DETAILED ACTION
This Office action for U.S. Patent Application No. 17/502,594 is responsive to communications filed 27 April 2026, in reply to the Non-Final Rejection of 30 January 2026.
Claims 1–20 are pending.
In the previous Office action, claims 1–7 and 13–18 were rejected under 35 U.S.C. § 102(a)(1) as anticipated by U.S. Patent Application Publication No. 2013/0129192 A1 (“Wang”). Claims 8–12, 19, and 20 were rejected under 35 U.S.C. § 103 as obvious over Wang in view of U.S. Patent Application Publication No. 2020/0228774 A1 (“Kar”).
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
Applicant's arguments filed with respect to the independent claims have been considered but are moot in view of new grounds of rejection. The new issues of the apparent incompatibility of the second image being generated “independently of modifying” a first image while also being based on a transformation of the first image aside (§ 112(b) rejections infra), the previous claim set gave details of this transformation and generation in the dependent claims, including claim 12 rejected over a combination of Wang and Kar. Applicant does not mention Kar at all, much less interact with the finding in the previous Office action that Kar at paragraph 0051 teaches reconstructing images with novel views by using a convolutional neural network to blend images. Even under the most charitable reading of the claims possible, the amendments are not patentable over a combination of Wang and Kar.
Claim Rejections - 35 U.S.C. § 112
The following is a quotation of 35 U.S.C. § 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claims 1, 15, and 16 are rejected under 35 U.S.C. § 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, regards as the invention.
Each of independent claims 1, 15, and 16 recites “generating . . . [a] second image of [an] object from [a] virtual camera position based on the first image of the object and [a] second plurality of transformations, wherein the second image is generated from the virtual camera position independently of modifying any specific actual captured frame toward a smoothed camera position”. The claims also recite the second transforms are transforms of coordinates of the first image, which are locations in the first image. It is unclear how the second image can be generated ex nihilo without use of the first image as required while at the same time being based on the first image and its transforms, or how the claimed second transforms are not modifications of the first image. The appeal to paragraphs 0104 and 0107 in the specification does not help. For example, in Figure 9, how is the frame at virtual position 918 generated independently of and not modifying the frame at actual camera position 910, whilst also being based on a transform of the frame at the actual camera position? Applicant’s explanation of the claims in their current form is also required to show how the claimed amendment satisfies the enablement and written description requirements under 35 U.S.C. § 112(a).
Claim Rejections - 35 U.S.C. § 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 absent any evidence to the contrary. Applicant is advised of the obligation under 37 C.F.R. § 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 1–20 are rejected under 35 U.S.C. § 103 as being unpatentable over U.S. Patent Application Publication No. 2013/0129192 A1 (“Wang”) in view of U.S. Patent Application Publication No. 2020/0228774 A1 (“Kar”).
Applicant has provided evidence in this file showing that the claimed invention and the subject matter disclosed in the “Kar” prior art reference were owned by, or subject to an obligation of assignment to, the same entity Fyusion, Inc. not later than the effective filing date of the claimed invention, or the subject matter disclosed in the prior art reference was developed and the claimed invention was made by, or on behalf of one or more parties to a joint research agreement in effect not later than the effective filing date of the claimed invention. However, although Kar has been excepted as prior art under 35 U.S.C. § 102(a)(2), because it names additional inventors, it is still applicable as prior art under 35 U.S.C. § 102(a)(1) that cannot be excepted under 35 U.S.C. § 102(b)(2)(C). M.P.E.P. § 2153.01(a).
Applicant may rely on the exception under 35 U.S.C. § 102(b)(1)(A) to overcome this rejection using a reference that qualifies as prior art under 35 U.S.C. § 102(a)(1) by a showing under 37 C.F.R. § 1.130(a) that the subject matter disclosed in the reference was obtained directly or indirectly from the inventor or a joint inventor of this application, and is therefore not prior art under 35 U.S.C. § 102(a)(1). M.P.E.P. §§ 2153.01, 2155.01, 717.01(a). Alternatively, applicant may rely on the exception under 35 U.S.C. § 102(b)(1)(B) by providing evidence of a prior public disclosure via an affidavit or declaration under 37 C.F.R. § 1.130(b). M.P.E.P. §§ 2153.02, 2155.02, 717.01(b).
