IP Library Granted Patent US 12,649,088
Granted Patent B1
US 12,649,088 · App. 19/247,715 · Granted Jun 9, 2026

Systems and methods for generating swing recommendations

Inventors: Gregory Alan Rose (Carlsbad, CA); Brett J. Killian (Oceanside, CA); Radu Orghidan (London, GB); Adrian Tamas (London, GB)
Assignee: Acushnet Company
A63B24/0006A63B24/0075A63B71/0622A63B2024/0015A63B2102/32A63B2214/00A63B2220/05A63B2220/806A63B2230/62
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Quick Facts
Patent No.
US 12,649,088
App. No.
19/247,715
Filed
Jun 24, 2025
Granted
Jun 9, 2026
Kind
B1
Art Unit
3711
USPC
473/219
Abstract

Systems, methods, and apparatuses for generating swing recommendations are disclosed herein. In accordance with the presently disclosed technology, a method may include obtaining a swing recommendation model, obtaining target swing characteristic data, and generating target swing recommendation data.

Claims (60)

1 . A method for generating swing recommendations, the method being implemented in a computer system comprising electronic storage, a display, and a physical computer processor, the method comprising:

obtaining, from the electronic storage, a conditioned swing point model, the conditioned swing point model having been generated by applying an initial swing point model to a training swing image set comprising one or more objects and training swing point data specifying swing points of the one or more objects as a function of position and time, thereby generating a set of swing point relationships between swing images and swing point data, wherein the training swing image set comprises one or more images with motion blur, and wherein the training swing image set comprises computer-generated images;

obtaining, from the electronic storage, a target swing image set captured by a capture system, wherein the target swing image set comprises one or more sequential images of at least part of a golf swing, and wherein the target swing image set comprises one or more images with motion blur;

generating, with the physical computer processor, target swing point data by applying the conditioned swing point model to the target swing image set, wherein the target swing point data specifies the swing points on the target swing image set;

obtaining, from the electronic storage, a swing characteristic model to track the swing points as a function of position and/or time, wherein the swing characteristic model comprises a set of swing characteristic relationships between the swing point data and swing characteristic data;

generating, with the physical computer processor, target swing characteristic data by applying the swing characteristic model to the target swing point data, wherein the target swing characteristic data comprises swing characteristics specifying swing characteristic values corresponding to the target swing point data;

obtaining, from the electronic storage, a swing recommendation model to recommend the swing recommendations based on a detected swing characteristic, wherein the swing recommendation model comprises a set of swing recommendation relationships between swing characteristic data and swing recommendation data;

obtaining, from the electronic storage, target swing characteristic data comprising swing characteristics specifying swing characteristic values; and

generating, with the physical computer processor, target swing recommendation data by applying the swing recommendation model to the target swing characteristic data, wherein the target swing recommendation data comprises the swing recommendations specifying swing recommendation values corresponding to the target swing characteristic data.

2 . The method of claim 1 , further comprising generating, with the physical computer processor, a swing recommendation representation of the target swing characteristic data using visual effects to depict at least some of the target swing recommendation data.

3 . The method of claim 2 , wherein the computer system further comprises a display, and wherein the method further comprises displaying the swing recommendation representation via the display.

4 . The method of claim 1 , wherein the swing recommendations comprise one or more of exercises, drills, equipment, swing changes, or why a prescription was recommended.

5 . The method of claim 4 , wherein the exercises comprise one or more of strength training, cardio, low impact training, or high impact interval training.

