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 .
Response to Amendment
This office action is responsive to the amendment received 04/23/2026.
In the response to the Non-Final Office Action 10/25/2025, the applicant states that claims 1, 4-5, 9, 15-16, 18, 20-35, 38, and 40-41 have been amended, claim 42 has been cancelled, and claims 43-54 are new claims. Therefore, claims 1-41, and 43-54 are pending in the application.
Claims 1, 4-5, 9, 15-16, 18, 20-35, 38, and 40-41 have been amended. Claim 42 has been cancelled, and claims 43-54 are new claims. In summary, claims 1-41, and 43-54 are pending in current application.
Response to Arguments
Applicant's arguments filed 04/23/2026 have been fully considered but they are not persuasive.
Regarding double patenting rejection, the applicant does not argue the double patenting rejection. The amendment does not overcome the double patenting rejection. The applicant requests the double patenting rejection be held in abeyance until it is the only remaining rejection. Therefore, the examiner maintains the double patenting rejection.
Regarding to objections of claim 29, the amendment has cured the basis of objections of claim 29, therefore, the objection of claim 29 is hereby withdrawn.
Regarding to claim 1, the applicant argues that cited arts fail to teach or suggest "A computer-implemented system configured for applying biomechanical analysis to a sequence of images of a user's movement during performance of a swing and determining pre-shot routine data of the user prior to initiating a swing to strike a ball, the pre-shot routine data comprising at least one practice swing without striking the ball;" "receive pre-shot routine data, for the user, from a data processing module, the pre-shout routine data comprising data regarding user actions prior to initiating a swing to strike the ball;" "determine, from the pre-shot routine action data over a period of time, a standard pre-shot routine for the user;" and "determine when a subsequent pre-shot routine deviates from the standard pre-shot routine." The arguments have been fully considered, but they are not persuasive. The examiner cannot concur with the applicant for following reasons:
The claim limitation “determining pre-shot routine data of the user prior to initiating a swing to strike a ball, the pre-shot routine data comprising at least one practice swing without striking the ball” is preamble. This claim limitation does not have patentable weight.
Ross discloses “pre-shot routine and determining pre-shot routine data of the user prior to initiating a swing to strike a ball”. For example, in paragraph [0023], Ross teaches optimizing a golf swing in a pre-shot routine by measuring and providing feedback. In paragraph [0042], Ross teaches during practice exercises; Ross further teaches testing and analysis. In paragraph [0047], Ross teaches calibration procedures are customized to the specific types of sensors being used in the testing environment; Ross further teaches the sensors are adjusted based on the initial conditions of the testing session; Ross further more teaches the initial conditions of the testing session include pre-shot routine. In paragraph [0048], Ross teaches initiating initial downswing, mid-downswing, and late downswing. In paragraph [0054], Ross teaches the rising clubhead initiated backswing is a set of actions; Ross further teaches in these actions, the user does not hit the ball; Ross further more teaches these actions are prior to hitting ball actions, i.e. prior to actual swing. In Fig. 2A and paragraph [0058], Ross teaches backswing, and upper torso rotation are a set of pre-shot actions; Ross further teaches in these actions, the user does not hit a ball and these actions are prior to hitting ball actions; Ross further more teaches initiating downswing, i.e. a swing, by the reversal of pelvic rotation followed by a reversal of upper torso rotation.
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Kosowsky discloses “the pre-shot routine data comprising at least one practice swing without striking the ball”. For example, in Fig. 3 and paragraph [0093], Kosowsky teaches the user is performing the golf swing without striking a ball; Kosowsky further teaches training without a ball.
Kosowsky further discloses “determine when a subsequent pre-shot routine deviates from the standard pre- shot routine”. For example, in paragraph [0057], Kosowsky teaches allowing the user to apply statistical analysis to compare their motions to the motions of other individuals or to the aggregated motions of a set of other individuals. In Fig. 3 and paragraph [0093], Kosowsky teaches the user is performing the golf swing without striking a ball; Kosowsky further teaches training without a ball; Kosowsky further teaches when training without a ball, detect the moment that the head of the golf club passes the location where the ball would have been located. In paragraph [0101], Kosowsky teaches the golfer may want to see how many times their head or part of the body stayed within the boundaries for swings of a particular type of club each of the preceding sessions. In paragraph [0102], Kosowsky teaches the user compares the visualizations of their good swings with their bad swings to help in their training. In Fig. 10 and paragraph [0103], Kosowsky teaches using machine learning algorithms, models 1003 or 1004 are computed that will predict the qualitative result—good or bad 1005—or the quantitative result—expected distance 1006 the golf ball will travel—given the series of timestamps and target positions 1001 or 1002; Kosowsky further teaches machine learning algorithms determine a golf swing is good, i.e., standard practice routine; machine learning algorithms determine a golf swing is bad, i.e., deviates from the standard practice routine. In Fig. 11 and paragraph [0104], Kosowsky teaches applying machine learning to predict the result of a swing from the series of target positions 1001 and 1002.
Regarding to claim 4, the applicant argues the cited arts fail to teach or suggest “receive, from at least one image capture device, the sequence of images of the user's movement during the performance of the swing as measurable attributes to be monitored during analysis of the sequence of images”; “a watch list engine configured to enable a user to select via a user interface one or more golf-specific, kinematic parameters and sequences of motion during the swing, and to correlate results of the watch list engine with additional context data to affect what information is presented to the user, wherein additional context data comprises non-kinematic information that is used to interpret performance and tailor feedback, visuals, or recommendations presented to the user; and alter visual representations based on the correlated results of the watch engine list with additional context data by changing at least one of (i) which kinematic parameters are displayed, (ii) a visual alert, or (iii) a recommendation displayed on a user device”. The arguments have been fully considered, but they are not persuasive. The examiner cannot concur with the applicant for following reasons:
“or” is optional;
Song discloses “receive, from at least one image capture device, the sequence of images of the user's movement during the performance of the swing as measurable attributes to be monitored during analysis of the sequence of images”. For example, in paragraph [0025], Song teaches these identified subject reference locations are mapped to the electronically captured images and stored in the metadata of these images. In Fig. 1 and paragraph [0042], Song teaches the electronic device 107 receives captured images; Song further teaches an image capture device electronically captures one or more electronically captured images 108 of the subject 103 performing the activity 104. In paragraph [0068], Song teaches an elbow angle of thirty degrees; Song further teaches however, the standard has an elbow angle of only five degrees. In paragraph [0075], Song teaches a stereoscopic camera 202 captures three dimensional images of the user performing the activity. In Fig. 10 and paragraph [0128], Song teaches a stereoscopic camera 202 captures one or more three-dimensional images 1001 of a subject 103 performing an activity 104. In Fig. 10 and paragraph [0130], Song teaches receiving the captured images with angles.
Kosowsky discloses “provide a watch list engine configured to enable a user to select via a user interface one or more golf-specific, kinematic parameters and sequences of motion during the swing and to correlate results of the watch list engine with additional context data to affect what information is presented to the user”. For example, in Fig. 2C and paragraph [0072], Kosowsky teaches the head 2c02 of the golfer 2c01 and the knee 2c04 of the golfer 2c01 are selected and tracked with a user interface, i.e. black and highlighted dot points;
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; Kosowsky further teaches the golfer is alerted when either the head 2c02 moves outside the boundaries depicted as rectangle 2c03, or the knee 2c04 moves outside the boundaries depicted as rectangle 2c05; Kosowsky further more teaches generating various alerts for various sets of combinations of targets crossing boundaries. In paragraph [0080], Kosowsky teaches the system provides for the user to specify any of these factors through a user interface on a smartphone. In Fig. 5 and paragraph [0084], Kosowsky teaches the user indicates their skill level and the swing they would like to practice by using a user interface on a smartphone. In Fig. 13 and paragraph [0106], Kosowsky teaches providing a user interface 1307 to interact with the user. In Fig. 17, Fig. 18, and paragraph [0119], Kosowsky teaches determining the most salient fault affecting a user's performance across a set of activities; Kosowsky further teaches this sorted list is presented to the user to inform them of the most important factors that affected their performance in the session.
Kosowsky further discloses “wherein additional context data comprises non-kinematic information that is used to interpret performance and tailor feedback, visuals, or recommendations presented to the user”. For example, in Fig. 17, Fig. 18, and paragraph [0119], Kosowsky teaches determining the most salient fault affecting a user's performance across a set of activities; Kosowsky further teaches this sorted list is presented to the user to inform them of the most important factors that affected their performance in the session; Kosowsky further more teaches suggesting the user try to alter his motion to eliminate the most severe fault. Kosowsky suggests providing feedback to the user as to the user's progress in addressing the focus or multiple foci over multiple performances of the activity; motion and focus are non-kinematic information.
Kosowsky further more discloses “alter visual representations based on the correlated results of the watch engine list with additional context data by changing at least one of (i) which kinematic parameters are displayed, (ii) a visual alert, or (iii) a recommendation displayed on a user device”. For example, in Fig. 14C and paragraph [0108], Kosowsky teaches the embodiment has alerted the user of this movement of the target outside of the boundaries visually perhaps by a text message displayed on the screen and a change in color of the specific boundary;
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. In paragraph [0130], Kosowsky teaches if the target is not found within the desired limits in the image, then the user is told, at step 2008, to either move back from or forward towards the smartphone; Kosowsky further teaches if the location of the target is too close to the top of the image, the user is told to move back.
Song discloses “alter visual representations based on the correlated results of the watch engine list with additional context data”. For example, in Fig. 8 and paragraph [0066], Song teaches the electronically altered image 801; Song further teaches outputting the differences between the one or more standard reference locations and the one or more corresponding subject reference locations. In Fig. 8 and paragraph [0067], Song teaches identifying the differences between the at least one standard reference location and the at least one corresponding subject reference location to appear in the electronically altered image.
Regarding to claim 9, the applicant argues that Song in view of Kosowsky and Ross fail to teach or suggest “display a graphical user interface with options to enable creation of one or more motion trackers comprising specific kinematic parameters comprising higher order derivatives of position with respect to time and sequences of motions, wherein the higher order derivatives comprise at least a third derivative and a fourth derivative computed from a quantitative measurement generated from the sequence of images captured on a user device”. The arguments have been fully considered, but they are not persuasive. The examiner cannot concur with the applicant for following reasons:
In Non-Final Office Action 10/25/2025, Russo (US 6710713 B1) teaches “higher order derivatives of position with respect to time and sequences of motions”.
Russo (US 6710713 B1) discloses “higher order derivatives of position with respect to time and sequences of motions, wherein the higher order derivatives comprise at least a third derivative and a fourth derivative computed from a quantitative measurement generated from the sequence of images captured on a user device”. For example, in col. 5, lines 45-55, Russo teaches one analyst selects particular athletes to track. In col. 6, lines 30-40, Russo teaches the first and second derivatives of position with respect to time. In col. 7, lines 1-10, Russo teaches calculations involve higher order derivatives of the athlete's change of position with respect to time. In col. 8, lines 5-15, Russo teaches an icon is a graphical data representation 166; Russo further teaches by touching one or more particular icons 166 on the screen with a finger or light pen, the analyst selects one or more athletes for further tracking.
Rose discloses “to enable creation of one or more motion trackers comprising specific kinematic parameters comprising derivatives and sequences of motions”. For example, in paragraph [0028], Rose teaches the derivative with respect to time of the upper torso rotation angle. In paragraph [0030], Rose teaches pelvic rotational velocity; Rose further teaches the derivative with respect to time of the pelvic rotation angle. In paragraph [0038], Rose teaches mechanical motion capture systems directly track body joint angles. In paragraph [0045], Rose teaches obtaining values for specific descriptive parameters, such as, pelvic and shoulder tilt, the relative difference between the rotation of the hips and the shoulders, free moment, and position of the head. In paragraph [0049], Rose teaches the graphical display includes videos of other subjects performing ideal or non-ideal golf swings with explanations and comparisons to the current subject's movement. In paragraph [0053], Rose teaches kinematic data were collected using an eight-camera optometric system for three-dimensional motion analysis at a sampling rate of 240 Hz; Rose further teaches the motion capture system.
