DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 18 August 2026 has been entered.
The Examiner acknowledges the amendments to claims 1 and 19-21. Claims 1-8, 10, 12, and 14-21 are pending, with claims 14-18 remaining as previously withdrawn.
Specification
The abstract of the disclosure is objected to because the abstract should read “inertial sensors (e.g., gyroscopes)” [line 2]. A corrected abstract of the disclosure is required and must be presented on a separate sheet, apart from any other text. See MPEP § 608.01(b).
The disclosure is objected to because of the following informalities:
The attempt to incorporate subject matter into this application by reference to “the Journal of Statistical Software” [“One example implementation of a DTW program package is available from the Journal of Statistical Software (www.jstatsoft.org/article/view/v03li07)” (Applicant’s Specification ¶86)] is ineffective because an incorporation by reference by hyperlink or other form of browser executable code is not permitted [37 C.F.R. § 1.57].
The attempt to incorporate subject matter into this application by reference to “The MathWorks, Inc. of Natick, Massachusetts” [Another example implementation of DTW is the “dtw” function provided in MATLAB™, which is a product of The MathWorks, Inc. of Natick, Massachusetts (www.mathworks.com/help/signal/ref/dtw.htmal)” (Applicant’s Specification ¶86)] is ineffective because an incorporation by reference by hyperlink or other form of browser executable code is not permitted [37 C.F.R. § 1.57].
Appropriate correction is required.
Claim Objections
Claim(s) 1 and 19-20 is/are objected to because of the following informalities:
Claim 1 should read “the calculated distances,” [line 27].
Claim 19 should read “determine [[the]] gait quality” [line 44].
Claim 20 should read “wherein the instructions for using the plurality of inertial sensors, when executed” [line 8].
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 30-31], 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 7-8 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 [wherein the angular velocities across all strides for a particular lower limb segment comprises sagittal angular velocities and wherein the sagittal angular velocities are averaged], or whether the angular velocity may comprise at least the sagittal angular velocity. The Examiner notes that claims 19-21 recite similar subject matter [lines 38-39 in claim 19; lines 36-37 in claim 20; lines 33-34 in claim 21] that is considered indefinite mutatis mutandis. For examination purposes, the Examiner has interpreted either interpretation to be applicable in light of any applied prior art references under § 102 or § 103.
Claim 19 recites the limitation “the subject performs the exercises of the exercise prescription” [line 52], which is considered indefinite, as the cited portion of claim 19 is not directed to the system, but rather to actions of the subject the system is implemented on, such that claim 19 is considered to claim both a system and the method steps of using the system [See MPEP § 2173.05(p)(II)]. Claim 20 is considered to recite similarly indefinite subject matter directed towards actions of the subject the non-transitory computer-readable medium having instructions stored therein is implemented on [line 49], such that claim 20 is considered to claim both a non-transitory computer-readable medium having instructions stored therein and the method steps of implementing the instructions stored therein [See MPEP § 2173.05(p)(II)].
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, the subject being human,
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, 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;
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,
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 lower limb segment, comparing stride graphs to consecutive ipsilateral strides, resulting in an angular velocity difference-repeatability measured in degrees, and
output the gait metrics;
determine the 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;
treat the subject with evidence-based rehabilitative treatment including identifying movement limitations related to balance and mobility for exercise prescription having exercises to be performed by the subject, and assessing gait quality, based on the determined gait quality, for Parkinson's disease, neurological disorders and/or musculoskeletal disorders; and
the subject performs the exercises of the exercise prescription.
(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, the subject being human” – 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, 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; 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]
“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 lower limb segment, comparing stride graphs to consecutive ipsilateral strides, resulting in an angular velocity difference-repeatability measured in degrees” – 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 ¶¶97-98]
“determine the 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 assessing gait quality, based on the determined gait quality, for Parkinson's disease, neurological disorders and/or musculoskeletal disorders” – 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., claim limitations identified under Step 2A Prong 1 directed towards the processor functions to calculate gait metrics] 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 and [for the sake of compact prosecution] pre-solution 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 extra-solution activity as identified above, 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 ‘units’ or ‘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 function, pre-solution data gathering (for the sake of compact prosecution)] 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 [outputting signals, etc.] 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, at least one hardware processor, one or more software modules stored on the at least one hardware processor, a computer system having the non-transitory computer-readable medium stored therein, each recited at a high level of generality and corresponding functions thereof], 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 having exercises to be performed by the subject… and the subject performs the exercises of the exercise prescription”, which the Examiner notes is a positive recitation of a treatment or prophylaxis, but is not considered to be a particular treatment or prophylaxis, as none of the identified claims positively recite or include language that is considered to be a particular treatment or prophylaxis 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. The recitation of the treatment being “evidence-based rehabilitative treatment” based on movement limitations related to balance and mobility [that are not specifically defined relative to any parameter as measured or determined in the claimed method/system] is not considered to be sufficiently particular and is recited at such a high level of generality to be interpreted as any type of specific treatment [The claim also recites "administering a lower than normal dosage of a beta blocker medication to a patient identified as having the poor metabolizer genotype." This administration step is particular, and it integrates the mental analysis step into a practical application. Conversely, consider a claim that recites the same abstract idea and "administering a suitable medication to a patient." This administration step is not particular, and is instead merely instructions to "apply" the exception in a generic way. Thus, the administration step does not integrate the mental analysis step into a practical application (MPEP § 2106.04(d)(2))].