6 . The method of claim 1 , wherein the swing recommendation values for an exercises swing recommendation comprise one or more of cats and dogs, supine pelvic tilts, pelvic tilts in golf stance, torso backswing neutral pelvis, spine foam rolling, crocodile breath press ups, assisted reachbacks, two arm cross body lat stretch, windmills, lunge stance one arm incline row, open book rib cage, lumbar lock (IR) reachbacks, lunge stance one arm incline rows, lunge stance one arm decline chest press, supine pillow presses, lumbar lock (IR) reachbacks, box presses, search and destroy with calf stretch, disassociation planks, half-kneeling bounce pass, brettzel, stork turns supported, starfish pattern 1, hip drops, helicopter turns, resisted half-kneeling lift no rotation to rotation, horizontal chops-wide to narrow base, split stance lunge turn, open clam shells, open clam shell hip extended, half kneeling narrow base med-ball bounce pass, single leg bridge, bird dog hip extension with internal rotation, pivot and post, lunge stance bounce pass, dead bugs opposite arm and leg, bird dog diagonals with pattern assistance, half kneeling med-ball lifts, cariocas, side step up, med-ball discus throws, flow row perpendicular foot, side wall press, pivot and post, half-kneeling bounce pass, starfish rolling pattern 1, palm presses, side step up open hip, squat to press to turns, or supine egyptian presses.

7 . The method of claim 1 , wherein the swing recommendation values for a drills swing recommendation comprise one or more of hip bar hinges, pubic bone to rib cage, w-turn backswings, sweep the dust, loss of posture, lead hip high lead shoulder low, lead arm supported swings, get closer, picket fence, control right knee flex, low hip, plumb bob, belt loop at ball, lead hand trail pocket, lift lead foot, trail leg only swings, corner of door way, two shafts show pivot, step change, side arm throw, barriers, change of direction, lead leg only swings, push ball drill, reach over the fence, impact fix drill, lead leg only swings, step into the pitch, pelvic punch, forehand topspin drill, pizza dumbbell, two hand forehand topspin, forehand topspin drill, lead arm only swings, or motorcycle.

8 . The method of claim 1 , wherein the swing recommendation values for an equipment swing recommendation comprises one or more of golf club recommendations or golf ball recommendations.

9 . A method for generating swing recommendations, the method being implemented in a computer system that includes a physical computer processor and electronic storage, the method comprising:

obtaining, from the electronic storage, a conditioned swing point model, the conditioned swing point model having been generated by applying an initial swing point model to a training swing image set comprising one or more objects and training swing point data specifying swing points of the one or more objects as a function of position and time, thereby generating a set of swing point relationships between swing images and swing point data, wherein the training swing image set comprises one or more images with motion blur, wherein the training swing image set is based on virtual body models;

obtaining, from the electronic storage, a target swing image set captured by a capture system, wherein the target swing image set comprises one or more sequential images of at least part of a golf swing, and wherein the target swing image set comprises one or more images with motion blur;

generating, with the physical computer processor, target swing point data by applying the conditioned swing point model to the target swing image set, wherein the target swing point data specifies the swing points on the target swing image set;

obtaining, from the electronic storage, a swing characteristic model to track the swing points as a function of position and/or time, wherein the swing characteristic model comprises a set of swing characteristic relationships between the swing point data and swing characteristic data;

generating, with the physical computer processor, target swing characteristic data by applying the swing characteristic model to the target swing point data, wherein the target swing characteristic data comprises swing characteristics specifying swing characteristic values corresponding to the target swing point data;

obtaining, from the electronic storage, a swing recommendation model to recommend the swing recommendations based on a detected swing characteristic, wherein the swing recommendation model comprises a set of swing recommendation relationships between swing characteristic data and swing recommendation data;

obtaining, from the electronic storage, target swing characteristic data comprising swing characteristics specifying swing characteristic values;

generating, with the physical computer processor, target swing recommendation data by applying the swing recommendation model to the target swing characteristic data, wherein the target swing recommendation data comprises the swing recommendations specifying swing recommendation values corresponding to the target swing characteristic data, wherein the swing recommendations comprise one or more of exercises, drills, equipment, swing changes, or why a prescription was recommended; and

generating, with the physical computer processor, a swing recommendation representation of the target swing characteristic data using visual effects to depict at least some of the target swing recommendation data.

