CTFR 18/266,414 CTFR 95778 DETAILED ACTION This action is responsive to the “AMENDMENT UNDER 37 C.F.R. § 1.111” filed 20 April 2026. The Examiner acknowledges the amendments to claims 1-11. Claims 1-11 are pending. Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. 07-30-03-h AIA Claim Interpretation Examiner Notes: currently, NO limitation invokes interpretation under § 112(f). Claim Rejections - 35 USC § 112 Examiner’s Note Regarding Machine Learning: the previously presented note acknowledging that the Applicant’s Specification is considered to provide sufficient written description support for the machine learning as presently claimed on p. 5 of the Non-Final Office Action dated 20 January 2026 is maintained. 07-30-02 AIA 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. 07-34-01 Claim(s) 11 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 11 recites the limitation “the processor” [4] , which is considered indefinite, as claim 11 depends from each of claims 1 and 8, which each recite unique processors [“a processor connected to the memory” (line 3 of claim 1); “a processor connected to the memory of the data processing device” (line 5 of claim 8)] , such that it is unclear which processor claim 11 refers to in order to perform the claimed functionality. For examination purposes, the Examiner has interpreted either processor of claims 1 and 8 to be applicable in light of any prior art applied under § 102 or § 103 . Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 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) 1-11 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) 1 [representing all independent claims] recite(s): A feature-amount generation device comprising: a memory storing instructions ; and a processor connected to the memory and configured to execute the instructions to : receive time-series data of sensor data from a data acquisition device including a three-axis acceleration sensor and a three-axis angular velocity sensor disposed on footwear worn by a user, the sensor data including spatial acceleration in three axial directions and spatial angular velocity around three axes measured at a predetermined sampling rate during walking of the user ; generate a gait waveform for one gait cycle from the time-series data of sensor data related to a motion of a foot by identifying a heel strike event as a starting point of the gait cycle and a subsequent heel strike event as an ending point of the gait cycle based on characteristic patterns in the sensor data ; divide the gait waveform into a plurality of gait phases, each gait phase representing a unit section of the one gait cycle ; extract a first feature amount from each gait phase of the generated gait waveform by analyzing at least one of: acceleration in three axial directions, angular velocity around three axes, and plantar angle calculated from the spatial acceleration and spatial angular velocity, wherein the first feature amount represents a biomechanical characteristic specific to the respective gait phase ; extract a gait phase cluster by identifying and integrating a plurality of temporally continuous gait phases from each of which a feature amount is extracted, wherein the gait phase cluster corresponds to a specific biomechanical motion phase selected from a group consisting of: an initial stance period, a mid-stance period, a terminal stance period, a pre-swing period, an initial swing period, a mid-swing period, and a terminal swing period ; generate a second feature amount of the gait phase cluster using a preset feature amount constitutive expression, wherein the feature-amount constitutive expression is a calculation expression that generates the second feature amount by performing at least one of: calculating an integral average value of the first feature amounts, calculating an arithmetic average value of the first feature amounts, calculating an inclination of the first feature amounts, and calculating a variation of the first feature amounts ; generate feature-amount data in which a gait cycle percentage range of the plurality of gait phases constituting the gait phase cluster and the second feature amount of the gait phase cluster are associated with each other ; and output the feature-amount data having a reduced data amount compared to the time-series data of the sensor data for the one gait cycle, wherein the feature-amount data comprises a plurality of second feature amounts each corresponding to a respective gait phase cluster, and wherein a total number of the second feature amounts is less than a total number of the gait phases from which the first feature amounts are extracted . (Emphasis added: abstract idea , additional element ) Step 2A Prong 1 Representative claim(s) 1 recites the following abstract ideas, which may be performed in the mind or by hand with the assistance of pen and paper: “ receive time-series data of sensor data from a data acquisition device including a three-axis acceleration sensor and a three-axis angular velocity sensor disposed on footwear worn by a user, the sensor data including spatial acceleration in three axial directions and spatial angular velocity around three axes measured at a predetermined sampling rate during walking of the user ” – may be performed by merely observing known or previously collected data, for at least a limited amount of data; wherein the Examiner notes that the claim language fails to positively recite any step of measuring spatial acceleration in three axial directions and spatial angular velocity around three axes, wherein the limitation is considered to merely limit the type of sensor data received; for the sake of compact prosecution, the Examiner has analyzed the identified limitation at Step 2B in the alternative “ generate a gait waveform for one gait cycle from the time-series data of sensor data related to a motion of a foot by identifying a heel strike event as a starting point of the gait cycle and a subsequent heel strike event as an ending point of the gait cycle based on characteristic patterns in the sensor data ” – may be performed by merely observing known or previously collected data, for at least a limited amount of data, drawing mental conclusions therefrom, and further mapping or graphing the limited amount of data “ divide the gait waveform into a plurality of gait phases, each gait phase representing a unit section of the one gait cycle ” – may be performed by merely observing known or previously collected data, for at least a limited amount of data, and drawing mental conclusions therefrom “ extract a first feature amount from each gait phase of the generated gait waveform by analyzing at least one of: acceleration in three axial directions, angular velocity around three axes, and plantar angle calculated from the spatial acceleration and spatial angular velocity, wherein the first feature amount represents a biomechanical characteristic specific to the respective gait phase ” – may be performed by merely observing known or previously collected data, for at least a limited amount of data, and drawing mental conclusions therefrom “ extract a gait phase cluster by identifying and integrating a plurality of temporally continuous gait phases from each of which a feature amount is extracted, wherein the gait phase cluster corresponds to a specific biomechanical motion phase selected from a group consisting of: an initial stance period, a mid-stance period, a terminal stance period, a pre-swing period, an initial swing period, a mid-swing period, and a terminal swing period ” – may be performed by merely applying derived or established mathematical equations or formulas on known or previously collected data, for at least a limited amount of data, and drawing mental conclusions therefrom “ generate a second feature amount of the gait phase cluster using a preset feature amount constitutive expression, wherein the feature-amount constitutive expression is a calculation expression that generates the second feature amount by performing at least one of: calculating an integral average value of the first feature amounts, calculating an arithmetic average value of the first feature amounts, calculating an inclination of the first feature amounts, and calculating a variation of the first feature amounts ” – may be performed by merely applying derived or established mathematical equations or formulas on known or previously collected data, for at least a limited amount of data, and drawing mental conclusions therefrom “ generate feature-amount data in which a gait cycle percentage range of the plurality of gait phases constituting the gait phase cluster and the second feature amount of the gait phase cluster are associated with each other ” – may be performed by merely observing known or previously collected data, for at least a limited amount of data, and drawing mental conclusions therefrom “ output the feature-amount data having a reduced data amount compared to the time-series data of the sensor data for the one gait cycle, wherein the feature-amount data comprises a plurality of second feature amounts each corresponding to a respective gait phase cluster, and wherein a total number of the second feature amounts is less than a total number of the gait phases from which the first feature amounts are extracted ” – may