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. Claim Objections Claim(s) 1-6 is/are objected to because of the following informalities: The limitations of lines 5-16 of claim 1 should be tabbed over to the right once to indicate the limitations that refer to the instructions executed by the processor. The limitations of lines 5-6 of claim 2 should be tabbed over to the right once to indicate the limitations that refer to the instructions executed by the processor. The limitations of lines 5-8 of claim 3 should be tabbed over to the right once to indicate the limitations that refer to the instructions executed by the processor. The limitations of lines 3-5 of claim 4 should be tabbed over to the right once to indicate the limitations that refer to the instructions executed by the processor. The limitations of lines 3-4 of claim 5 should be tabbed over to the right once to indicate the limitations that refer to the instructions executed by the processor. The limitations of lines 3-8 of claim 6 should be tabbed over to the right once to indicate the limitations that refer to the instructions executed by the processor. Appropriate correction is required. Claim Interpretation Examiner Notes: currently, NO limitation invokes interpretation under § 112(f). Intended Use: Claim 7 recites the limitation “the information is used for decision making to address the harmonic index” [lines 4-5] , which is considered to be an intended use of the claimed system, as the information being used for a certain purpose does not structurally or functionally limit the instant invention [See also Rowe v. Dror, 112 F.3d 473, 478, 42 USPQ2d 1550, 1553 (Fed. Cir. 1997) ("where a patentee defines a structurally complete invention in the claim body and uses the preamble only to state a purpose or intended use for the invention, the preamble is not a claim limitation"); To satisfy an intended use limitation which is limiting, a prior art structure which is capable of performing the intended use as recited in the preamble meets the claim (MPEP § 2111.02(II)); the Examiner notes that the cited portions of the MPEP are directed towards intended use as recited in the preamble, but are still considered to be applicable regarding the instant limitation] . For examination purposes, the Examiner has interpreted any prior art under § 102 or § 103 that is capable of teaching, disclosing, or suggesting, the structure and functionality of the invention of claim 7 to be capable of allowing for information to be “used for decision making to address the harmonic index” . Claim Rejections - 35 USC § 112 The following is a quotation of the first paragraph of 35 U.S.C. 112(a): (a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention. The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112: The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention. Claim(s) 7 is/are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. Claim 7 recites the limitation “the information is used for decision making to address the harmonic index” [lines 4-5] , which the Examiner notes lacks sufficient written description support as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same and shall set forth the best mode contemplated by the inventor of carrying out his invention, as the Examiner notes that the Applicant’s Specification is devoid of any disclosure regarding a harmonic index. Examiner’s Note Regarding Machine Learning: the claimed machine learning model of claim(s) 6-7 was considered under § 112(a), wherein the Examiner notes that the disclosure of machine learning models that may be applied [Applicant’s Specification ¶¶0085, 0122] of the Applicant’s Specification is considered to provide sufficient written description support for the machine learning model as presently claimed for one of ordinary skill in the art to understand that the Applicant possessed the instant invention at the time of filing. 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 appl icant regards as his invention. Claim(s) 1, 7, 9-10, 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 “the feature amount accumulated” [lines 12-13] , which is considered indefinite, as claim 1 defines a first feature amount [“extract a feature amount from the generated gait waveform” (line 7)] and a second feature amount [“generate a feature amount of the gait phase cluster using a preset feature-amount constitutive expression” (lines 10-11)] , such that it is unclear whether the indefinite limitation is meant to refer to the first feature amount, the second feature amount, or both feature amounts. For examination purposes, the Examiner has interpreted any identified interpretation to be applicable in light of any prior art applied under § 102 or § 103 . Claims 9-10 are each considered to recite similarly indefinite subject matter [line 11 in claim 9; line 12 in claim 10] , which are interpreted similar to the interpretation of claim 1 above mutatis mutandis . Claim 7 recites the limitations “the information” [line 4] and “the harmonic index” [lines 4-5] , which are considered to lack antecedent basis, as none of claims 1, 6, or 7 previously define either of “information” or a “harmonic index”. The recitation of “the information” in claim 7 is further considered to render claim 7 indefinite, as it is not clear what is meant to constitute the recited information. For examination purposes, the Examiner has interpreted any data or information extracted, generated, estimated, measured, or output from any of claims 1, 6, and/or 7 to define “the information” . Double Patenting The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg , 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman , 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi , 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum , 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel , 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington , 418 F.2d 528, 163 USPQ 644 (CCPA 1969). A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA. A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b). The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13. The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA/25, or