DETAILED ACTION
This action is responsive to the RESPONSE TO NON-FINAL OFFICE ACTION filed 17 December 2025. The Examiner acknowledges the amendments to claims 1, 10, 12, and 19-21, and the cancellation of claims 9 and 11. Claims 1-8, 10, 12, and 14-21 are pending, wherein claims 14-18 are currently withdrawn.
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 .
Claim Objections
Claim(s) 1 and 19-21 is/are objected to because of the following informalities:
Claim 1 should be amended such that the limitation “wherein the gait symmetry is calculated as… wherein thresholds is a threshold representing an estimated maximum possible mean of the calculated distances” [lines 16-20, including the recited equation] is recited after the limitation “calculating the gait symmetry metric comprises aligning contralateral pairs… and calculating a mean of the calculated distances” [lines 20-24], so as to properly order the claim language to provide sufficient antecedent basis to the claimed “calculated distances”. The Examiner notes that due to the similar subject matter and order of claim language in claims 19 [lines 19-29] and 20 [lines 20-28], claims 19 and 20 are similar objected to and similar suggestions as presented for claim 1 are presented mutatis mutandis. The Examiner further notes that lines 19-24 of claim 1 [including any additional amendments to the limitation beginning with “a gait symmetry metric that represents…” of line 14] should be tabbed to the right once, similar to the formatting of claims 19 [lines 19-29], 20 [lines 20-28], 21 [lines 20-25].
Claim 1 should read “averaging the segmented sagittal angular velocities across all strides for a particular lower limb segment” [lines 27-28, wherein the Examiner notes that there may be confusion when reciting only “segment” as segment has been used to refer to a lower limb “segment”, as well as the step to “segment” each signal into a plurality of stride signals].
Claim 20 should read “cause [[the]] a processor to” [line 9].
Appropriate correction is required.
Claim Interpretation
Examiner Notes: currently, NO limitation invokes interpretation under § 112(f).
Claim Rejections - 35 USC § 112
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(s) 1, 19-21, and those dependent therefrom is/are 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.
Claim 1 recites the limitation “averaging the segmented sagittal angular velocities across all strides for a particular lower limb segment” [lines 27-28], wherein the Examiner notes that “the segmented sagittal angular velocities” lacks antecedent basis, as the gait data comprising a signal from each of the plurality of inertial sensors was previously only recited as representing “an angular velocity of one of a plurality of lower limb segments” [lines 7-9 of claim 1], such that it is unclear whether the limitation lacking antecedent basis is meant to further limit the angular velocity as being sagittal angular velocity only in reference to the determination of the gait repeatability metric, or whether the angular velocity may comprise at least the sagittal angular velocity. For examination purposes, the Examiner has interpreted either interpretation to be applicable in light of any applied art under § 102 or § 103. The Examiner notes that claims 19-21 recite similar subject matter that is similarly rejected and interpreted to claim 1 as noted above mutatis mutandis.
Claim 20 recites the limitation “the non-transitory computer-readable medium therein and executing the instructions stored therein; wherein the instructions, when executed ” [lines 6-7], which is considered indefinite, as it is not clear whether the recited “non-transitory computer-readable medium therein” and corresponding “instructions” are meant to refer to the non-transitory computer-readable medium having instructions stored therein as defined by the preamble of claim 20 [lines 1-2] or the subset non-transitory computer-readable medium having instructions stored therein for using the plurality of inertial sensors [lines 4-5]. For examination purposes, the Examiner has interpreted the indefinite limitation to refer to the subset non-transitory computer-readable medium having instructions stored therein for using the plurality of inertial sensors [lines 4-5].
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.
Claim(s) 1-8, 10, 12, and 19-21 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more. Each claim has been analyzed to determine whether it is directed to any judicial exceptions.
Representative claim(s) 19 [representing all independent claims] recite(s):
A system comprising:
a plurality of inertial sensors;
a non-transitory computer-readable medium having instructions stored therein for using the plurality of inertial sensors;
at least one hardware processor; and
one or more software modules stored on the at least one hardware processor, that are configured to, when executed by the at least one hardware processor,
acquire gait data comprising a signal from each of the plurality of inertial sensors, wherein each signal represents an angular velocity of one of a plurality of lower limb segments of a subject during ambulation,
segment each signal into a plurality of stride signals, wherein each of the plurality of stride signals represents one of a plurality of strides during the ambulation,
calculate gait metrics based on the plurality of stride signals, wherein the gait metrics comprise
a gait symmetry metric that represents a similarity of the plurality of stride signals across two of the signals acquired for at least one pair of contralateral lower limb segments of the plurality of lower limb segments, wherein the gait symmetry metric is calculated as:
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wherein thresholds is a threshold representing an estimated maximum possible mean of the calculated distances, and calculating the gait symmetry metric comprises aligning contralateral pairs of the plurality of stride signals across the two signals acquired for the at least one pair of contralateral lower limb segments of the plurality of lower limb segments, and calculating a distance between each aligned contralateral pair of stride signals; and calculating a mean of the calculated distances;
a gait repeatability metric that represents a similarity between each of the plurality of stride signals within at least one of the signals and including averaging the segmented sagittal angular velocities across all strides for a particular segment, comparing stride graphs to consecutive ipsilateral strides, resulting in an angular velocity difference-repeatability measured in degrees, and
output the gait metrics;
determine gait quality of the subject using both the gait symmetry metric and the gait repeatability metric together to differentiate between a gait that is symmetrical but inconsistent and a gait that is asymmetrical but stable; and
treat the subject with evidence-based rehabilitative treatment including identifying movement limitations related to balance and mobility for exercise prescription and assessing gait quality, based on the determined gait quality, for Parkinson’s disease, neurological disorders and/or musculoskeletal disorders.
(Emphasis added: abstract idea, additional element)
Step 2A Prong 1
Representative claim(s) 19 recites the following abstract ideas, which may be performed in the mind or by hand with the assistance of pen and paper:
“acquire gait data comprising a signal from each of the plurality of inertial sensors, wherein each signal represents an angular velocity of one of a plurality of lower limb segments of a subject during ambulation” – may be performed by merely observing previously collected data [The angular velocities may be continually transmitted at periodic intervals (e.g., 60 Hz), so as to be acquired as a signal by the external processing system 100 (Applicant’s Specification ¶72)]
“segment each signal into a plurality of stride signals, wherein each of the plurality of stride signals represents one of a plurality of strides during the ambulation” – may be performed by merely observing previously collected data and drawing conclusions therefrom for at least a limited amount of data [Applicant’s Specification ¶84]
“calculate gait metrics based on the plurality of stride signals, wherein the gait metrics comprise a gait symmetry metric that represents a similarity of the plurality of stride signals across two of the signals acquired for at least one pair of contralateral lower limb segments of the plurality of lower limb segments, wherein the gait symmetry metric is calculated as:
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wherein thresholds is a threshold representing an estimated maximum possible mean of the calculated distances” – may be performed by applying known mathematical processes/calculations to previously observed or collected data for at least a limited amount of data [see mathematical calculations disclosed in ¶¶87-91 of Applicant’s Specification]
“calculating the gait symmetry metric comprises aligning contralateral pairs of the plurality of stride signals across the two signals acquired for the at least one pair of contralateral lower limb segments of the plurality of lower limb segments, and calculating a distance between each aligned contralateral pair of stride signals” –
“calculating a mean of the calculated distances” – may be performed by applying known mathematical processes/calculations to previously observed or collected data for at least a limited amount of data
“calculate gait metrics based on the plurality of stride signals, wherein the gait metrics comprise… a gait repeatability metric that represents a similarity between each of the plurality of stride signals within at least one of the signals and including averaging the segmented sagittal angular velocities across all strides for a particular segment” – may be performed by applying known mathematical processes/calculations to previously observed or collected data for at least a limited amount of data [Applicant’s Specification ¶97]
“comparing stride graphs to consecutive ipsilateral strides, resulting in an angular velocity difference-repeatability measured in degrees” – may be performed by merely observing previously collected data and drawing mental conclusions therefrom for at least a limited amount of data [Applicant’s Specification ¶98]
“determine gait quality of the subject using both the gait symmetry metric and the gait repeatability metric together to differentiate between a gait that is symmetrical but inconsistent and a gait that is asymmetrical but stable” – may be performed by merely observing known or determined information and drawing mental conclusions therefrom [Applicant’s Specification ¶¶156-157]
If a claim, under BRI, covers performance of the limitations in the mind but for the mere recitation of extra-solutionary activity (and otherwise generic computer elements) then the claim falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea under Step 2A Prong 1 of the Mayo framework as set forth in the 2019 PEG.