be performed by merely observing known or previously collected data, for at least a limited amount of data, and drawing mental conclusions therefrom, as well as further writing down or verbally communicating the mental conclusions 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., “ extract a gait phase cluster by identifying and integrating a plurality of temporally continuous gait phases from each of which a feature amount is extracted, wherein the gait phase cluster corresponds to a specific biomechanical motion phase selected from a group consisting of: an initial stance period, a mid-stance period, a terminal stance period, a pre-swing period, an initial swing period, a mid-swing period, and a terminal swing period ”, “ generate a second feature amount of the gait phase cluster using a preset feature amount constitutive expression, wherein the feature-amount constitutive expression is a calculation expression that generates the second feature amount by performing at least one of: calculating an integral average value of the first feature amounts, calculating an arithmetic average value of the first feature amounts, calculating an inclination of the first feature amounts, and calculating a variation of the first feature amounts ” ] 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 1 only recites additional elements of extra-solutionary activity – in particular, extra-solution activity [generic computer function] – 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, or a sufficiently particular form of display or computing architecture/structure). Dependent claim(s) 2-5 and 11 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) 6-8 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 9-10 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] 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 [acquiring, storing, 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 [memory and processor recited at a high level of generality and functions therein, data processing device of claim 6 recited at a high level of generality and functions therein] , 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 therein, 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 6 recites a “the data acquisition device that is disposed at footwear of a user to be measured, measures a spatial acceleration and a spatial angular velocity according to a gait of the user, generates sensor data based on the measured spatial acceleration and the measured spatial angular velocity, and outputs the generated sensor data to the feature-amount generation device, wherein the data acquisition device transmits feature-amount data including a feature amount of a gait phase cluster extracted by the feature-amount generation device”. The Examiner notes the limitation of representative claim 1 directed towards “receive time-series data of sensor data from a data acquisition device including a three-axis acceleration sensor and a three-axis angular velocity sensor disposed on footwear worn by a user, the sensor data including spatial acceleration in three axial directions and spatial angular velocity around three axes measured at a predetermined sampling rate during walking of the user” is also analyzed for the sake of compact prosecution. Such a “data acquisition device” 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 data acquisition device of the measurement device, and recites the acceleration sensor and angular velocity sensor that comprise the data acquisition device of the measurement device at a high level of generality [The measurement device 11 includes an acceleration sensor and an angular velocity sensor (Applicant’s Specification ¶0020); The measurement device 11 is achieved by, for example, an inertial measurement device including an acceleration sensor and an angular velocity sensor. An example of the inertial measurement device is an inertial measurement unit (IMU). The IMU includes a three-axis acceleration sensor and a three-axis angular velocity sensor. Examples of the inertial measurement device include a vertical gyro (VG), an attitude heading (AHRS), and a global positioning system/inertial navigation system (GPS/INS) (Applicant’s Specification ¶0024); The acceleration sensor 111 is a sensor that measures acceleration (also referred to as spatial acceleration) in the three axial directions. The acceleration sensor 111 outputs the measured acceleration to the control unit 113. For example, a sensor of a piezoelectric type, a piezoresistive type, a capacitance type, or the like can be used as the acceleration sensor 111. As long as the sensor used for the acceleration sensor 111 can measure an acceleration, the measurement method is not limited (Applicant’s Specification ¶0035); The angular velocity sensor 112 is a sensor that measures angular velocities around the three axes (also referred to as spatial angular velocities). Angular velocity sensor 112 outputs the measured angular velocity to control unit 113. For example, a sensor of a vibration type, a capacitance type, or the like can be used as the angular velocity sensor 112. As long as the sensor used for the angular velocity sensor 112 can measure an angular velocity, the measurement method is not limited (Applicant’s Specification ¶0036)] . 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 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] . Kiani (US-20150100105-A1) [the sensor unit 120 may collect sensor data at predetermined time intervals, such as every 10 milliseconds, for example (Kiani ¶0080); FIG. 6 illustrates an example sensor configuration 600 with the sensor unit 120 embedded in an insole 602 of a footwear (Kiani ¶0126); The sensor unit socket 604 can receive a component, such as 670, that houses the other modules of the sensor unit 120, such as… other sensors in the sensor module 450 (e.g., accelerometer, gyroscope, etc.) (Kiani ¶0127); Figure 6] Kirtley (US-20030009308-A1) [A light weight flexible insole 1… Two piezo-electric gyroscope sensors 3 and 6 (Murata ENC-03J) sense angular velocity about the longitudinal and transverse axes of the insole, respectively, while two bi-axial accelerometers 5 and 7 (Analog Devices ADXL202) sense acceleration in the three orthogonal directions (longitudinal, transverse and vertical) (Kirtley ¶0039); The micro-controller 1 receives analog inputs from up to eight sensors 2, and digital inputs from the accelerometers. This sampling is driven by the watch crystal 3 (Kirtley ¶0040); Figure 1] Pease (US-20130041617-A1) [The system 100, as shown in FIG. 1, includes one or more sensors 105 attached to (e.g., embedded within, fixedly coupled to, or releasably coupled to) a portion of a shoe 110 of a runner 115 to measure one or more data conditions/performance characteristics during athletic activity (e.g., a run) (Pease ¶0049); The sensor(s) may be integrally embedded within the shoe and, for example, within one or more portions of a sole (e.g., an outsole, midsole, or insole) of a shoe (Pease ¶0050); Various sensors may be utilized to measure one or more data conditions during athletic activity. Example sensors include, but are not limited to,… accelerometers,… gyroscopic sensors (Pease ¶0051); The sample rate may be set, for example, at between 500 to 5000 Hz or between 500 and 2000 Hz or, more particularly, between 800 and 1500 Hz and, for example, at about 900 Hz. In one embodiment, a sample rate of about 900 Hz may be set to ensure that at least about 300 samples are taken per stride (Pease ¶0093); a separate sensor (e.g., an accelerometer) may be used in the sensor unit in addition to the gyroscopic sensor, with the accelerometer being used to indicate when a foot strike event is taking place and the gyroscopic sensor only capturing angular velocity data (Pease ¶0106); Figures 2-4] Claim 11 recites “the estimation model is constructed by machine learning”. Such an estimation model is considered well-understood, routine, and conventional, as known by at least: Hu (“Intelligent Sensor Networks”, previously presented) [In supervised learning, the learner is provided with labeled input data. This data contains a sequence of input/output pairs of the form xi, yi, where xi is a possible input and yi is the correctly labeled output associated with it. The aim of the learner in supervised learning is to learn the mapping from inputs to outputs. The learning program is expected to learn a function f that accounts for the input/output pairs seen so far, f (xi) = yi, for all i. This function f is called a classifier if the output is discrete and a regression function if the output is continuous. The job of the classifier/regression function is to correctly predict the outputs of inputs it has not seen before (Hu, Page 5)] Huang (“Kernel Based Algorithms for Mining Huge Data Sets”, previously presented) [In supervised learning, the learner is provided with labeled input data. This data contains a sequence of input/output pairs of the form xi, yi, where xi is a possible input and yi is the correctly labeled output associated with it. The aim of the learner in supervised learning is to learn the mapping from inputs to outputs. The learning program is expected to learn a function f that accounts for the input/output pairs seen so far, f (xi) = yi, for all i. This function f is called a