Kosowsky discloses “display a graphical user interface with options to enable creation of one or more motion trackers”. For example, in paragraph [0004], Kosowsky teaches allowing a user to set boundaries around the motion of the head. In paragraph [0057], Kosowsky teaches the boundaries are user selectable. In paragraph [0059], Kosowsky teaches the user is able to set a threshold for movement in terms of real-world distance. In Fig. 14B and paragraph [0107], Kosowsky teaches displaying a set of boundaries, the positioning of said boundaries determined by a set of selectable options chosen by the user. In Fig. 15B and paragraph [0110], Kosowsky teaches displaying a button with a share icon 1512.
Russo discloses “display a graphical user interface with options to enable creation of one or more motion trackers”. For example, in col. 5, lines 45-55, Russo teaches one analyst selects particular athletes to track. In col. 8, lines 5-15, Russo teaches an icon is a graphical data representation 166; Russo further teaches by touching one or more particular icons 166 on the screen with a finger or light pen, the analyst selects one or more athletes for further tracking.
Regarding to claim 11, the applicant argues that cited arts fail to teach or suggest “display a graphical user interface with options to enable creation of one or more motion trackers comprising specific kinematic parameters and sequences of motions, the kinematic parameters comprising position, velocity, acceleration, and/or higher-order derivatives of the position with respect to time”. The arguments have been fully considered, but they are not persuasive. The examiner cannot concur with the applicant for following reasons:
The language “/or higher-order derivatives of the position with respect to time” in the claim is optional.
Rose discloses “to enable creation of one or more motion trackers comprising specific kinematic parameters and sequences of motions, the kinematic parameters comprising position, velocity, acceleration, and/or higher-order derivatives of the position with respect to time”. For example, in paragraph [0044], Rose teaches these sensors measure angular velocity; Rose further teaches accelerometers measure inclination or linear acceleration; Rose further more teaches cameras measure position of body segments or objects. For example, in paragraph [0028], Rose teaches the derivative with respect to time of the upper torso rotation angle. In paragraph [0030], Rose teaches pelvic rotational velocity; Rose further teaches the derivative with respect to time of the pelvic rotation angle. In paragraph [0038], Rose teaches mechanical motion capture systems directly track body joint angles. In paragraph [0045], Rose teaches obtaining values for specific descriptive parameters. In paragraph [0049], Rose teaches the graphical display may be on a computer monitor, a TV, a smartphone. In paragraph [0053], Rose teaches kinematic data were collected using an eight-camera optometric system for three-dimensional motion analysis at a sampling rate of 240 Hz; Rose further teaches the motion capture system. In paragraph [0054], Rose teaches swing phases were defined based on clubhead and ball kinematics. In paragraph [0060], Rose teaches all biomechanical parameters increased from easy to medium to hard swings among professional golfers. In paragraph [0061], Rose teaches the number of biomechanical factors during amateur hard swings that fell outside both one and two standard deviations of mean values for professional hard golf swings increased with handicap.
Kosowsky discloses “display a graphical user interface with options to enable creation of one or more motion trackers”. For example, in paragraph [0004], Kosowsky teaches allowing a user to set boundaries around the motion of the head. In paragraph [0057], Kosowsky teaches the boundaries are user selectable. In paragraph [0059], Kosowsky teaches the user is able to set a threshold for movement in terms of real-world distance. In Fig. 14B and paragraph [0107], Kosowsky teaches displaying a set of boundaries, the positioning of said boundaries determined by a set of selectable options chosen by the user. In Fig. 15B and paragraph [0110], Kosowsky teaches displaying a button with a share icon 1512.
Regarding to claim 25, the applicant argues that cited arts fail to teach or suggest “dynamically generates one or more watch lists comprising a list of measurable attributes of a performance for which the system is programmed to monitor in the images of the user's movement during performance of the swing; and store the watch lists in a memory of the system as watch list configuration data identifying the list of measurable attributes to be monitored and being retrievable for use in analysis of subsequent swings. The arguments have been fully considered, but they are not persuasive. The examiner cannot concur with the applicant for following reasons:
Kosowsky discloses “dynamically generate one or more watch lists comprising a list of measurable attributes of a performance for which the system is programmed to monitor in the images of the user's movement during performance of the swing. For example, in Fig. 2C and paragraph [0072], Kosowsky teaches the head 2c02 of the golfer 2c01 and the knee 2c04 of the golfer 2c01 are selected and tracked; Kosowsky further teaches the golfer is alerted when either the head 2c02 moves outside the boundaries depicted as rectangle 2c03, or the knee 2c04 moves outside the boundaries depicted as rectangle 2c05; Kosowsky further more teaches dynamically generating various alerts for various sets of combinations of targets crossing boundaries; in addition, Kosowsky teaches software modules and machine-learning body-part segmentation model dynamically label body parts in images of a body and dynamically generate various sets of combinations of targets crossing boundaries. In paragraph [0073], Kosowsky teaches Google Inc.'s ML Kit analyzes camera images and provides values that are used to describe the location of a face in device-based coordinates. In paragraph [0080], Kosowsky teaches the system dynamically determines the boundaries that determine if the target is in-bounds or out-of-bounds at step 37. In Fig. 14A and paragraph [0108], Kosowsky teaches the user causes the target to move outside of the boundaries in real physical space. In paragraph [0109]: indicate the location of the target at the moment the golf club strikes the ball; Fig. 17; Fig. 18; [0119]: determine the most salient fault affecting a user's performance across a set of activities; this sorted list is presented to the user to inform them of the most important factors that affected their performance in the session); and
Kosowsky further discloses “store the watch lists in a memory of the system as watch list configuration data identifying the list of measurable attributes to be monitored and being retrievable for use in analysis of subsequent swings”. For example, in paragraph [0094], Kosowsky teaches storing indicators relevant to the activity; Kosowsky further teaches storing date and location of the activity, and weather conditions. In Fig. 8 and paragraph [0097], Kosowsky teaches the image contains a display of stored target coordinates of the target 82 representing the path of motion during the activity. In paragraph [0101], Kosowsky teaches the user may choose to review the data collected by the system in many ways; Kosowsky further teaches storing statistical analyses for later retrieval to increase the responsiveness and usefulness of the system. In Fig. 14B and paragraph [0107], Kosowsky teaches displaying a set of boundaries, the positioning of said boundaries determined by a set of selectable options chosen by the user;
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Regarding to claim 35, the applicant argues that cited arts fail to teach or suggest a 3D avatar engine that (i) automatically generates a 3D avatar of the swing from quantitative measurements of kinematic parameters and sequences of motion, and (ii) generates that avatar by tailoring the avatar's component structures based on body points that correspond to pose, movement, or alignment features. The arguments have been fully considered, but they are not persuasive. The examiner cannot concur with the applicant for following reasons:
Song discloses “a 3D avatar engine configured to automatically generate a 3D avatar of the swing based on the quantitative measurement of the kinematic parameters and sequences of motion”. For example, in Fig. 8 and paragraph [0068], Song teaches geometric alignment 803 refers to the left elbow, and indicates the subject has an elbow angle of thirty degrees;
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; Song further teaches geometric alignment 805 refers to the right knee, and indicates the subject has a knee bend of eight degrees, while the standard has a knee bend of twenty-five degrees; Song further more teaches a 3D avatar is automatically generated as illustrated in Fig. 8. In Fig. 2 and paragraph [0087], Song teaches the virtual reality headset provides a three-dimensional experience to the user;
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. In Fig. 5 and paragraph [0114], Song teaches the one or more processors 401 are 3D avatar engine. In Fig. 5 and paragraph [0119], Song teaches automatically generating 3D avatar as illustrated in Fig. 5
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. In Fig. 1 and paragraph [0048]. Song teaches the electronically captured images 108 of the subject 103 performing the activity 104 are automatically converted to electronic data and models by processors. In Fig. 10 and paragraph [0130], Song teaches superimposing a representation 1003 of the standard upon the subject 1004 in the one or more three-dimensional images 1001 of a subject 103 performing an activity 104;
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; Song further teaches automatically generating a mixed and updated image with 3D avatars as illustrated in Fig. 10.
Song discloses “where the 3D avatar engine is configured to generate the 3D avatar by tailoring component structures of the 3D avatar based on body points that correspond with pose, movement, or alignment features”. For example, in Fig. 1 and paragraph [0050], Song teaches if the activity were playing golf, the predefined reference locations include the club head and club shaft. In paragraph [0068], Song teaches the subject is instructed to decrease the elbow angle to move its subject reference location of the elbow toward the standard reference location of the standard; Song further teaches geometric alignment 805 refers to the right knee, and indicates the subject has a knee bend of eight degrees, while the standard has a knee bend of twenty-five degrees. In Fig. 2 and paragraph [0082], Song teaches a piano player desiring practice feedback prefers predefined reference locations such as the fingertips, knuckles, joints between fingers, wrist bones, wrist and forearm; Song further teaches a golfer prefers predefined reference locations such as the club head, club shaft, grip, hands, elbows, shoulders, head, hips, knees, feet, and ball. In Fig. 2 and paragraph [0087], Song teaches the virtual reality headset provides a three-dimensional experience to the user;
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. In Fig. 5 and paragraph [0114], Song teaches the one or more processors 401 are 3D avatar engine. In Fig. 1, Fig. 10, and paragraph [0130], Song teaches superimposing a representation 1003 of the standard upon the subject 1004 in the one or more three-dimensional images 1001 of a subject 103 performing an activity 104;
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; Song further teaches the 3D avatar of piano player, golfer, and yoga player are tailored.
Claims 36-37 are not allowable due to the similar reasons as discussed above.
Regarding to claim 41, the applicant argues that cited arts fail to teach or suggest “Song in view of Rose and Kosowsky further discloses “determine information about a user’s standard pre-shot routine based on the pre-shot routine data over time, wherein the pre-shot routine data comprises at least one of (i) a number of practice swings, (ii) a duration of the pre-shot routine, or (iii) timing between pre-shot actions and initiation of the swing”. The arguments have been fully considered, but they are not persuasive. The examiner cannot concur with the applicant for following reasons:
or is optional;
Kosowsky discloses “determine information about a user’s standard pre-shot routine based on the pre-shot routine data over time, wherein the pre-shot routine data comprises at least one of (i) a number of practice swings, (ii) a duration of the pre-shot routine, or (iii) timing between pre-shot actions and initiation of the swing”. For example, in paragraph [0057], Kosowsky teaches allowing the user to apply statistical analysis to compare their motions to the motions of other individuals or to the aggregated motions of a set of other individuals. In Fig. 3 and paragraph [0093], Kosowsky teaches the user is performing the golf swing without striking a ball; Kosowsky further teaches analysis of other of the user's swings with or without a ball present; Kosowsky further more teaches when training without a ball, detect the moment that the head of the golf club passes the location where the ball would have been located. In paragraph [0101], Kosowsky teaches the golfer may want to see how many times their head or part of the body stayed within the boundaries for swings of a particular type of club each of the preceding sessions; Kosowsky further teaches the number of swings. In paragraph [0102], Kosowsky teaches the user compares the visualizations of their good swings with their bad swings to help in their training. In Fig. 10 and paragraph [0103], Kosowsky teaches using machine learning algorithms, models 1003 or 1004 are computed that will predict the qualitative result—good or bad 1005—or the quantitative result—expected distance 1006 the golf ball will travel—given the series of timestamps and target positions 1001 or 1002; machine learning algorithms determine a golf swing is good, i.e., standard practice routine; Kosowsky further teaches machine learning algorithms determine a golf swing is bad, i.e., deviates from the standard practice routine. In Fig. 11 and paragraph [0104], Kosowsky teaches detecting the defining moment of the golf club striking the golf ball; Kosowsky further teaches applying machine learning to predict the result of a swing from the series of target positions 1001 and 1002; Kosowsky further more teaches applying machine learning to compute models 1103 or 1104 to yield qualitative 1105 or quantitative 1106 results from such sound recordings 1101 and 1102.
Claims 43-54 are new claims. The rejection of claims 43-54 are in pages 70-80 of the current Final Office Action.
Claims 2-3, 5-8, 10, and 12-41, and 43-54 are not allowable due to the similar reasons as discussed above.
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.
Claims 1-4, 8, 11-13, 21-33, and 35 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-4, 8-10, and 18-30 of U.S. Patent No. US 11640725 B2. Although the claims at issue are not identical, they are not patentably distinct from each other because all the limitations in claim 1 is anticipated by claim 1 of the U.S. Patent No. US 11640725 B2.