10 . The method of claim 9 , wherein the computer system comprises a display, and wherein the method further comprises displaying the swing recommendation representation via the display.

11 . The method of claim 9 , wherein the exercises comprise one or more of strength training, cardio, low impact training, or high impact interval training.

12 . The method of claim 9 , wherein the swing recommendation values for an exercises swing recommendation comprise one or more of cats and dogs, supine pelvic tilts, pelvic tilts in golf stance, torso backswing neutral pelvis, spine foam rolling, crocodile breath press ups, assisted reachbacks, two arm cross body lat stretch, windmills, lunge stance one arm incline row, open book rib cage, lumbar lock (IR) reachbacks, lunge stance one arm incline rows, lunge stance one arm decline chest press, supine pillow presses, lumbar lock (IR) reachbacks, box presses, search and destroy with calf stretch, disassociation planks, half-kneeling bounce pass, brettzel, stork turns supported, starfish pattern 1, hip drops, helicopter turns, resisted half-kneeling lift no rotation to rotation, horizontal chops-wide to narrow base, split stance lunge turn, open clam shells, open clam shell hip extended, half kneeling narrow base med-ball bounce pass, single leg bridge, bird dog hip extension with internal rotation, pivot and post, lunge stance bounce pass, dead bugs opposite arm and leg, bird dog diagonals with pattern assistance, half kneeling med-ball lifts, cariocas, side step up, med-ball discus throws, flow row perpendicular foot, side wall press, pivot and post, half-kneeling bounce pass, starfish rolling pattern 1, palm presses, side step up open hip, squat to press to turns, or supine egyptian presses.

13 . The method of claim 9 , wherein the swing recommendation values for a drills swing recommendation comprise one or more of hip bar hinges, pubic bone to rib cage, w-turn backswings, sweep the dust, loss of posture, lead hip high lead shoulder low, lead arm supported swings, get closer, picket fence, control right knee flex, low hip, plumb bob, belt loop at ball, lead hand trail pocket, lift lead foot, trail leg only swings, corner of door way, two shafts show pivot, step change, side arm throw, barriers, change of direction, lead leg only swings, push ball drill, reach over the fence, impact fix drill, lead leg only swings, step into the pitch, pelvic punch, forehand topspin drill, pizza dumbbell, two hand forehand topspin, forehand topspin drill, lead arm only swings, or motorcycle.

14 . The method of claim 9 , wherein the swing recommendation values for an equipment swing recommendation comprises one or more of golf club recommendations or golf ball recommendations.

15 . A system for generating swing recommendations, the system comprising:

electronic storage;

a display; and

a physical computer processor configured by machine readable instructions to:

obtain, from the electronic storage, a conditioned swing point model, the conditioned swing point model having been generated by applying an initial swing point model to a training swing image set comprising one or more objects and training swing point data specifying swing points of the one or more objects as a function of position and time, thereby generating a set of swing point relationships between swing images and swing point data, wherein the training swing image set comprises one or more images with motion blur;

obtain, from the electronic storage, a target swing image set captured by a capture system, wherein the target swing image set comprises one or more sequential images of at least part of a golf swing, and wherein the target swing image set comprises one or more images with motion blur;

generate, with the physical computer processor, target swing point data by applying the conditioned swing point model to the target swing image set, wherein the target swing point data specifies the swing points on the target swing image set;

obtain, from the electronic storage, a swing characteristic model to track the swing points as a function of position and/or time, wherein the swing characteristic model comprises a set of swing characteristic relationships between the swing point data and swing characteristic data;

generate, with the physical computer processor, target swing characteristic data by applying the swing characteristic model to the target swing point data, wherein the target swing characteristic data comprises swing characteristics specifying swing characteristic values corresponding to the target swing point data;

obtain, from the electronic storage, a swing recommendation model to recommend the swing recommendations based on a detected swing characteristic, wherein the swing recommendation model comprises a set of swing recommendation relationships between swing characteristic data and swing recommendation data;

obtain, from the electronic storage, target swing characteristic data corresponding to at least part of a golf swing; and

generate, with the physical computer processor, target swing recommendation data by applying the swing recommendation model to the target swing characteristic data, wherein the target swing recommendation data comprises the swing recommendations specifying swing recommendation values corresponding to the target swing characteristic data, and wherein the swing recommendations comprise one or more of exercises, drills, equipment, swing changes, or why a prescription was recommended.

