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 with respect to claim(s) 1, 11, 19 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-2, 9-11, 17-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over ZHU (US 2022/0414821) in view of HACHIUMA (US 2026/0057672) and further in view of Atzmon et al. ["Frame Averaging for Equivariant Shape Space Learning"], hereinafter Atzmon.
As to claim 1, ZHU discloses a method comprising:
generating, via execution of one or more layers included in a neural network, a set of features associated with a first partition prediction for a plurality of points included in a scene (para. 0003, 0037, 0039);
applying, to the set of features, one or more transformations included in a frame associated with the plurality of points to generate a set of equivariant features (para. 0032, 0041, 0042);
generating a second partition prediction for the plurality of points based at least on the set of equivariant feature (para. 0043); and
causing an object tracking result associated with the plurality of points to be generated based at least on the second partition prediction (para. 0003, 0037).
ZHU is silent regarding causing an object tracking result.
HACHIUMA teaches causing an object tracking result (para. 0176, 0196 0211, 0212).
It would have been obvious to one of ordinary skill in the art to incorporate HACHIUMA’s teachings into ZHU since doing so would merely combine prior art elements according to known methods to yield predictable results, and improve detection systems for autonomous driving controls.
The combination of ZHU and HACHIUMA is silent regarding wherein the first partition prediction comprises a division of the plurality of points into discrete subsets of points that correspond to a plurality of piecewise equivariant regions within the scene.
Atzmon teaches that a partition prediction comprises a division of the plurality of points into discrete subsets of points that correspond to a plurality of piecewise equivariant regions within the scene (page 624, right column; e.g., “Piecewise Euclidean: Mesh -> mesh. In this scenario we generalize our framework to be equivariant to different Euclidean motions applied to different object parts”, “The k parts are defined using a partitioning weight matrix”).
It would have been obvious to one of ordinary skill in the art to incorporate Atzmon’s teachings into the combination of ZHU and HACHIUMA since doing so would merely combine prior art elements according to known methods to yield predictable results, and improve detection system performance.
As to claim 2, the combination of ZHU, HACHIUMA and Atzmon discloses the method of claim 1, wherein the generating the second partition prediction comprises:
determining a set of parameters associated with the first partition prediction based on the set of equivariant features (ZHU, para. 0043-0045, e.g., determining features and weights within the neural network); and
merging a plurality of parts associated with the first partition prediction based on one or more distances computed using the set of parameters (ZHU, para. 0040-0043, 0049, e.g., merging corresponds to “Registration in feature space may be performed at step 110 by aligning the decoded features for the first and second point clouds. The registration step may be included in a training loop for a machine learning network, directly encouraging the accurate registration of point clouds with noise”; one or more distances are inherently determined when “optional loss or noise in the implicit shape reconstruction may be determined” and “optimal rotation matrix to translate from the first to second point clouds may be determined at step 112”, e.g., “The registration loss may be determined as the mean squared error”).
As to claim 9, the combination of ZHU, HACHIUMA and Atzmon discloses the method of claim 1, wherein the one or more transformations include an average of the set of features over one or more equivariant frames (HACHIUMA, para. 0124, 0138, e.g., AveragePooling includes an average of the set of features; ZHU, para. 0033, 0045).
As to claim 10, the combination of ZHU, HACHIUMA and Atzmon discloses the method of claim 1, wherein the one or more layers include a fully connected layer and a max pooling layer (HACHIUMA, para. 0098; ZHU, para. 0033, 0035).
As to claims 11, 19, these claims recite features similar to those discussed above. Therefore, they are rejected for reasons similar to those discussed above.
As to claim 17, the combination of ZHU, HACHIUMA and Atzmon discloses the processor of claim 11, wherein the one or more layers include a PointNet architecture (ZHU, para. 0044).
As to claim 18, the combination of ZHU, HACHIUMA and Atzmon discloses the processor of claim 11, 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 one or more simulation operations; a system for performing one or more digital twin operations; a system for performing light transport simulation; a system for performing collaborative content creation for 3D assets; a system for performing one or more deep learning operations; a system implemented using an edge device; a system for generating or presenting at least one of virtual reality content, augmented reality content, or mixed reality content; a system implemented using a robot; a system for performing one or more conversational AI operations; a system for performing one or more generative AI operations; a system implemented using one or more large language models (LLMs); a system implemented using one or more vision language models (VLMs); 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 ((ZHU, para. 0036, 0037).
As to claim 20, the claim recites features similar to those discussed above. Therefore, claim 20 is rejected for reasons similar to those discussed above.
Claim(s) 3, 5-6, 12-14 is/are rejected under 35 U.S.C. 103 as being unpatentable over ZHU (US 2022/0414821) in view of HACHIUMA (US 2026/0057672), further in view of Atzmon et al. ["Frame Averaging for Equivariant Shape Space Learning"], hereinafter Atzmon, and further in view of VEEFKIND (US 2025/0086522).
