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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on December 04, 2024, is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the Examiner.
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.
Claims 20-33 are rejected under 35 U.S.C. 103 as being unpatentable over Gupta et al. (US 2024/0331170 A1), hereafter Gupta, in view of Jenny et al. (On the mechanical power output required for human running – Insight from an analytical model. Journal of Biomechanics. Volume 110.), hereafter Jenny.
Regarding claim 20, Gupta teaches a method performed within a mobile device (See mobile device labeled 110 in Fig. 1.), the method comprising: capturing video of a human runner to obtain one or more parameters (Fig. 1 shows capturing a video of a human runner using a mobile device for generating a motion performance metric.) including at least one of:
a speed u, a contact time tc, and a flight time tf ([0267] “Performance metrics include speed, velocity, acceleration, angular speed of joints, angles and change in angles during motion, stride frequency, stride length, ground contact time, air time, and other temporal-based performance metrics, amongst others.”); and
operating on the one or more parameters with a biomechanics model implemented by the mobile device to produce one or more running metrics ([0017-0018] “In an embodiment, extracting kinematic data of the subject includes recognizing human pose points on the subject. In an embodiment, the method includes the further step of: constructing a biomechanical model of the motion of the subject based on the extracted kinematic data, whereby the motion performance metric is formulated based on the constructed biomechanical model.” Furthermore, the biomechanical model can be compared to an ideal model to gain further insights and to provide feedback to the human runner.).
As shown above, Gupta teaches constructing a biomechanical model of the motion of the human runner to determine motion performance metrics, but Gupta does not teach producing one or more running metrics including at least one of: a total power P as a function of time t, an average power Psr, and a specific average power Psr/mu.
However, Jenny teaches wherein the one or more running metrics are including at least one of: a total power P as a function of time t, an average power Psr, and a specific average power Psr/mu ([Section 4.1] “Figure 6 depicts required specific mechanical power output as function of running speed. On the left P/m is shown for α={0,0.1,0.2,0.3} (solid, dashed, dash-dotted and dotted lines, respectively).”).
Gupta and Jenny are analogous in the art, because both teach methods of analyzing the strides of a human runner and calculating performance metrics using biomechanical principles and mathematical models which are specific to the biometrics of the human runner. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to extend Gupta’s invention to additionally calculate power as a motion performance metric. This modification would further enhance Gupta’s invention by providing additional performance metrics to the user for providing further advice and improvements to the user’s running form. For example, [Jenny Section 1] “The assumptions and approximations were examined and it can be concluded that they are justified. Simple algebraic expressions for the COM trajectory and required power output were derived and they lead to very interesting new insight. Comparisons of model predictions with reported performance data of world class athletes suggest that approximately a quarter of the impact energy during the first part of ground contact gets stored as elastic energy, which is then released and supports the runner during the remaining stance phase. Step rate, ground contact time as well as the runner’s mass and height are crucial parameters for the mechanical power output demand of running… Further, the model shows that a higher center of mass reduces the mechanical power output demand for running.”).
Furthermore, Gupta motivates further improvement by teaching a general architecture which can produce motion performance metrics by capturing a video of a human runner with a mobile device. A person of ordinary skill in the art could additionally include power metrics using Gupta’s invention. Jenny teaches and motivates calculating power directly from the motion performance metrics (speed, contact time, flight time, etc.) which are already calculated by Gupta [0267]. [Jenny Abstract] “Therefore, a mathematical model based on very few assumptions is devised. The purpose of the proposed model is to relate running speed and required mechanical power output as an algebraic function of the runner’s mass, height, step rate, ground contact time and wind speed.”).
Regarding claim 21, Gupta and Jenny teach the method of claim 20. Gupta further teaches wherein said capturing video is performed by a camera system of the mobile device (Fig. 1 shows capturing a video of a human runner using a mobile device for generating a motion performance metric.).
Regarding claim 22, Gupta and Jenny teach the method of claim 20. Gupta further teaches further comprising refining the one or more running metrics based on subsequent frames of the video (In [0263], Gupta teaches refining the tracking of the human runner’s pose in consecutive frames. The kinematic data and performance metrics are based on the pose information [0263] “In an embodiment, the “smooth” change in position of a pose point across frames is assumed to identify noisy pose points that are indicative of a pose point detection error… Any noisy data point is replaced by mean of pose points in two neighboring frames (the frame prior and the frame following the frame with the erroneous pose point).”).
