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 .
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.
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-4, 7-14, and 16-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Wang et al. (“3D Hierarchical Refinement and Augmentation for Unsupervised Learning of Depth and Pose From Monocular Video,” IEEE Transactions on Circuits and Systems for Video Technology, Vol. 33, No. 4, April 2023, published 19 October 2022), hereinafter referred to as Wang.
Regarding claim 1, Wang teaches a processor comprising:
one or more circuits (Wang pg. 1782: “All the experiments for 2 PoseNets are implemented on a NVIDIA RTX 2080Ti GPU … implemented on NVIDIA RTX 3090 GPU”) to:
generate, using a machine-learning model and based at least on a first image of a set of sequential images corresponding to a first viewpoint, a first transformed image corresponding to a second viewpoint (Wang Fig. 1: “the process of pose estimation refinement … given two adjacent frames Xt and Xt+1, with their corresponding camera view Pt and Pt+1, we utilize our 3D hierarchical refinement method in Sec. III-B to synthesize transitional camera view Pmwarp”; Wang pg. 1778-1779, §III-B: “Our method has multiple PoseNets, NP1, NP2, …, NPM, with the same network structure to refine the pose estimation … we use the image warping based on 2D-3D transformation to construct the intermediate image between Xt and Xt+1 to make next residual pose estimation easier”; Wang pg. 1782 left column: “11 long driving stereo sequences with available ground truth trajectories … using sequences 00-08 for training and sequences 09-10 for testing”; Wang pg. 1782 right column: “A snippet of two sequential images is used to estimate both forward pose and backward pose between two frames. The losses are used in two-directional transformation and image reconstruction”); and
update one or more parameters of the machine-learning model based at least on a loss determined according to the first transformed image and a second image of the set of the sequential images (Wang Fig. 1 & pg. 1778-1779, §III-B discussed above, Wang uses a hierarchical refinement method; also see Wang pg. 1778 right column: “Through the image reconstruction loss LR, geometry consistency loss LGC, and depth smooth loss Lsmooth, the multi-scale depths and multi-layer poses are trained together … we propose the data augmentation loss Laug based on 2D-3D transformation … The overall training loss is L … where α1, α2, α3, and α4 are hyperparameters”; Wang Fig. 2: see “Refine”).
Regarding claim 2, Wang teaches the processor of claim 1, wherein the one or more circuits are to:
identify a respective depth map associated with each image of the set of sequential images (Wang Fig. 2: “The DepthNet estimates depth maps at four scales … reconstruction loss is generated by all-scale depth maps and all-level poses”; Wang pg. 1780 left column: “to find the corresponding depth in depth map Dt for Dt+1”; Wang Fig. 6); and
update one or more parameters of the machine-learning model further based at least on a second loss determined according to depth values of one or more mesh faces of an output of the machine-learning model and a respective depth map associated with the second image (Wang Figs. 1-2 & pg. 1778-1779, §III-B discussed above; Wang 1781 left column: “more than one pixel in the original images may be projected to the same grid in the augmented images. To solve the collision problem, pixels with minimum depth in original images Xt are selected to be displayed on the augmented image Xaugt”; Wang Fig. 3).
Regarding claim 3, Wang teaches the processor of claim 2, wherein the one or more circuits are to update the one or more parameters of the machine-learning model further based at least on a third loss determined according to an estimated depth map of the output of the machine-learning model and a respective depth map associated with the first image (Wang Figs. 1-2 & pg. 1778-1779, §III-B discussed above; Wang eqs. (1), (14)-(16), (19)).
Regarding claim 4, Wang teaches the processor of claim 1, wherein the one or more circuits are to generate at least one mask for at least one image of the set of sequential images (Wang pg. 1779 right column: “Loss Functions with Masks”; Wang pg. 1780 left column: “the depth inconsistency map is used to generate the occlusion weight mask … The binary auto-mask [14] is also used to filter the objects which are static relative to the camera and textureless regions … The final masked image reconstruction loss of Dn and Tm”; Wang Fig. 4).
Regarding claim 7, Wang teaches the processor of claim 1, wherein the one or more circuits are to update one or more parameters of the machine-learning model to render the output in the second viewpoint of the second image of the set of sequential images (Wang Figs. 1-2 & pg. 1778-1779, §III-B discussed above).