PTO/AIA/26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer . Claim (s) 1 is/are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim ( s) 8 of copending Application No. 18/266,414 [hereinafter Huang] in view of Thompson (US-10610131-B1) . Claim 8 of the conflicting Huang reference patent application is considered to anticipate almost each and every limitation of instant claim 1 [see comparison below] , except the limitation “display information related to the gait according to the degree of progress of hallux valgus of the user on a screen of a mobile terminal used by the user ”. Thompson discloses a gait measurement system, wherein Thompson discloses wherein the system displays information related to a gait according to a physical feature of a user on a screen of a mobile terminal used by the user [The motion metrics may be displayed 622 on the mobile device (104) perhaps for immediate use or on a personal computer with a larger graphics display (Thompson Col 11:32-35)] . 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 Huang to employ the processor to display information related to the gait according to the physical feature of the user on a screen of a mobile terminal used by the user, so as to allow for user visualization of relevant data and information. Claim 1 of the Instant Application Claim 8 of Conflicting Patent Application 18/266,414 [hereinafter Huang] A gait measurement system comprising: A gait measurement system comprising: a measurement device including the feature-amount generation device according to claim 1 [claim 6] a memory storing instructions, and a memory storing instructions; [claim 1] a processor connected to the memory and configured to execute the instructions to: a processor connected to the memory and configured to execute the instructions to: [claim 1] generate a gait waveform for one gait cycle from time-series data of sensor data measured according to a gait of a user; generate a gait waveform for one gait cycle from time-series data of sensor data related to a motion of a foot [claim 1, wherein the motion of the foot is considered to be according to a gait of the user, as the generated waveform is a gait waveform for one gait cycle] extract a feature amount from the generated gait waveform; extract a feature amount from the generated gait waveform [claim 1] extract a gait phase cluster by integrating a plurality of temporally continuous gait phases from each of which a feature amount is extracted; extract a gait phase cluster by integrating a plurality of temporally continuous gait phases from each of which a feature amount is extracted [claim 1] generate a feature amount of the gait phase cluster using a preset feature-amount constitutive expression; and generate a feature amount of the gait phase cluster using a preset feature-amount constitutive expression [claim 1] estimate a degree of progress of hallux valgus of the user using the feature amount accumulated along with the gait of the user; and 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 input feature amount, and estimate a degree of hallux valgus of the user based on an estimation value output from the estimation model [claim 8, wherein “a degree of hallux valgus” is considered to be equivalent to “a degree of progress of hallux valgus”, as both are considered to be indicators of a level of hallux valgus] display information related to the gait according to the degree of progress of hallux valgus of the user on a screen of a mobile terminal used by the user. This is a provisional nonstatutory double patenting rejection. Claim (s) 1-10 is/are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim (s) 1-10 of copending Application No. 18/393,929 [hereinafter Huang II] in view of Najafi (WO-2019213399-A1, foreign reference attached) . Claim 1 of the conflicting Huang II reference patent application is considered to anticipate almost each and every limitation of instant claim 1 [see comparison below] , except wherein the “physical feature” of the conflicting Huang II reference is broader than the “degree of progress of hallux valgus” of the instant invention. Najafi discloses systems and methods for assessing user gait, wherein Najafi discloses estimating a degree of progress of hallux valgus based on a feature amount of the user’s gait and the gait of the user [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), wherein hallux valgus and bunion are considered to refer to the same physical condition/feature] . 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 Huang II to employ wherein the estimated physical feature of the user is an estimated degree of progress of hallux valgus, as hallux valgus/bunions are considered to be a physical feature that affects a user’s gait. Claims 2-8 of the conflicting Huang II reference as modified by Najafi are considered to render obvious instant claims 2-8. Claims 9-10 of the conflicting Huang II reference as modified by Najafi mutatis mutandis are considered to similarly render obvious instant claims 9-10. Claim 1 of the Instant Application Claim 1 of Conflicting Patent Application 18/393,929 [hereinafter Huang II] A gait measurement system comprising: A gait measurement system comprising: a memory storing instructions, and a memory storing instructions, and a processor connected to the memory and configured to execute the instructions to: a processor connected to the memory and configured to execute the instructions to: generate a gait waveform for one gait cycle from time-series data of sensor data measured according to a gait of a user; generate a gait waveform for one gait cycle from time-series data of sensor data measured according to a gait of a user; extract a feature amount from the generated gait waveform; extract a feature amount from the generated gait waveform; extract a gait phase cluster by integrating a plurality of temporally continuous gait phases from each of which a feature amount is extracted; extract a gait phase cluster by