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 .
Status of Claims
Applicant's arguments, filed 08/11/2026, have been fully considered. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application.
Applicants have amended their claims, filed 08/11/2026, and therefore rejections newly made in the instant office action have been necessitated by amendment.
Applicants have amended claims 143, 145, 154, 171, and 172.
Applicants have left claims 144, 146-153, and 155-170 as originally filed/previously presented.
Applicants have canceled/previously canceled claims 1-142.
Claims 143-172 are the current claims hereby under examination.
Claim Objections - Withdrawn and Newly Applied Necessitated by Applicant’s Amendments
Claim 162 is objected to because of the following informalities:
Regarding claim 162, line 2 recites “an injury”, however it appears it should read --the injury-- (emphasis added).
Response to Arguments
Applicant’s arguments, see page 8 of Remarks, filed 08/11/2026, with respect to claims 143, and 171-171 have been fully considered and are persuasive. Applicants have amended the claims, rendering the objections moot. The objections of claims 143, and 171-171 has been withdrawn. However, there are new grounds for objections.
Claim Rejections - 35 USC § 112(b) - Withdrawn and Maintained
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 172 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claim 172, the claim recites the limitation "the camera" in line 7. There is insufficient antecedent basis for this limitation in the claim. In light of the specification, it is currently unclear if “the camera” is the same as, related to, or different from “a digital recording device”. For the purposes of examination, “the camera” is further defining “a digital recording device”.
Response to Arguments
Applicant’s arguments, see pages 8-9 of Remarks, filed 08/11/2026, with respect to claims 145, 154, 171, and 172 have been fully considered and are persuasive. Applicants have amended claims 145 and 154, rendering the 112(b) rejections moot. Further, Applicant’s arguments regarding claims 171 and 172 claiming both an apparatus and the method steps of using the apparatus are considered persuasive. The 112(b) rejections of claims 145, 154, 171, and 172 has been withdrawn.
Applicant’s arguments did not address the 112(b) rejection of claim 172 regarding “the camera”, therefore, the Examiner cannot find a reason to withdraw the rejection.
Claim Rejections - 35 USC § 112(d) - Newly Applied Necessitated by Applicant’s Amendments
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claims 165-168 are rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends.
Regarding claim 165, the claim recites “the human has a disease or condition”, however, claim 143 recites “the subject has chronic pain, an injury, a musculoskeletal disease or condition …”, and claim 164 recites “the subject is a human”. Therefore, claim 165 fails to further limit the subject matter of the claim upon which it depends.
The dependent claims of the above rejected claim are rejected due to their dependency.
Regarding claim 167, the claim recites “the human has experienced an injury”, however, claim 143 recites “the subject has … an injury”, and claim 164 recites “the subject is a human”. Therefore, claim 167 fails to further limit the subject matter of the claim upon which it depends.
The dependent claims of the above rejected claim are rejected due to their dependency.
Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
Claim Rejections - 35 USC § 101 - Withdrawn and Maintained/Modified Necessitated by Applicant’s Amendments
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 171 and 172 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.
Analysis of independent claims 171 and 172:
Step 1 of the subject matter eligibility test (see MPEP 2106.03).
Claim 171 is directed to a device, which describes one of the four statutory categories of patentable subject matter, i.e., a machine. Claim 172 is directed to a system, which describes one of the four statutory categories of patentable subject matter, i.e., a machine. Therefore, further consideration is necessary.
Step 2A of the subject matter eligibility test (see MPEP 2106.04).
Prong One: Claims 171 and 172 recite an abstract idea. In particular, the claims recite the following:
Generating one or more musculoskeletal movement biomarkers from the time series data;
Producing the mobility assessment for the subject from the one or more musculoskeletal movement biomarkers; and
Providing a treatment recommendation to the subject based on the mobility assessment.
These elements recited in claims 171 and 172 are drawn to an abstract idea since (1) they involve a mental process that can be practically performed in the human mind including observation, evaluation, judgment, and opinion and using pen and paper.
Generating one or more musculoskeletal movement biomarkers from time series data is drawn to a mental process that can be practically performed in the human mind with the aid of pen and paper. For example, a person with ordinary skill in the art can reasonably view time series data of the subject on a piece of paper and visually determine biomarkers from the time series data, such as determining if the time series data is the same as ideal time series data. There is nothing to suggest an undue level of complexity in the generation of the biomarkers.
Producing a mobility assessment from the one or more musculoskeletal movement biomarkers is drawn to a mental process that can be practically performed in the human mind with the aid of pen and paper. For example, a person with ordinary skill in the art can reasonably produce a negative mobility assessment if the biomarkers are determined to be not similar to ideals, and a positive mobility assessment if the biomarkers are determined to be similar to ideals. There is nothing to suggest an undue level of complexity in the producing a mobility assessment.