No limitations are provided that would force the complexity of any of the identified evaluation steps to be non-performable by pen-and-paper practice.
Alternatively or additionally, these steps describe the concept of using implicit mathematical formula(s) [i.e., “calculate gait metrics based on the plurality of stride signals, wherein the gait metrics comprise a gait symmetry metric that represents a similarity of the plurality of stride signals across two of the signals acquired for at least one pair of contralateral lower limb segments of the plurality of lower limb segments, wherein the gait symmetry metric is calculated as:
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wherein thresholds is a threshold representing an estimated maximum possible mean of the calculated distances”, “calculating a distance between each aligned contralateral pair of stride signals”, “calculating a mean of the calculated distances”, “calculate gait metrics based on the plurality of stride signals, wherein the gait metrics comprise… a gait repeatability metric that represents a similarity between each of the plurality of stride signals within at least one of the signals and including averaging the segmented sagittal angular velocities across all strides for a particular segment”] to derive a conclusion based on input of data, which corresponds to concepts identified as abstract ideas by the courts [Diamond v. Diehr. 450 U.S. 175, 209 U.S.P.Q. 1 (1981), Parker v. Flook. 437 U.S. 584, 19 U.S.P.Q. 193 (1978), and In re Grams. 888 F.2d 835, 12 U.S.P.Q.2d 1824 (Fed. Cir. 1989)]. The concept of the recited limitations identified as mathematical concepts above is not meaningfully different than those mathematical concepts found by the courts to be abstract ideas.
The dependent claims merely include limitations that either further define the abstract idea [e.g. limitations relating to the data gathered or particular steps which are entirely embodied in the mental process] and amount to no more than generally linking the use of the abstract idea to a particular technological environment or field of use because they are merely incidental or token additions to the claims that do not alter or affect how the process steps are performed.
Thus, these concepts are similar to court decisions of abstract ideas of itself: collecting, displaying, and manipulating data [Int. Ventures v. Cap One Financial], collecting information, analyzing it, and displaying certain results of the collection and analysis [Electric Power Group], collection, storage, and recognition of data [Smart Systems Innovations].
Step 2A Prong 2
The judicial exception is not integrated into a practical application.
Representative claim 19 only recites additional elements of extra-solutionary activity – in particular, extra-solution activity of generic computer functions of outputting data/information, data gathering – without further sufficient detail that would tie the abstract portions of the claim into a specific practical application (2019 PEG p. 55 – the instant claim, for example does not tie into a particular machine, a sufficiently particular form of data or signal collection – via the claimed generic computer functions and data gathering, or a sufficiently particular form of display or computing architecture/structure).
Dependent claim(s) 2-3, 5-10, and 12 merely add detail to the abstract portions of the claim but do not otherwise encompass any additional elements which tie the claim(s) into a particular application/integration [the dependent claim(s) recite generic steps which encompass mere computer instructions to carry out an otherwise wholly abstract idea].
Dependent claim(s) 4 encounter substantially the same issues as the independent claim(s) from which they depend in that they encompass further generic extra-solutionary activity [generic data gathering] and/or generic computer elements [storage, memory per se].
Accordingly, the claim(s) are not integrated into a practical application under Step 2A Prong 2.
Step 2B
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Independent claims 1 and 19-21 as individual wholes fail to amount to significantly more than the judicial exception at Step 2B. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of extra-solutionary activity [i.e., generic computer functions of outputting data/information and data gathering] and generic computer elements cannot amount to significantly more than an abstract idea [MPEP § 2106.05(f)] and is further considered to merely implement an abstract idea on a generic computer [MPEP § 2106.05(d)(II) establishes computer-based elements which are considered to be well understood, routine, and conventional when recited at a high level of generality].
For the independent claim portions and dependent claims which provide additional elements of extra-solutionary data gathering, MPEP § 2106.05(g) establishes that mere data gathering for determining a result does not amount to significantly more. The extra-solutionary activity of processor steps/functions of outputting data/information as presently recited, cannot provide an inventive concept which amounts to significantly more than the recited abstract idea.
For the independent claims as well as the dependent claims merely reciting generic computer elements and functions [“a non-transitory computer-readable medium having instructions stored therein for using the plurality of inertial sensors”, “at least one hardware processor”, and “one or more software modules stored on the at least one hardware processor, that are configured to, when executed by the at least one hardware processor”], MPEP § 2106.05(d)(II) establishes computer-based elements which are considered to be well understood, routine, and conventional when recited at a high level of generality.
Accordingly, the generic computer elements and functions thereof, as presently limited, cannot provide an inventive concept since they fall under a generic structure and/or function that does not add a meaningful additional feature to the judicial exception(s) of the claim(s).
Claim(s) 1 and 19-21 recite providing “a plurality of inertial sensors”, claim 4 recites “one or more inertial measurement units positioned on the plurality of lower limb segments of the subject, wherein the one or more inertial measurement units comprise the plurality of inertial sensors”, and claim 7 recites “signals from the plurality of inertial sensors collected during a middle portion of the distance-based walk test”. Such a “plurality of inertial sensors” / “one or more inertial measurement units positioned on the plurality of lower limb segments of the subject” is considered well-understood, routine, and conventional, as known by at least:
Applicant’s disclosure is not particular regarding the particular structure of the generically claimed “plurality of inertial sensors” / “one or more inertial measurement units positioned on the plurality of lower limb segments of the subject”, and recites the “plurality of inertial sensors” / “one or more inertial measurement units positioned on the plurality of lower limb segments of the subject” at a high level of generality [For example, the system may comprise a plurality (e.g., four) inertial measurement units (IMUs) that each comprise at least one inertial sensor. IMUs are generally small, inexpensive, wireless devices that can be worn on the body, are not restricted by time and space, and can provide innumerable amounts of data regarding human motion without requiring the installation of bulky, heavy, expensive equipment. This makes IMUs especially useful for a point-of-care device. A typical IMU comprises two or more inertial sensors, including an accelerometer and a gyroscope, and, in some cases, a magnetometer. In an embodiment, each IMU comprises at least a gyroscope that continuously outputs a gyroscopic signal (Applicant’s Specification ¶46)]. This lack of disclosure is acceptable under 35 U.S.C. 112(a) since this hardware performs non-specialized functions known by those of ordinary skill in the medical technology arts. Thus, Applicant's specification essentially admits that this hardware is conventional and performs well understood, routine and conventional activities in the field of gait analysis. In other words, Applicant’s specification demonstrates the well-understood, routine, conventional nature of the above-identified additional element because it describes such an additional element in a manner that indicates that the additional element is sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. 112(a) [see Berkheimer memo from April 19, 2018, Page 3, (III)(A)(1), not attached]. Adding hardware that performs “well understood, routine, conventional activit[ies]’ previously known to the industry” will not make claims patent-eligible [TLI Communications].
Strausser (US-20150045703-A1, previously presented) [Inertial measurement units (IMUs) could be coupled to the leg support 212. An inertial measurement unit is generally composed of an accelerometer and a gyroscope and sometimes a magnetometer as well; in many modern sensors these devices are MEMS (Mico electromechanical systems) that have measurement in all three orthogonal axes on one or more microchips. The behavior of IMUs is well understood in the art (IMUs being used for applications from missile guidance to robotics to cell phones to hobbyist toys); they typically provide measurement of angular orientation with respect to gravity, as well as measurement of angular velocity with respect to earth and linear acceleration, all in three axes (Strausser ¶0025)]
Examiner’s Note Regarding Particular Treatment or Prophylaxis: Claim(s) 1 and 19-21 recite subject matter regarding “treat the subject with evidence-based rehabilitative treatment including identifying movement limitations related to balance and mobility for exercise prescription and assessing gait quality, based on the determined gait quality, for Parkinson’s disease, neurological disorders and/or musculoskeletal disorders”, which the Examiner notes is not considered to be a particular treatment or prophylaxis, as none of the identified claims positively recite or include language [the Examiner notes that merely performing non-specific operations “for exercise prescription” does not require that the exercise be performed by the subject] that is considered to be a particular treatment or prophylaxis [the Examiner notes that merely applying a non-specific “evidence-based rehabilitative treatment” for a broad range of disorders is not particular regarding any type of specific treatment] as an additional element to integrate the judicial exception into a practical application or allow the identified claims to amount to significantly more than the judicial exception [MPEP § 2106.04(d)(2)].
Accordingly, the claim(s) as whole(s) fail amount to significantly more than the judicial exception under Step 2B.
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.
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.
