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 .
DETAILED ACTION
This Office Action is in response to the Amendment After Non-Final Rejection filed 07/17/2026. Claims 1-20 are pending and have been examined.
Response to Arguments
Applicant’s arguments with respect to claims 1-20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-5, 8-12 and 16-19 are rejected under 35 U.S.C. 103 as being unpatentable over Eledath et al. (US 2013/0182119) in view of Pedagadi et al. (US 2023/0056531), herein Pedagadi.
Consider claim 1, Eledath clearly teaches a method for video generation in a sports event, (Fig. 6) the method comprising:
receiving, via a computer, a plurality of sports event video feeds; (Figs. 1, 2: Video capture module 22 receives video content from a plurality of cameras 24 at a venue, [0029], [0030].)
calibrating, via the computer, the plurality of sports event video feeds; (Fig. 13: Highlighted landmarks 118 are identified in each of the camera views, [0092].)
generating, via the computer, a panoramic video feed, wherein the panoramic video feed is generated by stitching together the calibrated plurality of sports event video feeds; (Figs. 6, 13: Landmarks 118 are used to stitch the camera views into a panoramic video by panoramic view generation module 54 and rectification module 56, [0069]-[0072], [0092], [0092].)
obtaining, via the computer, tracking data for at least one asset in the sports event; (Fig. 6: The motion analysis module 58, object detection and shape and appearance analysis module 60, and activity analysis module 62 track motion of objects in the video, [0073].) and
generating, via the computer, a tactical video feed based on the tracking data, wherein the tactical video feed is a subset of the panoramic video feed selected based on one or more video parameters. (Fig. 6: Region-of-interest (ROI) selector 63 uses the object tracking information to select a ROI in the panoramic video, [0068], [0078].)
However, Eledath does not explicitly teach obtaining, via a machine learning model and the computer, tracking data for at least one asset in the sports event; and.
In an analogous art, Pedagadi, which discloses a system for image processing, clearly teaches obtaining, via a machine learning model and the computer, tracking data for at least one asset in the sports event; and. (Fig. 3: Artificial intelligence models 116 generate tracking data 204 from panoramic video stream 202, [0023], [0025]-[0027], [0038], [0039].)
Therefore, before the effective filing date of the claimed invention, it would have been obvious to one with ordinary skill in the art to modify the system of Eledath by obtaining, via a machine learning model and the computer, tracking data for at least one asset in the sports event; and, as taught by Pedagadi, for the benefit of more effectively obtaining tracking data.
Consider claim 2, Eledath combined with Pedagadi clearly teaches the tracking data comprises tracking data for at least one player in the sports event. (Players in various sporting events can be tracked, [0080] Eledath.)
Consider claim 3, Eledath combined with Pedagadi clearly teaches calibrating the plurality of sports event video feeds further comprises: identifying, via the computer, common points between the plurality of sports event video feeds; and calculating, via the computer, a camera homography between each of the plurality of sports event video feeds, wherein the camera homography is based on the identified common points. (Fig. 13: Homography between the camera views is computed using the landmarks 118, [0092], [0093] Eledath.)
Consider claim 4, Eledath combined with Pedagadi clearly teaches the tactical video feed is dynamically updated, via the computer, based on updates to the tracking data. (A live video feed is analyzed to determine a ROI based on objects tracked in the live video, [0068], claim 13 Eledath.)
Consider claim 5, Eledath combined with Pedagadi clearly teaches generating a panoramic video feed further comprises: obtaining, via the computer, color data for each of the calibrated plurality of sports event video feeds; calculating, via the computer, a color balancing solution based on the color data for each of the calibrated plurality of sports event video feeds; and applying, via the computer, a color calibration to the panoramic video feed, wherein the color calibration is based on the color balancing solution. (Fig. 14: A white calibration object is placed in the venue and imaged by each camera to perform color correction for the panoramic video, [0070], [0095] Eledath.)
Consider claim 8, Eledath clearly teaches a system for video generation in a sports event, (Fig. 2) the system comprising:
a non-transitory computer readable medium configured to store processor-readable instructions; and a processor operatively connected to the non-transitory computer readable medium, and configured to execute the instructions to perform operations ([0033]) comprising:
receiving a plurality of sports event video feeds; (Figs. 1, 2: Video capture module 22 receives video content from a plurality of cameras 24 at a venue, [0029], [0030].)
calibrating the plurality of sports event video feeds; (Fig. 13: Highlighted landmarks 118 are identified in each of the camera views, [0092].)
generating a panoramic video feed, wherein the panoramic video feed is generated by stitching together the calibrated plurality of sports event video feeds; (Figs. 6, 13: Landmarks 118 are used to stitch the camera views into a panoramic video by panoramic view generation module 54 and rectification module 56, [0069]-[0072], [0092], [0092].)
obtaining tracking data for at least one asset in the sports event; (Fig. 6: The motion analysis module 58, object detection and shape and appearance analysis module 60, and activity analysis module 62 track motion of objects in the video, [0073].) and
generating a tactical video feed based on the tracking data, wherein the tactical video feed is a subset of the panoramic video feed selected based on one or more video parameters. (Fig. 6: Region-of-interest (ROI) selector 63 uses the object tracking information to select a ROI in the panoramic video, [0068], [0078].)
