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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 11 May 2026 has been entered.
Response to Arguments
Applicant’s arguments have been fully considered but they are moot in view of the new grounds of rejection presented in this Office Action.
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, 3-5, 17, 18, 20-23, and 25 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang et al. (“NeRF++: Analyzing and Improving Neural Radiance Fields”; hereinafter “Zhang”) in view of Khademi et al. (“View Synthesis In Casually Captured Scenes Using a Cylindrical Neural Radiance Field With Exposure Compensation”; hereinafter “Khademi”), and further in view of Galera et al. (US 2015/0332463; hereinafter “Galera”).
Regarding claim 1, Zhang discloses A method comprising: determining sample points on a camera ray (“sample points from the ray origin … the number of samples per camera ray,” pg. 6, sec. 5, para. 2) based on view-generation information comprising a scene viewing-position and a scene-viewing direction (“the radiance field is parameterized by both 3D position and viewing direction,” pg. 2, sec. 2, para. 1); determining, based on a virtual … coordinate system defined by a center with a radius (“unit sphere,” pg. 5, para. 1), location statuses of the respective sample points; determining whether the location status of a corresponding sample point among the sample points is foreground or background (“partition the scene space into two volumes, an inner unit sphere and an outer volume … The inner volume contains the foreground and all the cameras, while the outer volume contains the remainder of the environment,” pg. 5, para. 1; “the ray
r
=
o
+
t
d
is partitioned into two segments by the unit sphere … inside the sphere … outside the sphere,” pg. 5, para. 3); projecting and rendering a value of a pixel corresponding to the camera ray by, according to the location statuses, applying the view-generation information to a first neural network to generate a foreground image and to a second neural network to generate a background image, wherein the first neural network has been trained to generate foreground images and the second neural network has been trained to generate background images (“partition the scene space into two volumes, an inner unit sphere and an outer volume … The inner volume contains the foreground and all the cameras, while the outer volume contains the remainder of the environment. These two volumes are modelled with two separate NeRFs,” pg. 5, para. 1; Fig. 6 illustrates the claimed camera ray); and generating a rendered image based on blending the foreground image and the background image (“To render the color for a ray, they are raycast individually, followed by a final composition,” pg. 5, para. 2), wherein the determining of location statuses of the sample points comprises: determining a distance … between the center and the corresponding sample point (“
x
2
+
y
2
+
z
2
,” Zhang, pg. 5, para. 3) … and comparing the distance with the radius (“
x
2
+
y
2
+
z
2
> 1,” Zhang, pg. 5, para. 3), and wherein the determination of whether the location status of the corresponding sample point among the plurality of sample points is foreground or background is determined based on a result of comparing the distance with the radius (“inside the sphere … outside the sphere,” pg. 5, para. 3; “partition the scene space into two volumes,” pg. 5, paras. 1-2; “a 3D point
x
,
y
,
z
,
r
=
x
2
+
y
2
+
z
2
> 1 in the outer volume,” Zhang, pg. 5, para. 3).
Zhang discloses a spherical coordinate system, but does not disclose that the coordinate system is a cylindrical coordinate system.
In the same art of neural rendering, Khademi teaches partitioning a 3D space using a cylindrical coordinate system (“we introduce a new cylindrical parameterization that resembles the inverted sphere parameterization presented in [Zhang et al. 2020],” pg. 2, sec. 3, para. 2).
Before the effective filing date of the claimed invention, it would have been obvious to one having ordinary skill in the art to apply the cylindrical coordinate system of Khademi to Zhang. The motivation would have been that it “is more suitable to casual capture where the user spins in a circle and thus the top and bottom of the scene are unlikely to be observed” (Khademi, pg. 2, sec. 3, para. 2).
The combination of Zhang and Khademi does not disclose determining the distance in a two dimensional (2D) projection … by projecting the center and the corresponding sample point into an x, y plane of the virtual cylindrical coordinate system.