Claim(s) 1-8, 10, 12, 19-21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang (US-20170273601-A1, previously presented) in view of Li (“Gait Analysis for Post-Stroke Hemiparetic Patient by Multi-Features Fusion Method”, NPL previously presented), Jain (“Score normalization in multimodal biometric systems”, NPL previously presented), and Thompson (US-10610131-B1).
Regarding claim 1, Wang teaches
A method comprising:
providing a plurality of inertial sensors [a set of inertial measurement systems measure motion at multiple points. The points of measurement may be in the waist region, the upper leg, the lower leg, the foot, and/or any suitable location (Wang ¶0023)];
providing a non-transitory computer-readable medium having instructions stored therein for using the plurality of inertial sensors for carrying out the method, using at least one hardware processor to execute the instructions [The activity monitoring device 100 can additionally include any suitable components to support computational operation such as a processor, RAM, an EEPROM, user input elements (e.g., buttons, switches, capacitive sensors, touch screens, and the like), user output elements (e.g., status indicator lights, graphical display, speaker, audio jack, vibrational motor, and the like), communication components (e.g., Bluetooth LE, Zigbee, NFC, Wi-Fi, cellular data, and the like), and/or other suitable components (Wang ¶0021)] to:
acquire gait data comprising a signal from each of the plurality of inertial sensors [The signal processor module 120 functions to transform sensor data generated by the inertial measurement unit no (Wang ¶0028)], wherein each signal represents an angular velocity of one of a plurality of lower limb segments of a subject during ambulation [The relative angular orientation and displacement can be detected between the foot, thigh, and/or pelvic region. Similarly, relative velocities between a set of activity monitoring systems can be used to generate particular biomechanical signals (Wang ¶0024)];
segment each signal into a plurality of stride signals, wherein each of the plurality of stride signals represents one of a plurality of strides during the ambulation [The signal processor module 120 can include a step segmenter (Wang ¶0028); generating a set of biomechanical signals can include generating a set of stride-based biomechanical signals comprising segmenting kinematic data by steps and for at least a subset of the stride-based biomechanical signals generating a biomechanical signal based on step biomechanical properties (Wang ¶0044)];
calculate gait metrics based on the plurality of stride signals [Wang ¶0044], wherein the gait metrics comprise
a gait symmetry metric that represents a similarity of the plurality of stride signals across two of the signals acquired for at least one pair of contralateral lower limb segments of the plurality of lower limb segments [The set of stride-based biomechanical signals can include… stride symmetry (Wang ¶0044); Stride symmetry can be a measure of imbalances between different steps. It can account for various factors such as stride length, step duration, pelvic rotation, and/or other factors (Wang ¶0061)], and
a gait repeatability metric that represents a similarity between each of the plurality of stride signals within at least one of the signals [In a walking sensing mode the biomechanical signals can be based on step-wise windows of the kinematic data streams—looking at single steps, consecutive steps, or a sequence of steps. In one variation, generating a set of biomechanical signals can include generating a set of stride-based biomechanical signals comprising segmenting kinematic data by steps and for at least a subset of the stride-based biomechanical signals generating a biomechanical signal based on step biomechanical properties… The set of stride-based biomechanical signals can include cadence, ground contact time, braking, pelvic rotation, pelvic tilt, pelvic drop, vertical oscillation of the pelvis, lateral oscillation of the pelvis, forward oscillation, upper body trunk lean, forward velocity properties of the pelvis, step duration, stride or step length, step impact or shock, foot pronation, body loading ratio, foot lift, step and/or stride length, swing time, double-stance time, leg lift response time, activity transition time, stride symmetry, left and right step detection, motion paths, and/or other features (Wang ¶0044), wherein assessing individual steps relative to other steps is considered to read on the claimed repeatability metric]; and
outputting the gait metrics [The user application functions as one potential outlet of the biomechanical signal output (Wang ¶0031)]
determining gait quality of the subject using both the gait symmetry metric and the gait repeatability metric together to differentiate between a gait that is symmetrical but inconsistent and a gait that is asymmetrical but stable [updating a mobility quality score of a subject based on the set of biomechanical signals, functions to analyze the set of biomechanical signals to derive some assessment of the health or value of how the patient is moving. In one variation, the mobility quality score can be an abstraction of the set of biomechanical signals (Wang ¶0073), wherein the Examiner notes that determining a gait quality based on a calculated gait symmetry metric and a gait repeatability metric is considered to read on the language “to differentiate between a gait that is symmetrical but inconsistent and a gait that is asymmetrical but stable” as the gait quality is defined by each of the gait symmetry metric and the gait repeatability metric]; and
treating the subject with evidence-based rehabilitative treatment including identifying movement limitations related to balance and mobility for exercise prescription, and assessing gait quality, based on the determined gait quality, for Parkinson’s disease, neurological disorders and/or musculoskeletal disorders [Transmitting an electronic communication can be used in remotely monitoring a patient. In one example, transmitting an electronic communication can be used in a hospitalization use case. Similarly directing an action can be an electronic communication used in altering the operating mode of a device or system. In one example, the action may be altering delivery of treatment by a medical device (Wang ¶0080); The delivered health assessment can additionally include a report on treatment recommendations. The treatment recommendation can be a recommended or prescribed quantity of the treatment. Such recommendations can be used to generate or approve individual prescriptions. Such medication adjustments can enable treatments to be adjusted specifically to a patient based in part on the patient's mobility (Wang ¶0083); a physical therapy software application or virtual coach can provide the patient with real-time guidance and exercises (Wang ¶0088)].
However, Wang fails to explicitly disclose wherein the gait symmetry metric is calculated as:
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wherein thresholds is a threshold representing an estimated maximum possible mean of the calculated distances, and calculating the gait symmetry metric comprises aligning contralateral pairs of the plurality of stride signals across the two signals acquired for the at least one pair of contralateral lower limb segments of the plurality of lower limb segments, and calculating a distance between each aligned contralateral pair of stride signals; and calculating a mean of the calculated distances.
Li discloses that calculating a gait symmetry metric comprises: calculating a distance between aligned contralateral pair of stride signals [If one assumes that the gait electrostatic signal sequence generated by the subject’s left foot is L = {l1, l2,…, ln} of length n, the right foot sequence is R = {r1, r2,…, rm} of length m. The goal is to find an alignment between L and R with a minimal overall cost. Defining sequence W= {w1, w2,…, wk}, where k is satisfied min(n, m) < k ≤ max(n, m). The kth element of W is defined as wk = (i, j)k, where wk is the Euclidean distance between li and rj. DTW is the warping path with minimal total cost among all possible warping paths (Li Page 4, see Equation 1 on Page 4)]; and calculating a mean of the calculated distances [The mean and standard deviation (SD) of the features were compared between the hemiparetic patients (HP) and the healthy control (HC) group (Li Page 6, Table 1 on Page 8)].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of Wang to employ calculating the gait symmetry metric further comprises: aligning contralateral pairs of the plurality of stride signals for the at least one pair of contralateral lower limb segments of the plurality of lower limb segments, and calculating a distance between each aligned contralateral pair of stride signals; and calculating a mean of the calculated distances, so as to allow for the assessment of gait symmetry using the well-known method of dynamic time warping (DTW) [Li Page 4].
Jain discloses known mathematical processes for normalizing data in biometric systems [see Jain Abstract and Jain Introduction (Page 2270), which identifies gait as a known trait in biometric systems], wherein Jain discloses min-max normalization defined as
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as a method for shifting the minimum and maximum score of a dataset to 0 and 1 [Jain Page 2276], respectively. Under the current modification of Wang in view of Li, when applying min-max normalization to the calculated mean distance as modified by Li [Li Page 4, Table 1 on Page 8], it is understood that the minimum distance value is considered to be 0 [as perfectly symmetrical signals would not require DTW to align], such that the min-max normalization may be rewritten as
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. It is further understood that the additional mathematical processes as claimed of
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is considered to merely convert the normalized value [understood to refer to a fraction, as a mean value of a dataset is considered to be less than a max value of the same dataset] as a percentage of 100.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of Wang in view of Li to employ wherein the gait symmetry metric is calculated as:
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wherein thresholds is a threshold representing an estimated maximum possible mean of the calculated distances, based min-max normalization as disclosed by Jain and further known mathematical processes to calculate a percentage, as this modification would amount to merely applying known techniques [min-max normalization, converting a fraction into a percentage] to a known method ready for improvement to yield predictable results [convert the calculated mean distance representing gait symmetry (Li Table 1 Page 8) into a percentage score in order to be easier to understand relative to other scores] [MPEP § 2143(I)(D)].
