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 .
This office action is in response to amendment filed 07/13/2026 in which the claims 1-8, 16-20 are pending & claims 9-15 are withdrawn.
Response to Arguments
Applicant’s arguments, see pages 6-12, filed 07/13/2026, with respect to the rejections of claims have been fully considered and amended claims are moot in view of new grounds of rejection by relying on the teachings of Mettler May et al. (US 2019/0009133 A1) (as described below).
Double Patenting
Terminal disclaimer electronically filed and approved on 07/13/2026 in the present application over the patents US 12,014,503 B2, US 11,694,347 B2, US 11,348,256 B2, US 10,706,566 B2 are in accordance with 37 CFR 1.321 overcomes the nonstatutory double patenting rejections.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
8. Claims 1-4 are rejected under 35 U.S.C. 103 as being unpatentable over Marty et al. (US 2015/0332450 A1) (hereinafter Marty I) in view of Marty et al. (US 2007/0026975 A1) (hereinafter Marty II) and Mettler May et al. (US 2019/0009133 A1).
Regarding claim 1, Marty I discloses a system for determining performance attributes (Abstract teaches apparatus relating to predicting outcome in a sporting environment are described. The methods and apparatus are used to relate trajectory performance of an object to body motions and body orientation associated with a generating the trajectory of the object) comprising: at least one processor configured for network communication (Fig. 21 processor 204) with a plurality of image capturing devices (Fig. 21 machine vision system comprises one or more cameras 201 (e.g., a CCD camera)), and at least one server computer and/or at least one local computing device (Para[0163] teaches full motion virtual server, synthesis server, data server, broadcast replay server and video game development server are described with respect to FIGS. 13A-14B); wherein a first image capturing device of the plurality of image capturing devices generates video data (Para[0083] teaches range of input variables for an individual player may be determined by capturing data from actual trajectories generated by a player and then using an analysis, such as a statistical analysis, to determine a mean or average for a variable of interest and then to determine an amount of variability around the mean or average. For example, in basketball environment, a player may be asked to take twenty to thirty shots from a particular location. Trajectory parameters associated with each shot may be determined using a device employing video capture) and/or image data including an individual (Para[0058] teaches based upon video data captured of an individual shooting a basketball, body motions and an orientation of the individual as they are shooting the ball may be used to determine an initial force vector, i.e., a magnitude of forces and their associated direction including rotational forces if desired, for the basketball as it is released from the shooters hand. [0060] Using an analysis of body motions and body orientation to predict trajectory outcome, the individual doesn't necessarily have to launch or strike an object. For example, an individual in tennis may practice their service motion without hitting a tennis ball); wherein the at least one processor is operable to: develop a motion curve corresponding to the individual based on locations of one or more body parts of the individual in the video data and/or the image data (Para [0121] In 400, for one or more image frames objects in the frame may be identified. Examples of some objects that may be identified from image data include balls, sporting equipment, people and parts of people. Para [0163] teaches for a particular, the manner of articulation of the various body parts may be determined to match movements of a particular player captured using the trajectory capture system. Para [0167] teaches articulations of a player's various body parts during a swing may be extracted from capture video data and applied to a 3-D model of a golfer as a function of time. In 906, the 3-D simulation including one or more of golfers, golf balls being struck, trajectories of golf balls, etc. that may be seen on an actual golf course may be simulated from the view point selected in 904. Para[0213] teaches captured video data from one or more cameras may be used in 3-D simulations. To generate the 3-D simulation, a sub-composition may be applied to the captured video data. For example, in basketball, body parts (head, eyes, gaze direction, neck, shoulders, torso, upper arms, elbows, lower arms, hands, hand position, upper legs, lower legs, feet) of 10 players and X referees may be identified as logically connected groups and represented in a 3-D wire frame);