Providing a treatment recommendation to the subject based on the mobility assessment is drawn to a mental process that can be practically performed in the human mind with the aid of pen and paper. For example, a person with ordinary skill in the art can either mentally provide a treatment recommendation based on the mentally determined mobility assessment and/or utilize a lookup table to provide a treatment recommendation to the subject based on the mentally determined mobility assessment. There is nothing to suggest an undue level of complexity in the treatment recommendation step.
Prong Two: Claims 171 and 172 do not recite additional elements that integrate the exception into a practical application. Therefore, the claims are “directed to” the abstract idea. The additional elements merely:
Recite the words “apply it” or an equivalent with the judicial exception, or include instructions to implement the abstract idea on a computer, or merely use the computer as a tool to perform the abstract idea (e.g., “a processor … a memory … a dynamic algorithm” (claim 171), “a processor … a memory … a dynamic algorithm” (claim 172)), and
Add insignificant extra-solution activity (the pre-solution activity of: using generic data-gathering components (e.g. “a recording device “ (claim 171), “a display … a digital recording device … the camera” (claim 172)); the post-solution activity of: (e.g. N/A); using generic data-outputting components (e.g. N/A)).
As a whole, the additional elements merely serve to gather information to be used by the abstract idea, while generically implementing it on a computer. There is no practical application because the abstract idea is not applied, relied on, or used in a meaningful way. The processing performed remains in the abstract realm, i.e., the result is not used for a treatment. No improvement to the technology is evident. Therefore, the additional elements, alone or in combination, do not integrate the abstract idea into a practical application.
Per the Berkheimer requirement, the additional elements are well-understood, routine, and conventional. For example, “a recording device” is well-understood, routine, and conventional, as disclosed by Joseph Luciano (US 20200037929 A1) - para. [0024-0027].
For example, “a dynamic algorithm” is well-understood, routine, and conventional, as disclosed by Joseph Luciano (US 20200037929 A1) - para. [0036-0037] and Winterbach et al. (US 20200335222 A1) - para. [0077-0080].
Further, “a dynamic algorithm”, “a processor” and/or “a memory comprising instructions …” does not qualify as significantly more because this limitation is simply appending well-understood, routine and conventional activities previously known in the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known in the industry (see Electric Power Group, 830 F.3d 1350 (Fed. Cir. 2016); Alice Corp. v. CLS Bank Int’l, 110 USPQ2d 1976 (2014)) and/or a claim to an abstract idea requiring no more than being stored on a computer readable medium which is a well-understood, routine and conventional activity previously known in the industry (see Electric Power Group, 830 F.3d 1350 (Fed. Cir. 2016); Alice Corp. v. CLS Bank Int’l, 110 USPQ2d 1976 (2014); SAP Am. v. InvestPic, 890 F.3d 1016 (Fed. Circ. 2018)).
Step 2B of the subject matter eligibility test (see MPEP 2106.05).
Claims 171 and 172 do not include additional elements, alone or in combination, that are sufficient to amount to significantly more than the judicial exception (i.e., an inventive concept) for the same reasons as described above. E.g., all elements are directed to pre-solution activities of necessary data gathering and/or utilizing a generic computer to perform the abstract idea, which merely facilitate the abstract idea.
In view of the above, the additional elements individually do not integrate the exception into a practical application and do not amount to significantly more than the above-judicial exception (the abstract idea). Looking at the limitations as an ordered combination (that is, as a whole) adds nothing that is not already present when looking at the elements taking individually. There is no indication that the combination of elements improves the functioning of a computer, for example, or improves any other technology. There is no indication that the combination of elements permits automation of specific tasks that previously could not be automated. There is no indication that the combination of elements includes a particular solution to a computer-based problem or a particular way to achieve a desired computer-based outcome. Rather, the collective functions of the claimed invention merely provide conventional computer implementation, i.e., the computer is simply a tool to perform the process.
Taken alone or in combination, the additional elements do not integrate the judicial exception into a practical application at least because the abstract idea is not applied, relied on, or used in a meaningful way. The additional elements do not add anything significantly more than the abstract idea. The collective functions of the additional elements merely provide computer/electronic implementation and processing, and no additional elements beyond those of the abstract idea. There is no indication that the combination of elements permits automation of specific tasks that previously could not be automated. There is no indication that the combination of elements improves the functioning of a computer, output device, improves technology other than the technical field of the claimed invention, etc. Therefore, the claims are rejected as being directed to non-statutory subjection matter. Claims 171 and 172 are rejected.