However, while Wang discloses characterizing each of left and right and right strides in terms of angular velocity [Multiple points may be used for detecting foot gait attributes, knee flex angle, and/or distinguishing between right and left leg actions. Single point sensing may additionally be applied to right and left leg attributes (Wang ¶0024); Left and right step detection can function to detect individual steps. Any of the biomechanical signals could additionally be characterized for left and right sides (Wang ¶0062)], Wang in view of Li and Jain fails to explicitly disclose wherein the gait repeatability metric includes averaging the segmented sagittal angular velocities across all strides for a particular segment, comparing stride graphs to consecutive ipsilateral strides, resulting in an angular velocity difference-repeatability measured in degrees.
Thompson discloses systems and methods for assessing gait quality of a subject, wherein Thompson discloses that averaging data within a stride may provide useful summary information regarding limb motion metrics [These displacements may be used directly and/or indirectly (e.g., summations, averages, extrema magnitudes, RMS, or the like), perhaps to derive usable summary and point-by-point limb motion metrics (Thompson Col 10:9-12)], as well as steps for comparing stride graphs to consecutive ipsilateral strides, resulting in an angular velocity difference-repeatability measured in degrees [the usable stride 610 inertial sensor outputs (e.g., three-dimensional angular velocity roll, pitch and yaw components or the like) may be processed, perhaps to extract limb inertial motion metrics and even stride phase duration metrics 612 (Thompson Col 9:61-65); Additionally, differences between each of the angular displacement and angular velocity curves can be compared either for the same limb (unilateral) or between limbs (bilateral) on a point by point basis (perhaps for each data collection time point over the course of a stride). These differences can be averaged over multiple strides (aggregated across each run within a session for intra-session comparison or runs among sessions for multi-session comparison), and the results may be displayed, perhaps with animation and/or graphically, as an average difference curve (e.g., over the entire stride and/or for each stride phase or the like). These difference-curves can highlight non-uniform motion signatures and/or patterns that can be visualized and even recognized at a glance (Thompson Col 10:21-34); this measurement device could be applied to humans and other animals or the like (Thompson Col 5:1-3)].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of Wang in view of Li and Jain to employ wherein the gait repeatability metric includes averaging the segmented sagittal angular velocities across all strides for a particular segment, comparing stride graphs to consecutive ipsilateral strides, resulting in an angular velocity difference-repeatability measured in degrees, so as to allow for visualization and recognition of non-uniform motion signatures during gait.
Regarding claim 2, Wang in view of Li, Jain, and Thompson teaches
The method of Claim 1, wherein each signal is acquired for each of the plurality of lower limb segments of the subject during ambulation, and wherein the plurality of lower limb segments comprises a right thigh, right shank, left thigh, and left shank of the subject [Wang ¶0023, ¶0028].
Regarding claim 3, Wang in view of Li, Jain, and Thompson teaches
The method of Claim 1, wherein each signal represents the angular velocity in a sagittal plane of the subject over a time period of the ambulation [The individual kinematic data streams preferably correspond to distinct kinematic measurements along a defined axis. The kinematic measurements are preferably along a set of orthonormal axes (e.g., an x, y, z coordinate plane) (Wang ¶0039); Pelvic tilt (i.e., pitch) can be characterized as rotation in the sagittal plane (i.e., rotation about a lateral axis) (Wang ¶0048)].
Regarding claim 4, Wang in view of Li, Jain, and Thompson teaches
The method of Claim 1, wherein acquiring the gait data comprises receiving a wireless signal transmitted by one or more inertial measurement units [kinematic data or biomechanical signal data could be sent over Wi-Fi or a cellular network (Wang ¶0021)], positioned on the plurality of lower limb segments of the subject [Wang ¶0023], wherein the one or more inertial measurement units comprise the plurality of inertial sensors [Wang ¶0025].
Regarding claim 5, Wang in view of Li, Jain, and Thompson teaches
The method of Claim 1, wherein the ambulation comprises one or more ambulation tests [Wang ¶0044].
Regarding claim 6, Wang in view of Li, Jain, and Thompson teaches
The method of Claim 5, wherein the one or more ambulation tests comprise a distance-based walk test [an inertial measurement unit can include a Bluetooth communication channel to a smart phone, and the smart phone can track and retrieve data on geolocation, distance covered, elevation changes, and other data (Wang ¶0026); Forward velocity properties of the pelvis or the forward oscillation can be one or more signals characterizing the oscillation of distance over a step or stride, velocity, maximum velocity, minimum velocity, average velocity, or any suitable property of forward kinematic properties of the pelvis (Wang ¶0052)].
Regarding claim 7, Wang in view of Li, Jain, and Thompson teaches
The method of Claim 6, wherein the gait data consists of signals from the plurality of inertial sensors collected during a middle portion of the distance-based walk test [Wang ¶¶0026, 0044, 0052; wherein any signal that is not considered to be the first or last signal, as collected over time, may be considered to be collected during a “middle portion”].
Regarding claim 8, Wang in view of Li, Jain, and Thompson teaches
The method of Claim 1.
However, Wang in view of Li, Jain, and Thompson as presently modified fails to explicitly disclose wherein each of the plurality of stride signals represents a toe-off of one lower limb segment of the plurality of lower limb segments to a next toe-off of the same lower limb segment of the plurality of lower limb segments.
Thompson discloses a stride as comprising a first toe-off of one lower limb segment to a next toe-off of the same lower limb segment [Each stride is a full cycle of limb motion, perhaps including stance and swing phases (Thompson Col 2:6-7, Fig. 7), wherein as a stride is a cycle, the cycle as depicted in Thompson Fig. 7 may be considered to start at any point and end when the cycle reaches the same point again].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of Wang in view of Li, Jain, and Thompson to employ wherein each of the plurality of stride signals represents a toe-off of one lower limb segment of the plurality of lower limb segments to a next toe-off of the same lower limb segment of the plurality of lower limb segments, as known sequences of a stride of a gait cycle comprise a toe-off to a next toe-off of the same limb [Shim ¶0207, Figure 7A], such that it would amount to mere simple substitution of one known element [the undisclosed stride segmentation of Wang] for another [the toe-off to next toe-off stride segmentation of Thompson] to obtain predictable results [allow for stride segmentation] [see MPEP § 2143(I)(B)].
Regarding claim 10, Wang in view of Li, Jain, and Thompson teaches
The method of Claim 9, wherein aligning the contralateral pairs of stride signals comprises dynamic time warping [see § 103 modification of claim 9 above; Li Page 4].
Regarding claim 12, Wang in view of Li, Jain, and Thompson teaches
The method of Claim 1, wherein the distance is a Euclidean distance [see § 103 modification of claim 1 above; Li Page 4].
