DETAILED ACTION
Claims 1-24 filed December 6th 2025 are pending in the current action.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
Applicant's arguments filed December 6th 2025 have been fully considered but they are not persuasive.
With regards towards applicant’s arguments that Lodato et al. (US2018/0350134) in view of Xiong (US2025/0076969) do not teach “a first loop” the Examiner must respectfully disagree. It would be obvious given the context of “generating frames for viewing” that the operations described in a single pass would be repeated. Lodato Figs. 18, 20, 21 and ¶154 teach that method 2000 or method 2100 may be repeated for each other frame in the temporal sequence of frames to produce video output of a dynamic perspective image view of a virtual 3D space representative of a captured 3D scene. Thus, Lodato has support for teaching a loop.
With regards towards applicant’s argument pertaining to estimating a depth map, the Examiner must respectfully disagree. Lodato ¶147-149 teaches “The depth pass may include the virtual reality content rendering system transforming received depth data (e.g., captured depth data representing positions of objects in the 3D scene) and metadata (e.g., vantage point information) into 3D coordinates in a common virtual 3D space and projecting 2D input meshes for the vantage points into 3D coordinates in the virtual 3D space to form partial 3D meshes projected into the virtual 3D space, as described herein.” Thus, the depth map is a rough estimation of the sensed points from the sensor which must then be transformed using the additional metadata in order to generate the refined 3D mesh. The current claim language does not distinguish between using sensors or a calculation to perform the depth estimate.
With respect towards the iterative loop the Examiner must respectfully disagree. It would be obvious given the context of “generating frames for viewing” that the operations described in a single pass would be repeated. Lodato Figs. 18, 20, 21 and ¶154 teach that method 2000 or method 2100 may be repeated for each other frame in the temporal sequence of frames to produce video output of a dynamic perspective image view of a virtual 3D space representative of a captured 3D scene. Thus, Lodato has support for teaching a loop.
For the reasons above, the Examiner finds the Applicant’s arguments non-persuasive and the 103 rejection under Lodato and Xiong remains.
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.
Claim(s) 1 and 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lodato et al. (US2018/0350134) in view of Xiong (US2025/0076969)
Consider claim 1, where Lodato teaches a method for generating a three-dimensional (3D) interactive scene, the method comprising: executing a first process based on a two-dimensional (2D) source image, wherein the 2D source image is loaded from a storage unit; (See Lodato Figs. 5-7, 1 and ¶62-66, 46 where a camera 202 associated with a vantage point 204 captures a 2D mesh for eventually generating a 3D image and where data used for generating 3D images is stored in a storage facility 104) and generating the 3D interactive scene based on the 2D source image, wherein the first process comprises: estimating a corresponding depth map for the 2D source image; (See Lodato Figs. 5-7 and ¶62-66 where depth data is also captured and associated with each vantage point 204) creating a 3D mesh based on the estimated depth map; (See Lodato Figs. 5-7 and ¶66-68 where the projection of a 2D input mesh within a virtual 3D space may produce a partial 3D mesh of the captured scene) and executing a first loop iteratively to present the 3D interactive scene based on the 3D mesh and vantage point information. (See Lodato Figs. 13and ¶83-86, 154 where the 3D meshes are used to generate an image view 1302 of the virtual 3D space)
Lodato teaches vantage point information, however Lodato does not explicitly teach head pose information. However, in an analogous field of endeavor Xiong teaches head pose information. (See Xiong Figs. 2A-B and ¶47-48 where the head pose 206a, b is used as the camera position 204) Therefore, it would have been obvious for one of ordinary skill in the art that the vantage information of Lodato could comprise head position information in the case of XR applications where the camera is mounted to the head as taught by Xiong. One of ordinary skill in the art would have been motivated to perform the modification for the advantage of/ benefit of using existing configurations of cameras to yield predictable results.