classifier if the output is discrete and a regression function if the output is continuous. The job of the classifier/regression function is to correctly predict the outputs of inputs it has not seen before (Huang, Page 1)] Mitchell (“The Discipline of Machine Learning”, previously presented) [For example, we now have a variety of algorithms for supervised learning of classification and regression functions; that is, for learning some initially unknown function f : X [Calibri font/0xE0] Y given a set of labeled training examples {xi; yi} of inputs xi and outputs yi = f(xi) (Mitchell, Pages 3-4)] Examiner’s Note Regarding Particular Treatment or Prophylaxis: Claim(s) 8 recite subject matter regarding “an estimation model that outputs a degree of hallux valgus” [lines 8-9] and “estimate a degree of hallux valgus of the user” [line 10] , and claim 11 recites the subject matter regarding “output information including a message that allows the user to make a decision to visit a hospital depending on the degree of hallux valgus of the user” [lines 5-6] , which the Examiner notes is not considered to be a particular treatment or prophylaxis, as none of the identified claims positively recite or include language 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 Examiner notes that the wording of claim 11 is considered to recite an intended use of the claimed message] [MPEP § 2106.04(d)(2)] . Accordingly, the claim(s) as whole(s) fail amount to significantly more than the judicial exception under Step 2B. Claim Rejections - 35 USC § 103 07-20-aia AIA 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. 07-23-aia AIA 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. 07-20-02-aia AIA 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. 07-21-aia AIA Claim (s) 1-6 and 9-10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Thompson (US-10610131-B1, previously presented) in view of Selner (US-20170319368-A1, previously presented) . Regarding claim 1, Thompson teaches A feature-amount generation device comprising: a memory storing instructions [In addition and as to computer aspects and each aspect amenable to programming or other electronic automation, the applicant(s) should be understood to have support to claim and make a statement of invention to at least: xv) processes performed with the aid of or on a computer, machine, or computing machine as described throughout the above discussion, xvi) a programmable apparatus as described throughout the above discussion, xvii) a computer readable memory encoded with data to direct a computer comprising means or elements which function as described throughout the above discussion (Thompson Col 13:53-63)] ; and a processor connected to the memory and configured to execute the instructions [Thompson Col 13:53-63] to: receive time-series data of sensor data from a data acquisition device including a three-axis angular velocity sensor, the sensor data including spatial angular velocity around three axes measured at a predetermined sampling rate during walking of the user [this measurement device could be applied to humans and other animals or the like (Thompson Col 5:1-3); FIG. 1 illustrates an example of an equine limb inertial sensing system 100 according to embodiments of the present invention. A pair of inertial sensing devices 102 (left L and right R) may be located on the lower limb segment that may include the third metacarpal and/or metatarsal bone, perhaps depending on whether either one or both of the front or hind limb pairs are evaluated. The biosensor devices may include casings and inertial sensors, such as gyroscopes or the like, perhaps for measuring the three-dimensional motion of the lower limb (Thompson Col 5:61-6:3); As shown in FIG. 1, a portable controller and analytics unit 104 may be used to wirelessly collect gait data from each device pair using the device controller module 106 in addition to analyzing the data into limb motion metrics, perhaps using the gait analytics reporting module 108 (Thompson Col 6:41-45)] ; generate a gait waveform for one gait cycle from the time-series data of sensor data related to a motion of a foot by identifying a heel strike event as a starting point of the gait cycle and a subsequent heel strike event as an ending point of the gait cycle based on characteristic patterns in the sensor data [the raw stride inertial data values may be made available for transfer to facilitate further analysis and even sharing between practitioners and researchers (Thompson Col 11:64-67), wherein the raw stride inertial data is considered to be defined by a gait waveform, and wherein as based on Thompson Col 5:1-3 noting that the device is applicable to humans, Thompson Fig. 7 is considered to depict identifying a heel strike as a starting event of the gait cycle, wherein as gait is considered cyclical, a subsequent heel as the ending point] ; divide the gait waveform into a plurality of gait phases, each gait phase representing a unit section of the one gait cycle [Thompson Fig. 7, wherein the gait waveform being measured over time is considered to read on dividing the gait waveform into a plurality of phases (data per unit of time), based on the broadest reasonable interpretation of the Applicant’s definition of a “unit section” of a gait cycle (Applicant’s Specification p. 13:7-11)] ; extract a first feature amount from each gait phase of the generated gait waveform by analyzing at least one of: acceleration in three axial directions, angular velocity around three axes, and plantar angle calculated from the spatial acceleration and spatial angular velocity, wherein the first feature amount represents a biomechanical characteristic specific to the respective gait phase [wherein any particular amplitude of angular velocity at a specific point in time is considered to define a feature amount from the gait waveform representative of any non-specific biomechanical characteristic specific to the respective gait phase] ; extract a gait phase cluster by identifying and integrating a plurality of temporally continuous gait phases from each of which a feature amount is extracted, wherein the gait phase cluster corresponds to a specific biomechanical motion phase selected from a group consisting of: an initial stance period, a mid-stance period, a terminal stance period, a pre-swing period, an initial swing period, a mid-swing period, and a terminal swing period [The analytics and reporting module (108) may automatically segment the run into strides and may divide each stride into phases and sub-phases 610 perhaps based on detection of stride events and limb motion signature patterns (e.g., see FIG. 7 for an example illustration of a non-limiting sampling of types of stride phases, stride events, and sub-phases or the like)… The roll, pitch and yaw angular displacements over each stride phase may be determined by integrating the raw sensor angular velocities over the time interval determined by the start and end of each phase Thompson Col 9:37-43, 9:67-10:3), wherein each stride phase is considered to define a gait phase cluster, wherein the data segments combined to define each stride phase are considered to read on temporally continuous gait phases] ; generate a second feature amount of the gait phase cluster using a preset feature amount constitutive expression, wherein the feature-amount constitutive expression is a calculation expression that generates the second feature amount by performing at least one of: calculating an integral average value of the first feature amounts, calculating an arithmetic average value of the first feature amounts, calculating an inclination of the first feature amounts, and calculating a variation of the first feature amounts [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), which is considered to be an arithmetic average value of the first feature amounts (amplitudes)] ; generate feature-amount data in which a gait cycle percentage range of the plurality of gait phases constituting the gait phase cluster and the second feature amount of the gait phase cluster are associated with each other [wherein averaging displacements calculated over each stride phase is considered to “associate” a gait cycle percentage range of the plurality of gait phases constituting the gait phase cluster with the generated feature amount of the gait phase cluster] ; and output the feature-amount data having a reduced data amount compared to the time-series data of the sensor data for the one gait cycle, wherein the feature-amount data comprises a plurality of second feature amounts each corresponding to a respective gait phase cluster, and wherein a total number of the second feature amounts is less than a total number of the gait phases from which the first feature amounts are extracted [Thompson Col 10:9-12, wherein performing averages as part of a summary of limb motion metrics output, wherein the averages are of a gait cycle percentage range of the plurality of gait phases constituting the gait phase cluster (second feature amount), is considered to read on the total number of the second feature amounts being less than a total number of the gait phases from which the first feature amounts] . However, Thompson fails to explicitly disclose wherein the data acquisition device includes a three-axis acceleration sensor and wherein the sensor data includes spatial acceleration in three axial directions; and wherein the data acquisition device is disposed on footwear worn by a user. Selner discloses systems for monitoring foot movement of a subject during a gait cycle, wherein Selner discloses a measurement device including a processing system for processing data, and a data acquisition device, disposed at footwear of the subject to be measured, configured to measure spatial acceleration and spatial angular velocity [An electronic system, according to this block diagram, includes a microprocessor 104, a forefoot 3-axis accelerometer/3-axis gyroscope 101, an arch region 3-axis accelerometer/3-axis gyroscope 106, and a wireless transmitter 111. The components are embedded in the orthotic. Suitable components can include the Intel® Quark™ SE microcontroller, said to be the heart of the Intel Curie. The Quark™ SE CPU would be connected to a 3-axis accelerometer, and 3-axis gyroscope, and 3-axis magnetometer IC, also embedded in the orthotic. The STMicroelectronics LSM9DS0 9DOF IMU IC would be a suitable component for this purpose… The electronic system, overall, will measure and record raw sensor data, pre-process it for external analysis and analyze the data (Selner ¶0027)] . 