Regarding claim 8, Wang teaches the processor of claim 1, wherein the loss comprises one or more of a L1 loss, a structural similarity (SSIM) loss, or a minimal loss (Wang pg. 1778-1779, §III-B discussed above; also see Wang eq. (8), (12)-(16) & pg. 1780).
Regarding claim 9, Wang teaches the processor of claim 1, wherein the one or more circuits are to execute the machine-learning model to generate a set of transformed images corresponding to at least the second viewpoint (Wang Figs. 1-2 discussed above; also see Wang Figs. 5-6).
Regarding claim 10, Wang teaches the processor of claim 1, wherein the one or more circuits are to update one or more parameters of a second machine-learning model using the set of transformed images (Wang Fig. 2 & pg. 1778-1779, §III-B discussed above).
Regarding claim 11, Wang teaches the processor of claim 1, wherein the processor is comprised in at least one of:
a control system for an autonomous or semi-autonomous machine; a perception system for an autonomous or semi-autonomous machine; a system for performing simulation operations; a system for performing digital twin operations; a system for performing light transport simulation; a system for performing collaborative content creation for 3D assets; a system for performing deep learning operations; a system implemented using an edge device; a system implemented using a robot; a system for performing conversational Al operations; a system for performing generative Al operations using a large language model (LLM); a system for generating synthetic data; a system incorporating one or more virtual machines (VMs); a system implemented at least partially in a data center; or a system implemented at least partially using cloud computing resources (Wang Abstract: “autonomous robots and autonomous driving”; Wang pg. 1779 left column: “virtual intermediate view”; Wang pg. 1780 right column: “autonomous driving scenario”).
Regarding claim 12, Wang teaches a system comprising:
one or more processors (Wang pg. 1782 discussed above) to:
identify a first set of images corresponding to a first view point (Wang Fig. 1 discussed above);
generate, using a machine learning model and based at least on the first set of images, a second set of images corresponding to the first set of images and a second viewpoint (Wang Fig. 1, pg. 1778-1779, §III-B, pg. 1782 discussed above); and
update one or more parameters of a second machine-learning model using a dataset comprising the second set of images (Wang Figs. 1-2, & pg. 1778-1779, §III discussed above, also see Wang Fig. 5 describing pose augmentation learning).
Regarding claim 13, Wang teaches the system of claim 12, wherein the one or more processors are to iteratively execute the machine-learning model using a first image of the first set of images as input to generate a plurality of images include din the second set of images, each of the plurality of images corresponding to a respective viewpoint different from the first viewpoint (Wang Figs. 1-2, 5 & 1778-1780, §III-B discussed above).
Regarding claim 14, Wang teaches the system of claim 13, wherein the one or more processors are to execute the machine-learning model further using at least an indication of the second viewpoint (Wang Figs. 1-2, 4-5 & 1778-1780, §III-B discussed above – the final augmented image is based on the mask H; also see Wang Fig. 6).
Claim 16 is rejected using the same rationale as applied to claim 11 discussed above.
Regarding claim 17, Wang teaches that the processor and system perform a method comprising the processes described in claims 1 and 7. Therefore, claim 17 is rejected using the same rationale as applied to claims 1 and 12 discussed above.
Claim 18 is rejected using the same rationale as applied to claim 2 discussed above.
Claim 19 is rejected using the same rationale as applied to claim 3 discussed above.
Claim 20 is rejected using the same rationale as applied to claim 4 discussed above.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 5, 6, and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. (IEEE TCSVT, Vol. 33, No. 4, April 2023, published 19 October 2022), in view of Seo et al. (US 2020/0090322 A1), hereinafter referred to as Wang and Seo, respectively.
Regarding claim 5, Wang teaches the processor of claim 4, wherein the one or more circuits are to generate the at least one mask using a second machine-learning model updated to predict the at least one mask to correspond to one or more objects (Wang pg. 1779-1780 & Fig. 4 discussed above).
However, Wang does not appear to explicitly teach that the one or more objects are proximate to a device that captured the at least one image.
Pertaining to the same field of endeavor, Seo teaches that the one or more objects are proximate to a device that captured the at least one image (Seo ¶¶0041: “a region-based mask may be created that associates masks with regions in the image based on the importance of the respective regions”; Seo Fig. 2 & ¶¶0046: “the machine learning model(s) 104 may use image data representative of input image 210A as input and may output an image mask 210B including the image blindness regions 212 and 214”; Seo Fig. 5 & ¶¶0072: “the ground truth data 404 may include annotations for blindness region(s) 406 (e.g., blindness region 522), blindness classification(s) 408 (e.g., blocked area), and blindness attribute(s) 410 (e.g., object (e.g., pedestrian) in proximity, during the day) to train the machine learning model(s) 104 to recognize and classify sensor blindness based on the regions and associated causes thereof”).