16 . The system of claim 15 , wherein the physical computer processor is further configured by machine readable instructions to generate, with the physical computer processor, a swing recommendation representation of the target swing recommendation data using visual effects to depict at least some of the target swing recommendation data.

17 . The system of claim 16 , wherein the system further comprises a display, and wherein the physical computer processor is further configured by machine readable instructions to display the swing recommendation representation via the display.

18 . The system of claim 15 , wherein the exercises comprise one or more of strength training, cardio, low impact training, or high impact interval training.

19 . The system of claim 15 , wherein the swing recommendation values for an exercises swing recommendation comprise one or more of cats and dogs, supine pelvic tilts, pelvic tilts in golf stance, torso backswing neutral pelvis, spine foam rolling, crocodile breath press ups, assisted reachbacks, two arm cross body lat stretch, windmills, lunge stance one arm incline row, open book rib cage, lumbar lock (IR) reachbacks, lunge stance one arm incline rows, lunge stance one arm decline chest press, supine pillow presses, lumbar lock (IR) reachbacks, box presses, search and destroy with calf stretch, disassociation planks, half-kneeling bounce pass, brettzel, stork turns supported, starfish pattern 1, hip drops, helicopter turns, resisted half-kneeling lift no rotation to rotation, horizontal chops-wide to narrow base, split stance lunge turn, open clam shells, open clam shell hip extended, half kneeling narrow base med-ball bounce pass, single leg bridge, bird dog hip extension with internal rotation, pivot and post, lunge stance bounce pass, dead bugs opposite arm and leg, bird dog diagonals with pattern assistance, half kneeling med-ball lifts, cariocas, side step up, med-ball discus throws, flow row perpendicular foot, side wall press, pivot and post, half-kneeling bounce pass, starfish rolling pattern 1, palm presses, side step up open hip, squat to press to turns, or supine egyptian presses.

20 . The system of claim 15 , wherein the swing recommendation values for a drills swing recommendation comprise one or more of hip bar hinges, pubic bone to rib cage, w-turn backswings, sweep the dust, loss of posture, lead hip high lead shoulder low, lead arm supported swings, get closer, picket fence, control right knee flex, low hip, plumb bob, belt loop at ball, lead hand trail pocket, lift lead foot, trail leg only swings, corner of door way, two shafts show pivot, step change, side arm throw, barriers, change of direction, lead leg only swings, push ball drill, reach over the fence, impact fix drill, lead leg only swings, step into the pitch, pelvic punch, forehand topspin drill, pizza dumbbell, two hand forehand topspin, forehand topspin drill, lead arm only swings, or motorcycle.

21 . An apparatus for generating swing recommendations, the apparatus comprising:

a capture system to capture a target swing image set, wherein the target swing image set comprises one or more sequential images of at least part of a golf swing, and wherein the target swing image set comprises one or more images with motion blur; and

a computer system operatively linked to the capture system, wherein the computer system comprises:

electronic storage; and

a physical computer processor configured by machine readable instructions to:

obtain, from the electronic storage, a conditioned swing point model, the conditioned swing point model having been generated by applying an initial swing point model to a training swing image set comprising one or more objects and training swing point data specifying swing points of the one or more objects as a function of position and time, thereby generating a set of swing point relationships between swing images and swing point data, wherein the training swing image set comprises one or more images with motion blur;

obtain, from the electronic storage, the target swing image set captured by the capture system;