Consider claim 13, where Lodato teaches a computer system for generating a 3D interactive scene, comprising: a storage unit, storing a 2D source image and a program; a display unit; and a processing unit, communicable with the storage unit and the display unit, loading the program from the storage unit to execute a first process based on the two-dimensional (2D) source image loaded from the storage unit; (See Lodato Figs. 5-7, 1 and ¶62-66, 46 where a camera 202 associated with a vantage point 204 captures a 2D mesh for eventually generating a 3D image and where data used for generating 3D images is stored in a storage facility 104) wherein the first process comprises: estimating a corresponding depth map for the 2D source image; (See Lodato Figs. 5-7 and ¶62-66 where depth data is also captured and associated with each vantage point 204) creating the 3D mesh based on the estimated depth map; and executing a first loop iteratively to present the 3D interactive scene based on the 3D mesh and vantage point information. (See Lodato Figs. 13and ¶83-86, 154 where the 3D meshes are used to generate an image view 1302 of the virtual 3D space)
Lodato teaches vantage point information, however Lodato does not explicitly teach head pose information. However, in an analogous field of endeavor Xiong teaches head pose information. (See Xiong Figs. 2A-B and ¶47-48 where the head pose 206a, b is used as the camera position 204) Therefore, it would have been obvious for one of ordinary skill in the art that the vantage information of Lodato could comprise head position information in the case of XR applications where the camera is mounted to the head as taught by Xiong. One of ordinary skill in the art would have been motivated to perform the modification for the advantage of/ benefit of using existing configurations of cameras to yield predictable results.
Claim(s) 2 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lodato in view of Xiong as applied to claim 1, in further view of Kim et al. (US2019/0164346)
Consider claim 2, where the method as claimed in claim 1, wherein the first loop comprises: obtaining the head pose information; calculating a set of camera coordinates based on the vantage point information; constructing a transformation matrix based on the calculated set of camera coordinates; (See Lodato ¶54, 154 where the virtual 3D space uses a transformation matrix for each vantage point of each capture device) projecting each vertex of the 3D mesh onto a screen coordinate of an onscreen image using the transformation matrix; coloring the onscreen image based on 2D color information corresponding to the 2D source image; and causing a display unit to display the onscreen image. (See Lodato ¶56 where rendering facility 102 may perform one or more operations separate from the generating of the partial 3D mesh (after the partial 3D mesh is generated) to associate color samples from the 2D color data 210 with vertices and/or primitives of the partial 3D mesh projected into the virtual 3D space.)
Lodato teaches vantage point information, however Lodato does not explicitly teach head pose information. However, in an analogous field of endeavor Xiong teaches head pose information. (See Xiong Figs. 2A-B and ¶47-48 where the head pose 206a, b is used as the camera position 204) Therefore, it would have been obvious for one of ordinary skill in the art that the vantage information of Lodato could comprise head position information in the case of XR applications where the camera is mounted to the head as taught by Xiong. One of ordinary skill in the art would have been motivated to perform the modification for the advantage of/ benefit of using existing configurations of cameras to yield predictable results.
Lodato teaches 2D color data however Lodato does not explicitly call it texture information. However, in an analogous field of endeavor Kim teaches texture. (See Kim ¶94 where an area obtained by connecting the projected four vertices may be registered as the texture map. In addition, when performing synthesizing as AR and alpha map information is included in a 2D RGB image for alpha blending so that boundaries are naturally synthesized, natural alpha blending effect may be calculated when a 3D rendering engine renders an image by registering alpha information as alpha map mask information of the texture map when registering the RGB image area as the texture map. Thus, the 2D color map can be converted to a texture map) Therefore, it would have been obvious for one of ordinary skill in the art to further process the 2D color data of Lodato into texture data as taught by Kim. One of ordinary skill in the art would have been motivated to perform the modification for the advantage of/ benefit of using a known process to improve the 3D scene in a predictable manner.