As to claim 3, the combination of ZHU, HACHIUMA and Atzmon discloses generating the second partition prediction based on a set of parameters (ZHU, para. 0043).
The combination of ZHU, HACHIUMA and Atzmon is silent regarding updating the set of parameters based on the merged plurality of parts; and generating the second partition prediction based on the updated set of parameters.
VEEFKIND teaches updating the set of parameters based on the merged plurality of parts (para. 0080-0084); and generating the second partition prediction based on the updated set of parameters (para. 0085).
It would have been obvious to one of ordinary skill in the art to incorporate VEEFKIND’s teaching into the combination of ZHU, HACHIUMA and Atzmon since doing so would merely combine prior art elements according to known methods to yield predictable results, and improve prediction for the plurality of points.
As to claim 5, the combination of ZHU, HACHIUMA, Atzmon and VEEFKIND discloses the method of claim 2, wherein the determining the set of parameters comprises iteratively updating one or more parameters included in the set of parameters based on one or more additional parameters included in the set of parameters (VEEFKIND, para. 0083, 0084)
As to claim 6, the combination of ZHU, HACHIUMA and Atzmon does not disclose wherein the one or more distances comprise a Kullback-Leibler (KL) divergence.
VEEFKIND teaches wherein the one or more distances comprise a Kullback-Leibler (KL) divergence between a first distribution representing a first part included in the plurality of parts and a second distribution representing a second part included in the plurality of parts (para. 0060).
It would have been obvious to one of ordinary skill in the art to incorporate VEEFKIND’s teachings into the combination of ZHU, HACHIUMA and Atzmon since doing so would merely combine prior art elements according to known methods to yield predictable results, and improve prediction for the plurality of points.
As to claims 12-14, these claims recite features similar to those discussed above. Therefore, they are rejected for reasons similar to those discussed above.
Claim(s) 4, 15-16 is/are rejected under 35 U.S.C. 103 as being unpatentable over ZHU (US 2022/0414821) in view of HACHIUMA (US 2026/0057672), further in view of Atzmon et al. ["Frame Averaging for Equivariant Shape Space Learning"], hereinafter Atzmon, further in view of VEEFKIND (US 2025/0086522), and further in view of Jampani (US 2020/0320401).
As to claim 4, the combination of ZHU, HACHIUMA, Atzmon and VEEFKIND discloses computing a set of parts for the merged plurality of parts based on the updated set of parameters (ZHU, para. 0040-0043, 0049, VEEFKIND, para. 0080-0084); and generating a set of distributions corresponding to the second partition prediction based at least on the set of parts (ZHU, para. 0043)
The combination of ZHU, HACHIUMA, Atzmon and VEEFKIND is silent a set of part centers.
Jampani teaches computing a set of part centers (para. 0026).
It would have been obvious to one of ordinary skill in the art to incorporate Jampani’s teachings into the combination of ZHU, HACHIUMA, Atzmon and VEEFKIND since doing so would merely combine prior art elements according to known methods to yield predictable results, and improve prediction for the plurality of points.
As to claim 15, the combination of ZHU, HACHIUMA, Atzmon, VEEFKIND and Jampani discloses a set of part centers associated with the plurality of parts (Jampani, para. 0026); and a set of mixing coefficients associated with a Gaussian Mixture Model corresponding to the first partition prediction (ZHU, para. 0072).
As to claim 16, the claim recites features similar to those discussed above. Therefore, claim 16 is rejected for reasons similar to those discussed above.
Claim(s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over ZHU (US 2022/0414821) in view of HACHIUMA (US 2026/0057672), further in view of Atzmon et al. ["Frame Averaging for Equivariant Shape Space Learning"], hereinafter Atzmon, and further in view of Jampani (US 2020/0320401).
As to claim 6, the combination of ZHU, HACHIUMA and Atzmon does not disclose wherein the one or more distances comprise a Kullback-Leibler (KL) divergence.
Jampani teaches wherein the one or more distances comprise a Kullback-Leibler (KL) divergence between a first distribution representing a first part included in the plurality of parts and a second distribution representing a second part included in the plurality of parts (para. 0026).
It would have been obvious to one of ordinary skill in the art to incorporate Jampani’s teachings into the combination of ZHU, HACHIUMA and Atzmon since doing so would merely combine prior art elements according to known methods to yield predictable results, and improve prediction for the plurality of points.
Allowable Subject Matter
Claims 7-8 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: The prior art references disclose the claim limitations discussed above, but fail to disclose the combined features required by dependent claim 7.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Yu et al. disclose the claim limitation “a partition prediction comprises a division of the plurality of points into discrete subsets of points that correspond to a plurality of piecewise equivariant regions within the scene” (see abstract and Figure 2).
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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.
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/PHUOC TRAN/Primary Examiner, Art Unit 2668
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