Application 18309849
Claim 1
U.S. Patent No. US 11640725 B2
Claim 1
1. A computer-implemented system configured for applying biomechanical analysis to a sequence of images of a user's movement during performance of a swing to generate computer-generated three-dimensional (3D) avatars of the swing based on user-selected kinematic parameters and sequences of motion, the system comprising:
1. A computer-implemented system configured for applying biomechanical analysis to a sequence of images of a user's movement during performance of a golf swing to generate computer-generated three-dimensional (3D) avatars of the user's golf swing based on user selected kinematic parameters and sequences of motion, the system comprising:
one or more hardware processors configured by machine-readable instructions to:
one or more hardware processors configured by machine-readable instructions to:
receive, from at least one image capture device, the images of the user's movement during the performance of the golf swing;
provide a watch list engine configured to enable a user to select via a user interface one or more golf-specific, kinematic parameters and sequences of motion during the golf swing;
provide a movement module configured to:
provide a movement module configured to
quantitatively analyze the sequence of images of the user's movement during performance of the swing to generate a quantitative measurement of the kinematic parameters and sequences of motion, and
quantitatively analyze the images to measure and track the user's golf swing in 3D space across the images, including to measure and track the user selected, golf-specific, kinematic parameters and sequences of motion, wherein the movement module is configured to identify one or more objects other than the user in the images, wherein the one or more objects includes at least a portion of a golf club, track kinematic parameters of the one or more objects in the images, and generate context data based on the tracked kinematic parameters of the one or more objects, and
identify pre-shot routines where a user swings but does not hit a ball, wherein a pre-shot routine comprises a set of actions performed by the user prior to an actual swing in which the user hits the ball;
wherein the movement module is configured to identify pre-shot routines, wherein the user swings but does not hit a ball;
provide a machine learning module configured to determine a user's standard practice routine over time and determine when a new practice routine deviates from the standard practice routine;
provide a machine learning module configured to determine a user's standard pre-shot routine over time and determine when a pre-shot routine deviates from the standard pre-shot routine;
provide a display generator configured to display visual representations of the quantitative measurement of the kinematic parameters and sequences of motion on the user device, wherein the visual representations comprise user-selected kinematic parameters with a 3D avatar of the swing, animated to cause movement of the 3D avatar displayed on the user device.
provide a 3D avatar engine configured to automatically generate a 3D avatar of the user's golf swing based on the quantitative measurement of the golf-specific, kinematic parameters and sequences of motion, where the 3D avatar engine is configured to generate the 3D avatar by tailoring component structures of the 3D avatar based on golf-specific criteria including body points that correspond with pose, movement, and/or alignment features related to golf; and
provide a display generator configured to: i) display the 3D avatar of the user's golf swing and animate the 3D avatar to cause movement of the 3D avatar based on the measured, quantified movement from the sequence of images; and ii) display the user selected kinematic parameters with the 3D avatar of the user's golf swing.
Claims 2-3
Claim 1
Claim 4
Claims 1-2
Claim 8
Claim 4
Claim 11
Claim 8
Claim 12
Claim 9
Claim 13
Claim 10
Claims 21-33
Claims 18-30
Claim 35
Claim 1
Claim Objections
Claim 38 is objected to because of the following informalities: the language “a kinematic parameters and sequences” is not correct. Appropriate correction is required.
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 1-8, 10, 12-41, and 43-54 are rejected under 35 U.S.C. 103 as being unpatentable over Song (US 20210004981 A1) in view of Rose (US 20140257538 A1), and further in view of Kosowsky (US 20200398110 A1).
Regarding to claim 1 (Currently Amended), Song discloses a computer-implemented system configured for applying biomechanical analysis to a sequence of images of a user's movement during performance of a swing ([0025]: perform an activity and practice yoga; Fig. 1; [0040]: method and system to perform an activity; Fig. 1; [0050]: if the activity were playing golf, the predefined reference locations include the club head and club shaft; playing golf includes swing; [0061]: alter the one or more electronically captured images 108 to identify the differences between the at least one standard reference location and the at least one corresponding subject reference location in one or more electronically altered images 120; Fig. 1; [0070]: present the one or more electronically altered images 120 on the display 121 of the electronic device 107;
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; [0082]: a golfer might prefer predefined reference locations such as the club head, club shaft, grip, hands, elbows, shoulders, head, hips, knees, feet, and ball; Fig. 10; [0128]: captures one or more three-dimensional images 1001 of a subject 103 performing an activity 104; Fig. 10; [0130]: superimpose a representation 1003 of the standard upon the subject 1004;
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), the system comprising:
one or more hardware processors configured by machine-readable instructions to, which when executed, cause the processor to ([0039]: one or more processors; Fig. 1; [0041]: one or more processors):
determine, from the routine data over a period of time, a standard routine for the user ([0017]: identify standard reference locations from one or more electronic images retrieved from a memory device; [0026]: the standard includes a depiction of a trainer or professional performing the same activity over a period of time; [0027]: these identified standard reference locations are mapped to the retrieved electronic images and stored in the metadata of these images; [0030]: determine each standard reference location of the plurality of standard reference locations; Fig. 1; [0050]: the activity were playing golf over a period of time; the combination of paragraph [0026] and [0050] teaches the standard includes a depiction of a trainer or professional performing the same activity, i.e. playing golf, over a period of time; [0082]: a golfer prefers predefined reference locations); and
determine when a subsequent routine deviates from the standard routine ([0019]: compare standard reference locations from electronic images retrieved from memory with subject reference locations in one or more electronically captured images; identify and determine differences between the standard reference locations and the subject reference locations on a display of the electronic device; [0030]: identify the differences between the at least one standard reference location and the at least one corresponding subject reference location in one or more electronically altered images; Fig. 1; [0050]: the activity were playing golf; Fig. 8; [0067]: identify the differences between the at least one standard reference location and the at least one corresponding subject reference location to appear in the electronically altered image; Fig. 8; [0068]: decrease the elbow angle to move its subject reference location of the elbow toward the standard reference location of the standard;
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; Fig. 10; [0131]).
Song fails to explicitly disclose:
pre-shot routine and determining pre-shot routine data of the user prior to initiating a swing to strike a ball, the pre-shot routine data comprising at least one practice swing without striking the ball;
receive pre-shot routine data, for the user, from a data processing module, the pre-shot routine data comprising data regarding user actions prior to initiating a swing to strike the ball;
In same field of endeavor, Ross teaches:
pre-shot routine and determining pre-shot routine data of the user prior to initiating a swing to strike a ball ([0023]: optimize a golf swing in a pre-shot routines by measuring and providing feedback; [0042]: during practice exercises; testing and analysis; [0047]: calibration procedures are customized to the specific types of sensors being used in the testing environment; the sensors are adjusted based on the initial conditions of the testing session; the initial conditions of the testing session include pre-shot routine; [0054]: the rising clubhead initiated backswing is a set of actions; in these actions, the user does not hit the ball; these actions are prior to hitting ball actions, i.e. prior to actual swing; Fig. 2A; [0058]: backswing, and upper torso rotation are a set of pre-shot actions; in these actions, the user does not hit a ball; these actions are prior to hitting ball actions; downswing, i.e. a swing, was initiated by the reversal of pelvic rotation followed by a reversal of upper torso rotation),
receive pre-shot routine data, for the user, from a data processing module ([0023]: optimize a golf swing in a pre-shot routines by measuring and providing feedback; [0042]: during practice exercises; Fig. 1; [0048]: the beginning of backswing, initial downswing, mid-downswing, and late downswing are pre-shot routines, but not hit a ball;
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; Fig. 2A; [0058]: downswing was initiated by the reversal of pelvic rotation followed by a reversal of upper torso rotation;
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; backswing, downswing and upper torso rotation are pre-shot routines, but not hit a ball), the pre-shot routine data comprising data regarding user actions prior to initiating a swing to strike the ball ([0023]: optimize a golf swing in a pre-shot routines by measuring and providing feedback; [0042]: during practice exercises; testing and analysis; [0047]: calibration procedures are customized to the specific types of sensors being used in the testing environment; the sensors are adjusted based on the initial conditions of the testing session; the initial conditions of the testing session include pre-shot routine; [0054]: the rising clubhead initiated backswing is a set of actions; in these actions, the user does not hit the ball; these actions are prior to hitting ball actions; Fig. 2A; [0058]: backswing, downswing and upper torso rotation are a set of actions; in these actions, the user does not hit a ball; these actions are prior to hitting ball actions, i.e. prior to actual swing);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Song to include pre-shot routine and determining pre-shot routine data of the user prior to initiating a swing to strike a ball; receive pre-shot routine data, for the user, from a data processing module, the pre-shot routine data comprising data regarding user actions prior to initiating a swing to strike the ball as taught by Rose. The motivation for doing so would have been to analyze and improve a subject's golf swing; to provide data on how the subject can improve his or her technique; to average biomechanical factors of the professional golfers' hard swings within subjects as taught by Rose in paragraphs [0011], [0046], and [0056].
Song in view of Rose fails to explicitly disclose:
the pre-shot routine data comprising at least one practice swing without striking the ball;
In same field of endeavor, Kosowsky teaches:
the pre-shot routine data comprising at least one practice swing without striking the ball (Fig. 3; [0093]: the user is performing the golf swing without striking a ball; training without a ball);
determine when a subsequent pre-shot routine deviates from the standard pre- shot routine ([0057]: allow the user to apply statistical analysis to compare their motions to the motions of other individuals or to the aggregated motions of a set of other individuals; Fig. 3; [0093]: the user is performing the golf swing without striking a ball; training without a ball; when training without a ball, detect the moment that the head of the golf club passes the location where the ball would have been located; [0101]: the golfer may want to see how many times their head or part of the body stayed within the boundaries for swings of a particular type of club each of the preceding sessions; [0102]: the user compares the visualizations of their good swings with their bad swings to help in their training; Fig. 10; [0103]: using machine learning algorithms, models 1003 or 1004 are computed that will predict the qualitative result—good or bad 1005—or the quantitative result—expected distance 1006 the golf ball will travel—given the series of timestamps and target positions 1001 or 1002; machine learning algorithms determine a golf swing is good, i.e., standard practice routine; machine learning algorithms determine a golf swing is bad, i.e., deviates from the standard practice routine; Fig. 11; [0104]: apply machine learning to predict the result of a swing from the series of target positions 1001 and 1002).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Song in view of Rose to include the pre-shot routine data comprising at least one practice swing without striking the ball; determine when a subsequent pre-shot routine deviates from the standard pre- shot routine as taught by Kosowsky. The motivation for doing so would have been to improve the golfer's golf swing; to provide a system for alerting a user when the movement of a target exceeds a threshold as measured in distances in the real, physical space of the user; to determine a golf swing is good or bad using machine learning algorithms as taught by Kosowsky in Fig. 1, and paragraphs [0058-0059], and [0103-0104].
Regarding to claim 2 (Original), Song in view of Rose and Kosowsky discloses the computer-implemented system of claim 1, wherein the swing is a golf swing (Rose; [0023]: optimize a golf swing by measuring and providing feedback; Fig. 1; [0048]: the beginning of backswing, initial downswing, mid-downswing, and late downswing are pre-shot routines, but not hit a ball;
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; Fig. 1; [0054]: analyze Golf swings using in-house algorithms). Same motivation of claim 1 is applied here.
Regarding to claim 3 (Previously Presented), Song in view of Rose and Kosowsky discloses the computer-implemented system of claim 1, wherein a practice swing is a pre-shot routine in golf (Rose; [0023]: optimize a golf swing in a training by measuring and assessing these parameters for a subject; provide feedback that are utilized for training; [0042]: training tools are used in practice exercises, i.e., during practice exercises; Fig. 1; [0048]: the beginning of backswing, initial downswing, mid-downswing, and late downswing are pre-shot routines, but not hit a ball;
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; [0053]: a plastic practice ball was wrapped in light-reflective tape and placed on a synthetic grass mat; each subject performed three swings of different efforts; Fig. 1; [0054]: the initiation of downswing was defined by the transition of the clubhead direction at the top of backswing).
Same motivation of claim 1 is applied here.