Accordingly, the claim(s) as whole(s) fail amount to significantly more than the judicial exception under Step 2B.
Section 33(a) of the America Invents Act reads as follows:
Notwithstanding any other provision of law, no patent may issue on a claim directed to or encompassing a human organism.
Claim(s) 19-20 is/are rejected under 35 U.S.C. 101 and section 33(a) of the America Invents Act as being directed to or encompassing a human organism. See also Animals - Patentability, 1077 Off. Gaz. Pat. Office 24 (April 21, 1987) (indicating that human organisms are excluded from the scope of patentable subject matter under 35 U.S.C. 101).
Claim 19 recites the limitation “the subject performs the exercises of the exercise prescription” [line 52], which is considered to positively recite the human body [see emphasized portion].
Claim 20 recites the limitation “the subject performs the exercises of the exercise prescription” [line 49], which is considered to positively recite the human body [see emphasized portion].
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, and 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, filed 6 May 2015, previously presented).
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)]; the subject being human [Wang Fig. 1];
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)],
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 having exercises to be performed by the subject, 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); the system and method can offer a non-invasive technique to identifying underlying physiological and/or biomechanical problems with an individual, which may be used in diagnosing movement disorders such as Parkinson's disease (Wang ¶0093); a participant can be guided through a set of different activities. In performing the method for a diagnosis treatment stage, the method can include directing actions by a patient and generating biomechanical signals from kinematic data during the directed actions (Wang ¶0094)]; and
the subject performing the exercises of the exercise prescription [In addition, a physical therapy software application or virtual coach can provide the patient with real-time guidance and exercises. The application can reward the patient for achieving various exercises or range of motion objectives. The application could be turned into a game with the user performing exercises to complete virtual objectives, making exercises fun and engaging to help counteract traditionally poor patient compliance (Wang ¶0088); Data from one or more patients can be applied to determine recommended approaches and to customize rehabilitation steps for an individual. Patients may have unique individual responses to different exercises and rehabilitation steps. The system and method can provide a concrete metric to judge the effectiveness of techniques and adjust rehabilitation to achieve improved results (Wang ¶0089), wherein the real-time provision of exercises and analysis based on completion of said exercises is considered to read on the subject performing the exercises of the exercise prescription].
However, Wang fails to explicitly disclose wherein 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; 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.
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)].
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.
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].
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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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 lower limb 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 [Thompson Col 2:6-7, Fig. 7], 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 [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]; and
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)], the subject being human [Wang Fig. 1],
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)],
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 the 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];
treat the subject with evidence-based rehabilitative treatment including identifying movement limitations related to balance and mobility for exercise prescription having exercises to be performed by the subject, 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); the system and method can offer a non-invasive technique to identifying underlying physiological and/or biomechanical problems with an individual, which may be used in diagnosing movement disorders such as Parkinson's disease (Wang ¶0093); a participant can be guided through a set of different activities. In performing the method for a diagnosis treatment stage, the method can include directing actions by a patient and generating biomechanical signals from kinematic data during the directed actions (Wang ¶0094)]; and
the subject performs the exercises of the exercise prescription [In addition, a physical therapy software application or virtual coach can provide the patient with real-time guidance and exercises. The application can reward the patient for achieving various exercises or range of motion objectives. The application could be turned into a game with the user performing exercises to complete virtual objectives, making exercises fun and engaging to help counteract traditionally poor patient compliance (Wang ¶0088); Data from one or more patients can be applied to determine recommended approaches and to customize rehabilitation steps for an individual. Patients may have unique individual responses to different exercises and rehabilitation steps. The system and method can provide a concrete metric to judge the effectiveness of techniques and adjust rehabilitation to achieve improved results (Wang ¶0089), wherein the real-time provision of exercises and analysis based on completion of said exercises is considered to read on the subject performing the exercises of the exercise prescription].
However, Wang fails to explicitly disclose wherein 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; 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.
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)].
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.
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].