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 Thompson to employ wherein the data acquisition device includes a three-axis acceleration sensor and wherein the sensor data includes spatial acceleration in three axial directions; and wherein the data acquisition device is disposed on footwear worn by a user, so as to provide additional context regarding foot movement during the gait cycle [with respect to measuring spatial acceleration] , as well as amount to mere simple substitution of one known element for another with similar expected results [provide inertial sensor(s) for monitoring gait] [MPEP § 2143(I)(B)] . Regarding claim 2, Thompson in view of Selner teaches The feature-amount generation device according to claim 1, wherein in a case where a feature amount is extracted from a single gait phase that is not temporally continuous, the processor is configured to execute the instructions to: output a feature amount extracted from the single gait phase, and generate feature-amount data in which the single gait phase and the feature amount of the single gait phase are associated with each other [The analytics and reporting module (108) may automatically segment the run into strides and may divide each stride into phases and sub-phases 610 perhaps based on detection of stride events and limb motion signature patterns (e.g., see FIG. 7 for an example illustration of a non-limiting sampling of types of stride phases, stride events, and sub-phases or the like)… Once the invalid strides (e.g., change in gait, tripping, slipping, forging, turning, or the like) may be detected and even excluded, 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… The roll, pitch and yaw angular displacements over each stride phase may be determined by integrating the raw sensor angular velocities over the time interval determined by the start and end of each phase (Thompson Col 9:37-43, 9:59-65, 9:67-10:3), wherein applying the feature amount processes to any non-excluded stride phase or sub-phase is considered to read on being not temporally continuous, wherein applying an average to a displacement defined by a sub-phase is considered to read on the claimed limitations] . Regarding claim 3, Thompson in view of Selner teaches The feature-amount generation device according to claim 1, wherein in a case where a feature amount is extracted from a single gait phase that is not temporally continuous, the processor is configured to execute the instructions to-to: extract the single gait phase as a gait phase cluster, and generate feature-amount data in which the single gait phase extracted as the gait phase cluster and the feature amount of the single gait phase are associated with each other [Thompson Col 9:37-43, 9:59-65, 9:67-10:3, wherein applying the feature amount processes to any non-excluded stride phase or sub-phase is considered to read on being not temporally continuous, wherein applying an average to a displacement defined by a sub-phase is considered to read on the claimed limitations] . Regarding claim 4, Thompson in view of Selner teaches The feature-amount generation device according to claim 1, wherein the processor is configured to execute the instructions to: extract a feature amount from each gait phase constituting a preset gait phase cluster to be extracted [wherein any range of amplitudes of angular velocity at specific points in time is considered to define a feature amount of a gait phase constituting a preset gait phase cluster, due to the lack of particularity regarding the “preset” gait phase cluster] . Regarding claim 5, Thompson in view of Selner teaches The feature-amount generation device according to claim 1, wherein the processor is configured to execute the instructions to: extract a feature amount related to a gait affected by a specific physical feature [wherein any particular amplitude of angular velocity at a specific point in time is considered to define a feature amount from the gait waveform; and wherein the cluster being defined by the stride phase is considered to define the feature amounts as being “affected” by a specific physical feature (the foot of the subject)] . Regarding claim 6, Thompson in view of Selner teaches A gait measurement system comprising: the feature-amount generation device according to claim 1; the data acquisition device that is disposed at footwear of a user to be measured, measures a spatial acceleration and a spatial angular velocity according to a gait of the user, generates sensor data based on the measured spatial acceleration and the measured spatial angular velocity, and outputs the generated sensor data to the feature-amount generation device, wherein the data acquisition device transmits feature- amount data including a feature amount of a gait phase cluster extracted by the feature-amount generation device [see § 103 modification of claim 1 above; Thompson Col 5:1-3, 5:61-6:3, 6:41-45; Selner ¶0027; As shown in FIG. 1, a portable controller and analytics unit 104 may be used to wirelessly collect gait data from each device pair using the device controller module 106 in addition to analyzing the data into limb motion metrics, perhaps using the gait analytics reporting module 108 (Thompson Col 6:41-45)] ; and a data processing device that receives the feature amount of the gait phase cluster and performs data processing using the received feature amount of the gait phase cluster [Transmission of the information for further evaluation or review can be performed using the mobile device controller (104) which can send the information electronically (e.g., email, electronic communications, or the like) or from data downloaded to a personal computer (Thompson Col 8:51-56)] . Regarding claim 9, Thompson teaches A feature-amount generation method comprising: receiving time-series data of sensor data from a data acquisition device including a three-axis angular velocity sensor, the sensor data including spatial angular velocity around three axes measured at a predetermined sampling rate during walking of the user [this measurement device could be applied to humans and other animals or the like (Thompson Col 5:1-3); FIG. 1 illustrates an example of an equine limb inertial sensing system 100 according to embodiments of the present invention. A pair of inertial sensing devices 102 (left L and right R) may be located on the lower limb segment that may include the third metacarpal and/or metatarsal bone, perhaps depending on whether either one or both of the front or hind limb pairs are evaluated. The biosensor devices may include casings and inertial sensors, such as gyroscopes or the like, perhaps for measuring the three-dimensional motion of the lower limb (Thompson Col 5:61-6:3); As shown in FIG. 1, a portable controller and analytics unit 104 may be used to wirelessly collect gait data from each device pair using the device controller module 106 in addition to analyzing the data into limb motion metrics, perhaps using the gait analytics reporting module 108 (Thompson Col 6:41-45)] ; generating a gait waveform for one gait cycle from the time-series data of sensor data related to a motion of a foot by identifying a heel strike event as a starting point of the gait cycle and a subsequent heel strike event as an ending point of the gait cycle based on characteristic patterns in the sensor data [the raw stride inertial data values may be made available for transfer to facilitate further analysis and even sharing between practitioners and researchers (Thompson Col 11:64-67), wherein the raw stride inertial data is considered to be defined by a gait waveform, and wherein as based on Thompson Col 5:1-3 noting that the device is applicable to humans, Thompson Fig. 7 is considered to depict identifying a heel strike as a starting event of the gait cycle, wherein as gait is considered cyclical, a subsequent heel as the ending point] ; dividing the gait waveform into a plurality of gait phases, each gait phase representing a unit section of the one gait cycle [Thompson Fig. 7, wherein the gait