Response to Arguments
Applicant’s arguments, see pages 10-13 of Remarks, filed 08/11/2026, with respect to claims 143-170 have been fully considered and are persuasive. Applicants have amended independent claim 143 to recite at least “providing treatment to the subject based on the mobility assessment”, integrating the judicial exception into a practical application affirmatively reciting an action that effects a particular treatment for a disease or medical condition. The 101 rejection of claims 143-170 has been withdrawn.
Applicant's arguments regarding claims 171 and 172, filed 08/11/2026, have been fully considered but they are not persuasive.
Applicants have argued on pages 10-13 of Remarks, “claims … 171, and 172 include features that integrate any alleged judicial exception into a practical application by applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition … Applicant submits that the claims use any alleged judicial exception to effect a particular treatment for a medical condition.”
The Examiner respectfully disagrees. Claims 171 and 172 recite “providing a treatment recommendation to the subject based on the mobility assessment”, however, “providing a treatment recommendation” does not actually provide a treatment or prophylaxis because it is not a positive limitation that requires that the treatment recommendation be performed by the subject (see MPEP 2106.04(d)(2)). Therefore, the judicial exceptions are not integrated into a practical application.
Claim Rejections - 35 USC § 103 - Newly Applied Necessitated by Applicant’s Amendments
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 143-147, 149-153, and 156-170 are rejected under 35 U.S.C. 103 as being unpatentable over Joseph Luciano (US 20200037929 A1) (previously cited), hereinafter referred to as Luciano, in view of Winterbach et al. (US 20200335222 A1) (previously cited), hereinafter referred to as Winterbach, in view of Toth et al. (US 20230147888 A1), hereinafter referred to as Toth, in view of Varaklis et al. (US 20140153794 A1), hereinafter referred to as Varaklis.
Regarding claim 143, Luciano discloses a method of generating a mobility assessment for a subject (Abstract, Fig. 2, para. [0020]), the method comprising:
the subject performing an activity comprising an oscillatory motion (Fig. 2-10, para. [0024], “three-dimensional motion data from the patient”, para. [0036], para. [0038], “angular speed, inflection points, abduction, angle speed, right angle distribution, and speed distribution … motion of a joint …”, para. [0060-0061]), wherein the subject has chronic pain, an injury, a musculoskeletal disease or condition, or a risk of developing a musculoskeletal disease or condition (para. [0020], “injured body part …”, para. [0064], “progression and healing of the injury …”);
generating a visual recording of the subject performing the activity using a recording device (Fig. 2, element 210, para. [0024-0027], “sensor system may include one or more sensors which collect three-dimensional motion data from the patient … optical motion capture system … sensor system may include a camera …”, para. [0036-0037], “motion data may be collected from a patient …”);
extracting time series data from the visual recording using a dynamic algorithm (Fig. 2, Fig. 5A-D, Fig. 6A-D, para. [0036-0037], “visual motion algorithms … track the motion of the sensors from frame to frame to determine the position of the joint being measured …”, para. [0051]), wherein the dynamic algorithm is configured to change based on data processed by the dynamic algorithm and trained on visual images labeled with one or more body parts (para. [0036-0037], “motion data may be collected from a patient at a single joint … at multiple joints … from the full body … collect images that can be used to determine certain characteristics, such as inflection points, angular speed …”, para. [0039], “compared to one or more corresponding characteristics of the reference information … same characteristics from a plurality of subjects … a healthy body part or joint of a plurality of subjects …” - The visual motion algorithms are configured to change because multiple types of motion data (single join, multiple joint, fully body) are collected and multiple types of characteristics are collected. Further, the algorithms are trained using corresponding characteristics from similar body parts);
generating one or more musculoskeletal movement biomarkers from the time series data (Fig. 2, element 220, para. [0038], “all or a portion of the data is received by the processor and analyzed … determine one or more characteristics related to the patient’s motion … angular speed, inflection points, abduction (ABD), angle speed, right angle distribution, and speed distribution …”);
producing the mobility assessment for the subject from the one or more musculoskeletal movement biomarkers (Fig. 2, para. [0039-0044], “patient’s characteristics are compared to one or more corresponding characteristics of the reference information … determined whether the patient has a healthy body part, injured body part … report is generated …”).
However, Luciano does not explicitly disclose instructing the subject to perform the activity.