Marty I does not explicitly disclose wherein a second image capturing device of the plurality of image capturing devices generates video data and/or image data of an environment and detects one or more environmental conditions; cause frames and/or scan lines from the video data and/or the image data associated with the motion curve to be displayed on the at least one server computer and/or the at least one local computing device; and correlate the one or more environmental conditions to at least one of the performance attributes of the individual. However Marty II and cause frames and/or scan lines from the video data and/or the image data associated with the motion curve to be displayed on the at least one server computer and/or the at least one local computing device (Para[0147] teaches in analyzing a player's body mechanics, the trajectory device 722 may be used to determine a trajectory of one or more parts of the player's body during and after a shot. For example, the trajectory device 722 may determine an arc of a player's foot/ or knee to determine whether the player move upwards in a vertically aligned direction or in a direction off of vertical. In another example, the trajectory device may measure an arc of a player's elbow or hand to determine whether the player's arms is imparting sideways momentum to the ball and to determine whether the player's is properly following through on the shot. Para[0148]- [0151] teaches the visual display device 716 or a display device on 722 may be used to display a comparison of the different shots. From the comparison, differences in the player's biomechanics between the shots may be evident. When the trajectory device has performed an analysis and determined a biomechanical difference between the shots, such as a poor follow through for shots in one group versus a good follow through for shots in another group, the differences may be illustrated on the display with additional graphics added to the previously captured visual data. For instance, the visual recording of the player's hand or arm may be highlighted with additional graphics, such as an added arc that follows the motion of the player's hand, to illustrate what is good or not good about the player's follow through). It would have been obvious to one having ordinary skill in the art before the effective filing date of the invention to use the method of manner of articulation of the various body parts may be determined to match movements of a particular player captured using the trajectory capture system of Marty I with the method of capture a position or an alignment of a player's torso, head, arms, etc., during and/or after a shot of Marty II in order to provide a in order to provide a system that allows the player to improve their trajectory skills for games requiring such skills where training devices are non-intrusive, operable in an environment that approximates actual playing conditions, simple to set-up and operate and provides immediate and objective feedback to the user of the device.
Marty I in view of Marty II does not explicitly disclose wherein a second image capturing device of the at least two image capturing devices is configured to generate video data and/or image data of an environment and detect one or more environmental conditions; and correlate the one or more environmental conditions to one or more performance attributes of the individual. However Mettler May discloses wherein a second image capturing device of the at least two image capturing devices is configured to generate video data and/or image data of an environment and detect one or more environmental conditions (Para[0627] teaches use of video cameras 70 that capture the broader agent behavior and the task environment 50); and correlate the one or more environmental conditions to one or more performance attributes of the individual (Para[0628] teaches category of performance data measurements, these so-called first-person cameras capture data related to the interaction of the subject 10, the tennis racket 20, the ball 30, as well as the motion of other participants such as the opponent 53, and other relevant environment elements such as the court 51 and net 52. Combined with measurements of the gaze direction or vector 81, video cameras on the subjects and/or environment make it possible to determine which elements or events the performers are attending to at any given time, or at specific instants of the performance such as during specific movement phases or phase transitions 26, 27 on the path 25, opponent behaviors, or task elements such as related to the ball trajectory 36). It would have been obvious to one having ordinary skill in the art before the effective filing date of the invention to use the method of manner of articulation of the various body parts may be determined to match movements of a particular player captured using the trajectory capture system, capture a position or an alignment of a player's torso, head, arms, etc., during and/or after a shot of Marty I in view of Marty II with the method in which the interaction between a stroke motion and task and environment elements of Mettler May the of in order to provide system a uses movement skill assessment and diagnostics at distinct levels of the human movement system hierarchy to specify training goals for a user.
Regarding claim 2, Marty I discloses the system of claim 1, wherein the at least one processor is operable to develop the motion curve using respective temporal segment lengths from the video data and/or the image data (Para[0167] & Fig. 12 teaches articulations of a player's various body parts during a swing may be extracted from capture video data and applied to a 3-D model of a golfer as a function of time. [0209] teaches body motions may be used to predict an outcome of a trajectory of a shot basketball. For instance, the motions of the hands, wrist, arms of a player, while making a shot may be used to determine an initial force vector and position for the basketball as it is shot. The position data may be related to a distance from the hoop and a distance above the floor associated with the ball over time as the ball is shot). Motivation to combine as indicted in claim 1.