However, Eledath does not explicitly teach obtaining, via a machine learning model and the computer, tracking data for at least one asset in the sports event; and.
In an analogous art, Pedagadi, which discloses a system for image processing, clearly teaches obtaining, via a machine learning model and the computer, tracking data for at least one asset in the sports event; and. (Fig. 3: Artificial intelligence models 116 generate tracking data 204 from panoramic video stream 202, [0023], [0025]-[0027], [0038], [0039].)
Therefore, before the effective filing date of the claimed invention, it would have been obvious to one with ordinary skill in the art to modify the system of Eledath by obtaining, via a machine learning model and the computer, tracking data for at least one asset in the sports event; and, as taught by Pedagadi, for the benefit of more effectively obtaining tracking data.
Consider claim 9, Eledath combined with Pedagadi clearly teaches the tracking data comprises tracking data for at least one player in the sports event. (Players in various sporting events can be tracked, [0080] Eledath.)
Consider claim 10, Eledath combined with Pedagadi clearly teaches calibrating the plurality of sports event video feeds further comprises: identifying common points between the plurality of sports event video feeds; and calculating a camera homography between each of the plurality of sports event video feeds, wherein the camera homography is based on the identified common points. (Fig. 13: Homography between the camera views is computed using the landmarks 118, [0092], [0093] Eledath.)
Consider claim 11, Eledath combined with Pedagadi clearly teaches the tactical video feed is dynamically updated, via the computer, based on updates to the tracking data. (A live video feed is analyzed to determine a ROI based on objects tracked in the live video, [0068], claim 13 Eledath.)
Consider claim 12, Eledath combined with Pedagadi clearly teaches generating a panoramic video feed further comprises: obtaining color data for each of the calibrated plurality of sports event video feeds; calculating a color balancing solution based on the color data for each of the calibrated plurality of sports event video feeds; and applying a color calibration to the panoramic video feed, wherein the color calibration is based on the color balancing solution. (Fig. 14: A white calibration object is placed in the venue and imaged by each camera to perform color correction for the panoramic video, [0070], [0095] Eledath.)
Consider claim 15, Eledath clearly teaches a non-transitory computer readable medium configured to store processor-readable instructions, which when executed by a processor, cause a computing system to perform operations ([0033]) comprising:
receiving a plurality of sports event video feeds; (Figs. 1, 2: Video capture module 22 receives video content from a plurality of cameras 24 at a venue, [0029], [0030].)
calibrating the plurality of sports event video feeds; (Fig. 13: Highlighted landmarks 118 are identified in each of the camera views, [0092].)
generating a panoramic video feed, wherein the panoramic video feed is generated by stitching together the calibrated plurality of sports event video feeds; (Figs. 6, 13: Landmarks 118 are used to stitch the camera views into a panoramic video by panoramic view generation module 54 and rectification module 56, [0069]-[0072], [0092], [0092].)
obtaining tracking data for at least one asset in the sports event; (Fig. 6: The motion analysis module 58, object detection and shape and appearance analysis module 60, and activity analysis module 62 track motion of objects in the video, [0073].) and
generating a tactical video feed based on the tracking data, wherein the tactical video feed is a subset of the panoramic video feed selected based on one or more video parameters. (Fig. 6: Region-of-interest (ROI) selector 63 uses the object tracking information to select a ROI in the panoramic video, [0068], [0078].)
However, Eledath does not explicitly teach obtaining, via a machine learning model and the computer, tracking data for at least one asset in the sports event; and.
In an analogous art, Pedagadi, which discloses a system for image processing, clearly teaches obtaining, via a machine learning model and the computer, tracking data for at least one asset in the sports event; and. (Fig. 3: Artificial intelligence models 116 generate tracking data 204 from panoramic video stream 202, [0023], [0025]-[0027], [0038], [0039].)
Therefore, before the effective filing date of the claimed invention, it would have been obvious to one with ordinary skill in the art to modify the system of Eledath by obtaining, via a machine learning model and the computer, tracking data for at least one asset in the sports event; and, as taught by Pedagadi, for the benefit of more effectively obtaining tracking data.
Consider claim 16, Eledath combined with Pedagadi clearly teaches the tracking data comprises tracking data for at least one player in the sports event. (Players in various sporting events can be tracked, [0080] Eledath.)
Consider claim 17, Eledath combined with Pedagadi clearly teaches calibrating the plurality of sports event video feeds further comprises: identifying common points between the plurality of sports event video feeds; and calculating a camera homography between each of the plurality of sports event video feeds, wherein the camera homography is based on the identified common points. (Fig. 13: Homography between the camera views is computed using the landmarks 118, [0092], [0093] Eledath.)
Consider claim 18, Eledath combined with Pedagadi clearly teaches the tactical video feed is dynamically updated, via the computer, based on updates to the tracking data. (A live video feed is analyzed to determine a ROI based on objects tracked in the live video, [0068], claim 13 Eledath.)