Regarding claim 19, Wang teaches
A system comprising:
a plurality of inertial sensors [a set of inertial measurement systems measure motion at multiple points. The points of measurement may be in the waist region, the upper leg, the lower leg, the foot, and/or any suitable location (Wang ¶0023)];
a non-transitory computer-readable medium having instructions stored therein for using the plurality of inertial sensors for carrying out the method [The activity monitoring device 100 can additionally include any suitable components to support computational operation such as a processor, RAM, an EEPROM, user input elements (e.g., buttons, switches, capacitive sensors, touch screens, and the like), user output elements (e.g., status indicator lights, graphical display, speaker, audio jack, vibrational motor, and the like), communication components (e.g., Bluetooth LE, Zigbee, NFC, Wi-Fi, cellular data, and the like), and/or other suitable components (Wang ¶0021)];
at least one hardware processor [Wang ¶0021];
one or more software modules stored on the at least one hardware processor [Wang ¶0021], that are configured to, when executed by the at least one hardware processor,
acquire gait data comprising a signal from each of the plurality of inertial sensors [The signal processor module 120 functions to transform sensor data generated by the inertial measurement unit no (Wang ¶0028)], wherein each signal represents an angular velocity of one of a plurality of lower limb segments of a subject during ambulation [The relative angular orientation and displacement can be detected between the foot, thigh, and/or pelvic region. Similarly, relative velocities between a set of activity monitoring systems can be used to generate particular biomechanical signals (Wang ¶0024)];
segment each signal into a plurality of stride signals, wherein each of the plurality of stride signals represents one of a plurality of strides during the ambulation [The signal processor module 120 can include a step segmenter (Wang ¶0028); generating a set of biomechanical signals can include generating a set of stride-based biomechanical signals comprising segmenting kinematic data by steps and for at least a subset of the stride-based biomechanical signals generating a biomechanical signal based on step biomechanical properties (Wang ¶0044)];
calculate gait metrics based on the plurality of stride signals [Wang ¶0044], wherein the gait metrics comprise
a gait symmetry metric that represents a similarity of the plurality of stride signals across two of the signals acquired for at least one pair of contralateral lower limb segments of the plurality of lower limb segments [The set of stride-based biomechanical signals can include… stride symmetry (Wang ¶0044); Stride symmetry can be a measure of imbalances between different steps. It can account for various factors such as stride length, step duration, pelvic rotation, and/or other factors (Wang ¶0061)], and
a gait repeatability metric that represents a similarity between each of the plurality of stride signals within at least one of the signals [In a walking sensing mode the biomechanical signals can be based on step-wise windows of the kinematic data streams—looking at single steps, consecutive steps, or a sequence of steps. In one variation, generating a set of biomechanical signals can include generating a set of stride-based biomechanical signals comprising segmenting kinematic data by steps and for at least a subset of the stride-based biomechanical signals generating a biomechanical signal based on step biomechanical properties… The set of stride-based biomechanical signals can include cadence, ground contact time, braking, pelvic rotation, pelvic tilt, pelvic drop, vertical oscillation of the pelvis, lateral oscillation of the pelvis, forward oscillation, upper body trunk lean, forward velocity properties of the pelvis, step duration, stride or step length, step impact or shock, foot pronation, body loading ratio, foot lift, step and/or stride length, swing time, double-stance time, leg lift response time, activity transition time, stride symmetry, left and right step detection, motion paths, and/or other features (Wang ¶0044), wherein assessing individual steps relative to other steps is considered to read on the claimed repeatability metric]; and
output the gait metrics [The user application functions as one potential outlet of the biomechanical signal output (Wang ¶0031)]
determine gait quality of the subject using both the gait symmetry metric and the gait repeatability metric together to differentiate between a gait that is symmetrical but inconsistent and a gait that is asymmetrical but stable [updating a mobility quality score of a subject based on the set of biomechanical signals, functions to analyze the set of biomechanical signals to derive some assessment of the health or value of how the patient is moving. In one variation, the mobility quality score can be an abstraction of the set of biomechanical signals (Wang ¶0073), wherein the Examiner notes that determining a gait quality based on a calculated gait symmetry metric and a gait repeatability metric is considered to read on the language “to differentiate between a gait that is symmetrical but inconsistent and a gait that is asymmetrical but stable” as the gait quality is defined by each of the gait symmetry metric and the gait repeatability metric]; and
treat the subject with evidence-based rehabilitative treatment including identifying movement limitations related to balance and mobility for exercise prescription, and assessing gait quality, based on the determined gait quality, for Parkinson’s disease, neurological disorders and/or musculoskeletal disorders [Transmitting an electronic communication can be used in remotely monitoring a patient. In one example, transmitting an electronic communication can be used in a hospitalization use case. Similarly directing an action can be an electronic communication used in altering the operating mode of a device or system. In one example, the action may be altering delivery of treatment by a medical device (Wang ¶0080); The delivered health assessment can additionally include a report on treatment recommendations. The treatment recommendation can be a recommended or prescribed quantity of the treatment. Such recommendations can be used to generate or approve individual prescriptions. Such medication adjustments can enable treatments to be adjusted specifically to a patient based in part on the patient's mobility (Wang ¶0083); a physical therapy software application or virtual coach can provide the patient with real-time guidance and exercises (Wang ¶0088)].
However, Wang fails to explicitly disclose wherein the gait symmetry metric is calculated as:
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wherein thresholds is a threshold representing an estimated maximum possible mean of the calculated distances, and calculating the gait symmetry metric comprises aligning contralateral pairs of the plurality of stride signals across the two signals acquired for the at least one pair of contralateral lower limb segments of the plurality of lower limb segments, and calculating a distance between each aligned contralateral pair of stride signals; and calculating a mean of the calculated distances.
Li discloses that calculating a gait symmetry metric comprises: calculating a distance between aligned contralateral pair of stride signals [If one assumes that the gait electrostatic signal sequence generated by the subject’s left foot is L = {l1, l2,…, ln} of length n, the right foot sequence is R = {r1, r2,…, rm} of length m. The goal is to find an alignment between L and R with a minimal overall cost. Defining sequence W= {w1, w2,…, wk}, where k is satisfied min(n, m) < k ≤ max(n, m). The kth element of W is defined as wk = (i, j)k, where wk is the Euclidean distance between li and rj. DTW is the warping path with minimal total cost among all possible warping paths (Li Page 4, see Equation 1 on Page 4)]; and calculating a mean of the calculated distances [The mean and standard deviation (SD) of the features were compared between the hemiparetic patients (HP) and the healthy control (HC) group (Li Page 6, Table 1 on Page 8)].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Wang to employ calculating the gait symmetry metric further comprises: aligning contralateral pairs of the plurality of stride signals for the at least one pair of contralateral lower limb segments of the plurality of lower limb segments, and calculating a distance between each aligned contralateral pair of stride signals; and calculating a mean of the calculated distances, so as to allow for the assessment of gait symmetry using the well-known method of dynamic time warping (DTW) [Li Page 4].
Jain discloses known mathematical processes for normalizing data in biometric systems [see Jain Abstract and Jain Introduction (Page 2270), which identifies gait as a known trait in biometric systems], wherein Jain discloses min-max normalization defined as
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as a method for shifting the minimum and maximum score of a dataset to 0 and 1 [Jain Page 2276], respectively. Under the current modification of Wang in view of Li, when applying min-max normalization to the calculated mean distance as modified by Li [Li Page 4, Table 1 on Page 8], it is understood that the minimum distance value is considered to be 0 [as perfectly symmetrical signals would not require DTW to align], such that the min-max normalization may be rewritten as
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. It is further understood that the additional mathematical processes as claimed of
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is considered to merely convert the normalized value [understood to refer to a fraction, as a mean value of a dataset is considered to be less than a max value of the same dataset] as a percentage of 100.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Wang in view of Li to employ wherein the gait symmetry metric is calculated as:
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wherein thresholds is a threshold representing an estimated maximum possible mean of the calculated distances, based min-max normalization as disclosed by Jain and further known mathematical processes to calculate a percentage, as this modification would amount to merely applying known techniques [min-max normalization, converting a fraction into a percentage] to a known method ready for improvement to yield predictable results [convert the calculated mean distance representing gait symmetry (Li Table 1 Page 8) into a percentage score in order to be easier to understand relative to other scores] [MPEP § 2143(I)(D)].
However, while Wang discloses characterizing each of left and right and right strides in terms of angular velocity [Multiple points may be used for detecting foot gait attributes, knee flex angle, and/or distinguishing between right and left leg actions. Single point sensing may additionally be applied to right and left leg attributes (Wang ¶0024); Left and right step detection can function to detect individual steps. Any of the biomechanical signals could additionally be characterized for left and right sides (Wang ¶0062)], Wang in view of Li and Jain fails to explicitly disclose wherein the gait repeatability metric includes averaging the segmented sagittal angular velocities across all strides for a particular segment, comparing stride graphs to consecutive ipsilateral strides, resulting in an angular velocity difference-repeatability measured in degrees.
Thompson discloses systems and methods for assessing gait quality of a subject, wherein Thompson discloses that averaging data within a stride may provide useful summary information regarding limb motion metrics [These displacements may be used directly and/or indirectly (e.g., summations, averages, extrema magnitudes, RMS, or the like), perhaps to derive usable summary and point-by-point limb motion metrics (Thompson Col 10:9-12)], as well as steps for comparing stride graphs to consecutive ipsilateral strides, resulting in an angular velocity difference-repeatability measured in degrees [the usable stride 610 inertial sensor outputs (e.g., three-dimensional angular velocity roll, pitch and yaw components or the like) may be processed, perhaps to extract limb inertial motion metrics and even stride phase duration metrics 612 (Thompson Col 9:61-65); Additionally, differences between each of the angular displacement and angular velocity curves can be compared either for the same limb (unilateral) or between limbs (bilateral) on a point by point basis (perhaps for each data collection time point over the course of a stride). These differences can be averaged over multiple strides (aggregated across each run within a session for intra-session comparison or runs among sessions for multi-session comparison), and the results may be displayed, perhaps with animation and/or graphically, as an average difference curve (e.g., over the entire stride and/or for each stride phase or the like). These difference-curves can highlight non-uniform motion signatures and/or patterns that can be visualized and even recognized at a glance (Thompson Col 10:21-34); this measurement device could be applied to humans and other animals or the like (Thompson Col 5:1-3)].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of wherein the gait repeatability metric includes averaging the segmented sagittal angular velocities across all strides for a particular segment, comparing stride graphs to consecutive ipsilateral strides, resulting in an angular velocity difference-repeatability measured in degrees, so as to allow for visualization and recognition of non-uniform motion signatures during gait.