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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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 device (method, or product) 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 lower limb 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 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 20, Wang teaches
A non-transitory computer-readable medium having instructions stored therein, comprising:
a non-transitory computer-readable medium having instructions stored therein for using the plurality of inertial sensors [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 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)]; the 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 for using the plurality of inertial sensors [Wang ¶¶0021, 0023];
wherein the instructions, when executed by a processor in the computer system [Wang ¶0021], cause a 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)]; the subject being human [Wang Fig. 1];
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)],
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];
treat the subject with evidence-based rehabilitative treatment including identifying movement limitations related to balance and mobility for exercise prescription having exercised to be performed by the subject, 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); the system and method can offer a non-invasive technique to identifying underlying physiological and/or biomechanical problems with an individual, which may be used in diagnosing movement disorders such as Parkinson's disease (Wang ¶0093); a participant can be guided through a set of different activities. In performing the method for a diagnosis treatment stage, the method can include directing actions by a patient and generating biomechanical signals from kinematic data during the directed actions (Wang ¶0094)]; and
the subject performs the exercises of the exercise prescription [In addition, a physical therapy software application or virtual coach can provide the patient with real-time guidance and exercises. The application can reward the patient for achieving various exercises or range of motion objectives. The application could be turned into a game with the user performing exercises to complete virtual objectives, making exercises fun and engaging to help counteract traditionally poor patient compliance (Wang ¶0088); Data from one or more patients can be applied to determine recommended approaches and to customize rehabilitation steps for an individual. Patients may have unique individual responses to different exercises and rehabilitation steps. The system and method can provide a concrete metric to judge the effectiveness of techniques and adjust rehabilitation to achieve improved results (Wang ¶0089), wherein the real-time provision of exercises and analysis based on completion of said exercises is considered to read on the subject performing the exercises of the exercise prescription].
However, Wang fails to explicitly disclose wherein 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; 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.
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)].
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.
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 having instructions stored therein 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].
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 non-transitory computer-readable medium having instructions stored therein 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 lower limb 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 having instructions stored therein 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 21, Wang teaches
A method using at least one hardware processor, 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 [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)];
acquiring motion data comprising a signal from each of the plurality of 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 motion of one of a plurality of 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)]; the subject being human [Wang Fig. 1];
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 signal segments [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 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)],
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];
treating the subject with evidence-based rehabilitative treatment including identifying movement limitations related to balance and mobility for exercise prescription having exercises to be performed by the subject, 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); the system and method can offer a non-invasive technique to identifying underlying physiological and/or biomechanical problems with an individual, which may be used in diagnosing movement disorders such as Parkinson's disease (Wang ¶0093); a participant can be guided through a set of different activities. In performing the method for a diagnosis treatment stage, the method can include directing actions by a patient and generating biomechanical signals from kinematic data during the directed actions (Wang ¶0094)]; and
the subject performing the exercises of the exercise prescription [In addition, a physical therapy software application or virtual coach can provide the patient with real-time guidance and exercises. The application can reward the patient for achieving various exercises or range of motion objectives. The application could be turned into a game with the user performing exercises to complete virtual objectives, making exercises fun and engaging to help counteract traditionally poor patient compliance (Wang ¶0088); Data from one or more patients can be applied to determine recommended approaches and to customize rehabilitation steps for an individual. Patients may have unique individual responses to different exercises and rehabilitation steps. The system and method can provide a concrete metric to judge the effectiveness of techniques and adjust rehabilitation to achieve improved results (Wang ¶0089), wherein the real-time provision of exercises and analysis based on completion of said exercises is considered to read on the subject performing the exercises of the exercise prescription].
However, Wang fails to explicitly disclose wherein 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; 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.
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)].
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.
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].
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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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 lower limb 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. 14, filed 18 August 2026, with respect to the previously presented claim objections have been fully considered and are persuasive. The objections to claims 1 and 19-21 have been withdrawn.
Applicant's arguments, see Applicant’s Remarks p. 14, with respect to the previously applied rejections under § 112(b) have been fully considered but they are not persuasive.
The Examiner notes that not all of the previously applied rejections to claims 1 and 19-21 under § 112(b) have been specifically addressed by the Applicant’s Remarks or claim amendments. See above for maintained rejection(s).
Applicant's arguments, see Applicant’s Remarks p. 15, with respect to the previously applied rejections of claims 1, 19-21, and those dependent therefrom under § 101 have been fully considered but they are not persuasive.