Consider claim 14, where Lodato in view of Xiong teaches the computer system as claimed in claim 13, wherein the first loop comprises: obtaining the head pose information; calculating a set of camera coordinates based on the vantage point information; constructing a transformation matrix based on the calculated set of camera coordinates; (See Lodato ¶54, 154 where the virtual 3D space uses a transformation matrix for each vantage point of each capture device) projecting each vertex of the 3D mesh onto a screen coordinate of an onscreen image using the transformation matrix; coloring the onscreen image based on 2D color information corresponding to the 2D source image; and causing a display unit to display the onscreen image. (See Lodato ¶56 where rendering facility 102 may perform one or more operations separate from the generating of the partial 3D mesh (after the partial 3D mesh is generated) to associate color samples from the 2D color data 210 with vertices and/or primitives of the partial 3D mesh projected into the virtual 3D space.)
Lodato teaches vantage point information, however Lodato does not explicitly teach head pose information. However, in an analogous field of endeavor Xiong teaches head pose information. (See Xiong Figs. 2A-B and ¶47-48 where the head pose 206a, b is used as the camera position 204) Therefore, it would have been obvious for one of ordinary skill in the art that the vantage information of Lodato could comprise head position information in the case of XR applications where the camera is mounted to the head as taught by Xiong. One of ordinary skill in the art would have been motivated to perform the modification for the advantage of/ benefit of using existing configurations of cameras to yield predictable results.
Lodato teaches 2D color data however Lodato does not explicitly call it texture information. However, in an analogous field of endeavor Kim teaches texture. (See Kim ¶94 where an area obtained by connecting the projected four vertices may be registered as the texture map. In addition, when performing synthesizing as AR and alpha map information is included in a 2D RGB image for alpha blending so that boundaries are naturally synthesized, natural alpha blending effect may be calculated when a 3D rendering engine renders an image by registering alpha information as alpha map mask information of the texture map when registering the RGB image area as the texture map. Thus, the 2D color map can be converted to a texture map) Therefore, it would have been obvious for one of ordinary skill in the art to further process the 2D color data of Lodato into texture data as taught by Kim. One of ordinary skill in the art would have been motivated to perform the modification for the advantage of/ benefit of using a known process to improve the 3D scene in a predictable manner.
Claim(s) 3 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lodato in view of Xiong in view of Kim as applied to claim 2 above, in further view of Juliani et al. (US2020/0122040)
Consider claim 3, where Lodato in view of Xiong in view of Kim teaches the method as claimed in claim 2, however they do not explicitly teach wherein the first loop further comprises: detecting a pause request; in response to the pause request, pausing the iteration of the first loop and waiting for a resume request; and in response to the resume request, resuming the next iteration of the first loop. However, in an analogous field of endeavor Juliani teaches detecting a pause request; in response to the pause request, pausing the iteration of the first loop and waiting for a resume request; and in response to the resume request, resuming the next iteration of the first loop. (see Juliani Fig. 2 and ¶12, 26-28 where a learning system may pause a process to ask for a user to take over and resume with more correct examples. See Fig. 2 steps 214, 224) Therefore, it would have been obvious for one of ordinary skill in the art to modify the computer processes of Lodato to pause and request user input to resume operations as taught by Juliani. One of ordinary skill in the art would have been motivated to perform the modification for the advantage of/ benefit of using known processes in the art to improve a process in a similar manner.
Consider claim 15, where Lodato in view of Xiong teaches the computer system as claimed in claim 14, wherein the first loop further comprises: detecting a pause request; in response to the pause request, pausing the iteration of the first loop and waiting for a resume request; and in response to the resume request, resuming the next iteration of the first loop. However, in an analogous field of endeavor Juliani teaches detecting a pause request; in response to the pause request, pausing the iteration of the first loop and waiting for a resume request; and in response to the resume request, resuming the next iteration of the first loop. (see Juliani Fig. 2 and ¶12, 26-28 where a learning system may pause a process to ask for a user to take over and resume with more correct examples. See Fig. 2 steps 214, 224) Therefore, it would have been obvious for one of ordinary skill in the art to modify the computer processes of Lodato to pause and request user input to resume operations as taught by Juliani. One of ordinary skill in the art would have been motivated to perform the modification for the advantage of/ benefit of using known processes in the art to improve a process in a similar manner.