generate, with the physical computer processor, target swing point data by applying the conditioned swing point model to the target swing image set, wherein the target swing point data specifies the swing points on the target swing image set;

obtain, from the electronic storage, a swing characteristic model to track swing points as a function of position and/or time, wherein the swing characteristic model comprises a set of swing characteristic relationships between swing point data and swing characteristic data;

generate, with the physical computer processor, target swing characteristic data by applying the swing characteristic model to the target swing point data, wherein the target swing characteristic data comprises swing characteristics specifying swing characteristic values corresponding to the target swing point data;

obtain, from the electronic storage, a swing recommendation model to recommend the swing recommendations based on a detected swing characteristic, wherein the swing recommendation model comprises a set of swing recommendation relationships between the swing characteristic data and the swing recommendation data; and

generate, with the physical computer processor, target swing recommendation data by applying the swing recommendation model to the target swing characteristic data, wherein the target swing recommendation data comprises the swing recommendations specifying swing recommendation values corresponding to the target swing characteristic data.

Assignments (1)
SECURITY INTEREST Recorded Nov 22, 2025
From: ACUSHNET COMPANY
To: JPMORGAN CHASE BANK, N.A.
Reel/Frame 073689/0515 →
References Cited (92)
US 6406336B1 · Stansbury · 2002 [cited by examiner]
US 7395696B2 · Bissonnette et al. · 2008 [cited by applicant]
US 8175326B2 · Siegel · 2012 [cited by examiner]
US 8235870B2 · Hamilton · 2012 [cited by examiner]
US 8696450B2 · Rose · 2014 [cited by examiner]
US 8755569B2 · Shen · 2014 [cited by examiner]
US 8758201B2 · Ashby et al. · 2014 [cited by applicant]
US 9355305B2 · Tanabiki · 2016 [cited by examiner]
US 9364714B2 · Koduri et al. · 2016 [cited by applicant]
US 9697417B2 · Aonuma · 2017 [cited by examiner]
US 9697609B2 · Kim · 2017 [cited by examiner]
US 9782654B2 · Molinari et al. · 2017 [cited by applicant]
US 9898675B2 · Yee et al. · 2018 [cited by applicant]
US 10137348B2 · Molinari et al. · 2018 [cited by applicant]
US 10668342B2 · Amarant et al. · 2020 [cited by applicant]
US 10799759B2 · Hixenbaugh et al. · 2020 [cited by applicant]
US 10929654B2 · Iqbal et al. · 2021 [cited by applicant]
US 11222428B2 · Lee et al. · 2022 [cited by applicant]
US 11263919B2 · Vikram · 2022 [cited by applicant]
US 11430308B1 · Zhang · 2022 [cited by examiner]
US 11458362B1 · Berme et al. · 2022 [cited by applicant]
US 11461905B2 · Lee et al. · 2022 [cited by applicant]
US 11521733B2 · Song · 2022 [cited by examiner]
US 11615648B2 · Lee et al. · 2023 [cited by applicant]
US 11620858B2 · Lee et al. · 2023 [cited by applicant]
US 11636777B2 · Shteren et al. · 2023 [cited by applicant]
US 11640725B2 · Menaker et al. · 2023 [cited by applicant]
US 11642047B2 · Zhang et al. · 2023 [cited by applicant]
US 11645873B1 · Lui · 2023 [cited by applicant]
US 11727728B2 · Mehl et al. · 2023 [cited by applicant]
US 11759126B2 · Juhas et al. · 2023 [cited by applicant]