In the same art of computer graphics, Galera teaches determining a distance in a two dimensional (2D) projection … by projecting the center and the corresponding sample point into an x, y plane (“project the three-dimensional objects onto the X-Y plane resulting in two-dimensional projections. The system then calculates the minimum distance between the two-dimensional projections,” para. 77).
Before the effective filing date of the claimed invention, it would have been obvious to one having ordinary skill in the art to apply the teachings of Galera to the virtual cylindrical coordinate system of the combination of Zhang and Khademi. The motivation would have been “In order to simplify the calculation of distances” (Galera, para. 77).
Regarding claim 3, the combination of Zhang, Khademi, and Galera renders obvious determining the location status of the corresponding sample point as foreground based on the distance being smaller than the radius; and determining the location of the corresponding sample point as background based on the distance being greater than or equal to the radius (“a 3D point
x
,
y
,
z
,
r
=
x
2
+
y
2
+
z
2
> 1 in the outer volume … the ray
r
=
o
+
t
d
is partitioned into two segments by the unit sphere … inside the sphere … outside the sphere,” Zhang, pg. 5, para. 3; note that using a “greater than” or “greater than or equal to” operator to determine an interior or exterior of a bounding volume is considered a trivial and obvious variation).
Regarding claim 4, the combination of Zhang, Khademi, and Galera renders obvious applying the view-generation information to the first neural network in response to the location status of the corresponding sample point being determined to be foreground (“The inner volume contains the foreground and all the cameras … These two volumes are modelled with two separate NeRFs,” Zhang, pg. 5, para. 1).
Regarding claim 5, the combination of Zhang, Khademi, and Galera renders obvious based on the location status of the corresponding sample point being determined to be background, changing the view-generation information to comprise the inverse of the radius; and applying the changed view-generation information to the second neural network (“For the outer NeRF, we apply an inverted sphere parametrization,” Zhang, pg. 5, para. 2; “the inverse radius,” Zhang, pg. 5, para. 3; see Fig. 6).
Regarding claim 17, it is rejected using the same citations and rationales described in the rejection of claim 1, with the additional limitations of An apparatus comprising: processing hardware; storage hardware storing instructions configured to configure the processing hardware, which is taught by Zhang (“RTX 2080 Ti GPUs,” pg. 7, sec. 6, para. 1).
Regarding claim 18, the combination of Zhang, Khademi, and Galera renders obvious rendering an image based on blending a result of applying the first neural network and the second neural network (“To render the color for a ray, they are raycast individually, followed by a final composition,” Zhang, pg. 5, para. 2).
Regarding claim 20, it is rejected using the same citations and rationales described in the rejection of claim 3.
Regarding claim 21, it is rejected using the same citations and rationales described in the rejections of claims 4 and 5.
Regarding claim 22, Zhang discloses A method performed by a computing device, the method comprising: projecting camera rays from a virtual camera pose to a scene to determine sample points of the respective camera rays (“sample points from the ray origin … the number of samples per camera ray,” pg. 6, sec. 5, para. 2); according to a virtual [sphere] defining by a center with a radius (“unit sphere,” pg. 5, para. 1) and the virtual camera pose, determining first of the sample points as corresponding to a foreground of the scene and based thereon generating respective first pixel values of the scene by applying the virtual camera pose to a first neural network; according to the virtual [sphere] and the virtual camera pose, determining second of the sample points as corresponding to a background of the scene and based thereon generating respective second pixel values of the scene by applying a transform of the virtual camera pose to a second neural network (“partition the scene space into two volumes, an inner unit sphere and an outer volume represented by an inverted sphere covering the complement of the inner volume,” pg. 5, para. 1; “These two volumes are modelled with two separate NeRFs,” pg. 5, para. 2; “the ray
r
=
o
+
t
d
is partitioned into two segments by the unit sphere … inside the sphere … outside the sphere,” pg. 5, para. 3); and rendering an image of the scene by blending the first pixel values and the second pixel values (“To render the color for a ray, they are raycast individually, followed by a final composition,” pg. 5, para. 2), wherein the determination of the first of the sample points as corresponding to the foreground of the scene and the determination of the second of the sample points as corresponding to the background of the scene comprises: determining a distance … between the center and a corresponding point among the sample points (“
x
2
+
y
2
+
z
2
,” Zhang, pg. 5, para. 3) … comparing the distance with the radius (“
x
2
+
y
2
+
z
2
> 1,” Zhang, pg. 5, para. 3); and determining the corresponding sample point as corresponding to either the foreground or the background based on a result of comparing the distance with the radius (“inside the sphere … outside the sphere,” pg. 5, para. 3; “partition the scene space into two volumes,” pg. 5, paras. 1-2; “a 3D point
x
,
y
,
z
,
r
=
x
2
+
y
2
+
z
2
> 1 in the outer volume,” Zhang, pg. 5, para. 3).