Regarding claim 3, Marty I discloses the system of claim 1, wherein the at least one processor is operable to intertwine and temporally dispose the video data and/or the image data along a common timing reference and discard temporally adjacent images (Para[0299] teaches only a portion of the raw data, such as video frame data, may be sent to archival storage. Further, the data may be filtered for bad data prior to being sent to archival storage 525. The archival storage 525 may include a database used to relate trajectory data from one or more trajectory sessions to the conditions of the trajectory session, such as time place and location, and player identification information).
Regarding claim 4, Marty I discloses the system of claim 1, wherein the at least one server computer and/or the at least one local computing device is operable to display frames corresponding to an identified spatial point on the motion curve (Para[0170] teaches accurately predict where a shot on a virtual 3-D course would have settled if the shot had been taken on the real course from the view point of what a player actually playing on the course would see).
9. Claims 5, 8 are rejected under 35 U.S.C. 103 as being unpatentable over Marty et al. (US 2015/0332450 A1) (hereinafter Marty I) in view of Marty et al. (US 2007/0026975 A1) (hereinafter Marty II) and Mettler May et al. (US 2019/0009133 A1) in further view of Wurmlin et al. (US 2009/0315978 A1).
Regarding claim 5, Marty I in view of Marty II and Mettler May discloses the system of claim 1, Marty I in view of Marty II and Mettler May does not explicitly disclose wherein a three-dimensional (3D) position is determined by merging the video data and/or the image data using a least squares or other error reduction technique. However Wurmlin discloses wherein a three-dimensional (3D) position is determined by merging the video data and/or the image data using a least squares or other error reduction technique (Para[0020] teaches determining the 3D position of the objects by combining the information from the at least two video streams. Para[0178] teaches The minimum then serves as the new position of the bounding box and the differences between maximum and minimum in each direction is taken as the new width (in u-direction) and height (in v-direction) of the bounding box. The reference or anchor point 604 for the 3D position calculation method is taken). It would have been obvious to one having ordinary skill in the art before the effective filing date of the invention to use the method of manner of articulation of the various body parts may be determined to match movements of a particular player captured using the trajectory capture system, capture a position or an alignment of a player's torso, head, arms, etc., during and/or after a shot of Marty I in view of Marty II and Mettler May with method of movement of the objects in the two video streams is tracked using the position information derived from the three dimensional (3D) position of the objects in the earlier instants in time of Wurmlin in order to provide a system which improves quality, speed and robustness of two dimensional tracking in video streams.
Regarding claim 8, Marty I in view of Marty II and Mettler May discloses the system of claim 1, Marty I in view of Marty II and Mettler May does not explicitly disclose wherein the motion curve is a multi-dimensional motion curve. However Wurmlin discloses wherein the motion curve is a multi-dimensional motion curve (Para[0140] teaches the 2D trajectories of the tracked object based on just the tracked 2D positions is depicted as lines with arrows 311a,b for object A and B, resulting in an expected 2D position 312a,b in the previous frame 301 where another tracking algorithm would start searching for the object). It would have been obvious to one having ordinary skill in the art before the effective filing date of the invention to use the method of manner of articulation of the various body parts may be determined to match movements of a particular player captured using the trajectory capture system, capture a position or an alignment of a player's torso, head, arms, etc., during and/or after a shot of Marty I in view of Marty II and Mettler May with method of movement of the objects in the two video streams is tracked using the position information derived from the three dimensional (3D) position of the objects in the earlier instants in time of Wurmlin in order to provide a system which improves quality, speed and robustness of two dimensional tracking in video streams.
10. Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Marty et al. (US 2015/0332450 A1) (hereinafter Marty I) in view of Marty et al. (US 2007/0026975 A1) (hereinafter Marty II) and Mettler May et al. (US 2019/0009133 A1) in further view of Marty et al. (US 2024/0087137 A1) (hereinafter Marty III).