Regarding claim 20, Wang teaches
A non-transitory computer-readable medium comprising:
a non-transitory computer-readable medium having instructions stored therein for using the plurality of inertial sensors for carrying out the method [The activity monitoring device 100 can additionally include any suitable components to support computational operation such as a processor, RAM, an EEPROM, user input elements (e.g., buttons, switches, capacitive sensors, touch screens, and the like), user output elements (e.g., status indicator lights, graphical display, speaker, audio jack, vibrational motor, and the like), communication components (e.g., Bluetooth LE, Zigbee, NFC, Wi-Fi, cellular data, and the like), and/or other suitable components (Wang ¶0021)]; a plurality of inertial sensors being operatively connected to a computer system having the non-transitory computer-readable medium therein and executing the instructions stored therein [a set of inertial measurement systems measure motion at multiple points. The points of measurement may be in the waist region, the upper leg, the lower leg, the foot, and/or any suitable location (Wang ¶0023); Wang ¶0021];
wherein the instructions, when executed by a processor in the computer system, cause the processor to:
acquire gait data comprising a signal from each of the plurality of inertial sensors [The signal processor module 120 functions to transform sensor data generated by the inertial measurement unit no (Wang ¶0028)], wherein each signal represents an angular velocity of one of a plurality of lower limb segments of a subject during ambulation [The relative angular orientation and displacement can be detected between the foot, thigh, and/or pelvic region. Similarly, relative velocities between a set of activity monitoring systems can be used to generate particular biomechanical signals (Wang ¶0024)];
segment each signal into a plurality of stride signals, wherein each of the plurality of stride signals represents one of a plurality of strides during the ambulation [The signal processor module 120 can include a step segmenter (Wang ¶0028); generating a set of biomechanical signals can include generating a set of stride-based biomechanical signals comprising segmenting kinematic data by steps and for at least a subset of the stride-based biomechanical signals generating a biomechanical signal based on step biomechanical properties (Wang ¶0044)];
calculate gait metrics based on the plurality of stride signals [Wang ¶0044], wherein the gait metrics comprise
a gait symmetry metric that represents a similarity of the plurality of stride signals across two of the signals acquired for at least one pair of contralateral lower limb segments of the plurality of lower limb segments [The set of stride-based biomechanical signals can include… stride symmetry (Wang ¶0044); Stride symmetry can be a measure of imbalances between different steps. It can account for various factors such as stride length, step duration, pelvic rotation, and/or other factors (Wang ¶0061)], and
a gait repeatability metric that represents a similarity between each of the plurality of stride signals within at least one of the signals [In a walking sensing mode the biomechanical signals can be based on step-wise windows of the kinematic data streams—looking at single steps, consecutive steps, or a sequence of steps. In one variation, generating a set of biomechanical signals can include generating a set of stride-based biomechanical signals comprising segmenting kinematic data by steps and for at least a subset of the stride-based biomechanical signals generating a biomechanical signal based on step biomechanical properties… The set of stride-based biomechanical signals can include cadence, ground contact time, braking, pelvic rotation, pelvic tilt, pelvic drop, vertical oscillation of the pelvis, lateral oscillation of the pelvis, forward oscillation, upper body trunk lean, forward velocity properties of the pelvis, step duration, stride or step length, step impact or shock, foot pronation, body loading ratio, foot lift, step and/or stride length, swing time, double-stance time, leg lift response time, activity transition time, stride symmetry, left and right step detection, motion paths, and/or other features (Wang ¶0044), wherein assessing individual steps relative to other steps is considered to read on the claimed repeatability metric]; and
output the gait metrics [The user application functions as one potential outlet of the biomechanical signal output (Wang ¶0031)]
determine gait quality of the subject using both the gait symmetry metric and the gait repeatability metric together to differentiate between a gait that is symmetrical but inconsistent and a gait that is asymmetrical but stable [updating a mobility quality score of a subject based on the set of biomechanical signals, functions to analyze the set of biomechanical signals to derive some assessment of the health or value of how the patient is moving. In one variation, the mobility quality score can be an abstraction of the set of biomechanical signals (Wang ¶0073), wherein the Examiner notes that determining a gait quality based on a calculated gait symmetry metric and a gait repeatability metric is considered to read on the language “to differentiate between a gait that is symmetrical but inconsistent and a gait that is asymmetrical but stable” as the gait quality is defined by each of the gait symmetry metric and the gait repeatability metric]; and
treat the subject with evidence-based rehabilitative treatment including identifying movement limitations related to balance and mobility for exercise prescription, and assessing gait quality, based on the determined gait quality, for Parkinson’s disease, neurological disorders and/or musculoskeletal disorders [Transmitting an electronic communication can be used in remotely monitoring a patient. In one example, transmitting an electronic communication can be used in a hospitalization use case. Similarly directing an action can be an electronic communication used in altering the operating mode of a device or system. In one example, the action may be altering delivery of treatment by a medical device (Wang ¶0080); The delivered health assessment can additionally include a report on treatment recommendations. The treatment recommendation can be a recommended or prescribed quantity of the treatment. Such recommendations can be used to generate or approve individual prescriptions. Such medication adjustments can enable treatments to be adjusted specifically to a patient based in part on the patient's mobility (Wang ¶0083); a physical therapy software application or virtual coach can provide the patient with real-time guidance and exercises (Wang ¶0088)].
However, Wang fails to explicitly disclose wherein the gait symmetry metric is calculated as:
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wherein thresholds is a threshold representing an estimated maximum possible mean of the calculated distances, and calculating the gait symmetry metric comprises aligning contralateral pairs of the plurality of stride signals across the two signals acquired for the at least one pair of contralateral lower limb segments of the plurality of lower limb segments, and calculating a distance between each aligned contralateral pair of stride signals; and calculating a mean of the calculated distances.
Li discloses that calculating a gait symmetry metric comprises: calculating a distance between aligned contralateral pair of stride signals [If one assumes that the gait electrostatic signal sequence generated by the subject’s left foot is L = {l1, l2,…, ln} of length n, the right foot sequence is R = {r1, r2,…, rm} of length m. The goal is to find an alignment between L and R with a minimal overall cost. Defining sequence W= {w1, w2,…, wk}, where k is satisfied min(n, m) < k ≤ max(n, m). The kth element of W is defined as wk = (i, j)k, where wk is the Euclidean distance between li and rj. DTW is the warping path with minimal total cost among all possible warping paths (Li Page 4, see Equation 1 on Page 4)]; and calculating a mean of the calculated distances [The mean and standard deviation (SD) of the features were compared between the hemiparetic patients (HP) and the healthy control (HC) group (Li Page 6, Table 1 on Page 8)].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the non-transitory computer-readable medium of Wang to employ calculating the gait symmetry metric further comprises: aligning contralateral pairs of the plurality of stride signals for the at least one pair of contralateral lower limb segments of the plurality of lower limb segments, and calculating a distance between each aligned contralateral pair of stride signals; and calculating a mean of the calculated distances, so as to allow for the assessment of gait symmetry using the well-known method of dynamic time warping (DTW) [Li Page 4].
Jain discloses known mathematical processes for normalizing data in biometric systems [see Jain Abstract and Jain Introduction (Page 2270), which identifies gait as a known trait in biometric systems], wherein Jain discloses min-max normalization defined as
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as a method for shifting the minimum and maximum score of a dataset to 0 and 1 [Jain Page 2276], respectively. Under the current modification of Wang in view of Li, when applying min-max normalization to the calculated mean distance as modified by Li [Li Page 4, Table 1 on Page 8], it is understood that the minimum distance value is considered to be 0 [as perfectly symmetrical signals would not require DTW to align], such that the min-max normalization may be rewritten as
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It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the non-transitory computer-readable medium of Wang in view of Li to employ wherein the gait symmetry metric is calculated as:
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wherein thresholds is a threshold representing an estimated maximum possible mean of the calculated distances, based min-max normalization as disclosed by Jain and further known mathematical processes to calculate a percentage, as this modification would amount to merely applying known techniques [min-max normalization, converting a fraction into a percentage] to a known method ready for improvement to yield predictable results [convert the calculated mean distance representing gait symmetry (Li Table 1 Page 8) into a percentage score in order to be easier to understand relative to other scores] [MPEP § 2143(I)(D)].