Regarding to claim 4 (Currently Amended), Song in view of Rose and Kosowsky discloses the computer-implemented system of claim 1, wherein the one or more hardware processors are further configured to (same as rejected in claim 1):
receive, from at least one image capture device, the sequence of images of the user's movement during the performance of the swing as measurable attributes to be monitored during analysis of the sequence of images (Song; [0025]: these identified subject reference locations are mapped to the electronically captured images and stored in the metadata of these images; Fig. 1; [0042]: the electronic device 107 receives captured images; an image capture device electronically captures one or more electronically captured images 108 of the subject 103 performing the activity 104; [0068]: an elbow angle of thirty degrees; However, the standard has an elbow angle of only five degrees; [0075]: a stereoscopic camera 202 captures three dimensional images of the user performing the activity; Fig. 10; [0128]: a stereoscopic camera 202 captures one or more three-dimensional images 1001 of a subject 103 performing an activity 104; Fig. 10; [0130]: receive the captured images); and
provide a watch list engine configured to enable a user to select via a user interface one or more golf-specific, kinematic parameters and sequences of motion during the swing (Kosowsky; Fig. 2C; [0072]: the head 2c02 of the golfer 2c01 and the knee 2c04 of the golfer 2c01 are selected and tracked; the golfer is alerted when either the head 2c02 moves outside the boundaries depicted as rectangle 2c03, or the knee 2c04 moves outside the boundaries depicted as rectangle 2c05; generate various alerts for various sets of combinations of targets crossing boundaries; Fig. 17; Fig. 18; [0119]: determine the most salient fault affecting a user's performance across a set of activities; this sorted list is presented to the user to inform them of the most important factors that affected their performance in the session) and to correlate results of the watch list engine with additional context data to affect what information is presented to the user (Kosowsky; Fig. 2C; [0072]: the head 2c02 of the golfer 2c01 and the knee 2c04 of the golfer 2c01 are selected and tracked; the golfer is alerted when either the head 2c02 moves outside the boundaries depicted as rectangle 2c03, or the knee 2c04 moves outside the boundaries depicted as rectangle 2c05; generate various alerts for various sets of combinations of targets crossing boundaries; Fig. 17; Fig. 18; [0119]: determine the most salient fault affecting a user's performance across a set of activities; this sorted list is presented to the user to inform them of the most important factors that affected their performance in the session), wherein additional context data comprises non-kinematic information that is used to interpret performance and tailor feedback, visuals, or recommendations presented to the user (or is optional; Kosowsky; Fig. 17; Fig. 18; [0119]: determine the most salient fault affecting a user's performance across a set of activities; this sorted list is presented to the user to inform them of the most important factors that affected their performance in the session; suggest the user try to alter his motion to eliminate the most severe fault; provide feedback to the user as to the user's progress in addressing the focus or multiple foci over multiple performances of the activity; motion and focus are non-kinematic information), and at least one of environmental condition data, club selection data, or user-entered pre-shot comments (Kosowsky; [0101]: swings of a particular type of club; Fig. 15C; [0111]: select a specific type of golf club, a wedge); and
alter visual representations based on the correlated results of the watch engine list with additional context data by changing at least one of (i) which kinematic parameters are displayed, (ii) a visual alert, or (iii) a recommendation displayed on a user device (Kosowsky; Fig. 14C; [0108]: the embodiment has alerted the user of this movement of the target outside of the boundaries visually perhaps by a text message displayed on the screen and a change in color of the specific boundary;
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; [0130]: if the target is not found within the desired limits in the image, then the user is told, at step 2008, to either move back from or forward towards the smartphone; If the location of the target is too close to the top of the image, the user is told to move back).
Song in view of Rose and Kosowsky further discloses alter visual representations based on the correlated results of the watch engine list with additional context data (Song; Fig. 8; [0066]: the electronically altered image 801; output the differences between the one or more standard reference locations and the one or more corresponding subject reference locations; Fig. 8; [0067]).
Same motivation of claim 1 is applied here.
Regarding to claim 5 (Currently Amended), Song in view of Rose and Kosowsky discloses the computer-implemented system of claim 1, wherein the one or more hardware processors are further configured to identify objects other than the user in the sequence of images, wherein the objects include at least a portion of a golf club (Rose; [0034]: measure body and club movements associated with a golf swing; [0040]: the motion capture system measures club motions; [0054]: swing phases were defined based on clubhead and ball kinematics).
Same motivation of claim 1 is applied here.
Regarding to claim 6 (Original), Song in view of Rose and Kosowsky discloses the computer-implemented system of claim 1, wherein the one or more hardware processors are further configured to (same as rejected in claim 1):
provide a watch list engine configured to enable creation of one or more watch lists comprising a list of measurable attributes of the swing for which the system is configured to monitor during analysis of the sequence of images (Kosowsky; Fig. 2C; [0072]: the head 2c02 of the golfer 2c01 and the knee 2c04 of the golfer 2c01 are selected and tracked; the golfer is alerted when either the head 2c02 moves outside the boundaries depicted as rectangle 2c03, or the knee 2c04 moves outside the boundaries depicted as rectangle 2c05; generate various alerts for various sets of combinations of targets crossing boundaries; Fig. 14A; [0108]: the user causes the target to move outside of the boundaries in real physical space; Fig. 17; Fig. 18; [0119]: determine the most salient fault affecting a user's performance across a set of activities; this sorted list is presented to the user to inform them of the most important factors that affected their performance in the session).
Regarding to claim 7 (Original), Song in view of Rose and Kosowsky discloses the computer-implemented system of claim 1, wherein the one or more hardware processors are further configured to (same as rejected in claim 1):
provide a watch list engine configured to enable creation of one or more golf-specific watch lists (Kosowsky; Fig. 2C; [0072]: the head 2c02 of the golfer 2c01 and the knee 2c04 of the golfer 2c01 are selected and tracked; the golfer is alerted when either the head 2c02 moves outside the boundaries depicted as rectangle 2c03, or the knee 2c04 moves outside the boundaries depicted as rectangle 2c05; generate various alerts for various sets of combinations of targets crossing boundaries; Fig. 14A; [0108]: the user causes the target to move outside of the boundaries in real physical space; [0109]: indicate the location of the target at the moment the golf club strikes the ball; Fig. 17; Fig. 18; [0119]: determine the most salient fault affecting a user's performance across a set of activities; this sorted list is presented to the user to inform them of the most important factors that affected their performance in the session).
Same motivation of claim 1 is applied here.
Regarding to claim 8 (Original), Song in view of Rose and Kosowsky discloses the computer-implemented system of claim 1, wherein the one or more hardware processors are further configured to (same motivation of claim 1 is applied here):
provide a watch list engine is configured to enable creation of one or more golf-specific watch lists and specification of a weighting of kinematic parameters and sequences for analysis of images of swings, wherein the kinematic parameters correspond with measurable attributes of the swings (Kosowsky; Fig. 2C; [0072]: the head 2c02 of the golfer 2c01 and the knee 2c04 of the golfer 2c01 are selected and tracked; the golfer is alerted when either the head 2c02 moves outside the boundaries depicted as rectangle 2c03, or the knee 2c04 moves outside the boundaries depicted as rectangle 2c05; generate various alerts for various sets of combinations of targets crossing boundaries; Fig. 14A; [0108]: the user causes the target to move outside of the boundaries in real physical space; Fig. 15A; Fig. 15B; [0109]: the image contains a display 1504 representing the path of motion during the activity determined by the stored target coordinates of the target; indicate the location of the target at the moment the golf club strikes the ball; Fig. 17; Fig. 18; [0119]: each fault in the combined list is assigned a weighted severity; multiply the number of occurrences of the fault across all the activities in the session by the fault's severity index; determine the most salient fault affecting a user's performance across a set of activities; this sorted list is presented to the user to inform them of the most important factors that affected their performance in the session).
Regarding to claim 10 (Previously Presented), Song in view of Rose and Kosowsky discloses the computer-implemented system of claim 1, wherein the one or more hardware processors are further configured to (same as rejected in claim 1):
display a graphical user interface with options to enable creation of one or more golf-specific motion trackers measuring actual values taken throughout performance of an activity (Kosowsky; Fig. 9; [0100]: images or videos are captured by the camera as the user performs the activity; Fig. 14A; Fig. 14B; [0107]: a series of images of screens are displayed to a user; [0131]: the system tracks targets including body parts such as the face, head, knee or hip, or equipment such as clubs, rackets, bats and balls, by incorporating image processing algorithms capable of detecting the target).
Same motivation of claim 1 is applied here.
Regarding to claim 12 (Previously Presented), Song in view of Rose and Kosowsky discloses the computer-implemented system of claim 1, further comprising a 3D avatar engine is further configured to apply recorded 3D motion data from the user to an avatar (Song; Fig. 10; [0130]: superimpose a representation 1003 of the standard upon the subject 1004 in the one or more three-dimensional images 1001 of a subject 103 performing an activity 104;
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).
Song in view of Rose and Kosowsky further discloses Song in view of Rose and Kosowsky discloses wherein the 3D avatar engine is further configured to apply recorded 3D motion data from the user to an avatar (Rose; [0049]: the graphical display includes images of the an avatar performing the movement, with superimposed visual indicators showing optimal movement accomplishment; the display uses perspective techniques to display a three-dimensional animation such that body movements can be seen from different points of view in a three-dimensional space; the graphical display may include videos of other subjects performing ideal or non-ideal golf swings with explanations and/or comparisons to the current subject's movement).
Same motivation of claim 1 is applied here.
Regarding to claim 13 (Previously Presented), Song in view of Rose and Kosowsky discloses the computer-implemented system of claim 12, wherein the 3D avatar engine is further configured to model a user's body as an articulated structure including a set of human skeletal components connected via joints and a measured spatial relationship among a user's skeletal components and animate the model along 3D space based on the measured movement of the user along the 3D space (Rose; Fig. 2A; [0049]: series of variables are taken over time, as well as target parameters, and the like; the graphical display may include images of the an avatar performing the movement, with superimposed visual indicators showing optimal movement accomplishment;
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Song in view of Rose and Kosowsky further discloses wherein the 3D avatar engine is further configured to model a user's body as an articulated structure including a set of human skeletal components connected via joints and a measured spatial relationship among a user's skeletal components and animate the model along 3D space based on the measured movement of the user along the 3D space (Song; Fig. 10; [0130]: superimpose a representation 1003 of the standard upon the subject 1004 in the one or more three-dimensional images 1001 of a subject 103 performing an activity 104;
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).
Same motivation of claim 1 is applied here.
Regarding to claim 14 (Previously Presented), Song in view of Rose and Kosowsky discloses the computer-implemented system of claim 12, wherein the 3D avatar engine is further configured to customize a model of a user's body based on a golf-specific, kinematic parameters and sequences of motion (Rose; Fig. 2A; [0049]: series of variables are taken over time, as well as target parameters, and the like; the graphical display includes images of the an avatar performing the movement, with superimposed visual indicators showing optimal movement accomplishment;
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Song in view of Rose and Kosowsky further discloses wherein the 3D avatar engine is further configured to customize a model of a user's body based on a golf-specific, kinematic parameters and sequences of motion (Song; Fig. 1; [0061]: electronically alter the one or more electronically captured images 108 to identify the differences between the at least one standard reference location and the at least one corresponding subject reference location in one or more electronically altered images 120; Fig. 8; [0066-0067]).
Same motivation of claim 1 is applied here.
Regarding to claim 15 (Currently Amended), Song in view of Rose and Kosowsky discloses the computer-implemented system of claim 1, wherein the one or more hardware processors are further configured to:
compare user-selected kinematic parameters and sequences of motion, a measured movements in the performance of the swing by the user to a stored set of target measured movements of a reference performance of the swing (Song; Fig. 1; [0019]: compare standard reference locations from electronic images retrieved from memory with subject reference locations in one or more electronically captured images; [0028]: the proper comparison between the subject reference locations and the standard reference locations occurs; [0030]: compare, with the one or more processors, each standard reference location of the plurality of standard reference locations to each corresponding subject reference location of the plurality of subject reference locations).