Regarding claim 6, Marty I in view of Marty II and Mettler May discloses the system of claim 1, Marty I in view of Marty II and Mettler May does not explicitly disclose wherein three-dimensional (3D) coordinates of pre-registered references spots are known, and wherein when a pre-registered reference spot is captured in an image, coordinates of the image are automatically converted to the 3D coordinates. However Marty III discloses wherein three-dimensional (3D) coordinates of pre-registered references spots are known, and wherein when a pre-registered reference spot is captured in an image, coordinates of the image are automatically converted to the 3D coordinates (Para[0151] & Fig. 9 teaches In 452, a reference point on the course for a particular shot may be selected and positions of identified objects relative to the reference point may be determine. In 454 and 456, the captured and derived information may be tracked over time allowing important parameters to be analyzed, including position, self and relative characteristics and identifiers. For Example, identifiers may be that Jim Smith takes 2nd shot on 3rd hole of Spyglass course from X1, Y1, Z1 position with 7-iron and ball goes to height of Y before bouncing 3 times and coming to rest in sand trap at position X2, Y2, Z2. In 458 and 460, digital capture of information allows views to be, aggregated, stored and searched via an interface of some type. For example, in 460, the interface may be used to perform a search, such as all shots taken by Jim Smith in the past 20 games with a 7-iron. Based on this query, relevant information could be automatically assembled for review, coaching and learning and displayed on the interface). It would have been obvious to one having ordinary skill in the art before the effective filing date of the invention to use the method of manner of articulation of the various body parts may be determined to match movements of a particular player captured using the trajectory capture system, capture a position or an alignment of a player's torso, head, arms, etc., during and/or after a shot of Marty I in view of Marty II and Mettler May with a method in which the sensors detect physical information used to characterize a trajectory of an object launched along its trajectory by a human of Marty III in order to provide a system allows the player to improve their trajectory skills for games requiring such skills where training devices are non-intrusive, operable in an environment that approximates actual playing conditions, simple to set-up and operate and provides immediate and objective feedback to the user of the device.
11. Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Marty et al. (US 2015/0332450 A1) (hereinafter Marty I) in view of Marty et al. (US 2007/0026975 A1) (hereinafter Marty II) and Mettler May et al. (US 2019/0009133 A1) in further view of Stirling et al. (US 7602301 B1)
Regarding claim 7, Marty I in view of Marty II and Mettler May discloses the system of claim 1, Marty I in view of Marty II and Mettler May does not explicitly disclose wherein a database stores in-the-field biometric attributes including a heart rate, breathing rate, a perspiration level, a blood pressure, a galvanic skin response, a topical temperature , and/or cranial electrical activity of a sports participant recorded at the same time as a play action activity. However Stirling discloses wherein a database stores in-the-field biometric attributes including a heart rate, breathing rate, a perspiration level, a blood pressure, a galvanic skin response, a topical temperature , and/or cranial electrical activity of a sports participant recorded at the same time as a play action activity. (col 10 lines 4-17 teaches sensors 130 can gather data relating to various physical characteristics, positions, changes, performance, or properties of the subject. This data can be referred to as "biometric" data. Biometric data includes biomedical and biomechanical data, and can include any of the following: data tracing the trajectory, speed, acceleration, position, orientation, etc. of a subject's appendage or other body part; data showing the heart rate, blood pressure, temperature, stress level, moisture content, toxin level, viability, respiration rate, etc. of a subject; data showing whether or not a subject is performing a signal or communication movement (e.g., teeth closed, arm cocked, etc.); data showing the posture or other status of a subject (e.g., prone or erect, breathing or not, moving or not); data showing the emotional state of a subject; etc. For example, the sensors can track movement of the subject and/or tension in the subject's muscles). It would have been obvious to one having ordinary skill in the art before the effective filing date of the invention to use the method of manner of articulation of the various body parts may be determined to match movements of a particular player captured using the trajectory capture system, capture a position or an alignment of a player's torso, head, arms, etc., during and/or after a shot of Marty I in view of Marty II and Mettler May with the method in which the biometric data gathered of Stirling in order to provide a system in which the digital assessment tools are provided to evaluate performance to the user.