integrating a plurality of temporally continuous gait phases from each of which a feature amount is extracted; generate a feature amount of the gait phase cluster using a preset feature-amount constitutive expression; and generate a feature amount of the gait phase cluster using a preset feature-amount constitutive expression; and estimate a degree of progress of hallux valgus of the user using the feature amount accumulated along with the gait of the user; and estimate a physical feature of the user using the feature amount accumulated along with the gait of the user; and display information related to the gait according to the degree of progress of hallux valgus of the user on a screen of a mobile terminal used by the user. display information related to the gait according to the physical feature of the user on a screen of a mobile terminal used by the user. This is a provisional nonstatutory double patenting rejection. 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) 1-10 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 gait measurement system comprising: a memory storing instructions , and a processor connected to the memory and configured to execute the instructions to: generate a gait waveform for one gait cycle from time-series data of sensor data measured according to a gait of a user ; extract a feature amount from the generated gait waveform ; extract a gait phase cluster by integrating a plurality of temporally continuous gait phases from each of which a feature amount is extracted ; generate a feature amount of the gait phase cluster using a preset feature-amount constitutive expression ; and estimate a degree of progress of hallux valgus of the user using the feature amount accumulated along with the gait of the user ; and display information related to the gait according to the degree of progress of hallux valgus of the user on a screen of a mobile terminal used by the user . (Emphasis added: abstract idea , additional element ) Step 2A Prong 1 Representative claim(s) REF RepresentativeClaim101 \h \* MERGEFORMAT 1 recites the following abstract ideas, which may be performed in the mind or by hand with the assistance of pen and paper: “ generate a gait waveform for one gait cycle from time-series data of sensor data measured according to a gait of a user ” – may be performed by merely applying known mathematical formulas or equations to at least a limited amount of data under no particular time constraints [Applicant’s Specification ¶0066] “ extract a feature amount from the generated gait waveform ” – may be performed by merely observing at least a limited amount of previously collected or known data under no particular time constraints and drawing mental conclusions therefrom [Applicant’s Specification ¶0080] “ extract a gait phase cluster by integrating a plurality of temporally continuous gait phases from each of which a feature amount is extracted ” – may be performed by merely applying known mathematical formulas or equations to at least a limited amount of data under no particular time constraints and drawing mental conclusions therefrom [Applicant’s Specification ¶0064] “ generate a feature amount of the gait phase cluster using a preset feature-amount constitutive expression ” – may be performed by merely applying known mathematical formulas or equations to at least a limited amount of data under no particular time constraints [Applicant’s Specification ¶0028] “ estimate a degree of progress of hallux valgus of the user using the feature amount accumulated along with the gait of the user ” – may be performed by merely observing at least a limited amount of previously collected or known data under no particular time constraints and drawing mental conclusions therefrom [Applicant’s Specification ¶0089] 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 integrating a plurality of temporally continuous gait phases from each of which a feature amount is extracted ”; “ generate a feature amount of the gait phase cluster using a preset feature-amount constitutive expression ” ] 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 REF RepresentativeClaim101 \h \* MERGEFORMAT 1 only recites additional elements of extra-solutionary activity – in particular, extra-solution activity [generic computer function; the Examiner notes there is no positive recitation of any 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, or a sufficiently particular form of display or computing architecture/structure). Dependent claim(s) 2-5 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) 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, transmitting 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 [processor, memory, display each recited at a high level of generality and generic 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 6-7 recite a “machine learning model” that is “constructed by machine learning”. Such a machine learning model is considered well-understood, routine, and conventional, as known by at least: Hu (“Intelligent Sensor Networks”, NPL attached) [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”, NPL attached) [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”, NPL attached) [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 Y given a set of labeled training examples {xi; yi} of inputs xi and outputs yi = f(xi) (Mitchell, Pages 3-4)] Claim 8 recites “a data acquisition device configured to measure a spatial acceleration and a spatial angular velocity, and generate the sensor data based on the spatial acceleration and the spatial angular velocity”. 