Regarding to claim 16 (Currently Amended), Song in view of Rose and Kosowsky discloses the computer-implemented system of claim 1, wherein the one or more hardware processors are further configured to:
compare user-selected kinematic parameters and sequences of motion, the measured movements in the performance of the swing by the user to a stored set of target measured movements of a reference performance of the swing (Song; Fig. 1; [0019]: compare standard reference locations from electronic images retrieved from memory with subject reference locations in one or more electronically captured images; [0028]: the proper comparison between the subject reference locations and the standard reference locations occurs; [0030]: compare, with the one or more processors, each standard reference location of the plurality of standard reference locations to each corresponding subject reference location of the plurality of subject reference locations); and
provide 3D avatar engine configured to simultaneously display an avatar of the swing and animate a 3D avatar to cause movement of the 3D avatar based on the measured, tracked movement from the sequence of images based on the swing during the performance of the swing and a reference avatar based on the reference performance of the swing (Song; Fig. 1; [0061]: alter the one or more electronically captured images 108 to identify the differences between the at least one standard reference location and the at least one corresponding subject reference location in one or more electronically altered images 120; Fig. 6; [0063]: cause the depiction 606 of the standard to appear superimposed atop a depiction 607 of the subject performing the activity in the electronically altered image 601; the depiction 606 of the standard can be caused to appear superimposed alongside the depiction 607 of the subject performing the activity in the electronically altered image; Fig. 10; [0128-0130]).
Regarding to claim 17 (Previously Presented), Song in view of Rose and Kosowsky discloses the computer-implemented system of claim 1, wherein the one or more hardware processors are further configured to (same as rejected in claim 1):
compare measured movements in the swing to a stored set of a measured movements of a second user's performance of a second swing (Song; Fig. 1; [0019]: compare standard reference locations from electronic images retrieved from memory with subject reference locations in one or more electronically captured images; [0026]: the standard comprises a depiction of a trainer or professional performing the same activity; the standard may be a depiction of a professional yogi performing the chair pose in an electronic image; [0028]: the proper comparison between the subject reference locations and the standard reference locations occurs; [0030]: compare, with the one or more processors, each standard reference location of the plurality of standard reference locations to each corresponding subject reference location of the plurality of subject reference locations).
Regarding to claim 18 (Currently Amended), Song in view of Rose and Kosowsky discloses the computer-implemented system of claim 1, wherein the one or more hardware processors are further configured to (same as rejected in claim 1):
compare user-selected kinematic parameters and sequences of motion, measured movements of during performance of the swing by the user to a stored set of a measured movements, including the user selected, kinematic parameters and sequences of motion, of a second user's performance of the swing (Song; Fig. 1; [0019]: comparing standard reference locations from electronic images retrieved from memory with subject reference locations in one or more electronically captured images; [0026]: the standard comprises a depiction of a trainer or professional performing the same activity; the standard may be a depiction of a professional yogi performing the chair pose in an electronic image; [0028]: the proper comparison between the subject reference locations and the standard reference locations occurs; [0030]: compare, with the one or more processors, each standard reference location of the plurality of standard reference locations to each corresponding subject reference location of the plurality of subject reference locations); and
provide a 3D avatar engine configured to simultaneously display an avatar based on performance of an activity by the user and an avatar based on the second user's performance of a second swing (Song; Fig. 1; [0061]: alter the one or more electronically captured images 108 to identify the differences between the at least one standard reference location and the at least one corresponding subject reference location in one or more electronically altered images 120; Fig. 6; [0063]: cause the depiction 606 of the standard to appear superimposed atop a depiction 607 of the subject performing the activity in the electronically altered image 601; the depiction 606 of the standard can be caused to appear superimposed alongside the depiction 607 of the subject performing the activity in the electronically altered image).
Regarding to claim 19 (Previously Presented), Song in view of Rose and Kosowsky discloses the computer-implemented system of claim 1, wherein the one or more hardware processors are further configured to (same as rejected in claim 1):
compare measured movements in a user's first performance of the swing to a stored set of target measured movements of the user's second performance of a second swing (Rose; videos of other subjects, i.e., users, perform ideal or non-ideal golf swings with explanations; compare ideal with non-ideal golf swings).
Song in view of Rose and Kosowsky further discloses compare the measured movements in a user's first performance of the swing to a stored set of target measured movements of the user's second performance of a second swing (Song; Fig. 1; [0019]: compare standard reference locations from electronic images retrieved from memory with subject reference locations in one or more electronically captured images; [0026]: the standard comprises a depiction of a trainer or professional performing the same activity; the standard may be a depiction of a professional yogi performing the chair pose in an electronic image; [0028]: the proper comparison between the subject reference locations and the standard reference locations occurs; [0030]: compare, with the one or more processors, each standard reference location of the plurality of standard reference locations to each corresponding subject reference location of the plurality of subject reference locations).
Same motivation of claim 1 is applied here.
Regarding to claim 20 (Currently Amended), Song in view of Rose and Kosowsky discloses the computer-implemented system of claim 1, wherein the one or more hardware processors are further configured to:
compare the user-selected kinematic parameters and sequences of motion, measured movements in a user's first performance of the swing by the user to a stored set of target measured movements, including the user selected kinematic parameters and sequences of motion, of the user's second performance of a second swing (Rose; videos of other subjects, i.e., users, perform ideal or non-ideal golf swings with explanations; compare ideal with non-ideal golf swings);
Song in view of Rose and Kosowsky further discloses compare, for the user-selected kinematic parameters and sequences of motion, measured movements in a user's first performance of the swing by the user to a stored set of target measured movements, including the user selected kinematic parameters and sequences of motion, of the user's second performance of a second swing (Song; Fig. 1; [0019]: compare standard reference locations from electronic images retrieved from memory with subject reference locations in one or more electronically captured images; [0026]: the standard comprises a depiction of a trainer or professional performing the same activity; the standard may be a depiction of a professional yogi performing the chair pose in an electronic image; [0028]: the proper comparison between the subject reference locations and the standard reference locations occurs; [0030]: compare, with the one or more processors, each standard reference location of the plurality of standard reference locations to each corresponding subject reference location of the plurality of subject reference locations), and
provide a 3D avatar engine configured to simultaneously display an avatar based on the user's first performance of the swing and an avatar based on the user's second performance of the second swing (Song; Fig. 1; [0061]: alter the one or more electronically captured images 108 to identify the differences between the at least one standard reference location and the at least one corresponding subject reference location in one or more electronically altered images 120; Fig. 6; [0063]: cause the depiction 606 of the standard to appear superimposed atop a depiction 607 of the subject performing the activity in the electronically altered image 601; the depiction 606 of the standard can be caused to appear superimposed alongside the depiction 607 of the subject performing the activity in the electronically altered image).
The same motivation of claim 1 is applied here.
Regarding to claim 21 (Currently Amended), Song in view of Rose and Kosowsky discloses the computer-implemented system of claim 1, wherein the one or more hardware processors are further configured to enable a user to playback a user's performances of the swing, including display of a video of the user's performance and an animated 3D avatar display of the user's performance (Kosowsky; [0057]: the system may allow the user to display and analyze the record of their motions in multiple instances of the activity; [0099]: the image displayed could display a replay of the motion of the target by moving a display object along the path; the image includes the amount of time elapsed from the moment when the initial position was determined until the defining moment representing the impact of the club with the ball); and further comprising user selectable display elements that enable the user to control a perspective from which the animated 3D avatar is displayed (Kosowsky; [0099]: the image displayed could display a replay of the motion of the target by moving a display object along the path; the image could also include the amount of time elapsed from the moment when the initial position was determined until the defining moment representing the impact of the club with the ball).
Same motivation of claim 1 is applied here.
Regarding to claim 22 (Currently Amended), Song in view of Rose and Kosowsky discloses the computer-implemented system of claim 1, wherein the one or more hardware processors are further configured to identify one or more objects other than the user in the images (Rose; [0054]: swing phases were defined based on clubhead and ball kinematics), track kinematic parameters of the one or more objects in the images (Rose; [0054]: swing phases were defined based on clubhead and ball kinematics), and
Song in view of Rose and Kosowsky further discloses generating context data based on the tracked kinematic parameters of the one or more objects (Song; Fig. 8; [0068]: geometric alignment 803 refers to the left elbow, and indicates the subject has an elbow angle of thirty degrees;
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; Fig. 10; [0128]; [0130]).
Same motivation of claim 1 is applied here.
Regarding to claim 23 (Currently Amended), Song in view of Rose and Kosowsky discloses the computer-implemented system of claim 1, wherein the one or more hardware processors are further configured to identify one or more objects other than the user in the images (Rose; [0054]: swing phases were defined based on clubhead and ball kinematics), track and measure kinematic parameters of the one or more objects in the images (Rose; [0054]: swing phases were defined based on clubhead and ball kinematics) and
Song in view of Rose and Kosowsky further discloses display a 3D avatar relative to the one or more objects based on measured movements of the user in the images and the kinematic parameters of the one or more objects in the images (Song; Fig. 8; [0068]: geometric alignment 803 refers to the left elbow, and indicates the subject has an elbow angle of thirty degrees;
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; Fig. 10; [0130]:
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).
Same motivation of claim 1 is applied here.
Regarding to claim 24 (Currently Amended), Song in view of Rose and Kosowsky discloses the computer-implemented system of claim 1, wherein the one or more hardware processors are further configured to measure and quantify user-selected movements of the user via a motion tracker (Song; Fig. 8; [0068]: three angles of body movements are measured as illustrated in Fig. 8; the subject moves and has an elbow angle of thirty degrees;
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; leg angle is measured to be 18 degree as illustrated in Fig. 8),
Song in view of Rose and Kosowsky further discloses wherein the motion tracker is configured to generate measurements of a predetermined sequence of motions (Rose; [0038]: Mechanical motion capture systems directly track body joint angles; measure the subject's relative motion).
Same motivation of claim 1 is applied here.
Regarding to claim 25 (Currently Amended), Song in view of Rose and Kosowsky discloses the computer-implemented system of claim 1, wherein the one or more hardware processors are further configured to:
dynamically generate one or more watch lists comprising a list of measurable attributes of a performance for which the system is programmed to monitor in the images of the user's movement during performance of the swing (Kosowsky; Fig. 2C; [0072]: the head 2c02 of the golfer 2c01 and the knee 2c04 of the golfer 2c01 are selected and tracked; the golfer is alerted when either the head 2c02 moves outside the boundaries depicted as rectangle 2c03, or the knee 2c04 moves outside the boundaries depicted as rectangle 2c05; generate various alerts for various sets of combinations of targets crossing boundaries; Fig. 14A; [0108]: the user causes the target to move outside of the boundaries in real physical space; [0109]: indicate the location of the target at the moment the golf club strikes the ball; Fig. 17; Fig. 18; [0119]: determine the most salient fault affecting a user's performance across a set of activities; this sorted list is presented to the user to inform them of the most important factors that affected their performance in the session); and store the watch lists in a memory of the system as watch list configuration data identifying the list of measurable attributes to be monitored and being retrievable for use in analysis of subsequent swings (Kosowsky; [0094]: store indicators relevant to the activity; store date and location of the activity, and weather conditions; Fig. 8; [0097]: the image contains a display of stored target coordinates of the target 82 representing the path of motion during the activity; [0101]: The user may choose to review the data collected by the system in many ways; store statistical analyses for later retrieval to increase the responsiveness and usefulness of the system; Fig. 14B; [0107]: display a set of boundaries, the positioning of said boundaries determined by a set of selectable options chosen by the user;
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; Fig. 4B; [0108]).
Same motivation of claim 1 is applied here.
Regarding to claim 26 (Currently Amended), Song in view of Rose and Kosowsky discloses the computer-implemented system of claim 1, wherein the one or more hardware processors are further configured to:
display a graphical user interface with options to for user selection of one or more components of the user's movement during performance of the swing (Song; Fig. 2; [0077]: a camera 219 of smartphone 201 with options electronically captures one or more electronically captured images 208 of a subject performing an activity; [0087]: electronic adjustments to the one or more electronically altered images); and
store the one or more components in a memory of the system as one or more motion trackers (Song; Fig. 1; [0019]: memory; [0026]: retrieve the stored data with the one or more processors from a memory; [0093]: a storage device, such as memory 306, can optionally store the executable software code and data; [0096]: predefined authentication references are stored in memory 306).