Regarding claim 23, Gupta and Jenny teach the method of claim 20. Jenny further teaches wherein said operating on the one or more parameters includes:
determining a vertical power Pvert as a function of time t (In Section 2.3 and Eq. 12-13, Jenny teaches calculating the energy dissipation due to vertical oscillation Po.);
determining a horizontal power Ptr as a function of time t (In Section 2.3 and Eq. 14-15, Jenny teaches calculating the energy dissipation due to the ground reaction force Pb. These equations include determination of the horizontal ground reaction force [Eq. 14]. Additionally, the energy dissipation due to aerodynamic drag Pa is determined by Eq. 11.); and
calculating the total power P based at least in part on the vertical power Pvert and the horizontal power Ptr (Eq. 1 shows the mathematical model for determining power. The equation includes Pmech, which is the mechanical power required by the runner for movement. Sections 2.1-2.3 discuss the steps for calculating the mechanical power, and Eq. 16-17 show that mechanical power is calculated by summing several different energy dissipation factors (powers) which consider both horizontal and vertical elements. [Eq. 17] “Pmech = Pa + Po + Pb”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to extend Gupta’s invention to additionally calculate power as a motion performance metric. This modification would further enhance Gupta’s invention by providing additional performance metrics to the user for providing further advice and improvements to the user’s running form. See the full rationale applied to claim 1.
Regarding claim 24, Gupta and Jenny teach the method of claim 23. Jenny further teaches wherein said determining the vertical power Pvert includes:
deriving a normal force Fn as a function of time t for the human runner based on the one or more parameters obtained from capturing the video ([Section 2.1] “Figure 1 shows the resulting vertical ground reaction force component during ground contact (normalized by mg).” Eq. 8 shows determining the normal force as a function of time.);
converting the normal force Fn into a vertical acceleration component ay of a center of mass, a vertical velocity component vy of the center of mass ([Section 2.1] “Note that independent of its shape the area under the F⊥ /(mg)-curve (limited from below by the horizontal thin dashed line) must be equal to 100(tf+tc)/tc. Knowing the vertical acceleration, the vertical velocity component can be computed as…” See Eq. 9.), and a vertical position component y of the center of mass ([Section 2.1] “For the COM-height one obtains…” See Eq. 10.);
determining the vertical power Pvert as a function of time t based on the normal force Fn and the vertical velocity component vy (See Section 2.3. Eq. 12-13 show calculating the energy dissipation due to vertical oscillation, represented as Po. These equations consider the change in height of the center of mass over time and the vertical forces. As shown in Section 2.1 and Eq. 9-10, the vertical velocity is considered when determining changes in height of the center of mass.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to extend Gupta’s invention to additionally calculate power as a motion performance metric. This modification would further enhance Gupta’s invention by providing additional performance metrics to the user for providing further advice and improvements to the user’s running form. See the full rationale applied to claim 1.
Regarding claim 25, Gupta and Jenny teach the method of claim 24. Jenny further teaches wherein said determining the horizontal power Ptr includes: deriving a horizontal projection xr of the center of mass as a function of time t ([Section 1] “The mathematical model is presented in Section 2; first, an expression for the center of mass trajectory is derived as function of running speed, step rate and ground contact time; then an expression for the required mechanical power output is derived...” In Section 2.1, Jenny teaches calculating the position of the center of mass over time; see the equations and figures within Section 2.1. Fig. 2 shows the trajectory of the center of mass as calculated in Section 2.1.);
combining the normal force Fn ([Eq. 5]), horizontal projection xr ([Fig. 1]), and vertical position component y ([Eq. 10]), to obtain a horizontal force FT ([Section 2.3] “The horizontal ground reaction force can be computed as…” See Eq. 14. “It is fully determined by the vertical ground reaction force, running speed, COM height, step rate and ground contact length.” Eq. 14 calculates the horizontal ground reaction force as a function of time, and this equation considers the normal force, horizonal speed, and center of mass height. Additionally, see Section 2.1 and Eq. 1-10. Fig. 1 shows vertical and horizontal ground reaction forces calculated in Section 2.1. These equations consider the normal force F⊥, horizontal center of mass trajectory [Fig. 1], and the vertical position h(t).);
determining the horizontal power Ptr as a function of time t based on the horizontal force Ft and a runner speed u (See Eq. 15, which calculates the energy dissipation (power) from the ground resistance and braking. Eq. 15 includes the horizontal force as a function of time as calculated in Eq. 14, which considered the runner speed.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to extend Gupta’s invention to additionally calculate power as a motion performance metric. This modification would further enhance Gupta’s invention by providing additional performance metrics to the user for providing further advice and improvements to the user’s running form. See the full rationale applied to claim 1.