waveform being measured over time is considered to read on dividing the gait waveform into a plurality of phases (data per unit of time), based on the broadest reasonable interpretation of the Applicant’s definition of a “unit section” of a gait cycle (Applicant’s Specification p. 13:7-11)] ; extracting a first feature amount from each gait phase of the generated gait waveform by analyzing at least one of: acceleration in three axial directions, angular velocity around three axes, and plantar angle calculated from the spatial acceleration and spatial angular velocity, wherein the first feature amount represents a biomechanical characteristic specific to the respective gait phase [wherein any particular amplitude of angular velocity at a specific point in time is considered to define a feature amount from the gait waveform representative of any non-specific biomechanical characteristic specific to the respective gait phase] ; extracting a gait phase cluster by identifying and integrating a plurality of temporally continuous gait phases from each of which a feature amount is extracted, wherein the gait phase cluster corresponds to a specific biomechanical motion phase selected from a group consisting of: an initial stance period, a mid-stance period, a terminal stance period, a pre-swing period, an initial swing period, a mid-swing period, and a terminal swing period [The analytics and reporting module (108) may automatically segment the run into strides and may divide each stride into phases and sub-phases 610 perhaps based on detection of stride events and limb motion signature patterns (e.g., see FIG. 7 for an example illustration of a non-limiting sampling of types of stride phases, stride events, and sub-phases or the like)… The roll, pitch and yaw angular displacements over each stride phase may be determined by integrating the raw sensor angular velocities over the time interval determined by the start and end of each phase Thompson Col 9:37-43, 9:67-10:3), wherein each stride phase is considered to define a gait phase cluster, wherein the data segments combined to define each stride phase are considered to read on temporally continuous gait phases] ; generating a second feature amount of the gait phase cluster using a feature-amount constitutive expression, wherein the feature-amount constitutive expression is a calculation expression that generates the second feature amount by performing at least one of: calculating an integral average value of the first feature amounts, calculating an arithmetic average value of the first feature amounts, calculating an inclination of the first feature amounts, and calculating a variation of the first feature amounts [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), which is considered to be an arithmetic average value of the first feature amounts (amplitudes)] ; and generating feature-amount data in which a gait cycle percentage range of the plurality of gait phases constituting the gait phase cluster and the second feature amount of the gait phase cluster are associated with each other [wherein averaging displacements calculated over each stride phase is considered to “associate” a gait cycle percentage range of the plurality of gait phases constituting the gait phase cluster with the generated feature amount of the gait phase cluster] ; and outputting the feature-amount data having a reduced data amount compared to the time- series data of the sensor data for the one gait cycle, wherein the feature-amount data comprises a plurality of second feature amounts each corresponding to a respective gait phase cluster, and wherein a total number of the second feature amounts is less than a total number of the gait phases from which the first feature amounts are extracted [Thompson Col 10:9-12, wherein performing averages as part of a summary of limb motion metrics output, wherein the averages are of a gait cycle percentage range of the plurality of gait phases constituting the gait phase cluster (second feature amount), is considered to read on the total number of the second feature amounts being less than a total number of the gait phases from which the first feature amounts] . However, Thompson fails to explicitly disclose wherein the data acquisition device includes a three-axis acceleration sensor and wherein the sensor data includes spatial acceleration in three axial directions; and wherein the data acquisition device is disposed on footwear worn by a user. Selner discloses systems for monitoring foot movement of a subject during a gait cycle, wherein Selner discloses a measurement device including a processing system for processing data, and a data acquisition device, disposed at footwear of the subject to be measured, configured to measure spatial acceleration and spatial angular velocity [An electronic system, according to this block diagram, includes a microprocessor 104, a forefoot 3-axis accelerometer/3-axis gyroscope 101, an arch region 3-axis accelerometer/3-axis gyroscope 106, and a wireless transmitter 111. The components are embedded in the orthotic. Suitable components can include the Intel® Quark™ SE microcontroller, said to be the heart of the Intel Curie. The Quark™ SE CPU would be connected to a 3-axis accelerometer, and 3-axis gyroscope, and 3-axis magnetometer IC, also embedded in the orthotic. The STMicroelectronics LSM9DS0 9DOF IMU IC would be a suitable component for this purpose… The electronic system, overall, will measure and record raw sensor data, pre-process it for external analysis and analyze the data (Selner ¶0027)] . 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 Thompson to employ wherein the data acquisition device includes a three-axis acceleration sensor and wherein the sensor data includes spatial acceleration in three axial directions; and wherein the data acquisition device is disposed on footwear worn by a user, so as to provide additional context regarding foot movement during the gait cycle [with respect to measuring spatial acceleration] , as well as amount to mere simple substitution of one known element for another with similar expected results [provide inertial sensor(s) for monitoring gait] [MPEP § 2143(I)(B)] . Regarding claim 10, Thompson teaches A non-transitory recording medium recording a program for causing a computer to execute: a process of receiving time-series data of sensor data from a data acquisition device including a three-axis angular velocity sensor, the sensor data including spatial angular velocity around three axes measured at a predetermined sampling rate during walking of the user [this measurement device could be applied to humans and other animals or the like (Thompson Col 5:1-3); FIG. 1 illustrates an example of an equine limb inertial sensing system 100 according to embodiments of the present invention. A pair of inertial sensing devices 102 (left L and right R) may be located on the lower limb segment that may include the third metacarpal and/or metatarsal bone, perhaps depending on whether either one or both of the front or hind limb pairs are evaluated. The biosensor devices may include casings and inertial sensors, such as gyroscopes or the like, perhaps for measuring the three-dimensional motion of the lower limb (Thompson Col 5:61-6:3); As shown in FIG. 1, a portable controller and analytics unit 104 may be used to wirelessly collect gait data from each device pair using the device controller module 106 in addition to analyzing the data into limb motion metrics, perhaps using the gait analytics reporting module 108 (Thompson Col 6:41-45)] ; a process of generating a gait waveform for one gait cycle from the time-series data of sensor data related to a motion of a foot by identifying a heel strike event as a starting point of the gait cycle and a subsequent heel strike event as an ending point of the gait cycle based on characteristic patterns in the sensor data [the raw stride inertial data values may be made available for transfer to facilitate further analysis and even sharing between practitioners and researchers (Thompson Col 11:64-67), wherein the raw stride inertial data is considered to be defined by a gait waveform, and wherein as based on Thompson Col 5:1-3 noting that the device is applicable to humans, Thompson Fig. 7 is considered to depict identifying a heel strike as a starting event of the gait cycle, wherein as gait is considered cyclical, a subsequent heel as the ending point] ; a process of dividing the gait waveform into a plurality of gait phases, each gait phase representing a unit section of the one gait cycle [Thompson Fig. 7, wherein the gait waveform being measured over time is considered to read on dividing the gait waveform into a plurality of phases (data per unit of time), based on the broadest reasonable interpretation of the Applicant’s definition of a “unit section” of a gait cycle (Applicant’s Specification p. 13:7-11)] ; a process of extracting a first feature amount from each gait phase of the generated gait waveform by analyzing at least one of: acceleration in three axial directions, angular velocity around three axes, and plantar angle calculated from the spatial acceleration and spatial angular velocity, wherein the first feature amount represents a biomechanical characteristic specific to the respective gait phase [wherein