Winterbach teaches an analogous method of generating a mobility assessment for a subject by generating one or more musculoskeletal movement biomarkers from time series data of a visual recording (Abstract, Fig. 5, Fig. 13, para. [0017], para. [0052], para. [0111-0115]). Winterbach further teaches instructing the subject to perform an activity for generating the mobility assessment (Fig. 10, para. [0087-0088], “instruct the patient how to complete the assessment … movement exercise shown in user interface … may select start … perform the range of motion test …”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method disclosed by Luciano to explicitly include instructing the subject to perform the activity, as taught by Winterbach. This is because Winterbach teaches instructing the user to perform the activity allows for progress updates to be made over time to be monitored by a clinician (para. [0088-0089]).
However, modified Luciano does not explicitly disclose the one or more musculoskeletal movement biomarkers from the time series are generated by performing bispectral coherence analysis on principal components identified in the extracted time series data.
Toth teaches an analogous method for comparing similarities and/or differences between data (Abstract, para. [0143]). Toth further teaches performing bicoherence analysis on data to determine if the data is correlated (para. [0143]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method taught by modified Luciano to additionally perform bispectral coherence analysis on principal components identified in the extracted time series data to generate the one or more musculoskeletal movement biomarkers from the time series data, as suggested by Toth. This is because Toth teaches bicoherence analysis is a known method for determining correlation between time series datasets (para. [0143]), and one of ordinary skill in the art would recognize bicoherence analysis would improve the correlation method taught by Toth in a similar and predictable way, for obtaining biomarkers between two time series data.
However, modified Luciano does not explicitly disclose providing treatment to the subject based on the mobility assessment.
Varaklis teaches an analogous method for generating a mobility assessment for a subject by instructing the subject to perform an activity comprising an oscillatory motion (Fig. 3, element 302, para. [0087]), generating a visual recording of the subject performing the activity using a recording device (Fig. 3, element 306, para. [0087]), and producing a mobility assessment based on analysis (Fig. 3, element 308, para. [0087]). Varaklis further teaches providing treatment to the subjected based on the mobility assessment (para. [0089-0090], para. [0104-0107]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method taught by modified Luciano to additionally provide treatment to the subject based on the mobility assessment, as taught by Varaklis. This is because Varaklis teaches providing treatment based on the mobility assessment allows for a closed-loop system, allowing for treatment algorithms to be provided without a human clinician, thereby allowing a faster recovery time of the subject (para. [0090]).
Regarding claim 144, modified Luciano discloses the method according to claim 143, wherein the oscillatory motion comprises the repeated abduction, adduction, flexion, extension, or circumduction of one or more body parts of the subject (Fig. 5A-D, para. [0038], para. [0051]).
Regarding claim 145, modified Luciano discloses the method according to claim 144, wherein the one or more body parts comprises the subjects arms, legs, pelvis, hips, back, thorax, shoulders, shoulder girdles, ankles, or knees (para. [0005], “shoulder, hip, or leg”, para. [0036], para. [0038], para. [0060]).
Regarding claim 146, modified Luciano discloses the method according to claim 143, wherein the visual recording comprises the use of a motion tracking marker or sensor (para. [0024-0027], “optical motion capture system … optical markers …”).
Regarding claim 147, modified Luciano discloses the method according to claim 146.
However, modified Luciano does not explicitly disclose wherein the motion tracking marker or sensor is a smartwatch.
Winterbach further teaches a motion tracking sensor is a smartwatch (Fig. 2, para. [0029-0036]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the motion tracking sensor taught by modified Luciano to explicitly be a smartwatch, as taught by Winterbach. This is because Winterbach teaches a smartwatch, including a gyroscope, allows for motions of a user to be detected (para. [0034], para. [0058]).
Regarding claim 149, modified Luciano discloses the method according to claim 143, wherein the recording device is a webcam or smartphone (para. [0026], “camera”).
Regarding claim 150, modified Luciano discloses the method according to claim 143, wherein the time series data comprises a waveform generated by graphing the location of a body part of the subject on an axis over time (Fig. 4-5, para. [0037-0038], “position, orientation, velocity, and/or acceleration of the joint(s) and/or body being measured … position data may be in the form of cartesian coordinates or other such coordinate system …”).
Regarding claim 151, modified Luciano discloses the method according to claim 150, wherein the axis is selected from a vertical axis, a horizontal axis, and a depth axis (Fig. 4-5, para. [0037-0038], “position, orientation, velocity, and/or acceleration of the joint(s) and/or body being measured … position data may be in the form of cartesian coordinates or other such coordinate system …”).
Regarding claim 152, modified Luciano discloses the method according to claim 143.
However, modified Luciano does not explicitly disclose wherein the dynamic algorithm comprises a machine learning algorithm.