US 11790536B1 · Berme et al. · 2023 [cited by applicant]
US 11798318B2 · Prince et al. · 2023 [cited by applicant]
US 11819734B2 · Lee et al. · 2023 [cited by applicant]
US 11850490B1 · Destefano · 2023 [cited by applicant]
US 11918883B2 · Kim · 2024 [cited by applicant]
US 11935330B2 · Lee et al. · 2024 [cited by applicant]
US 11941915B2 · Tyomkin · 2024 [cited by applicant]
US 11941916B2 · Lee et al. · 2024 [cited by applicant]
US 11992745B2 · Kulangara Muriyil et al. · 2024 [cited by applicant]
US 12008839B2 · Menaker et al. · 2024 [cited by applicant]
US D1035720S · Lee et al. · 2024 [cited by applicant]
US D1035721S · Lee et al. · 2024 [cited by applicant]
US D1036464S · Lee et al. · 2024 [cited by applicant]
US 12048868B2 · Augustin et al. · 2024 [cited by applicant]
US 12097422B2 · Tawiah · 2024 [cited by applicant]
US 12257478B1 · Yao et al. · 2025 [cited by applicant]
US 12257479B2 · Hixenbaugh et al. · 2025 [cited by applicant]
US 12412428B1 · Alex et al. · 2025 [cited by applicant]
US 20050215337A1 · Shirai et al. · 2005 [cited by applicant]
US 20060247070A1 · Funk et al. · 2006 [cited by applicant]
US 20130304417A1 · Mooney et al. · 2013 [cited by applicant]
US 20140079289A1 · Yamamoto et al. · 2014 [cited by applicant]
US 20140156040A1 · Mooney · 2014 [cited by applicant]
US 20140342844A1 · Mooney · 2014 [cited by applicant]
US 20160360378A1 · Borowski et al. · 2016 [cited by applicant]
US 20170203181A1 · Ito et al. · 2017 [cited by applicant]
US 20180169471A1 · Kondo · 2018 [cited by applicant]
US 20180200605A1 · Syed et al. · 2018 [cited by applicant]
US 20180357472A1 · Dreessen · 2018 [cited by applicant]
US 20190224528A1 · Omid-Zohoor et al. · 2019 [cited by applicant]
US 20190362506A1 · Leroyer · 2019 [cited by examiner]
US 20220198834A1 · Fujimoto · 2022 [cited by examiner]
US 20220262013A1 · Decker et al. · 2022 [cited by applicant]
US 20220273984A1 · Lee · 2022 [cited by applicant]
US 20220273998A1 · Park · 2022 [cited by applicant]
US 20220362630A1 · Lee · 2022 [cited by applicant]
US 20230181970A1 · Lee · 2023 [cited by applicant]
US 20230267768A1 · Menaker et al. · 2023 [cited by applicant]
US 20230285802A1 · Lee · 2023 [cited by applicant]
US 20230285806A1 · Webster · 2023 [cited by examiner]
US 20230342969A1 · Jiang et al. · 2023 [cited by applicant]
US 20230356033A1 · Augustin et al. · 2023 [cited by applicant]
US 20230381584A1 · Lee · 2023 [cited by applicant]
US 20230398408A1 · Lee · 2023 [cited by applicant]
US 20230419731A1 · Prince et al. · 2023 [cited by applicant]
US 20240123284A1 · Barbalinardo et al. · 2024 [cited by applicant]
US 20240123290A1 · Mccants et al. · 2024 [cited by applicant]
US 20240185637A1 · Lee et al. · 2024 [cited by applicant]
US 20240185638A1 · Lee et al. · 2024 [cited by applicant]
US 20240216774A1 · Rahman et al. · 2024 [cited by applicant]
US 20240245972A1 · Jurczak · 2024 [cited by applicant]
US 20240299803A1 · Lee · 2024 [cited by applicant]
US 20240355142A1 · Li et al. · 2024 [cited by applicant]
CN 115223002A · 2022 [cited by applicant]
WO 2022251680A1 · 2022 [cited by applicant]
WO 2022251686A1 · 2022 [cited by applicant]
WO 2022251688A1 · 2022 [cited by applicant]
WO 2022256912A1 · 2022 [cited by applicant]
WO 2023277381A1 · 2023 [cited by applicant]
WO 2023205423A1 · 2023 [cited by applicant]
WO 2024159402A1 · 2024 [cited by applicant]