12. Claims 16, 17, 20 are rejected under 35 U.S.C. 103 as being unpatentable over Marty et al. (US 2015/0332450 A1) (hereinafter Marty I) in view of Mettler May et al. (US 2019/0009133 A1).
Regarding claim 16, Wurmlin discloses a system for determining performance attributes comprising: at least one processor configured for network communication with at least one image capturing device (Abstract & para[0124]–[0125] teaches the system 100 may be implemented on a general purpose data processing device or computer comprising a processing unit. Para[0128] teaches a data acquisition method 102 which captures (digitizes) these two or more video streams 120 and observing the same 3D scene (102)), and at least one server computer and/or at least one local computing device (Para[0125] teaches the system 100 may further comprise or be linked to a resource data module 110 and an environment data module 113); wherein a first image capturing device of the at least two image capturing device generates video data and/or image data including an individual (Abstract & Figs. 3-4 & Para[0138] –[0143] teaches tracking the movement of objects (310a,b, 312a,b; 330a,b, 331 a,b, 332a,b; 410a,b, 411a,b; 430a,b, 431a,b; 420a,b, 421 a,b) in the at least two video streams (104); determining the identity of the objects in the at least two video streams (105), para[0025] teaches In a sports setting, objects are e.g. players, a ball and a referee); wherein the at least one processor is operable to: determine the location of the individual based on the video data and/or the image data (Abstract & Figs. 3-4 & Para[0138] –[0143] teaches determining the 3D position of the objects by combining the information from the at least two video streams (106); wherein the step of tracking (104) the movement of objects in the at least two video streams uses position information derived from the 3D position of the objects in one or more earlier instants in time. Para[0047] teaches Identifying an object in an image means that an object, as seen in an image, is associated with being "referee" or "ball" or "player Vroomfondel" or another player etc. Ideally, identifying an object can be done by clicking on (or otherwise selecting) an object in only one of the still images. Assuming that all objects are located at the level of the playing field, the 3D position of the selected object on the playing field is determined by intersecting the vector pointing from the camera to the object position as seen by the camera with the plane of the playing field); determine a common timing reference for the video data and/or the image data (Para[0191] teaches 3D position calculation method 107 assigns each modeled object a 3D position for each time (video frame) and outputs these 3D positions of all objects 128, para[0147] teaches the object identification method 105 associates, for each visible object in each video stream, the object's 2D position and shape in the color texture data 123 with a real object (e.g. players, goalkeepers, referees, ball, etc.) based on the camera calibration data 122, the information on the real-world objects 132 contained in a resource data module (or simply "resource") 110, and possibly also the extrapolated 3D object position 130 and the 2D position and shape 123 for essentially all objects in all frames of all cameras provided by the tracking method 104, Para[0148] teaches In the course of the initialization for the frame at time t_init, the user associates each 2D position and shape information 123 of one camera with a specific real-world object 132 which all are known previously (player names, goalkeeper names, referees, ball, etc.) from the resource 110.); intertwine the video data and/or the image data such that the video data and/or the image data are approximately disposed temporally along the common timing reference (Abstract teaches determining the 3D position of the objects by combining the information from the at least two video streams (106); wherein the step of tracking (104) the movement of objects in the at least two video streams uses position information derived from the 3D position of the objects in one or more earlier instants in time. Para[0148] teaches In the course of the initialization for the frame at time t_init, the user associates each 2D position and shape information 123 of one camera with a specific real-world object 132 which all are known previously (player names, goalkeeper names, referees, ball, etc.) from the resource 110. For the other camera frames at time t_init, the object identification method preferably automatically suggests the identification (e.g. a name) by carrying out the following steps:).