Regarding claim 26, Gupta and Jenny teach the method of claim 23. Jenny further teaches wherein said operating on the one or more parameters includes: determining an aerodynamic drag power Pa ([Section 2.3] “Energy dissipation due to aerodynamic drag is equal to the running speed times the aerodynamic resistance. Latter is proportional to the air density times the square of the virtual air velocity faced by the runner (running velocity u minus wind speed w) times its projected area A seen from the front.” See equation 11.),
wherein said calculating the total power P is also based on the aerodynamic drag power Pa (The power calculations taught by Jenny consider aerodynamic drag power, which is determined by equation 11. [Section 3] “Aerodynamic drag coefficient: The aerodynamic coefficient γ is deduced from aerodynamic data published in Pritchard (1993). There it is reported that for a runner with a mass of m=70kg a projected area of A=0.45m2 is representative, and they suggest to use Cd=1. From this and with pa kg/m3 (air density at roughly 1500 m above sea level) one obtains γ
≈
0.013
m2 kg−2/3, which is used throughout the following studies.”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to extend Gupta’s invention to additionally calculate power as a motion performance metric. This modification would further enhance Gupta’s invention by providing additional performance metrics to the user for providing further advice and improvements to the user’s running form. See the full rationale applied to claim 1.
Regarding claim 27, Gupta teaches a mobile device comprising a camera system, a processor, and a run-analysis application (See mobile device labeled 110 in Fig. 1.) configuring the processor to:
use the camera system to capture video of a human runner (Fig. 1 shows capturing a video of a human runner using a mobile device for generating a motion performance metric.);
process the video to obtain one or more parameters including at least one of: a speed u, a contact time tc, and a flight time tf ([0267] “Performance metrics include speed, velocity, acceleration, angular speed of joints, angles and change in angles during motion, stride frequency, stride length, ground contact time, air time, and other temporal-based performance metrics, amongst others.”); and
operate on the one or more parameters with a biomechanics model to produce one or more running metrics ([0017-0018] “In an embodiment, extracting kinematic data of the subject includes recognizing human pose points on the subject. In an embodiment, the method includes the further step of: constructing a biomechanical model of the motion of the subject based on the extracted kinematic data, whereby the motion performance metric is formulated based on the constructed biomechanical model.” Furthermore, the biomechanical model can be compared to an ideal model to gain further insights and to provide feedback to the human runner.).
As shown above, Gupta teaches constructing a biomechanical model of the motion of the human runner to determine motion performance metrics, but Gupta does not teach producing one or more running metrics including at least one of: a total power P as a function of time t, an average power Psr, and a specific average power Psr/mu.
However, Jenny teaches wherein the one or more running metrics are including at least one of: a total power P as a function of time t, an average power Psr, and a specific average power Psr/mu ([Section 4.1] “Figure 6 depicts required specific mechanical power output as function of running speed. On the left P/m is shown for α={0,0.1,0.2,0.3} (solid, dashed, dash-dotted and dotted lines, respectively).”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to extend Gupta’s invention to additionally calculate power as a motion performance metric. This modification would further enhance Gupta’s invention by providing additional performance metrics to the user for providing further advice and improvements to the user’s running form. See the full rationale applied to claim 1.
Regarding claim 28, Gupta and Jenny teach the mobile device of claim 27. Gupta further teaches wherein the run-analysis application further configures the processor to refine the one or more running metrics based on subsequent frames of the video (In [0263], Gupta teaches refining the tracking of the human runner’s pose in consecutive frames. The kinematic data and performance metrics are based on the pose information [0263] “In an embodiment, the “smooth” change in position of a pose point across frames is assumed to identify noisy pose points that are indicative of a pose point detection error… Any noisy data point is replaced by mean of pose points in two neighboring frames (the frame prior and the frame following the frame with the erroneous pose point).”).
