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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 6/11/2026 has been entered.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1-20 have been considered but are moot in view of the new grounds of rejection.
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, 3, 5-9, 11, 13, and 15-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al ("Normalized object coordinate space for category-level 6d object pose and size estimation." Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2019, pages 2642-2651, retrieved from the Internet on 9/19/2025) in view of Yu et al ("pixelnerf: Neural radiance fields from one or few images." Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2021, pages 4578-4587, retrieved from the Internet on 9/19/2025) and Muller et al (“AutoRF: Learning 3D Object Radiance Fields from Single View Observations”, https://doi.org/10.48550/arXiv.2204.03593, 4/7/2022, pages 1-14, retrieved from the Internet on 6/26/2026).
Regarding claim 1, Wang teaches a computer-implemented method for training a model (abstract, Our region-based neural network is then trained to directly infer the correspondence from observed pixels to this shared object representation (NOCS) along with other object information such as class label and instance mask), comprising:
training a size estimation model to generate an estimated object size (fig. 3; section 5, Figure 3 shows our method for 6D pose and size estimation of multiple previously unseen objects from an RGB-D image. A CNN predicts class labels, masks, and NOCS maps of objects. It would be necessary to train the CNN) using a training dataset with differing levels of annotation (abstract, To train our network, we present a new contextaware technique to generate large amounts of fully annotated mixed reality data. To further improve our model and evaluate its performance on real data, we also provide a fully annotated real-world dataset with large environment and instance variation; section 4.1);
performing two-dimensional object detection on a training image to identify an object (fig. 1; section 5.1);
cropping the training image around the object (section 3, Mask R-CNN; section 5.1.1, For each proposed region of interest (ROI), the output of a head is of size 28×28×N, where N is the number of categories and each category containing the x (or y, z) coordinates for all detected objects in that category. It would be obvious to only use the ROI);
training a normalized coordinate model using the training image and ground truth information (abstract, Our region-based neural network is then trained to directly infer the correspondence from observed pixels to this shared object representation (NOCS); section 5.1.1, During training, only the NOCS map 2646 component from the ground truth object category is used in the loss function).
Wang fails to teach generating a category-level shape reconstruction using a neural radiance field (NeRF) model; and
training a model using information from the category-level shape reconstruction.
However Yu teaches generating a category-level shape reconstruction using a neural radiance field (NeRF) model (section 1; Our experiments show that pixelNeRF can generate novel views from a single image input for both category-specific and category-agnostic settings, even in the case of unseen object categories; section 2, PixelNeRF operates in view-space, which has been shown to allow better reconstruction of unseen object categories; section 4.1); and
training a model using information from the category-level shape reconstruction (section 5.1.2, During both training and evaluation, a random view is selected as the input view for each object; section 5.2, As seen in Fig. 8, the network trained
on synthetic data effectively infers shape and texture of the real cars).
Therefore taking the combined teachings of Wang and Yu as a whole, it would have been obvious to one of ordinary skill in the art at the time the invention was filed to incorporate the steps of Yu into the method of Wang. The motivation to combine Yu and Wang would be to achieve noticeably superior results (section 5.1.1 of Yu).
Wang also fails to teach wherein training the normalized coordinate model includes optimizing a loss function that includes an occupancy term and a color information term.
However Muller teaches wherein training a normalized coordinate model (page 4 left side, Normalized Object Coordinate Space; section 5) includes optimizing a loss function that includes an occupancy term and a color information term (section 3.3, To train our architecture we rely on two loss terms: a photometric loss and an occupancy loss).
Therefore taking the combined teachings of Wang and Muller as a whole, it would have been obvious to one of ordinary skill in the art at the time the invention was filed to incorporate the steps of Muller into the method of Wang. The motivation to combine Muller and Wang would be to improve reconstruction quality (abstract of Muller).
Regarding claim 3, the modified invention of Wang teaches a method wherein training the normal coordinate model includes training a neural network model using a deep learning process (abstract of Wang, Our region-based neural network).
Regarding claim 5, the modified invention of Wang teaches a method wherein the training dataset is derived from multiple different domains having differing degrees of annotation (abstract and section 4.1 of Wang).
Regarding claim 6, the modified invention of Wang teaches a method wherein at least one domain of the training dataset has object size annotation (section 1 of Wang, We also present a real-world dataset for training and testing with 18 different scenes and ground truth 6D pose and size annotations for 6 object categories).
Wang fails to teach wherein at least one domain of the training dataset lacks location and orientation annotation. However it would be obvious to omit undesired data from the dataset to reduce storage requirements.
Regarding claim 7, the modified invention of Wang teaches a method wherein the multiple different domains reflect differences in sensor configuration and/or location (abstract of Wang, we also provide a fully annotated real-world dataset with large environment and instance variation).