Wurmlin does not explicitly disclose wherein a second image capturing device of the at least two image capturing devices is configured to generate video data and/or image data of an environment and detect one or more environmental conditions; and correlate the one or more environmental conditions to one or more performance attributes of the individual. However Mettler May discloses wherein a second image capturing device of the at least two image capturing devices is configured to generate video data and/or image data of an environment and detect one or more environmental conditions (Para[0627] teaches use of video cameras 70 that capture the broader agent behavior and the task environment 50); and correlate the one or more environmental conditions to one or more performance attributes of the individual (Para[0628] teaches category of performance data measurements, these so-called first-person cameras capture data related to the interaction of the subject 10, the tennis racket 20, the ball 30, as well as the motion of other participants such as the opponent 53, and other relevant environment elements such as the court 51 and net 52. Combined with measurements of the gaze direction or vector 81, video cameras on the subjects and/or environment make it possible to determine which elements or events the performers are attending to at any given time, or at specific instants of the performance such as during specific movement phases or phase transitions 26, 27 on the path 25, opponent behaviors, or task elements such as related to the ball trajectory 36). It would have been obvious to one having ordinary skill in the art before the effective filing date of the invention to use the method of movement of the objects in the two video streams is tracked using the position information derived from the three dimensional (3D) position of the objects in the earlier instants in time of Wurmlin with the method in which the interaction between a stroke motion and task and environment elements of Mettler May the of in order to provide system a uses movement skill assessment and diagnostics at distinct levels of the human movement system hierarchy to specify training goals for a user.
Regarding claim 17, Wurmlin discloses the system of claim 16, wherein the at least one processor is operable to develop a motion curve (para[0004] teaches of the ball and the players' heads are computed after manually specifying their image positions in a few key frames, Para[0140] teaches the 2D trajectories of the tracked object based on just the tracked 2D positions is depicted as lines with arrows 311a,b for object A and B, resulting in an expected 2D position 312a,b in the previous frame 301 where another tracking algorithm would start searching for the object).
Regarding claim 20, Wurmlin discloses the system of claim 16, wherein the at least one processor is operable to automatically determine the start and/or end of a play action activity and generate a unique ID label for the play action activity (Para[0045]-[0047] teaches a user selecting, in a first still image, of a first one of the video streams, one object and assigning it a unique identifier; and automatic identification assistance" reduces the work of identifying each object in each of the set of still images (one for each video stream, and under the precondition that all images are taken at the same time). Identifying an object in an image means that an object, as seen in an image, is associated with being "referee" or "ball" or "player Vroomfondel" or another player etc. Ideally, identifying an object can be done by clicking on (or otherwise selecting) an object in only one of the still images. Assuming that all objects are located at the level of the playing field, the 3D position of the selected object on the playing field is determined by intersecting the vector pointing from the camera to the object position as seen by the camera with the plane of the playing field).
13. Claims 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Wurmlin et al. (US 2009/0315978 A1) in view of Mettler May et al. (US 2019/0009133 A1) and Marty et al. (US 2007/0026975 A1) (hereinafter Marty II)
Regarding claim 18, Wurmlin in view of Mettler May discloses the system of claim 17, Wurmlin in view of Mettler May does not explicitly disclose wherein the at least one processor is operable to identify frames and/or scan lines that correspond with spatial points along the motion curve. However Marty II discloses wherein the at least one processor is operable to identify frames and/or scan lines that correspond with spatial points along the motion curve (Para[0034] teaches determining a position of the object in a plurality of the video frames from the video frame data generating a curve-fit of the trajectory of the object from the determined positions, para[0086] & Fig. 2 ). Para[0059] teaches The captured video frames may show a sequence of states of the basketball 109 at different times along its trajectory 102. For instance, the camera 118 may capture 1) an initial state 105 of the trajectory shortly after the ball leaves the shooter's hand, 2) a number of states along the trajectory 102, such as 120, 121, 122 and 123 at times t.sub.1, t.sub.2, t.sub.3 and t.sub.4 and 3) a termination point 107 in the basketball hoop 103 , Para[0086] & Fig. 2 teaches once the position of the object is determined from each frame. A curve-fit for the trajectory may be developed in a computational space 205 with a coordinate system 216. In the figure, for illustrative purposes only, four points, 206, 207, 208 and 209 corresponding to times t.sub.1, t.sub.2, t.sub.3 and t.sub.4 are shown. Para[0088] teaches for instance, with enough position data near a particular location on the trajectory, such as the termination point 213, then an entry angle may be calculated by simply fitting a line through available data points near the termination points). It would have been obvious to one having ordinary skill in the art before the effective filing date of the invention to use the method of movement of the objects in the two video streams is tracked using the position information derived from the three dimensional (3D) position of the objects in the earlier instants in time of Wurmlin in view of Mettler May with the method of curve-fit may be used to generate trajectory parameters corresponding to different states along the object's trajectory such as an initial state of the trajectory, a final state of the trajectory or any of the states of the trajectory between the initial state and the final state of Marty II in order to provide a system allows the player to improve their trajectory skills for games requiring such skills where training devices are non-intrusive, operable in an environment that approximates actual playing conditions, simple to set-up and operate and provides immediate and objective feedback to the user of the device.