Wang and Seo are considered to be analogous art because they are directed to neural networks for augmenting image data. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the 3D hierarchical refinement and augmentation for unsupervised learning of depth and pose from videos (as taught by Wang) to detect and estimate objects that are proximate to the image capture device (as taught by Seo) because the combination allows the machine learning model to recognize a road region or less important regions (e.g., sky) even when an input image includes variations in color or positioning (Seo ¶¶0066).
Regarding claim 6, Wang teaches the processor of claim 4, wherein the one or more circuits are to generate the at least one mask using a second machine-learning model updated to predict the at least one mask to correspond to a sky depicted in the at least one image (Seo Figs. 2, 5 & ¶¶0041, ¶¶0046, ¶¶0072 discussed above; further see Seo ¶¶0073: “The sky region may be annotated in training image 560 to indicate the sky near the horizon in the image 560 … The labeling may also train the machine learning model(s) to learn that the sky region—when blocked or blurred—is not as important of a region for determining usability of sensor data”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the 3D hierarchical refinement and augmentation for unsupervised learning of depth and pose from videos (as taught by Wang) to detect and estimate sky regions (as taught by Seo) because the combination allows the machine learning model to recognize a road region or less important regions (e.g., sky) even when an input image includes variations in color or positioning (Seo ¶¶0066).
Regarding claim 15, Wang teaches the system of claim 12, but does not appear to explicitly teach that the second machine-learning model comprises a segmentation model.
Pertaining to the same field of endeavor, Seo teaches that the second machine-learning model comprises a segmentation model (Seo Fig. 5 & ¶0071: “the machine learning model(s) 104 may be trained to predict potential blindness regions 108 as well as blindness classification(s) 110 and/or blindness attribute(s) 112 associated therewith” – different classified regions are segmented).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the 3D hierarchical refinement and augmentation for unsupervised learning of depth and pose from videos (as taught by Wang) to use a segmentation model (as taught by Seo) because the combination allows the machine learning model to ignore ego-vehicle regions and further include contextual information (Seo ¶¶0071).
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1 and 7-20 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of copending Application No. 18/818,221 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because both applications are directed to machine learning models for generating synthetic data corresponding to viewpoints of an image sequence.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Claims 2-4 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of copending Application No. 18/818,221, in view of Wang et al. (IEEE TCSVT, Vol. 33, No. 4, April 2023, published 19 October 2022).
Regarding claim 2, the copending application teaches the processor of claim 1 (see 18/818,221 claim 1), wherein the one or more circuits are to update one or more parameters of the machine-learning model (18/818,221 claim 10).
However, 18/818,221 does not appear to explicitly teach identifying a respective depth map associated with each image of the set of sequential images and updating one or more parameters of the machine-learning model further based at least on a second loss determined according to depth values of one or more mesh faces of an output of the machine-learning model and a respective depth map associated with the second image.
Pertaining to the same field of endeavor, Wang teaches that the one or more circuits are to identify a respective depth map associated with each image of the set of sequential images (Wang Fig. 2, 6, & pg. 1780 left column discussed above); and
update one or more parameters of the machine-learning model further based at least on a second loss determined according to depth values of one or more mesh faces of an output of the machine-learning model and a respective depth map associated with the second image (Wang Figs. 1-3 & pg. 1781, & 1778-1779, §III-B discussed above).
U.S. Patent Application S/N 18/818,221 and Wang are considered to be analogous art because they are directed to neural networks for augmenting image data. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the generative model for rendering novel views of 3D scenes (as taught by 18/818,221) to identify depth maps and update the model parameters (as taught by Wang) because the combination improves autonomous robot localization and navigation (Wang pg. 1776 right column).
This is a provisional nonstatutory double patenting rejection.
Regarding claim 3, 18/818,221, in view of Wang, teaches the processor of claim 2, wherein the one or more circuits are to update the one or more parameters of the machine-learning model further based at least on a third loss determined according to an estimated depth map of the output of the machine-learning model and a respective depth map associated with the first image (Wang Figs. 1-2 & pg. 1778-1779, §III-B discussed above; Wang eqs. (1), (14)-(16), (19)).