any particular amplitude of angular velocity at a specific point in time is considered to define a feature amount from the gait waveform representative of any non-specific biomechanical characteristic specific to the respective gait phase] ; a process of extracting a gait phase cluster by identifying and integrating a plurality of temporally continuous gait phases from each of which a feature amount is extracted, wherein the gait phase cluster corresponds to a specific biomechanical motion phase selected from a group consisting of: an initial stance period, a mid-stance period, a terminal stance period, a pre-swing period, an initial swing period, a mid-swing period, and a terminal swing period [The analytics and reporting module (108) may automatically segment the run into strides and may divide each stride into phases and sub-phases 610 perhaps based on detection of stride events and limb motion signature patterns (e.g., see FIG. 7 for an example illustration of a non-limiting sampling of types of stride phases, stride events, and sub-phases or the like)… The roll, pitch and yaw angular displacements over each stride phase may be determined by integrating the raw sensor angular velocities over the time interval determined by the start and end of each phase Thompson Col 9:37-43, 9:67-10:3), wherein each stride phase is considered to define a gait phase cluster, wherein the data segments combined to define each stride phase are considered to read on temporally continuous gait phases] ; a process of generating a second feature amount of the gait phase cluster using a feature- amount constitutive expression, wherein the feature-amount constitutive expression is a calculation expression that generates the second feature amount by performing at least one of: calculating an integral average value of the first feature amounts, calculating an arithmetic average value of the first feature amounts, calculating an inclination of the first feature amounts, and calculating a variation of the first feature amounts [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), which is considered to be an arithmetic average value of the first feature amounts (amplitudes)] ; a process of generating feature-amount data in which a gait cycle percentage range of the plurality of gait phases constituting the gait phase cluster and the second feature amount of the gait phase cluster are associated with each other [wherein averaging displacements calculated over each stride phase is considered to “associate” a gait cycle percentage range of the plurality of gait phases constituting the gait phase cluster with the generated feature amount of the gait phase cluster] ; and a process of outputting the feature-amount data having a reduced data amount compared to the time-series data of the sensor data for the one gait cycle, wherein the feature-amount data comprises a plurality of second feature amounts each corresponding to a respective gait phase cluster, and wherein a total number of the second feature amounts is less than a total number of the gait phases from which the first feature amounts are extracted [Thompson Col 10:9-12, wherein performing averages as part of a summary of limb motion metrics output, wherein the averages are of a gait cycle percentage range of the plurality of gait phases constituting the gait phase cluster (second feature amount), is considered to read on the total number of the second feature amounts being less than a total number of the gait phases from which the first feature amounts] . However, Thompson fails to explicitly disclose wherein the data acquisition device includes a three-axis acceleration sensor and wherein the sensor data includes spatial acceleration in three axial directions; and wherein the data acquisition device is disposed on footwear worn by a user. Selner discloses systems for monitoring foot movement of a subject during a gait cycle, wherein Selner discloses a measurement device including a processing system for processing data, and a data acquisition device, disposed at footwear of the subject to be measured, configured to measure spatial acceleration and spatial angular velocity [An electronic system, according to this block diagram, includes a microprocessor 104, a forefoot 3-axis accelerometer/3-axis gyroscope 101, an arch region 3-axis accelerometer/3-axis gyroscope 106, and a wireless transmitter 111. The components are embedded in the orthotic. Suitable components can include the Intel® Quark™ SE microcontroller, said to be the heart of the Intel Curie. The Quark™ SE CPU would be connected to a 3-axis accelerometer, and 3-axis gyroscope, and 3-axis magnetometer IC, also embedded in the orthotic. The STMicroelectronics LSM9DS0 9DOF IMU IC would be a suitable component for this purpose… The electronic system, overall, will measure and record raw sensor data, pre-process it for external analysis and analyze the data (Selner ¶0027)] . 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 recording medium recording a program to employ wherein the data acquisition device includes a three-axis acceleration sensor and wherein the sensor data includes spatial acceleration in three axial directions; and wherein the data acquisition device is disposed on footwear worn by a user, so as to provide additional context regarding foot movement during the gait cycle [with respect to measuring spatial acceleration] , as well as amount to mere simple substitution of one known element for another with similar expected results [provide inertial sensor(s) for monitoring gait] [MPEP § 2143(I)(B)] . 07-21-aia AIA Claim (s) 7-8 and 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Thompson in view of Selner, as applied to claim 6 above, in further view of Najafi (WO-2019213399-A1, previously presented) . Regarding claim 7, Thompson in view of Selner teaches The gait measurement system according to claim 6, wherein the data processing device includes a memory storing instructions [Thompson Col 8:51-56, wherein a computer is considered to be defined by a processor and memory] , and a processor connected to the memory of the data processing device [Thompson Col 8:51-56] . However, while Thompson discloses analyzing the feature amount based on historical data to assess a physical feature of the user [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 as indicative of the type and/or source of lameness (Thompson Col 10:21-35); The goal of the uniformity analysis process may be to quantify the difference in the motion of one limb compared to itself (unilateral consistency) or to compare the difference between contralateral limbs (bilateral symmetry) (refer to FIG. 8). With both approaches, the horse may serve as their own reference for sensitive detection of limb motion that may be considered abnormal for that particular horse (Thompson Col 10:37-44); wherein based on Thompson Col 5:1-3, the analysis is considered to be similarly applicable to a human] , Thompson in view of Selner fails to explicitly disclose wherein the processor connected to the memory of the data processing device is configured to execute the instructions to: input a feature amount of the gait phase cluster extracted from time-series data of the sensor data measured along with a gait of the user to an estimation model that outputs a physical feature according to the input feature amount, and estimate a physical feature of the user based on an estimation value output from the estimation model. Najafi discloses systems and methods for assessing user gait, wherein Najafi discloses inputting a feature amount of a user’s gait and a gait of the user to an estimation model that outputs a physical feature according to the input feature amount, and estimate a physical feature of the user based on an estimation value output from the estimation model that uses angular velocity information as an input [A neural network model 900, as shown in FIGURE 9, may be used to analyze lower extremity motion data and establish reliability of a predictive relationship between gait characteristics and a frailty level. For example, a neural network model may determine whether it is possible to accurately predict a frailty level based on a set of gait characteristics. The neural network 900 may analyze a discriminating power of gait characteristics 912-922 to differentiate between non-frail, pre-frail, and frail individuals. The neural network model may receive input data from a right leg sensor 902 and/or a left leg sensor 904, such as angular velocity data from gyroscopes of the left and right leg sensors 902, 904 at a sensor configuration 906. Sensed data, such as angular velocity data, may be input into a neural network processing algorithm 908 (Najafi ¶0047); For example, if a propulsion efficiency calculated based on one or more gait parameters falls below a predetermined threshold, a determination may be made that a foot is at risk of diabetic foot ulcers or deformity, such as bunions, hammer toes, overlapping toes, and other deformities (Najafi ¶0052)] . 