Winterbach further teaches the dynamic algorithm comprises a machine learning algorithm (para. [0077-0080]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method taught by modified Luciano to additionally have the dynamic algorithm comprise a machine learning algorithm, as taught by Winterbach. This is because Winterbach teaches a machine learning algorithm, including a convolutional neural network, allows for data to be extracted from captured images and videos to be used for producing a mobility assessment (para. [0077-0080]).
Regarding claim 153, modified Luciano discloses the method according to claim 152, wherein the machine learning algorithm comprises a neural network, selected from a convolutional neural network, a recurrent neural network, a ResNet, an InceptionNet, a VGGNet, a GoogLeNet, AlexNet, an EfficientNet, and a YOLONet neural network (Winterbach, para. [0077-0080], Further, see the rejection of claim 152).
Regarding claim 156, modified Luciano discloses the method according to claim 143, wherein one or more of the musculoskeletal movement biomarkers is selected from the group consisting of oscillatory motion amplitude, duration, full width at half maximum, acceleration, and velocity (Fig. 3-10, para. [0037-0038], “position, orientation, velocity, and/or acceleration of the joint(s) and/or body being measured … position data may be in the form of cartesian coordinates or other such coordinate system …”).
Regarding claim 157, modified Luciano discloses the method according to claim 143, wherein one or more of the musculoskeletal movement biomarkers is related to the symmetry of the oscillatory motion (Fig. 5A-D, para. [0051]).
Regarding claim 158, modified Luciano discloses the method according to claim 157, wherein the symmetry is between abduction and adduction movements, between reaching and pulling movements, between flexion and extension movements, or between arclengths of one or more circumduction movements (Fig. 5A-D, para. [0051], “peaks and/or troughs …”).
Regarding claim 159, modified Luciano discloses the method according to claim 143.
However, modified Luciano does not explicitly disclose wherein one or more of the musculoskeletal movement biomarkers is related to the correlation between separate body parts performing the oscillatory motion, wherein the separate body parts perform the oscillatory motion concurrently and/or wherein the separate body parts perform the oscillatory motion at separate times.
Winterbach further teaches one or more of the musculoskeletal movement biomarkers is related to the correlation between separate body parts performing the oscillatory motion, wherein the separate body parts perform the oscillatory motion concurrently and/or wherein the separate body parts perform the oscillatory motion at separate times (para. [0033-0035], para. [0058]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method taught by modified Luciano to additionally have the one or more musculoskeletal movement biomarkers be related to the correlation between separate body parts performing the oscillatory motion, wherein the separate body parts perform the oscillatory motion concurrently and/or wherein the separate body parts perform the oscillatory motion at separate times, as taught by Winterbach. This is because Winterbach teaches correlating between an injured shoulder and a bad shoulder allows for a range of expectations to be determined specific for the patient being evaluated (para. [0058]).
Regarding claim 160, modified Luciano discloses the method according to claim 143, wherein the mobility assessment comprises one or more of: a quantitative score of movement quality, a diagnosis of a disease or condition, a determination regarding whether one or more body parts is being compensated for, a determination regarding whether one or more body parts is compensating for another body part, and an assessment of the subject's fitness for performing a task (Fig. 2, para. [0039-0044], “patient’s characteristics are compared to one or more corresponding characteristics of the reference information … determined whether the patient has a healthy body part, injured body part … report is generated …”, para. [0063], “statistical rating of the relative severity of the injury … a rating of 10 …”).
Regarding claim 161, modified Luciano discloses the method according to claim 143, wherein the visual recording is generated at two or more timepoints to generate two or more mobility assessments (para. [0064], “patient can be tested a second time, after a certain amount of time has passed … track the progress and development of the injury”).
Regarding claim 162, modified Luciano discloses the method according to claim 161, wherein at least one timepoint of the two or more timepoints occurs after an injury of the subject (para. [0064], “patient can be tested a second time, after a certain amount of time has passed … track the progress and development of the injury”).
Regarding claim 163, modified Luciano discloses the method according to claim 161, wherein at least one timepoint of the two or more timepoints occurs before or after the subject has received a medical intervention (para. [0064], “patient can be tested a second time, after a certain amount of time has passed … repeated a plurality of times along the rehabilitation timeline to track the progress and development of the injury”).
Regarding claim 164, modified Luciano discloses the method according to claim 143, wherein the subject is a human (para. [0005], para. [0020]).
Regarding claim 165, modified Luciano discloses the method according to claim 164, wherein the human has a disease or condition (para. [0020], para. [0064]).
Regarding claim 166, modified Luciano discloses the method according to claim 165, wherein the disease or condition is selected from one or more of: arthritis, tendonitis, a tendon or myotendinous tear, or a hernia (para. [0039], “injured shoulder …”).