Wang, directed to converting 2-D video to 3-D video, teaches with respect to claim 1 a method comprising:
projecting via a processor a plurality of three-dimensional points onto first locations in a first image of an object captured from a first position in three-dimensional space relative to the object (¶¶ 0045, 0058; extract 3-D point cloud of scene),
the first position associated with a first actual camera position along a path of actual camera positions (Figs. 4–5, ¶¶ 0070–71; input camera path 420, input camera path graph 480) . . . ;
projecting via a processor the plurality of three-dimensional points onto second locations a virtual camera position located at a second position at a second position in three-dimensional space relative to the object (¶ 0084, warping 530 image 530 to target viewpoint 520),
the second position corresponding to a location along a smoothed trajectory for a plurality of virtual camera positions (Figs. 4–5, ¶¶ 0070–71, smoothed camera path 430, smoothed camera path graph 485),
wherein a plurality of locations along the smoothed trajectory are determined by using rotational path modeling comprising determining an optimization function having a loss function (¶ 0069, directly measuring the camera position using position sensors along with a motion algorithm structure to estimate camera positions; ¶¶ 0070–71, smoothing camera path from input camera positions, e.g., using a cubic smoothing spline described as “well-known in the art”; see D.S.G. Pollock, “Smoothing with Cubic Splines”, in Handbook of Time Series Analysis, Signal Processing, and Dynamics 293-322 (1999) as demonstration that cubic smoothing spline minimizes curvature and norm from data points, as a valid optimized regression curve)
and minimizing one more loss functions (¶ 0071, smoothing spline function “well-known in the art” is a curve known to minimize norm from data points);
determining via a processor a first plurality of transformations, each of the first plurality of transformations linking a respective one of the first locations with a respective one of the second locations (¶ 0090, equation 1 of pixel position from image frame to virtual view);
based on the plurality of transformations, determining via the processor a second plurality of transformations transforming first coordinates for the first image of the object to second coordinates for a second image of the object so that the second image corresponds to a view along the smoothed trajectory (¶ 0091, pixel correspondence function using the Equation 1 transformation relates the pixel coordinates in a video frame to a corresponding warped image with the target viewpoint); and
generating via the processor the second image of the object from the virtual camera position to a second plurality of transformations (¶¶ 0084, 92; synthesizing the warped output image).
Claim 1 differs from Wang in that the claim specifies the illogical feat of generating the second images “independently of modifying any specific actual captured frame” while doing so “based on the first image” and a “second plurality of transforms” therefrom, wherein the “second plurality of transforms transform[ ] first coordinates for the first image of the object to second coordinates for [the] second image of the object” (that is, a modification of the first image). This application already has three Requests for Continued Examination, and probably one thing agreeable to Applicant and the examiner is that a fourth solely to resolve outstanding and latent Section 112 issues before resuming examination on the prior art would waste time. To the examiner’s best guess, the closest the specification comes to this final limitation is in paragraph 0160, which allows for generating synthetic intermediate views between stabilized frames. Wang produces the stabilized frames themselves (¶¶ 0074–76), but not these synthetic intermediate views as given in the specification and possibly attempted to be claimed. However, Kar, directed to free-viewpoint image synthesis, teaches with respect to claim 1,
wherein the second image is generated from the virtual camera position as a synthetic intermediate view representing a smoothed camera position (Kar ¶ 0051, using a neural network to blend renderings of neighboring multi-plane images to reconstruct and synthesize novel views).
It would have been obvious to one of ordinary skill in the art at the time of effective filing to further generate intermediate novel view frames from the original images as a part of generating the smoothed trajectory, as taught by Kar, in order to avoid jumps between stabilized frames. Kar ¶ 0178.
Regarding claim 2, Wang in view of Kar teaches the method of claim 1, wherein the first coordinates correspond to a first two-dimensional mesh overlain on the first image of the object (Wang ¶ 0091, pixel coordinates of ith video frame),
and wherein the second coordinates correspond to a two-dimensional mesh overlain on the second image of the object (id., pixel coordinates of corresponding warped image Wi).
Regarding claim 3, Wang in view of Kar teaches the method of claim 1, wherein the first image of the object is one of a first plurality of images captured by a camera moving along an input path through space around the object (Wang Fig. 5, input camera path 480),
and wherein the second image is one of a second plurality of images generated at respective virtual camera positions relative to the object (id., smoothed camera path 485).
Regarding claim 4, Wang in view of Kar teaches the method of claim 3, the method further comprising:
determining a smoothed path through space around the object based on the input path (Wang Fig. 5, camera path 485 is smoothed version of input camera path 480); and
determining the virtual camera position based on the smoothed path (¶¶ 0071–76, modifying viewpoint of digital image to be at smoothed camera position).
Regarding claim 5, Wang in view of Kar teaches the method of claim 1, wherein the plurality of three-dimensional points are determined at least in part via motion data captured from an inertial measurement unit at the mobile computing device (Wang ¶ 0056–57, camera location and position in space determined from external parameters from position sensor such as gyroscope, accelerometer, or GPS).
Regarding claim 6, Wang in view of Kar teaches the method of claim 1, wherein the motion data includes data selected from the group consisting of: accelerometer data, gyroscopic data, and global positioning system (GPS) data (Wang ¶ 0057, “Types of position sensors used in digital cameras commonly include gyroscopes, accelerometers and global positioning system (GPS) sensors”).
Regarding claim 7, Wang in view of Kar teaches the method of claim 1, wherein the plurality of three-dimensional points are determined at least in part based on depth sensor data captured from a depth sensor (Wang ¶ 0107, application to Microsoft Kinect imaging system that includes an RGB digital camera and a depth sensor).
Regarding claim 8, Wang in view of Kar teaches the method of claim 1, wherein the second plurality of transformations is generated via a neural network (Kar ¶ 0051, reconstructing image with novel view by using a convolutional neural network to blend images).
