independent claims have the claim limitation of “upon determining that the probability distribution of identifications of the body position of the user does not exceed the identification threshold, disregarding the probability distribution of identifications of the body position of the user and awaiting a new image of the body of the user from the camera system” in combination with the other claim limitations overcome the prior art of record of Raleigh (US 2012/0312961 A1), Arar (US 20210182625 A1), Tamersoy (US 20200297237 A1). A new search was conducted and found the prior of Shouldice (WO 2022162589 A1) that teaches determining body positions based on thresholded data, however it did not teach disregarding probability distributions of identification of the body position of the user.
Response to Arguments
The arguments filed 3/31/2026 have been fully considered.
Regarding the arguments pertaining to the claim objections are persuasive. The claims have been sufficiently amended and the claim objection has been withdrawn.
Regarding the arguments pertaining to the 103 rejection, these arguments are persuasive. The claim amendments to the independent claims overcome the prior art of record and the rejection has been withdrawn.
Regarding the arguments pertaining to the 101 rejection, these arguments are not persuasive. Applicant argues that the transferring and processing based on output of a machine learning model improves computer system performance by limiting transfers and a portion of processing to occur when certain conditions are met. Examiner respectfully disagrees. The use of the output of probability distribution is to compare the data to an identification threshold (paragraph 29) and creates a decision tree in which the system proceeds to block 308 when the threshold is exceeded or disregarded if the probability distribution does not exceed the identification threshold (paragraph 30, Figure 3). The system is comparing and parsing data of the module and does not provide an improvement on technology. The processing system performance and resource utilization is not present in the specification as the specification teaches a general-use computing device as shown in the rejection above and is silent on improved technology to parse the data. Under broadest reasonable interpretation, the process shown in Figure 3 of the claimed invention does not provide an improvement but rather implementation of parsing data without significantly more to the abstract idea.
Applicant further argues that the claimed invention provides a practical application of providing a range of solutions to better accommodate imaging variations. However, improving the imaging techniques through a remote user adjusting imaging of a remote patient is part of the abstract idea. Applicant argues that the claimed invention is performed without requiring observation and feedback from a remote participant. However, the claimed invention recites “and displaying a correction message based on the correction angle of the body of the user by outputting the correction message as feedback to the user”, wherein the user is provided feedback based on training data that is analyzed by a subject matter expert (paragraph 36) Improving the medical field by improving the interactions between a remote user and a remote patient as evidenced in the specification show that the claims are directed to organizing human activity. The “medical field” is not necessarily a “technical field”, nor is a treatment effected. Classen is an example of adding a meaningful limitation to the claims that create a practical application, however Classen integrated the results of the analysis into a specific and tangible method that resulted in the method “moving from abstract scientific principle to specific application” (Classen Immunotherapies Inc. v. Biogen IDEC). The current claimed limitations fail to provide this practical application and the 101 rejection is maintained.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Shouldice (WO 2022162589 A10 teaches determining body positions based on thresholded data however it did not teach disregarding probability distributions of identification of the body position of the user.
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/KIMBERLY A. SASS/Examiner, Art Unit 3686
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