Regarding claim 167, modified Luciano discloses the method according to claim 164, wherein the human has experienced an injury (para. [0064], “patient can be tested a second time, after a certain amount of time has passed … repeated a plurality of times along the rehabilitation timeline to track the progress and development of the injury”).
Regarding claim 168, modified Luciano discloses the method according to claim 167, wherein the injury is selected from one or more of: a musculoskeletal injury, an injury of the shoulder, an injury of the rotator cuff, a muscle strain or a muscle tear, and a sprain (para. [0039], “injured shoulder …”).
Regarding claim 169, modified Luciano discloses the method according to claim 143, wherein the mobility assessment is produced using a computer or smartphone (Fig. 1, para. [0024], “computing devices …”, para. [0028-0032], para. [0044]).
Regarding claim 170, modified Luciano discloses the method according to claim 143, wherein the mobility assessment is produced using a computer or smartphone app (Fig. 1, para. [0024], “computing devices …”, para. [0028-0032], “one or more processors, memory … instructions that can be executable by the one or more processors …”, para. [0044]).
Claims 148 and 155 are rejected under 35 U.S.C. 103 as being unpatentable over Joseph Luciano (US 20200037929 A1) (previously cited), hereinafter referred to as Luciano, in view of Winterbach et al. (US 20200335222 A1) (previously cited), hereinafter referred to as Winterbach, in view of Toth et al. (US 20230147888 A1), hereinafter referred to as Toth, in view of Varaklis et al. (US 20140153794 A1), hereinafter referred to as Varaklis as applied to claim 143 above, and further in view of Ofir et al. (US 20210015415 A1) (previously cited), hereinafter referred to as Ofir.
Regarding claim 148, modified Luciano discloses the method according to claim 143.
However, modified Luciano does not explicitly disclose wherein the recording device comprises a LiDAR scanner.
Ofir teaches an analogous method for generating a visual recording of the subject performing an activity using a recording device (Fig. 1, element 102, Fig. 2, para. [0044-0047]). Ofir further teaches the recording device comprises a LiDAR scanner (para. [0047]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the recording device taught by modified Luciano to explicitly be a LiDAR scanner, as taught by Ofir. This is because Ofir teaches a LiDAR scanner is a known recording device for generating visual recordings of a subject and extracting gestures (para. [0046-0047]).
Regarding claim 155, modified Luciano discloses the method according to claim 143.
However, modified Luciano does not explicitly disclose wherein the extracted time series data is filtered using a high pass filter, a low pass filter, a signal processing filter, a Butterworth filter, a Chebyshev filter, an Elliptic filter, a Linkwitz-Riley filter, or a Savitzky-Golay filter.
Ofir teaches an analogous method for generating a visual recording of the subject performing an activity using a recording device and extracting time series data from the visual recording using a dynamic algorithm (Fig. 1, element 102, Fig. 2, para. [0044-0049]). Ofir further teaches the extracted time series data is filtered using a high pass filter, a low pass filter, a signal processing filter, a Butterworth filter, a Chebyshev filter, an Elliptic filter, a Linkwitz-Riley filter, or a Savitzky-Golay filter (para. [0031]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method taught by modified Luciano to additionally filter the extracted time series data using a high pass filter, a low pass filter, a signal processing filter, a Butterworth filter, a Chebyshev filter, an Elliptic filter, a Linkwitz-Riley filter, or a Savitzky-Golay filter, as taught by Ofir. This is because Ofir teaches filtering the data allows for noise to be eliminated, producing more accurate data (para. [0031]).
Claim 154 is rejected under 35 U.S.C. 103 as being unpatentable over Joseph Luciano (US 20200037929 A1) (previously cited), hereinafter referred to as Luciano, in view of Winterbach et al. (US 20200335222 A1) (previously cited), hereinafter referred to as Winterbach, in view of Toth et al. (US 20230147888 A1), hereinafter referred to as Toth, in view of Varaklis et al. (US 20140153794 A1), hereinafter referred to as Varaklis as applied to claim 143 above, and further in view of Baek et al. (US 20200327465 A1) (previously cited), hereinafter referred to as Baek.
Regarding claim 154, modified Luciano discloses the method according to claim 143.
However, modified Luciano does not explicitly disclose wherein the dynamic algorithm is trained using one or more of: DeepPoseKit, LEAP, SLEAP, Anipose, ImageNet, COCO, OID, or PASCAL data set.