Regarding claim 9, Wang in view of Kar teaches the method of claim 8, wherein the first plurality of transformations are provided as reprojection constraints to the neural network (Kar ¶ 0174, points of interest are constrained in search in stabilization algorithm).
Regarding claim 10, Wang in view of Kar teaches the method of claim 8, wherein the neural network includes one or more similarity constraints that penalize deformation of first two-dimensional mesh via the second plurality of transformations (Wang ¶ 0047, 0060–62, candidate images must score above predefined similarity threshold).
Regarding claim 11, Wang in view of Kar teaches the method of claim 1, the method further comprising generating a multiview interactive digital media representation (MVIDMR) that includes the second set of images (Kar ¶¶ 0161–288, application to creating MVIDMR),
the MVIDMR being navigable in one or more dimensions along one or more smoothed trajectories (¶¶ 0122, 0124; apparent property of MVIDMR to be navigable between viewpoints).
Regarding claim 12, Wang in view of Kar teaches the method of claim 1, wherein the second image is generated via a neural network (Kar ¶ 0051, reconstructing image with novel view by using a convolutional neural network to blend images).
Regarding claim 13, Wang in view of Kar teaches the method of claim 1, wherein the processor is located within a mobile computing device that includes a camera, the first image being captured by the camera (Wang ¶¶ 0070–71, camera moves on path).
Regarding claim 14, Wang in view of Kar teaches the method of claim 1, wherein the processor is connected to a camera, the first image being captured by the camera (Wang ¶¶ 0070–71, camera moves on path).
Regarding claim 15, Wang in view of Kar teaches a non-transitory computer-readable storage medium . . . including instructions that when executed by a computer, cause the computer to (Wang ¶ 0120, implementation on non-transitory computer readable storage medium storing instructions for controlling a computer to practice the described method):
[perform the claim 1 method] (claim 1 rejection supra).
Regarding claim 16, Wang in view of Kar teaches a computing apparatus comprising:
a processor (Wang ¶ 0120, computer that practices the described method); and
a memory storing instructions that, when executed by the processor (id., storage medium storing instructions for controlling the computer to practice the method), configure the apparatus to:
[perform the claim 1 method] (claim 1 rejection supra).
Regarding claim 17, Wang in view of Kar teaches the computing apparatus of claim 16, wherein the first image of the object is one of a first plurality of images captured by a camera moving along an input path through space around the object (Wang Fig. 5, input camera path 480),
and wherein the second image is one of a second plurality of images generated at respective virtual camera positions relative to the object (id., smoothed camera path 485).
Regarding claim 18, Wang in view of Kar teaches the computing apparatus of claim 17, wherein the instructions further configure the apparatus to:
determine a smoothed path through space around the object based on the input path (Wang Fig. 5, camera path 485 is smoothed version of input camera path 480); and
determine the virtual camera position based on the smoothed path (¶¶ 0071–76, modifying viewpoint of digital image to be at smoothed camera position).
Regarding claim 19, Wang in view of Kar teaches the computing apparatus of claim 16, wherein the second plurality of transformations is generated via a neural network (Kar ¶ 0051, reconstructing image with novel view by using a convolutional neural network to blend images),
and wherein the first plurality of transformations are provided as reprojection constraints to the neural network (Kar ¶ 0174, points of interest are constrained in search in stabilization algorithm).
It would have been obvious to one of ordinary skill in the art at the time of effective filing to perform the Wang method using a neural network, since it has been held that updating prior art systems to use modern electronics is considered within the ordinary skill in the art. Leapfrog Enterprises v. Fisher-Price, Inc., 485 F.3d 1157, 1161–62 (Fed. Cir. 2007).
Regarding claim 20, Wang in view of Kar teaches the computing apparatus of claim 19, wherein the neural network includes one or more similarity constraints that penalize deformation of first two-dimensional mesh via the second plurality of transformations (Wang ¶¶ 0047, 0060–62; candidate images must score above predefined similarity threshold).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
US 2019/0114832 A1
US 2017/0278219 A1
WO 2019/175286 A1
The following prior art was found using an Artificial Intelligence assisted search using an internal AI tool that uses the classification of the application under the Cooperative Patent Classification (CPC) system, as well as from the specification, including the claims and abstract, of the application as contextual information. The documents are ranked from most to least relevant. Where possible, English-language equivalents are given, and redundant results within the same patent families are eliminated. See “New Artificial Intelligence Functionality in PE2E Search”, 1504 OG 359 (15 November 2022), “Automated Search Pilot Program”, 90 F.R. 48,161 (8 October 2025).
US 2020/0234466 A1
US 2017/0124680 A1
US 2022/0198750 A1
US 2017/0046868 A1
Applicant's amendment necessitated the new grounds of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See M.P.E.P. § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 C.F.R. § 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 C.F.R. § 1.17(a)) pursuant to 37 C.F.R. § 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 David N Werner whose telephone number is (571)272-9662. The examiner can normally be reached M--F 7:30--4:00 Central.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Dave Czekaj can be reached at 571.272.7327. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/David N Werner/Primary Examiner, Art Unit 2487
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