This is a provisional nonstatutory double patenting rejection
Regarding claim 4, 18/818,221 teaches the processor of claim 1, but does not appear to explicitly teach generating at least one mask for at least one image of the set of sequential images.
Pertaining to the same field of endeavor, Wang teaches that the one or more circuits are to generate at least one mask for at least one image of the set of sequential images (Wang pg. 1779 right column: “Loss Functions with Masks”; Wang pg. 1780 left column: “the depth inconsistency map is used to generate the occlusion weight mask … The binary auto-mask [14] is also used to filter the objects which are static relative to the camera and textureless regions … The final masked image reconstruction loss of Dn and Tm”; Wang Fig. 4).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the generative model for rendering novel views of 3D scenes (as taught by 18/818,221) to use a mask (as taught by Wang) because the combination can solve the problem of occlusion and dynamic objects (Wang pg. 1776 right column).
This is a provisional nonstatutory double patenting rejection.
Claims 5-6 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of copending Application No. 18/818,221, in view of Wang et al. (IEEE TCSVT, Vol. 33, No. 4, April 2023, published 19 October 2022), and further in view of Seo et al. (US 2020/0090322 A1).
Regarding claim 5, 18/818,221, in view of Wang, teaches the processor of claim 4, wherein the one or more circuits are to generate the at least one mask using a second machine-learning model updated to predict the at least one mask to correspond to one or more objects (Wang pg. 1779-1780 & Fig. 4 discussed above).
However, 18/818,221, in view of Wang, does not appear to explicitly teach that the one or more objects are proximate to a device that captured the at least one image.
Pertaining to the same field of endeavor, Seo teaches that the one or more objects are proximate to a device that captured the at least one image (Seo ¶¶0041: “a region-based mask may be created that associates masks with regions in the image based on the importance of the respective regions”; Seo Fig. 2 & ¶¶0046: “the machine learning model(s) 104 may use image data representative of input image 210A as input and may output an image mask 210B including the image blindness regions 212 and 214”; Seo Fig. 5 & ¶¶0072: “the ground truth data 404 may include annotations for blindness region(s) 406 (e.g., blindness region 522), blindness classification(s) 408 (e.g., blocked area), and blindness attribute(s) 410 (e.g., object (e.g., pedestrian) in proximity, during the day) to train the machine learning model(s) 104 to recognize and classify sensor blindness based on the regions and associated causes thereof”).
18/818,221, in view of Wang, and Seo are considered to be analogous art because they are directed to neural networks for augmenting image data. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the generative model for rendering novel views and unsupervised learning of depth and pose from videos (as taught by 18/818,221, in view of Wang) to detect and estimate objects that are proximate to the image capture device (as taught by Seo) because the combination allows the machine learning model to recognize a road region or less important regions (e.g., sky) even when an input image includes variations in color or positioning (Seo ¶¶0066).
This is a provisional nonstatutory double patenting rejection.
Regarding claim 6, 18/818,221, in view of Wang, teaches the processor of claim 4, but does not appear to explicitly teach that the one or more circuits are to generate the at least one mask using a second machine-learning model updated to predict the at least one mask to correspond to a sky depicted in the at least one image.
Pertaining to the same field of endeavor, Seo teaches that the one or more circuits are to generate the at least one mask using a second machine-learning model updated to predict the at least one mask to correspond to a sky depicted in the at least one image (Seo Figs. 2, 5 & ¶¶0041, ¶¶0046, ¶¶0072 discussed above; further see Seo ¶¶0073: “The sky region may be annotated in training image 560 to indicate the sky near the horizon in the image 560 … The labeling may also train the machine learning model(s) to learn that the sky region—when blocked or blurred—is not as important of a region for determining usability of sensor data”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the generative model for rendering novel views and unsupervised learning of depth and pose from videos (as taught by 18/818,221, in view of Wang) to detect and estimate sky regions (as taught by Seo) because the combination allows the machine learning model to recognize a road region or less important regions (e.g., sky) even when an input image includes variations in color or positioning (Seo ¶¶0066).
This is a provisional nonstatutory double patenting rejection.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SOO J SHIN whose telephone number is (571)272-9753. The examiner can normally be reached M-F; 10-6.
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/Soo Shin/Primary Examiner, Art Unit 2667 571-272-9753
soo.shin@uspto.gov
Prosecution Insights