Zhang discloses a spherical coordinate system, but does not disclose that the coordinate system is based on a cylinder.
In the same art of neural rendering, Khademi teaches partitioning a 3D space using a cylindrical coordinate system (“we introduce a new cylindrical parameterization that resembles the inverted sphere parameterization presented in [Zhang et al. 2020],” pg. 2, sec. 3, para. 2).
Before the effective filing date of the claimed invention, it would have been obvious to one having ordinary skill in the art to apply the cylindrical coordinate system of Khademi to Zhang. The motivation would have been that it “is more suitable to casual capture where the user spins in a circle and thus the top and bottom of the scene are unlikely to be observed” (Khademi, pg. 2, sec. 3, para. 2).
The combination of Zhang and Khademi does not disclose determining the distance in a two dimensional (2D) projection … by projecting the center and the corresponding sample point into an x, y plane of the virtual cylindrical coordinate system.
In the same art of computer graphics, Galera teaches determining a distance in a two dimensional (2D) projection … by projecting the center and the corresponding sample point into an x, y plane (“project the three-dimensional objects onto the X-Y plane resulting in two-dimensional projections. The system then calculates the minimum distance between the two-dimensional projections,” para. 77).
Before the effective filing date of the claimed invention, it would have been obvious to one having ordinary skill in the art to apply the teachings of Galera to the virtual cylindrical coordinate system of the combination of Zhang and Khademi. The motivation would have been “In order to simplify the calculation of distances” (Galera, para. 77).
Regarding claim 23, the combination of Zhang, Khademi, and Galera renders obvious wherein determining that a first sample point of the first sample points corresponds to the foreground corresponds to determining that the first sample point is inside the virtual cylinder, and wherein determining that a second sample point of the second sample points corresponds to the background corresponds to determining that the second sample point is outside the virtual cylinder (“the ray
r
=
o
+
t
d
is partitioned into two segments by the unit sphere … inside the sphere … outside the sphere,” Zhang, pg. 5, para. 3; “cylindrical parameterization,” Khademi, pg. 2, sec. 3, para. 2; see claim 22 for motivation to combine).
Regarding claim 25, the combination of Zhang, Khademi, and Galera renders obvious wherein the first neural network and the second neural network are trained by minimizing a loss function (“minimizing the discrepancy between the ground truth observed images … and the predicted images,” Zhang, pg. 2, sec. 2, para. 2) based on a 360-degree image (“training/testing images … of hand-held 360° captures,” Zhang, pg. 6, sec. 5, para. 2).
Claims 6-8 are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Zhang, Khademi, and Galera, and further in view of Rematas et al. (US 2023/0281913; hereinafter “Rematas”).
Regarding claim 6, the combination of Zhang, Khademi, and Galera renders obvious wherein the first neural network comprises: a neural network trained to output a color (“NeRF is an implicit MLP-based model that maps 5D vectors—3D coordinates plus 2D viewing directions—to opacity and color values,” Zhang, pg. 1, sec. 1, para. 2; “cylindrical parameterization,” Khademi, pg. 2, sec. 3, para. 2; see claim 1 for motivation to combine).
Zhang refers to “opacity” rather than a volume density.