Baek teaches an analogous method of generating a mobility assessment for a subject (Abstract, Fig. 1, para. [0044-0048). Baek further teaches training a dynamic algorithm using one or more of: DeepPoseKit, LEAP, SLEAP, Anipose, ImageNet, COCO, OID, or PASCAL data set (para. [0054]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the dynamic algorithm taught by modified Luciano to additionally be trained using one or more of: DeepPoseKit, LEAP, SLEAP, Anipose, ImageNet, COCO, OID, or PASCAL data set, as taught by Baek. This is because Baek teaches ImageNet is a large image data base that allows for training of a CNN (para. [0054]).
Claims 171 and 172 are rejected under 35 U.S.C. 103 as being unpatentable over Joseph Luciano (US 20200037929 A1) (previously cited), hereinafter referred to as Luciano, in view of Winterbach et al. (US 20200335222 A1) (previously cited), hereinafter referred to as Winterbach, in view of Varaklis et al. (US 20140153794 A1), hereinafter referred to as Varaklis.
Regarding claim 171, Luciano discloses a device for generating a mobility assessment for a subject (Abstract, Fig. 1, Fig. 2, para. [0020], para. [0023]), comprising:
a processor (Fig. 1, element 112, para. [0032]); and
a memory comprising instructions that when executed by the processor direct the processor to (Fig. 1, element 114, para. [0028-0030]):
generate a visual recording of the subject performing the activity using a recording device (Fig. 2, element 210, para. [0024-0027], “sensor system may include one or more sensors which collect three-dimensional motion data from the patient … optical motion capture system … sensor system may include a camera …”, para. [0036-0037], “motion data may be collected from a patient …”);
extract time series data from the visual recording using a dynamic algorithm (Fig. 2, Fig. 5A-D, Fig. 6A-D, para. [0036-0037], “visual motion algorithms … track the motion of the sensors from frame to frame to determine the position of the joint being measured …”, para. [0051]);
generate one or more musculoskeletal movement biomarkers from the time series data (Fig. 2, element 220, para. [0038], “all or a portion of the data is received by the processor and analyzed … determine one or more characteristics related to the patient’s motion … angular speed, inflection points, abduction (ABD), angle speed, right angle distribution, and speed distribution …”);
produce the mobility assessment for the subject from the one or more musculoskeletal movement biomarkers (Fig. 2, para. [0039-0044], “patient’s characteristics are compared to one or more corresponding characteristics of the reference information … determined whether the patient has a healthy body part, injured body part … report is generated …”).
Luciano discloses the subject performs an activity comprising an oscillatory motion (Fig. 2-10, para. [0024], “three-dimensional motion data from the patient”, para. [0036], para. [0038], “angular speed, inflection points, abduction, angle speed, right angle distribution, and speed distribution … motion of a joint …”, para. [0060-0061]), wherein the subject has chronic pain, an injury, a musculoskeletal disease or condition, or a risk of developing a musculoskeletal disease or condition (para. [0020], “injured body part …”, para. [0064], “progression and healing of the injury …”). However, Luciano does not explicitly disclose directing the processor to instruct the subject to perform the activity comprising the oscillatory motion (emphasis added).
Winterbach teaches an analogous device for generating a mobility assessment for a subject by generating one or more musculoskeletal movement biomarkers from time series data of a visual recording (Abstract, Fig. 5, Fig. 13, para. [0017], para. [0052], para. [0111-0115]). Winterbach further teaches directing the processor to instruct the subject to perform an activity comprising an oscillatory motion (Fig. 10, para. [0087-0088], “instruct the patient how to complete the assessment … movement exercise shown in user interface … may select start … perform the range of motion test …”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the device disclosed by Luciano to explicitly direct the processor to instruct the subject to perform an activity comprising an oscillatory motion, as taught by Winterbach. This is because Winterbach teaches instructing the user to perform the activity allows for progress updates to be made over time to be monitored by a clinician (para. [0088-0089]).
However, modified Luciano does not explicitly disclose the processor provides a treatment recommendation to the subject based on the mobility assessment.
Varaklis teaches an analogous device for generating a mobility assessment for a subject by instructing the subject to perform an activity comprising an oscillatory motion (Fig. 3, element 302, para. [0087]), generating a visual recording of the subject performing the activity using a recording device (Fig. 3, element 306, para. [0087]), and producing a mobility assessment based on analysis (Fig. 3, element 308, para. [0087]). Varaklis further teaches providing a treatment recommendation to the subject based on the mobility assessment (para. [0089-0090], para. [0104-0107]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the processor taught by modified Luciano to additionally a treatment recommendation to the subject based on the mobility assessment, as taught by Varaklis. This is because Varaklis teaches providing treatment based on the mobility assessment allows for a closed-loop system, allowing for treatment algorithms to be provided without a human clinician, thereby allowing a faster recovery time of the subject (para. [0090]).