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 Thompson in view of Selner to employ wherein the processor connected to the memory of the data processing device is configured to execute the instructions to: input a feature amount of the gait phase cluster extracted from time-series data of the sensor data measured along with a gait of the user to an estimation model that outputs a physical feature according to the input feature amount, and estimate a physical feature of the user based on an estimation value output from the estimation model, so as to allow for correlation and determination of user frailty based on measured parameters, as well as allow for assessment of treatment plans [Analysis of gait characteristics can also allow for more reliable treatment plans to be implemented. For example, gait characteristics may be analyzed to determine if a treatment plan is likely to have a positive impact on a lower extremity risk level. A determination may also be made, based on gait characteristics, that one or more treatment options, such as offloading, lower extremity amputation, and/or foot surgery, may have a negative impact on a foot risk level and/or a frailty of an individual, and alternative treatment options with a greater likelihood of success may be suggested (Najafi ¶0055); Monitoring of gait characteristics over time may be particularly useful in assessing the effectiveness of prescribed treatment regimens, such as specialized footwear, orthotics, lower extremity surgery, exercise, and physical therapy, for lower extremity maladies. For example, if a physical therapy regimen or a prescribed offloading causes deterioration in gait characteristics, such as propulsion efficiency, such deterioration may be detected and an individual or doctor may be informed, to allow for a treatment adjustment (Najafi ¶0056)] . Regarding claim 8, Thompson in view of Selner teaches The gait measurement system according to claim 6, wherein the data processing device includes a memory storing instructions [Thompson Col 8:51-56] , and a processor connected to the memory of the data processing device [Thompson Col 8:51-56] . However, while Thompson discloses analyzing the feature amount based on historical data to assess a physical feature of the user [Thompson Col 10:21-35, 10:37-44, wherein based on Thompson Col 5:1-3, the analysis is considered to be similarly applicable to a human] , Thompson in view of Selner fails to explicitly disclose wherein the processor connected to the memory of the data processing device is configured to execute the instructions to: input a feature amount of the gait phase cluster extracted from time-series data of the sensor data measured along with a gait of the user to an estimation model that outputs a degree of hallux valgus according to the input feature amount, and estimate a degree of hallux valgus of the user based on an estimation value output from the estimation model that outputs a degree of hallux valgus. Najafi discloses systems and methods for assessing user gait, wherein Najafi discloses inputting a feature amount of a user’s gait and a gait of the user to an estimation model that outputs a degree of hallux valgus according to the input feature amount, and estimate a degree of hallux valgus of the user based on an estimation value output from the estimation model that uses angular velocity information as an input [A neural network model 900, as shown in FIGURE 9, may be used to analyze lower extremity motion data and establish reliability of a predictive relationship between gait characteristics and a frailty level. For example, a neural network model may determine whether it is possible to accurately predict a frailty level based on a set of gait characteristics. The neural network 900 may analyze a discriminating power of gait characteristics 912-922 to differentiate between non-frail, pre-frail, and frail individuals. The neural network model may receive input data from a right leg sensor 902 and/or a left leg sensor 904, such as angular velocity data from gyroscopes of the left and right leg sensors 902, 904 at a sensor configuration 906. Sensed data, such as angular velocity data, may be input into a neural network processing algorithm 908 (Najafi ¶0047); For example, if a propulsion efficiency calculated based on one or more gait parameters falls below a predetermined threshold, a determination may be made that a foot is at risk of diabetic foot ulcers or deformity, such as bunions, hammer toes, overlapping toes, and other deformities (Najafi ¶0052)] . 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 Thompson in view of Selner to employ wherein the processor connected to the memory of the data processing device is configured to execute the instructions to: input a feature amount of the gait phase cluster extracted from time-series data of the sensor data measured along with a gait of the user to an estimation model that outputs a degree of hallux valgus according to the input feature amount, and estimate a degree of hallux valgus of the user based on an estimation value output from the estimation model that outputs a degree of hallux valgus, so as to allow for correlation and determination of user frailty based on measured parameters, as well as allow for assessment of treatment plans [Najafi ¶¶0055-0056] . Regarding claim 11, Thompson in view of Selner and Najafi teaches The gait measurement system according to claim 8, wherein the estimation model is constructed by machine learning [Najafi ¶0047] , and the processor is configured to execute the instructions to: output information including a message that allows the user to make a decision to visit a hospital depending on the degree of hallux valgus of the user [the Examiner notes that the wording of the claim is considered to recite an intended use of the claimed message, however for the sake of compact prosecution Najafi ¶¶0055-0056 is considered to read on the claimed limitation] . Response to Arguments 07-38-01 AIA Applicant’s arguments, see Applicant’s Remarks p. 12 , filed 20 April 2026, with respect to the previously presented claim objections have been fully considered and are persuasive. The objections to claims 1-8 have been withdrawn. Applicant’s arguments, see Applicant’s Remarks p. 12 , with respect to the previously applied rejection(s) under § 112(a) have been fully considered and are persuasive. The rejection of claim 11 under § 112(a) has been withdrawn. Applicant’s arguments, see Applicant’s Remarks p. 12-13, with respect to the previously applied rejection(s) under have been fully considered and are persuasive. The rejections of claims 2, 4, 6, 8, 11, and those dependent therefrom have been withdrawn. Applicant’s arguments, see Applicant’s Remarks p. 13, with respect to the Non-Statutory Double Patenting provisional rejections have been fully considered and are persuasive. The provisional rejections of claims 1-3, 5, and 9-10 have been withdrawn. Applicant's arguments, see Applicant’s Remarks p. 13-19, with respect to the previously applied rejections under § 101 have been fully considered but they are not persuasive. The Applicant asserts that claim 1 has integrated the alleged exception into a practical application at Step 2A Prong 2 by improving the functioning of a computer or improving another technology or technical field, wherein the Applicant notes that the limitation “time-series data of sensor data from a data acquisition device including a three-axis acceleration sensor and a three-axis angular velocity sensor disposed on footwear worn by a user” is an integration of a wearable sensor as a specific technical component for data acquisition that represents a specific technological implementation, not a generic computer implementation. The Applicant further asserts that the limitations directed towards “generate feature-amount data…” and “output the feature-amount data…” allow for accurate estimation of the physical feature even when the number of feature amounts to be verified is reduced [Applicant’s Specification ¶0093] represents a concrete technological improvement to the accuracy of gait analysis and estimation. The Applicant also asserts that the claimed embodiment implements a novel process for analyzing and/or estimating a user’s gait and demonstrates an improvement in how computers are used as tools in the same way as CardioNet , and therefore provides a practical application for the alleged abstract idea. However, the Examiner disagrees with the Applicant’s argument regarding the an integration of a wearable sensor in claim 1 as a specific technical component for data acquisition that represents a specific technological implementation, not a generic computer implementation, as the Examiner notes that claim 1 fails to positively recite “a data acquisition device including a three-axis acceleration sensor and a three-axis angular velocity sensor disposed on footwear worn by a user”, as claim 1 merely refers to such a device to define the type of data that is received. Moreover, while claim 6 does positively recite such a data acquisition device and assuming [for the sake of compact prosecution] that claim 1 does positively recite such a data acquisition device, the device is considered to be well-understood, routine, and conventional at Step 2B based on the Applicant’s own recitation of the device at a high level of generality and the presently cited Kiani, Kirtley, and Pease references. Furthermore, the Examiner disagrees with the Applicant’s arguments regarding the limitations directed towards “generate feature-amount data…” and “output the