In the same art of using separate NeRFs for foreground and background samples, Rematas teaches a neural network trained to output a color and volume density (“the predicted view synthesis output can include one or more predicted color values … and one or more predicted opacity values (e.g., a probability value associated with the density of occupancy at given positions in a rendering),” para. 43).
Before the effective filing date of the claimed invention, it would have been obvious to one having ordinary skill in the art to apply the teachings of Rematas to the combination of Zhang, Khademi, and Galera. The motivation would have been “to produce more accurate outputs” (Rematas, para. 47).
Regarding claim 7, the combination of Zhang, Khademi, and Galera renders obvious wherein the second neural network comprises: a neural network trained to output a color (“NeRF is an implicit MLP-based model that maps 5D vectors—3D coordinates plus 2D viewing directions—to opacity and color values,” Zhang, pg. 1, sec. 1, para. 2; “cylindrical parameterization,” Khademi, pg. 2, sec. 3, para. 2; see claim 1 for motivation to combine).
Zhang refers to “opacity” rather than a volume density.
In the same art of using separate NeRFs for foreground and background samples, Rematas teaches a neural network trained to output a color and volume density (“the predicted view synthesis output can include one or more predicted color values … and one or more predicted opacity values (e.g., a probability value associated with the density of occupancy at given positions in a rendering),” para. 43).
Before the effective filing date of the claimed invention, it would have been obvious to one having ordinary skill in the art to apply the teachings of Rematas to the combination of Zhang, Khademi, and Galera. The motivation would have been “to produce more accurate outputs” (Rematas, para. 47).
Regarding claim 8, the combination of Zhang, Khademi, and Galera renders obvious projecting and rendering a color (“These two volumes are modelled with two separate NeRFs. To render the color for a ray, they are raycast individually, followed by a final composition,” Zhang, pg. 5, para. 2).
Zhang refers to “opacity” rather than a volume density.
In the same art of using separate NeRFs for foreground and background samples, Rematas teaches projecting and rendering a color and volume density of a pixel (“the predicted view synthesis output can include one or more predicted color values … and one or more predicted opacity values (e.g., a probability value associated with the density of occupancy at given positions in a rendering),” para. 43).
Before the effective filing date of the claimed invention, it would have been obvious to one having ordinary skill in the art to apply the teachings of Rematas to the combination of Zhang, Khademi, and Galera. The motivation would have been “to produce more accurate outputs” (Rematas, para. 47).
Claims 9-16 are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Zhang, Khademi, and Galera, and further in view of Matthews et al. (US 2024/0371081; hereinafter “Matthews”).
Regarding claim 9, Zhang discloses A method comprising: … an input image captured by a 360-degree camera (“training/testing images … of hand-held 360° captures,” pg. 6, sec. 5, para. 2); encoding location statuses of respective points sampled for each of camera rays formed (“samples per camera ray,” pg. 6, sec. 5, para. 2; “the ray
r
=
o
+
t
d
,” pg. 5, para. 3), based on a virtual (“unit sphere,” pg. 5, para. 1); determining, based on the virtual (“partition the scene space into two volumes, an inner unit sphere and an outer volume … The inner volume contains the foreground and all the cameras, while the outer volume contains the remainder of the environment,” pg. 5, para. 1; “the ray
r
=
o
+
t
d
is partitioned into two segments by the unit sphere … inside the sphere … outside the sphere,” pg. 5, para. 3); adding and rendering pixel values obtained by determining which of a first neural network or a second neural network to apply the encoded points to based on the location statuses, wherein the first neural network is configured to generate a foreground image and the second neural network is configured to generate a background image (“partition the scene space into two volumes, an inner unit sphere and an outer volume … The inner volume contains the foreground and all the cameras, while the outer volume contains the remainder of the environment. These two volumes are modelled with two separate NeRFs,” pg. 5, para. 1; “ray-casts both inner and outer volumes,” pg. 6, sec. 5, para. 2); and training the first neural network and the second neural network based on a pixel value of a camera ray, the pixel value obtained by blending a rendering result for each of the camera rays (“To render the color for a ray, they are raycast individually, followed by a final composition,” pg. 5, para. 2), wherein the determining of the location statuses of the points comprises: determining a distance … between the center and a corresponding point among the respective points (“
x
2
+
y
2
+
z
2
,” Zhang, pg. 5, para. 3) … comparing the distance with the radius (“
x
2
+
y
2
+
z
2
> 1,” Zhang, pg. 5, para. 3); and determining the location status of the corresponding point as either foreground or background based on a result of comparing the distance with the radius (“inside the sphere … outside the sphere,” pg. 5, para. 3; “partition the scene space into two volumes,” pg. 5, paras. 1-2; “a 3D point
x
,
y
,
z
,
r
=
x
2
+
y
2
+
z
2
> 1 in the outer volume,” Zhang, pg. 5, para. 3).
