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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 7/29/2026 has been entered.
Response to Amendment
The amendment filed on 7/29/2026 has been entered and made of record. Claims 1, 8 and 15 are amended. Claims 1-20 are pending.
Response to Arguments
Applicant’s arguments with respect to the rejections of independent claims 1, 8 and 15 have been fully considered but they are moot because the arguments do not apply to the references being used in the current rejection.
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 of this title, 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 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Camous et al. (US 2023/0074860 A1) in view of Wang et al. (Sample-adaptive Augmentation for Point Cloud Recognition Against Real-world Corruptions, arXiv:2309.10431v1 [cs.CV] 19 Sep 2023) and Kilickaya et al. (WO 2021/097378 A1).
As to Claim 1, Camous teaches An apparatus, comprising: one or more memories; and one or more processors coupled to the one or more memories and configured to:
obtain, with a backbone artificial neural network, an original feature map of point cloud data (Camous, [0079, 0105-0106] and Fig 5B below
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).
For the sake of completeness, the combination of Wang further teaches following limitations:
deform the point cloud data, with a deformation artificial neural network, into a plurality of deformed point cloud objects based on the original feature map of point cloud data, the deformation artificial neural network configured to map the original feature map of point cloud data to a set of control point perturbations and to apply the set of control point perturbations to deform the point cloud data (Camous discloses pooling function in [0070, 0081]; “One or both of the selected LiDAR point clouds may be transformed to align the respective LiDAR point clouds to account for different locations and/or orientations of the LiDAR sensor that captured the LiDAR point cloud” in [0092], see also misalignment transformation in [0119,0122]. Wang further discloses
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, see also mapping function at p. 4 and Fig 2 below:
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);
combine the plurality of deformed point cloud objects into a mixed point cloud (Camous discloses “For example, the at least one functional network 510E, 510F, 510G may receive one feature map in the case that the at least two LiDAR point clouds are merged… The at least one functional network may not include the concatenation network 510E in the case that the at least two LiDAR point clouds are processed as a merged point cloud” in [0107]. Wang also teaches auto-augmentation framework under section “Data Augmentation on Point Cloud” at p. 2 and Fig 1);
extract, with the backbone artificial neural network, a mixed feature map from the mixed point cloud; extract a plurality of deformed feature maps from the plurality of deformed point cloud objects (Camous discloses “The classifier network may extract features from the pair of LiDAR point clouds, and compute a probability score of the pair of LiDAR point clouds being aligned or misaligned” in [0022]; “The feature backbone 510C may be a feature extraction network” in [0106]; see also [0096]. Wang, Fig 1-2); and
compute, with a contrastive module, a loss for the backbone artificial neural network and for the deformation artificial neural network based on the mixed feature map and the plurality of deformed feature maps (Camous discloses “The output datasets may include a binary classification (aligned or misaligned), a probability score, and/or a confidence score, and the like.” in [0098]; “For instance, the classifier network 504 may be trained on training data including labeled sets of misaligned and aligned pairs of LiDAR point clouds with appropriate loss functions providing feedback to adjust the classifier network 504.” in [0117]; see also Fig 7B. Wang further discloses
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It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the invention of Camous with the teaching of Wang so as to provide an alternative to make sample-adaptive transformations based on the structure of the sample to cope with potential corruption via an auto-augmentation framework (Wang, Abstract).
Camous and Wang don’t teach non-weight sharing. The combination of Kilickaya further teaches following limitation:
the deformation artificial neural network comprising first and second instances that do not share weights (Kilickaya discloses “The late fusion neural network 412 may be a non-weight sharing neural network and used to separately encode the representations 422, 420. The predictions of the non-weight sharing neural networks are combined at a classifier layer” in [0056]. Here, a late fusion neural network separately encode representations as shown in Fig 4A.
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It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the invention of Camous and Wang with the teaching of Kilickaya so as to separately encode the representations to improve system performance by increasing scalable capability (Kilickaya, [0027]).
As to Claim 2, Camous in view of Wang and Kilickaya teaches The apparatus of claim 1, in which the deformation artificial neural network comprises a multilayer perceptron (Camous discloses “In some embodiments, perception system 402, planning system 404, localization system 406, and/or control system 408 implement at least one machine learning model (e.g., at least one multilayer perceptron (MLP), at least one convolutional neural network (CNN), at least one recurrent neural network (RNN), at least one autoencoder, at least one transformer, and/or the like)” in [0066]. Wang also teaches multi-layer perceptron (MLP) at p. 4.)
As to Claim 3, Camous in view of Wang and Kilickaya teaches The apparatus of claim 1, in which the one or more processors is further configured to combine the plurality of deformed point cloud objects by performing linear interpolation between the plurality of deformed point cloud objects (Wang discloses trilinear interpolation at p. 4.)
As to Claim 4, Camous in view of Wang and Kilickaya teaches The apparatus of claim 1, in which the one or more processors is further configured to deform the point cloud data by mapping each point cloud feature of the original feature map of point cloud data to a control point perturbation to obtain a set of control point perturbations in a lattice and to deform the lattice (Wang discloses
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see also mapping function at p. 4 and Fig 2.)
As to Claim 5, Camous in view of Wang and Kilickaya teaches The apparatus of claim 1, in which the one or more processors is further configured to optimize the backbone artificial neural network and the deformation artificial neural network based on the loss (Wang teaches loss function under section 3.2 Leaning Objectives at p. 5.)
As to Claim 6, Camous in view of Wang and Kilickaya teaches The apparatus of claim 5, in which the one or more processors is further configured to compute the loss based on a regularization that penalizes similarity between the plurality of deformed point cloud objects (Wang, section 3.2 Leaning Objectives at p. 5.)
As to Claim 7, Camous in view of Wang and Kilickaya teaches The apparatus of claim 1, in which the deformation artificial neural network comprises two instances of an artificial neural network (Camous, [0104] and Fig 5B. Wang, Fig 1-2.)
Claim 8 recites similar limitations as claim 1 but in a method form. Therefore, the same rationale used for claim 1 is applied.
Claim 9 is rejected based upon similar rationale as Claim 2.
Claim 10 is rejected based upon similar rationale as Claim 3.
Claim 11 is rejected based upon similar rationale as Claim 4.
Claim 12 is rejected based upon similar rationale as Claim 5.
Claim 13 is rejected based upon similar rationale as Claim 6.
Claim 14 is rejected based upon similar rationale as Claim 7.
Claim 15 recites similar limitations as claim 1 but in a computer readable medium form. Therefore, the same rationale used for claim 1 is applied.
Claim 16 is rejected based upon similar rationale as Claim 2.
Claim 17 is rejected based upon similar rationale as Claim 3.
Claim 18 is rejected based upon similar rationale as Claim 4.
Claim 19 is rejected based upon similar rationale as Claim 5.
Claim 20 is rejected based upon similar rationale as Claim 6.
Conclusion
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/WEIMING HE/
Primary Examiner, Art Unit 2611
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