However, while Wang discloses characterizing each of left and right and right strides in terms of angular velocity [Multiple points may be used for detecting foot gait attributes, knee flex angle, and/or distinguishing between right and left leg actions. Single point sensing may additionally be applied to right and left leg attributes (Wang ¶0024); Left and right step detection can function to detect individual steps. Any of the biomechanical signals could additionally be characterized for left and right sides (Wang ¶0062)], Wang in view of Li and Jain fails to explicitly disclose wherein the gait repeatability metric includes averaging the segmented sagittal angular velocities across all strides for a particular segment, comparing stride graphs to consecutive ipsilateral strides, resulting in an angular velocity difference-repeatability measured in degrees.
Thompson discloses systems and methods for assessing gait quality of a subject, wherein Thompson discloses that averaging data within a stride may provide useful summary information regarding limb motion metrics [These displacements may be used directly and/or indirectly (e.g., summations, averages, extrema magnitudes, RMS, or the like), perhaps to derive usable summary and point-by-point limb motion metrics (Thompson Col 10:9-12)], as well as steps for comparing stride graphs to consecutive ipsilateral strides, resulting in an angular velocity difference-repeatability measured in degrees [the usable stride 610 inertial sensor outputs (e.g., three-dimensional angular velocity roll, pitch and yaw components or the like) may be processed, perhaps to extract limb inertial motion metrics and even stride phase duration metrics 612 (Thompson Col 9:61-65); Additionally, differences between each of the angular displacement and angular velocity curves can be compared either for the same limb (unilateral) or between limbs (bilateral) on a point by point basis (perhaps for each data collection time point over the course of a stride). These differences can be averaged over multiple strides (aggregated across each run within a session for intra-session comparison or runs among sessions for multi-session comparison), and the results may be displayed, perhaps with animation and/or graphically, as an average difference curve (e.g., over the entire stride and/or for each stride phase or the like). These difference-curves can highlight non-uniform motion signatures and/or patterns that can be visualized and even recognized at a glance (Thompson Col 10:21-34); this measurement device could be applied to humans and other animals or the like (Thompson Col 5:1-3)].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the non-transitory computer-readable medium of wherein the gait repeatability metric includes averaging the segmented sagittal angular velocities across all strides for a particular segment, comparing stride graphs to consecutive ipsilateral strides, resulting in an angular velocity difference-repeatability measured in degrees, so as to allow for visualization and recognition of non-uniform motion signatures during gait.
Regarding claim 21, Wang teaches
A method using at least one hardware processor [The activity monitoring device 100 can additionally include any suitable components to support computational operation such as a processor, RAM, an EEPROM, user input elements (e.g., buttons, switches, capacitive sensors, touch screens, and the like), user output elements (e.g., status indicator lights, graphical display, speaker, audio jack, vibrational motor, and the like), communication components (e.g., Bluetooth LE, Zigbee, NFC, Wi-Fi, cellular data, and the like), and/or other suitable components (Wang ¶0021)], comprising the steps of:
providing a plurality of inertial sensors [a set of inertial measurement systems measure motion at multiple points. The points of measurement may be in the waist region, the upper leg, the lower leg, the foot, and/or any suitable location (Wang ¶0023)];
providing a non-transitory computer-readable medium having instructions stored therein for using the plurality of inertial sensors for carrying out the method [Wang ¶0021];
acquiring gait data comprising a signal from each of the plurality of inertial sensors [The signal processor module 120 functions to transform sensor data generated by the inertial measurement unit no (Wang ¶0028)], wherein each signal represents an angular velocity of one of a plurality of lower limb segments of a subject during a motion test [The relative angular orientation and displacement can be detected between the foot, thigh, and/or pelvic region. Similarly, relative velocities between a set of activity monitoring systems can be used to generate particular biomechanical signals (Wang ¶0024)];
segment each signal into a plurality of signal segments, wherein each of the plurality of signal segments represents one of a plurality of repetitive motions during the motion test [The signal processor module 120 can include a step segmenter (Wang ¶0028); generating a set of biomechanical signals can include generating a set of stride-based biomechanical signals comprising segmenting kinematic data by steps and for at least a subset of the stride-based biomechanical signals generating a biomechanical signal based on step biomechanical properties (Wang ¶0044)];
calculate gait metrics based on the plurality of stride signals [Wang ¶0044], wherein the gait metrics comprise
a gait symmetry metric that represents a similarity of the plurality of signal segments across two of the signals acquired for at least one pair of contralateral lower limb segments of the plurality of lower limb segments [The set of stride-based biomechanical signals can include… stride symmetry (Wang ¶0044); Stride symmetry can be a measure of imbalances between different steps. It can account for various factors such as stride length, step duration, pelvic rotation, and/or other factors (Wang ¶0061)], and
a gait repeatability metric that represents a similarity between each of the plurality of signal segments within at least one of the signals [In a walking sensing mode the biomechanical signals can be based on step-wise windows of the kinematic data streams—looking at single steps, consecutive steps, or a sequence of steps. In one variation, generating a set of biomechanical signals can include generating a set of stride-based biomechanical signals comprising segmenting kinematic data by steps and for at least a subset of the stride-based biomechanical signals generating a biomechanical signal based on step biomechanical properties… The set of stride-based biomechanical signals can include cadence, ground contact time, braking, pelvic rotation, pelvic tilt, pelvic drop, vertical oscillation of the pelvis, lateral oscillation of the pelvis, forward oscillation, upper body trunk lean, forward velocity properties of the pelvis, step duration, stride or step length, step impact or shock, foot pronation, body loading ratio, foot lift, step and/or stride length, swing time, double-stance time, leg lift response time, activity transition time, stride symmetry, left and right step detection, motion paths, and/or other features (Wang ¶0044), wherein assessing individual steps relative to other steps is considered to read on the claimed repeatability metric]; and
output the gait metrics [The user application functions as one potential outlet of the biomechanical signal output (Wang ¶0031)]
determining gait quality of the subject using both the gait symmetry metric and the gait repeatability metric together to differentiate between a gait that is symmetrical but inconsistent and a gait that is asymmetrical but stable [updating a mobility quality score of a subject based on the set of biomechanical signals, functions to analyze the set of biomechanical signals to derive some assessment of the health or value of how the patient is moving. In one variation, the mobility quality score can be an abstraction of the set of biomechanical signals (Wang ¶0073), wherein the Examiner notes that determining a gait quality based on a calculated gait symmetry metric and a gait repeatability metric is considered to read on the language “to differentiate between a gait that is symmetrical but inconsistent and a gait that is asymmetrical but stable” as the gait quality is defined by each of the gait symmetry metric and the gait repeatability metric]; and
treating the subject with evidence-based rehabilitative treatment including identifying movement limitations related to balance and mobility for exercise prescription, and assessing gait quality, based on the determined gait quality, for Parkinson’s disease, neurological disorders and/or musculoskeletal disorders [Transmitting an electronic communication can be used in remotely monitoring a patient. In one example, transmitting an electronic communication can be used in a hospitalization use case. Similarly directing an action can be an electronic communication used in altering the operating mode of a device or system. In one example, the action may be altering delivery of treatment by a medical device (Wang ¶0080); The delivered health assessment can additionally include a report on treatment recommendations. The treatment recommendation can be a recommended or prescribed quantity of the treatment. Such recommendations can be used to generate or approve individual prescriptions. Such medication adjustments can enable treatments to be adjusted specifically to a patient based in part on the patient's mobility (Wang ¶0083); a physical therapy software application or virtual coach can provide the patient with real-time guidance and exercises (Wang ¶0088)].
However, Wang fails to explicitly disclose wherein the gait symmetry metric is calculated as:
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wherein thresholds is a threshold representing an estimated maximum possible mean of the calculated distances, and calculating the gait symmetry metric comprises aligning contralateral pairs of the plurality of stride signals across the two signals acquired for the at least one pair of contralateral lower limb segments of the plurality of lower limb segments, and calculating a distance between each aligned contralateral pair of stride signals; and calculating a mean of the calculated distances.