Zhang discloses a spherical coordinate system, but does not disclose that the coordinate system is a cylindrical coordinate system.
In the same art of neural rendering, Khademi teaches partitioning a 3D space using a cylindrical coordinate system (“we introduce a new cylindrical parameterization that resembles the inverted sphere parameterization presented in [Zhang et al. 2020],” pg. 2, sec. 3, para. 2).
Before the effective filing date of the claimed invention, it would have been obvious to one having ordinary skill in the art to apply the cylindrical coordinate system of Khademi to Zhang. The motivation would have been that it “is more suitable to casual capture where the user spins in a circle and thus the top and bottom of the scene are unlikely to be observed” (Khademi, pg. 2, sec. 3, para. 2).
The combination of Zhang and Khademi does not disclose determining the distance in a two dimensional (2D) projection … by projecting the center and the corresponding sample point into an x, y plane of the virtual cylindrical coordinate system.
In the same art of computer graphics, Galera teaches determining a distance in a two dimensional (2D) projection … by projecting the center and the corresponding sample point into an x, y plane (“project the three-dimensional objects onto the X-Y plane resulting in two-dimensional projections. The system then calculates the minimum distance between the two-dimensional projections,” para. 77).
Before the effective filing date of the claimed invention, it would have been obvious to one having ordinary skill in the art to apply the teachings of Galera to the virtual cylindrical coordinate system of the combination of Zhang and Khademi. The motivation would have been “In order to simplify the calculation of distances” (Galera, para. 77).
The combination of Zhang, Khademi, and Galera does not disclose generating pose information corresponding to at least one object included in each image frame of a plurality of image frames or encoding location statuses based on the pose information.
In the same art of using separate NeRFs for foreground and background samples, Matthews teaches generating pose information corresponding to at least one object included in each image frame of a plurality of image frames(“determine an orientation of the object depicted and/or for depth determination for specific features of the object,” para. 43; “The foreground may be the object of interest,” para. 51) and encoding location statuses based on the pose information (“The sample outputs from the landmarker and the segmenter networks for the two input identities can convey the location of the foreground object and the location of specific characterizing features,” para. 130; “the systems and methods can include foreground-background decomposition … a separate model can be used to handle the generation of background details,” para. 165).
Before the effective filing date of the claimed invention, it would have been obvious to one having ordinary skill in the art to apply the teachings of Matthews to the combination of Zhang, Khademi, and Galera. The motivation would have been to “reconstruct images directly with a more efficient and scalable stochastic sampling process” (Matthews, para. 154).
Regarding claim 10, the combination of Zhang, Khademi, Galera, and Matthews renders obvious estimating first pose information corresponding to at least a first object and a second object included in each of the image frames (“a plurality of different objects,” Matthews, para. 6; “determine an orientation of the object depicted and/or for depth determination for specific features of the object,” Matthews, para. 43; see claim 9 for motivation to combine); separating and encoding points corresponding to the first object as foreground based on a direction from which the 360-degree camera views the first object being inside a virtual cylinder corresponding to the virtual cylindrical coordinate system, and separating and encoding points corresponding to the second object as background based on being outside the virtual cylinder, based on the virtual cylindrical coordinate system (“partition the scene space into two volumes, an inner unit sphere and an outer volume,” Zhang, pg. 5, para. 1; “the ray
r
=
o
+
t
d
is partitioned into two segments by the unit sphere … inside the sphere … outside the sphere,” Zhang, pg. 5, para. 3; Fig. 7 of Zhang illustrates a foreground truck object and multiple background objects; “cylindrical parameterization,” Khademi, pg. 2, sec. 3, para. 2; see claim 9 for motivation to combine).