Regarding claim 8, the modified invention of Wang teaches a method wherein cropping the image excludes information from the training image outside of a bounding box determined by the object detection (section 3 and fig. 3 of Wang).
Regarding claim 9, the modified invention of Wang teaches a method further comprising determining a three-dimensional pose of the object based on normalized coordinates from the normalized coordinate model and the estimated object size (section 5.2 and 7 of Wang).
Regarding claim 11, the claim recites similar subject matter as claim 1 and is rejected for the same reasons as stated above.
Regarding claim 13, the claim recites similar subject matter as claim 3 and is rejected for the same reasons as stated above.
Regarding claim 15, the claim recites similar subject matter as claim 5 and is rejected for the same reasons as stated above.
Regarding claim 16, the claim recites similar subject matter as claim 6 and is rejected for the same reasons as stated above.
Regarding claim 17, the claim recites similar subject matter as claim 7 and is rejected for the same reasons as stated above.
Regarding claim 18, the claim recites similar subject matter as claim 8 and is rejected for the same reasons as stated above.
Regarding claim 19, the claim recites similar subject matter as claim 9 and is rejected for the same reasons as stated above.
Claim(s) 2 and 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al ("Normalized object coordinate space for category-level 6d object pose and size estimation." Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2019, pages 2642-2651, retrieved from the Internet on 9/19/2025), Yu et al ("pixelnerf: Neural radiance fields from one or few images." Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2021, pages 4578-4587, retrieved from the Internet on 9/19/2025) and Muller et al (“AutoRF: Learning 3D Object Radiance Fields from Single View Observations”, https://doi.org/10.48550/arXiv.2204.03593, 4/7/2022, pages 1-14, retrieved from the Internet on 6/26/2026) in view of Cohen et al (US20240005261).
Regarding claim 2, the modified invention of Wang teaches a method wherein the training image is of a navigable environment (abstract of Wang, we also provide a
fully annotated real-world dataset with large environment and instance variation) and the object is a navigation obstacle (fig. 1 and section 5.1 of Wang).
Wang fails to teach wherein the navigable environment is a healthcare facility.
However Cohen teaches a training image of a healthcare facility (para. [0068], In various embodiments, a physical environment such as a warehouse, hospital, or office may be provisioned with sensors, emitters, or other data collection and/or data producing systems during training. This apparatus may be used to create a richer set of training data that can be used as inputs and/or labels for training a ML-based model).
Therefore taking the combined teachings of Wang, Muller and Yu with Cohen as a whole, it would have been obvious to one of ordinary skill in the art at the time the invention was filed to incorporate the steps of Cohen into the method of Wang, Muller and Yu. The motivation to combine Cohen, Yu, Muller and Wang would be to create a richer set of training data (para. [0068] of Cohen).
Regarding claim 12, the claim recites similar subject matter as claim 2 and is rejected for the same reasons as stated above.
Claim(s) 10 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al ("Normalized object coordinate space for category-level 6d object pose and size estimation." Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2019, pages 2642-2651, retrieved from the Internet on 9/19/2025), Yu et al ("pixelnerf: Neural radiance fields from one or few images." Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2021, pages 4578-4587, retrieved from the Internet on 9/19/2025) and Muller et al (“AutoRF: Learning 3D Object Radiance Fields from Single View Observations”, https://doi.org/10.48550/arXiv.2204.03593, 4/7/2022, pages 1-14, retrieved from the Internet on 6/26/2026)in view of Zakharov et al (US20220300770).
Regarding claim 10, the modified invention of Wang fails to teach a method further comprising using the normalized coordinates and the three-dimensional pose of the object in an autonomous vehicle to navigate through an environment.
However Zakharov teaches using normalized coordinates (para. [0032]) and the three-dimensional pose of an object (para. [0034]) in an autonomous vehicle to navigate through an environment (para. [0048], [0073]).
Therefore taking the combined teachings of Wang, Muller and Yu with Zakharov as a whole, it would have been obvious to one of ordinary skill in the art at the time the invention was filed to incorporate the steps of Zakharov into the method of Wang, Muller and Yu. The motivation to combine Zakharov, Muller, Yu and Wang would be to improve transferability to real-world datasets (para. [0001] of Zakharov).
Regarding claim 20, the claim recites similar subject matter as claim 10 and is rejected for the same reasons as stated above.
Allowable Subject Matter
Claims 4 and 14 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.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LEON VIET Q NGUYEN whose telephone number is (571)270-1185. The examiner can normally be reached Mon-Fri 11AM-7PM.
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/LEON VIET Q NGUYEN/Primary Examiner, Art Unit 2663
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