Regarding to claim 27 (Currently Amended), Song in view of Rose and Kosowsky discloses the computer-implemented system of claim 1, wherein the one or more hardware processors are further configured to track a continuous movement of the swing across a sequence of images (Rose; Fig. 1; [0015]: primary phases of the golf swing; Fig. 2A; [0058]: backswing began with a clockwise rotation of the pelvis and upper torso in the horizontal plane; [0060]: all biomechanical parameters are increased from easy to medium to hard swings among professional golfers; [0061]: FIGS. 3A through 3I are graphs of golf swing rotational biomechanics for an amateur golfer compared to the average of the professional golfers).
Same motivation of claim 1 is applied here.
Regarding to claim 28 (Currently Amended), Song in view of Rose and Kosowsky discloses the computer-implemented system of claim 12, wherein the 3D avatar engine is further configured to generate a 3D avatar based on a user selected sequence of measurements (Song; Fig. 10; [0129]: identify a plurality of subject reference locations situated at predefined features of a subject depicted performing an activity in the one or more three-dimensional images 1001 of a subject 103 performing an activity 104;
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; Fig. 10; [0130]: generate 3D subject 1004, i.e. 3D avatar, in the one or more three-dimensional images 1001 of a subject 103 and the subject performs an activity 104 as illustrated in Fig. 10).
Regarding to claim 29 (Currently Amended), Song in view of Rose and Kosowsky discloses the computer-implemented system of claim 12, wherein the 3D avatar engine is further configured to:
i) model the user's body as an articulated structure, including a set of human skeletal components connected via joints and a measured spatial relationship among the set of human skeletal components for the user’s body (Song; Fig. 8; [0068]: the subject has an elbow angle of thirty degrees; a knee bend of twenty-five degrees);
ii) customize the model of the user's body based on the performance of the swing being golf-specific (Song; [0023]: a person doing yoga; [0024]: training, yoga, gaming, or in whichever activity the person may be engaged; [0025]: practice yoga), the customization including a determination of the set of human skeletal components and/or joints to include in the model based on at least the swing (Song; Fig. 8; [0068]: the subject has an elbow angle of thirty degrees; a knee bend of twenty-five degrees; Fig. 10; [0128]: a subject 103 performs an activity of yoga); and
iii) animate the model along three-dimensional 3D space based on the recorded 3D motion data (Song; [0108]: output components such as video; the output components include a video output component; Fig. 10; [0130]: superimpose a representation 1003 of the standard upon the subject 1004 in the one or more three-dimensional images 1001 of a subject 103 performing an activity 10).
Song in view of Rose and Kosowsky further discloses iii) animate the model along three-dimensional 3D space based on the recorded 3D motion data (Rose; [0049]: the display may use perspective techniques to display a three-dimensional animation such that body movements can be seen from different points of view in a three-dimensional space.).
Same motivation of claim 1 is applied here.
Regarding to claim 30 (Currently Amended), Song in view of Rose and Kosowsky discloses the computer-implemented system of claim 4, wherein the at least one image capture device comprises a single camera and a 3D avatar engine is further configured to automatically generate a 3D avatar of the user from a single-viewpoint recording by the single camera (Song; Fig. 10; [0128]: the stereoscopic camera 202 simply employs a depth imager (314) to capture the one or more three-dimensional images 1001 of a subject 103 performing an activity 104).
Regarding to claim 31 (Currently Amended), Song in view of Rose and Kosowsky discloses the computer-implemented system of claim 1, further comprising:
a watch list engine configured to generate a dynamic watch list (Kosowsky; Fig. 2C; [0072]: the head 2c02 of the golfer 2c01 and the knee 2c04 of the golfer 2c01 are selected and tracked; Fig. 14A; [0108]: the user causes the target to move outside of the boundaries in real physical space; [0109]: indicate the location of the target at the moment the golf club strikes the ball; Fig. 17; Fig. 18; [0119]: determine the most salient fault affecting a user's performance across a set of activities; this sorted list is presented to the user to inform them of the most important factors that affected their performance in the session);
a user interface configured to present options for a user to make selections for the dynamic watch lists by selecting one or more components, including one or more body parts or context objects (Kosowsky; Fig. 2C; [0072]: the head 2c02 of the golfer 2c01 and the knee 2c04 of the golfer 2c01 are selected and tracked; Fig. 14A; [0108]: the user causes the target to move outside of the boundaries in real physical space; [0109]: indicate the location of the target at the moment the golf club strikes the ball; Fig. 17; Fig. 18; [0119]: determine the most salient fault affecting a user's performance across a set of activities; this sorted list is presented to the user to inform them of the most important factors that affected their performance in the session); and
one or more motion trackers configured to determine kinematic parameters of one or more selected components to assess performance of the swing (Kosowsky; Fig. 2C; [0072]: the head 2c02 of the golfer 2c01 and the knee 2c04 of the golfer 2c01 are selected and tracked).
Same motivation of claim 1 is applied here.
Regarding to claim 32 (Currently Amended), Song in view of Rose and Kosowsky discloses the computer-implemented system of claim 12, wherein the 3D avatar engine is further configured to automatically generate a 3D avatar of the user by conversion of a 2D pose estimation from an image into a 3D space (Song; Fig. 8; [0068]: indicates the subject has an elbow angle of thirty degrees; Fig. 2; [0087]: the virtual reality headset provides a three-dimensional experience to the user;
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; Fig. 1; [0048]: the electronically captured images 108 of the subject 103 performing the activity 104 are automatically converted to electronic data and models by processors; [0080]: convert the one or more electronically captured images 208 depicting the subject performing the activity to data; Fig. 5; [0114]: the one or more processors 401 are 3D avatar engine).
Regarding to claim 33 (Currently Amended), Song in view of Rose and Kosowsky discloses the computer-implemented system of claim 12, wherein the 3D avatar engine is further configured to:
automatically generate a 3D avatar of the user from a 2D image by modeling a user's body as an articulated structure, including a predetermined set of human skeletal components connected via joints and a measured spatial relationship among set of human skeletal components for the user’s body (Fig. 1; [0048]: the electronically captured images 108 of the subject 103 performing the activity 104 are automatically converted to electronic data and models by processors; [0080]: convert the one or more electronically captured images 208 depicting the subject performing the activity to data; Fig. 2; [0087]: the virtual reality headset provides a three-dimensional experience to the user;
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; Fig. 5; [0114]: the one or more processors 401 are 3D avatar engine; Fig. 10; [0130]: the one or more processors of the local or remote electronic device perform a Procrustes superimposition operation one the one or more three-dimensional electronic images 1002 to superimpose a representation 1003 of the standard upon the subject 1004 in the one or more three-dimensional images 1001 of a subject 103 performing an activity 104;
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); and
detecting the joints through 3D pose estimation (Song; [0050]: the predefined reference locations may comprise the fingertips, knuckles, joints between fingers, wrist bones, wrist and forearm; [0082]: joints between fingers; a golfer might prefer predefined reference locations such as the club head, club shaft, grip, hands, elbows, shoulders, head, hips, knees, feet, and ball; Fig. 8; [0068]: geometric alignment 803 refers to the left elbow, and indicates the subject has an elbow angle of thirty degrees; the standard has an elbow angle of only five degrees).
Regarding to claim 34 (Currently Amended), Song in view of Rose and Kosowsky discloses the computer-implemented system of claim 1, further comprising:
a 3D avatar engine configured to apply recorded 3D motion data from the user to a 3D avatar for: 1) playback of the swing to enable the user to watch their own swing from any angle (Kosowsky; [0057]: the system allows the user to display and analyze the record of their motions in multiple instances of the activity; [0099]: the image displayed could display a replay, i.e. playback, of the motion of the target by moving a display object along the path; the image could also include the amount of time elapsed from the moment when the initial position was determined until the defining moment representing the impact of the club with the ball); and
Song in view of Rose and Kosowsky further discloses ii) apply a second user's recorded kinematic parameter sequence to a second avatar, to enable a coach, whose swing is recorded to model performances for the user (Song; Fig. 8; [0068]: geometric alignment 803 refers to the left elbow, and indicates the subject has an elbow angle of thirty degrees; the standard has an elbow angle of only five degrees; Fig. 10; [0130]).
Same motivation of claim 1 is applied here.
Regarding to claim 35 (Currently Amended), Song in view of Rose and Kosowsky discloses the computer-implemented system of claim 1, further comprising:
a 3D avatar engine configured to automatically generate a 3D avatar of the swing based on the quantitative measurement of the kinematic parameters and sequences of motion (Song; Fig. 8; [0068]: geometric alignment 803 refers to the left elbow, and indicates the subject has an elbow angle of thirty degrees;
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; geometric alignment 805 refers to the right knee, and indicates the subject has a knee bend of eight degrees, while the standard has a knee bend of twenty-five degrees; Fig. 2; [0087]: the virtual reality headset provides a three-dimensional experience to the user;
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; Fig. 5; [0114]: the one or more processors 401 are 3D avatar engine; Fig. 1; [0048]: the electronically captured images 108 of the subject 103 performing the activity 104 are automatically converted to electronic data and models by processors; Fig. 10; [0130]: superimpose a representation 1003 of the standard upon the subject 1004 in the one or more three-dimensional images 1001 of a subject 103 performing an activity 104;
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), where the 3D avatar engine is configured to generate the 3D avatar by tailoring component structures of the 3D avatar based on body points that correspond with pose, movement, or alignment features (Song; Fig. 1; [0050]: If the activity were playing golf, the predefined reference locations include the club head and club shaft; [0068]: the subject is instructed to decrease the elbow angle to move its subject reference location of the elbow toward the standard reference location of the standard; geometric alignment 805 refers to the right knee, and indicates the subject has a knee bend of eight degrees, while the standard has a knee bend of twenty-five degrees; Fig. 2; [0082]: a piano player desiring practice feedback prefers predefined reference locations such as the fingertips, knuckles, joints between fingers, wrist bones, wrist and forearm; a golfer prefers predefined reference locations such as the club head, club shaft, grip, hands, elbows, shoulders, head, hips, knees, feet, and ball; Fig. 2; [0087]: the virtual reality headset provides a three-dimensional experience to the user;
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; Fig. 5; [0114]: the one or more processors 401 are 3D avatar engine; Fig. 1; Fig. 10; [0130]: superimpose a representation 1003 of the standard upon the subject 1004 in the one or more three-dimensional images 1001 of a subject 103 performing an activity 104;
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; the 3D avatar of piano player, golfer, and yoga player are tailored), wherein tailoring the component structures comprises determining a set of human skeletal components and/or joints to include in the 3D avatar based at least on the swing being golf-specific (Song; Fig. 8; [0067]: cause one or more geometric alignments 803,804,805 identifying the differences; Fig. 5; [0119]:
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Regarding to claim 36 (Previously Presented), Song in view of Rose and Kosowsky discloses the computer-implemented system of claim 35, wherein the 3D avatar engine is further configured to display an overlay of an avatar based on performance of an activity by the user over a reference avatar based on a reference performance of a prior performance of the activity (Song; Fig. 1; [0061]: alter the one or more electronically captured images 108 to identify the differences between the at least one standard reference location and the at least one corresponding subject reference location in one or more electronically altered images 120; Fig. 6; [0063]: cause the depiction 606 of the standard to appear superimposed atop a depiction 607 of the subject performing the activity in the electronically altered image 601; the depiction 606 of the standard can be caused to appear superimposed alongside the depiction 607 of the subject performing the activity in the electronically altered image; Fig. 10; [0128-0130]; Fig. 8; [0068]: geometric alignment 803 refers to the left elbow, and indicates the subject has an elbow angle of thirty degrees;
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; the subject is instructed to decrease the elbow angle to move its subject reference location of the elbow toward the standard reference location of the standard; geometric alignment 805 refers to the right knee, and indicates the subject has a knee bend of eight degrees, while the standard has a knee bend of twenty-five degrees; Fig. 1; Fig. 10; [0130]: superimpose a representation 1003 of the standard upon the subject 1004 in the one or more three-dimensional images 1001 of a subject 103 performing an activity 104;
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; the 3D avatar of piano player, golfer, and yoga player are tailored).