Regarding claim 11, the combination of Zhang, Khademi, Galera, and Matthews renders obvious based on the determining of the location statuses, encoding each location status of the respective points (“the ray
r
=
o
+
t
d
is partitioned into two segments by the unit sphere … inside the sphere … outside the sphere,” Zhang, pg. 5, para. 3; “a 3D point
x
,
y
,
z
,
r
=
x
2
+
y
2
+
z
2
> 1 in the outer volume,” Zhang, pg. 5, para. 3).
Regarding claim 13, the combination of Zhang, Khademi, Galera, and Matthews renders obvious determining the location status of the corresponding point as foreground based on the corresponding distance being less than the radius; and determining the location status of the corresponding point as background based on the corresponding distance being greater than or equal to the radius (“a 3D point
x
,
y
,
z
,
r
=
x
2
+
y
2
+
z
2
> 1 in the outer volume … the ray
r
=
o
+
t
d
is partitioned into two segments by the unit sphere … inside the sphere … outside the sphere,” Zhang, pg. 5, para. 3; note that using a “greater than” or “greater than or equal to” operator to determine an interior or exterior of a bounding volume is considered a trivial and obvious variation).
Regarding claim 14, the combination of Zhang, Khademi, Galera, and Matthews renders obvious wherein the rendering comprises at least one of: first rendering by the first neural network according to the location status of the corresponding point being foreground; or second rendering by the second neural network according to the location status of the corresponding point being background (“These two volumes are modelled with two separate NeRFs. To render the color for a ray, they are raycast individually, followed by a final composition,” Zhang, pg. 5, para. 2).
Regarding claim 15, the combination of Zhang, Khademi, Galera, and Matthews renders obvious training the first neural network and the second neural network based on a difference between a pixel value generated by blending a result of the first rendering and a result of the second rendering (“To render the color for a ray, they are raycast individually, followed by a final composition,” Zhang, pg. 5, para. 2; “minimizing the discrepancy between the ground truth observed images … and the predicted images,” Zhang, pg. 2, sec. 2, para. 2).
Regarding claim 16, the combination of Zhang, Khademi, Galera, and Matthews renders obvious A non-transitory computer-readable storage medium storing instructions that, when executed by a processor, cause the processor to perform the method of claim 9 (“Training NeRF++ on a node with 4 RTX 2080 Ti GPUs,” Zhang, pg. 7, sec. 6, para. 1).
Claim 26 is rejected under 35 U.S.C. 103 as being unpatentable over the combination of Zhang, Khademi, and Galera, and further in view of Yano (US 2021/0176450).
Regarding claim 26, the combination of Zhang, Khademi, and Galera does not disclose wherein the virtual camera pose is positioned outside the virtual cylinder.
In the same art of novel view generation, Yano teaches wherein the virtual camera pose is positioned outside the virtual [boundary] (“the setting region to be a region in a three-dimensional space in which a three-dimensional model of an object is present,” para. 52; “the virtual camera can move within the movable range … and can also move to the outside of the movable range,” para. 59).
Before the effective filing date of the claimed invention, it would have been obvious to one having ordinary skill in the art to apply the teachings of Yano to the virtual cylinder boundary of the combination of Zhang, Khademi, and Galera. The motivation would have been “the degree of freedom with which the virtual camera can be navigated can be expanded” (Yano, para. 76).
Conclusion
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/RYAN MCCULLEY/Primary Examiner, Art Unit 2611
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