Regarding claim 29, Gupta and Jenny teach the mobile device of claim 27. Jenny further teaches wherein as part of operating on the one or more parameters, the run-analysis application configures the processor to: determine a vertical power Pvert as a function of time t (In Section 2.3 and Eq. 12-13, Jenny teaches calculating the energy dissipation due to vertical oscillation Po.);
determine a horizontal power Ptr as a function of time t (In Section 2.3 and Eq. 14-15, Jenny teaches calculating the energy dissipation due to the ground reaction force Pb. These equations include determination of the horizontal ground reaction force [Eq. 14]. Additionally, the energy dissipation due to aerodynamic drag Pa is determined by Eq. 11.);
determine an aerodynamic drag power Pa as a function of time t ([Section 2.3] “Energy dissipation due to aerodynamic drag is equal to the running speed times the aerodynamic resistance. Latter is proportional to the air density times the square of the virtual air velocity faced by the runner (running velocity u minus wind speed w) times its projected area A seen from the front.” See equation 11.); and
calculate the total power P based at least in part on the vertical power Pvert, the horizontal power Ptr, and the aerodynamic drag power Pa (Eq. 1 shows the mathematical model for determining power. The equation includes Pmech, which is the mechanical power required by the runner for movement. Sections 2.1-2.3 discuss the steps for calculating the mechanical power, and Eq. 16-17 show that mechanical power is calculated by summing several different energy dissipation factors (powers) which consider horizontal, vertical, and aerodynamic drag elements. [Eq. 17] “Pmech = Pa + Po + Pb”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to extend Gupta’s invention to additionally calculate power as a motion performance metric. This modification would further enhance Gupta’s invention by providing additional performance metrics to the user for providing further advice and improvements to the user’s running form. See the full rationale applied to claim 1.
Regarding claim 30, Gupta and Jenny teach the method of claim 29. Jenny further teaches wherein as part of determining the vertical power, the run-analysis application configures the processor to: derive a normal force Fn as a function of time t for the human runner based on said one or more parameters ([Section 2.1] “Figure 1 shows the resulting vertical ground reaction force component during ground contact (normalized by mg).” Eq. 8 shows determining the normal force as a function of time.);
convert the normal force Fn into a vertical acceleration component ay of a center of mass, a vertical velocity component vy of the center of mass (See Section 2.3. Eq. 12-13 show calculating the energy dissipation due to vertical oscillation, represented as Po. These equations consider the change in height of the center of mass over time and the vertical forces. As shown in Section 2.1 and Eq. 9-10, the vertical velocity is considered when determining changes in height of the center of mass.), and a vertical position component y of the center of mass ([Section 2.1] “For the COM-height one obtains…” See Eq. 10.); and
determine the vertical power Pvert as a function of time t based on the normal force Fn and the vertical velocity component vy (See Section 2.3. Eq. 12-13 show calculating the energy dissipation due to vertical oscillation, represented as Po. These equations consider the change in height of the center of mass over time and the vertical forces. As shown in Section 2.1 and Eq. 9-10, the vertical velocity is considered when determining changes in height of the center of mass.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to extend Gupta’s invention to additionally calculate power as a motion performance metric. This modification would further enhance Gupta’s invention by providing additional performance metrics to the user for providing further advice and improvements to the user’s running form. See the full rationale applied to claim 1.
Regarding claim 31, Gupta and Jenny teach the method of claim 30. Jenny further teaches wherein as part of determining the horizontal power Ptr, the run-analysis application configures the processor to: derive a horizontal projection xr of the center of mass as a function of time t ([Section 1] “The mathematical model is presented in Section 2; first, an expression for the center of mass trajectory is derived as function of running speed, step rate and ground contact time; then an expression for the required mechanical power output is derived...” In Section 2.1, Jenny teaches calculating the position of the center of mass over time; see the equations and figures within Section 2.1. Fig. 2 shows the trajectory of the center of mass as calculated in Section 2.1.);
combine the normal force Fn, horizontal projection xr, and vertical position component y, to obtain a horizontal force FT ([Section 2.3] “The horizontal ground reaction force can be computed as…” See Eq. 14. “It is fully determined by the vertical ground reaction force, running speed, COM height, step rate and ground contact length.” Eq. 14 calculates the horizontal ground reaction force as a function of time, and this equation considers the normal force, horizonal speed, and center of mass height. Additionally, see Section 2.1 and Eq. 1-10. Fig. 1 shows vertical and horizontal ground reaction forces calculated in Section 2.1. These equations consider the normal force F⊥, horizontal center of mass trajectory [Fig. 1], and the vertical position h(t).);
determine the horizontal power Ptr as a function of time t based on the horizontal force Ft and a runner speed u (See Eq. 15, which calculates the energy dissipation (power) from the ground resistance and braking. Eq. 15 includes the horizontal force as a function of time as calculated in Eq. 14, which considered the runner speed.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to extend Gupta’s invention to additionally calculate power as a motion performance metric. This modification would further enhance Gupta’s invention by providing additional performance metrics to the user for providing further advice and improvements to the user’s running form. See the full rationale applied to claim 1.