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, and recites the data acquisition 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 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) [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 recite subject matter regarding “ estimate a degree of progress of hallux valgus of the user using the feature amount accumulated along with the gait of the user; and display information related to the gait according to the degree of progress of hallux valgus of the user on a screen of a mobile terminal used by the user ” [the Examiner notes that claims 9 and 10 recite similar subject matter] , 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 [MPEP § 2106.04(d)(2)] . Accordingly, the claim(s) as whole(s) fail amount to significantly more than the judicial exception under Step 2B. Claim Rejections - 35 USC § 103 The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action: A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made. The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows: 1. Determining the scope and contents of the prior art. 2. Ascertaining the differences between the prior art and the claims at issue. 3. Resolving the level of ordinary skill in the pertinent art. 4. Considering objective evidence present in the application indicating obviousness or nonobviousness. This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention. Claim (s) 1-7 and 9-10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Thompson (US-10610131-B1) in view of Najafi (WO-2019213399-A1, foreign reference attached) . Regarding claim 1, Thompson teaches A gait measurement system 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: generate a gait waveform for one gait cycle from time-series data of sensor data measured according to a gait of a user [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] ; extract a feature amount from the generated gait waveform [wherein any particular amplitude of angular velocity at a specific point in time is considered to define a feature amount from the gait waveform] ; extract a gait phase cluster by integrating a plurality of temporally continuous gait phases from each of which a feature amount is extracted [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 feature amount of the gait phase cluster using a preset feature-amount constitutive expression [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), wherein averaging displacements calculated over each stride phase is considered to read on using a preset feature-amount constitutive expression as defined by the Applicant’s Specification ¶0028] ; and estimate a degree of a physical feature of the user using the feature amount accumulated along with the gait 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, wherein an estimation of contralateral symmetry is considered to be an estimation of a “physical feature” of the user] ; and display information related to the gait according to the degree of the physical feature of the user on a screen of a mobile terminal used by the user [The motion metrics may be displayed 622 on the mobile device (104) perhaps for immediate use or on a personal computer with a larger graphics display (Thompson Col 11:32-35)] . However, Thompson fails to explicitly disclose wherein the estimation of a physical feature of the user using the feature amount accumulated along with the gait of the user is an estimation of a degree of progress of hallux valgus. Najafi discloses systems and methods for assessing user gait, wherein Najafi discloses estimating a degree of progress of hallux valgus based on a feature amount of the user’s gait and the gait of the user [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), wherein hallux valgus and bunion are considered to refer to the same physical condition/feature] . 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 estimation of a physical feature of the user using the feature amount accumulated along with the gait of the user is an estimation of a degree of progress of hallux valgus, so as to allow for correlation and determination of foot deformities based on measured parameters. Regarding claim 2, Thompson in view of Najafi teaches The gait measurement system 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 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 Najafi teaches The gait measurement system 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 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 [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 4, Thompson in view of Najafi teaches The gait measurement system according to claim 1, wherein the processor is configured to execute the instructions to extract the feature amount of the gait phases forming the walking phase cluster related to hallux valgus of the user based on preset conditions [wherein extracting any particular sub-phase of the cluster is considered to be based on unspecified “preset conditions”] . Regarding claim 5, Thompson in view of Najafi teaches The gait measurement system according to claim 1, wherein the processor is configured to execute the instructions to extract a feature amount related to a gait affected by influence of hallux valgus [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 being used to determine a physical feature of the user is considered to read on be related to a gait affected by influence of hallux valgus based on the § 103 modification of claim 1 above] . Regarding claim 6, Thompson in view of Najafi teaches The gait measurement system according to claim 1. However, Thompson in view of Najafi as presently modified fails to explicitly disclose wherein the processor is configured to execute the instructions to input the feature amount of the gait phase cluster extracted from the time-series data of the sensor data measured along with the gait of the user to a machine learning model that outputs the degree of progress of hallux valgus according to input the feature amount, and estimate the degree of progress of hallux valgus of the user based on an estimation value output from the machine learning model. Najafi does disclose inputting a feature amount of a user’s gait and a gait of the user to an estimation model that outputs the degree of progress of hallux valgus according to input feature amount, and steps to estimate the degree of progress of hallux valgus of the user based on an estimation value output from the estimation model that uses angular velocity information as an input [Najafi ¶¶0047, 0052] . It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to hav