Regarding claim 172, Luciano discloses a system for generating a mobility assessment for a subject (Abstract, Fig. 1, Fig. 2, para. [0020], para. [0023]), the system comprising:
a display configured to provide visual information (para. [0033], para. [0045]);
a digital recording device configured to generate a visual recording of the subject performing the activity comprising the oscillatory motion (Fig. 2, element 210, para. [0024-0027], “sensor system may include one or more sensors which collect three-dimensional motion data from the patient … optical motion capture system … sensor system may include a camera …”, para. [0036-0037], “motion data may be collected from a patient …”);
a processor configured to receive the visual recording generated by the camera (Fig. 1, element 112, para. [0024-0027], para. [0032], para. [0036-0037]); and
memory operably coupled to the processor wherein the memory comprises instructions stored thereon, which when executed by the processor, cause the processor to (Fig. 1, element 114, para. [0028-0030]) extract time series data from the visual recording using a dynamic algorithm (Fig. 2, Fig. 5A-D, Fig. 6A-D, para. [0036-0037], “visual motion algorithms … track the motion of the sensors from frame to frame to determine the position of the joint being measured …”, para. [0051]), generate one or more musculoskeletal movement biomarkers from the time series data (Fig. 2, element 220, para. [0038], “all or a portion of the data is received by the processor and analyzed … determine one or more characteristics related to the patient’s motion … angular speed, inflection points, abduction (ABD), angle speed, right angle distribution, and speed distribution …”), produce the mobility assessment for the subject from the one or more musculoskeletal movement biomarkers (Fig. 2, para. [0039-0044], “patient’s characteristics are compared to one or more corresponding characteristics of the reference information … determined whether the patient has a healthy body part, injured body part … report is generated …”).
However, Luciano does not explicitly disclose the visual information is for instructing the subject to perform an activity comprising an oscillatory motion.
Winterbach teaches an analogous system for generating a mobility assessment for a subject by generating one or more musculoskeletal movement biomarkers from time series data of a visual recording (Abstract, Fig. 5, Fig. 13, para. [0017], para. [0052], para. [0111-0115]). Winterbach further teaches the visual information is for instructing the subject to perform an activity comprising an oscillatory motion (Fig. 10, para. [0087-0088], “instruct the patient how to complete the assessment … movement exercise shown in user interface … may select start … perform the range of motion test …”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system and display disclosed by Luciano to explicitly have the visual information instruct the subject to perform an activity comprising an oscillatory motion, as taught by Winterbach. This is because Winterbach teaches instructing the user to perform the activity allows for progress updates to be made over time to be monitored by a clinician (para. [0088-0089]).
However, modified Luciano does not explicitly disclose the processor provides a treatment recommendation to the subject based on the mobility assessment.
Varaklis teaches an analogous system for generating a mobility assessment for a subject by instructing the subject to perform an activity comprising an oscillatory motion (Fig. 3, element 302, para. [0087]), generating a visual recording of the subject performing the activity using a recording device (Fig. 3, element 306, para. [0087]), and producing a mobility assessment based on analysis (Fig. 3, element 308, para. [0087]). Varaklis further teaches providing a treatment recommendation to the subject based on the mobility assessment (para. [0089-0090], para. [0104-0107]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the processor taught by modified Luciano to additionally a treatment recommendation to the subject based on the mobility assessment, as taught by Varaklis. This is because Varaklis teaches providing treatment based on the mobility assessment allows for a closed-loop system, allowing for treatment algorithms to be provided without a human clinician, thereby allowing a faster recovery time of the subject (para. [0090]).
Response to Arguments
Applicant's arguments filed 08/11/2026 have been fully considered but they are not persuasive.
Applicants have argued on pages 14-15 of Remarks, filed 08/11/2026, that “Luciano fails to teach “extracting time series data from the visual recording using a dynamic algorithm, wherein the dynamic algorithm is configured to change based on data processed by the dynamic algorithm and trained on visual images labeled with one or more body parts”.
The Examiner respectfully disagrees. As recited above in the modified rejection, Luciano discloses the claimed limitation.
Applicant’s arguments, see pages 15-18 of Remarks, filed 08/11/2026, with respect to the rejection(s) of claim(s) 143-170 under 35 USC 103 have been fully considered and are persuasive. Specifically, Applicant’s amendments and arguments regarding “performing bispectral coherence analysis on principal components identified in the extracted time series data” is considered persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Toth et al. (US 20230147888 A1), hereinafter referred to as Toth.
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.
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/K.W.K./Examiner, Art Unit 3791
/JASON M SIMS/Supervisory Patent Examiner, Art Unit 3791