feature-amount data…” represent a concrete technological improvement to the accuracy of gait analysis and estimation, as the Examiner notes that in the § 101 analysis of the cited case of CardioNet , the claims focused on specific means or methods that improved cardiac monitoring technology , by more accurately detecting occurrence of atrial fibrillation and atrial flutter as distinct from other arrhythmias, rather than being directed to a result or effect that itself was an abstract idea and merely invoking well-understood, routine, and conventional processes and machinery . The Examiner notes that the alleged improvement is recited within limitations that have been identified as being abstract ideas implemented on a generic computer with additional elements that are considered to be well-understood, routine, and conventional. The “improvements” are not considered to be additional elements, as “generate feature-amount data…” and “output the feature-amount data…”, are identified as being abstract ideas. As such, under MPEP 2106.05(a), "an improvement in the abstract idea itself ( e.g. a recited fundamental economic concept) is not an improvement in technology". Specifically, the "improvements" analysis in Step 2A determines whether the claim pertains to an improvement to the functioning of a computer or to another technology without reference to what is well-understood, routine, conventional activity [MPEP § 2106.04(d)(1)] . It is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements. See the discussion of Diamond v. Diehr , 450 U.S. 175, 187 and 191-92, 209 USPQ 1, 10 (1981)) in subsection II, below. In addition, the improvement can be provided by the additional element(s) in combination with the recited judicial exception [MPEP § 2106.05(a)] . It is important to note that in order for a method claim to improve computer functionality, the broadest reasonable interpretation of the claim must be limited to computer implementation. That is, a claim whose entire scope can be performed mentally, cannot be said to improve computer technology. Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 120 USPQ2d 1473 (Fed. Cir. 2016) (a method of translating a logic circuit into a hardware component description of a logic circuit was found to be ineligible because the method did not employ a computer and a skilled artisan could perform all the steps mentally) [MPEP § 2106.05(a)(I)] . As such, the claims do not recite additional elements that may integrate the abstract ideas into a practical application of the abstract ideas, and thus the claimed invention is not considered to improve other technology or technical field. The Applicant asserts that assuming, merely arguendo , that the claim is not considered to integrate into a practical application, that the amended claim recites significantly more than any abstract idea, wherein the Applicant argues that as the combination of features of claim 1 are not disclosed in the cited art, as detailed in the Applicant’s Remarks with respect to the § 102/103 rejection, that claim 1 provides a “non-conventional” technical solution that amounts to significantly more than any abstract idea. Furthermore, the Applicant directs attention to p. 5 of the August 4, 2025 Memorandum wherein “Examiners are reminded that if it is a ‘close call’ as to whether a claim is eligible, they should only make a rejection when it is more likely than not (i.e., more than 50%) that the claim is ineligible under 35 U.S.C. 101”, which the Applicant considered favors a finding of patent eligibility. However, the Examiner disagrees with the Applicant’s argument regarding the Applicant’s arguments with respect to the rejection(s) under § 102/103 rendering claim 1 as a “non-conventional” technical solution, as the Examiner notes that the examination under 35 U.S.C. 101 is distinct and separate from the examination under 35 U.S.C. 102 and 35 U.S.C. 103, as recited in the MPEP : As made clear by the courts, the "‘novelty’ of any element or steps in a process, or even of the process itself, is of no relevance in determining whether the subject matter of a claim falls within the § 101 categories of possibly patentable subject matter." Intellectual Ventures I v. Symantec Corp., 838 F.3d 1307, 1315, 120 USPQ2d 1353, 1358 (Fed. Cir. 2016) (quoting Diamond v. Diehr, 450 U.S. at 188–89, 209 USPQ at 9). See also Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 1151, 120 USPQ2d 1473, 1483 (Fed. Cir. 2016) ("a claim for a new abstract idea is still an abstract idea. The search for a § 101 inventive concept is thus distinct from demonstrating § 102 novelty.")…Because they are separate and distinct requirements from eligibility, patentability of the claimed invention under 35 U.S.C. 102 and 103 with respect to the prior art is neither required for, nor a guarantee of, patent eligibility under 35 U.S.C. 101 [MPEP § 2106.05(I)] . Furthermore, the Examiner disagrees with the Applicant’s citation of the August 4, 2025 Memorandum as favoring a finding of patent eligibility, as detailed in the § 101 analysis above, it is more than likely (i.e., more than 50%) that the claim is ineligible, as all the processor functions of the claim are considered to be directed towards abstract idea(s), wherein claims 1/9/10 or those dependent therefrom fail to recite any additional elements to integrate the judicial exception into a practical application or allow any claim to amount to significantly more. The Applicant asserts that the feature of dependent claim 8 of “estimate a degree of hallux valgus of the user based on an estimation value output from the estimation model that outputs a degree of hallux valgus” renders the claim patent-eligible by applying the alleged judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition. However, the Examiner disagrees with the Applicant’s argument, as the Examiner notes that there is no language in claim 8 or any other claim in which a particular treatment/prophylaxis is positively recited , as the broadest reasonable interpretation of the argued limitation to “estimate a degree of hallux valgus of the user based on an estimation value output from the estimation model that outputs a degree of hallux valgus” is considered to be an abstract idea that may be performed in the mind or by hand by merely observing known or previously collected data and drawing mental conclusions therefrom, such that no treatment or prophylaxis is actually being applied in claim 8 [The treatment or prophylaxis limitation must be "particular," i.e., specifically identified so that it does not encompass all applications of the judicial exception(s). For example, consider a claim that recites mentally analyzing information to identify if a patient has a genotype associated with poor metabolism of beta blocker medications. This falls within the mental process grouping of abstract ideas enumerated in MPEP § 2106.04(a) (MPEP § 2106.04(d)(2))] . Applicant’s arguments, see Applicant’s Remarks p. 19-22, with respect to the rejection(s) of claim(s) 1, 9-10, and those dependent therefrom under § 102 and § 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Thompson (US-10610131-B1, previously presented) in view of Selner (US-20170319368-A1, previously presented). The Applicant assert that Thompson fails to disclose or suggest at least the amended features of claim 1 [“data acquisition device…”, “generate feature amount data…”, “output the feature-amount data…”] . However, the Examiner notes that Applicant’s arguments with respect to claim(s) 1/9/10 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. Thompson (US-10610131-B1, previously presented) is presently further modified in view of Selner (US-20170319368-A1, previously presented), so as to teach the argued data acquisition device as claimed [Thompson Col 5:1-3, 5:61-6:3, 6:41-45; Selner ¶0027] . Moreover, the Examiner disagrees with the Applicant’s arguments that Thompson fails to explicitly disclose the limitations to “generate feature amount data…”, “output the feature-amount data…”, as Thompson is considered to teach the argued limitations [wherein averaging displacements calculated over each stride phase is considered to “associate” a gait cycle percentage range of the plurality of gait phases constituting the gait phase cluster with the generated feature amount of the gait phase cluster; Thompson Col 10:9-12, wherein performing averages as part of a summary of limb motion metrics output, wherein the averages are of a gait cycle percentage range of the plurality of gait phases constituting the gait phase cluster (second feature amount), is considered to read on the total number of the second feature amounts being less than a total number of the gait phases from which the first feature amounts] . Conclusion 07-40 AIA Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL . See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a). A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action. 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. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Charles Marmor II can be reached at (571) 272-4730. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /SEVERO ANTONIO P LOPEZ/Examiner, Art Unit 3791 Application/Control Number: 18/266,414 Page 2 Art Unit: 3791
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