Regarding to claim 37 (Previously Presented), Song in view of Rose and Kosowsky discloses the computer-implemented system of claim 35, wherein the 3D avatar engine is further configured to display a time synchronized overlay of a 3D avatar based on performance by the user of an activity over a video recording of a user’s prior performance of the activity (Song; Fig. 1; [0061]: alter the one or more electronically captured images 108 to identify the differences between the at least one standard reference location and the at least one corresponding subject reference location in one or more electronically altered images 120; Fig. 6; [0063]: cause the depiction 606 of the standard to appear superimposed atop a depiction 607 of the subject performing the activity in the electronically altered image 601; the depiction 606 of the standard can be caused to appear superimposed alongside the depiction 607 of the subject performing the activity in the electronically altered image; Fig. 10; [0128-0130]; Fig. 8; [0068]: geometric alignment 803 refers to the left elbow, and indicates the subject has an elbow angle of thirty degrees;
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; the subject is instructed to decrease the elbow angle to move its subject reference location of the elbow toward the standard reference location of the standard; geometric alignment 805 refers to the right knee, and indicates the subject has a knee bend of eight degrees, while the standard has a knee bend of twenty-five degrees; Fig. 1; Fig. 10; [0130]: superimpose a representation 1003 of the standard upon the subject 1004 in the one or more three-dimensional images 1001 of a subject 103 performing an activity 104;
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; the 3D avatar of piano player, golfer, and yoga player are tailored).
Regarding to claim 38 (Currently Amended), Song in view of Rose and Kosowsky discloses the computer-implemented system of claim 1, wherein the one or more hardware processors are further configured to identify objects other than the user in the sequence of images (Rose; [0040]: club motions are measured by the motion capture system; club motions drive the joints and segments of the model; Fig. 2; [0049]: the display may use perspective techniques to display a three-dimensional animation such that body movements are seen from different points of view in a three-dimensional space;
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), wherein the objects include at least a portion of a golf club and a golf ball while analyzing the sequence of images of the user’s movement captured on a user device during performance of the swing to generate a quantitative measurement of a kinematic parameters and sequences of motion (Rose; [0040]: club motions are measured by the motion capture system; club motions drive the joints and segments of the model; Fig. 2; [0049]: the display may use perspective techniques to display a three-dimensional animation such that body movements are seen from different points of view in a three-dimensional space;
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).
Regarding to claim 39 (Previously Presented), Song in view of Rose and Kosowsky discloses the computer-implemented system of claim 25, wherein the one or more hardware processors are further configured to (same as rejected in claim 1):
provide a 3D avatar engine configured to alter values in a kinematic parameter sequence to simulate changes in performance of an action and results of those changes (Kosowsky; Fig. 2C; [0072]: the golfer is alerted when either the head 2c02 moves outside the boundaries depicted as rectangle 2c03, or the knee 2c04 moves outside the boundaries depicted as rectangle 2c05; [0101]: the golfer may want to see his progress in reducing the occurrence of performance faults detected by the system; [0118]: indicate to the user a fault with each performance of the activity).
Same motivation of claim 1 is applied here.
Regarding to claim 40 (Currently Amended), Song in view of Rose and Kosowsky discloses the computer-implemented system of claim 1, wherein the one or more hardware processors are further configured to (same as rejected in claim 1):
generate and display an animated overlay comprising a 3D avatar and at least a reference video of a reference performance, and to cause simultaneous animation of the 3D avatar and the reference video within the display such that differences between the reference video and the 3D avatar are visually highlighted (Song; Fig. 1; [0061]: alter the one or more electronically captured images 108 to identify the differences between the at least one standard reference location and the at least one corresponding subject reference location in one or more electronically altered images 120; Fig. 6; [0063]: cause the depiction 606 of the standard to appear superimposed atop a depiction 607 of the subject performing the activity in the electronically altered image 601; the depiction 606 of the standard can be caused to appear superimposed alongside the depiction 607 of the subject performing the activity in the electronically altered image; Fig. 10; [0128-0130]; Fig. 8; [0068]: geometric alignment 803 refers to the left elbow, and indicates the subject has an elbow angle of thirty degrees;
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; the subject is instructed to decrease the elbow angle to move its subject reference location of the elbow toward the standard reference location of the standard; geometric alignment 805 refers to the right knee, and indicates the subject has a knee bend of eight degrees, while the standard has a knee bend of twenty-five degrees; Fig. 1; Fig. 10; [0130]: superimpose a representation 1003 of the standard upon the subject 1004 in the one or more three-dimensional images 1001 of a subject 103 performing an activity 104;
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; the 3D avatar of piano player, golfer, and yoga player are tailored).
Regarding to claim 41 (Currently Amended), Song in view of Rose and Kosowsky discloses the computer-implemented system of claim 1, wherein the one or more hardware processors are further configured by machine-readable instructions to:
capture pre-shot routine data across a plurality of swings (Rose; [0023]: optimize a golf swing in a pre-shot routines by measuring and providing feedback; [0042]: during practice exercises; Fig. 1; [0048]: the beginning of backswing, initial downswing, mid-downswing, and late downswing are pre-shot routines, but not hit a ball;
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; Fig. 2A; [0058]: downswing was initiated by the reversal of pelvic rotation followed by a reversal of upper torso rotation;
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; backswing, downswing and upper torso rotation are pre-shot routines, but not hit a ball) and
Song in view of Rose and Kosowsky further discloses determine information about a user’s standard pre-shot routine based on the pre-shot routine data over time, wherein the pre-shot routine data comprises at least one of (i) a number of practice swings, (ii) a duration of the pre-shot routine, or (iii) timing between pre-shot actions and initiation of the swing (or is optional; Kosowsky; [0057]: allow the user to apply statistical analysis to compare their motions to the motions of other individuals or to the aggregated motions of a set of other individuals; [0101]: the golfer may want to see how many times their head or part of the body stayed within the boundaries for swings of a particular type of club each of the preceding sessions; [0102]: the user compares the visualizations of their good swings with their bad swings to help in their training; Fig. 10; [0103]: using machine learning algorithms, models 1003 or 1004 are computed that will predict the qualitative result—good or bad 1005—or the quantitative result—expected distance 1006 the golf ball will travel—given the series of timestamps and target positions 1001 or 1002; machine learning algorithms determine a golf swing is good, i.e., standard practice routine; machine learning algorithms determine a golf swing is bad, i.e., deviates from the standard practice routine; Fig. 11; [0104]: detect the defining moment of the golf club striking the golf ball; apply machine learning to predict the result of a swing from the series of target positions 1001 and 1002; apply machine learning to compute models 1103 or 1104 to yield qualitative 1105 or quantitative 1106 results from such sound recordings 1101 and 1102).
Same motivation of claim 1 is applied here.
Regarding to claim 43 (New), Song in view of Rose and Kosowsky discloses the computer-implemented system of claim 1, wherein the one or more hardware processors are further configured by machine-readable instructions to (same as rejected in claim 1):
quantitatively analyze the sequence of images of the user's movement captured on a user device during performance of the swing to generate a quantitative measurement of kinematic parameters and sequences of motion (Song; Fig. 1; [0050]: If the activity were playing golf, the predefined reference locations include the club head and club shaft; [0082]: a golfer might prefer predefined reference locations such as the club head, club shaft, grip, hands, elbows, shoulders, head, hips, knees, feet, and ball; Fig. 5; [0116]: the position and pose reference point detection engine 407 identify a plurality of subject reference locations situated at predefined features of a subject depicted performing the activity in the one or more electronically captured images 208; [0017]: identify and analyze subject reference locations in one or more electronically captured images; identify and analyze standard reference locations from one or more electronic images retrieved from a memory device; Fig. 1; [0054]: the plurality of standard reference locations 128,129,130 correspond to the plurality of subject reference locations 124,125,126 situated at predefined features of the depiction 132 of the subject 103 depicted performing the activity 104 in one or more electronically captured images 108 on a one-to-one basis; Fig. 8; [0068]: geometric alignment 803 refers to the left elbow, and indicates the subject has an elbow angle of thirty degrees;
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; Fig. 10; [0131]: identify the differences between the at least one standard reference location and the at least one corresponding subject reference location in one or more electronically altered three-dimensional images 1005);
cause execution of an application configured to receive and store user-entered pre-shot comments prior to performance of the swing, to time-stamp the user-entered pre-shot
comments, and to associate the time-stamped pre-shot comments with the swing as non- kinematic context data (Ross; [0023]: optimize a golf swing in a pre-shot routines by measuring and providing feedback; [0042]: during practice exercises; Fig. 1; [0048]: the beginning of backswing, initial downswing, mid-downswing, and late downswing are pre-shot routines, but not hit a ball;
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; Fig. 2A; [0058]: downswing was initiated by the reversal of pelvic rotation followed by a reversal of upper torso rotation;
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; backswing, downswing and upper torso rotation are pre-shot routines, but not hit a ball; Fig. 2A; [0058]: backswing is associated with time as illustrated in Fig. 2A); and
execute a context-data module configured to determine and store environmental condition data contemporaneous with the swing as non-kinematic context data, the environmental condition data comprising at least one of wind speed, wind direction, temperature, humidity, or precipitation (or is optional; Ross; [044]: temperature sensors measure ambient heat, i.e. environmental condition data; [0047]: testing environment; temperature).
Regarding to claim 44 (New), Song in view of Rose and Kosowsky discloses the computer-implemented system of claim 4, wherein the visual representations comprise user-selected kinematic parameters with a 3D avatar of the swing, animated to cause movement of the 3D avatar displayed on the user device (Song; [0048]: the electronically captured images 108 of the subject 103 performing the activity 104 are automatically converted to electronic data and models by processors; Fig. 1; [0050]: If the activity were playing golf, the predefined reference locations include the club head and club shaft; Fig. 6; [0063]: cause the depiction 606 of the standard to appear superimposed atop a depiction 607 of the subject performing the activity in the electronically altered image 601; Fig. 8; [0066]: the audio data 802 instructs the subject to move a predefined feature of the subject toward a predefined standard reference location; move left elbow right!; Fig. 8; [0068]: geometric alignment 805 refers to the right knee, and indicates the subject has a knee bend of eight degrees, while the standard has a knee bend of twenty-five degrees;
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; Fig. 1; [0070]: the one or more standard reference locations 128,129,130 are selected parameters; Fig. 2; [0082]: these predefined features 218 are selected as reference locations of both the subject and the standard; a golfer prefers predefined reference locations such as the club head, club shaft, grip, hands, elbows, shoulders, head, hips, knees, feet, and ball; Fig. 2; [0087]: the virtual reality headset provides a three-dimensional experience to the user;
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; [0108]: a video output component displays a sequence images; Fig. 1; Fig. 10; [0130]: superimpose a representation 1003 of the standard upon the subject 1004 in the one or more three-dimensional images 1001 of a subject 103 performing an activity 104;
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; the 3D avatar of piano player, golfer, and yoga player are tailored; [0137]: instruct the subject to move a predefined feature of the subject toward a predefined standard reference location to appear in the one or more electronically captured images).
Regarding to claim 45 (New), Song in view of Rose and Kosowsky discloses the computer-implemented system of claim 1, wherein the one or more hardware processors are further configured to:
execute a machine learning module configured to determine and store characteristics of a user's practice routine over time as a standard pre-shot routine (Kosowsky; [0057]: allow the user to apply statistical analysis to compare their motions to the motions of other individuals or to the aggregated motions of a set of other individuals; [0101]: the golfer may want to see how many times their head or part of the body stayed within the boundaries for swings of a particular type of club each of the preceding sessions; [0102]: the user compares the visualizations of their good swings with their bad swings to help in their training; Fig. 10; [0103]: using machine learning algorithms, models 1003 or 1004 are computed that will predict the qualitative result—good or bad 1005—or the quantitative result—expected distance 1006 the golf ball will travel—given the series of timestamps and target positions 1001 or 1002; machine learning algorithms determine a golf swing is good, i.e., standard practice routine; machine learning algorithms determine a golf swing is bad, i.e., deviates from the standard practice routine; Fig. 11; [0104]: apply machine learning to predict the result of a swing from the series of target positions 1001 and 1002).
Regarding to claim 46 (New), Song in view of Rose and Kosowsky discloses the computer-implemented system of claim 45, wherein determining the standard practice routine over time comprises calculating how many seconds it takes to complete a pre-shot routine and hit the ball (Ross; Fig. 2A; [0058]:
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; it takes over six seconds to hit the ball as illustrated in Fig. 2A).