Consider claim 19, Eledath combined with Pedagadi clearly teaches generating a panoramic video feed further comprises: obtaining color data for each of the calibrated plurality of sports event video feeds; calculating a color balancing solution based on the color data for each of the calibrated plurality of sports event video feeds; and applying a color calibration to the panoramic video feed, wherein the color calibration is based on the color balancing solution. (Fig. 14: A white calibration object is placed in the venue and imaged by each camera to perform color correction for the panoramic video, [0070], [0095] Eledath.)
Claims 6 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Eledath et al. (US 2013/0182119) in view of Pedagadi et al. (US 2023/0056531) in view of Yamauchi (US 2022/0172401).
Consider claim 6, Eledath combined with Pedagadi clearly teaches the color data is obtained via the computer. (Fig. 14, [0095])
However, Eledath combined with Pedagadi does not explicitly teach the color data is obtained, via the computer, prior to a start of the sports event.
In an analogous art, Yamauchi, which discloses a system for image processing, clearly teaches the color data is obtained, via the computer, prior to a start of the sports event. (A background image is captured prior to the start of a game and used for color correction, [0053].)
Therefore, before the effective filing date of the claimed invention, it would have been obvious to one with ordinary skill in the art to modify the system of Eledath combined with Pedagadi by the color data is obtained, via the computer, prior to a start of the sports event, as taught by Yamauchi, to achieve the predictable result of calibrating the color of the images.
Consider claim 13, Eledath combined with Pedagadi and Yamauchi clearly teaches the color data is obtained, via the computer, prior to a start of the sports event. (A background image is captured prior to the start of a game and used for color correction, [0053] Yamauchi.)
Claims 7, 14 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Eledath et al. (US 2013/0182119) in view of Pedagadi et al. (US 2023/0056531) in view of Yu et al. (US 10,084,959), herein Yu.
Consider claim 7, Eledath combined with Pedagadi clearly teaches calculating a color balancing solution. (Fig. 14, [0095])
However, Eledath combined with Pedagadi does not explicitly teach extracting, via the computer, at least one prominent color from at least one of the calibrated plurality of sports event video feeds; matching, via the computer, the at least one prominent color to at least one prominent color from a different calibrated sports event video feed to generate at least one matched color pair; and incorporating, via the computer, the at least one matched color pair into the color data.
In an analogous art, Yu, which discloses a system for image processing, clearly teaches extracting, via the computer, at least one prominent color from at least one of the calibrated plurality of sports event video feeds; matching, via the computer, the at least one prominent color to at least one prominent color from a different calibrated sports event video feed to generate at least one matched color pair; and incorporating, via the computer, the at least one matched color pair into the color data. (Fig. 1A: Colors representing the same object, e.g. different shades of blue representing the sky, in adjacent frames are matched and used for color correction processing, col. 8 lines 4-21, col. 23 lines 29-49.)
Therefore, before the effective filing date of the claimed invention, it would have been obvious to one with ordinary skill in the art to modify the system of Eledath combined with Pedagadi by extracting, via the computer, at least one prominent color from at least one of the calibrated plurality of sports event video feeds; matching, via the computer, the at least one prominent color to at least one prominent color from a different calibrated sports event video feed to generate at least one matched color pair; and incorporating, via the computer, the at least one matched color pair into the color data, as taught by Yu, for the benefit of smoothing the transition between adjacent frames of the panoramic video.
Consider claim 14, Eledath combined with Pedagadi and Yu clearly teaches calculating a color balancing solution further comprises: extracting at least one prominent color from at least one of the calibrated plurality of sports event video feeds; matching the at least one prominent color to at least one prominent color from a different calibrated sports event video feed to generate at least one matched color pair; and incorporating the at least one matched color pair into the color data. (Fig. 1A: Colors representing the same object, e.g. different shades of blue representing the sky, in adjacent frames are matched and used for color correction processing, col. 8 lines 4-21, col. 23 lines 29-49 Yu.)
Consider claim 20, Eledath combined with Pedagadi and Yu clearly teaches calculating a color balancing solution further comprises: extracting at least one prominent color from at least one of the calibrated plurality of sports event video feeds; matching the at least one prominent color to at least one prominent color from a different calibrated sports event video feed to generate at least one matched color pair; and incorporating the at least one matched color pair into the color data. (Fig. 1A: Colors representing the same object, e.g. different shades of blue representing the sky, in adjacent frames are matched and used for color correction processing, col. 8 lines 4-21, col. 23 lines 29-49 Yu.)
Conclusion
In the case of amending the claimed invention, applicant is respectfully requested to indicate the portion(s) of the specification which dictate(s) the structure relied on for proper interpretation and also to verify and ascertain the metes and bounds of the claimed invention.
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 JOHN R SCHNURR whose telephone number is (571)270-1458. The examiner can normally be reached M-F 6a-4p.
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/JOHN R SCHNURR/ Primary Examiner, Art Unit 2425
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