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 .
Response to Arguments
Applicant's arguments filed February 2, 2026 have been fully considered but they are not persuasive.
Applicant asserts that Walker predicts the final resting position of a golf ball by determining bounce and roll behavior of a golf ball on impact and asserts that Walker does not teach or suggest a machine learning model configured to determine aerodynamic coefficients at multiple different times during the golf ball flight. Examiner notes that Walker’s use of bounce and roll behavior does not preclude the use of aerodynamic coefficients at multiple different times during the golf ball flight. The use of both methods is shown by the fact that Walker does use aerodynamic coefficients at multiple different times during the golf ball flight as noted below.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1, 4-12 and 16-20 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by U.S. Pub. 2024/0173591 by Walker.
Regarding claim 1, Walker discloses a computer-implemented method (abstract) comprising:
receiving, by one or more computing devices, first data associated with first launch conditions of a first golf ball after the first golf ball moves from a first initial position (para. 43, 52 – see measurement of impact variables during a golf ball strike);
determining, by the one or more computing devices, a simulated landing position of the first golf ball, the simulated landing position determined based at least in part on the launch conditions (para. 53 – see predicted final resting position);
receiving, by the one or more computing devices, an actual landing position of the first golf ball (para. 62 – see actual final resting position);
determining, by the one or more computing devices, second data associated with an error between the simulated landing position and the actual landing position (para. 62 – see difference between actual and predicted);
training, by the one or more computing devices, a machine learning model using the error between the simulated landing position and the actual landing position (para. 62, 82 – see updated model and utilization of machine learning);
wherein the machine learning model is configured to: determine first aerodynamic coefficients of a golf ball at a first time, the first time being after the golf ball moves from an initial position and before the golf ball lands (para. 81-82 – see aerodynamic flight prediction variables in the model); and
determine second aerodynamic coefficients of the golf ball at a second time, the second time being different than the first time and being after the golf ball moves from the initial position and before the golf ball lands (para. 81-82 – see aerodynamic flight prediction variables in the model);
receiving, by the one or more computing devices, third data associated with second launch conditions of a second golf ball after the second golf ball moves from a second initial position (para. 52 – see updates during play); and
determining, by the one or more computing devices and based at least in part on inputting the third data into the trained machine learning model, a predicted landing position of the second golf ball (para. 52, 62, 82 – see updated model and utilization of machine learning and data collection during play),
the predicted landing position being based at least in part on the first aerodynamic coefficients and the second aerodynamic coefficients (para. 51-52 – see final resting position and underlying conditions).
Regarding claim 4, Walker discloses the computer-implemented method of claim 1, wherein the first launch conditions and the second launch conditions comprise one or more of a ball speed, a launch angle, an azimuth, a total spin, a spin-tilt axis, a linear velocity, or an angular velocity (para. 43 – see velocity and trajectory).
Regarding claim 5, Walker discloses the computer-implemented method of claim 3, wherein the first aerodynamic coefficients and the second aerodynamic coefficients comprise one or more of drag or lift (para. 81-82 – see drag).
Regarding claim 6, Walker discloses the computer-implemented method of claim 1, further comprising determining, by the one or more computing devices and based at least in part on the trained machine learning model, a predicted ball flight of the second golf ball, the predicted ball flight including a predicted location of the second golf ball at various time steps between a first time and a second time (para. 79 – see at least path prediction).
Regarding claim 7, Walker discloses the computer-implemented method of claim 6, wherein the first time is associated with initial movement of the second ball from the initial position and the second time is associated with a landing of the second golf ball (para. 62, 78-82 – see path, final resting position and prediction).
Regarding claim 8, Walker discloses the computer-implemented method of claim 1, wherein the machine learning model comprises one or more of a supervised learning algorithm, an unsupervised learning algorithm, a semi-supervised learning algorithm, or a reinforcement learning algorithm (para. 65 – see unsupervised feedback loop).
Regarding claim 9, Walker discloses the computer-implemented method of claim 1, wherein the one or more computing devices determine the simulated landing position using a mathematical physics model (para. 21 – see the use of mathematical physics variables).
Regarding claim 10, Walker discloses the computer-implemented method of claim 1, wherein the one or more computing devices determine the predicted landing position using a mathematical physics model (para. 21 – see the use of mathematical physics variables).
Regarding claim 11, Walker discloses this claim as noted above regarding claim 1 and the iterative modeling as noted in para. 52.
Regarding claim 12, Walker discloses this claim as noted above regarding claim 1.
Regarding claim 16, Walker discloses the computer-implemented method of claim 1, wherein a difference between the first aerodynamic coefficients and the second aerodynamic coefficients is based at least in part on a spin decay of the golf ball between the first time and the second time (para. 52 – see iterative modeling and spin rate change meeting the limitation “spin decay”).
Regarding claim 17, Walker discloses the computer-implemented method of claim 1, wherein a difference between the first aerodynamic coefficients and the second aerodynamic coefficients is based at least in part on a change in velocity of the golf ball between the first time and the second time (para. 52 – see iterative modeling and velocity measurement/input).
Regarding claim 18, Walker discloses the computer-implemented method of claim 1, wherein the machine learning model is configured to determine instantaneous aerodynamic coefficients at a plurality of time steps during a flight of the golf ball, the predicted landing position being based at least in part on the instantaneous aerodynamic coefficients (para. 52 – see iterative modeling and the listed aerodynamic coefficient, e.g. velocity, spin).
Regarding claim 19, Walker discloses the computer-implemented method of claim 1, further comprising determining, based at least in part on the first aerodynamic coefficients and the second aerodynamic coefficients, at least one of a lift force or a drag force acting on the second golf ball during flight (para. 52 – see iterative modeling and the listed aerodynamic coefficient, e.g. velocity, spin).
Regarding claim 20, Walker discloses the computer-implemented method of claim 1, wherein determining the predicted landing position comprises performing a forward pass through the machine learning model at each of a plurality of time steps during a simulated flight of the second golf ball to determine aerodynamic coefficients at each time step (para. 52 – see iterative modeling and the listed aerodynamic coefficient, e.g. velocity, spin).
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.
Claim(s) 14 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Walker and further in view of U.S. Pub. 2024/0216774 by Rahman.
Regarding claims 14 and 15, While Walker discloses the predictive ball launching method, it does not provide for modification of a physical club or simulated club in a simulated golf environment. Rahman discloses both of these uses at para. 82-87. Because the references are from a similar art and concerned with a similar problem, see simulated golf environments, it would have been obvious to one having ordinary skill in the art at the time of filing to construct Walker with Rahman’s virtual club fitting/adjustment implementation. One having ordinary skill in the art at the time of filing would have been motivated to do so because the use of a simulated golf ball flight algorithm allows for enhanced club fitting and suggestion through the predictive power of the simulated golf experience. The predictive nature of the simulation reduces the guesswork and extra effort that would normally accompany club fitting/adjustment.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/PETER J IANNUZZI/ Primary Examiner, Art Unit 3715