Same motivation of claim 1 is applied here
Regarding to claim 47 (New), Song in view of Rose and Kosowsky discloses the computer-implemented system of claim 45, wherein the machine learning module is further configured to determine a relative consistency of pre-shot routines across a plurality of shots (Kosowsky; Fig. 3; [0093]: the user is performing the golf swing without striking a ball; training without a ball; [0101]: the golfer may want to see how many times their head or part of the body stayed within the boundaries for swings of a particular type of club each of the preceding sessions; [0102]: the user compares the visualizations of their good swings with their bad swings to help in their training; Fig. 10; [0103]: using machine learning algorithms, models 1003 or 1004 are computed that will predict the qualitative result—good or bad 1005—or the quantitative result—expected distance 1006 the golf ball will travel—given the series of timestamps and target positions 1001 or 1002; machine learning algorithms determine a golf swing is good, i.e., standard practice routine; machine learning algorithms determine a golf swing is bad, i.e., deviates from the standard practice routine).
Same motivation of claim 1 is applied here.
Regarding to claim 48 (New), Song in view of Rose and Kosowsky discloses the computer-implemented system of claim 45, wherein the machine learning module is further configured to determine whether the pre-shot routine is consistent across different shots (Kosowsky; Fig. 3; [0093]: the user is performing the golf swing without striking a ball; training without a ball; [0101]: the golfer may want to see how many times their head or part of the body stayed within the boundaries for swings of a particular type of club each of the preceding sessions; Fig. 10; [0103]: machine learning algorithms determine a golf swing is good, i.e., standard practice routine; machine learning algorithms determine a golf swing is bad, i.e., deviates from the standard practice routine).
Same motivation of claim 1 is applied here.
Regarding to claim 49 (New), Song in view of Rose and Kosowsky discloses the computer-implemented system of claim 1, wherein the one or more hardware processors are further configured to:
identify pre-shot routines by determining, for a respective pre-shot routine, (i) at least one practice swing in which the user swings but does not hit the ball (Kosowsky; Fig. 3; [0093]: the user is performing the golf swing without striking a ball; training without a ball), and
Song in view of Rose and Kosowsky further discloses (ii) at least one additional pre-shot routine characteristic comprising at least one of: (a) a number of steps taken by the user to step into a shot, or (b) a wait time before starting a swing that hits the ball (Ross; Fig. 2A; [0058]:
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; it takes multiple steps to hit the ball as illustrated in Fig. 2A).
Same motivation of claim 1 is applied here.
Regarding to claim 50 (New), Song in view of Rose and Kosowsky discloses the computer-implemented system of claim 49, wherein the at least one additional pre-shot routine characteristic comprises the wait time before starting a swing that hits the ball (Kosowsky; [0084]: after the user is ready; [0107]: instruct the user to set up and perform actions in order to indicate to the embodiment that the user is ready; [0114]: the ratio of the time interval from the start of the swing to the top of the swing to the time interval from the top of the swing to impact with the ball) and
Song in view of Rose and Kosowsky further discloses a duration to complete the pre-shot routine and hit the ball (Ross; Fig. 2A; [0058]:
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; it takes multiple steps to hit the ball as illustrated in Fig. 2A).
Same motivation of claim 1 is applied here.
Regarding to claim 51 (New), Song in view of Rose and Kosowsky discloses the computer-implemented system of claim 1, wherein the pre-shot routines include a number of steps the user took to step into a shot (Kosowsky; [0107]: instruct the user to set up and perform actions in order to indicate to the embodiment that the user is ready; [0114]: the ratio of the time interval from the start of the swing to the top of the swing to the time interval from the top of the swing to impact with the ball).
Same motivation of claim 1 is applied here.
Regarding to claim 52 (New), Song in view of Rose and Kosowsky discloses the computer-implemented system of claim 1, wherein the pre-shot routines include a wait time before starting a swing that hits the ball (Kosowsky; [0107]: instruct the user to set up and perform actions in order to indicate to the embodiment that the user is ready; [0114]: the ratio of the time interval from the start of the swing to the top of the swing to the time interval from the top of the swing to impact with the ball).
Same motivation of claim 1 is applied here.
Regarding to claim 53 (New), Song in view of Rose and Kosowsky discloses the computer-implemented system of claim 1, wherein the pre-shot routines include a number of practice swings (Kosowsky; Fig. 3; [0093]: the user is performing the golf swing without striking a ball; training without a ball; [0101]: the golfer may want to see how many times their head or part of the body stayed within the boundaries for swings of a particular type of club each of the preceding sessions; Fig. 10; [0103]: machine learning algorithms determine a golf swing is good, i.e., standard practice routine; machine learning algorithms determine a golf swing is bad, i.e., deviates from the standard practice routine).
Regarding to claim 54 (New), Song in view of Rose and Kosowsky discloses the computer-implemented method of claim 1, wherein the one or more hardware processors are further configured to:
alter visual representations based on correlated results by changing at least one of: (i) which kinematic parameters are displayed, (ii) a visual alert, or (iii) a recommendation displayed on a user device (Or is optional; Kosowsky; Fig. 14C; [0108]: the embodiment has alerted the user of this movement of the target outside of the boundaries visually perhaps by a text message displayed on the screen and a change in color of the specific boundary;
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; [0130]: if the target is not found within the desired limits in the image, then the user is told, at step 2008, to either move back from or forward towards the smartphone; If the location of the target is too close to the top of the image, the user is told to move back).
Same motivation of claim 1 is applied here.
Claims 9 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Song (US 20210004981 A1) in view of Rose (US 20140257538 A1), in view of Kosowsky (US 20200398110 A1), and further in view of Russo (US 6710713 B1).
Regarding to claim 9 (Currently Amended), Song in view of Rose and Kosowsky discloses the computer-implemented system of claim 1, wherein the one or more hardware processors are further configured to (same as rejected in claim 1):
to enable creation of one or more motion trackers comprising specific kinematic parameters comprising derivatives respect to time and sequences of motions (Rose; [0028]: the derivative with respect to time of the upper torso rotation angle; [0030]: pelvic rotational velocity; the derivative with respect to time of the pelvic rotation angle; [0038]: mechanical motion capture systems directly track body joint angles; [0045]: obtain values for specific descriptive parameters, such as, pelvic and shoulder tilt, the relative difference between the rotation of the hips and the shoulders, free moment, and position of the head; [0049]: the graphical display includes videos of other subjects performing ideal or non-ideal golf swings with explanations and comparisons to the current subject's movement; [0053]: kinematic data were collected using an eight-camera optometric system for three-dimensional motion analysis at a sampling rate of 240 Hz; the motion capture system.).
Song in view of Rose and Kosowsky further discloses display a graphical user interface with options to enable creation of one or more motion trackers (Kosowsky; [0004]: allow a user to set boundaries around the motion of the head; [0057]: the boundaries are user selectable; [0059]: the user is able to set a threshold for movement in terms of real-world distance; Fig. 14B; [0107]: displays a set of boundaries, the positioning of said boundaries determined by a set of selectable options chosen by the user; Fig. 15B; [0110]: display a button with a share icon 1512).
Same motivation of claim 1 is applied here.
Song in view of Rose and Kosowsky fails to explicitly disclose:
higher order derivatives of position with respect to time;
wherein the higher order derivatives comprise at least a third derivative and a fourth derivative computed from a quantitative measurement generated from the sequence of images captured on a user device.
In same field of endeavor, Russo teaches:
display a graphical user interface with options to enable creation of one or more motion trackers (col. 5, lines 45-55: one analyst selects particular athletes to track; col. 8, lines 5-15: an icon is a graphical data representation 166; by touching one or more particular icons 166 on the screen with a finger or light pen, the analyst Selects one or more athletes for further tracking.);
higher order derivatives of position with respect to time (col. 6, lines 30-40: the first and second derivatives of position with respect to time; col. 7, lines 1-10: calculations involve higher order derivatives of the athlete's change of position with respect to time);
wherein the higher order derivatives comprise at least a third derivative and a fourth derivative computed from a quantitative measurement generated from the sequence of images captured on a user device (col. 5, lines 45-55: one analyst selects particular athletes to track; col. 6, lines 30-40: the first and second derivatives of position with respect to time; col. 7, lines 1-10: calculations involve higher order derivatives of the athlete's change of position with respect to time; col. 8, lines 5-15: an icon is a graphical data representation 166; Russo further teaches by touching one or more particular icons 166 on the screen with a finger or light pen, the analyst selects one or more athletes for further tracking).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Song in view of Rose and Kosowsky to include display a graphical user interface with options to enable creation of one or more motion trackers; higher order derivatives of position with respect to time; wherein the higher order derivatives comprise at least a third derivative and a fourth derivative computed from a quantitative measurement generated from the sequence of images captured on a user device as taught by Russo. The motivation for doing so would have been analyze the motion data of a particular athlete; to calculate, from the stored athlete motion data 132, the first and second derivatives of position with respect to time; to involve higher order derivatives of the athlete's change of position with respect to time as taught by Russo in col. 2, lines 45-55, col. 6, lines 30-40 and col. 7, lines 1-10.
Regarding to claim 11 (Original), Song in view of Rose and Kosowsky discloses the computer-implemented system of claim 1, wherein the one or more hardware processors are further configured to (same motivation of claim 1 is applied here.):
to enable creation of one or more motion trackers comprising specific kinematic parameters and sequences of motions, the kinematic parameters comprising position, velocity, acceleration, and/or higher-order derivatives of the position with respect to time (or is optional; Rose; [0028]: the derivative with respect to time of the upper torso rotation angle; [0030]: pelvic rotational velocity; the derivative with respect to time of the pelvic rotation angle; [0038]: mechanical motion capture systems directly track body joint angles; [0044]: these sensors measure angular velocity; accelerometers measure inclination or linear acceleration; cameras measure position of body segments or objects; [0045]: obtain values for specific descriptive parameters, such as, pelvic and shoulder tilt, the relative difference between the rotation of the hips and the shoulders, free moment, and position of the head; [0049]: the graphical display may be on a computer monitor, a TV, a smartphone; [0053]: kinematic data were collected using an eight-camera optometric system for three-dimensional motion analysis at a sampling rate of 240 Hz; the motion capture system; [0054]: swing phases were defined based on clubhead and ball kinematics; [0060]: all biomechanical parameters increased from easy to medium to hard swings among professional golfers; [0061]: the number of biomechanical factors during amateur hard swings that fell outside both one and two standard deviations of mean values for professional hard golf swings increased with handicap).
Song in view of Rose and Kosowsky further discloses display a graphical user interface with options to enable creation of one or more motion trackers (Kosowsky; [0004]: allow a user to set boundaries around the motion of the head; [0057]: the boundaries are user selectable; [0059]: the user is able to set a threshold for movement in terms of real-world distance; Fig. 14B; [0107]: displays a set of boundaries, the positioning of said boundaries determined by a set of selectable options chosen by the user; Fig. 15B; [0110]: display a button with a share icon 1512 ).
Same motivation of claim 1 is applied here.
Song in view of Rose and Kosowsky fails to explicitly disclose:
higher order derivatives of position with respect to time.
In same field of endeavor, Russo teaches:
display a graphical user interface with options to enable creation of one or more motion trackers (col. 5, lines 45-55: one analyst selects particular athletes to track; col. 8, lines 5-15: an icon is a graphical data representation 166; by touching one or more particular icons 166 on the screen with a finger or light pen, the analyst Selects one or more athletes for further tracking.);
higher order derivatives of position with respect to time (col. 6, lines 30-40: the first and second derivatives of position with respect to time; col. 7, lines 1-10: calculations involve higher order derivatives of the athlete's change of position with respect to time).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Song in view of Rose and Kosowsky to include display a graphical user interface with options to enable creation of one or more motion trackers; higher order derivatives of position with respect to time as taught by Russo. The motivation for doing so would have been analyze the motion data of a particular athlete; to calculate, from the stored athlete motion data 132, the first and second derivatives of position with respect to time; to involve higher order derivatives of the athlete's change of position with respect to time as taught by Russo in col. 2, lines 45-55, col. 6, lines 30-40 and col. 7, lines 1-10.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Hai Tao Sun whose telephone number is (571)272-5630. The examiner can normally be reached 9:00AM-6:00PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Daniel Hajnik can be reached at 5712727642. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/HAI TAO SUN/Primary Examiner, Art Unit 2616
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