Claim(s) 12 and 24 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lodato in view of Xiong as applied to claim 1 above, in further view of Katz et al. (US2020/0213572)
Consider claim 12, where Lodato in view of Xiong teaches the method as claimed in claim 1, however they do not explicitly teach further comprising: acquiring a handle to a desktop wallpaper, wherein the 3D interactive scene is presented as the desktop wallpaper. However, in an analogous field of endeavor Katz teaches acquiring a handle to a desktop wallpaper, wherein the 3D interactive scene is presented as the desktop wallpaper. (See Katz ¶70, 149 where two left and right images are interpolated to generate the wallpaper image and the interpolation is based on the disparity maps generated from the two original RGB images. This provides an appearance of a 3D world sensation by rotating images that are not even real, but only requires two modified two-dimensional images (frames) to produce the light field effect.) Therefore, it would have been obvious for one of ordinary skill in the art that the 3D scene generated by Lodato can be used as a wallpaper as taught by Katz. One of ordinary skill in the art would have been motivated to perform the modification for the advantage of/ benefit of using other known uses for 3D scenes to improve the display in a predictable manner.
Consider claim 24, where Lodato in view of Xiong teaches the computer system as claimed in claim 13, however they do not explicitly teach wherein the 3D interactive scene is presented as a desktop wallpaper; and wherein the processing unit further acquires a handle to the desktop wallpaper. However, in an analogous field of endeavor Katz teaches wherein the 3D interactive scene is presented as a desktop wallpaper; and wherein the processing unit further acquires a handle to the desktop wallpaper. (See Katz ¶70, 149 where two left and right images are interpolated to generate the wallpaper image and the interpolation is based on the disparity maps generated from the two original RGB images. This provides an appearance of a 3D world sensation by rotating images that are not even real, but only requires two modified two-dimensional images (frames) to produce the light field effect.) Therefore, it would have been obvious for one of ordinary skill in the art that the 3D scene generated by Lodato can be used as a wallpaper as taught by Katz. One of ordinary skill in the art would have been motivated to perform the modification for the advantage of/ benefit of using other known uses for 3D scenes to improve the display in a predictable manner.
Allowable Subject Matter
Claims 4-12 and 16-23 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter: Within the field of endeavor, the Examiner was unable to find the limitation: “wherein the pause request is triggered in an event that an area proportion of the onscreen image obscured by windows of other programs exceeds a specified percentage.” Thus, claims 4 and 16 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Claims 5 and 17 recite the limitation: “executing a second process based on a 2D source video loaded from the storage unit, wherein the second process comprises: determining whether a depth video corresponding to the 2D source video is stored in the storage unit, wherein the depth video comprises a depth frame sequence; in an event that the depth video is stored in the storage unit, initializing the 3D mesh, and executing a second loop iteratively to present the 3D interactive scene based on the head pose information; and in an event that the depth video is not stored in the storage unit, starting a background inference thread for generating the depth video and storing the generated depth video in the storage unit.” The Examiner was able to find pertinent art in Li et al. (US2021/0287430) ¶522 where inferences using neural networks are used to reconstruct 3D meshes from 2D images are run on separate threads. However, neural networks do not produce consistent results. Thus, the combination of Lodato in view of Xiong with Li would yield unpredictable results. Claims 6-11 and 18-23 are objected to based upon their dependency from claims 5 and 17.
Conclusion
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 WILLIAM LU whose telephone number is (571)270-1809. The examiner can normally be reached 10am-6:30pm.
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WILLIAM LU
Primary Examiner
Art Unit 2624
/WILLIAM LU/Primary Examiner, Art Unit 2624