The Applicant asserts that the amendments to claim 1 [and similar amendments to claims 19-21] amount to significantly more than the judicial exception under Step 2B, as claims 1 and 19-21 expressly call for the (human) subject to expressly perform the exercises of the exercise prescription, wherein the Applicant notes that such performance of an exercise of the exercise prescription cannot be carried out in the mind and is not an abstract claim element. The Examiner agrees that the performance of an exercise of the exercise prescription is positively recited in each of the independent claims, and at Step 2A Prong 1 is not identified as referring to an abstract idea that may be performed in the mind or by hand. However, the Examiner disagrees that the amendment as recited is considered to render each of claims 1 and 19-21 significantly more than the judicial exception at Step 2A Prong 2 and Step 2B, as while the subject positively performing “exercises of the exercise prescription”, defined as “evidence-based rehabilitative treatment including identifying movement limitations related to balance and mobility”, is a positive recitation of a treatment or prophylaxis, is not considered to be a particular treatment or prophylaxis, as the movement limitations related to balance and mobility are not specifically defined relative to any parameter as measured or determined in the claimed method/system to be sufficiently particular and is recited at such a high level of generality to be interpreted as any type of specific treatment [The claim also recites "administering a lower than normal dosage of a beta blocker medication to a patient identified as having the poor metabolizer genotype." This administration step is particular, and it integrates the mental analysis step into a practical application. Conversely, consider a claim that recites the same abstract idea and "administering a suitable medication to a patient." This administration step is not particular, and is instead merely instructions to "apply" the exception in a generic way. Thus, the administration step does not integrate the mental analysis step into a practical application (MPEP § 2106.04(d)(2))].
Applicant's arguments, see Applicant’s Remarks p. 15-19, with respect to the previously applied rejections of claims 1, 19-21, and those dependent therefrom under § 103 have been fully considered but they are not persuasive.
The Applicant asserts that the prior art rejection under § 103 reflects impermissible hindsight reconstruction and that the Examiner has not articulated a sufficient rationale for why a person of ordinary skill in the art would combine all four references in the claimed manner, particular the Thompson reference. The Applicant asserts that Thompson discloses gait analysis for animals (primarily equines) not for humans, such that a person of skill in the art would not have been motivated to apply Thompson’s techniques (which are designed for quadruped gait) to the human gait analysis context of the claimed invention, and that there would be no reasonable expectation of success in doing so. The Applicant further notes that Wang [¶0018, Figs. 1, 4, 5A-G], Li, and Jain are clearly directed to gait analysis of human subjects and not for gait analysis of equines, such that Thompson is not combinable with Wang (and Li and Jain) because the support for the combining an equine gait sensing system with a human gait sensing system is missing. In response to applicant’s argument that the examiner’s conclusion of obviousness is based upon improper hindsight reasoning, it must be recognized that any judgment on obviousness is in a sense necessarily a reconstruction based upon hindsight reasoning. But so long as it takes into account only knowledge which was within the level of ordinary skill at the time the claimed invention was made, and does not include knowledge gleaned only from the applicant’s disclosure, such a reconstruction is proper. See In re McLaughlin, 443 F.2d 1392, 170 USPQ 209 (CCPA 1971). As at least each of Wang, Li, and Thompson are directed towards gait analysis, and at least Wang [Wang ¶¶0044, 0061] and Thompson [Thompson Col 9:61-65, 10:21-41, Fig. 8] are directed towards analysis of gait symmetry and repeatability, the modification of Wang in view of Li, Jain, and Thompson is not considered to be impermissible hindsight. Furthermore, the Examiner disagrees with the Applicant’s argument that Thompson is not combinable with Wang, Li, and Jain, as the Examiner notes that while Thompson discloses embodiments directed towards analysis of equine gait, Thompson does disclose that the invention of Thompson may be applied to humans [this measurement device could be applied to humans and other animals or the like (Thompson Col 5:1-3)] and further discloses and depicts the analysis of gait phases that are considered to overlap with/are applicable to human gait [FIG. 7 shows a schematic diagram of stride phases (stance, swing), subphases (e.g. stance landing, weight-bearing and breakover), and stride events (hoof impact, heel off, toe off), in accordance with various embodiments of the present invention (Thompson Col 5:28-32, Fig. 7)], as further indicated by at least Wang [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)] and Li [The EFS method can obtain several gait temporal parameters accurately. These parameters, such as gait cycle, stance phase duration, swing phase duration, gait cadence, etc. (Li p. 2); The local maxima point of the waveform coincided with the moments when the foot separated from the ground, the local minima point of the waveform coincided with the moments when the foot contacted the ground. Figure 3 shows a plot of all gait cycle waveforms of HP and HC in the same illustration (Li p. 7, Fig. 3a-b)], such that Thompson is still considered to be relevant in the analysis of human gait.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SEVERO ANTONIO P LOPEZ whose telephone number is (571)272-7378. The examiner can normally be reached M-F 9-6 EST.
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/SEVERO ANTONIO P LOPEZ/Examiner, Art Unit 3791
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