Li discloses that calculating a gait symmetry metric comprises: calculating a distance between aligned contralateral pair of stride signals [If one assumes that the gait electrostatic signal sequence generated by the subject’s left foot is L = {l1, l2,…, ln} of length n, the right foot sequence is R = {r1, r2,…, rm} of length m. The goal is to find an alignment between L and R with a minimal overall cost. Defining sequence W= {w1, w2,…, wk}, where k is satisfied min(n, m) < k ≤ max(n, m). The kth element of W is defined as wk = (i, j)k, where wk is the Euclidean distance between li and rj. DTW is the warping path with minimal total cost among all possible warping paths (Li Page 4, see Equation 1 on Page 4)]; and calculating a mean of the calculated distances [The mean and standard deviation (SD) of the features were compared between the hemiparetic patients (HP) and the healthy control (HC) group (Li Page 6, Table 1 on Page 8)].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of Wang to employ calculating the gait symmetry metric further comprises: aligning contralateral pairs of the plurality of stride signals for the at least one pair of contralateral lower limb segments of the plurality of lower limb segments, and calculating a distance between each aligned contralateral pair of stride signals; and calculating a mean of the calculated distances, so as to allow for the assessment of gait symmetry using the well-known method of dynamic time warping (DTW) [Li Page 4].
Jain discloses known mathematical processes for normalizing data in biometric systems [see Jain Abstract and Jain Introduction (Page 2270), which identifies gait as a known trait in biometric systems], wherein Jain discloses min-max normalization defined as
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as a method for shifting the minimum and maximum score of a dataset to 0 and 1 [Jain Page 2276], respectively. Under the current modification of Wang in view of Li, when applying min-max normalization to the calculated mean distance as modified by Li [Li Page 4, Table 1 on Page 8], it is understood that the minimum distance value is considered to be 0 [as perfectly symmetrical signals would not require DTW to align], such that the min-max normalization may be rewritten as
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. It is further understood that the additional mathematical processes as claimed of
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is considered to merely convert the normalized value [understood to refer to a fraction, as a mean value of a dataset is considered to be less than a max value of the same dataset] as a percentage of 100.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of Wang in view of Li to employ wherein the gait symmetry metric is calculated as:
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wherein thresholds is a threshold representing an estimated maximum possible mean of the calculated distances, based min-max normalization as disclosed by Jain and further known mathematical processes to calculate a percentage, as this modification would amount to merely applying known techniques [min-max normalization, converting a fraction into a percentage] to a known method ready for improvement to yield predictable results [convert the calculated mean distance representing gait symmetry (Li Table 1 Page 8) into a percentage score in order to be easier to understand relative to other scores] [MPEP § 2143(I)(D)].
However, while Wang discloses characterizing each of left and right and right strides in terms of angular velocity [Multiple points may be used for detecting foot gait attributes, knee flex angle, and/or distinguishing between right and left leg actions. Single point sensing may additionally be applied to right and left leg attributes (Wang ¶0024); Left and right step detection can function to detect individual steps. Any of the biomechanical signals could additionally be characterized for left and right sides (Wang ¶0062)], Wang in view of Li and Jain fails to explicitly disclose wherein the gait repeatability metric includes averaging the segmented sagittal angular velocities across all strides for a particular segment, comparing stride graphs to consecutive ipsilateral strides, resulting in an angular velocity difference-repeatability measured in degrees.
Thompson discloses systems and methods for assessing gait quality of a subject, wherein Thompson discloses that averaging data within a stride may provide useful summary information regarding limb motion metrics [These displacements may be used directly and/or indirectly (e.g., summations, averages, extrema magnitudes, RMS, or the like), perhaps to derive usable summary and point-by-point limb motion metrics (Thompson Col 10:9-12)], as well as steps for comparing stride graphs to consecutive ipsilateral strides, resulting in an angular velocity difference-repeatability measured in degrees [the usable stride 610 inertial sensor outputs (e.g., three-dimensional angular velocity roll, pitch and yaw components or the like) may be processed, perhaps to extract limb inertial motion metrics and even stride phase duration metrics 612 (Thompson Col 9:61-65); Additionally, differences between each of the angular displacement and angular velocity curves can be compared either for the same limb (unilateral) or between limbs (bilateral) on a point by point basis (perhaps for each data collection time point over the course of a stride). These differences can be averaged over multiple strides (aggregated across each run within a session for intra-session comparison or runs among sessions for multi-session comparison), and the results may be displayed, perhaps with animation and/or graphically, as an average difference curve (e.g., over the entire stride and/or for each stride phase or the like). These difference-curves can highlight non-uniform motion signatures and/or patterns that can be visualized and even recognized at a glance (Thompson Col 10:21-34); this measurement device could be applied to humans and other animals or the like (Thompson Col 5:1-3)].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of Wang in view of Li and Jain to employ wherein the gait repeatability metric includes averaging the segmented sagittal angular velocities across all strides for a particular segment, comparing stride graphs to consecutive ipsilateral strides, resulting in an angular velocity difference-repeatability measured in degrees, so as to allow for visualization and recognition of non-uniform motion signatures during gait.
Response to Arguments
Applicant’s arguments, see Applicant’s Remarks p. 12, filed 17 December 2025, with respect to the previously presented claim objections have been fully considered and are persuasive. The objections of claims 1 and 19-21 have been withdrawn.
Applicant's arguments, see Applicant’s Remarks p. 12-13, with respect to the previously applied claim rejections under § 112(b) have been fully considered but they are not considered entirely persuasive. Not all of the previously applied rejections under § 112(b) have been properly addressed. See above § 112(b) rejections for maintained rejections.
Applicant's arguments, see Applicant’s Remarks p. 13-14, with respect to the previously applied claim rejections under § 101 have been fully considered but they are not persuasive.
The Applicant asserts that the amendment to claims 1 and 19-21 to include “evidence-based rehabilitative treatment including identifying movement limitations related to balance and mobility for exercise prescription” is a delineated treatment that cannot be performed by pen-and-paper practice or in the mind, and further integrates the judicial exception into a practical application to allow the claims to amount to significantly more than the judicial exception. However, the Examiner disagrees with the Applicant’s argument, as the Examiner notes that the amended limitation, while considered to refer to an additional element at Step 2A Prong 2 and Step 2B, fails to define a particular treatment or prophylaxis, as the amended language fails to positively recite or include language [merely performing non-specific operations “for exercise prescription” does not require that the exercise be performed by the subject] that is considered to be a particular treatment or prophylaxis [merely applying a non-specific “evidence-based rehabilitative treatment” for a broad range of disorders is not particular regarding any type of specific treatment] as an additional element to integrate the judicial exception into a practical application or allow the identified claims to amount to significantly more than the judicial exception [MPEP § 2106.04(d)(2)].
Applicant’s arguments, see Applicant’s Remarks p. 15-18, with respect to the rejection(s) of claim(s) 1, 19-21, and those dependent therefrom under § 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Wang (US-20170273601-A1, previously presented) in view of Li (“Gait Analysis for Post-Stroke Hemiparetic Patient by Multi-Features Fusion Method”, NPL previously presented), Jain (“Score normalization in multimodal biometric systems”, NPL previously presented), and Thompson (US-10610131-B1).
The Applicant asserts that the previously argued features regarding gait repeatability that were not recited in the rejected claims and as such were not considered by the Examiner for scope and application of prior art have been amended into independent claims 1 and 19-21, wherein the Applicant further asserts that the previously applied Wang reference fails to teach determining gait repeatability as claimed. Applicant’s arguments with respect to claim(s) 1 and 19-12 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. Wang in view of Li and Jain is further modified by Thompson (US-10610131-B1), as Thompson discloses that averaging data within a stride may provide useful summary information regarding limb motion metrics [These displacements may be used directly and/or indirectly (e.g., summations, averages, extrema magnitudes, RMS, or the like), perhaps to derive usable summary and point-by-point limb motion metrics (Thompson Col 10:9-12)], as well as steps for comparing stride graphs to consecutive ipsilateral strides, resulting in an angular velocity difference-repeatability measured in degrees [the usable stride 610 inertial sensor outputs (e.g., three-dimensional angular velocity roll, pitch and yaw components or the like) may be processed, perhaps to extract limb inertial motion metrics and even stride phase duration metrics 612 (Thompson Col 9:61-65); Additionally, differences between each of the angular displacement and angular velocity curves can be compared either for the same limb (unilateral) or between limbs (bilateral) on a point by point basis (perhaps for each data collection time point over the course of a stride). These differences can be averaged over multiple strides (aggregated across each run within a session for intra-session comparison or runs among sessions for multi-session comparison), and the results may be displayed, perhaps with animation and/or graphically, as an average difference curve (e.g., over the entire stride and/or for each stride phase or the like). These difference-curves can highlight non-uniform motion signatures and/or patterns that can be visualized and even recognized at a glance (Thompson Col 10:21-34); this measurement device could be applied to humans and other animals or the like (Thompson Col 5:1-3)].
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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/CHARLES A MARMOR II/Supervisory Patent Examiner
Art Unit 3791
/S.P.L./Examiner, Art Unit 3791