Regarding claim 32, Gupta teaches a system comprising:
an end-user display; and an edge device having a camera system, a processor, and a run-analysis application stored in memory (See mobile device labeled 110 in Fig. 1. Mobile devices are edge devices.), the run-analysis application configuring the edge device to:
use the camera system to capture video of a human runner (Fig. 1 shows capturing a video of a human runner using a mobile device for generating a motion performance metric.);
process the video to obtain one or more parameters including at least one of: a speed u, a contact time tc, and a flight time tf ([0267] “Performance metrics include speed, velocity, acceleration, angular speed of joints, angles and change in angles during motion, stride frequency, stride length, ground contact time, air time, and other temporal-based performance metrics, amongst others.”);
operate on the one or more parameters with a biomechanics model to produce one or more running metrics ([0017-0018] “In an embodiment, extracting kinematic data of the subject includes recognizing human pose points on the subject. In an embodiment, the method includes the further step of: constructing a biomechanical model of the motion of the subject based on the extracted kinematic data, whereby the motion performance metric is formulated based on the constructed biomechanical model.” Furthermore, the biomechanical model can be compared to an ideal model to gain further insights and to provide feedback to the human runner.); and
to transfer the one or running metrics to the end-user display ([0025-0027] “In an embodiment, the method includes the further step of outputting the motion performance metric for visual display on a display device. In an embodiment, the display device is a smartphone. In an embodiment, the motion performance metric is outputted and displayed as one or more of: a graph; a number; and a dynamically moving gauge.”).
As shown above, Gupta teaches constructing a biomechanical model of the motion of the human runner to determine motion performance metrics, but Gupta does not teach producing one or more running metrics including at least one of: a total power P as a function of time t, an average power Psr, and a specific average power Psr/mu.
However, Jenny teaches wherein the one or more running metrics are including at least one of: a total power P as a function of time t, an average power Psr, and a specific average power Psr/mu ([Section 4.1] “Figure 6 depicts required specific mechanical power output as function of running speed. On the left P/m is shown for α={0,0.1,0.2,0.3} (solid, dashed, dash-dotted and dotted lines, respectively).”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to extend Gupta’s invention to additionally calculate power as a motion performance metric. This modification would further enhance Gupta’s invention by providing additional performance metrics to the user for providing further advice and improvements to the user’s running form. See the full rationale applied to claim 1.
Regarding claim 33, Gupta and Jenny teach the system of claim 32. Gupta further teaches wherein the run-analysis application further configures the edge device to accept biometric data input from the user, the biometric data input including a mass of the human runner, and wherein the run-analysis application configures the edge device to produce the one or more running metrics based in part on the biometric data input ([0360] “Generally speaking, all performance metrics, data and associated video and feedback is preferably automatically (but otherwise manually) associated with an athlete profile of a subject using one or more of the biometric identification methods recited above, or manual input, to be used as a basis of measurement at the time. The captured data can not only be compared to past performance of the same subject over time, but also another different subject, for example two different runners, in a manner similar to the overlying metric illustrated in FIG. 8.” [0180] “The existing profile of an athlete may include data and analysis taken from previous visual data captures which can therefore serve as a basis of comparison to the present captured visual data of that same athlete and an appropriate performance metric is formulated (more on this below). Further, athlete profiles will include: athlete date of birth; athlete height and weight at a particular date; past injuries of athlete; and body measurements.” Additionally, Jenny also teaches computing the power for the human runner using biometric data as variables. [Section 1] “Step rate, ground contact time as well as the runner’s mass and height are crucial parameters for the mechanical power output demand of running.”).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Qui et al. (US 2022/0114839 A1) teaches systems and methods for analyzing the gait of a human runner from a video clip. The methods involve tracking the pose of the human runner over time, determining the center-of-gravity coordinates of the runner over time, and determining safety and behavior information of the human runner’s form based on the gait data.
Mulligan et al. (A minimal power model for human running performance. PLoS ONE. Volume 13. Issue 11.) teaches mathematical models for analyzing human running performance by quantifying the relative metabolic power output over time of a human runner.
Dunn et al. (Non-invasive, Spatio-temporal Gait Analysis for Sprint Running Using a Single Camera. Procedia Engineering. Volume 112. Pages 528-533. ISSN 1877-7058.) teaches analyzing the gait of athletes during sprint running by capturing video of the athletes and determining velocity and determining the positions when each foot contacts the ground.
Su et al. (CN 114818989 B) teaches systems and methods for identifying gait behavior from a video of a human runner.
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/Eric Shoemaker/
Patent Examiner
/JENNIFER MEHMOOD/Supervisory Patent Examiner, Art Unit 2664
Prosecution Insights