Regarding claim 19, Wurmlin in view of Mettler May and Marty II discloses the system of claim 17, Marty II further discloses wherein the at least one processor is operable to fit the motion curve with curve fit optimization techniques relative to the video data and/or the image data (Para[0014], [0034] teaches generate a curve fit from the physical information that approximates the object's trajectory and the trajectory parameters. In particular, the curve fit may be a parabolic arc. The curve-fit may be used to generate trajectory parameters corresponding to different states along the object's trajectory such as an initial state of the trajectory, a final state of the trajectory or any of the states of the trajectory between the initial state and the final state, Para[0086] & Fig. 2 teaches curve-fit for the trajectory) where a subset of the video data and/or the image data is given greater weight due to closeness and/or a better point of view (POV) (Paras[0076] teaches to provide measures of variability of different data sets representing different playing conditions, the system 100 may divide the trajectory data into different subsets, such as grouping according to types of shots, locations of shots, shots where the shooter is guarded, shots where the shooter is unguarded, made shots, swished shots, missed shots, shots made earlier in the session versus shots made later in the session, and combinations of these groupings. Para[0148]-[0149] teaches after or during a training session, the trajectory device 722 may analyze the shots, group them in some manner and then determine whether there are characteristics of each group related to a player's body mechanics that distinguish one group from the other group. during a training session, the trajectory device 702 may group a first number of shots a player has taken as good shots (e.g., shots with an arc within a proscribed range and made) and group a second number of shots as bad shots (e.g., shots with an arc out of a proscribed range that were made or missed. Para[0147] teaches the trajectory device 722 may determine an arc of a player's foot/ or knee to determine whether the player move upwards in a vertically aligned direction or in a direction off of vertical. In another example, the trajectory device may measure an arc of a player's elbow or hand to determine whether the player's arms is imparting sideways momentum to the ball and to determine whether the player's is properly following through on the shot). It would have been obvious to one having ordinary skill in the art before the effective filing date of the invention to use the method of movement of the objects in the two video streams is tracked using the position information derived from the three dimensional (3D) position of the objects in the earlier instants in time of Wurmlin in view of Mettler May with the method of curve-fit may be used to generate trajectory parameters corresponding to different states along the object's trajectory such as an initial state of the trajectory, a final state of the trajectory or any of the states of the trajectory between the initial state and the final state of Marty II in order to provide a system allows the player to improve their trajectory skills for games requiring such skills where training devices are non-intrusive, operable in an environment that approximates actual playing conditions, simple to set-up and operate and provides immediate and objective feedback to the user of the device.
Conclusion
14. THIS ACTION IS MADE FINAL. 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 ROWINA J CATTUNGAL whose telephone number is (571)270-5922. The examiner can normally be reached Monday-Thursday 7:30am-6pm.
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/ROWINA J CATTUNGAL/Primary Examiner, Art Unit 2425
